From 5d51794607d71e1bbffd7d9d5a1eed417de771ae Mon Sep 17 00:00:00 2001 From: filtered <176114999+webfiltered@users.noreply.github.com> Date: Tue, 22 Apr 2025 06:13:00 +1000 Subject: [PATCH 001/193] Add node type hint for socketless option (#7714) * Add node type hint for socketless option * nit - Doc --- comfy/comfy_types/node_typing.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/comfy/comfy_types/node_typing.py b/comfy/comfy_types/node_typing.py index 42ed5174e..a348791a9 100644 --- a/comfy/comfy_types/node_typing.py +++ b/comfy/comfy_types/node_typing.py @@ -115,6 +115,11 @@ class InputTypeOptions(TypedDict): """When a link exists, rather than receiving the evaluated value, you will receive the link (i.e. `["nodeId", ]`). Designed for node expansion.""" tooltip: NotRequired[str] """Tooltip for the input (or widget), shown on pointer hover""" + socketless: NotRequired[bool] + """All inputs (including widgets) have an input socket to connect links. When ``true``, if there is a widget for this input, no socket will be created. + Available from frontend v1.17.5 + Ref: https://github.com/Comfy-Org/ComfyUI_frontend/pull/3548 + """ # class InputTypeNumber(InputTypeOptions): # default: float | int min: NotRequired[float] From 9d57b8afd8c9f14776b1464919472ae17de2b03e Mon Sep 17 00:00:00 2001 From: "Alexander G. Morano" Date: Mon, 21 Apr 2025 18:51:31 -0400 Subject: [PATCH 002/193] Update nodes_primitive.py (#7716) Allow FLOAT and INT types to support negative numbers. Caps the numbers at the user's own system min and max. --- comfy_extras/nodes_primitive.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_primitive.py b/comfy_extras/nodes_primitive.py index b770104fb..184b990c3 100644 --- a/comfy_extras/nodes_primitive.py +++ b/comfy_extras/nodes_primitive.py @@ -1,6 +1,8 @@ # Primitive nodes that are evaluated at backend. from __future__ import annotations +import sys + from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, IO @@ -23,7 +25,7 @@ class Int(ComfyNodeABC): @classmethod def INPUT_TYPES(cls) -> InputTypeDict: return { - "required": {"value": (IO.INT, {"control_after_generate": True})}, + "required": {"value": (IO.INT, {"min": -sys.maxsize, "max": sys.maxsize, "control_after_generate": True})}, } RETURN_TYPES = (IO.INT,) @@ -38,7 +40,7 @@ class Float(ComfyNodeABC): @classmethod def INPUT_TYPES(cls) -> InputTypeDict: return { - "required": {"value": (IO.FLOAT, {})}, + "required": {"value": (IO.FLOAT, {"min": -sys.maxsize, "max": sys.maxsize})}, } RETURN_TYPES = (IO.FLOAT,) From 5d0d4ee98a24b6c72c94635fc5a6e93af2b005bc Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 21 Apr 2025 16:36:20 -0700 Subject: [PATCH 003/193] Add strength control for vace. (#7717) --- comfy/ldm/wan/model.py | 3 ++- comfy/model_base.py | 3 +++ comfy_extras/nodes_wan.py | 7 ++++--- 3 files changed, 9 insertions(+), 4 deletions(-) diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py index 5e7848bd5..4ef86d5f2 100644 --- a/comfy/ldm/wan/model.py +++ b/comfy/ldm/wan/model.py @@ -582,6 +582,7 @@ class VaceWanModel(WanModel): t, context, vace_context, + vace_strength=1.0, clip_fea=None, freqs=None, transformer_options={}, @@ -629,7 +630,7 @@ class VaceWanModel(WanModel): ii = self.vace_layers_mapping.get(i, None) if ii is not None: c_skip, c = self.vace_blocks[ii](c, x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) - x += c_skip + x += c_skip * vace_strength # head x = self.head(x, e) diff --git a/comfy/model_base.py b/comfy/model_base.py index 04a101526..b0c6a465b 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -1068,6 +1068,9 @@ class WAN21_Vace(WAN21): mask = torch.ones(noise_shape, device=noise.device, dtype=noise.dtype) out['vace_context'] = comfy.conds.CONDRegular(torch.cat([vace_frames.to(noise), mask.to(noise)], dim=1)) + + vace_strength = kwargs.get("vace_strength", 1.0) + out['vace_strength'] = comfy.conds.CONDConstant(vace_strength) return out diff --git a/comfy_extras/nodes_wan.py b/comfy_extras/nodes_wan.py index 19a6cdfa4..9dda64597 100644 --- a/comfy_extras/nodes_wan.py +++ b/comfy_extras/nodes_wan.py @@ -203,6 +203,7 @@ class WanVaceToVideo: "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1000.0, "step": 0.01}), }, "optional": {"control_video": ("IMAGE", ), "control_masks": ("MASK", ), @@ -217,7 +218,7 @@ class WanVaceToVideo: EXPERIMENTAL = True - def encode(self, positive, negative, vae, width, height, length, batch_size, control_video=None, control_masks=None, reference_image=None): + def encode(self, positive, negative, vae, width, height, length, batch_size, strength, control_video=None, control_masks=None, reference_image=None): latent_length = ((length - 1) // 4) + 1 if control_video is not None: control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) @@ -267,8 +268,8 @@ class WanVaceToVideo: trim_latent = reference_image.shape[2] mask = mask.unsqueeze(0) - positive = node_helpers.conditioning_set_values(positive, {"vace_frames": control_video_latent, "vace_mask": mask}) - negative = node_helpers.conditioning_set_values(negative, {"vace_frames": control_video_latent, "vace_mask": mask}) + positive = node_helpers.conditioning_set_values(positive, {"vace_frames": control_video_latent, "vace_mask": mask, "vace_strength": strength}) + negative = node_helpers.conditioning_set_values(negative, {"vace_frames": control_video_latent, "vace_mask": mask, "vace_strength": strength}) latent = torch.zeros([batch_size, 16, latent_length, height // 8, width // 8], device=comfy.model_management.intermediate_device()) out_latent = {} From 1f3fba2af518073551a73582c8dce7bae4ad7716 Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Tue, 22 Apr 2025 08:15:32 +0800 Subject: [PATCH 004/193] Unified Weight Adapter system for better maintainability and future feature of Lora system (#7540) --- comfy/lora.py | 321 ++----------------------------- comfy/weight_adapter/__init__.py | 13 ++ comfy/weight_adapter/base.py | 94 +++++++++ comfy/weight_adapter/glora.py | 93 +++++++++ comfy/weight_adapter/loha.py | 100 ++++++++++ comfy/weight_adapter/lokr.py | 133 +++++++++++++ comfy/weight_adapter/lora.py | 142 ++++++++++++++ 7 files changed, 592 insertions(+), 304 deletions(-) create mode 100644 comfy/weight_adapter/__init__.py create mode 100644 comfy/weight_adapter/base.py create mode 100644 comfy/weight_adapter/glora.py create mode 100644 comfy/weight_adapter/loha.py create mode 100644 comfy/weight_adapter/lokr.py create mode 100644 comfy/weight_adapter/lora.py diff --git a/comfy/lora.py b/comfy/lora.py index bc9f3022a..8760a21fb 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -20,6 +20,7 @@ from __future__ import annotations import comfy.utils import comfy.model_management import comfy.model_base +import comfy.weight_adapter as weight_adapter import logging import torch @@ -49,139 +50,12 @@ def load_lora(lora, to_load, log_missing=True): dora_scale = lora[dora_scale_name] loaded_keys.add(dora_scale_name) - reshape_name = "{}.reshape_weight".format(x) - reshape = None - if reshape_name in lora.keys(): - try: - reshape = lora[reshape_name].tolist() - loaded_keys.add(reshape_name) - except: - pass - - regular_lora = "{}.lora_up.weight".format(x) - diffusers_lora = "{}_lora.up.weight".format(x) - diffusers2_lora = "{}.lora_B.weight".format(x) - diffusers3_lora = "{}.lora.up.weight".format(x) - mochi_lora = "{}.lora_B".format(x) - transformers_lora = "{}.lora_linear_layer.up.weight".format(x) - A_name = None - - if regular_lora in lora.keys(): - A_name = regular_lora - B_name = "{}.lora_down.weight".format(x) - mid_name = "{}.lora_mid.weight".format(x) - elif diffusers_lora in lora.keys(): - A_name = diffusers_lora - B_name = "{}_lora.down.weight".format(x) - mid_name = None - elif diffusers2_lora in lora.keys(): - A_name = diffusers2_lora - B_name = "{}.lora_A.weight".format(x) - mid_name = None - elif diffusers3_lora in lora.keys(): - A_name = diffusers3_lora - B_name = "{}.lora.down.weight".format(x) - mid_name = None - elif mochi_lora in lora.keys(): - A_name = mochi_lora - B_name = "{}.lora_A".format(x) - mid_name = None - elif transformers_lora in lora.keys(): - A_name = transformers_lora - B_name ="{}.lora_linear_layer.down.weight".format(x) - mid_name = None - - if A_name is not None: - mid = None - if mid_name is not None and mid_name in lora.keys(): - mid = lora[mid_name] - loaded_keys.add(mid_name) - patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid, dora_scale, reshape)) - loaded_keys.add(A_name) - loaded_keys.add(B_name) - - - ######## loha - hada_w1_a_name = "{}.hada_w1_a".format(x) - hada_w1_b_name = "{}.hada_w1_b".format(x) - hada_w2_a_name = "{}.hada_w2_a".format(x) - hada_w2_b_name = "{}.hada_w2_b".format(x) - hada_t1_name = "{}.hada_t1".format(x) - hada_t2_name = "{}.hada_t2".format(x) - if hada_w1_a_name in lora.keys(): - hada_t1 = None - hada_t2 = None - if hada_t1_name in lora.keys(): - hada_t1 = lora[hada_t1_name] - hada_t2 = lora[hada_t2_name] - loaded_keys.add(hada_t1_name) - loaded_keys.add(hada_t2_name) - - patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale)) - loaded_keys.add(hada_w1_a_name) - loaded_keys.add(hada_w1_b_name) - loaded_keys.add(hada_w2_a_name) - loaded_keys.add(hada_w2_b_name) - - - ######## lokr - lokr_w1_name = "{}.lokr_w1".format(x) - lokr_w2_name = "{}.lokr_w2".format(x) - lokr_w1_a_name = "{}.lokr_w1_a".format(x) - lokr_w1_b_name = "{}.lokr_w1_b".format(x) - lokr_t2_name = "{}.lokr_t2".format(x) - lokr_w2_a_name = "{}.lokr_w2_a".format(x) - lokr_w2_b_name = "{}.lokr_w2_b".format(x) - - lokr_w1 = None - if lokr_w1_name in lora.keys(): - lokr_w1 = lora[lokr_w1_name] - loaded_keys.add(lokr_w1_name) - - lokr_w2 = None - if lokr_w2_name in lora.keys(): - lokr_w2 = lora[lokr_w2_name] - loaded_keys.add(lokr_w2_name) - - lokr_w1_a = None - if lokr_w1_a_name in lora.keys(): - lokr_w1_a = lora[lokr_w1_a_name] - loaded_keys.add(lokr_w1_a_name) - - lokr_w1_b = None - if lokr_w1_b_name in lora.keys(): - lokr_w1_b = lora[lokr_w1_b_name] - loaded_keys.add(lokr_w1_b_name) - - lokr_w2_a = None - if lokr_w2_a_name in lora.keys(): - lokr_w2_a = lora[lokr_w2_a_name] - loaded_keys.add(lokr_w2_a_name) - - lokr_w2_b = None - if lokr_w2_b_name in lora.keys(): - lokr_w2_b = lora[lokr_w2_b_name] - loaded_keys.add(lokr_w2_b_name) - - lokr_t2 = None - if lokr_t2_name in lora.keys(): - lokr_t2 = lora[lokr_t2_name] - loaded_keys.add(lokr_t2_name) - - if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): - patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale)) - - #glora - a1_name = "{}.a1.weight".format(x) - a2_name = "{}.a2.weight".format(x) - b1_name = "{}.b1.weight".format(x) - b2_name = "{}.b2.weight".format(x) - if a1_name in lora: - patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale)) - loaded_keys.add(a1_name) - loaded_keys.add(a2_name) - loaded_keys.add(b1_name) - loaded_keys.add(b2_name) + for adapter_cls in weight_adapter.adapters: + adapter = adapter_cls.load(x, lora, alpha, dora_scale, loaded_keys) + if adapter is not None: + patch_dict[to_load[x]] = adapter + loaded_keys.update(adapter.loaded_keys) + continue w_norm_name = "{}.w_norm".format(x) b_norm_name = "{}.b_norm".format(x) @@ -408,26 +282,6 @@ def model_lora_keys_unet(model, key_map={}): return key_map -def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function): - dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype) - lora_diff *= alpha - weight_calc = weight + function(lora_diff).type(weight.dtype) - weight_norm = ( - weight_calc.transpose(0, 1) - .reshape(weight_calc.shape[1], -1) - .norm(dim=1, keepdim=True) - .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1)) - .transpose(0, 1) - ) - - weight_calc *= (dora_scale / weight_norm).type(weight.dtype) - if strength != 1.0: - weight_calc -= weight - weight += strength * (weight_calc) - else: - weight[:] = weight_calc - return weight - def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Tensor: """ Pad a tensor to a new shape with zeros. @@ -482,6 +336,16 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32, ori if isinstance(v, list): v = (calculate_weight(v[1:], v[0][1](comfy.model_management.cast_to_device(v[0][0], weight.device, intermediate_dtype, copy=True), inplace=True), key, intermediate_dtype=intermediate_dtype), ) + if isinstance(v, weight_adapter.WeightAdapterBase): + output = v.calculate_weight(weight, key, strength, strength_model, offset, function, intermediate_dtype, original_weights) + if output is None: + logging.warning("Calculate Weight Failed: {} {}".format(v.name, key)) + else: + weight = output + if old_weight is not None: + weight = old_weight + continue + if len(v) == 1: patch_type = "diff" elif len(v) == 2: @@ -508,157 +372,6 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32, ori diff_weight = comfy.model_management.cast_to_device(target_weight, weight.device, intermediate_dtype) - \ comfy.model_management.cast_to_device(original_weights[key][0][0], weight.device, intermediate_dtype) weight += function(strength * comfy.model_management.cast_to_device(diff_weight, weight.device, weight.dtype)) - elif patch_type == "lora": #lora/locon - mat1 = comfy.model_management.cast_to_device(v[0], weight.device, intermediate_dtype) - mat2 = comfy.model_management.cast_to_device(v[1], weight.device, intermediate_dtype) - dora_scale = v[4] - reshape = v[5] - - if reshape is not None: - weight = pad_tensor_to_shape(weight, reshape) - - if v[2] is not None: - alpha = v[2] / mat2.shape[0] - else: - alpha = 1.0 - - if v[3] is not None: - #locon mid weights, hopefully the math is fine because I didn't properly test it - mat3 = comfy.model_management.cast_to_device(v[3], weight.device, intermediate_dtype) - final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] - mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) - try: - lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape) - if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) - else: - weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) - except Exception as e: - logging.error("ERROR {} {} {}".format(patch_type, key, e)) - elif patch_type == "lokr": - w1 = v[0] - w2 = v[1] - w1_a = v[3] - w1_b = v[4] - w2_a = v[5] - w2_b = v[6] - t2 = v[7] - dora_scale = v[8] - dim = None - - if w1 is None: - dim = w1_b.shape[0] - w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w1_b, weight.device, intermediate_dtype)) - else: - w1 = comfy.model_management.cast_to_device(w1, weight.device, intermediate_dtype) - - if w2 is None: - dim = w2_b.shape[0] - if t2 is None: - w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype)) - else: - w2 = torch.einsum('i j k l, j r, i p -> p r k l', - comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype)) - else: - w2 = comfy.model_management.cast_to_device(w2, weight.device, intermediate_dtype) - - if len(w2.shape) == 4: - w1 = w1.unsqueeze(2).unsqueeze(2) - if v[2] is not None and dim is not None: - alpha = v[2] / dim - else: - alpha = 1.0 - - try: - lora_diff = torch.kron(w1, w2).reshape(weight.shape) - if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) - else: - weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) - except Exception as e: - logging.error("ERROR {} {} {}".format(patch_type, key, e)) - elif patch_type == "loha": - w1a = v[0] - w1b = v[1] - if v[2] is not None: - alpha = v[2] / w1b.shape[0] - else: - alpha = 1.0 - - w2a = v[3] - w2b = v[4] - dora_scale = v[7] - if v[5] is not None: #cp decomposition - t1 = v[5] - t2 = v[6] - m1 = torch.einsum('i j k l, j r, i p -> p r k l', - comfy.model_management.cast_to_device(t1, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype)) - - m2 = torch.einsum('i j k l, j r, i p -> p r k l', - comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype)) - else: - m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype)) - m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype)) - - try: - lora_diff = (m1 * m2).reshape(weight.shape) - if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) - else: - weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) - except Exception as e: - logging.error("ERROR {} {} {}".format(patch_type, key, e)) - elif patch_type == "glora": - dora_scale = v[5] - - old_glora = False - if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]: - rank = v[0].shape[0] - old_glora = True - - if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]: - if old_glora and v[1].shape[0] == weight.shape[0] and weight.shape[0] == weight.shape[1]: - pass - else: - old_glora = False - rank = v[1].shape[0] - - a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype) - a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype) - b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype) - b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype) - - if v[4] is not None: - alpha = v[4] / rank - else: - alpha = 1.0 - - try: - if old_glora: - lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape) #old lycoris glora - else: - if weight.dim() > 2: - lora_diff = torch.einsum("o i ..., i j -> o j ...", torch.einsum("o i ..., i j -> o j ...", weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) - else: - lora_diff = torch.mm(torch.mm(weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) - lora_diff += torch.mm(b1, b2).reshape(weight.shape) - - if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) - else: - weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) - except Exception as e: - logging.error("ERROR {} {} {}".format(patch_type, key, e)) else: logging.warning("patch type not recognized {} {}".format(patch_type, key)) diff --git a/comfy/weight_adapter/__init__.py b/comfy/weight_adapter/__init__.py new file mode 100644 index 000000000..e6cd805b6 --- /dev/null +++ b/comfy/weight_adapter/__init__.py @@ -0,0 +1,13 @@ +from .base import WeightAdapterBase +from .lora import LoRAAdapter +from .loha import LoHaAdapter +from .lokr import LoKrAdapter +from .glora import GLoRAAdapter + + +adapters: list[type[WeightAdapterBase]] = [ + LoRAAdapter, + LoHaAdapter, + LoKrAdapter, + GLoRAAdapter, +] diff --git a/comfy/weight_adapter/base.py b/comfy/weight_adapter/base.py new file mode 100644 index 000000000..54af3babe --- /dev/null +++ b/comfy/weight_adapter/base.py @@ -0,0 +1,94 @@ +from typing import Optional + +import torch +import torch.nn as nn + +import comfy.model_management + + +class WeightAdapterBase: + name: str + loaded_keys: set[str] + weights: list[torch.Tensor] + + @classmethod + def load(cls, x: str, lora: dict[str, torch.Tensor]) -> Optional["WeightAdapterBase"]: + raise NotImplementedError + + def to_train(self) -> "WeightAdapterTrainBase": + raise NotImplementedError + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + raise NotImplementedError + + +class WeightAdapterTrainBase(nn.Module): + def __init__(self): + super().__init__() + + # [TODO] Collaborate with LoRA training PR #7032 + + +def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function): + dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype) + lora_diff *= alpha + weight_calc = weight + function(lora_diff).type(weight.dtype) + weight_norm = ( + weight_calc.transpose(0, 1) + .reshape(weight_calc.shape[1], -1) + .norm(dim=1, keepdim=True) + .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1)) + .transpose(0, 1) + ) + + weight_calc *= (dora_scale / weight_norm).type(weight.dtype) + if strength != 1.0: + weight_calc -= weight + weight += strength * (weight_calc) + else: + weight[:] = weight_calc + return weight + + +def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Tensor: + """ + Pad a tensor to a new shape with zeros. + + Args: + tensor (torch.Tensor): The original tensor to be padded. + new_shape (List[int]): The desired shape of the padded tensor. + + Returns: + torch.Tensor: A new tensor padded with zeros to the specified shape. + + Note: + If the new shape is smaller than the original tensor in any dimension, + the original tensor will be truncated in that dimension. + """ + if any([new_shape[i] < tensor.shape[i] for i in range(len(new_shape))]): + raise ValueError("The new shape must be larger than the original tensor in all dimensions") + + if len(new_shape) != len(tensor.shape): + raise ValueError("The new shape must have the same number of dimensions as the original tensor") + + # Create a new tensor filled with zeros + padded_tensor = torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) + + # Create slicing tuples for both tensors + orig_slices = tuple(slice(0, dim) for dim in tensor.shape) + new_slices = tuple(slice(0, dim) for dim in tensor.shape) + + # Copy the original tensor into the new tensor + padded_tensor[new_slices] = tensor[orig_slices] + + return padded_tensor diff --git a/comfy/weight_adapter/glora.py b/comfy/weight_adapter/glora.py new file mode 100644 index 000000000..939abbba5 --- /dev/null +++ b/comfy/weight_adapter/glora.py @@ -0,0 +1,93 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import WeightAdapterBase, weight_decompose + + +class GLoRAAdapter(WeightAdapterBase): + name = "glora" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["GLoRAAdapter"]: + if loaded_keys is None: + loaded_keys = set() + a1_name = "{}.a1.weight".format(x) + a2_name = "{}.a2.weight".format(x) + b1_name = "{}.b1.weight".format(x) + b2_name = "{}.b2.weight".format(x) + if a1_name in lora: + weights = (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale) + loaded_keys.add(a1_name) + loaded_keys.add(a2_name) + loaded_keys.add(b1_name) + loaded_keys.add(b2_name) + return cls(loaded_keys, weights) + else: + return None + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + dora_scale = v[5] + + old_glora = False + if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]: + rank = v[0].shape[0] + old_glora = True + + if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]: + if old_glora and v[1].shape[0] == weight.shape[0] and weight.shape[0] == weight.shape[1]: + pass + else: + old_glora = False + rank = v[1].shape[0] + + a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype) + a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype) + b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype) + b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype) + + if v[4] is not None: + alpha = v[4] / rank + else: + alpha = 1.0 + + try: + if old_glora: + lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape) #old lycoris glora + else: + if weight.dim() > 2: + lora_diff = torch.einsum("o i ..., i j -> o j ...", torch.einsum("o i ..., i j -> o j ...", weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) + else: + lora_diff = torch.mm(torch.mm(weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) + lora_diff += torch.mm(b1, b2).reshape(weight.shape) + + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy/weight_adapter/loha.py b/comfy/weight_adapter/loha.py new file mode 100644 index 000000000..ce79abad5 --- /dev/null +++ b/comfy/weight_adapter/loha.py @@ -0,0 +1,100 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import WeightAdapterBase, weight_decompose + + +class LoHaAdapter(WeightAdapterBase): + name = "loha" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["LoHaAdapter"]: + if loaded_keys is None: + loaded_keys = set() + + hada_w1_a_name = "{}.hada_w1_a".format(x) + hada_w1_b_name = "{}.hada_w1_b".format(x) + hada_w2_a_name = "{}.hada_w2_a".format(x) + hada_w2_b_name = "{}.hada_w2_b".format(x) + hada_t1_name = "{}.hada_t1".format(x) + hada_t2_name = "{}.hada_t2".format(x) + if hada_w1_a_name in lora.keys(): + hada_t1 = None + hada_t2 = None + if hada_t1_name in lora.keys(): + hada_t1 = lora[hada_t1_name] + hada_t2 = lora[hada_t2_name] + loaded_keys.add(hada_t1_name) + loaded_keys.add(hada_t2_name) + + weights = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale) + loaded_keys.add(hada_w1_a_name) + loaded_keys.add(hada_w1_b_name) + loaded_keys.add(hada_w2_a_name) + loaded_keys.add(hada_w2_b_name) + return cls(loaded_keys, weights) + else: + return None + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + w1a = v[0] + w1b = v[1] + if v[2] is not None: + alpha = v[2] / w1b.shape[0] + else: + alpha = 1.0 + + w2a = v[3] + w2b = v[4] + dora_scale = v[7] + if v[5] is not None: #cp decomposition + t1 = v[5] + t2 = v[6] + m1 = torch.einsum('i j k l, j r, i p -> p r k l', + comfy.model_management.cast_to_device(t1, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype)) + + m2 = torch.einsum('i j k l, j r, i p -> p r k l', + comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype)) + else: + m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype)) + m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype)) + + try: + lora_diff = (m1 * m2).reshape(weight.shape) + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy/weight_adapter/lokr.py b/comfy/weight_adapter/lokr.py new file mode 100644 index 000000000..51233db2d --- /dev/null +++ b/comfy/weight_adapter/lokr.py @@ -0,0 +1,133 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import WeightAdapterBase, weight_decompose + + +class LoKrAdapter(WeightAdapterBase): + name = "lokr" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["LoKrAdapter"]: + if loaded_keys is None: + loaded_keys = set() + lokr_w1_name = "{}.lokr_w1".format(x) + lokr_w2_name = "{}.lokr_w2".format(x) + lokr_w1_a_name = "{}.lokr_w1_a".format(x) + lokr_w1_b_name = "{}.lokr_w1_b".format(x) + lokr_t2_name = "{}.lokr_t2".format(x) + lokr_w2_a_name = "{}.lokr_w2_a".format(x) + lokr_w2_b_name = "{}.lokr_w2_b".format(x) + + lokr_w1 = None + if lokr_w1_name in lora.keys(): + lokr_w1 = lora[lokr_w1_name] + loaded_keys.add(lokr_w1_name) + + lokr_w2 = None + if lokr_w2_name in lora.keys(): + lokr_w2 = lora[lokr_w2_name] + loaded_keys.add(lokr_w2_name) + + lokr_w1_a = None + if lokr_w1_a_name in lora.keys(): + lokr_w1_a = lora[lokr_w1_a_name] + loaded_keys.add(lokr_w1_a_name) + + lokr_w1_b = None + if lokr_w1_b_name in lora.keys(): + lokr_w1_b = lora[lokr_w1_b_name] + loaded_keys.add(lokr_w1_b_name) + + lokr_w2_a = None + if lokr_w2_a_name in lora.keys(): + lokr_w2_a = lora[lokr_w2_a_name] + loaded_keys.add(lokr_w2_a_name) + + lokr_w2_b = None + if lokr_w2_b_name in lora.keys(): + lokr_w2_b = lora[lokr_w2_b_name] + loaded_keys.add(lokr_w2_b_name) + + lokr_t2 = None + if lokr_t2_name in lora.keys(): + lokr_t2 = lora[lokr_t2_name] + loaded_keys.add(lokr_t2_name) + + if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): + weights = (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale) + return cls(loaded_keys, weights) + else: + return None + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + w1 = v[0] + w2 = v[1] + w1_a = v[3] + w1_b = v[4] + w2_a = v[5] + w2_b = v[6] + t2 = v[7] + dora_scale = v[8] + dim = None + + if w1 is None: + dim = w1_b.shape[0] + w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1_b, weight.device, intermediate_dtype)) + else: + w1 = comfy.model_management.cast_to_device(w1, weight.device, intermediate_dtype) + + if w2 is None: + dim = w2_b.shape[0] + if t2 is None: + w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype)) + else: + w2 = torch.einsum('i j k l, j r, i p -> p r k l', + comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype)) + else: + w2 = comfy.model_management.cast_to_device(w2, weight.device, intermediate_dtype) + + if len(w2.shape) == 4: + w1 = w1.unsqueeze(2).unsqueeze(2) + if v[2] is not None and dim is not None: + alpha = v[2] / dim + else: + alpha = 1.0 + + try: + lora_diff = torch.kron(w1, w2).reshape(weight.shape) + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy/weight_adapter/lora.py b/comfy/weight_adapter/lora.py new file mode 100644 index 000000000..b2e623924 --- /dev/null +++ b/comfy/weight_adapter/lora.py @@ -0,0 +1,142 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import WeightAdapterBase, weight_decompose, pad_tensor_to_shape + + +class LoRAAdapter(WeightAdapterBase): + name = "lora" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["LoRAAdapter"]: + if loaded_keys is None: + loaded_keys = set() + + reshape_name = "{}.reshape_weight".format(x) + regular_lora = "{}.lora_up.weight".format(x) + diffusers_lora = "{}_lora.up.weight".format(x) + diffusers2_lora = "{}.lora_B.weight".format(x) + diffusers3_lora = "{}.lora.up.weight".format(x) + mochi_lora = "{}.lora_B".format(x) + transformers_lora = "{}.lora_linear_layer.up.weight".format(x) + A_name = None + + if regular_lora in lora.keys(): + A_name = regular_lora + B_name = "{}.lora_down.weight".format(x) + mid_name = "{}.lora_mid.weight".format(x) + elif diffusers_lora in lora.keys(): + A_name = diffusers_lora + B_name = "{}_lora.down.weight".format(x) + mid_name = None + elif diffusers2_lora in lora.keys(): + A_name = diffusers2_lora + B_name = "{}.lora_A.weight".format(x) + mid_name = None + elif diffusers3_lora in lora.keys(): + A_name = diffusers3_lora + B_name = "{}.lora.down.weight".format(x) + mid_name = None + elif mochi_lora in lora.keys(): + A_name = mochi_lora + B_name = "{}.lora_A".format(x) + mid_name = None + elif transformers_lora in lora.keys(): + A_name = transformers_lora + B_name = "{}.lora_linear_layer.down.weight".format(x) + mid_name = None + + if A_name is not None: + mid = None + if mid_name is not None and mid_name in lora.keys(): + mid = lora[mid_name] + loaded_keys.add(mid_name) + reshape = None + if reshape_name in lora.keys(): + try: + reshape = lora[reshape_name].tolist() + loaded_keys.add(reshape_name) + except: + pass + weights = (lora[A_name], lora[B_name], alpha, mid, dora_scale, reshape) + loaded_keys.add(A_name) + loaded_keys.add(B_name) + return cls(loaded_keys, weights) + else: + return None + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + mat1 = comfy.model_management.cast_to_device( + v[0], weight.device, intermediate_dtype + ) + mat2 = comfy.model_management.cast_to_device( + v[1], weight.device, intermediate_dtype + ) + dora_scale = v[4] + reshape = v[5] + + if reshape is not None: + weight = pad_tensor_to_shape(weight, reshape) + + if v[2] is not None: + alpha = v[2] / mat2.shape[0] + else: + alpha = 1.0 + + if v[3] is not None: + # locon mid weights, hopefully the math is fine because I didn't properly test it + mat3 = comfy.model_management.cast_to_device( + v[3], weight.device, intermediate_dtype + ) + final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] + mat2 = ( + torch.mm( + mat2.transpose(0, 1).flatten(start_dim=1), + mat3.transpose(0, 1).flatten(start_dim=1), + ) + .reshape(final_shape) + .transpose(0, 1) + ) + try: + lora_diff = torch.mm( + mat1.flatten(start_dim=1), mat2.flatten(start_dim=1) + ).reshape(weight.shape) + if dora_scale is not None: + weight = weight_decompose( + dora_scale, + weight, + lora_diff, + alpha, + strength, + intermediate_dtype, + function, + ) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight From 3ab231f01f26f9cec03bd94382ae5b6289789d9e Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 21 Apr 2025 20:36:12 -0700 Subject: [PATCH 005/193] Fix issue with WAN VACE implementation. (#7724) --- comfy/ldm/wan/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py index 4ef86d5f2..b8eec3afb 100644 --- a/comfy/ldm/wan/model.py +++ b/comfy/ldm/wan/model.py @@ -630,7 +630,7 @@ class VaceWanModel(WanModel): ii = self.vace_layers_mapping.get(i, None) if ii is not None: c_skip, c = self.vace_blocks[ii](c, x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) - x += c_skip * vace_strength + x += c_skip * vace_strength # head x = self.head(x, e) From 966c43ce268341de6e60762ef18e7628f7d311bf Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Tue, 22 Apr 2025 16:59:47 +0800 Subject: [PATCH 006/193] Add OFT/BOFT algorithm in weight adapter (#7725) --- comfy/weight_adapter/__init__.py | 4 ++ comfy/weight_adapter/boft.py | 115 +++++++++++++++++++++++++++++++ comfy/weight_adapter/oft.py | 94 +++++++++++++++++++++++++ 3 files changed, 213 insertions(+) create mode 100644 comfy/weight_adapter/boft.py create mode 100644 comfy/weight_adapter/oft.py diff --git a/comfy/weight_adapter/__init__.py b/comfy/weight_adapter/__init__.py index e6cd805b6..d2a1d0151 100644 --- a/comfy/weight_adapter/__init__.py +++ b/comfy/weight_adapter/__init__.py @@ -3,6 +3,8 @@ from .lora import LoRAAdapter from .loha import LoHaAdapter from .lokr import LoKrAdapter from .glora import GLoRAAdapter +from .oft import OFTAdapter +from .boft import BOFTAdapter adapters: list[type[WeightAdapterBase]] = [ @@ -10,4 +12,6 @@ adapters: list[type[WeightAdapterBase]] = [ LoHaAdapter, LoKrAdapter, GLoRAAdapter, + OFTAdapter, + BOFTAdapter, ] diff --git a/comfy/weight_adapter/boft.py b/comfy/weight_adapter/boft.py new file mode 100644 index 000000000..c85adc7ab --- /dev/null +++ b/comfy/weight_adapter/boft.py @@ -0,0 +1,115 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import WeightAdapterBase, weight_decompose + + +class BOFTAdapter(WeightAdapterBase): + name = "boft" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["BOFTAdapter"]: + if loaded_keys is None: + loaded_keys = set() + blocks_name = "{}.boft_blocks".format(x) + rescale_name = "{}.rescale".format(x) + + blocks = None + if blocks_name in lora.keys(): + blocks = lora[blocks_name] + if blocks.ndim == 4: + loaded_keys.add(blocks_name) + + rescale = None + if rescale_name in lora.keys(): + rescale = lora[rescale_name] + loaded_keys.add(rescale_name) + + if blocks is not None: + weights = (blocks, rescale, alpha, dora_scale) + return cls(loaded_keys, weights) + else: + return None + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + blocks = v[0] + rescale = v[1] + alpha = v[2] + dora_scale = v[3] + + blocks = comfy.model_management.cast_to_device(blocks, weight.device, intermediate_dtype) + if rescale is not None: + rescale = comfy.model_management.cast_to_device(rescale, weight.device, intermediate_dtype) + + boft_m, block_num, boft_b, *_ = blocks.shape + + try: + # Get r + I = torch.eye(boft_b, device=blocks.device, dtype=blocks.dtype) + # for Q = -Q^T + q = blocks - blocks.transpose(1, 2) + normed_q = q + if alpha > 0: # alpha in boft/bboft is for constraint + q_norm = torch.norm(q) + 1e-8 + if q_norm > alpha: + normed_q = q * alpha / q_norm + # use float() to prevent unsupported type in .inverse() + r = (I + normed_q) @ (I - normed_q).float().inverse() + r = r.to(original_weight) + + inp = org = original_weight + + r_b = boft_b//2 + for i in range(boft_m): + bi = r[i] + g = 2 + k = 2**i * r_b + if strength != 1: + bi = bi * strength + (1-strength) * I + inp = ( + inp.unflatten(-1, (-1, g, k)) + .transpose(-2, -1) + .flatten(-3) + .unflatten(-1, (-1, boft_b)) + ) + inp = torch.einsum("b n m, b n ... -> b m ...", inp, bi) + inp = ( + inp.flatten(-2).unflatten(-1, (-1, k, g)).transpose(-2, -1).flatten(-3) + ) + + if rescale is not None: + inp = inp * rescale + + lora_diff = inp - org + lora_diff = comfy.model_management.cast_to_device(lora_diff, weight.device, intermediate_dtype) + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy/weight_adapter/oft.py b/comfy/weight_adapter/oft.py new file mode 100644 index 000000000..0ea229b79 --- /dev/null +++ b/comfy/weight_adapter/oft.py @@ -0,0 +1,94 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import WeightAdapterBase, weight_decompose + + +class OFTAdapter(WeightAdapterBase): + name = "oft" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["OFTAdapter"]: + if loaded_keys is None: + loaded_keys = set() + blocks_name = "{}.oft_blocks".format(x) + rescale_name = "{}.rescale".format(x) + + blocks = None + if blocks_name in lora.keys(): + blocks = lora[blocks_name] + if blocks.ndim == 3: + loaded_keys.add(blocks_name) + + rescale = None + if rescale_name in lora.keys(): + rescale = lora[rescale_name] + loaded_keys.add(rescale_name) + + if blocks is not None: + weights = (blocks, rescale, alpha, dora_scale) + return cls(loaded_keys, weights) + else: + return None + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + blocks = v[0] + rescale = v[1] + alpha = v[2] + dora_scale = v[3] + + blocks = comfy.model_management.cast_to_device(blocks, weight.device, intermediate_dtype) + if rescale is not None: + rescale = comfy.model_management.cast_to_device(rescale, weight.device, intermediate_dtype) + + block_num, block_size, *_ = blocks.shape + + try: + # Get r + I = torch.eye(block_size, device=blocks.device, dtype=blocks.dtype) + # for Q = -Q^T + q = blocks - blocks.transpose(1, 2) + normed_q = q + if alpha > 0: # alpha in oft/boft is for constraint + q_norm = torch.norm(q) + 1e-8 + if q_norm > alpha: + normed_q = q * alpha / q_norm + # use float() to prevent unsupported type in .inverse() + r = (I + normed_q) @ (I - normed_q).float().inverse() + r = r.to(original_weight) + lora_diff = torch.einsum( + "k n m, k n ... -> k m ...", + (r * strength) - strength * I, + original_weight, + ) + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight From 454a635c1b8aae9f635e7fb4f696bf7ac2e1fd1f Mon Sep 17 00:00:00 2001 From: Terry Jia Date: Tue, 22 Apr 2025 05:00:28 -0400 Subject: [PATCH 007/193] upstream MaskPreview from ComfyUI_essentials (#7719) --- comfy_extras/nodes_mask.py | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py index 13d2b4bab..99b264a32 100644 --- a/comfy_extras/nodes_mask.py +++ b/comfy_extras/nodes_mask.py @@ -3,7 +3,10 @@ import scipy.ndimage import torch import comfy.utils import node_helpers +import folder_paths +import random +import nodes from nodes import MAX_RESOLUTION def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False): @@ -362,6 +365,30 @@ class ThresholdMask: mask = (mask > value).float() return (mask,) +# Mask Preview - original implement from +# https://github.com/cubiq/ComfyUI_essentials/blob/9d9f4bedfc9f0321c19faf71855e228c93bd0dc9/mask.py#L81 +# upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes +class MaskPreview(nodes.SaveImage): + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + self.compress_level = 4 + + @classmethod + def INPUT_TYPES(s): + return { + "required": {"mask": ("MASK",), }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + FUNCTION = "execute" + CATEGORY = "mask" + + def execute(self, mask, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) + return self.save_images(preview, filename_prefix, prompt, extra_pnginfo) + NODE_CLASS_MAPPINGS = { "LatentCompositeMasked": LatentCompositeMasked, @@ -376,6 +403,7 @@ NODE_CLASS_MAPPINGS = { "FeatherMask": FeatherMask, "GrowMask": GrowMask, "ThresholdMask": ThresholdMask, + "MaskPreview": MaskPreview } NODE_DISPLAY_NAME_MAPPINGS = { From a8f63c0d5b40b4ed12faa1376e973b0e790b1c0d Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Tue, 22 Apr 2025 17:01:27 +0800 Subject: [PATCH 008/193] Support dora_scale on both axis (#7727) --- comfy/weight_adapter/base.py | 24 +++++++++++++++++------- 1 file changed, 17 insertions(+), 7 deletions(-) diff --git a/comfy/weight_adapter/base.py b/comfy/weight_adapter/base.py index 54af3babe..29873519d 100644 --- a/comfy/weight_adapter/base.py +++ b/comfy/weight_adapter/base.py @@ -43,13 +43,23 @@ def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediat dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype) lora_diff *= alpha weight_calc = weight + function(lora_diff).type(weight.dtype) - weight_norm = ( - weight_calc.transpose(0, 1) - .reshape(weight_calc.shape[1], -1) - .norm(dim=1, keepdim=True) - .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1)) - .transpose(0, 1) - ) + + wd_on_output_axis = dora_scale.shape[0] == weight_calc.shape[0] + if wd_on_output_axis: + weight_norm = ( + weight.reshape(weight.shape[0], -1) + .norm(dim=1, keepdim=True) + .reshape(weight.shape[0], *[1] * (weight.dim() - 1)) + ) + else: + weight_norm = ( + weight_calc.transpose(0, 1) + .reshape(weight_calc.shape[1], -1) + .norm(dim=1, keepdim=True) + .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1)) + .transpose(0, 1) + ) + weight_norm = weight_norm + torch.finfo(weight.dtype).eps weight_calc *= (dora_scale / weight_norm).type(weight.dtype) if strength != 1.0: From 2d6805ce57cede78acb6515112439c5092c7b257 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 22 Apr 2025 03:17:38 -0700 Subject: [PATCH 009/193] Add option for using fp8_e8m0fnu for model weights. (#7733) Seems to break every model I have tried but worth testing? --- comfy/cli_args.py | 1 + comfy/model_management.py | 2 ++ 2 files changed, 3 insertions(+) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index 81f29f098..1b971be3c 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -66,6 +66,7 @@ fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the diff fpunet_group.add_argument("--fp16-unet", action="store_true", help="Run the diffusion model in fp16") fpunet_group.add_argument("--fp8_e4m3fn-unet", action="store_true", help="Store unet weights in fp8_e4m3fn.") fpunet_group.add_argument("--fp8_e5m2-unet", action="store_true", help="Store unet weights in fp8_e5m2.") +fpunet_group.add_argument("--fp8_e8m0fnu-unet", action="store_true", help="Store unet weights in fp8_e8m0fnu.") fpvae_group = parser.add_mutually_exclusive_group() fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.") diff --git a/comfy/model_management.py b/comfy/model_management.py index 19e6c8dff..43e402243 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -725,6 +725,8 @@ def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, tor return torch.float8_e4m3fn if args.fp8_e5m2_unet: return torch.float8_e5m2 + if args.fp8_e8m0fnu_unet: + return torch.float8_e8m0fnu fp8_dtype = None if weight_dtype in FLOAT8_TYPES: From 92cdc692f47188e6e4c48c5666ac802281240a37 Mon Sep 17 00:00:00 2001 From: Alex Butler Date: Tue, 22 Apr 2025 22:57:17 +0100 Subject: [PATCH 010/193] Replace aom-av1 with svt-av1 for saving webm videos, use preset 6 + yuv420p10le pixel format (#7736) * Add support for saving svt-av1 webm videos & yuv420p10le pixel format * Replace aom-av1 with svt-av1 Use yuv420p10le for av1 --- comfy_extras/nodes_video.py | 6 ++++-- requirements.txt | 2 +- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/comfy_extras/nodes_video.py b/comfy_extras/nodes_video.py index 97ca513d8..a9e244ebe 100644 --- a/comfy_extras/nodes_video.py +++ b/comfy_extras/nodes_video.py @@ -50,13 +50,15 @@ class SaveWEBM: for x in extra_pnginfo: container.metadata[x] = json.dumps(extra_pnginfo[x]) - codec_map = {"vp9": "libvpx-vp9", "av1": "libaom-av1"} + codec_map = {"vp9": "libvpx-vp9", "av1": "libsvtav1"} stream = container.add_stream(codec_map[codec], rate=Fraction(round(fps * 1000), 1000)) stream.width = images.shape[-2] stream.height = images.shape[-3] - stream.pix_fmt = "yuv420p" + stream.pix_fmt = "yuv420p10le" if codec == "av1" else "yuv420p" stream.bit_rate = 0 stream.options = {'crf': str(crf)} + if codec == "av1": + stream.options["preset"] = "6" for frame in images: frame = av.VideoFrame.from_ndarray(torch.clamp(frame[..., :3] * 255, min=0, max=255).to(device=torch.device("cpu"), dtype=torch.uint8).numpy(), format="rgb24") diff --git a/requirements.txt b/requirements.txt index 5c3a854ce..90eb04612 100644 --- a/requirements.txt +++ b/requirements.txt @@ -22,4 +22,4 @@ psutil kornia>=0.7.1 spandrel soundfile -av +av>=14.1.0 From 0738e4ea5dd5ecac34d8cf61bb381ea6d159394b Mon Sep 17 00:00:00 2001 From: Robin Huang Date: Tue, 22 Apr 2025 23:18:08 -0700 Subject: [PATCH 011/193] [API nodes] Add backbone for supporting api nodes in ComfyUI (#7745) * Add Ideogram generate node. * Add staging api. * COMFY_API_NODE_NAME node property * switch to boolean flag and use original node name for id * add optional to type * Add API_NODE and common error for missing auth token (#5) * Add Minimax Video Generation + Async Task queue polling example (#6) * [Minimax] Show video preview and embed workflow in ouput (#7) * [API Nodes] Send empty request body instead of empty dictionary. (#8) * Fixed: removed function from rebase. * Add pydantic. * Remove uv.lock * Remove polling operations. * Update stubs workflow. * Remove polling comments. * Update stubs. * Use pydantic v2. * Use pydantic v2. * Add basic OpenAITextToImage node * Add. * convert image to tensor. * Improve types. * Ruff. * Push tests. * Handle multi-form data. - Don't set content-type for multi-part/form - Use data field instead of JSON * Change to api.comfy.org * Handle error code 409. * Remove nodes. --------- Co-authored-by: bymyself Co-authored-by: Yoland Y <4950057+yoland68@users.noreply.github.com> --- comfy/comfy_types/node_typing.py | 4 +- comfy_api_nodes/__init__.py | 0 comfy_api_nodes/apis/client.py | 337 +++++++++++++++++++++++++++++++ requirements.txt | 1 + server.py | 3 + 5 files changed, 344 insertions(+), 1 deletion(-) create mode 100644 comfy_api_nodes/__init__.py create mode 100644 comfy_api_nodes/apis/client.py diff --git a/comfy/comfy_types/node_typing.py b/comfy/comfy_types/node_typing.py index a348791a9..0bdda032e 100644 --- a/comfy/comfy_types/node_typing.py +++ b/comfy/comfy_types/node_typing.py @@ -1,7 +1,7 @@ """Comfy-specific type hinting""" from __future__ import annotations -from typing import Literal, TypedDict +from typing import Literal, TypedDict, Optional from typing_extensions import NotRequired from abc import ABC, abstractmethod from enum import Enum @@ -229,6 +229,8 @@ class ComfyNodeABC(ABC): """Flags a node as experimental, informing users that it may change or not work as expected.""" DEPRECATED: bool """Flags a node as deprecated, indicating to users that they should find alternatives to this node.""" + API_NODE: Optional[bool] + """Flags a node as an API node.""" @classmethod @abstractmethod diff --git a/comfy_api_nodes/__init__.py b/comfy_api_nodes/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py new file mode 100644 index 000000000..cd81d5a1d --- /dev/null +++ b/comfy_api_nodes/apis/client.py @@ -0,0 +1,337 @@ +import logging + +""" +API Client Framework for api.comfy.org. + +This module provides a flexible framework for making API requests from ComfyUI nodes. +It supports both synchronous and asynchronous API operations with proper type validation. + +Key Components: +-------------- +1. ApiClient - Handles HTTP requests with authentication and error handling +2. ApiEndpoint - Defines a single HTTP endpoint with its request/response models +3. ApiOperation - Executes a single synchronous API operation + +Usage Examples: +-------------- + +# Example 1: Synchronous API Operation +# ------------------------------------ +# For a simple API call that returns the result immediately: + +# 1. Create the API client +api_client = ApiClient( + base_url="https://api.example.com", + api_key="your_api_key_here", + timeout=30.0, + verify_ssl=True +) + +# 2. Define the endpoint +user_info_endpoint = ApiEndpoint( + path="/v1/users/me", + method=HttpMethod.GET, + request_model=EmptyRequest, # No request body needed + response_model=UserProfile, # Pydantic model for the response + query_params=None +) + +# 3. Create the request object +request = EmptyRequest() + +# 4. Create and execute the operation +operation = ApiOperation( + endpoint=user_info_endpoint, + request=request +) +user_profile = operation.execute(client=api_client) # Returns immediately with the result + +""" + +from typing import ( + Dict, + Type, + Optional, + Any, + TypeVar, + Generic, +) +from pydantic import BaseModel +from enum import Enum +import json +import requests +from urllib.parse import urljoin + +T = TypeVar("T", bound=BaseModel) +R = TypeVar("R", bound=BaseModel) + +class EmptyRequest(BaseModel): + """Base class for empty request bodies. + For GET requests, fields will be sent as query parameters.""" + + pass + + +class HttpMethod(str, Enum): + GET = "GET" + POST = "POST" + PUT = "PUT" + DELETE = "DELETE" + PATCH = "PATCH" + + +class ApiClient: + """ + Client for making HTTP requests to an API with authentication and error handling. + """ + + def __init__( + self, + base_url: str, + api_key: Optional[str] = None, + timeout: float = 30.0, + verify_ssl: bool = True, + ): + self.base_url = base_url + self.api_key = api_key + self.timeout = timeout + self.verify_ssl = verify_ssl + + def get_headers(self) -> Dict[str, str]: + """Get headers for API requests, including authentication if available""" + headers = {"Content-Type": "application/json", "Accept": "application/json"} + + if self.api_key: + headers["Authorization"] = f"Bearer {self.api_key}" + + return headers + + def request( + self, + method: str, + path: str, + params: Optional[Dict[str, Any]] = None, + json: Optional[Dict[str, Any]] = None, + files: Optional[Dict[str, Any]] = None, + headers: Optional[Dict[str, str]] = None, + ) -> Dict[str, Any]: + """ + Make an HTTP request to the API + + Args: + method: HTTP method (GET, POST, etc.) + path: API endpoint path (will be joined with base_url) + params: Query parameters + json: JSON body data + files: Files to upload + headers: Additional headers + + Returns: + Parsed JSON response + + Raises: + requests.RequestException: If the request fails + """ + url = urljoin(self.base_url, path) + self.check_auth_token(self.api_key) + # Combine default headers with any provided headers + request_headers = self.get_headers() + if headers: + request_headers.update(headers) + + # Let requests handle the content type when files are present. + if files: + del request_headers["Content-Type"] + + logging.debug(f"[DEBUG] Request Headers: {request_headers}") + logging.debug(f"[DEBUG] Files: {files}") + logging.debug(f"[DEBUG] Params: {params}") + logging.debug(f"[DEBUG] Json: {json}") + + try: + # If files are present, use data parameter instead of json + if files: + form_data = {} + if json: + form_data.update(json) + response = requests.request( + method=method, + url=url, + params=params, + data=form_data, # Use data instead of json + files=files, + headers=request_headers, + timeout=self.timeout, + verify=self.verify_ssl, + ) + else: + response = requests.request( + method=method, + url=url, + params=params, + json=json, + headers=request_headers, + timeout=self.timeout, + verify=self.verify_ssl, + ) + + # Raise exception for error status codes + response.raise_for_status() + except requests.ConnectionError: + raise Exception( + f"Unable to connect to the API server at {self.base_url}. Please check your internet connection or verify the service is available." + ) + + except requests.Timeout: + raise Exception( + f"Request timed out after {self.timeout} seconds. The server might be experiencing high load or the operation is taking longer than expected." + ) + + except requests.HTTPError as e: + status_code = e.response.status_code if hasattr(e, "response") else None + error_message = f"HTTP Error: {str(e)}" + + # Try to extract detailed error message from JSON response + try: + if hasattr(e, "response") and e.response.content: + error_json = e.response.json() + if "error" in error_json and "message" in error_json["error"]: + error_message = f"API Error: {error_json['error']['message']}" + if "type" in error_json["error"]: + error_message += f" (Type: {error_json['error']['type']})" + else: + error_message = f"API Error: {error_json}" + except Exception as json_error: + # If we can't parse the JSON, fall back to the original error message + logging.debug(f"[DEBUG] Failed to parse error response: {str(json_error)}") + + logging.debug(f"[DEBUG] API Error: {error_message} (Status: {status_code})") + if hasattr(e, "response") and e.response.content: + logging.debug(f"[DEBUG] Response content: {e.response.content}") + if status_code == 401: + error_message = "Unauthorized: Please login first to use this node." + if status_code == 402: + error_message = "Payment Required: Please add credits to your account to use this node." + if status_code == 409: + error_message = "There is a problem with your account. Please contact support@comfy.org. " + if status_code == 429: + error_message = "Rate Limit Exceeded: Please try again later." + raise Exception(error_message) + + # Parse and return JSON response + if response.content: + return response.json() + return {} + + def check_auth_token(self, auth_token): + """Verify that an auth token is present.""" + if auth_token is None: + raise Exception("Please login first to use this node.") + return auth_token + + +class ApiEndpoint(Generic[T, R]): + """Defines an API endpoint with its request and response types""" + + def __init__( + self, + path: str, + method: HttpMethod, + request_model: Type[T], + response_model: Type[R], + query_params: Optional[Dict[str, Any]] = None, + ): + """Initialize an API endpoint definition. + + Args: + path: The URL path for this endpoint, can include placeholders like {id} + method: The HTTP method to use (GET, POST, etc.) + request_model: Pydantic model class that defines the structure and validation rules for API requests to this endpoint + response_model: Pydantic model class that defines the structure and validation rules for API responses from this endpoint + query_params: Optional dictionary of query parameters to include in the request + """ + self.path = path + self.method = method + self.request_model = request_model + self.response_model = response_model + self.query_params = query_params or {} + + +class SynchronousOperation(Generic[T, R]): + """ + Represents a single synchronous API operation. + """ + + def __init__( + self, + endpoint: ApiEndpoint[T, R], + request: T, + files: Optional[Dict[str, Any]] = None, + api_base: str = "https://api.comfy.org", + auth_token: Optional[str] = None, + timeout: float = 60.0, + verify_ssl: bool = True, + ): + self.endpoint = endpoint + self.request = request + self.response = None + self.error = None + self.api_base = api_base + self.auth_token = auth_token + self.timeout = timeout + self.verify_ssl = verify_ssl + self.files = files + def execute(self, client: Optional[ApiClient] = None) -> R: + """Execute the API operation using the provided client or create one""" + try: + # Create client if not provided + if client is None: + if self.api_base is None: + raise ValueError("Either client or api_base must be provided") + client = ApiClient( + base_url=self.api_base, + api_key=self.auth_token, + timeout=self.timeout, + verify_ssl=self.verify_ssl, + ) + + # Convert request model to dict, but use None for EmptyRequest + request_dict = None if isinstance(self.request, EmptyRequest) else self.request.model_dump(exclude_none=True) + + # Debug log for request + logging.debug(f"[DEBUG] API Request: {self.endpoint.method.value} {self.endpoint.path}") + logging.debug(f"[DEBUG] Request Data: {json.dumps(request_dict, indent=2)}") + logging.debug(f"[DEBUG] Query Params: {self.endpoint.query_params}") + + # Make the request + resp = client.request( + method=self.endpoint.method.value, + path=self.endpoint.path, + json=request_dict, + params=self.endpoint.query_params, + files=self.files, + ) + + # Debug log for response + logging.debug("=" * 50) + logging.debug("[DEBUG] RESPONSE DETAILS:") + logging.debug("[DEBUG] Status Code: 200 (Success)") + logging.debug(f"[DEBUG] Response Body: {json.dumps(resp, indent=2)}") + logging.debug("=" * 50) + + # Parse and return the response + return self._parse_response(resp) + + except Exception as e: + logging.debug(f"[DEBUG] API Exception: {str(e)}") + raise Exception(str(e)) + + def _parse_response(self, resp): + """Parse response data - can be overridden by subclasses""" + # The response is already the complete object, don't extract just the "data" field + # as that would lose the outer structure (created timestamp, etc.) + + # Parse response using the provided model + self.response = self.endpoint.response_model.model_validate(resp) + logging.debug(f"[DEBUG] Parsed Response: {self.response}") + return self.response diff --git a/requirements.txt b/requirements.txt index 90eb04612..f8ad908ca 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,3 +23,4 @@ kornia>=0.7.1 spandrel soundfile av>=14.1.0 +pydantic~=2.0 diff --git a/server.py b/server.py index 0cc97b248..f64ec27d4 100644 --- a/server.py +++ b/server.py @@ -580,6 +580,9 @@ class PromptServer(): info['deprecated'] = True if getattr(obj_class, "EXPERIMENTAL", False): info['experimental'] = True + + if hasattr(obj_class, 'API_NODE'): + info['api_node'] = obj_class.API_NODE return info @routes.get("/object_info") From 552615235dc043f0b07d11e1ff2e6571e6f90d4d Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 23 Apr 2025 01:12:52 -0700 Subject: [PATCH 012/193] Fix for dino lowvram. (#7748) --- comfy/image_encoders/dino2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/image_encoders/dino2.py b/comfy/image_encoders/dino2.py index 130ed6fd7..976f98c65 100644 --- a/comfy/image_encoders/dino2.py +++ b/comfy/image_encoders/dino2.py @@ -116,7 +116,7 @@ class Dino2Embeddings(torch.nn.Module): def forward(self, pixel_values): x = self.patch_embeddings(pixel_values) # TODO: mask_token? - x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1) + x = torch.cat((self.cls_token.to(device=x.device, dtype=x.dtype).expand(x.shape[0], -1, -1), x), dim=1) x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype) return x From 21a11ef817e3749047c6b548231210ff84fe331d Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 23 Apr 2025 02:12:59 -0700 Subject: [PATCH 013/193] Pytorch stable 2.7 is out and support cu128 (#7749) --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index cf6df7e55..62800bb4f 100644 --- a/README.md +++ b/README.md @@ -216,9 +216,9 @@ Additional discussion and help can be found [here](https://github.com/comfyanony Nvidia users should install stable pytorch using this command: -```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu126``` +```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu128``` -This is the command to install pytorch nightly instead which supports the new blackwell 50xx series GPUs and might have performance improvements. +This is the command to install pytorch nightly instead which might have performance improvements. ```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128``` From 7eaff81be106fa5e1479cfa69f5fd06265611f2e Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 23 Apr 2025 02:28:24 -0700 Subject: [PATCH 014/193] fp16 accumulation can now be enabled on the stable package. (#7750) --- .../run_nvidia_gpu_fast_fp16_accumulation.bat | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename .ci/{windows_nightly_base_files => windows_base_files}/run_nvidia_gpu_fast_fp16_accumulation.bat (100%) diff --git a/.ci/windows_nightly_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat b/.ci/windows_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat similarity index 100% rename from .ci/windows_nightly_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat rename to .ci/windows_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat From 3eaad0590e51bc186b1d533fef906e3f296cdd42 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 23 Apr 2025 02:47:09 -0700 Subject: [PATCH 015/193] Lower size of release package. (#7751) --- .github/workflows/windows_release_package.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/windows_release_package.yml b/.github/workflows/windows_release_package.yml index 416544f71..8300c2faf 100644 --- a/.github/workflows/windows_release_package.yml +++ b/.github/workflows/windows_release_package.yml @@ -50,7 +50,7 @@ jobs: - uses: actions/checkout@v4 with: - fetch-depth: 0 + fetch-depth: 150 persist-credentials: false - shell: bash run: | @@ -82,7 +82,7 @@ jobs: cd .. - "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable + "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=256m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z cd ComfyUI_windows_portable From 154f2911aaf0333db576a237c6098ed0a8160a7d Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 23 Apr 2025 03:33:09 -0700 Subject: [PATCH 016/193] Lower size of release package more. (#7754) --- .github/workflows/stable-release.yml | 6 +++--- .github/workflows/windows_release_nightly_pytorch.yml | 2 +- .github/workflows/windows_release_package.yml | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/stable-release.yml b/.github/workflows/stable-release.yml index f7d30a9a4..40df7ab88 100644 --- a/.github/workflows/stable-release.yml +++ b/.github/workflows/stable-release.yml @@ -36,7 +36,7 @@ jobs: - uses: actions/checkout@v4 with: ref: ${{ inputs.git_tag }} - fetch-depth: 0 + fetch-depth: 150 persist-credentials: false - uses: actions/cache/restore@v4 id: cache @@ -70,7 +70,7 @@ jobs: cd .. git clone --depth 1 https://github.com/comfyanonymous/taesd - cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/ + cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/ mkdir ComfyUI_windows_portable mv python_embeded ComfyUI_windows_portable @@ -85,7 +85,7 @@ jobs: cd .. - "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable + "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=512m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_nvidia.7z cd ComfyUI_windows_portable diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml index 24599249a..eb5ed9c91 100644 --- a/.github/workflows/windows_release_nightly_pytorch.yml +++ b/.github/workflows/windows_release_nightly_pytorch.yml @@ -56,7 +56,7 @@ jobs: cd .. git clone --depth 1 https://github.com/comfyanonymous/taesd - cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/ + cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/ mkdir ComfyUI_windows_portable_nightly_pytorch mv python_embeded ComfyUI_windows_portable_nightly_pytorch diff --git a/.github/workflows/windows_release_package.yml b/.github/workflows/windows_release_package.yml index 8300c2faf..dc79b1f4a 100644 --- a/.github/workflows/windows_release_package.yml +++ b/.github/workflows/windows_release_package.yml @@ -67,7 +67,7 @@ jobs: cd .. git clone --depth 1 https://github.com/comfyanonymous/taesd - cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/ + cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/ mkdir ComfyUI_windows_portable mv python_embeded ComfyUI_windows_portable @@ -82,7 +82,7 @@ jobs: cd .. - "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=256m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable + "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=512m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z cd ComfyUI_windows_portable From dea1c7474a8e663732a755204970e09006df68c7 Mon Sep 17 00:00:00 2001 From: Robin Huang Date: Wed, 23 Apr 2025 12:38:34 -0700 Subject: [PATCH 017/193] Add support for API Nodes in ComfyUI. (#7726) * Add Ideogram generate node. * Add staging api. * COMFY_API_NODE_NAME node property * switch to boolean flag and use original node name for id * add optional to type * Add API_NODE and common error for missing auth token (#5) * Add Minimax Video Generation + Async Task queue polling example (#6) * [Minimax] Show video preview and embed workflow in ouput (#7) * [API Nodes] Send empty request body instead of empty dictionary. (#8) * Fixed: removed function from rebase. * Add pydantic. * Remove uv.lock * Remove polling operations. * Update stubs workflow. * Remove polling comments. * Update stubs. * Use pydantic v2. * Use pydantic v2. * Add basic OpenAITextToImage node * Add. * convert image to tensor. * Improve types. * Ruff. * Push tests. * Handle multi-form data. - Don't set content-type for multi-part/form - Use data field instead of JSON * Change to api.comfy.org * Handle error code 409. * separate out nodes per openai model * Update error message. * fix wrong output type * re-categorize nodes, remove ideogram (for now) * oops, fix mappings * fix ruff * Update frontend to 1.17.9 * embargo lift rename nodes * remove unused autogenerated model code * fix API type error and add b64 support for 4o * fix ruff * oops forgot mask scaling code * Remove unused types. --------- Co-authored-by: bymyself Co-authored-by: Yoland Y <4950057+yoland68@users.noreply.github.com> Co-authored-by: thot-experiment --- .github/workflows/update-api-stubs.yml | 47 +++ comfy_api_nodes/apis/PixverseController.py | 17 + comfy_api_nodes/apis/PixverseDto.py | 57 +++ comfy_api_nodes/apis/__init__.py | 422 ++++++++++++++++++++ comfy_api_nodes/apis/client.py | 2 +- comfy_api_nodes/nodes_api.py | 425 +++++++++++++++++++++ nodes.py | 9 + requirements.txt | 2 +- 8 files changed, 979 insertions(+), 2 deletions(-) create mode 100644 .github/workflows/update-api-stubs.yml create mode 100644 comfy_api_nodes/apis/PixverseController.py create mode 100644 comfy_api_nodes/apis/PixverseDto.py create mode 100644 comfy_api_nodes/apis/__init__.py create mode 100644 comfy_api_nodes/nodes_api.py diff --git a/.github/workflows/update-api-stubs.yml b/.github/workflows/update-api-stubs.yml new file mode 100644 index 000000000..2ae99b673 --- /dev/null +++ b/.github/workflows/update-api-stubs.yml @@ -0,0 +1,47 @@ +name: Generate Pydantic Stubs from api.comfy.org + +on: + schedule: + - cron: '0 0 * * 1' + workflow_dispatch: + +jobs: + generate-models: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.10' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install 'datamodel-code-generator[http]' + + - name: Generate API models + run: | + datamodel-codegen --use-subclass-enum --url https://api.comfy.org/openapi --output comfy_api_nodes/apis --output-model-type pydantic_v2.BaseModel + + - name: Check for changes + id: git-check + run: | + git diff --exit-code comfy_api_nodes/apis || echo "changes=true" >> $GITHUB_OUTPUT + + - name: Create Pull Request + if: steps.git-check.outputs.changes == 'true' + uses: peter-evans/create-pull-request@v5 + with: + commit-message: 'chore: update API models from OpenAPI spec' + title: 'Update API models from api.comfy.org' + body: | + This PR updates the API models based on the latest api.comfy.org OpenAPI specification. + + Generated automatically by the a Github workflow. + branch: update-api-stubs + delete-branch: true + base: main diff --git a/comfy_api_nodes/apis/PixverseController.py b/comfy_api_nodes/apis/PixverseController.py new file mode 100644 index 000000000..29a3ab33b --- /dev/null +++ b/comfy_api_nodes/apis/PixverseController.py @@ -0,0 +1,17 @@ +# generated by datamodel-codegen: +# filename: https://api.comfy.org/openapi +# timestamp: 2025-04-23T15:56:33+00:00 + +from __future__ import annotations + +from typing import Optional + +from pydantic import BaseModel + +from . import PixverseDto + + +class ResponseData(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp: Optional[PixverseDto.V2OpenAPII2VResp] = None diff --git a/comfy_api_nodes/apis/PixverseDto.py b/comfy_api_nodes/apis/PixverseDto.py new file mode 100644 index 000000000..399512214 --- /dev/null +++ b/comfy_api_nodes/apis/PixverseDto.py @@ -0,0 +1,57 @@ +# generated by datamodel-codegen: +# filename: https://api.comfy.org/openapi +# timestamp: 2025-04-23T15:56:33+00:00 + +from __future__ import annotations + +from typing import Optional + +from pydantic import BaseModel, Field, constr + + +class V2OpenAPII2VResp(BaseModel): + video_id: Optional[int] = Field(None, description='Video_id') + + +class V2OpenAPIT2VReq(BaseModel): + aspect_ratio: str = Field( + ..., description='Aspect ratio (16:9, 4:3, 1:1, 3:4, 9:16)', examples=['16:9'] + ) + duration: int = Field( + ..., + description='Video duration (5, 8 seconds, --model=v3.5 only allows 5,8; --quality=1080p does not support 8s)', + examples=[5], + ) + model: str = Field( + ..., description='Model version (only supports v3.5)', examples=['v3.5'] + ) + motion_mode: Optional[str] = Field( + 'normal', + description='Motion mode (normal, fast, --fast only available when duration=5; --quality=1080p does not support fast)', + examples=['normal'], + ) + negative_prompt: Optional[constr(max_length=2048)] = Field( + None, description='Negative prompt\n' + ) + prompt: constr(max_length=2048) = Field(..., description='Prompt') + quality: str = Field( + ..., + description='Video quality ("360p"(Turbo model), "540p", "720p", "1080p")', + examples=['540p'], + ) + seed: Optional[int] = Field(None, description='Random seed, range: 0 - 2147483647') + style: Optional[str] = Field( + None, + description='Style (effective when model=v3.5, "anime", "3d_animation", "clay", "comic", "cyberpunk") Do not include style parameter unless needed', + examples=['anime'], + ) + template_id: Optional[int] = Field( + None, + description='Template ID (template_id must be activated before use)', + examples=[302325299692608], + ) + water_mark: Optional[bool] = Field( + False, + description='Watermark (true: add watermark, false: no watermark)', + examples=[False], + ) diff --git a/comfy_api_nodes/apis/__init__.py b/comfy_api_nodes/apis/__init__.py new file mode 100644 index 000000000..e7ea9b332 --- /dev/null +++ b/comfy_api_nodes/apis/__init__.py @@ -0,0 +1,422 @@ +# generated by datamodel-codegen: +# filename: https://api.comfy.org/openapi +# timestamp: 2025-04-23T15:56:33+00:00 + +from __future__ import annotations + +from datetime import datetime +from enum import Enum +from typing import Any, Dict, List, Optional + +from pydantic import AnyUrl, BaseModel, Field, confloat, conint + +class Customer(BaseModel): + createdAt: Optional[datetime] = Field( + None, description='The date and time the user was created' + ) + email: Optional[str] = Field(None, description='The email address for this user') + id: str = Field(..., description='The firebase UID of the user') + name: Optional[str] = Field(None, description='The name for this user') + updatedAt: Optional[datetime] = Field( + None, description='The date and time the user was last updated' + ) + + +class Error(BaseModel): + details: Optional[List[str]] = Field( + None, + description='Optional detailed information about the error or hints for resolving it.', + ) + message: Optional[str] = Field( + None, description='A clear and concise description of the error.' + ) + + +class ErrorResponse(BaseModel): + error: str + message: str + +class ImageRequest(BaseModel): + aspect_ratio: Optional[str] = Field( + None, + description="Optional. The aspect ratio (e.g., 'ASPECT_16_9', 'ASPECT_1_1'). Cannot be used with resolution. Defaults to 'ASPECT_1_1' if unspecified.", + ) + color_palette: Optional[Dict[str, Any]] = Field( + None, description='Optional. Color palette object. Only for V_2, V_2_TURBO.' + ) + magic_prompt_option: Optional[str] = Field( + None, description="Optional. MagicPrompt usage ('AUTO', 'ON', 'OFF')." + ) + model: str = Field(..., description="The model used (e.g., 'V_2', 'V_2A_TURBO')") + negative_prompt: Optional[str] = Field( + None, + description='Optional. Description of what to exclude. Only for V_1, V_1_TURBO, V_2, V_2_TURBO.', + ) + num_images: Optional[conint(ge=1, le=8)] = Field( + 1, description='Optional. Number of images to generate (1-8). Defaults to 1.' + ) + prompt: str = Field( + ..., description='Required. The prompt to use to generate the image.' + ) + resolution: Optional[str] = Field( + None, + description="Optional. Resolution (e.g., 'RESOLUTION_1024_1024'). Only for model V_2. Cannot be used with aspect_ratio.", + ) + seed: Optional[conint(ge=0, le=2147483647)] = Field( + None, description='Optional. A number between 0 and 2147483647.' + ) + style_type: Optional[str] = Field( + None, + description="Optional. Style type ('AUTO', 'GENERAL', 'REALISTIC', 'DESIGN', 'RENDER_3D', 'ANIME'). Only for models V_2 and above.", + ) + + +class Datum(BaseModel): + is_image_safe: Optional[bool] = Field( + None, description='Indicates whether the image is considered safe.' + ) + prompt: Optional[str] = Field( + None, description='The prompt used to generate this image.' + ) + resolution: Optional[str] = Field( + None, description="The resolution of the generated image (e.g., '1024x1024')." + ) + seed: Optional[int] = Field( + None, description='The seed value used for this generation.' + ) + style_type: Optional[str] = Field( + None, + description="The style type used for generation (e.g., 'REALISTIC', 'ANIME').", + ) + url: Optional[str] = Field(None, description='URL to the generated image.') + + +class Code(Enum): + int_1100 = 1100 + int_1101 = 1101 + int_1102 = 1102 + int_1103 = 1103 + + +class Code1(Enum): + int_1000 = 1000 + int_1001 = 1001 + int_1002 = 1002 + int_1003 = 1003 + int_1004 = 1004 + + +class AspectRatio(str, Enum): + field_16_9 = '16:9' + field_9_16 = '9:16' + field_1_1 = '1:1' + + +class Config(BaseModel): + horizontal: Optional[confloat(ge=-10.0, le=10.0)] = None + pan: Optional[confloat(ge=-10.0, le=10.0)] = None + roll: Optional[confloat(ge=-10.0, le=10.0)] = None + tilt: Optional[confloat(ge=-10.0, le=10.0)] = None + vertical: Optional[confloat(ge=-10.0, le=10.0)] = None + zoom: Optional[confloat(ge=-10.0, le=10.0)] = None + + +class Type(str, Enum): + simple = 'simple' + down_back = 'down_back' + forward_up = 'forward_up' + right_turn_forward = 'right_turn_forward' + left_turn_forward = 'left_turn_forward' + + +class CameraControl(BaseModel): + config: Optional[Config] = None + type: Optional[Type] = Field(None, description='Predefined camera movements type') + + +class Duration(str, Enum): + field_5 = 5 + field_10 = 10 + + +class Mode(str, Enum): + std = 'std' + pro = 'pro' + + +class TaskInfo(BaseModel): + external_task_id: Optional[str] = None + + +class Video(BaseModel): + duration: Optional[str] = Field(None, description='Total video duration') + id: Optional[str] = Field(None, description='Generated video ID') + url: Optional[AnyUrl] = Field(None, description='URL for generated video') + + +class TaskResult(BaseModel): + videos: Optional[List[Video]] = None + + +class TaskStatus(str, Enum): + submitted = 'submitted' + processing = 'processing' + succeed = 'succeed' + failed = 'failed' + + +class Data(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult] = None + task_status: Optional[TaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class AspectRatio1(str, Enum): + field_16_9 = '16:9' + field_9_16 = '9:16' + field_1_1 = '1:1' + field_4_3 = '4:3' + field_3_4 = '3:4' + field_3_2 = '3:2' + field_2_3 = '2:3' + field_21_9 = '21:9' + + +class ImageReference(str, Enum): + subject = 'subject' + face = 'face' + + +class Image(BaseModel): + index: Optional[int] = Field(None, description='Image Number (0-9)') + url: Optional[AnyUrl] = Field(None, description='URL for generated image') + + +class TaskResult1(BaseModel): + images: Optional[List[Image]] = None + + +class Data1(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_result: Optional[TaskResult1] = None + task_status: Optional[TaskStatus] = None + task_status_msg: Optional[str] = Field(None, description='Task status information') + updated_at: Optional[int] = Field(None, description='Task update time') + + +class AspectRatio2(str, Enum): + field_16_9 = '16:9' + field_9_16 = '9:16' + field_1_1 = '1:1' + + +class CameraControl1(BaseModel): + config: Optional[Config] = None + type: Optional[Type] = Field(None, description='Predefined camera movements type') + + +class ModelName2(str, Enum): + kling_v1 = 'kling-v1' + kling_v1_6 = 'kling-v1-6' + + +class TaskResult2(BaseModel): + videos: Optional[List[Video]] = None + + +class Data2(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult2] = None + task_status: Optional[TaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class Code2(Enum): + int_1200 = 1200 + int_1201 = 1201 + int_1202 = 1202 + int_1203 = 1203 + + +class ResourcePackType(str, Enum): + decreasing_total = 'decreasing_total' + constant_period = 'constant_period' + + +class Status(str, Enum): + toBeOnline = 'toBeOnline' + online = 'online' + expired = 'expired' + runOut = 'runOut' + + +class ResourcePackSubscribeInfo(BaseModel): + effective_time: Optional[int] = Field( + None, description='Effective time, Unix timestamp in ms' + ) + invalid_time: Optional[int] = Field( + None, description='Expiration time, Unix timestamp in ms' + ) + purchase_time: Optional[int] = Field( + None, description='Purchase time, Unix timestamp in ms' + ) + remaining_quantity: Optional[float] = Field( + None, description='Remaining quantity (updated with a 12-hour delay)' + ) + resource_pack_id: Optional[str] = Field(None, description='Resource package ID') + resource_pack_name: Optional[str] = Field(None, description='Resource package name') + resource_pack_type: Optional[ResourcePackType] = Field( + None, + description='Resource package type (decreasing_total=decreasing total, constant_period=constant periodicity)', + ) + status: Optional[Status] = Field(None, description='Resource Package Status') + total_quantity: Optional[float] = Field(None, description='Total quantity') + +class Background(str, Enum): + transparent = 'transparent' + opaque = 'opaque' + + +class Moderation(str, Enum): + low = 'low' + auto = 'auto' + + +class OutputFormat(str, Enum): + png = 'png' + webp = 'webp' + jpeg = 'jpeg' + + +class Quality(str, Enum): + low = 'low' + medium = 'medium' + high = 'high' + + +class OpenAIImageEditRequest(BaseModel): + background: Optional[str] = Field( + None, description='Background transparency', examples=['opaque'] + ) + model: str = Field( + ..., description='The model to use for image editing', examples=['gpt-image-1'] + ) + moderation: Optional[Moderation] = Field( + None, description='Content moderation setting', examples=['auto'] + ) + n: Optional[int] = Field( + None, description='The number of images to generate', examples=[1] + ) + output_compression: Optional[int] = Field( + None, description='Compression level for JPEG or WebP (0-100)', examples=[100] + ) + output_format: Optional[OutputFormat] = Field( + None, description='Format of the output image', examples=['png'] + ) + prompt: str = Field( + ..., + description='A text description of the desired edit', + examples=['Give the rocketship rainbow coloring'], + ) + quality: Optional[str] = Field( + None, description='The quality of the edited image', examples=['low'] + ) + size: Optional[str] = Field( + None, description='Size of the output image', examples=['1024x1024'] + ) + user: Optional[str] = Field( + None, + description='A unique identifier for end-user monitoring', + examples=['user-1234'], + ) + + +class Quality1(str, Enum): + low = 'low' + medium = 'medium' + high = 'high' + standard = 'standard' + hd = 'hd' + + +class ResponseFormat(str, Enum): + url = 'url' + b64_json = 'b64_json' + + +class Style(str, Enum): + vivid = 'vivid' + natural = 'natural' + + +class OpenAIImageGenerationRequest(BaseModel): + background: Optional[Background] = Field( + None, description='Background transparency', examples=['opaque'] + ) + model: Optional[str] = Field( + None, description='The model to use for image generation', examples=['dall-e-3'] + ) + moderation: Optional[Moderation] = Field( + None, description='Content moderation setting', examples=['auto'] + ) + n: Optional[int] = Field( + None, + description='The number of images to generate (1-10). Only 1 supported for dall-e-3.', + examples=[1], + ) + output_compression: Optional[int] = Field( + None, description='Compression level for JPEG or WebP (0-100)', examples=[100] + ) + output_format: Optional[OutputFormat] = Field( + None, description='Format of the output image', examples=['png'] + ) + prompt: str = Field( + ..., + description='A text description of the desired image', + examples=['Draw a rocket in front of a blackhole in deep space'], + ) + quality: Optional[Quality1] = Field( + None, description='The quality of the generated image', examples=['high'] + ) + response_format: Optional[ResponseFormat] = Field( + None, description='Response format of image data', examples=['b64_json'] + ) + size: Optional[str] = Field( + None, + description='Size of the image (e.g., 1024x1024, 1536x1024, auto)', + examples=['1024x1536'], + ) + style: Optional[Style] = Field( + None, description='Style of the image (only for dall-e-3)', examples=['vivid'] + ) + user: Optional[str] = Field( + None, + description='A unique identifier for end-user monitoring', + examples=['user-1234'], + ) + + +class Datum1(BaseModel): + b64_json: Optional[str] = Field(None, description='Base64 encoded image data') + revised_prompt: Optional[str] = Field(None, description='Revised prompt') + url: Optional[str] = Field(None, description='URL of the image') + + +class OpenAIImageGenerationResponse(BaseModel): + data: Optional[List[Datum1]] = None +class User(BaseModel): + email: Optional[str] = Field(None, description='The email address for this user.') + id: Optional[str] = Field(None, description='The unique id for this user.') + isAdmin: Optional[bool] = Field( + None, description='Indicates if the user has admin privileges.' + ) + isApproved: Optional[bool] = Field( + None, description='Indicates if the user is approved.' + ) + name: Optional[str] = Field(None, description='The name for this user.') diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py index cd81d5a1d..9bc3d76d5 100644 --- a/comfy_api_nodes/apis/client.py +++ b/comfy_api_nodes/apis/client.py @@ -226,7 +226,7 @@ class ApiClient: def check_auth_token(self, auth_token): """Verify that an auth token is present.""" if auth_token is None: - raise Exception("Please login first to use this node.") + raise Exception("Unauthorized: Please login first to use this node.") return auth_token diff --git a/comfy_api_nodes/nodes_api.py b/comfy_api_nodes/nodes_api.py new file mode 100644 index 000000000..92f4a0c87 --- /dev/null +++ b/comfy_api_nodes/nodes_api.py @@ -0,0 +1,425 @@ +import io +from inspect import cleandoc + +from comfy.utils import common_upscale +from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict +from comfy_api_nodes.apis import ( + OpenAIImageGenerationRequest, + OpenAIImageEditRequest, + OpenAIImageGenerationResponse +) +from comfy_api_nodes.apis.client import ApiEndpoint, HttpMethod, SynchronousOperation + +import numpy as np +from PIL import Image +import requests +import torch +import math +import base64 + +def downscale_input(image): + samples = image.movedim(-1,1) + #downscaling input images to roughly the same size as the outputs + total = int(1536 * 1024) + scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) + if scale_by >= 1: + return image + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + + s = common_upscale(samples, width, height, "lanczos", "disabled") + s = s.movedim(1,-1) + return s + +def validate_and_cast_response (response): + # validate raw JSON response + data = response.data + if not data or len(data) == 0: + raise Exception("No images returned from API endpoint") + + # Get base64 image data + image_url = data[0].url + b64_data = data[0].b64_json + if not image_url and not b64_data: + raise Exception("No image was generated in the response") + + if b64_data: + img_data = base64.b64decode(b64_data) + img = Image.open(io.BytesIO(img_data)) + + elif image_url: + img_response = requests.get(image_url) + if img_response.status_code != 200: + raise Exception("Failed to download the image") + img = Image.open(io.BytesIO(img_response.content)) + + img = img.convert("RGB") # Ensure RGB format + + # Convert to numpy array, normalize to float32 between 0 and 1 + img_array = np.array(img).astype(np.float32) / 255.0 + + # Convert to torch tensor and add batch dimension + return torch.from_numpy(img_array)[None,] + +class OpenAIDalle2(ComfyNodeABC): + """ + Generates images synchronously via OpenAI's DALL·E 2 endpoint. + + Uses the proxy at /proxy/openai/images/generations. Returned URLs are short‑lived, + so download or cache results if you need to keep them. + """ + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": (IO.STRING, { + "multiline": True, + "default": "", + "tooltip": "Text prompt for DALL·E", + }), + }, + "optional": { + "seed": (IO.INT, { + "default": 0, + "min": 0, + "max": 2**31-1, + "step": 1, + "display": "number", + "tooltip": "not implemented yet in backend", + }), + "size": (IO.COMBO, { + "options": ["256x256", "512x512", "1024x1024"], + "default": "1024x1024", + "tooltip": "Image size", + }), + "n": (IO.INT, { + "default": 1, + "min": 1, + "max": 8, + "step": 1, + "display": "number", + "tooltip": "How many images to generate", + }), + "image": (IO.IMAGE, { + "default": None, + "tooltip": "Optional reference image for image editing.", + }), + "mask": (IO.MASK, { + "default": None, + "tooltip": "Optional mask for inpainting (white areas will be replaced)", + }), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG" + } + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + def api_call(self, prompt, seed=0, image=None, mask=None, n=1, size="1024x1024", auth_token=None): + model = "dall-e-2" + path = "/proxy/openai/images/generations" + request_class = OpenAIImageGenerationRequest + img_binary = None + + if image is not None and mask is not None: + path = "/proxy/openai/images/edits" + request_class = OpenAIImageEditRequest + + input_tensor = image.squeeze().cpu() + height, width, channels = input_tensor.shape + rgba_tensor = torch.ones(height, width, 4, device="cpu") + rgba_tensor[:, :, :channels] = input_tensor + + if mask.shape[1:] != image.shape[1:-1]: + raise Exception("Mask and Image must be the same size") + rgba_tensor[:,:,3] = (1-mask.squeeze().cpu()) + + rgba_tensor = downscale_input(rgba_tensor.unsqueeze(0)).squeeze() + + image_np = (rgba_tensor.numpy() * 255).astype(np.uint8) + img = Image.fromarray(image_np) + img_byte_arr = io.BytesIO() + img.save(img_byte_arr, format='PNG') + img_byte_arr.seek(0) + img_binary = img_byte_arr#.getvalue() + img_binary.name = "image.png" + elif image is not None or mask is not None: + raise Exception("Dall-E 2 image editing requires an image AND a mask") + + # Build the operation + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=request_class, + response_model=OpenAIImageGenerationResponse + ), + request=request_class( + model=model, + prompt=prompt, + n=n, + size=size, + seed=seed, + ), + files={ + "image": img_binary, + } if img_binary else None, + auth_token=auth_token + ) + + response = operation.execute() + + img_tensor = validate_and_cast_response(response) + return (img_tensor,) + +class OpenAIDalle3(ComfyNodeABC): + """ + Generates images synchronously via OpenAI's DALL·E 3 endpoint. + + Uses the proxy at /proxy/openai/images/generations. Returned URLs are short‑lived, + so download or cache results if you need to keep them. + """ + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": (IO.STRING, { + "multiline": True, + "default": "", + "tooltip": "Text prompt for DALL·E", + }), + }, + "optional": { + "seed": (IO.INT, { + "default": 0, + "min": 0, + "max": 2**31-1, + "step": 1, + "display": "number", + "tooltip": "not implemented yet in backend", + }), + "quality" : (IO.COMBO, { + "options": ["standard","hd"], + "default": "standard", + "tooltip": "Image quality", + }), + "style": (IO.COMBO, { + "options": ["natural","vivid"], + "default": "natural", + "tooltip": "Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images.", + }), + "size": (IO.COMBO, { + "options": ["1024x1024", "1024x1792", "1792x1024"], + "default": "1024x1024", + "tooltip": "Image size", + }), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG" + } + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + def api_call(self, prompt, seed=0, style="natural", quality="standard", size="1024x1024", auth_token=None): + model = "dall-e-3" + + # build the operation + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/openai/images/generations", + method=HttpMethod.POST, + request_model=OpenAIImageGenerationRequest, + response_model=OpenAIImageGenerationResponse + ), + request=OpenAIImageGenerationRequest( + model=model, + prompt=prompt, + quality=quality, + size=size, + style=style, + seed=seed, + ), + auth_token=auth_token + ) + + response = operation.execute() + + img_tensor = validate_and_cast_response(response) + return (img_tensor,) + +class OpenAIGPTImage1(ComfyNodeABC): + """ + Generates images synchronously via OpenAI's GPT Image 1 endpoint. + + Uses the proxy at /proxy/openai/images/generations. Returned URLs are short‑lived, + so download or cache results if you need to keep them. + """ + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": (IO.STRING, { + "multiline": True, + "default": "", + "tooltip": "Text prompt for GPT Image 1", + }), + }, + "optional": { + "seed": (IO.INT, { + "default": 0, + "min": 0, + "max": 2**31-1, + "step": 1, + "display": "number", + "tooltip": "not implemented yet in backend", + }), + "quality": (IO.COMBO, { + "options": ["low","medium","high"], + "default": "low", + "tooltip": "Image quality, affects cost and generation time.", + }), + "background": (IO.COMBO, { + "options": ["opaque","transparent"], + "default": "opaque", + "tooltip": "Return image with or without background", + }), + "size": (IO.COMBO, { + "options": ["auto", "1024x1024", "1024x1536", "1536x1024"], + "default": "auto", + "tooltip": "Image size", + }), + "n": (IO.INT, { + "default": 1, + "min": 1, + "max": 8, + "step": 1, + "display": "number", + "tooltip": "How many images to generate", + }), + "image": (IO.IMAGE, { + "default": None, + "tooltip": "Optional reference image for image editing.", + }), + "mask": (IO.MASK, { + "default": None, + "tooltip": "Optional mask for inpainting (white areas will be replaced)", + }), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG" + } + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + def api_call(self, prompt, seed=0, quality="low", background="opaque", image=None, mask=None, n=1, size="1024x1024", auth_token=None): + model = "gpt-image-1" + path = "/proxy/openai/images/generations" + request_class = OpenAIImageGenerationRequest + img_binary = None + mask_binary = None + + + if image is not None: + path = "/proxy/openai/images/edits" + request_class = OpenAIImageEditRequest + + scaled_image = downscale_input(image).squeeze() + + image_np = (scaled_image.numpy() * 255).astype(np.uint8) + img = Image.fromarray(image_np) + img_byte_arr = io.BytesIO() + img.save(img_byte_arr, format='PNG') + img_byte_arr.seek(0) + img_binary = img_byte_arr#.getvalue() + img_binary.name = "image.png" + + if mask is not None: + if image is None: + raise Exception("Cannot use a mask without an input image") + if mask.shape[1:] != image.shape[1:-1]: + raise Exception("Mask and Image must be the same size") + batch, height, width = mask.shape + rgba_mask = torch.zeros(height, width, 4, device="cpu") + rgba_mask[:,:,3] = (1-mask.squeeze().cpu()) + + scaled_mask = downscale_input(rgba_mask.unsqueeze(0)).squeeze() + + mask_np = (scaled_mask.numpy() * 255).astype(np.uint8) + mask_img = Image.fromarray(mask_np) + mask_img_byte_arr = io.BytesIO() + mask_img.save(mask_img_byte_arr, format='PNG') + mask_img_byte_arr.seek(0) + mask_binary = mask_img_byte_arr#.getvalue() + mask_binary.name = "mask.png" + + files = {} + if img_binary: + files["image"] = img_binary + if mask_binary: + files["mask"] = mask_binary + + # Build the operation + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=request_class, + response_model=OpenAIImageGenerationResponse + ), + request=request_class( + model=model, + prompt=prompt, + quality=quality, + background=background, + n=n, + seed=seed, + size=size, + ), + files=files if files else None, + auth_token=auth_token + ) + + response = operation.execute() + + img_tensor = validate_and_cast_response(response) + return (img_tensor,) + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "OpenAIDalle2": OpenAIDalle2, + "OpenAIDalle3": OpenAIDalle3, + "OpenAIGPTImage1": OpenAIGPTImage1, +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "OpenAIDalle2": "OpenAI DALL·E 2", + "OpenAIDalle3": "OpenAI DALL·E 3", + "OpenAIGPTImage1": "OpenAI GPT Image 1", +} diff --git a/nodes.py b/nodes.py index b1ab62aad..73a62d930 100644 --- a/nodes.py +++ b/nodes.py @@ -2260,11 +2260,20 @@ def init_builtin_extra_nodes(): "nodes_fresca.py", ] + api_nodes_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_api_nodes") + api_nodes_files = [ + "nodes_api.py", + ] + import_failed = [] for node_file in extras_files: if not load_custom_node(os.path.join(extras_dir, node_file), module_parent="comfy_extras"): import_failed.append(node_file) + for node_file in api_nodes_files: + if not load_custom_node(os.path.join(api_nodes_dir, node_file), module_parent="comfy_api_nodes"): + import_failed.append(node_file) + return import_failed diff --git a/requirements.txt b/requirements.txt index f8ad908ca..2ac241261 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.16.9 +comfyui-frontend-package==1.17.9 comfyui-workflow-templates==0.1.3 torch torchsde From e8ddc2be95e3c70363414dfca94f57d6dad25c8f Mon Sep 17 00:00:00 2001 From: filtered <176114999+webfiltered@users.noreply.github.com> Date: Thu, 24 Apr 2025 06:02:41 +1000 Subject: [PATCH 018/193] [BugFix] Update frontend to 1.17.10 (#7762) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 2ac241261..291f81838 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.17.9 +comfyui-frontend-package==1.17.10 comfyui-workflow-templates==0.1.3 torch torchsde From 2c1d686ec61f26f3a64bb4c1afdcdb78bb943a4f Mon Sep 17 00:00:00 2001 From: thot experiment <94414189+thot-experiment@users.noreply.github.com> Date: Wed, 23 Apr 2025 13:10:10 -0700 Subject: [PATCH 019/193] implement multi image prompting for gpt-image-1 and fix transparency in outputs (#7763) * implement multi image prompting for GPTI Image 1 * fix transparency not working * fix ruff --- comfy_api_nodes/nodes_api.py | 43 ++++++++++++++++++++++-------------- 1 file changed, 26 insertions(+), 17 deletions(-) diff --git a/comfy_api_nodes/nodes_api.py b/comfy_api_nodes/nodes_api.py index 92f4a0c87..7bca0b503 100644 --- a/comfy_api_nodes/nodes_api.py +++ b/comfy_api_nodes/nodes_api.py @@ -53,7 +53,7 @@ def validate_and_cast_response (response): raise Exception("Failed to download the image") img = Image.open(io.BytesIO(img_response.content)) - img = img.convert("RGB") # Ensure RGB format + img = img.convert("RGBA") # Convert to numpy array, normalize to float32 between 0 and 1 img_array = np.array(img).astype(np.float32) / 255.0 @@ -339,25 +339,38 @@ class OpenAIGPTImage1(ComfyNodeABC): model = "gpt-image-1" path = "/proxy/openai/images/generations" request_class = OpenAIImageGenerationRequest - img_binary = None + img_binaries = [] mask_binary = None - + files = [] if image is not None: path = "/proxy/openai/images/edits" request_class = OpenAIImageEditRequest - scaled_image = downscale_input(image).squeeze() + batch_size = image.shape[0] - image_np = (scaled_image.numpy() * 255).astype(np.uint8) - img = Image.fromarray(image_np) - img_byte_arr = io.BytesIO() - img.save(img_byte_arr, format='PNG') - img_byte_arr.seek(0) - img_binary = img_byte_arr#.getvalue() - img_binary.name = "image.png" + + for i in range(batch_size): + single_image = image[i:i+1] + scaled_image = downscale_input(single_image).squeeze() + + image_np = (scaled_image.numpy() * 255).astype(np.uint8) + img = Image.fromarray(image_np) + img_byte_arr = io.BytesIO() + img.save(img_byte_arr, format='PNG') + img_byte_arr.seek(0) + img_binary = img_byte_arr + img_binary.name = f"image_{i}.png" + + img_binaries.append(img_binary) + if batch_size == 1: + files.append(("image", img_binary)) + else: + files.append(("image[]", img_binary)) if mask is not None: + if image.shape[0] != 1: + raise Exception("Cannot use a mask with multiple image") if image is None: raise Exception("Cannot use a mask without an input image") if mask.shape[1:] != image.shape[1:-1]: @@ -373,14 +386,10 @@ class OpenAIGPTImage1(ComfyNodeABC): mask_img_byte_arr = io.BytesIO() mask_img.save(mask_img_byte_arr, format='PNG') mask_img_byte_arr.seek(0) - mask_binary = mask_img_byte_arr#.getvalue() + mask_binary = mask_img_byte_arr mask_binary.name = "mask.png" + files.append(("mask", mask_binary)) - files = {} - if img_binary: - files["image"] = img_binary - if mask_binary: - files["mask"] = mask_binary # Build the operation operation = SynchronousOperation( From 188b383c35f0a790e407cb337dd554fccb188f6f Mon Sep 17 00:00:00 2001 From: thot experiment <94414189+thot-experiment@users.noreply.github.com> Date: Wed, 23 Apr 2025 14:53:34 -0700 Subject: [PATCH 020/193] change timeout to 7 days (#7765) --- comfy_api_nodes/apis/client.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py index 9bc3d76d5..384e559dc 100644 --- a/comfy_api_nodes/apis/client.py +++ b/comfy_api_nodes/apis/client.py @@ -269,7 +269,7 @@ class SynchronousOperation(Generic[T, R]): files: Optional[Dict[str, Any]] = None, api_base: str = "https://api.comfy.org", auth_token: Optional[str] = None, - timeout: float = 60.0, + timeout: float = 604800.0, verify_ssl: bool = True, ): self.endpoint = endpoint From 11b68ebd22c2137661ec6a70f39943a337edf897 Mon Sep 17 00:00:00 2001 From: filtered <176114999+webfiltered@users.noreply.github.com> Date: Thu, 24 Apr 2025 08:16:12 +1000 Subject: [PATCH 021/193] [BugFix] Update frontend to 1.17.11 (#7766) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 291f81838..10cc177af 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.17.10 +comfyui-frontend-package==1.17.11 comfyui-workflow-templates==0.1.3 torch torchsde From e2eed9eb9b70f1b2290d5384fd8cfb739c092b44 Mon Sep 17 00:00:00 2001 From: thot experiment <94414189+thot-experiment@users.noreply.github.com> Date: Wed, 23 Apr 2025 18:28:36 -0700 Subject: [PATCH 022/193] throw away alpha channel in clip vision preprocessor (#7769) saves users having to explicitly discard the channel --- comfy/clip_vision.py | 1 + 1 file changed, 1 insertion(+) diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py index 11bc57789..00aab9164 100644 --- a/comfy/clip_vision.py +++ b/comfy/clip_vision.py @@ -18,6 +18,7 @@ class Output: setattr(self, key, item) def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True): + image = image[:, :, :, :3] if image.shape[3] > 3 else image mean = torch.tensor(mean, device=image.device, dtype=image.dtype) std = torch.tensor(std, device=image.device, dtype=image.dtype) image = image.movedim(-1, 1) From 5c80da31dbfe6382da5b489098b57a411e7f58ed Mon Sep 17 00:00:00 2001 From: thot experiment <94414189+thot-experiment@users.noreply.github.com> Date: Thu, 24 Apr 2025 00:29:05 -0700 Subject: [PATCH 023/193] fix multiple image return from api nodes (#7772) --- comfy_api_nodes/nodes_api.py | 46 +++++++++++++++++++++--------------- 1 file changed, 27 insertions(+), 19 deletions(-) diff --git a/comfy_api_nodes/nodes_api.py b/comfy_api_nodes/nodes_api.py index 7bca0b503..4105ba7e1 100644 --- a/comfy_api_nodes/nodes_api.py +++ b/comfy_api_nodes/nodes_api.py @@ -31,35 +31,43 @@ def downscale_input(image): s = s.movedim(1,-1) return s -def validate_and_cast_response (response): +def validate_and_cast_response(response): # validate raw JSON response data = response.data if not data or len(data) == 0: raise Exception("No images returned from API endpoint") - # Get base64 image data - image_url = data[0].url - b64_data = data[0].b64_json - if not image_url and not b64_data: - raise Exception("No image was generated in the response") + # Initialize list to store image tensors + image_tensors = [] - if b64_data: - img_data = base64.b64decode(b64_data) - img = Image.open(io.BytesIO(img_data)) + # Process each image in the data array + for image_data in data: + image_url = image_data.url + b64_data = image_data.b64_json - elif image_url: - img_response = requests.get(image_url) - if img_response.status_code != 200: - raise Exception("Failed to download the image") - img = Image.open(io.BytesIO(img_response.content)) + if not image_url and not b64_data: + raise Exception("No image was generated in the response") - img = img.convert("RGBA") + if b64_data: + img_data = base64.b64decode(b64_data) + img = Image.open(io.BytesIO(img_data)) - # Convert to numpy array, normalize to float32 between 0 and 1 - img_array = np.array(img).astype(np.float32) / 255.0 + elif image_url: + img_response = requests.get(image_url) + if img_response.status_code != 200: + raise Exception("Failed to download the image") + img = Image.open(io.BytesIO(img_response.content)) - # Convert to torch tensor and add batch dimension - return torch.from_numpy(img_array)[None,] + img = img.convert("RGBA") + + # Convert to numpy array, normalize to float32 between 0 and 1 + img_array = np.array(img).astype(np.float32) / 255.0 + img_tensor = torch.from_numpy(img_array) + + # Add to list of tensors + image_tensors.append(img_tensor) + + return torch.stack(image_tensors, dim=0) class OpenAIDalle2(ComfyNodeABC): """ From 5acb7058577ca81d26107ace01dd5c5c7a4a5f27 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 24 Apr 2025 10:58:31 -0700 Subject: [PATCH 024/193] Switch LTXVPreprocess to libx264 (#7776) --- comfy_extras/nodes_lt.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py index 525889200..ff3fe5cdc 100644 --- a/comfy_extras/nodes_lt.py +++ b/comfy_extras/nodes_lt.py @@ -385,7 +385,7 @@ def encode_single_frame(output_file, image_array: np.ndarray, crf): container = av.open(output_file, "w", format="mp4") try: stream = container.add_stream( - "h264", rate=1, options={"crf": str(crf), "preset": "veryfast"} + "libx264", rate=1, options={"crf": str(crf), "preset": "veryfast"} ) stream.height = image_array.shape[0] stream.width = image_array.shape[1] From a97f2f850abd7dd330e6363c8d8074bb243eb413 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 24 Apr 2025 16:03:01 -0400 Subject: [PATCH 025/193] ComfyUI version 0.3.30 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index f9161b37e..67d27f942 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.29" +__version__ = "0.3.30" diff --git a/pyproject.toml b/pyproject.toml index e8fc9555d..eadca662e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.29" +version = "0.3.30" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9" From f935d42d8ee399e57028d33e0142730d0c163a91 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 25 Apr 2025 03:11:14 -0400 Subject: [PATCH 026/193] Support SimpleTuner lycoris lora format for HiDream. --- comfy/lora.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/comfy/lora.py b/comfy/lora.py index 8760a21fb..fff524be2 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -279,6 +279,13 @@ def model_lora_keys_unet(model, key_map={}): key_map["transformer.{}".format(key_lora)] = k key_map["diffusion_model.{}".format(key_lora)] = k # Old loras + if isinstance(model, comfy.model_base.HiDream): + for k in sdk: + if k.startswith("diffusion_model."): + if k.endswith(".weight"): + key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") + key_map["lycoris_{}".format(key_lora)] = k #SimpleTuner lycoris format + return key_map From 78992c4b25ce7ef1305113872aa1f1e6aa6a070b Mon Sep 17 00:00:00 2001 From: AustinMroz Date: Fri, 25 Apr 2025 12:35:07 -0500 Subject: [PATCH 027/193] [NodeDef] Add documentation on widgetType (#7768) * [NodeDef] Add documentation on widgetType * Document required version for widgetType --- comfy/comfy_types/node_typing.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/comfy/comfy_types/node_typing.py b/comfy/comfy_types/node_typing.py index 0bdda032e..4ceeb3468 100644 --- a/comfy/comfy_types/node_typing.py +++ b/comfy/comfy_types/node_typing.py @@ -120,6 +120,10 @@ class InputTypeOptions(TypedDict): Available from frontend v1.17.5 Ref: https://github.com/Comfy-Org/ComfyUI_frontend/pull/3548 """ + widgetType: NotRequired[str] + """Specifies a type to be used for widget initialization if different from the input type. + Available from frontend v1.18.0 + https://github.com/Comfy-Org/ComfyUI_frontend/pull/3550""" # class InputTypeNumber(InputTypeOptions): # default: float | int min: NotRequired[float] From 23e39f2ba7c38d5fc21206da31ce7d357b232e15 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 25 Apr 2025 16:36:00 -0700 Subject: [PATCH 028/193] Add a T5TokenizerOptions node to set options for the T5 tokenizer. (#7803) --- comfy/sd.py | 10 ++++++++++ comfy/sd1_clip.py | 17 +++++++++++------ comfy/sdxl_clip.py | 4 ++-- comfy/text_encoders/flux.py | 4 ++-- comfy/text_encoders/hidream.py | 8 ++++---- comfy/text_encoders/hunyuan_video.py | 4 ++-- comfy/text_encoders/hydit.py | 4 ++-- comfy/text_encoders/sd3_clip.py | 6 +++--- comfy_extras/nodes_cond.py | 25 ++++++++++++++++++++++++- 9 files changed, 60 insertions(+), 22 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index 8aba5d655..748f6c1ec 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -120,6 +120,7 @@ class CLIP: self.layer_idx = None self.use_clip_schedule = False logging.info("CLIP/text encoder model load device: {}, offload device: {}, current: {}, dtype: {}".format(load_device, offload_device, params['device'], dtype)) + self.tokenizer_options = {} def clone(self): n = CLIP(no_init=True) @@ -127,6 +128,7 @@ class CLIP: n.cond_stage_model = self.cond_stage_model n.tokenizer = self.tokenizer n.layer_idx = self.layer_idx + n.tokenizer_options = self.tokenizer_options.copy() n.use_clip_schedule = self.use_clip_schedule n.apply_hooks_to_conds = self.apply_hooks_to_conds return n @@ -134,10 +136,18 @@ class CLIP: def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): return self.patcher.add_patches(patches, strength_patch, strength_model) + def set_tokenizer_option(self, option_name, value): + self.tokenizer_options[option_name] = value + def clip_layer(self, layer_idx): self.layer_idx = layer_idx def tokenize(self, text, return_word_ids=False, **kwargs): + tokenizer_options = kwargs.get("tokenizer_options", {}) + if len(self.tokenizer_options) > 0: + tokenizer_options = {**self.tokenizer_options, **tokenizer_options} + if len(tokenizer_options) > 0: + kwargs["tokenizer_options"] = tokenizer_options return self.tokenizer.tokenize_with_weights(text, return_word_ids, **kwargs) def add_hooks_to_dict(self, pooled_dict: dict[str]): diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index 2ca5ed9ba..ac61babe9 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -457,13 +457,14 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No return embed_out class SDTokenizer: - def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, tokenizer_data={}, tokenizer_args={}): + def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, tokenizer_data={}, tokenizer_args={}): if tokenizer_path is None: tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args) self.max_length = tokenizer_data.get("{}_max_length".format(embedding_key), max_length) self.min_length = min_length self.end_token = None + self.min_padding = min_padding empty = self.tokenizer('')["input_ids"] self.tokenizer_adds_end_token = has_end_token @@ -518,13 +519,15 @@ class SDTokenizer: return (embed, leftover) - def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): + def tokenize_with_weights(self, text:str, return_word_ids=False, tokenizer_options={}, **kwargs): ''' Takes a prompt and converts it to a list of (token, weight, word id) elements. Tokens can both be integer tokens and pre computed CLIP tensors. Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens. Returned list has the dimensions NxM where M is the input size of CLIP ''' + min_length = tokenizer_options.get("{}_min_length".format(self.embedding_key), self.min_length) + min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding) text = escape_important(text) parsed_weights = token_weights(text, 1.0) @@ -603,10 +606,12 @@ class SDTokenizer: #fill last batch if self.end_token is not None: batch.append((self.end_token, 1.0, 0)) - if self.pad_to_max_length: + if min_padding is not None: + batch.extend([(self.pad_token, 1.0, 0)] * min_padding) + if self.pad_to_max_length and len(batch) < self.max_length: batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch))) - if self.min_length is not None and len(batch) < self.min_length: - batch.extend([(self.pad_token, 1.0, 0)] * (self.min_length - len(batch))) + if min_length is not None and len(batch) < min_length: + batch.extend([(self.pad_token, 1.0, 0)] * (min_length - len(batch))) if not return_word_ids: batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens] @@ -634,7 +639,7 @@ class SD1Tokenizer: def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} - out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids) + out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): diff --git a/comfy/sdxl_clip.py b/comfy/sdxl_clip.py index ea7f5d10f..c8cef14e4 100644 --- a/comfy/sdxl_clip.py +++ b/comfy/sdxl_clip.py @@ -28,8 +28,8 @@ class SDXLTokenizer: def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} - out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) - out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) + out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py index 0666dde7f..d61ef6668 100644 --- a/comfy/text_encoders/flux.py +++ b/comfy/text_encoders/flux.py @@ -19,8 +19,8 @@ class FluxTokenizer: def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} - out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) - out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) + out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): diff --git a/comfy/text_encoders/hidream.py b/comfy/text_encoders/hidream.py index 8e1abcfc1..dbcf52784 100644 --- a/comfy/text_encoders/hidream.py +++ b/comfy/text_encoders/hidream.py @@ -16,11 +16,11 @@ class HiDreamTokenizer: def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} - out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) - out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) - t5xxl = self.t5xxl.tokenize_with_weights(text, return_word_ids) + out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) + t5xxl = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) out["t5xxl"] = [t5xxl[0]] # Use only first 128 tokens - out["llama"] = self.llama.tokenize_with_weights(text, return_word_ids) + out["llama"] = self.llama.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): diff --git a/comfy/text_encoders/hunyuan_video.py b/comfy/text_encoders/hunyuan_video.py index 33ac22497..b02148b33 100644 --- a/comfy/text_encoders/hunyuan_video.py +++ b/comfy/text_encoders/hunyuan_video.py @@ -49,13 +49,13 @@ class HunyuanVideoTokenizer: def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, image_embeds=None, image_interleave=1, **kwargs): out = {} - out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) if llama_template is None: llama_text = self.llama_template.format(text) else: llama_text = llama_template.format(text) - llama_text_tokens = self.llama.tokenize_with_weights(llama_text, return_word_ids) + llama_text_tokens = self.llama.tokenize_with_weights(llama_text, return_word_ids, **kwargs) embed_count = 0 for r in llama_text_tokens: for i in range(len(r)): diff --git a/comfy/text_encoders/hydit.py b/comfy/text_encoders/hydit.py index e7273f425..ac6994529 100644 --- a/comfy/text_encoders/hydit.py +++ b/comfy/text_encoders/hydit.py @@ -41,8 +41,8 @@ class HyditTokenizer: def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} - out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids) - out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids) + out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids, **kwargs) + out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): diff --git a/comfy/text_encoders/sd3_clip.py b/comfy/text_encoders/sd3_clip.py index 6c2fbeca4..ff5d412db 100644 --- a/comfy/text_encoders/sd3_clip.py +++ b/comfy/text_encoders/sd3_clip.py @@ -45,9 +45,9 @@ class SD3Tokenizer: def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} - out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) - out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) - out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids) + out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) + out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): diff --git a/comfy_extras/nodes_cond.py b/comfy_extras/nodes_cond.py index 4c3a1d5bf..574262178 100644 --- a/comfy_extras/nodes_cond.py +++ b/comfy_extras/nodes_cond.py @@ -20,6 +20,29 @@ class CLIPTextEncodeControlnet: c.append(n) return (c, ) +class T5TokenizerOptions: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "clip": ("CLIP", ), + "min_padding": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}), + "min_length": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}), + } + } + + RETURN_TYPES = ("CLIP",) + FUNCTION = "set_options" + + def set_options(self, clip, min_padding, min_length): + clip = clip.clone() + for t5_type in ["t5xxl", "pile_t5xl", "t5base", "mt5xl", "umt5xxl"]: + clip.set_tokenizer_option("{}_min_padding".format(t5_type), min_padding) + clip.set_tokenizer_option("{}_min_length".format(t5_type), min_length) + + return (clip, ) + NODE_CLASS_MAPPINGS = { - "CLIPTextEncodeControlnet": CLIPTextEncodeControlnet + "CLIPTextEncodeControlnet": CLIPTextEncodeControlnet, + "T5TokenizerOptions": T5TokenizerOptions, } From b685b8a4e098237919adae580eb29e8d861b738f Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 26 Apr 2025 01:43:12 -0700 Subject: [PATCH 029/193] Update portable package workflow to cu128 (#7812) --- .github/workflows/stable-release.yml | 4 ++-- .github/workflows/windows_release_dependencies.yml | 4 ++-- .github/workflows/windows_release_package.yml | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/stable-release.yml b/.github/workflows/stable-release.yml index 40df7ab88..c4302cdd6 100644 --- a/.github/workflows/stable-release.yml +++ b/.github/workflows/stable-release.yml @@ -12,7 +12,7 @@ on: description: 'CUDA version' required: true type: string - default: "126" + default: "128" python_minor: description: 'Python minor version' required: true @@ -22,7 +22,7 @@ on: description: 'Python patch version' required: true type: string - default: "9" + default: "10" jobs: diff --git a/.github/workflows/windows_release_dependencies.yml b/.github/workflows/windows_release_dependencies.yml index 7a8ec5782..dfdb96d50 100644 --- a/.github/workflows/windows_release_dependencies.yml +++ b/.github/workflows/windows_release_dependencies.yml @@ -17,7 +17,7 @@ on: description: 'cuda version' required: true type: string - default: "126" + default: "128" python_minor: description: 'python minor version' @@ -29,7 +29,7 @@ on: description: 'python patch version' required: true type: string - default: "9" + default: "10" # push: # branches: # - master diff --git a/.github/workflows/windows_release_package.yml b/.github/workflows/windows_release_package.yml index dc79b1f4a..80a45b321 100644 --- a/.github/workflows/windows_release_package.yml +++ b/.github/workflows/windows_release_package.yml @@ -7,7 +7,7 @@ on: description: 'cuda version' required: true type: string - default: "126" + default: "128" python_minor: description: 'python minor version' @@ -19,7 +19,7 @@ on: description: 'python patch version' required: true type: string - default: "9" + default: "10" # push: # branches: # - master From 0dcc75ca547b533a129699208aefa95c6742f1b6 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 26 Apr 2025 13:11:21 -0700 Subject: [PATCH 030/193] Add experimental --async-offload lowvram weight offloading. (#7820) This should speed up the lowvram mode a bit. It currently is only enabled when --async-offload is used but it will be enabled by default in the future if there are no problems. --- comfy/cli_args.py | 1 + comfy/model_management.py | 47 ++++++++++++++++++++++++++++++++++++--- comfy/ops.py | 7 ++++-- 3 files changed, 50 insertions(+), 5 deletions(-) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index 1b971be3c..f89a7aab4 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -128,6 +128,7 @@ vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for e parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.") +parser.add_argument("--async-offload", action="store_true", help="Use async weight offloading.") parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha1', 'sha256', 'sha512'], default='sha256', help="Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256.") diff --git a/comfy/model_management.py b/comfy/model_management.py index 43e402243..d118f6b91 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -939,15 +939,56 @@ def force_channels_last(): #TODO return False -def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False): + +STREAMS = {} +NUM_STREAMS = 1 +if args.async_offload: + NUM_STREAMS = 2 + logging.info("Using async weight offloading with {} streams".format(NUM_STREAMS)) + +stream_counter = 0 +def get_offload_stream(device): + global stream_counter + if NUM_STREAMS <= 1: + return None + + if device in STREAMS: + ss = STREAMS[device] + s = ss[stream_counter] + stream_counter = (stream_counter + 1) % len(ss) + if is_device_cuda(device): + ss[stream_counter].wait_stream(torch.cuda.current_stream()) + return s + elif is_device_cuda(device): + ss = [] + for k in range(NUM_STREAMS): + ss.append(torch.cuda.Stream(device=device, priority=10)) + STREAMS[device] = ss + s = ss[stream_counter] + stream_counter = (stream_counter + 1) % len(ss) + return s + return None + +def sync_stream(device, stream): + if stream is None: + return + if is_device_cuda(device): + torch.cuda.current_stream().wait_stream(stream) + +def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None): if device is None or weight.device == device: if not copy: if dtype is None or weight.dtype == dtype: return weight return weight.to(dtype=dtype, copy=copy) - r = torch.empty_like(weight, dtype=dtype, device=device) - r.copy_(weight, non_blocking=non_blocking) + if stream is not None: + with stream: + r = torch.empty_like(weight, dtype=dtype, device=device) + r.copy_(weight, non_blocking=non_blocking) + else: + r = torch.empty_like(weight, dtype=dtype, device=device) + r.copy_(weight, non_blocking=non_blocking) return r def cast_to_device(tensor, device, dtype, copy=False): diff --git a/comfy/ops.py b/comfy/ops.py index aae6cafac..62daf447b 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -37,20 +37,23 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): if device is None: device = input.device + offload_stream = comfy.model_management.get_offload_stream(device) bias = None non_blocking = comfy.model_management.device_supports_non_blocking(device) if s.bias is not None: has_function = len(s.bias_function) > 0 - bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function) + bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream) if has_function: for f in s.bias_function: bias = f(bias) has_function = len(s.weight_function) > 0 - weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function) + weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream) if has_function: for f in s.weight_function: weight = f(weight) + + comfy.model_management.sync_stream(device, offload_stream) return weight, bias class CastWeightBiasOp: From ac10a0d69e9905662296c5280bcea61945c39762 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 26 Apr 2025 16:56:22 -0700 Subject: [PATCH 031/193] Make loras work with --async-offload (#7824) --- comfy/ops.py | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/comfy/ops.py b/comfy/ops.py index 62daf447b..032787915 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -22,6 +22,7 @@ import comfy.model_management from comfy.cli_args import args, PerformanceFeature import comfy.float import comfy.rmsnorm +import contextlib cast_to = comfy.model_management.cast_to #TODO: remove once no more references @@ -38,20 +39,28 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): device = input.device offload_stream = comfy.model_management.get_offload_stream(device) + if offload_stream is not None: + wf_context = offload_stream + else: + wf_context = contextlib.nullcontext() + bias = None non_blocking = comfy.model_management.device_supports_non_blocking(device) if s.bias is not None: has_function = len(s.bias_function) > 0 bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream) + if has_function: - for f in s.bias_function: - bias = f(bias) + with wf_context: + for f in s.bias_function: + bias = f(bias) has_function = len(s.weight_function) > 0 weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream) if has_function: - for f in s.weight_function: - weight = f(weight) + with wf_context: + for f in s.weight_function: + weight = f(weight) comfy.model_management.sync_stream(device, offload_stream) return weight, bias From 542b4b36b694148504656ad54433b8ddf0c38c4d Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 26 Apr 2025 17:52:56 -0700 Subject: [PATCH 032/193] Prevent custom nodes from hooking certain functions. (#7825) --- hook_breaker_ac10a0.py | 17 +++++++++++++++++ main.py | 5 ++++- 2 files changed, 21 insertions(+), 1 deletion(-) create mode 100644 hook_breaker_ac10a0.py diff --git a/hook_breaker_ac10a0.py b/hook_breaker_ac10a0.py new file mode 100644 index 000000000..c3e1c0633 --- /dev/null +++ b/hook_breaker_ac10a0.py @@ -0,0 +1,17 @@ +# Prevent custom nodes from hooking anything important +import comfy.model_management + +HOOK_BREAK = [(comfy.model_management, "cast_to")] + + +SAVED_FUNCTIONS = [] + + +def save_functions(): + for f in HOOK_BREAK: + SAVED_FUNCTIONS.append((f[0], f[1], getattr(f[0], f[1]))) + + +def restore_functions(): + for f in SAVED_FUNCTIONS: + setattr(f[0], f[1], f[2]) diff --git a/main.py b/main.py index ac9d24b7b..f3f56597a 100644 --- a/main.py +++ b/main.py @@ -141,7 +141,7 @@ import nodes import comfy.model_management import comfyui_version import app.logger - +import hook_breaker_ac10a0 def cuda_malloc_warning(): device = comfy.model_management.get_torch_device() @@ -215,6 +215,7 @@ def prompt_worker(q, server_instance): comfy.model_management.soft_empty_cache() last_gc_collect = current_time need_gc = False + hook_breaker_ac10a0.restore_functions() async def run(server_instance, address='', port=8188, verbose=True, call_on_start=None): @@ -268,7 +269,9 @@ def start_comfyui(asyncio_loop=None): prompt_server = server.PromptServer(asyncio_loop) q = execution.PromptQueue(prompt_server) + hook_breaker_ac10a0.save_functions() nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes) + hook_breaker_ac10a0.restore_functions() cuda_malloc_warning() From c8cd7ad795ec4ecc5256bdfe2c12c352eef26e3b Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 27 Apr 2025 02:38:11 -0700 Subject: [PATCH 033/193] Use stream for casting if enabled. (#7833) --- comfy/model_management.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/comfy/model_management.py b/comfy/model_management.py index d118f6b91..516b6e2f1 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -980,6 +980,9 @@ def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, str if not copy: if dtype is None or weight.dtype == dtype: return weight + if stream is not None: + with stream: + return weight.to(dtype=dtype, copy=copy) return weight.to(dtype=dtype, copy=copy) if stream is not None: From 8115a7895bf07a75ccf0c4b65122cbf4cd8b0e2b Mon Sep 17 00:00:00 2001 From: Benjamin Lu Date: Sun, 27 Apr 2025 20:06:55 -0400 Subject: [PATCH 034/193] Add `/api/v2/userdata` endpoint (#7817) * Add list_userdata_v2 * nit * nit * nit * nit * please set me free * \\\\ * \\\\ --- app/user_manager.py | 106 ++++++++++++++++++ .../prompt_server_test/user_manager_test.py | 58 ++++++++++ 2 files changed, 164 insertions(+) diff --git a/app/user_manager.py b/app/user_manager.py index e7381e621..d31da5b9b 100644 --- a/app/user_manager.py +++ b/app/user_manager.py @@ -197,6 +197,112 @@ class UserManager(): return web.json_response(results) + @routes.get("/v2/userdata") + async def list_userdata_v2(request): + """ + List files and directories in a user's data directory. + + This endpoint provides a structured listing of contents within a specified + subdirectory of the user's data storage. + + Query Parameters: + - path (optional): The relative path within the user's data directory + to list. Defaults to the root (''). + + Returns: + - 400: If the requested path is invalid, outside the user's data directory, or is not a directory. + - 404: If the requested path does not exist. + - 403: If the user is invalid. + - 500: If there is an error reading the directory contents. + - 200: JSON response containing a list of file and directory objects. + Each object includes: + - name: The name of the file or directory. + - type: 'file' or 'directory'. + - path: The relative path from the user's data root. + - size (for files): The size in bytes. + - modified (for files): The last modified timestamp (Unix epoch). + """ + requested_rel_path = request.rel_url.query.get('path', '') + + # URL-decode the path parameter + try: + requested_rel_path = parse.unquote(requested_rel_path) + except Exception as e: + logging.warning(f"Failed to decode path parameter: {requested_rel_path}, Error: {e}") + return web.Response(status=400, text="Invalid characters in path parameter") + + + # Check user validity and get the absolute path for the requested directory + try: + base_user_path = self.get_request_user_filepath(request, None, create_dir=False) + + if requested_rel_path: + target_abs_path = self.get_request_user_filepath(request, requested_rel_path, create_dir=False) + else: + target_abs_path = base_user_path + + except KeyError as e: + # Invalid user detected by get_request_user_id inside get_request_user_filepath + logging.warning(f"Access denied for user: {e}") + return web.Response(status=403, text="Invalid user specified in request") + + + if not target_abs_path: + # Path traversal or other issue detected by get_request_user_filepath + return web.Response(status=400, text="Invalid path requested") + + # Handle cases where the user directory or target path doesn't exist + if not os.path.exists(target_abs_path): + # Check if it's the base user directory that's missing (new user case) + if target_abs_path == base_user_path: + # It's okay if the base user directory doesn't exist yet, return empty list + return web.json_response([]) + else: + # A specific subdirectory was requested but doesn't exist + return web.Response(status=404, text="Requested path not found") + + if not os.path.isdir(target_abs_path): + return web.Response(status=400, text="Requested path is not a directory") + + results = [] + try: + for root, dirs, files in os.walk(target_abs_path, topdown=True): + # Process directories + for dir_name in dirs: + dir_path = os.path.join(root, dir_name) + rel_path = os.path.relpath(dir_path, base_user_path).replace(os.sep, '/') + results.append({ + "name": dir_name, + "path": rel_path, + "type": "directory" + }) + + # Process files + for file_name in files: + file_path = os.path.join(root, file_name) + rel_path = os.path.relpath(file_path, base_user_path).replace(os.sep, '/') + entry_info = { + "name": file_name, + "path": rel_path, + "type": "file" + } + try: + stats = os.stat(file_path) # Use os.stat for potentially better performance with os.walk + entry_info["size"] = stats.st_size + entry_info["modified"] = stats.st_mtime + except OSError as stat_error: + logging.warning(f"Could not stat file {file_path}: {stat_error}") + pass # Include file with available info + results.append(entry_info) + except OSError as e: + logging.error(f"Error listing directory {target_abs_path}: {e}") + return web.Response(status=500, text="Error reading directory contents") + + # Sort results alphabetically, directories first then files + results.sort(key=lambda x: (x['type'] != 'directory', x['name'].lower())) + + return web.json_response(results) + def get_user_data_path(request, check_exists = False, param = "file"): file = request.match_info.get(param, None) if not file: diff --git a/tests-unit/prompt_server_test/user_manager_test.py b/tests-unit/prompt_server_test/user_manager_test.py index 7e523cbf4..b939d8e68 100644 --- a/tests-unit/prompt_server_test/user_manager_test.py +++ b/tests-unit/prompt_server_test/user_manager_test.py @@ -229,3 +229,61 @@ async def test_move_userdata_full_info(aiohttp_client, app, tmp_path): assert not os.path.exists(tmp_path / "source.txt") with open(tmp_path / "dest.txt", "r") as f: assert f.read() == "test content" + + +async def test_listuserdata_v2_empty_root(aiohttp_client, app): + client = await aiohttp_client(app) + resp = await client.get("/v2/userdata") + assert resp.status == 200 + assert await resp.json() == [] + + +async def test_listuserdata_v2_nonexistent_subdirectory(aiohttp_client, app): + client = await aiohttp_client(app) + resp = await client.get("/v2/userdata?path=does_not_exist") + assert resp.status == 404 + + +async def test_listuserdata_v2_default(aiohttp_client, app, tmp_path): + os.makedirs(tmp_path / "test_dir" / "subdir") + (tmp_path / "test_dir" / "file1.txt").write_text("content") + (tmp_path / "test_dir" / "subdir" / "file2.txt").write_text("content") + + client = await aiohttp_client(app) + resp = await client.get("/v2/userdata?path=test_dir") + assert resp.status == 200 + data = await resp.json() + file_paths = {item["path"] for item in data if item["type"] == "file"} + assert file_paths == {"test_dir/file1.txt", "test_dir/subdir/file2.txt"} + + +async def test_listuserdata_v2_normalized_separators(aiohttp_client, app, tmp_path, monkeypatch): + # Force backslash as os separator + monkeypatch.setattr(os, 'sep', '\\') + monkeypatch.setattr(os.path, 'sep', '\\') + os.makedirs(tmp_path / "test_dir" / "subdir") + (tmp_path / "test_dir" / "subdir" / "file1.txt").write_text("x") + + client = await aiohttp_client(app) + resp = await client.get("/v2/userdata?path=test_dir") + assert resp.status == 200 + data = await resp.json() + for item in data: + assert "/" in item["path"] + assert "\\" not in item["path"]\ + +async def test_listuserdata_v2_url_encoded_path(aiohttp_client, app, tmp_path): + # Create a directory with a space in its name and a file inside + os.makedirs(tmp_path / "my dir") + (tmp_path / "my dir" / "file.txt").write_text("content") + + client = await aiohttp_client(app) + # Use URL-encoded space in path parameter + resp = await client.get("/v2/userdata?path=my%20dir&recurse=false") + assert resp.status == 200 + data = await resp.json() + assert len(data) == 1 + entry = data[0] + assert entry["name"] == "file.txt" + # Ensure the path is correctly decoded and uses forward slash + assert entry["path"] == "my dir/file.txt" From cb9ac3db586b675a96ebd2604bf099bc189c1b28 Mon Sep 17 00:00:00 2001 From: Andrew Kvochko Date: Mon, 28 Apr 2025 19:59:17 +0300 Subject: [PATCH 035/193] ltxv: add strength parameter to conditioning. (#7849) This commit adds strength parameter to the LTXVImgToVideo node. --- comfy_extras/nodes_lt.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py index ff3fe5cdc..1a667e01a 100644 --- a/comfy_extras/nodes_lt.py +++ b/comfy_extras/nodes_lt.py @@ -38,6 +38,7 @@ class LTXVImgToVideo: "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), "length": ("INT", {"default": 97, "min": 9, "max": nodes.MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") @@ -46,7 +47,7 @@ class LTXVImgToVideo: CATEGORY = "conditioning/video_models" FUNCTION = "generate" - def generate(self, positive, negative, image, vae, width, height, length, batch_size): + def generate(self, positive, negative, image, vae, width, height, length, batch_size, strength): pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) encode_pixels = pixels[:, :, :, :3] t = vae.encode(encode_pixels) @@ -59,7 +60,7 @@ class LTXVImgToVideo: dtype=torch.float32, device=latent.device, ) - conditioning_latent_frames_mask[:, :, :t.shape[2]] = 0 + conditioning_latent_frames_mask[:, :, :t.shape[2]] = 1.0 - strength return (positive, negative, {"samples": latent, "noise_mask": conditioning_latent_frames_mask}, ) From 30159a7fe6bfa54fc1b9ba4c80b9041837038819 Mon Sep 17 00:00:00 2001 From: Pam <42671363+pamparamm@users.noreply.github.com> Date: Mon, 28 Apr 2025 22:03:21 +0500 Subject: [PATCH 036/193] Save v pred zsnr metadata (#7840) --- comfy/model_sampling.py | 3 ++- comfy_extras/nodes_model_merging.py | 3 +++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py index b79af1e92..7e7291476 100644 --- a/comfy/model_sampling.py +++ b/comfy/model_sampling.py @@ -111,13 +111,14 @@ class ModelSamplingDiscrete(torch.nn.Module): self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end + self.zsnr = zsnr # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32)) # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32)) # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32)) sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5 - if zsnr: + if self.zsnr: sigmas = rescale_zero_terminal_snr_sigmas(sigmas) self.set_sigmas(sigmas) diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py index ccf601158..78d284889 100644 --- a/comfy_extras/nodes_model_merging.py +++ b/comfy_extras/nodes_model_merging.py @@ -209,6 +209,9 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi metadata["modelspec.predict_key"] = "epsilon" elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION: metadata["modelspec.predict_key"] = "v" + extra_keys["v_pred"] = torch.tensor([]) + if getattr(model_sampling, "zsnr", False): + extra_keys["ztsnr"] = torch.tensor([]) if not args.disable_metadata: metadata["prompt"] = prompt_info From 5a50c3c7e59a867ddf0b3b7cc207c877a7b422fb Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 28 Apr 2025 10:07:21 -0700 Subject: [PATCH 037/193] Fix stream priority to support older pytorch. (#7856) --- comfy/model_management.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 516b6e2f1..78317af3c 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -962,7 +962,7 @@ def get_offload_stream(device): elif is_device_cuda(device): ss = [] for k in range(NUM_STREAMS): - ss.append(torch.cuda.Stream(device=device, priority=10)) + ss.append(torch.cuda.Stream(device=device, priority=0)) STREAMS[device] = ss s = ss[stream_counter] stream_counter = (stream_counter + 1) % len(ss) From 772b4c594549fb42f70833053be8613d79932965 Mon Sep 17 00:00:00 2001 From: Andrew Kvochko Date: Mon, 28 Apr 2025 20:42:04 +0300 Subject: [PATCH 038/193] ltxv: overwrite existing mask on conditioned frame. (#7845) This commit overwrites the noise mask on the latent frame that is being conditioned with keyframe conditioning, setting it to one. --- comfy_extras/nodes_lt.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py index 1a667e01a..e6dc122ca 100644 --- a/comfy_extras/nodes_lt.py +++ b/comfy_extras/nodes_lt.py @@ -153,6 +153,15 @@ class LTXVAddGuide: return node_helpers.conditioning_set_values(cond, {"keyframe_idxs": keyframe_idxs}) def append_keyframe(self, positive, negative, frame_idx, latent_image, noise_mask, guiding_latent, strength, scale_factors): + _, latent_idx = self.get_latent_index( + cond=positive, + latent_length=latent_image.shape[2], + guide_length=guiding_latent.shape[2], + frame_idx=frame_idx, + scale_factors=scale_factors, + ) + noise_mask[:, :, latent_idx:latent_idx + guiding_latent.shape[2]] = 1.0 + positive = self.add_keyframe_index(positive, frame_idx, guiding_latent, scale_factors) negative = self.add_keyframe_index(negative, frame_idx, guiding_latent, scale_factors) From c15909bb621b31b0ede693f30cc927021e473c72 Mon Sep 17 00:00:00 2001 From: chaObserv <154517000+chaObserv@users.noreply.github.com> Date: Tue, 29 Apr 2025 01:51:35 +0800 Subject: [PATCH 039/193] CFG++ for gradient estimation sampler (#7809) --- comfy/k_diffusion/sampling.py | 34 +++++++++++++++++++++++++++++----- comfy/samplers.py | 2 +- 2 files changed, 30 insertions(+), 6 deletions(-) diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py index 6388d3faf..77ef748e8 100644 --- a/comfy/k_diffusion/sampling.py +++ b/comfy/k_diffusion/sampling.py @@ -1345,28 +1345,52 @@ def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, cal return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True) @torch.no_grad() -def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.): +def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2., cfg_pp=False): """Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK""" extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) old_d = None + uncond_denoised = None + def post_cfg_function(args): + nonlocal uncond_denoised + uncond_denoised = args["uncond_denoised"] + return args["denoised"] + + if cfg_pp: + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) - d = to_d(x, sigmas[i], denoised) + if cfg_pp: + d = to_d(x, sigmas[i], uncond_denoised) + else: + d = to_d(x, sigmas[i], denoised) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) dt = sigmas[i + 1] - sigmas[i] if i == 0: # Euler method - x = x + d * dt + if cfg_pp: + x = denoised + d * sigmas[i + 1] + else: + x = x + d * dt else: # Gradient estimation - d_bar = ge_gamma * d + (1 - ge_gamma) * old_d - x = x + d_bar * dt + if cfg_pp: + d_bar = (ge_gamma - 1) * (d - old_d) + x = denoised + d * sigmas[i + 1] + d_bar * dt + else: + d_bar = ge_gamma * d + (1 - ge_gamma) * old_d + x = x + d_bar * dt old_d = d return x +@torch.no_grad() +def sample_gradient_estimation_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.): + return sample_gradient_estimation(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, ge_gamma=ge_gamma, cfg_pp=True) + @torch.no_grad() def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, noise_scaler=None, max_stage=3): """ diff --git a/comfy/samplers.py b/comfy/samplers.py index 27dfce45a..67ae09a25 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -710,7 +710,7 @@ KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_c "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp", - "gradient_estimation", "er_sde", "seeds_2", "seeds_3"] + "gradient_estimation", "gradient_estimation_cfg_pp", "er_sde", "seeds_2", "seeds_3"] class KSAMPLER(Sampler): def __init__(self, sampler_function, extra_options={}, inpaint_options={}): From 7d329771f9f966fec20aa72079fc39202d877cbd Mon Sep 17 00:00:00 2001 From: Yoland Yan <4950057+yoland68@users.noreply.github.com> Date: Mon, 28 Apr 2025 10:59:22 -0700 Subject: [PATCH 040/193] Add moderation level option to OpenAIGPTImage1 node and update api_call method signature (#7804) --- comfy_api_nodes/nodes_api.py | 27 +++++++++++++++++---------- 1 file changed, 17 insertions(+), 10 deletions(-) diff --git a/comfy_api_nodes/nodes_api.py b/comfy_api_nodes/nodes_api.py index 4105ba7e1..a977bb9b7 100644 --- a/comfy_api_nodes/nodes_api.py +++ b/comfy_api_nodes/nodes_api.py @@ -1,21 +1,22 @@ +import base64 import io +import math from inspect import cleandoc -from comfy.utils import common_upscale +import numpy as np +import requests +import torch +from PIL import Image + from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict +from comfy.utils import common_upscale from comfy_api_nodes.apis import ( - OpenAIImageGenerationRequest, OpenAIImageEditRequest, - OpenAIImageGenerationResponse + OpenAIImageGenerationRequest, + OpenAIImageGenerationResponse, ) from comfy_api_nodes.apis.client import ApiEndpoint, HttpMethod, SynchronousOperation -import numpy as np -from PIL import Image -import requests -import torch -import math -import base64 def downscale_input(image): samples = image.movedim(-1,1) @@ -331,6 +332,11 @@ class OpenAIGPTImage1(ComfyNodeABC): "default": None, "tooltip": "Optional mask for inpainting (white areas will be replaced)", }), + "moderation": (IO.COMBO, { + "options": ["low","auto"], + "default": "low", + "tooltip": "Moderation level", + }), }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG" @@ -343,7 +349,7 @@ class OpenAIGPTImage1(ComfyNodeABC): DESCRIPTION = cleandoc(__doc__ or "") API_NODE = True - def api_call(self, prompt, seed=0, quality="low", background="opaque", image=None, mask=None, n=1, size="1024x1024", auth_token=None): + def api_call(self, prompt, seed=0, quality="low", background="opaque", image=None, mask=None, n=1, size="1024x1024", auth_token=None, moderation="low"): model = "gpt-image-1" path = "/proxy/openai/images/generations" request_class = OpenAIImageGenerationRequest @@ -415,6 +421,7 @@ class OpenAIGPTImage1(ComfyNodeABC): n=n, seed=seed, size=size, + moderation=moderation, ), files=files if files else None, auth_token=auth_token From 83d04717b6cd84fca7af31ee655f7e5a3585a371 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 28 Apr 2025 12:01:15 -0700 Subject: [PATCH 041/193] Support HiDream E1 model. (#7857) --- comfy/ldm/hidream/model.py | 3 +++ comfy/model_base.py | 3 +++ 2 files changed, 6 insertions(+) diff --git a/comfy/ldm/hidream/model.py b/comfy/ldm/hidream/model.py index fcb5a9c51..0305747bf 100644 --- a/comfy/ldm/hidream/model.py +++ b/comfy/ldm/hidream/model.py @@ -699,10 +699,13 @@ class HiDreamImageTransformer2DModel(nn.Module): y: Optional[torch.Tensor] = None, context: Optional[torch.Tensor] = None, encoder_hidden_states_llama3=None, + image_cond=None, control = None, transformer_options = {}, ) -> torch.Tensor: bs, c, h, w = x.shape + if image_cond is not None: + x = torch.cat([x, image_cond], dim=-1) hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) timesteps = t pooled_embeds = y diff --git a/comfy/model_base.py b/comfy/model_base.py index b0c6a465b..d2aa4ce7a 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -1104,4 +1104,7 @@ class HiDream(BaseModel): conditioning_llama3 = kwargs.get("conditioning_llama3", None) if conditioning_llama3 is not None: out['encoder_hidden_states_llama3'] = comfy.conds.CONDRegular(conditioning_llama3) + image_cond = kwargs.get("concat_latent_image", None) + if image_cond is not None: + out['image_cond'] = comfy.conds.CONDNoiseShape(self.process_latent_in(image_cond)) return out From 68f0d3529667a2b34b27cc0ac5051bc0e8c45b49 Mon Sep 17 00:00:00 2001 From: guill Date: Tue, 29 Apr 2025 02:58:00 -0700 Subject: [PATCH 042/193] Add support for VIDEO as a built-in type (#7844) * Add basic support for videos as types This PR adds support for VIDEO as first-class types. In order to avoid unnecessary costs, VIDEO outputs must implement the `VideoInput` ABC, but their implementation details can vary. Included are two implementations of this type which can be returned by other nodes: * `VideoFromFile` - Created with either a path on disk (as a string) or a `io.BytesIO` containing the contents of a file in a supported format (like .mp4). This implementation won't actually load the video unless necessary. It will also avoid re-encoding when saving if possible. * `VideoFromComponents` - Created from an image tensor and an optional audio tensor. Currently, only h264 encoded videos in .mp4 containers are supported for saving, but the plan is to add additional encodings/containers in the near future (particularly .webm). * Add optimization to avoid parsing entire video * Improve type declarations to reduce warnings * Make sure bytesIO objects can be read many times * Fix a potential issue when saving long videos * Fix incorrect type annotation * Add a `LoadVideo` node to make testing easier * Refactor new types out of the base comfy folder I've created a new `comfy_api` top-level module. The intention is that anything within this folder would be covered by semver-style versioning that would allow custom nodes to rely on them not introducing breaking changes. * Fix linting issue --- comfy/comfy_types/node_typing.py | 9 +- comfy_api/input/__init__.py | 8 + comfy_api/input/basic_types.py | 20 +++ comfy_api/input/video_types.py | 45 ++++++ comfy_api/input_impl/__init__.py | 7 + comfy_api/input_impl/video_types.py | 224 ++++++++++++++++++++++++++++ comfy_api/util/__init__.py | 8 + comfy_api/util/video_types.py | 51 +++++++ comfy_extras/nodes_video.py | 164 +++++++++++++++++++- folder_paths.py | 4 +- 10 files changed, 532 insertions(+), 8 deletions(-) create mode 100644 comfy_api/input/__init__.py create mode 100644 comfy_api/input/basic_types.py create mode 100644 comfy_api/input/video_types.py create mode 100644 comfy_api/input_impl/__init__.py create mode 100644 comfy_api/input_impl/video_types.py create mode 100644 comfy_api/util/__init__.py create mode 100644 comfy_api/util/video_types.py diff --git a/comfy/comfy_types/node_typing.py b/comfy/comfy_types/node_typing.py index 4ceeb3468..2ffc9c021 100644 --- a/comfy/comfy_types/node_typing.py +++ b/comfy/comfy_types/node_typing.py @@ -48,6 +48,7 @@ class IO(StrEnum): FACE_ANALYSIS = "FACE_ANALYSIS" BBOX = "BBOX" SEGS = "SEGS" + VIDEO = "VIDEO" ANY = "*" """Always matches any type, but at a price. @@ -273,7 +274,7 @@ class ComfyNodeABC(ABC): Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing """ - OUTPUT_IS_LIST: tuple[bool] + OUTPUT_IS_LIST: tuple[bool, ...] """A tuple indicating which node outputs are lists, but will be connected to nodes that expect individual items. Connected nodes that do not implement `INPUT_IS_LIST` will be executed once for every item in the list. @@ -292,7 +293,7 @@ class ComfyNodeABC(ABC): Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing """ - RETURN_TYPES: tuple[IO] + RETURN_TYPES: tuple[IO, ...] """A tuple representing the outputs of this node. Usage:: @@ -301,12 +302,12 @@ class ComfyNodeABC(ABC): Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-types """ - RETURN_NAMES: tuple[str] + RETURN_NAMES: tuple[str, ...] """The output slot names for each item in `RETURN_TYPES`, e.g. ``RETURN_NAMES = ("count", "filter_string")`` Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-names """ - OUTPUT_TOOLTIPS: tuple[str] + OUTPUT_TOOLTIPS: tuple[str, ...] """A tuple of strings to use as tooltips for node outputs, one for each item in `RETURN_TYPES`.""" FUNCTION: str """The name of the function to execute as a literal string, e.g. `FUNCTION = "execute"` diff --git a/comfy_api/input/__init__.py b/comfy_api/input/__init__.py new file mode 100644 index 000000000..66667946f --- /dev/null +++ b/comfy_api/input/__init__.py @@ -0,0 +1,8 @@ +from .basic_types import ImageInput, AudioInput +from .video_types import VideoInput + +__all__ = [ + "ImageInput", + "AudioInput", + "VideoInput", +] diff --git a/comfy_api/input/basic_types.py b/comfy_api/input/basic_types.py new file mode 100644 index 000000000..033fb7e27 --- /dev/null +++ b/comfy_api/input/basic_types.py @@ -0,0 +1,20 @@ +import torch +from typing import TypedDict + +ImageInput = torch.Tensor +""" +An image in format [B, H, W, C] where B is the batch size, C is the number of channels, +""" + +class AudioInput(TypedDict): + """ + TypedDict representing audio input. + """ + + waveform: torch.Tensor + """ + Tensor in the format [B, C, T] where B is the batch size, C is the number of channels, + """ + + sample_rate: int + diff --git a/comfy_api/input/video_types.py b/comfy_api/input/video_types.py new file mode 100644 index 000000000..0676e0e66 --- /dev/null +++ b/comfy_api/input/video_types.py @@ -0,0 +1,45 @@ +from __future__ import annotations +from abc import ABC, abstractmethod +from typing import Optional +from comfy_api.util import VideoContainer, VideoCodec, VideoComponents + +class VideoInput(ABC): + """ + Abstract base class for video input types. + """ + + @abstractmethod + def get_components(self) -> VideoComponents: + """ + Abstract method to get the video components (images, audio, and frame rate). + + Returns: + VideoComponents containing images, audio, and frame rate + """ + pass + + @abstractmethod + def save_to( + self, + path: str, + format: VideoContainer = VideoContainer.AUTO, + codec: VideoCodec = VideoCodec.AUTO, + metadata: Optional[dict] = None + ): + """ + Abstract method to save the video input to a file. + """ + pass + + # Provide a default implementation, but subclasses can provide optimized versions + # if possible. + def get_dimensions(self) -> tuple[int, int]: + """ + Returns the dimensions of the video input. + + Returns: + Tuple of (width, height) + """ + components = self.get_components() + return components.images.shape[2], components.images.shape[1] + diff --git a/comfy_api/input_impl/__init__.py b/comfy_api/input_impl/__init__.py new file mode 100644 index 000000000..02901b8b9 --- /dev/null +++ b/comfy_api/input_impl/__init__.py @@ -0,0 +1,7 @@ +from .video_types import VideoFromFile, VideoFromComponents + +__all__ = [ + # Implementations + "VideoFromFile", + "VideoFromComponents", +] diff --git a/comfy_api/input_impl/video_types.py b/comfy_api/input_impl/video_types.py new file mode 100644 index 000000000..12e5783db --- /dev/null +++ b/comfy_api/input_impl/video_types.py @@ -0,0 +1,224 @@ +from __future__ import annotations +from av.container import InputContainer +from av.subtitles.stream import SubtitleStream +from fractions import Fraction +from typing import Optional +from comfy_api.input import AudioInput +import av +import io +import json +import numpy as np +import torch +from comfy_api.input import VideoInput +from comfy_api.util import VideoContainer, VideoCodec, VideoComponents + +class VideoFromFile(VideoInput): + """ + Class representing video input from a file. + """ + + def __init__(self, file: str | io.BytesIO): + """ + Initialize the VideoFromFile object based off of either a path on disk or a BytesIO object + containing the file contents. + """ + self.__file = file + + def get_dimensions(self) -> tuple[int, int]: + """ + Returns the dimensions of the video input. + + Returns: + Tuple of (width, height) + """ + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) # Reset the BytesIO object to the beginning + with av.open(self.__file, mode='r') as container: + for stream in container.streams: + if stream.type == 'video': + assert isinstance(stream, av.VideoStream) + return stream.width, stream.height + raise ValueError(f"No video stream found in file '{self.__file}'") + + def get_components_internal(self, container: InputContainer) -> VideoComponents: + # Get video frames + frames = [] + for frame in container.decode(video=0): + img = frame.to_ndarray(format='rgb24') # shape: (H, W, 3) + img = torch.from_numpy(img) / 255.0 # shape: (H, W, 3) + frames.append(img) + + images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 3, 0, 0) + + # Get frame rate + video_stream = next(s for s in container.streams if s.type == 'video') + frame_rate = Fraction(video_stream.average_rate) if video_stream and video_stream.average_rate else Fraction(1) + + # Get audio if available + audio = None + try: + container.seek(0) # Reset the container to the beginning + for stream in container.streams: + if stream.type != 'audio': + continue + assert isinstance(stream, av.AudioStream) + audio_frames = [] + for packet in container.demux(stream): + for frame in packet.decode(): + assert isinstance(frame, av.AudioFrame) + audio_frames.append(frame.to_ndarray()) # shape: (channels, samples) + if len(audio_frames) > 0: + audio_data = np.concatenate(audio_frames, axis=1) # shape: (channels, total_samples) + audio_tensor = torch.from_numpy(audio_data).unsqueeze(0) # shape: (1, channels, total_samples) + audio = AudioInput({ + "waveform": audio_tensor, + "sample_rate": int(stream.sample_rate) if stream.sample_rate else 1, + }) + except StopIteration: + pass # No audio stream + + metadata = container.metadata + return VideoComponents(images=images, audio=audio, frame_rate=frame_rate, metadata=metadata) + + def get_components(self) -> VideoComponents: + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) # Reset the BytesIO object to the beginning + with av.open(self.__file, mode='r') as container: + return self.get_components_internal(container) + raise ValueError(f"No video stream found in file '{self.__file}'") + + def save_to( + self, + path: str, + format: VideoContainer = VideoContainer.AUTO, + codec: VideoCodec = VideoCodec.AUTO, + metadata: Optional[dict] = None + ): + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) # Reset the BytesIO object to the beginning + with av.open(self.__file, mode='r') as container: + container_format = container.format.name + video_encoding = container.streams.video[0].codec.name if len(container.streams.video) > 0 else None + reuse_streams = True + if format != VideoContainer.AUTO and format not in container_format.split(","): + reuse_streams = False + if codec != VideoCodec.AUTO and codec != video_encoding and video_encoding is not None: + reuse_streams = False + + if not reuse_streams: + components = self.get_components_internal(container) + video = VideoFromComponents(components) + return video.save_to( + path, + format=format, + codec=codec, + metadata=metadata + ) + + streams = container.streams + with av.open(path, mode='w', options={"movflags": "use_metadata_tags"}) as output_container: + # Copy over the original metadata + for key, value in container.metadata.items(): + if metadata is None or key not in metadata: + output_container.metadata[key] = value + + # Add our new metadata + if metadata is not None: + for key, value in metadata.items(): + if isinstance(value, str): + output_container.metadata[key] = value + else: + output_container.metadata[key] = json.dumps(value) + + # Add streams to the new container + stream_map = {} + for stream in streams: + if isinstance(stream, (av.VideoStream, av.AudioStream, SubtitleStream)): + out_stream = output_container.add_stream_from_template(template=stream, opaque=True) + stream_map[stream] = out_stream + + # Write packets to the new container + for packet in container.demux(): + if packet.stream in stream_map and packet.dts is not None: + packet.stream = stream_map[packet.stream] + output_container.mux(packet) + +class VideoFromComponents(VideoInput): + """ + Class representing video input from tensors. + """ + + def __init__(self, components: VideoComponents): + self.__components = components + + def get_components(self) -> VideoComponents: + return VideoComponents( + images=self.__components.images, + audio=self.__components.audio, + frame_rate=self.__components.frame_rate + ) + + def save_to( + self, + path: str, + format: VideoContainer = VideoContainer.AUTO, + codec: VideoCodec = VideoCodec.AUTO, + metadata: Optional[dict] = None + ): + if format != VideoContainer.AUTO and format != VideoContainer.MP4: + raise ValueError("Only MP4 format is supported for now") + if codec != VideoCodec.AUTO and codec != VideoCodec.H264: + raise ValueError("Only H264 codec is supported for now") + with av.open(path, mode='w', options={'movflags': 'use_metadata_tags'}) as output: + # Add metadata before writing any streams + if metadata is not None: + for key, value in metadata.items(): + output.metadata[key] = json.dumps(value) + + frame_rate = Fraction(round(self.__components.frame_rate * 1000), 1000) + # Create a video stream + video_stream = output.add_stream('h264', rate=frame_rate) + video_stream.width = self.__components.images.shape[2] + video_stream.height = self.__components.images.shape[1] + video_stream.pix_fmt = 'yuv420p' + + # Create an audio stream + audio_sample_rate = 1 + audio_stream: Optional[av.AudioStream] = None + if self.__components.audio: + audio_sample_rate = int(self.__components.audio['sample_rate']) + audio_stream = output.add_stream('aac', rate=audio_sample_rate) + audio_stream.sample_rate = audio_sample_rate + audio_stream.format = 'fltp' + + # Encode video + for i, frame in enumerate(self.__components.images): + img = (frame * 255).clamp(0, 255).byte().cpu().numpy() # shape: (H, W, 3) + frame = av.VideoFrame.from_ndarray(img, format='rgb24') + frame = frame.reformat(format='yuv420p') # Convert to YUV420P as required by h264 + packet = video_stream.encode(frame) + output.mux(packet) + + # Flush video + packet = video_stream.encode(None) + output.mux(packet) + + if audio_stream and self.__components.audio: + # Encode audio + samples_per_frame = int(audio_sample_rate / frame_rate) + num_frames = self.__components.audio['waveform'].shape[2] // samples_per_frame + for i in range(num_frames): + start = i * samples_per_frame + end = start + samples_per_frame + # TODO(Feature) - Add support for stereo audio + chunk = self.__components.audio['waveform'][0, 0, start:end].unsqueeze(0).numpy() + audio_frame = av.AudioFrame.from_ndarray(chunk, format='fltp', layout='mono') + audio_frame.sample_rate = audio_sample_rate + audio_frame.pts = i * samples_per_frame + for packet in audio_stream.encode(audio_frame): + output.mux(packet) + + # Flush audio + for packet in audio_stream.encode(None): + output.mux(packet) + diff --git a/comfy_api/util/__init__.py b/comfy_api/util/__init__.py new file mode 100644 index 000000000..9019c46db --- /dev/null +++ b/comfy_api/util/__init__.py @@ -0,0 +1,8 @@ +from .video_types import VideoContainer, VideoCodec, VideoComponents + +__all__ = [ + # Utility Types + "VideoContainer", + "VideoCodec", + "VideoComponents", +] diff --git a/comfy_api/util/video_types.py b/comfy_api/util/video_types.py new file mode 100644 index 000000000..d09663db9 --- /dev/null +++ b/comfy_api/util/video_types.py @@ -0,0 +1,51 @@ +from __future__ import annotations +from dataclasses import dataclass +from enum import Enum +from fractions import Fraction +from typing import Optional +from comfy_api.input import ImageInput, AudioInput + +class VideoCodec(str, Enum): + AUTO = "auto" + H264 = "h264" + + @classmethod + def as_input(cls) -> list[str]: + """ + Returns a list of codec names that can be used as node input. + """ + return [member.value for member in cls] + +class VideoContainer(str, Enum): + AUTO = "auto" + MP4 = "mp4" + + @classmethod + def as_input(cls) -> list[str]: + """ + Returns a list of container names that can be used as node input. + """ + return [member.value for member in cls] + + @classmethod + def get_extension(cls, value) -> str: + """ + Returns the file extension for the container. + """ + if isinstance(value, str): + value = cls(value) + if value == VideoContainer.MP4 or value == VideoContainer.AUTO: + return "mp4" + return "" + +@dataclass +class VideoComponents: + """ + Dataclass representing the components of a video. + """ + + images: ImageInput + frame_rate: Fraction + audio: Optional[AudioInput] = None + metadata: Optional[dict] = None + diff --git a/comfy_extras/nodes_video.py b/comfy_extras/nodes_video.py index a9e244ebe..61f7171b2 100644 --- a/comfy_extras/nodes_video.py +++ b/comfy_extras/nodes_video.py @@ -5,9 +5,13 @@ import av import torch import folder_paths import json +from typing import Optional, Literal from fractions import Fraction -from comfy.comfy_types import FileLocator - +from comfy.comfy_types import IO, FileLocator, ComfyNodeABC +from comfy_api.input import ImageInput, AudioInput, VideoInput +from comfy_api.util import VideoContainer, VideoCodec, VideoComponents +from comfy_api.input_impl import VideoFromFile, VideoFromComponents +from comfy.cli_args import args class SaveWEBM: def __init__(self): @@ -75,7 +79,163 @@ class SaveWEBM: return {"ui": {"images": results, "animated": (True,)}} # TODO: frontend side +class SaveVideo(ComfyNodeABC): + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type: Literal["output"] = "output" + self.prefix_append = "" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "video": (IO.VIDEO, {"tooltip": "The video to save."}), + "filename_prefix": ("STRING", {"default": "video/ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}), + "format": (VideoContainer.as_input(), {"default": "auto", "tooltip": "The format to save the video as."}), + "codec": (VideoCodec.as_input(), {"default": "auto", "tooltip": "The codec to use for the video."}), + }, + "hidden": { + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO" + }, + } + + RETURN_TYPES = () + FUNCTION = "save_video" + + OUTPUT_NODE = True + + CATEGORY = "image/video" + DESCRIPTION = "Saves the input images to your ComfyUI output directory." + + def save_video(self, video: VideoInput, filename_prefix, format, codec, prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + width, height = video.get_dimensions() + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path( + filename_prefix, + self.output_dir, + width, + height + ) + results: list[FileLocator] = list() + saved_metadata = None + if not args.disable_metadata: + metadata = {} + if extra_pnginfo is not None: + metadata.update(extra_pnginfo) + if prompt is not None: + metadata["prompt"] = prompt + if len(metadata) > 0: + saved_metadata = metadata + file = f"{filename}_{counter:05}_.{VideoContainer.get_extension(format)}" + video.save_to( + os.path.join(full_output_folder, file), + format=format, + codec=codec, + metadata=saved_metadata + ) + + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + return { "ui": { "images": results, "animated": (True,) } } + +class CreateVideo(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": (IO.IMAGE, {"tooltip": "The images to create a video from."}), + "fps": ("FLOAT", {"default": 30.0, "min": 1.0, "max": 120.0, "step": 1.0}), + }, + "optional": { + "audio": (IO.AUDIO, {"tooltip": "The audio to add to the video."}), + } + } + + RETURN_TYPES = (IO.VIDEO,) + FUNCTION = "create_video" + + CATEGORY = "image/video" + DESCRIPTION = "Create a video from images." + + def create_video(self, images: ImageInput, fps: float, audio: Optional[AudioInput] = None): + return (VideoFromComponents( + VideoComponents( + images=images, + audio=audio, + frame_rate=Fraction(fps), + ) + ),) + +class GetVideoComponents(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "video": (IO.VIDEO, {"tooltip": "The video to extract components from."}), + } + } + RETURN_TYPES = (IO.IMAGE, IO.AUDIO, IO.FLOAT) + RETURN_NAMES = ("images", "audio", "fps") + FUNCTION = "get_components" + + CATEGORY = "image/video" + DESCRIPTION = "Extracts all components from a video: frames, audio, and framerate." + + def get_components(self, video: VideoInput): + components = video.get_components() + + return (components.images, components.audio, float(components.frame_rate)) + +class LoadVideo(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + files = folder_paths.filter_files_content_types(files, ["video"]) + return {"required": + {"file": (sorted(files), {"video_upload": True})}, + } + + CATEGORY = "image/video" + + RETURN_TYPES = (IO.VIDEO,) + FUNCTION = "load_video" + def load_video(self, file): + video_path = folder_paths.get_annotated_filepath(file) + return (VideoFromFile(video_path),) + + @classmethod + def IS_CHANGED(cls, file): + video_path = folder_paths.get_annotated_filepath(file) + mod_time = os.path.getmtime(video_path) + # Instead of hashing the file, we can just use the modification time to avoid + # rehashing large files. + return mod_time + + @classmethod + def VALIDATE_INPUTS(cls, file): + if not folder_paths.exists_annotated_filepath(file): + return "Invalid video file: {}".format(file) + + return True NODE_CLASS_MAPPINGS = { "SaveWEBM": SaveWEBM, + "SaveVideo": SaveVideo, + "CreateVideo": CreateVideo, + "GetVideoComponents": GetVideoComponents, + "LoadVideo": LoadVideo, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "SaveVideo": "Save Video", + "CreateVideo": "Create Video", + "GetVideoComponents": "Get Video Components", + "LoadVideo": "Load Video", } diff --git a/folder_paths.py b/folder_paths.py index 9a525e5a1..f0b3fd103 100644 --- a/folder_paths.py +++ b/folder_paths.py @@ -4,7 +4,7 @@ import os import time import mimetypes import logging -from typing import Literal +from typing import Literal, List from collections.abc import Collection from comfy.cli_args import args @@ -141,7 +141,7 @@ def get_directory_by_type(type_name: str) -> str | None: return get_input_directory() return None -def filter_files_content_types(files: list[str], content_types: Literal["image", "video", "audio", "model"]) -> list[str]: +def filter_files_content_types(files: list[str], content_types: List[Literal["image", "video", "audio", "model"]]) -> list[str]: """ Example: files = os.listdir(folder_paths.get_input_directory()) From 005a91ce2b6455d7e76f7a5cf2e77dbd020afa1f Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 29 Apr 2025 03:29:38 -0700 Subject: [PATCH 043/193] Latest desktop and portable should work on blackwell. (#7861) Removed the mention about the cards from the readme. --- README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/README.md b/README.md index 62800bb4f..24a65b942 100644 --- a/README.md +++ b/README.md @@ -149,8 +149,6 @@ Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you If you have trouble extracting it, right click the file -> properties -> unblock -If you have a 50 series Blackwell card like a 5090 or 5080 see [this discussion thread](https://github.com/comfyanonymous/ComfyUI/discussions/6643) - #### How do I share models between another UI and ComfyUI? See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor. From 45503f649925fdf8e6ddce85fabee551ee3c446b Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Tue, 29 Apr 2025 06:32:34 -0400 Subject: [PATCH 044/193] Add release process section to README (#7855) * Add release process section to README * move * Update README.md --- README.md | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 24a65b942..0f39cfce2 100644 --- a/README.md +++ b/README.md @@ -49,7 +49,6 @@ Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, ## [Examples](https://comfyanonymous.github.io/ComfyUI_examples/) See what ComfyUI can do with the [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/). - ## Features - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything. - Image Models @@ -99,6 +98,23 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/) +## Release Process + +ComfyUI follows a weekly release cycle every Friday, with three interconnected repositories: + +1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)** + - Releases a new stable version (e.g., v0.7.0) + - Serves as the foundation for the desktop release + +2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)** + - Builds a new release using the latest stable core version + - Version numbers match the core release (e.g., Desktop v1.7.0 uses Core v1.7.0) + +3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)** + - Weekly frontend updates are merged into the core repository + - Features are frozen for the upcoming core release + - Development continues for the next release cycle + ## Shortcuts | Keybind | Explanation | From 5c5457a4ef151f9f855020ff544b4362cdcf1201 Mon Sep 17 00:00:00 2001 From: Terry Jia Date: Tue, 29 Apr 2025 11:28:04 -0400 Subject: [PATCH 045/193] support more example folders (#7836) * support more example folders * add warning message --- app/custom_node_manager.py | 43 ++++++++++++++++++++++++-------------- 1 file changed, 27 insertions(+), 16 deletions(-) diff --git a/app/custom_node_manager.py b/app/custom_node_manager.py index 42b0d75ba..27d85d9ce 100644 --- a/app/custom_node_manager.py +++ b/app/custom_node_manager.py @@ -93,16 +93,20 @@ class CustomNodeManager: def add_routes(self, routes, webapp, loadedModules): + example_workflow_folder_names = ["example_workflows", "example", "examples", "workflow", "workflows"] + @routes.get("/workflow_templates") async def get_workflow_templates(request): """Returns a web response that contains the map of custom_nodes names and their associated workflow templates. The ones without templates are omitted.""" - files = [ - file - for folder in folder_paths.get_folder_paths("custom_nodes") - for file in glob.glob( - os.path.join(folder, "*/example_workflows/*.json") - ) - ] + + files = [] + + for folder in folder_paths.get_folder_paths("custom_nodes"): + for folder_name in example_workflow_folder_names: + pattern = os.path.join(folder, f"*/{folder_name}/*.json") + matched_files = glob.glob(pattern) + files.extend(matched_files) + workflow_templates_dict = ( {} ) # custom_nodes folder name -> example workflow names @@ -118,15 +122,22 @@ class CustomNodeManager: # Serve workflow templates from custom nodes. for module_name, module_dir in loadedModules: - workflows_dir = os.path.join(module_dir, "example_workflows") - if os.path.exists(workflows_dir): - webapp.add_routes( - [ - web.static( - "/api/workflow_templates/" + module_name, workflows_dir - ) - ] - ) + for folder_name in example_workflow_folder_names: + workflows_dir = os.path.join(module_dir, folder_name) + + if os.path.exists(workflows_dir): + if folder_name != "example_workflows": + logging.warning( + "WARNING: Found example workflow folder '%s' for custom node '%s', consider renaming it to 'example_workflows'", + folder_name, module_name) + + webapp.add_routes( + [ + web.static( + "/api/workflow_templates/" + module_name, workflows_dir + ) + ] + ) @routes.get("/i18n") async def get_i18n(request): From 0a66d4b0afe4a78a200809b7d1d3beec6c6a2a8f Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 29 Apr 2025 17:28:52 -0700 Subject: [PATCH 046/193] Per device stream counters for async offload. (#7873) --- comfy/model_management.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 78317af3c..44aff3762 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -946,9 +946,9 @@ if args.async_offload: NUM_STREAMS = 2 logging.info("Using async weight offloading with {} streams".format(NUM_STREAMS)) -stream_counter = 0 +stream_counters = {} def get_offload_stream(device): - global stream_counter + stream_counter = stream_counters.get(device, 0) if NUM_STREAMS <= 1: return None @@ -958,6 +958,7 @@ def get_offload_stream(device): stream_counter = (stream_counter + 1) % len(ss) if is_device_cuda(device): ss[stream_counter].wait_stream(torch.cuda.current_stream()) + stream_counters[device] = stream_counter return s elif is_device_cuda(device): ss = [] @@ -966,6 +967,7 @@ def get_offload_stream(device): STREAMS[device] = ss s = ss[stream_counter] stream_counter = (stream_counter + 1) % len(ss) + stream_counters[device] = stream_counter return s return None From 7ee96455e2ed29293aa6076db9b4866862d41142 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 29 Apr 2025 17:38:45 -0700 Subject: [PATCH 047/193] Bump minimum pyav version to 14.2.0 (#7874) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 10cc177af..f64a05947 100644 --- a/requirements.txt +++ b/requirements.txt @@ -22,5 +22,5 @@ psutil kornia>=0.7.1 spandrel soundfile -av>=14.1.0 +av>=14.2.0 pydantic~=2.0 From dbc726f80c9ac0512d2611fad63d984b8c03886f Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 29 Apr 2025 17:42:00 -0700 Subject: [PATCH 048/193] Better vace memory estimation. (#7875) --- comfy/ldm/wan/model.py | 1 + comfy/supported_models.py | 4 ++++ 2 files changed, 5 insertions(+) diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py index b8eec3afb..66bee7480 100644 --- a/comfy/ldm/wan/model.py +++ b/comfy/ldm/wan/model.py @@ -631,6 +631,7 @@ class VaceWanModel(WanModel): if ii is not None: c_skip, c = self.vace_blocks[ii](c, x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) x += c_skip * vace_strength + del c_skip # head x = self.head(x, e) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 5e55035cf..69bcee1f7 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -993,6 +993,10 @@ class WAN21_Vace(WAN21_T2V): "model_type": "vace", } + def __init__(self, unet_config): + super().__init__(unet_config) + self.memory_usage_factor = 1.2 * self.memory_usage_factor + def get_model(self, state_dict, prefix="", device=None): out = model_base.WAN21_Vace(self, image_to_video=False, device=device) return out From b1c7291569daaeec34ccf6df25d57886eb73d98e Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 30 Apr 2025 11:18:20 -0700 Subject: [PATCH 049/193] Test updater in the windows release workflow. (#7886) --- .github/workflows/windows_release_package.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/windows_release_package.yml b/.github/workflows/windows_release_package.yml index 80a45b321..3926a65f3 100644 --- a/.github/workflows/windows_release_package.yml +++ b/.github/workflows/windows_release_package.yml @@ -88,6 +88,8 @@ jobs: cd ComfyUI_windows_portable python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu + python_embeded/python.exe -s ./update/update.py ComfyUI/ + ls - name: Upload binaries to release From 39c27a37052b5e18850a6c63cd0a4b92131db3ba Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 30 Apr 2025 11:42:18 -0700 Subject: [PATCH 050/193] Add updater test to stable release workflow. (#7887) --- .github/workflows/stable-release.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/stable-release.yml b/.github/workflows/stable-release.yml index c4302cdd6..a046ff9ea 100644 --- a/.github/workflows/stable-release.yml +++ b/.github/workflows/stable-release.yml @@ -91,6 +91,8 @@ jobs: cd ComfyUI_windows_portable python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu + python_embeded/python.exe -s ./update/update.py ComfyUI/ + ls - name: Upload binaries to release From 4ca3d842774421968cc19c319ee96daa58a98fbb Mon Sep 17 00:00:00 2001 From: Silver <65376327+silveroxides@users.noreply.github.com> Date: Thu, 1 May 2025 02:57:00 +0200 Subject: [PATCH 051/193] Support for Chroma - Flux1 Schnell distilled with CFG (#7355) * Upload files for Chroma Implementation * Remove trailing whitespace * trim more trailing whitespace..oops * remove unused imports * Add supported_inference_dtypes * Set min_length to 0 and remove attention_mask=True * Set min_length to 1 * get_mdulations added from blepping and minor changes * Add lora conversion if statement in lora.py * Update supported_models.py * update model_base.py * add uptream commits * set modelType.FLOW, will cause beta scheduler to work properly * Adjust memory usage factor and remove unnecessary code * fix mistake * reduce code duplication * remove unused imports * refactor for upstream sync * sync chroma-support with upstream via syncbranch patch * Update sd.py * Add Chroma as option for the OptimalStepsScheduler node --- comfy/ldm/chroma/layers.py | 183 +++++++++++++++++++ comfy/ldm/chroma/math.py | 44 +++++ comfy/ldm/chroma/model.py | 271 +++++++++++++++++++++++++++++ comfy/lora.py | 2 +- comfy/model_base.py | 62 +++++++ comfy/model_detection.py | 26 +++ comfy/sd.py | 5 + comfy/supported_models.py | 30 +++- comfy/text_encoders/chroma.py | 43 +++++ comfy_extras/nodes_optimalsteps.py | 3 +- nodes.py | 2 +- 11 files changed, 667 insertions(+), 4 deletions(-) create mode 100644 comfy/ldm/chroma/layers.py create mode 100644 comfy/ldm/chroma/math.py create mode 100644 comfy/ldm/chroma/model.py create mode 100644 comfy/text_encoders/chroma.py diff --git a/comfy/ldm/chroma/layers.py b/comfy/ldm/chroma/layers.py new file mode 100644 index 000000000..dd0b72f70 --- /dev/null +++ b/comfy/ldm/chroma/layers.py @@ -0,0 +1,183 @@ +import torch +from torch import Tensor, nn + +from .math import attention +from comfy.ldm.flux.layers import ( + MLPEmbedder, + RMSNorm, + QKNorm, + SelfAttention, + ModulationOut, +) + + + +class ChromaModulationOut(ModulationOut): + @classmethod + def from_offset(cls, tensor: torch.Tensor, offset: int = 0) -> ModulationOut: + return cls( + shift=tensor[:, offset : offset + 1, :], + scale=tensor[:, offset + 1 : offset + 2, :], + gate=tensor[:, offset + 2 : offset + 3, :], + ) + + + + +class Approximator(nn.Module): + def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers = 5, dtype=None, device=None, operations=None): + super().__init__() + self.in_proj = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device) + self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)]) + self.norms = nn.ModuleList([RMSNorm(hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)]) + self.out_proj = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device) + + @property + def device(self): + # Get the device of the module (assumes all parameters are on the same device) + return next(self.parameters()).device + + def forward(self, x: Tensor) -> Tensor: + x = self.in_proj(x) + + for layer, norms in zip(self.layers, self.norms): + x = x + layer(norms(x)) + + x = self.out_proj(x) + + return x + + +class DoubleStreamBlock(nn.Module): + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None): + super().__init__() + + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.num_heads = num_heads + self.hidden_size = hidden_size + self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) + + self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.img_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), + nn.GELU(approximate="tanh"), + operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + + self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) + + self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.txt_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), + nn.GELU(approximate="tanh"), + operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + self.flipped_img_txt = flipped_img_txt + + def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None): + (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec + + # prepare image for attention + img_modulated = self.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_qkv = self.img_attn.qkv(img_modulated) + img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = self.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_qkv = self.txt_attn.qkv(txt_modulated) + txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention + attn = attention(torch.cat((txt_q, img_q), dim=2), + torch.cat((txt_k, img_k), dim=2), + torch.cat((txt_v, img_v), dim=2), + pe=pe, mask=attn_mask) + + txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] + + # calculate the img bloks + img = img + img_mod1.gate * self.img_attn.proj(img_attn) + img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) + + # calculate the txt bloks + txt += txt_mod1.gate * self.txt_attn.proj(txt_attn) + txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) + + if txt.dtype == torch.float16: + txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) + + return img, txt + + +class SingleStreamBlock(nn.Module): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + qk_scale: float = None, + dtype=None, + device=None, + operations=None + ): + super().__init__() + self.hidden_dim = hidden_size + self.num_heads = num_heads + head_dim = hidden_size // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.mlp_hidden_dim = int(hidden_size * mlp_ratio) + # qkv and mlp_in + self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device) + # proj and mlp_out + self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device) + + self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) + + self.hidden_size = hidden_size + self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + + self.mlp_act = nn.GELU(approximate="tanh") + + def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor: + mod = vec + x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift + qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) + + q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k = self.norm(q, k, v) + + # compute attention + attn = attention(q, k, v, pe=pe, mask=attn_mask) + # compute activation in mlp stream, cat again and run second linear layer + output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) + x += mod.gate * output + if x.dtype == torch.float16: + x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) + return x + + +class LastLayer(nn.Module): + def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None): + super().__init__() + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, out_channels, bias=True, dtype=dtype, device=device) + + def forward(self, x: Tensor, vec: Tensor) -> Tensor: + shift, scale = vec + shift = shift.squeeze(1) + scale = scale.squeeze(1) + x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] + x = self.linear(x) + return x diff --git a/comfy/ldm/chroma/math.py b/comfy/ldm/chroma/math.py new file mode 100644 index 000000000..36b67931c --- /dev/null +++ b/comfy/ldm/chroma/math.py @@ -0,0 +1,44 @@ +import torch +from einops import rearrange +from torch import Tensor + +from comfy.ldm.modules.attention import optimized_attention +import comfy.model_management + + +def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor: + q_shape = q.shape + k_shape = k.shape + + q = q.float().reshape(*q.shape[:-1], -1, 1, 2) + k = k.float().reshape(*k.shape[:-1], -1, 1, 2) + q = (pe[..., 0] * q[..., 0] + pe[..., 1] * q[..., 1]).reshape(*q_shape).type_as(v) + k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v) + + heads = q.shape[1] + x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask) + return x + + +def rope(pos: Tensor, dim: int, theta: int) -> Tensor: + assert dim % 2 == 0 + if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled(): + device = torch.device("cpu") + else: + device = pos.device + + scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device) + omega = 1.0 / (theta**scale) + out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega) + out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) + out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) + return out.to(dtype=torch.float32, device=pos.device) + + +def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor): + xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) + xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) + xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] + xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] + return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) + diff --git a/comfy/ldm/chroma/model.py b/comfy/ldm/chroma/model.py new file mode 100644 index 000000000..636748fc5 --- /dev/null +++ b/comfy/ldm/chroma/model.py @@ -0,0 +1,271 @@ +#Original code can be found on: https://github.com/black-forest-labs/flux + +from dataclasses import dataclass + +import torch +from torch import Tensor, nn +from einops import rearrange, repeat +import comfy.ldm.common_dit + +from comfy.ldm.flux.layers import ( + EmbedND, + timestep_embedding, +) + +from .layers import ( + DoubleStreamBlock, + LastLayer, + SingleStreamBlock, + Approximator, + ChromaModulationOut, +) + + +@dataclass +class ChromaParams: + in_channels: int + out_channels: int + context_in_dim: int + hidden_size: int + mlp_ratio: float + num_heads: int + depth: int + depth_single_blocks: int + axes_dim: list + theta: int + patch_size: int + qkv_bias: bool + in_dim: int + out_dim: int + hidden_dim: int + n_layers: int + + + + +class Chroma(nn.Module): + """ + Transformer model for flow matching on sequences. + """ + + def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): + super().__init__() + self.dtype = dtype + params = ChromaParams(**kwargs) + self.params = params + self.patch_size = params.patch_size + self.in_channels = params.in_channels + self.out_channels = params.out_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError( + f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" + ) + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.in_dim = params.in_dim + self.out_dim = params.out_dim + self.hidden_dim = params.hidden_dim + self.n_layers = params.n_layers + self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) + self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device) + self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device) + # set as nn identity for now, will overwrite it later. + self.distilled_guidance_layer = Approximator( + in_dim=self.in_dim, + hidden_dim=self.hidden_dim, + out_dim=self.out_dim, + n_layers=self.n_layers, + dtype=dtype, device=device, operations=operations + ) + + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + dtype=dtype, device=device, operations=operations + ) + for _ in range(params.depth) + ] + ) + + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations) + for _ in range(params.depth_single_blocks) + ] + ) + + if final_layer: + self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations) + + self.skip_mmdit = [] + self.skip_dit = [] + self.lite = False + + def get_modulations(self, tensor: torch.Tensor, block_type: str, *, idx: int = 0): + # This function slices up the modulations tensor which has the following layout: + # single : num_single_blocks * 3 elements + # double_img : num_double_blocks * 6 elements + # double_txt : num_double_blocks * 6 elements + # final : 2 elements + if block_type == "final": + return (tensor[:, -2:-1, :], tensor[:, -1:, :]) + single_block_count = self.params.depth_single_blocks + double_block_count = self.params.depth + offset = 3 * idx + if block_type == "single": + return ChromaModulationOut.from_offset(tensor, offset) + # Double block modulations are 6 elements so we double 3 * idx. + offset *= 2 + if block_type in {"double_img", "double_txt"}: + # Advance past the single block modulations. + offset += 3 * single_block_count + if block_type == "double_txt": + # Advance past the double block img modulations. + offset += 6 * double_block_count + return ( + ChromaModulationOut.from_offset(tensor, offset), + ChromaModulationOut.from_offset(tensor, offset + 3), + ) + raise ValueError("Bad block_type") + + + def forward_orig( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + guidance: Tensor = None, + control = None, + transformer_options={}, + attn_mask: Tensor = None, + ) -> Tensor: + patches_replace = transformer_options.get("patches_replace", {}) + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + + # distilled vector guidance + mod_index_length = 344 + distill_timestep = timestep_embedding(timesteps.detach().clone(), 16).to(img.device, img.dtype) + # guidance = guidance * + distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype) + + # get all modulation index + modulation_index = timestep_embedding(torch.arange(mod_index_length), 32).to(img.device, img.dtype) + # we need to broadcast the modulation index here so each batch has all of the index + modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype) + # and we need to broadcast timestep and guidance along too + timestep_guidance = torch.cat([distill_timestep, distil_guidance], dim=1).unsqueeze(1).repeat(1, mod_index_length, 1).to(img.dtype).to(img.device, img.dtype) + # then and only then we could concatenate it together + input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1).to(img.device, img.dtype) + + mod_vectors = self.distilled_guidance_layer(input_vec) + + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.double_blocks): + if i not in self.skip_mmdit: + double_mod = ( + self.get_modulations(mod_vectors, "double_img", idx=i), + self.get_modulations(mod_vectors, "double_txt", idx=i), + ) + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"], out["txt"] = block(img=args["img"], + txt=args["txt"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("double_block", i)]({"img": img, + "txt": txt, + "vec": double_mod, + "pe": pe, + "attn_mask": attn_mask}, + {"original_block": block_wrap}) + txt = out["txt"] + img = out["img"] + else: + img, txt = block(img=img, + txt=txt, + vec=double_mod, + pe=pe, + attn_mask=attn_mask) + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + img += add + + img = torch.cat((txt, img), 1) + + for i, block in enumerate(self.single_blocks): + if i not in self.skip_dit: + single_mod = self.get_modulations(mod_vectors, "single", idx=i) + if ("single_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("single_block", i)]({"img": img, + "vec": single_mod, + "pe": pe, + "attn_mask": attn_mask}, + {"original_block": block_wrap}) + img = out["img"] + else: + img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask) + + if control is not None: # Controlnet + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + img[:, txt.shape[1] :, ...] += add + + img = img[:, txt.shape[1] :, ...] + final_mod = self.get_modulations(mod_vectors, "final") + img = self.final_layer(img, vec=final_mod) # (N, T, patch_size ** 2 * out_channels) + return img + + def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs): + bs, c, h, w = x.shape + patch_size = 2 + x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size)) + + img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) + + h_len = ((h + (patch_size // 2)) // patch_size) + w_len = ((w + (patch_size // 2)) // patch_size) + img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) + img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) + img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) + img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) + + txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) + out = self.forward_orig(img, img_ids, context, txt_ids, timestep, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None)) + return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w] diff --git a/comfy/lora.py b/comfy/lora.py index fff524be2..dbabd3335 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -252,7 +252,7 @@ def model_lora_keys_unet(model, key_map={}): key_lora = k[len("diffusion_model."):-len(".weight")] key_map["base_model.model.{}".format(key_lora)] = k #official hunyuan lora format - if isinstance(model, comfy.model_base.Flux): #Diffusers lora Flux + if isinstance(model, comfy.model_base.Flux) or isinstance(model, comfy.model_base.Chroma): #Diffusers lora Flux or a diffusers lora Chroma diffusers_keys = comfy.utils.flux_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") for k in diffusers_keys: if k.endswith(".weight"): diff --git a/comfy/model_base.py b/comfy/model_base.py index d2aa4ce7a..1a06bb503 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -38,6 +38,7 @@ import comfy.ldm.lumina.model import comfy.ldm.wan.model import comfy.ldm.hunyuan3d.model import comfy.ldm.hidream.model +import comfy.ldm.chroma.model import comfy.model_management import comfy.patcher_extension @@ -1108,3 +1109,64 @@ class HiDream(BaseModel): if image_cond is not None: out['image_cond'] = comfy.conds.CONDNoiseShape(self.process_latent_in(image_cond)) return out + +class Chroma(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.chroma.model.Chroma) + + def concat_cond(self, **kwargs): + try: + #Handle Flux control loras dynamically changing the img_in weight. + num_channels = self.diffusion_model.img_in.weight.shape[1] + except: + #Some cases like tensorrt might not have the weights accessible + num_channels = self.model_config.unet_config["in_channels"] + + out_channels = self.model_config.unet_config["out_channels"] + + if num_channels <= out_channels: + return None + + image = kwargs.get("concat_latent_image", None) + noise = kwargs.get("noise", None) + device = kwargs["device"] + + if image is None: + image = torch.zeros_like(noise) + + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + image = utils.resize_to_batch_size(image, noise.shape[0]) + image = self.process_latent_in(image) + if num_channels <= out_channels * 2: + return image + + #inpaint model + mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) + if mask is None: + mask = torch.ones_like(noise)[:, :1] + + mask = torch.mean(mask, dim=1, keepdim=True) + mask = utils.common_upscale(mask.to(device), noise.shape[-1] * 8, noise.shape[-2] * 8, "bilinear", "center") + mask = mask.view(mask.shape[0], mask.shape[2] // 8, 8, mask.shape[3] // 8, 8).permute(0, 2, 4, 1, 3).reshape(mask.shape[0], -1, mask.shape[2] // 8, mask.shape[3] // 8) + mask = utils.resize_to_batch_size(mask, noise.shape[0]) + return torch.cat((image, mask), dim=1) + + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + # upscale the attention mask, since now we + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + shape = kwargs["noise"].shape + mask_ref_size = kwargs["attention_mask_img_shape"] + # the model will pad to the patch size, and then divide + # essentially dividing and rounding up + (h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size)) + attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok)) + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + guidance = 0.0 + out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor((guidance,))) + return out diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 76de78a8a..daf6d04e7 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -154,6 +154,32 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config["guidance_embed"] = len(guidance_keys) > 0 return dit_config + if '{}distilled_guidance_layer.0.norms.0.scale'.format(key_prefix) in state_dict_keys or '{}distilled_guidance_layer.norms.0.scale'.format(key_prefix) in state_dict_keys: #Chroma + dit_config = {} + dit_config["image_model"] = "chroma" + dit_config["depth"] = 48 + dit_config["in_channels"] = 64 + patch_size = 2 + dit_config["patch_size"] = patch_size + in_key = "{}img_in.weight".format(key_prefix) + if in_key in state_dict_keys: + dit_config["in_channels"] = state_dict[in_key].shape[1] + dit_config["out_channels"] = 64 + dit_config["context_in_dim"] = 4096 + dit_config["hidden_size"] = 3072 + dit_config["mlp_ratio"] = 4.0 + dit_config["num_heads"] = 24 + dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.') + dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.') + dit_config["axes_dim"] = [16, 56, 56] + dit_config["theta"] = 10000 + dit_config["qkv_bias"] = True + dit_config["in_dim"] = 64 + dit_config["out_dim"] = 3072 + dit_config["hidden_dim"] = 5120 + dit_config["n_layers"] = 5 + return dit_config + if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and '{}img_in.weight'.format(key_prefix) in state_dict_keys: #Flux dit_config = {} dit_config["image_model"] = "flux" diff --git a/comfy/sd.py b/comfy/sd.py index 748f6c1ec..454b5929a 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -42,6 +42,7 @@ import comfy.text_encoders.cosmos import comfy.text_encoders.lumina2 import comfy.text_encoders.wan import comfy.text_encoders.hidream +import comfy.text_encoders.chroma import comfy.model_patcher import comfy.lora @@ -714,6 +715,7 @@ class CLIPType(Enum): LUMINA2 = 12 WAN = 13 HIDREAM = 14 + CHROMA = 15 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}): @@ -829,6 +831,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data), clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None, llama_scaled_fp8=None) clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer + elif clip_type == CLIPType.CHROMA: + clip_target.clip = comfy.text_encoders.chroma.chroma_te(**t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.chroma.ChromaT5Tokenizer else: #CLIPType.MOCHI clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 69bcee1f7..f03f2790e 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -17,6 +17,7 @@ import comfy.text_encoders.hunyuan_video import comfy.text_encoders.cosmos import comfy.text_encoders.lumina2 import comfy.text_encoders.wan +import comfy.text_encoders.chroma from . import supported_models_base from . import latent_formats @@ -1068,7 +1069,34 @@ class HiDream(supported_models_base.BASE): def clip_target(self, state_dict={}): return None # TODO +class Chroma(supported_models_base.BASE): + unet_config = { + "image_model": "chroma", + } -models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream] + unet_extra_config = { + } + + sampling_settings = { + "multiplier": 1.0, + } + + latent_format = comfy.latent_formats.Flux + + memory_usage_factor = 3.2 + + supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] + + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Chroma(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.chroma.ChromaTokenizer, comfy.text_encoders.chroma.chroma_te(**t5_detect)) + +models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma] models += [SVD_img2vid] diff --git a/comfy/text_encoders/chroma.py b/comfy/text_encoders/chroma.py new file mode 100644 index 000000000..aa8dffb25 --- /dev/null +++ b/comfy/text_encoders/chroma.py @@ -0,0 +1,43 @@ +from comfy import sd1_clip +import comfy.text_encoders.t5 +import os +from transformers import T5TokenizerFast + + +class T5XXLModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=False, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json") + t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None) + if t5xxl_scaled_fp8 is not None: + model_options = model_options.copy() + model_options["scaled_fp8"] = t5xxl_scaled_fp8 + + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + + +class ChromaT5XXL(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) + + +class T5XXLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data) + + +class ChromaT5Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) + + +def chroma_te(dtype_t5=None, t5xxl_scaled_fp8=None): + class ChromaTEModel_(ChromaT5XXL): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 + if dtype is None: + dtype = dtype_t5 + super().__init__(device=device, dtype=dtype, model_options=model_options) + return ChromaTEModel_ diff --git a/comfy_extras/nodes_optimalsteps.py b/comfy_extras/nodes_optimalsteps.py index f6928199b..102958734 100644 --- a/comfy_extras/nodes_optimalsteps.py +++ b/comfy_extras/nodes_optimalsteps.py @@ -20,13 +20,14 @@ def loglinear_interp(t_steps, num_steps): NOISE_LEVELS = {"FLUX": [0.9968, 0.9886, 0.9819, 0.975, 0.966, 0.9471, 0.9158, 0.8287, 0.5512, 0.2808, 0.001], "Wan":[1.0, 0.997, 0.995, 0.993, 0.991, 0.989, 0.987, 0.985, 0.98, 0.975, 0.973, 0.968, 0.96, 0.946, 0.927, 0.902, 0.864, 0.776, 0.539, 0.208, 0.001], +"Chroma": [0.9919999837875366, 0.9900000095367432, 0.9879999756813049, 0.9850000143051147, 0.9819999933242798, 0.9779999852180481, 0.9729999899864197, 0.9679999947547913, 0.9610000252723694, 0.953000009059906, 0.9430000185966492, 0.9309999942779541, 0.9169999957084656, 0.8999999761581421, 0.8809999823570251, 0.8579999804496765, 0.8320000171661377, 0.8019999861717224, 0.7689999938011169, 0.7310000061988831, 0.6899999976158142, 0.6460000276565552, 0.5989999771118164, 0.550000011920929, 0.5009999871253967, 0.45100000500679016, 0.4020000100135803, 0.35499998927116394, 0.3109999895095825, 0.27000001072883606, 0.23199999332427979, 0.19900000095367432, 0.16899999976158142, 0.14300000667572021, 0.11999999731779099, 0.10100000351667404, 0.08399999886751175, 0.07000000029802322, 0.057999998331069946, 0.04800000041723251, 0.0], } class OptimalStepsScheduler: @classmethod def INPUT_TYPES(s): return {"required": - {"model_type": (["FLUX", "Wan"], ), + {"model_type": (["FLUX", "Wan", "Chroma"], ), "steps": ("INT", {"default": 20, "min": 3, "max": 1000}), "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), } diff --git a/nodes.py b/nodes.py index 73a62d930..f2ced2c35 100644 --- a/nodes.py +++ b/nodes.py @@ -917,7 +917,7 @@ class CLIPLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ), - "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream"], ), + "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma"], ), }, "optional": { "device": (["default", "cpu"], {"advanced": True}), From 08ff5fa08a92e0b3f23b9abec979a830a6cffb03 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 30 Apr 2025 20:57:30 -0400 Subject: [PATCH 052/193] Cleanup chroma PR. --- comfy/ldm/chroma/layers.py | 2 +- comfy/ldm/chroma/math.py | 44 --------------------- comfy/lora.py | 2 +- comfy/model_base.py | 63 ++++-------------------------- comfy/model_detection.py | 41 ++++++------------- comfy/sd.py | 6 +-- comfy/supported_models.py | 3 +- comfy/text_encoders/chroma.py | 43 -------------------- comfy_extras/nodes_optimalsteps.py | 2 +- 9 files changed, 25 insertions(+), 181 deletions(-) delete mode 100644 comfy/ldm/chroma/math.py delete mode 100644 comfy/text_encoders/chroma.py diff --git a/comfy/ldm/chroma/layers.py b/comfy/ldm/chroma/layers.py index dd0b72f70..35da91ee2 100644 --- a/comfy/ldm/chroma/layers.py +++ b/comfy/ldm/chroma/layers.py @@ -1,7 +1,7 @@ import torch from torch import Tensor, nn -from .math import attention +from comfy.ldm.flux.math import attention from comfy.ldm.flux.layers import ( MLPEmbedder, RMSNorm, diff --git a/comfy/ldm/chroma/math.py b/comfy/ldm/chroma/math.py deleted file mode 100644 index 36b67931c..000000000 --- a/comfy/ldm/chroma/math.py +++ /dev/null @@ -1,44 +0,0 @@ -import torch -from einops import rearrange -from torch import Tensor - -from comfy.ldm.modules.attention import optimized_attention -import comfy.model_management - - -def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor: - q_shape = q.shape - k_shape = k.shape - - q = q.float().reshape(*q.shape[:-1], -1, 1, 2) - k = k.float().reshape(*k.shape[:-1], -1, 1, 2) - q = (pe[..., 0] * q[..., 0] + pe[..., 1] * q[..., 1]).reshape(*q_shape).type_as(v) - k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v) - - heads = q.shape[1] - x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask) - return x - - -def rope(pos: Tensor, dim: int, theta: int) -> Tensor: - assert dim % 2 == 0 - if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled(): - device = torch.device("cpu") - else: - device = pos.device - - scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device) - omega = 1.0 / (theta**scale) - out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega) - out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) - out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) - return out.to(dtype=torch.float32, device=pos.device) - - -def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor): - xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) - xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) - xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] - xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] - return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) - diff --git a/comfy/lora.py b/comfy/lora.py index dbabd3335..fff524be2 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -252,7 +252,7 @@ def model_lora_keys_unet(model, key_map={}): key_lora = k[len("diffusion_model."):-len(".weight")] key_map["base_model.model.{}".format(key_lora)] = k #official hunyuan lora format - if isinstance(model, comfy.model_base.Flux) or isinstance(model, comfy.model_base.Chroma): #Diffusers lora Flux or a diffusers lora Chroma + if isinstance(model, comfy.model_base.Flux): #Diffusers lora Flux diffusers_keys = comfy.utils.flux_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") for k in diffusers_keys: if k.endswith(".weight"): diff --git a/comfy/model_base.py b/comfy/model_base.py index 1a06bb503..3d33086d8 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -787,8 +787,8 @@ class PixArt(BaseModel): return out class Flux(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLUX, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.flux.model.Flux) + def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.flux.model.Flux): + super().__init__(model_config, model_type, device=device, unet_model=unet_model) def concat_cond(self, **kwargs): try: @@ -1110,63 +1110,14 @@ class HiDream(BaseModel): out['image_cond'] = comfy.conds.CONDNoiseShape(self.process_latent_in(image_cond)) return out -class Chroma(BaseModel): +class Chroma(Flux): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.chroma.model.Chroma) - def concat_cond(self, **kwargs): - try: - #Handle Flux control loras dynamically changing the img_in weight. - num_channels = self.diffusion_model.img_in.weight.shape[1] - except: - #Some cases like tensorrt might not have the weights accessible - num_channels = self.model_config.unet_config["in_channels"] - - out_channels = self.model_config.unet_config["out_channels"] - - if num_channels <= out_channels: - return None - - image = kwargs.get("concat_latent_image", None) - noise = kwargs.get("noise", None) - device = kwargs["device"] - - if image is None: - image = torch.zeros_like(noise) - - image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") - image = utils.resize_to_batch_size(image, noise.shape[0]) - image = self.process_latent_in(image) - if num_channels <= out_channels * 2: - return image - - #inpaint model - mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) - if mask is None: - mask = torch.ones_like(noise)[:, :1] - - mask = torch.mean(mask, dim=1, keepdim=True) - mask = utils.common_upscale(mask.to(device), noise.shape[-1] * 8, noise.shape[-2] * 8, "bilinear", "center") - mask = mask.view(mask.shape[0], mask.shape[2] // 8, 8, mask.shape[3] // 8, 8).permute(0, 2, 4, 1, 3).reshape(mask.shape[0], -1, mask.shape[2] // 8, mask.shape[3] // 8) - mask = utils.resize_to_batch_size(mask, noise.shape[0]) - return torch.cat((image, mask), dim=1) - - def extra_conds(self, **kwargs): out = super().extra_conds(**kwargs) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - # upscale the attention mask, since now we - attention_mask = kwargs.get("attention_mask", None) - if attention_mask is not None: - shape = kwargs["noise"].shape - mask_ref_size = kwargs["attention_mask_img_shape"] - # the model will pad to the patch size, and then divide - # essentially dividing and rounding up - (h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size)) - attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok)) - out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) - guidance = 0.0 - out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor((guidance,))) + + guidance = kwargs.get("guidance", 0) + if guidance is not None: + out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) return out diff --git a/comfy/model_detection.py b/comfy/model_detection.py index daf6d04e7..9254843ea 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -154,32 +154,6 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config["guidance_embed"] = len(guidance_keys) > 0 return dit_config - if '{}distilled_guidance_layer.0.norms.0.scale'.format(key_prefix) in state_dict_keys or '{}distilled_guidance_layer.norms.0.scale'.format(key_prefix) in state_dict_keys: #Chroma - dit_config = {} - dit_config["image_model"] = "chroma" - dit_config["depth"] = 48 - dit_config["in_channels"] = 64 - patch_size = 2 - dit_config["patch_size"] = patch_size - in_key = "{}img_in.weight".format(key_prefix) - if in_key in state_dict_keys: - dit_config["in_channels"] = state_dict[in_key].shape[1] - dit_config["out_channels"] = 64 - dit_config["context_in_dim"] = 4096 - dit_config["hidden_size"] = 3072 - dit_config["mlp_ratio"] = 4.0 - dit_config["num_heads"] = 24 - dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.') - dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.') - dit_config["axes_dim"] = [16, 56, 56] - dit_config["theta"] = 10000 - dit_config["qkv_bias"] = True - dit_config["in_dim"] = 64 - dit_config["out_dim"] = 3072 - dit_config["hidden_dim"] = 5120 - dit_config["n_layers"] = 5 - return dit_config - if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and '{}img_in.weight'.format(key_prefix) in state_dict_keys: #Flux dit_config = {} dit_config["image_model"] = "flux" @@ -190,7 +164,9 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): if in_key in state_dict_keys: dit_config["in_channels"] = state_dict[in_key].shape[1] // (patch_size * patch_size) dit_config["out_channels"] = 16 - dit_config["vec_in_dim"] = 768 + vec_in_key = '{}vector_in.in_layer.weight'.format(key_prefix) + if vec_in_key in state_dict_keys: + dit_config["vec_in_dim"] = state_dict[vec_in_key].shape[1] dit_config["context_in_dim"] = 4096 dit_config["hidden_size"] = 3072 dit_config["mlp_ratio"] = 4.0 @@ -200,7 +176,16 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config["axes_dim"] = [16, 56, 56] dit_config["theta"] = 10000 dit_config["qkv_bias"] = True - dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys + if '{}distilled_guidance_layer.0.norms.0.scale'.format(key_prefix) in state_dict_keys or '{}distilled_guidance_layer.norms.0.scale'.format(key_prefix) in state_dict_keys: #Chroma + dit_config["image_model"] = "chroma" + dit_config["in_channels"] = 64 + dit_config["out_channels"] = 64 + dit_config["in_dim"] = 64 + dit_config["out_dim"] = 3072 + dit_config["hidden_dim"] = 5120 + dit_config["n_layers"] = 5 + else: + dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys return dit_config if '{}t5_yproj.weight'.format(key_prefix) in state_dict_keys: #Genmo mochi preview diff --git a/comfy/sd.py b/comfy/sd.py index 454b5929a..da9b36d0e 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -42,7 +42,6 @@ import comfy.text_encoders.cosmos import comfy.text_encoders.lumina2 import comfy.text_encoders.wan import comfy.text_encoders.hidream -import comfy.text_encoders.chroma import comfy.model_patcher import comfy.lora @@ -820,7 +819,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip elif clip_type == CLIPType.LTXV: clip_target.clip = comfy.text_encoders.lt.ltxv_te(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer - elif clip_type == CLIPType.PIXART: + elif clip_type == CLIPType.PIXART or clip_type == CLIPType.CHROMA: clip_target.clip = comfy.text_encoders.pixart_t5.pixart_te(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.pixart_t5.PixArtTokenizer elif clip_type == CLIPType.WAN: @@ -831,9 +830,6 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data), clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None, llama_scaled_fp8=None) clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer - elif clip_type == CLIPType.CHROMA: - clip_target.clip = comfy.text_encoders.chroma.chroma_te(**t5xxl_detect(clip_data)) - clip_target.tokenizer = comfy.text_encoders.chroma.ChromaT5Tokenizer else: #CLIPType.MOCHI clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer diff --git a/comfy/supported_models.py b/comfy/supported_models.py index f03f2790e..d5210cfac 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -17,7 +17,6 @@ import comfy.text_encoders.hunyuan_video import comfy.text_encoders.cosmos import comfy.text_encoders.lumina2 import comfy.text_encoders.wan -import comfy.text_encoders.chroma from . import supported_models_base from . import latent_formats @@ -1095,7 +1094,7 @@ class Chroma(supported_models_base.BASE): def clip_target(self, state_dict={}): pref = self.text_encoder_key_prefix[0] t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) - return supported_models_base.ClipTarget(comfy.text_encoders.chroma.ChromaTokenizer, comfy.text_encoders.chroma.chroma_te(**t5_detect)) + return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.pixart_te(**t5_detect)) models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma] diff --git a/comfy/text_encoders/chroma.py b/comfy/text_encoders/chroma.py deleted file mode 100644 index aa8dffb25..000000000 --- a/comfy/text_encoders/chroma.py +++ /dev/null @@ -1,43 +0,0 @@ -from comfy import sd1_clip -import comfy.text_encoders.t5 -import os -from transformers import T5TokenizerFast - - -class T5XXLModel(sd1_clip.SDClipModel): - def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=False, model_options={}): - textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json") - t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None) - if t5xxl_scaled_fp8 is not None: - model_options = model_options.copy() - model_options["scaled_fp8"] = t5xxl_scaled_fp8 - - super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) - - -class ChromaT5XXL(sd1_clip.SD1ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) - - -class T5XXLTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") - super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data) - - -class ChromaT5Tokenizer(sd1_clip.SD1Tokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) - - -def chroma_te(dtype_t5=None, t5xxl_scaled_fp8=None): - class ChromaTEModel_(ChromaT5XXL): - def __init__(self, device="cpu", dtype=None, model_options={}): - if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: - model_options = model_options.copy() - model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 - if dtype is None: - dtype = dtype_t5 - super().__init__(device=device, dtype=dtype, model_options=model_options) - return ChromaTEModel_ diff --git a/comfy_extras/nodes_optimalsteps.py b/comfy_extras/nodes_optimalsteps.py index 102958734..e7c851ca2 100644 --- a/comfy_extras/nodes_optimalsteps.py +++ b/comfy_extras/nodes_optimalsteps.py @@ -20,7 +20,7 @@ def loglinear_interp(t_steps, num_steps): NOISE_LEVELS = {"FLUX": [0.9968, 0.9886, 0.9819, 0.975, 0.966, 0.9471, 0.9158, 0.8287, 0.5512, 0.2808, 0.001], "Wan":[1.0, 0.997, 0.995, 0.993, 0.991, 0.989, 0.987, 0.985, 0.98, 0.975, 0.973, 0.968, 0.96, 0.946, 0.927, 0.902, 0.864, 0.776, 0.539, 0.208, 0.001], -"Chroma": [0.9919999837875366, 0.9900000095367432, 0.9879999756813049, 0.9850000143051147, 0.9819999933242798, 0.9779999852180481, 0.9729999899864197, 0.9679999947547913, 0.9610000252723694, 0.953000009059906, 0.9430000185966492, 0.9309999942779541, 0.9169999957084656, 0.8999999761581421, 0.8809999823570251, 0.8579999804496765, 0.8320000171661377, 0.8019999861717224, 0.7689999938011169, 0.7310000061988831, 0.6899999976158142, 0.6460000276565552, 0.5989999771118164, 0.550000011920929, 0.5009999871253967, 0.45100000500679016, 0.4020000100135803, 0.35499998927116394, 0.3109999895095825, 0.27000001072883606, 0.23199999332427979, 0.19900000095367432, 0.16899999976158142, 0.14300000667572021, 0.11999999731779099, 0.10100000351667404, 0.08399999886751175, 0.07000000029802322, 0.057999998331069946, 0.04800000041723251, 0.0], +"Chroma": [0.992, 0.99, 0.988, 0.985, 0.982, 0.978, 0.973, 0.968, 0.961, 0.953, 0.943, 0.931, 0.917, 0.9, 0.881, 0.858, 0.832, 0.802, 0.769, 0.731, 0.69, 0.646, 0.599, 0.55, 0.501, 0.451, 0.402, 0.355, 0.311, 0.27, 0.232, 0.199, 0.169, 0.143, 0.12, 0.101, 0.084, 0.07, 0.058, 0.048, 0.001], } class OptimalStepsScheduler: From c6c19e99803a2538cee17e2f94314f77b7d48e98 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Thu, 1 May 2025 00:24:32 -0700 Subject: [PATCH 053/193] fix bug (#7894) --- comfy_api_nodes/apis/client.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py index 384e559dc..d3cd9ad2f 100644 --- a/comfy_api_nodes/apis/client.py +++ b/comfy_api_nodes/apis/client.py @@ -297,6 +297,10 @@ class SynchronousOperation(Generic[T, R]): # Convert request model to dict, but use None for EmptyRequest request_dict = None if isinstance(self.request, EmptyRequest) else self.request.model_dump(exclude_none=True) + if request_dict: + for key, value in request_dict.items(): + if isinstance(value, Enum): + request_dict[key] = value.value # Debug log for request logging.debug(f"[DEBUG] API Request: {self.endpoint.method.value} {self.endpoint.path}") From aa9d759df36faa2e34cc5722463749a09a7f529b Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 1 May 2025 03:33:42 -0700 Subject: [PATCH 054/193] Switch ltxv to use the pytorch RMSNorm. (#7897) --- comfy/ldm/lightricks/model.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py index 6e8e06181..056e101a4 100644 --- a/comfy/ldm/lightricks/model.py +++ b/comfy/ldm/lightricks/model.py @@ -1,7 +1,6 @@ import torch from torch import nn import comfy.ldm.modules.attention -from comfy.ldm.genmo.joint_model.layers import RMSNorm import comfy.ldm.common_dit from einops import rearrange import math @@ -262,8 +261,8 @@ class CrossAttention(nn.Module): self.heads = heads self.dim_head = dim_head - self.q_norm = RMSNorm(inner_dim, dtype=dtype, device=device) - self.k_norm = RMSNorm(inner_dim, dtype=dtype, device=device) + self.q_norm = operations.RMSNorm(inner_dim, dtype=dtype, device=device) + self.k_norm = operations.RMSNorm(inner_dim, dtype=dtype, device=device) self.to_q = operations.Linear(query_dim, inner_dim, bias=True, dtype=dtype, device=device) self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device) From 6d32dc049e676a1373dddc4fd912f2ba1311d4cb Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Thu, 1 May 2025 10:57:54 -0400 Subject: [PATCH 055/193] Update frontend to v1.18 (#7898) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index f64a05947..74a4ceb02 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.17.11 +comfyui-frontend-package==1.18.5 comfyui-workflow-templates==0.1.3 torch torchsde From 8d0661d0ba35fd54a72e4b49f68d4625febafc10 Mon Sep 17 00:00:00 2001 From: catboxanon <122327233+catboxanon@users.noreply.github.com> Date: Thu, 1 May 2025 19:32:04 -0400 Subject: [PATCH 056/193] Lint instance methods (#7903) --- comfy_extras/nodes_post_processing.py | 1 + comfy_extras/nodes_webcam.py | 2 +- pyproject.toml | 1 + 3 files changed, 3 insertions(+), 1 deletion(-) diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py index 5b9542015..cb1a0d883 100644 --- a/comfy_extras/nodes_post_processing.py +++ b/comfy_extras/nodes_post_processing.py @@ -141,6 +141,7 @@ class Quantize: CATEGORY = "image/postprocessing" + @staticmethod def bayer(im, pal_im, order): def normalized_bayer_matrix(n): if n == 0: diff --git a/comfy_extras/nodes_webcam.py b/comfy_extras/nodes_webcam.py index 31eddb2d6..062b15cf8 100644 --- a/comfy_extras/nodes_webcam.py +++ b/comfy_extras/nodes_webcam.py @@ -20,7 +20,7 @@ class WebcamCapture(nodes.LoadImage): CATEGORY = "image" - def load_capture(s, image, **kwargs): + def load_capture(self, image, **kwargs): return super().load_image(folder_paths.get_annotated_filepath(image)) diff --git a/pyproject.toml b/pyproject.toml index eadca662e..a9c028c7e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,6 +12,7 @@ documentation = "https://docs.comfy.org/" [tool.ruff] lint.select = [ + "N805", # invalid-first-argument-name-for-method "S307", # suspicious-eval-usage "S102", # exec "T", # print-usage From ff99861650a195234f2858b864663920ca811c55 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 2 May 2025 02:15:32 -0700 Subject: [PATCH 057/193] Make clipsave work with more TE models. (#7908) --- comfy_extras/nodes_model_merging.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py index 78d284889..f20beab7d 100644 --- a/comfy_extras/nodes_model_merging.py +++ b/comfy_extras/nodes_model_merging.py @@ -276,7 +276,7 @@ class CLIPSave: comfy.model_management.load_models_gpu([clip.load_model()], force_patch_weights=True) clip_sd = clip.get_sd() - for prefix in ["clip_l.", "clip_g.", ""]: + for prefix in ["clip_l.", "clip_g.", "clip_h.", "t5xxl.", "pile_t5xl.", "mt5xl.", "umt5xxl.", "t5base.", "gemma2_2b.", "llama.", "hydit_clip.", ""]: k = list(filter(lambda a: a.startswith(prefix), clip_sd.keys())) current_clip_sd = {} for x in k: From 551fe8dceebb07abed486580d8326a6202d0cf7a Mon Sep 17 00:00:00 2001 From: catboxanon <122327233+catboxanon@users.noreply.github.com> Date: Fri, 2 May 2025 05:28:05 -0400 Subject: [PATCH 058/193] Add node to extend sigmas (#7901) * Add ExpandSigmas node * Rename, add interpolation functions Co-authored-by: liesen * Move computed interpolation outside loop * Add type hints --------- Co-authored-by: liesen --- comfy_extras/nodes_custom_sampler.py | 51 ++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index c9689b745..3e5be3d3c 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -1,3 +1,4 @@ +import math import comfy.samplers import comfy.sample from comfy.k_diffusion import sampling as k_diffusion_sampling @@ -249,6 +250,55 @@ class SetFirstSigma: sigmas[0] = sigma return (sigmas, ) +class ExtendIntermediateSigmas: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sigmas": ("SIGMAS", ), + "steps": ("INT", {"default": 2, "min": 1, "max": 100}), + "start_at_sigma": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 20000.0, "step": 0.01, "round": False}), + "end_at_sigma": ("FLOAT", {"default": 12.0, "min": 0.0, "max": 20000.0, "step": 0.01, "round": False}), + "spacing": (['linear', 'cosine', 'sine'],), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/sigmas" + + FUNCTION = "extend" + + def extend(self, sigmas: torch.Tensor, steps: int, start_at_sigma: float, end_at_sigma: float, spacing: str): + if start_at_sigma < 0: + start_at_sigma = float("inf") + + interpolator = { + 'linear': lambda x: x, + 'cosine': lambda x: torch.sin(x*math.pi/2), + 'sine': lambda x: 1 - torch.cos(x*math.pi/2) + }[spacing] + + # linear space for our interpolation function + x = torch.linspace(0, 1, steps + 1, device=sigmas.device)[1:-1] + computed_spacing = interpolator(x) + + extended_sigmas = [] + for i in range(len(sigmas) - 1): + sigma_current = sigmas[i] + sigma_next = sigmas[i+1] + + extended_sigmas.append(sigma_current) + + if end_at_sigma <= sigma_current <= start_at_sigma: + interpolated_steps = computed_spacing * (sigma_next - sigma_current) + sigma_current + extended_sigmas.extend(interpolated_steps.tolist()) + + # Add the last sigma value + if len(sigmas) > 0: + extended_sigmas.append(sigmas[-1]) + + extended_sigmas = torch.FloatTensor(extended_sigmas) + + return (extended_sigmas,) + class KSamplerSelect: @classmethod def INPUT_TYPES(s): @@ -735,6 +785,7 @@ NODE_CLASS_MAPPINGS = { "SplitSigmasDenoise": SplitSigmasDenoise, "FlipSigmas": FlipSigmas, "SetFirstSigma": SetFirstSigma, + "ExtendIntermediateSigmas": ExtendIntermediateSigmas, "CFGGuider": CFGGuider, "DualCFGGuider": DualCFGGuider, From d9a87c1e6a390eb0e915b544e35baf4d807919db Mon Sep 17 00:00:00 2001 From: catboxanon <122327233+catboxanon@users.noreply.github.com> Date: Fri, 2 May 2025 05:28:27 -0400 Subject: [PATCH 059/193] Fix outdated comment about Internet connectivity (#7827) --- main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/main.py b/main.py index f3f56597a..5c21542b3 100644 --- a/main.py +++ b/main.py @@ -13,7 +13,7 @@ import logging import sys if __name__ == "__main__": - #NOTE: These do not do anything on core ComfyUI which should already have no communication with the internet, they are for custom nodes. + #NOTE: These do not do anything on core ComfyUI, they are for custom nodes. os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1' os.environ['DO_NOT_TRACK'] = '1' From 2ab9618732b738b52c96ae128597371b9b9fff96 Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Sat, 3 May 2025 01:12:37 +0800 Subject: [PATCH 060/193] Fix the bugs in OFT/BOFT moule (#7909) * Correct calculate_weight and load for OFT * Correct calculate_weight and loading for BOFT --- comfy/weight_adapter/boft.py | 34 +++++++++++++++++----------------- comfy/weight_adapter/oft.py | 20 +++++++++++--------- 2 files changed, 28 insertions(+), 26 deletions(-) diff --git a/comfy/weight_adapter/boft.py b/comfy/weight_adapter/boft.py index c85adc7ab..b2a2f1bd4 100644 --- a/comfy/weight_adapter/boft.py +++ b/comfy/weight_adapter/boft.py @@ -24,7 +24,7 @@ class BOFTAdapter(WeightAdapterBase): ) -> Optional["BOFTAdapter"]: if loaded_keys is None: loaded_keys = set() - blocks_name = "{}.boft_blocks".format(x) + blocks_name = "{}.oft_blocks".format(x) rescale_name = "{}.rescale".format(x) blocks = None @@ -32,17 +32,18 @@ class BOFTAdapter(WeightAdapterBase): blocks = lora[blocks_name] if blocks.ndim == 4: loaded_keys.add(blocks_name) + else: + blocks = None + if blocks is None: + return None rescale = None if rescale_name in lora.keys(): rescale = lora[rescale_name] loaded_keys.add(rescale_name) - if blocks is not None: - weights = (blocks, rescale, alpha, dora_scale) - return cls(loaded_keys, weights) - else: - return None + weights = (blocks, rescale, alpha, dora_scale) + return cls(loaded_keys, weights) def calculate_weight( self, @@ -71,7 +72,7 @@ class BOFTAdapter(WeightAdapterBase): # Get r I = torch.eye(boft_b, device=blocks.device, dtype=blocks.dtype) # for Q = -Q^T - q = blocks - blocks.transpose(1, 2) + q = blocks - blocks.transpose(-1, -2) normed_q = q if alpha > 0: # alpha in boft/bboft is for constraint q_norm = torch.norm(q) + 1e-8 @@ -79,9 +80,8 @@ class BOFTAdapter(WeightAdapterBase): normed_q = q * alpha / q_norm # use float() to prevent unsupported type in .inverse() r = (I + normed_q) @ (I - normed_q).float().inverse() - r = r.to(original_weight) - - inp = org = original_weight + r = r.to(weight) + inp = org = weight r_b = boft_b//2 for i in range(boft_m): @@ -91,14 +91,14 @@ class BOFTAdapter(WeightAdapterBase): if strength != 1: bi = bi * strength + (1-strength) * I inp = ( - inp.unflatten(-1, (-1, g, k)) - .transpose(-2, -1) - .flatten(-3) - .unflatten(-1, (-1, boft_b)) + inp.unflatten(0, (-1, g, k)) + .transpose(1, 2) + .flatten(0, 2) + .unflatten(0, (-1, boft_b)) ) - inp = torch.einsum("b n m, b n ... -> b m ...", inp, bi) + inp = torch.einsum("b i j, b j ...-> b i ...", bi, inp) inp = ( - inp.flatten(-2).unflatten(-1, (-1, k, g)).transpose(-2, -1).flatten(-3) + inp.flatten(0, 1).unflatten(0, (-1, k, g)).transpose(1, 2).flatten(0, 2) ) if rescale is not None: @@ -109,7 +109,7 @@ class BOFTAdapter(WeightAdapterBase): if dora_scale is not None: weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) else: - weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + weight += function((strength * lora_diff).type(weight.dtype)) except Exception as e: logging.error("ERROR {} {} {}".format(self.name, key, e)) return weight diff --git a/comfy/weight_adapter/oft.py b/comfy/weight_adapter/oft.py index 0ea229b79..25009eca3 100644 --- a/comfy/weight_adapter/oft.py +++ b/comfy/weight_adapter/oft.py @@ -32,17 +32,18 @@ class OFTAdapter(WeightAdapterBase): blocks = lora[blocks_name] if blocks.ndim == 3: loaded_keys.add(blocks_name) + else: + blocks = None + if blocks is None: + return None rescale = None if rescale_name in lora.keys(): rescale = lora[rescale_name] loaded_keys.add(rescale_name) - if blocks is not None: - weights = (blocks, rescale, alpha, dora_scale) - return cls(loaded_keys, weights) - else: - return None + weights = (blocks, rescale, alpha, dora_scale) + return cls(loaded_keys, weights) def calculate_weight( self, @@ -79,16 +80,17 @@ class OFTAdapter(WeightAdapterBase): normed_q = q * alpha / q_norm # use float() to prevent unsupported type in .inverse() r = (I + normed_q) @ (I - normed_q).float().inverse() - r = r.to(original_weight) + r = r.to(weight) + _, *shape = weight.shape lora_diff = torch.einsum( "k n m, k n ... -> k m ...", (r * strength) - strength * I, - original_weight, - ) + weight.view(block_num, block_size, *shape), + ).view(-1, *shape) if dora_scale is not None: weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) else: - weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + weight += function((strength * lora_diff).type(weight.dtype)) except Exception as e: logging.error("ERROR {} {} {}".format(self.name, key, e)) return weight From 530494588d9e58a8bcaf55b471f4c3ce2b97ce65 Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Fri, 2 May 2025 13:14:52 -0400 Subject: [PATCH 061/193] [BugFix] Update frontend 1.18.6 (#7910) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 74a4ceb02..05ceba00a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.18.5 +comfyui-frontend-package==1.18.6 comfyui-workflow-templates==0.1.3 torch torchsde From 065d855f14968406051a1340e3f2f26461a00e5d Mon Sep 17 00:00:00 2001 From: Terry Jia Date: Fri, 2 May 2025 13:15:54 -0400 Subject: [PATCH 062/193] upstream Preview Any from rgthree-comfy (#7815) * upstream Preview Any from rgthree-comfy * use IO.ANY --- comfy_extras/nodes_preview_any.py | 43 +++++++++++++++++++++++++++++++ nodes.py | 1 + 2 files changed, 44 insertions(+) create mode 100644 comfy_extras/nodes_preview_any.py diff --git a/comfy_extras/nodes_preview_any.py b/comfy_extras/nodes_preview_any.py new file mode 100644 index 000000000..e6805696f --- /dev/null +++ b/comfy_extras/nodes_preview_any.py @@ -0,0 +1,43 @@ +import json +from comfy.comfy_types.node_typing import IO + +# Preview Any - original implement from +# https://github.com/rgthree/rgthree-comfy/blob/main/py/display_any.py +# upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes +class PreviewAny(): + @classmethod + def INPUT_TYPES(cls): + return { + "required": {"source": (IO.ANY, {})}, + } + + RETURN_TYPES = () + FUNCTION = "main" + OUTPUT_NODE = True + + CATEGORY = "utils" + + def main(self, source=None): + value = 'None' + if isinstance(source, str): + value = source + elif isinstance(source, (int, float, bool)): + value = str(source) + elif source is not None: + try: + value = json.dumps(source) + except Exception: + try: + value = str(source) + except Exception: + value = 'source exists, but could not be serialized.' + + return {"ui": {"text": (value,)}} + +NODE_CLASS_MAPPINGS = { + "PreviewAny": PreviewAny, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PreviewAny": "Preview Any", +} diff --git a/nodes.py b/nodes.py index f2ced2c35..92b8ca6ae 100644 --- a/nodes.py +++ b/nodes.py @@ -2258,6 +2258,7 @@ def init_builtin_extra_nodes(): "nodes_optimalsteps.py", "nodes_hidream.py", "nodes_fresca.py", + "nodes_preview_any.py", ] api_nodes_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_api_nodes") From 486ad8fdc55495a705a211c22cc50bec9ec95643 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 2 May 2025 21:28:10 -0700 Subject: [PATCH 063/193] Fix updater issue with newer portable. (#7917) --- .ci/update_windows/update.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/.ci/update_windows/update.py b/.ci/update_windows/update.py index 731b6bc53..51a263203 100755 --- a/.ci/update_windows/update.py +++ b/.ci/update_windows/update.py @@ -63,7 +63,12 @@ except: print("checking out master branch") # noqa: T201 branch = repo.lookup_branch('master') if branch is None: - ref = repo.lookup_reference('refs/remotes/origin/master') + try: + ref = repo.lookup_reference('refs/remotes/origin/master') + except: + print("pulling.") # noqa: T201 + pull(repo) + ref = repo.lookup_reference('refs/remotes/origin/master') repo.checkout(ref) branch = repo.lookup_branch('master') if branch is None: From 7689917113fe521adfaba2a4fff952ef1805ad2b Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 3 May 2025 00:34:01 -0400 Subject: [PATCH 064/193] ComfyUI version 0.3.31 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 67d27f942..8d2068de7 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.30" +__version__ = "0.3.31" diff --git a/pyproject.toml b/pyproject.toml index a9c028c7e..8b549a0b6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.30" +version = "0.3.31" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9" From 3041e5c354511ad8e4a6fe73ed397e41845caae2 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 3 May 2025 16:07:55 -0700 Subject: [PATCH 065/193] Switch mochi and wan modes to use pytorch RMSNorm. (#7925) * Switch genmo model to native RMSNorm. * Switch WAN to native RMSNorm. --- comfy/ldm/genmo/joint_model/asymm_models_joint.py | 9 ++++----- comfy/ldm/genmo/joint_model/layers.py | 11 ----------- comfy/ldm/wan/model.py | 7 +++---- 3 files changed, 7 insertions(+), 20 deletions(-) diff --git a/comfy/ldm/genmo/joint_model/asymm_models_joint.py b/comfy/ldm/genmo/joint_model/asymm_models_joint.py index 2c46c24bf..366a8b713 100644 --- a/comfy/ldm/genmo/joint_model/asymm_models_joint.py +++ b/comfy/ldm/genmo/joint_model/asymm_models_joint.py @@ -13,7 +13,6 @@ from comfy.ldm.modules.attention import optimized_attention from .layers import ( FeedForward, PatchEmbed, - RMSNorm, TimestepEmbedder, ) @@ -90,10 +89,10 @@ class AsymmetricAttention(nn.Module): # Query and key normalization for stability. assert qk_norm - self.q_norm_x = RMSNorm(self.head_dim, device=device, dtype=dtype) - self.k_norm_x = RMSNorm(self.head_dim, device=device, dtype=dtype) - self.q_norm_y = RMSNorm(self.head_dim, device=device, dtype=dtype) - self.k_norm_y = RMSNorm(self.head_dim, device=device, dtype=dtype) + self.q_norm_x = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) + self.k_norm_x = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) + self.q_norm_y = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) + self.k_norm_y = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) # Output layers. y features go back down from dim_x -> dim_y. self.proj_x = operations.Linear(dim_x, dim_x, bias=out_bias, device=device, dtype=dtype) diff --git a/comfy/ldm/genmo/joint_model/layers.py b/comfy/ldm/genmo/joint_model/layers.py index 51d979559..e310bd717 100644 --- a/comfy/ldm/genmo/joint_model/layers.py +++ b/comfy/ldm/genmo/joint_model/layers.py @@ -151,14 +151,3 @@ class PatchEmbed(nn.Module): x = self.norm(x) return x - - -class RMSNorm(torch.nn.Module): - def __init__(self, hidden_size, eps=1e-5, device=None, dtype=None): - super().__init__() - self.eps = eps - self.weight = torch.nn.Parameter(torch.empty(hidden_size, device=device, dtype=dtype)) - self.register_parameter("bias", None) - - def forward(self, x): - return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps) diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py index 66bee7480..fc5ff40c5 100644 --- a/comfy/ldm/wan/model.py +++ b/comfy/ldm/wan/model.py @@ -9,7 +9,6 @@ from einops import repeat from comfy.ldm.modules.attention import optimized_attention from comfy.ldm.flux.layers import EmbedND from comfy.ldm.flux.math import apply_rope -from comfy.ldm.modules.diffusionmodules.mmdit import RMSNorm import comfy.ldm.common_dit import comfy.model_management @@ -49,8 +48,8 @@ class WanSelfAttention(nn.Module): self.k = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.v = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.o = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.norm_q = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() - self.norm_k = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() + self.norm_q = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() + self.norm_k = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() def forward(self, x, freqs): r""" @@ -114,7 +113,7 @@ class WanI2VCrossAttention(WanSelfAttention): self.k_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.v_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) # self.alpha = nn.Parameter(torch.zeros((1, ))) - self.norm_k_img = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() + self.norm_k_img = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() def forward(self, x, context, context_img_len): r""" From 9187a09483514c9d363bf064481c4614563951b0 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 4 May 2025 03:26:20 -0700 Subject: [PATCH 066/193] Change cosmos and hydit models to use the native RMSNorm. (#7934) --- comfy/ldm/cosmos/blocks.py | 11 +++++------ comfy/ldm/cosmos/model.py | 4 +--- comfy/ldm/hydit/models.py | 4 ++-- 3 files changed, 8 insertions(+), 11 deletions(-) diff --git a/comfy/ldm/cosmos/blocks.py b/comfy/ldm/cosmos/blocks.py index 84fd6d839..a12f892d2 100644 --- a/comfy/ldm/cosmos/blocks.py +++ b/comfy/ldm/cosmos/blocks.py @@ -23,7 +23,6 @@ from einops import rearrange, repeat from einops.layers.torch import Rearrange from torch import nn -from comfy.ldm.modules.diffusionmodules.mmdit import RMSNorm from comfy.ldm.modules.attention import optimized_attention @@ -37,11 +36,11 @@ def apply_rotary_pos_emb( return t_out -def get_normalization(name: str, channels: int, weight_args={}): +def get_normalization(name: str, channels: int, weight_args={}, operations=None): if name == "I": return nn.Identity() elif name == "R": - return RMSNorm(channels, elementwise_affine=True, eps=1e-6, **weight_args) + return operations.RMSNorm(channels, elementwise_affine=True, eps=1e-6, **weight_args) else: raise ValueError(f"Normalization {name} not found") @@ -120,15 +119,15 @@ class Attention(nn.Module): self.to_q = nn.Sequential( operations.Linear(query_dim, inner_dim, bias=qkv_bias, **weight_args), - get_normalization(qkv_norm[0], norm_dim), + get_normalization(qkv_norm[0], norm_dim, weight_args=weight_args, operations=operations), ) self.to_k = nn.Sequential( operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args), - get_normalization(qkv_norm[1], norm_dim), + get_normalization(qkv_norm[1], norm_dim, weight_args=weight_args, operations=operations), ) self.to_v = nn.Sequential( operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args), - get_normalization(qkv_norm[2], norm_dim), + get_normalization(qkv_norm[2], norm_dim, weight_args=weight_args, operations=operations), ) self.to_out = nn.Sequential( diff --git a/comfy/ldm/cosmos/model.py b/comfy/ldm/cosmos/model.py index 06d0baef3..4836e0b69 100644 --- a/comfy/ldm/cosmos/model.py +++ b/comfy/ldm/cosmos/model.py @@ -27,8 +27,6 @@ from torchvision import transforms from enum import Enum import logging -from comfy.ldm.modules.diffusionmodules.mmdit import RMSNorm - from .blocks import ( FinalLayer, GeneralDITTransformerBlock, @@ -195,7 +193,7 @@ class GeneralDIT(nn.Module): if self.affline_emb_norm: logging.debug("Building affine embedding normalization layer") - self.affline_norm = RMSNorm(model_channels, elementwise_affine=True, eps=1e-6) + self.affline_norm = operations.RMSNorm(model_channels, elementwise_affine=True, eps=1e-6, device=device, dtype=dtype) else: self.affline_norm = nn.Identity() diff --git a/comfy/ldm/hydit/models.py b/comfy/ldm/hydit/models.py index 359f6a965..5ba2b76e0 100644 --- a/comfy/ldm/hydit/models.py +++ b/comfy/ldm/hydit/models.py @@ -3,7 +3,7 @@ import torch import torch.nn as nn import comfy.ops -from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed, RMSNorm +from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed from comfy.ldm.modules.diffusionmodules.util import timestep_embedding from torch.utils import checkpoint @@ -51,7 +51,7 @@ class HunYuanDiTBlock(nn.Module): if norm_type == "layer": norm_layer = operations.LayerNorm elif norm_type == "rms": - norm_layer = RMSNorm + norm_layer = operations.RMSNorm else: raise ValueError(f"Unknown norm_type: {norm_type}") From 80a44b97f5cbcb890896e2b9e65d177f1ac6a588 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 4 May 2025 03:39:23 -0700 Subject: [PATCH 067/193] Change lumina to native RMSNorm. (#7935) --- comfy/ldm/lumina/model.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/comfy/ldm/lumina/model.py b/comfy/ldm/lumina/model.py index ccd5d2c0e..f8dc4d7db 100644 --- a/comfy/ldm/lumina/model.py +++ b/comfy/ldm/lumina/model.py @@ -8,7 +8,7 @@ import torch.nn as nn import torch.nn.functional as F import comfy.ldm.common_dit -from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, RMSNorm +from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder from comfy.ldm.modules.attention import optimized_attention_masked from comfy.ldm.flux.layers import EmbedND @@ -64,8 +64,8 @@ class JointAttention(nn.Module): ) if qk_norm: - self.q_norm = RMSNorm(self.head_dim, elementwise_affine=True, **operation_settings) - self.k_norm = RMSNorm(self.head_dim, elementwise_affine=True, **operation_settings) + self.q_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.k_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) else: self.q_norm = self.k_norm = nn.Identity() @@ -242,11 +242,11 @@ class JointTransformerBlock(nn.Module): operation_settings=operation_settings, ) self.layer_id = layer_id - self.attention_norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings) - self.ffn_norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings) + self.attention_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.ffn_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.attention_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings) - self.ffn_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings) + self.attention_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.ffn_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.modulation = modulation if modulation: @@ -431,7 +431,7 @@ class NextDiT(nn.Module): self.t_embedder = TimestepEmbedder(min(dim, 1024), **operation_settings) self.cap_embedder = nn.Sequential( - RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, **operation_settings), + operation_settings.get("operations").RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), operation_settings.get("operations").Linear( cap_feat_dim, dim, @@ -457,7 +457,7 @@ class NextDiT(nn.Module): for layer_id in range(n_layers) ] ) - self.norm_final = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings) + self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) self.final_layer = FinalLayer(dim, patch_size, self.out_channels, operation_settings=operation_settings) assert (dim // n_heads) == sum(axes_dims) From cd18582578d38081af5614d018781bcdfbe95e0b Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Sun, 4 May 2025 20:26:57 -0700 Subject: [PATCH 068/193] Support saving Comfy `VIDEO` type to buffer (#7939) * get output format when saving to buffer * add unit tests for writing to file or stream with correct fmt * handle `to_format=None` * fix formatting --- comfy_api/input_impl/video_types.py | 46 +++++++++- tests-unit/comfy_api_test/input_impl_test.py | 91 ++++++++++++++++++++ 2 files changed, 135 insertions(+), 2 deletions(-) create mode 100644 tests-unit/comfy_api_test/input_impl_test.py diff --git a/comfy_api/input_impl/video_types.py b/comfy_api/input_impl/video_types.py index 12e5783db..d0b0b36d2 100644 --- a/comfy_api/input_impl/video_types.py +++ b/comfy_api/input_impl/video_types.py @@ -12,6 +12,46 @@ import torch from comfy_api.input import VideoInput from comfy_api.util import VideoContainer, VideoCodec, VideoComponents + +def container_to_output_format(container_format: str | None) -> str | None: + """ + A container's `format` may be a comma-separated list of formats. + E.g., iso container's `format` may be `mov,mp4,m4a,3gp,3g2,mj2`. + However, writing to a file/stream with `av.open` requires a single format, + or `None` to auto-detect. + """ + if not container_format: + return None # Auto-detect + + if "," not in container_format: + return container_format + + formats = container_format.split(",") + return formats[0] + + +def get_open_write_kwargs( + dest: str | io.BytesIO, container_format: str, to_format: str | None +) -> dict: + """Get kwargs for writing a `VideoFromFile` to a file/stream with `av.open`""" + open_kwargs = { + "mode": "w", + # If isobmff, preserve custom metadata tags (workflow, prompt, extra_pnginfo) + "options": {"movflags": "use_metadata_tags"}, + } + + is_write_to_buffer = isinstance(dest, io.BytesIO) + if is_write_to_buffer: + # Set output format explicitly, since it cannot be inferred from file extension + if to_format == VideoContainer.AUTO: + to_format = container_format.lower() + elif isinstance(to_format, str): + to_format = to_format.lower() + open_kwargs["format"] = container_to_output_format(to_format) + + return open_kwargs + + class VideoFromFile(VideoInput): """ Class representing video input from a file. @@ -89,7 +129,7 @@ class VideoFromFile(VideoInput): def save_to( self, - path: str, + path: str | io.BytesIO, format: VideoContainer = VideoContainer.AUTO, codec: VideoCodec = VideoCodec.AUTO, metadata: Optional[dict] = None @@ -116,7 +156,9 @@ class VideoFromFile(VideoInput): ) streams = container.streams - with av.open(path, mode='w', options={"movflags": "use_metadata_tags"}) as output_container: + + open_kwargs = get_open_write_kwargs(path, container_format, format) + with av.open(path, **open_kwargs) as output_container: # Copy over the original metadata for key, value in container.metadata.items(): if metadata is None or key not in metadata: diff --git a/tests-unit/comfy_api_test/input_impl_test.py b/tests-unit/comfy_api_test/input_impl_test.py new file mode 100644 index 000000000..5fc21a9a7 --- /dev/null +++ b/tests-unit/comfy_api_test/input_impl_test.py @@ -0,0 +1,91 @@ +import io +from comfy_api.input_impl.video_types import ( + container_to_output_format, + get_open_write_kwargs, +) +from comfy_api.util import VideoContainer + + +def test_container_to_output_format_empty_string(): + """Test that an empty string input returns None. `None` arg allows default auto-detection.""" + assert container_to_output_format("") is None + + +def test_container_to_output_format_none(): + """Test that None input returns None.""" + assert container_to_output_format(None) is None + + +def test_container_to_output_format_comma_separated(): + """Test that a comma-separated list returns a valid singular format from the list.""" + comma_separated_format = "mp4,mov,m4a" + output_format = container_to_output_format(comma_separated_format) + assert output_format in comma_separated_format + + +def test_container_to_output_format_single(): + """Test that a single format string (not comma-separated list) is returned as is.""" + assert container_to_output_format("mp4") == "mp4" + + +def test_get_open_write_kwargs_filepath_no_format(): + """Test that 'format' kwarg is NOT set when dest is a file path.""" + kwargs_auto = get_open_write_kwargs("output.mp4", "mp4", VideoContainer.AUTO) + assert "format" not in kwargs_auto, "Format should not be set for file paths (AUTO)" + + kwargs_specific = get_open_write_kwargs("output.avi", "mp4", "avi") + fail_msg = "Format should not be set for file paths (Specific)" + assert "format" not in kwargs_specific, fail_msg + + +def test_get_open_write_kwargs_base_options_mode(): + """Test basic kwargs for file path: mode and movflags.""" + kwargs = get_open_write_kwargs("output.mp4", "mp4", VideoContainer.AUTO) + assert kwargs["mode"] == "w", "mode should be set to write" + + fail_msg = "movflags should be set to preserve custom metadata tags" + assert "movflags" in kwargs["options"], fail_msg + assert kwargs["options"]["movflags"] == "use_metadata_tags", fail_msg + + +def test_get_open_write_kwargs_bytesio_auto_format(): + """Test kwargs for BytesIO dest with AUTO format.""" + dest = io.BytesIO() + container_fmt = "mov,mp4,m4a" + kwargs = get_open_write_kwargs(dest, container_fmt, VideoContainer.AUTO) + + assert kwargs["mode"] == "w" + assert kwargs["options"]["movflags"] == "use_metadata_tags" + + fail_msg = ( + "Format should be a valid format from the container's format list when AUTO" + ) + assert kwargs["format"] in container_fmt, fail_msg + + +def test_get_open_write_kwargs_bytesio_specific_format(): + """Test kwargs for BytesIO dest with a specific single format.""" + dest = io.BytesIO() + container_fmt = "avi" + to_fmt = VideoContainer.MP4 + kwargs = get_open_write_kwargs(dest, container_fmt, to_fmt) + + assert kwargs["mode"] == "w" + assert kwargs["options"]["movflags"] == "use_metadata_tags" + + fail_msg = "Format should be the specified format (lowercased) when output format is not AUTO" + assert kwargs["format"] == "mp4", fail_msg + + +def test_get_open_write_kwargs_bytesio_specific_format_list(): + """Test kwargs for BytesIO dest with a specific comma-separated format.""" + dest = io.BytesIO() + container_fmt = "avi" + to_fmt = "mov,mp4,m4a" # A format string that is a list + kwargs = get_open_write_kwargs(dest, container_fmt, to_fmt) + + assert kwargs["mode"] == "w" + assert kwargs["options"]["movflags"] == "use_metadata_tags" + + fail_msg = "Format should be a valid format from the specified format list when output format is not AUTO" + assert kwargs["format"] in to_fmt, fail_msg From 3e62c5513a5ab5311a1fb4c87ad787fdaad06ea2 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Sun, 4 May 2025 20:27:23 -0700 Subject: [PATCH 069/193] make audio chunks contiguous before encoding (#7942) --- comfy_api/input_impl/video_types.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/comfy_api/input_impl/video_types.py b/comfy_api/input_impl/video_types.py index d0b0b36d2..ae48dbaa4 100644 --- a/comfy_api/input_impl/video_types.py +++ b/comfy_api/input_impl/video_types.py @@ -253,7 +253,12 @@ class VideoFromComponents(VideoInput): start = i * samples_per_frame end = start + samples_per_frame # TODO(Feature) - Add support for stereo audio - chunk = self.__components.audio['waveform'][0, 0, start:end].unsqueeze(0).numpy() + chunk = ( + self.__components.audio["waveform"][0, 0, start:end] + .unsqueeze(0) + .contiguous() + .numpy() + ) audio_frame = av.AudioFrame.from_ndarray(chunk, format='fltp', layout='mono') audio_frame.sample_rate = audio_sample_rate audio_frame.pts = i * samples_per_frame From d9c80a85e54ebdd5aaba3374d76a6e32e1988c97 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 5 May 2025 04:49:07 -0700 Subject: [PATCH 070/193] This should not be a warning. (#7946) --- app/custom_node_manager.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/app/custom_node_manager.py b/app/custom_node_manager.py index 27d85d9ce..281febca9 100644 --- a/app/custom_node_manager.py +++ b/app/custom_node_manager.py @@ -127,8 +127,8 @@ class CustomNodeManager: if os.path.exists(workflows_dir): if folder_name != "example_workflows": - logging.warning( - "WARNING: Found example workflow folder '%s' for custom node '%s', consider renaming it to 'example_workflows'", + logging.debug( + "Found example workflow folder '%s' for custom node '%s', consider renaming it to 'example_workflows'", folder_name, module_name) webapp.add_routes( From 1271c4ef9df2b4eb037688da514f63e1bd8bd727 Mon Sep 17 00:00:00 2001 From: Jedrzej Kosinski Date: Tue, 6 May 2025 03:23:00 -0500 Subject: [PATCH 071/193] More API Nodes (#7956) * Add Ideogram generate node. * Add staging api. * Add API_NODE and common error for missing auth token (#5) * Add Minimax Video Generation + Async Task queue polling example (#6) * [Minimax] Show video preview and embed workflow in ouput (#7) * Remove uv.lock * Remove polling operations. * Revert "Remove polling operations." * Update stubs. * Added Ideogram and Minimax back in. * Added initial BFL Flux 1.1 [pro] Ultra node (#11) * Add --comfy-api-base launch arg (#13) * Add instructions for staging development. (#14) * remove validation to make it easier to run against LAN copies of the API * Manually add BFL polling status response schema (#15) * Add function for uploading files. (#18) * Add Luma nodes (#16) * Refactor util functions (#20) * Add VIDEO type (#21) * Add rest of Luma node functionality (#19) * Fix image_luma_ref not working (#28) * [Bug] Remove duplicated option T2V-01 in MinimaxTextToVideoNode (#31) * Add utils to map from pydantic model fields to comfy node inputs (#30) * add veo2, bump av req (#32) * Add Recraft nodes (#29) * Add Kling Nodes (#12) * Add Camera Concepts (luma_concepts) to Luma Video nodes (#33) * Add Runway nodes (#17) * Convert Minimax node to use VIDEO output type (#34) * Standard `CATEGORY` system for api nodes (#35) * Set `Content-Type` header when uploading files (#36) * add better error propagation to veo2 (#37) * Add Realistic Image and Logo Raster styles for Recraft v3 (#38) * Fix runway image upload and progress polling (#39) * Fix image upload for Luma: only include `Content-Type` header field if it's set explicitly (#40) * Moved Luma nodes to nodes_luma.py (#47) * Moved Recraft nodes to nodes_recraft.py (#48) * Add Pixverse nodes (#46) * Move and fix BFL nodes to node_bfl.py (#49) * Move and edit Minimax node to nodes_minimax.py (#50) * Add Minimax Image to Video node + Cleanup (#51) * Add Recraft Text to Vector node, add Save SVG node to handle its output (#53) * Added pixverse_template support to Pixverse Text to Video node (#54) * Added Recraft Controls + Recraft Color RGB nodes (#57) * split remaining nodes out of nodes_api, make utility lib, refactor ideogram (#61) * Add types and doctstrings to utils file (#64) * Fix: `PollingOperation` progress bar update progress by absolute value (#65) * Use common download function in kling nodes module (#67) * Fix: Luma video nodes in `api nodes/image` category (#68) * Set request type explicitly (#66) * Add `control_after_generate` to all seed inputs (#69) * Fix bug: deleting `Content-Type` when property does not exist (#73) * Add preview to Save SVG node (#74) * change default poll interval (#76), rework veo2 * Add Pixverse and updated Kling types (#75) * Added Pixverse Image to VIdeo node (#77) * Add Pixverse Transition Video node (#79) * Proper ray-1-6 support as fix has been applied in backend (#80) * Added Recraft Style - Infinite Style Library node (#82) * add ideogram v3 (#83) * [Kling] Split Camera Control config to its own node (#81) * Add Pika i2v and t2v nodes (#52) * Temporary Fix for Runway (#87) * Added Stability Stable Image Ultra node (#86) * Remove Runway nodes (#88) * Fix: Prompt text can't be validated in Kling nodes when using primitive nodes (#90) * Fix: typo in node name "Stabiliy" => "Stability" (#91) * Add String (Multiline) node (#93) * Update Pika Duration and Resolution options (#94) * Change base branch to master. Not main. (#95) * Fix UploadRequest file_name param (#98) * Removed Infinite Style Library until later (#99) * fix ideogram style types (#100) * fix multi image return (#101) * add metadata saving to SVG (#102) * Bump templates version to include API node template workflows (#104) * Fix: `download_url_to_video_output` return type (#103) * fix 4o generation bug (#106) * Serve SVG files directly (#107) * Add a bunch of nodes, 3 ready to use, the rest waiting for endpoint support (#108) * Revert "Serve SVG files directly" (#111) * Expose 4 remaining Recraft nodes (#112) * [Kling] Add `Duration` and `Video ID` outputs (#105) * Fix: datamodel-codegen sets string#binary type to non-existent `bytes_aliased` variable (#114) * Fix: Dall-e 2 not setting request content-type dynamically (#113) * Default request timeout: one hour. (#116) * Add Kling nodes: camera control, start-end frame, lip-sync, video extend (#115) * Add 8 nodes - 4 BFL, 4 Stability (#117) * Fix error for Recraft ImageToImage error for nonexistent random_seed param (#118) * Add remaining Pika nodes (#119) * Make controls input work for Recraft Image to Image node (#120) * Use upstream PR: Support saving Comfy VIDEO type to buffer (#123) * Use Upstream PR: "Fix: Error creating video when sliced audio tensor chunks are non-c-contiguous" (#127) * Improve audio upload utils (#128) * Fix: Nested `AnyUrl` in request model cannot be serialized (Kling, Runway) (#129) * Show errors and API output URLs to the user (change log levels) (#131) * Fix: Luma I2I fails when weight is <=0.01 (#132) * Change category of `LumaConcepts` node from image to video (#133) * Fix: `image.shape` accessed before `image` is null-checked (#134) * Apply small fixes and most prompt validation (if needed to avoid API error) (#135) * Node name/category modifications (#140) * Add back Recraft Style - Infinite Style Library node (#141) * Fixed Kling: Check attributes of pydantic types. (#144) * Bump `comfyui-workflow-templates` version (#142) * [Kling] Print response data when error validating response (#146) * Fix: error validating Kling image response, trying to use `"key" in` on Pydantic class instance (#147) * [Kling] Fix: Correct/verify supported subset of input combos in Kling nodes (#149) * [Kling] Fix typo in node description (#150) * [Kling] Fix: CFG min/max not being enforced (#151) * Rebase launch-rebase (private) on prep-branch (public copy of master) (#153) * Bump templates version (#154) * Fix: Kling image gen nodes don't return entire batch when `n` > 1 (#152) * Remove pixverse_template from PixVerse Transition Video node (#155) * Invert image_weight value on Luma Image to Image node (#156) * Invert and resize mask for Ideogram V3 node to match masking conventions (#158) * [Kling] Fix: image generation nodes not returning Tuple (#159) * [Bug] [Kling] Fix Kling camera control (#161) * Kling Image Gen v2 + improve node descriptions for Flux/OpenAI (#160) * [Kling] Don't return video_id from dual effect video (#162) * Bump frontend to 1.18.8 (#163) * Use 3.9 compat syntax (#164) * Use Python 3.10 * add example env var * Update templates to 0.1.11 * Bump frontend to 1.18.9 --------- Co-authored-by: Robin Huang Co-authored-by: Christian Byrne Co-authored-by: thot experiment <94414189+thot-experiment@users.noreply.github.com> --- .github/workflows/test-launch.yml | 2 +- .github/workflows/update-api-stubs.yml | 13 +- .gitignore | 3 + comfy/cli_args.py | 7 + comfy_api_nodes/README.md | 41 + comfy_api_nodes/apinode_utils.py | 575 +++ comfy_api_nodes/apis/PixverseController.py | 4 +- comfy_api_nodes/apis/PixverseDto.py | 12 +- comfy_api_nodes/apis/__init__.py | 3857 ++++++++++++++++- comfy_api_nodes/apis/bfl_api.py | 156 + comfy_api_nodes/apis/client.py | 373 +- comfy_api_nodes/apis/luma_api.py | 253 ++ comfy_api_nodes/apis/pixverse_api.py | 146 + comfy_api_nodes/apis/recraft_api.py | 263 ++ comfy_api_nodes/apis/stability_api.py | 127 + comfy_api_nodes/mapper_utils.py | 116 + comfy_api_nodes/nodes_api.py | 449 -- comfy_api_nodes/nodes_bfl.py | 906 ++++ comfy_api_nodes/nodes_ideogram.py | 777 ++++ comfy_api_nodes/nodes_kling.py | 1563 +++++++ comfy_api_nodes/nodes_luma.py | 702 +++ comfy_api_nodes/nodes_minimax.py | 306 ++ comfy_api_nodes/nodes_openai.py | 487 +++ comfy_api_nodes/nodes_pika.py | 749 ++++ comfy_api_nodes/nodes_pixverse.py | 492 +++ comfy_api_nodes/nodes_recraft.py | 1217 ++++++ comfy_api_nodes/nodes_stability.py | 609 +++ comfy_api_nodes/nodes_veo2.py | 283 ++ comfy_api_nodes/redocly-dev.yaml | 10 + comfy_api_nodes/redocly.yaml | 10 + comfy_extras/nodes_primitive.py | 17 + nodes.py | 12 +- requirements.txt | 4 +- .../comfy_api_nodes_test/mapper_utils_test.py | 297 ++ 34 files changed, 14101 insertions(+), 737 deletions(-) create mode 100644 comfy_api_nodes/README.md create mode 100644 comfy_api_nodes/apinode_utils.py create mode 100644 comfy_api_nodes/apis/bfl_api.py create mode 100644 comfy_api_nodes/apis/luma_api.py create mode 100644 comfy_api_nodes/apis/pixverse_api.py create mode 100644 comfy_api_nodes/apis/recraft_api.py create mode 100644 comfy_api_nodes/apis/stability_api.py create mode 100644 comfy_api_nodes/mapper_utils.py delete mode 100644 comfy_api_nodes/nodes_api.py create mode 100644 comfy_api_nodes/nodes_bfl.py create mode 100644 comfy_api_nodes/nodes_ideogram.py create mode 100644 comfy_api_nodes/nodes_kling.py create mode 100644 comfy_api_nodes/nodes_luma.py create mode 100644 comfy_api_nodes/nodes_minimax.py create mode 100644 comfy_api_nodes/nodes_openai.py create mode 100644 comfy_api_nodes/nodes_pika.py create mode 100644 comfy_api_nodes/nodes_pixverse.py create mode 100644 comfy_api_nodes/nodes_recraft.py create mode 100644 comfy_api_nodes/nodes_stability.py create mode 100644 comfy_api_nodes/nodes_veo2.py create mode 100644 comfy_api_nodes/redocly-dev.yaml create mode 100644 comfy_api_nodes/redocly.yaml create mode 100644 tests-unit/comfy_api_nodes_test/mapper_utils_test.py diff --git a/.github/workflows/test-launch.yml b/.github/workflows/test-launch.yml index c56283c2d..1735fd83b 100644 --- a/.github/workflows/test-launch.yml +++ b/.github/workflows/test-launch.yml @@ -17,7 +17,7 @@ jobs: path: "ComfyUI" - uses: actions/setup-python@v4 with: - python-version: '3.9' + python-version: '3.10' - name: Install requirements run: | python -m pip install --upgrade pip diff --git a/.github/workflows/update-api-stubs.yml b/.github/workflows/update-api-stubs.yml index 2ae99b673..c99ec9fc1 100644 --- a/.github/workflows/update-api-stubs.yml +++ b/.github/workflows/update-api-stubs.yml @@ -22,10 +22,19 @@ jobs: run: | python -m pip install --upgrade pip pip install 'datamodel-code-generator[http]' + npm install @redocly/cli + + - name: Download OpenAPI spec + run: | + curl -o openapi.yaml https://api.comfy.org/openapi + + - name: Filter OpenAPI spec with Redocly + run: | + npx @redocly/cli bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly.yaml --remove-unused-components - name: Generate API models run: | - datamodel-codegen --use-subclass-enum --url https://api.comfy.org/openapi --output comfy_api_nodes/apis --output-model-type pydantic_v2.BaseModel + datamodel-codegen --use-subclass-enum --input filtered-openapi.yaml --output comfy_api_nodes/apis --output-model-type pydantic_v2.BaseModel - name: Check for changes id: git-check @@ -44,4 +53,4 @@ jobs: Generated automatically by the a Github workflow. branch: update-api-stubs delete-branch: true - base: main + base: master diff --git a/.gitignore b/.gitignore index 61881b8a4..4e8cea71e 100644 --- a/.gitignore +++ b/.gitignore @@ -21,3 +21,6 @@ venv/ *.log web_custom_versions/ .DS_Store +openapi.yaml +filtered-openapi.yaml +uv.lock diff --git a/comfy/cli_args.py b/comfy/cli_args.py index f89a7aab4..ef5ab6277 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -192,6 +192,13 @@ parser.add_argument("--user-directory", type=is_valid_directory, default=None, h parser.add_argument("--enable-compress-response-body", action="store_true", help="Enable compressing response body.") +parser.add_argument( + "--comfy-api-base", + type=str, + default="https://api.comfy.org", + help="Set the base URL for the ComfyUI API. (default: https://api.comfy.org)", +) + if comfy.options.args_parsing: args = parser.parse_args() else: diff --git a/comfy_api_nodes/README.md b/comfy_api_nodes/README.md new file mode 100644 index 000000000..e2633a769 --- /dev/null +++ b/comfy_api_nodes/README.md @@ -0,0 +1,41 @@ +# ComfyUI API Nodes + +## Introduction + +Below are a collection of nodes that work by calling external APIs. More information available in our [docs](https://docs.comfy.org/tutorials/api-nodes/overview#api-nodes). + +## Development + +While developing, you should be testing against the Staging environment. To test against staging: + +**Install ComfyUI_frontend** + +Follow the instructions [here](https://github.com/Comfy-Org/ComfyUI_frontend) to start the frontend server. By default, it will connect to Staging authentication. + +> **Hint:** If you use --front-end-version argument for ComfyUI, it will use production authentication. + +```bash +python run main.py --comfy-api-base https://stagingapi.comfy.org +``` + +API stubs are generated through automatic codegen tools from OpenAPI definitions. Since the Comfy Org OpenAPI definition contains many things from the Comfy Registry as well, we use redocly/cli to filter out only the paths relevant for API nodes. + +### Redocly Instructions + +**Tip** +When developing locally, use the `redocly-dev.yaml` file to generate pydantic models. This lets you use stubs for APIs that are not marked `Released` yet. + +Before your API node PR merges, make sure to add the `Released` tag to the `openapi.yaml` file and test in staging. + +```bash +# Download the OpenAPI file from prod server. +curl -o openapi.yaml https://stagingapi.comfy.org/openapi + +# Filter out unneeded API definitions. +npm install -g @redocly/cli +redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly-dev.yaml --remove-unused-components + +# Generate the pydantic datamodels for validation. +datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel + +``` diff --git a/comfy_api_nodes/apinode_utils.py b/comfy_api_nodes/apinode_utils.py new file mode 100644 index 000000000..bd3b8908b --- /dev/null +++ b/comfy_api_nodes/apinode_utils.py @@ -0,0 +1,575 @@ +import io +import logging +from typing import Optional +from comfy.utils import common_upscale +from comfy_api.input_impl import VideoFromFile +from comfy_api.util import VideoContainer, VideoCodec +from comfy_api.input.video_types import VideoInput +from comfy_api.input.basic_types import AudioInput +from comfy_api_nodes.apis.client import ( + ApiClient, + ApiEndpoint, + HttpMethod, + SynchronousOperation, + UploadRequest, + UploadResponse, +) + + +import numpy as np +from PIL import Image +import requests +import torch +import math +import base64 +import uuid +from io import BytesIO +import av + + +def download_url_to_video_output(video_url: str, timeout: int = None) -> VideoFromFile: + """Downloads a video from a URL and returns a `VIDEO` output. + + Args: + video_url: The URL of the video to download. + + Returns: + A Comfy node `VIDEO` output. + """ + video_io = download_url_to_bytesio(video_url, timeout) + if video_io is None: + error_msg = f"Failed to download video from {video_url}" + logging.error(error_msg) + raise ValueError(error_msg) + return VideoFromFile(video_io) + + +def downscale_image_tensor(image, total_pixels=1536 * 1024) -> torch.Tensor: + """Downscale input image tensor to roughly the specified total pixels.""" + samples = image.movedim(-1, 1) + total = int(total_pixels) + scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) + if scale_by >= 1: + return image + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + + s = common_upscale(samples, width, height, "lanczos", "disabled") + s = s.movedim(1, -1) + return s + + +def validate_and_cast_response(response, timeout: int = None) -> torch.Tensor: + """Validates and casts a response to a torch.Tensor. + + Args: + response: The response to validate and cast. + timeout: Request timeout in seconds. Defaults to None (no timeout). + + Returns: + A torch.Tensor representing the image (1, H, W, C). + + Raises: + ValueError: If the response is not valid. + """ + # validate raw JSON response + data = response.data + if not data or len(data) == 0: + raise ValueError("No images returned from API endpoint") + + # Initialize list to store image tensors + image_tensors: list[torch.Tensor] = [] + + # Process each image in the data array + for image_data in data: + image_url = image_data.url + b64_data = image_data.b64_json + + if not image_url and not b64_data: + raise ValueError("No image was generated in the response") + + if b64_data: + img_data = base64.b64decode(b64_data) + img = Image.open(io.BytesIO(img_data)) + + elif image_url: + img_response = requests.get(image_url, timeout=timeout) + if img_response.status_code != 200: + raise ValueError("Failed to download the image") + img = Image.open(io.BytesIO(img_response.content)) + + img = img.convert("RGBA") + + # Convert to numpy array, normalize to float32 between 0 and 1 + img_array = np.array(img).astype(np.float32) / 255.0 + img_tensor = torch.from_numpy(img_array) + + # Add to list of tensors + image_tensors.append(img_tensor) + + return torch.stack(image_tensors, dim=0) + + +def validate_aspect_ratio( + aspect_ratio: str, + minimum_ratio: float, + maximum_ratio: float, + minimum_ratio_str: str, + maximum_ratio_str: str, +) -> float: + """Validates and casts an aspect ratio string to a float. + + Args: + aspect_ratio: The aspect ratio string to validate. + minimum_ratio: The minimum aspect ratio. + maximum_ratio: The maximum aspect ratio. + minimum_ratio_str: The minimum aspect ratio string. + maximum_ratio_str: The maximum aspect ratio string. + + Returns: + The validated and cast aspect ratio. + + Raises: + Exception: If the aspect ratio is not valid. + """ + # get ratio values + numbers = aspect_ratio.split(":") + if len(numbers) != 2: + raise TypeError( + f"Aspect ratio must be in the format X:Y, such as 16:9, but was {aspect_ratio}." + ) + try: + numerator = int(numbers[0]) + denominator = int(numbers[1]) + except ValueError as exc: + raise TypeError( + f"Aspect ratio must contain numbers separated by ':', such as 16:9, but was {aspect_ratio}." + ) from exc + calculated_ratio = numerator / denominator + # if not close to minimum and maximum, check bounds + if not math.isclose(calculated_ratio, minimum_ratio) or not math.isclose( + calculated_ratio, maximum_ratio + ): + if calculated_ratio < minimum_ratio: + raise TypeError( + f"Aspect ratio cannot reduce to any less than {minimum_ratio_str} ({minimum_ratio}), but was {aspect_ratio} ({calculated_ratio})." + ) + elif calculated_ratio > maximum_ratio: + raise TypeError( + f"Aspect ratio cannot reduce to any greater than {maximum_ratio_str} ({maximum_ratio}), but was {aspect_ratio} ({calculated_ratio})." + ) + return aspect_ratio + + +def mimetype_to_extension(mime_type: str) -> str: + """Converts a MIME type to a file extension.""" + return mime_type.split("/")[-1].lower() + + +def download_url_to_bytesio(url: str, timeout: int = None) -> BytesIO: + """Downloads content from a URL using requests and returns it as BytesIO. + + Args: + url: The URL to download. + timeout: Request timeout in seconds. Defaults to None (no timeout). + + Returns: + BytesIO object containing the downloaded content. + """ + response = requests.get(url, stream=True, timeout=timeout) + response.raise_for_status() # Raises HTTPError for bad responses (4XX or 5XX) + return BytesIO(response.content) + + +def bytesio_to_image_tensor(image_bytesio: BytesIO, mode: str = "RGBA") -> torch.Tensor: + """Converts image data from BytesIO to a torch.Tensor. + + Args: + image_bytesio: BytesIO object containing the image data. + mode: The PIL mode to convert the image to (e.g., "RGB", "RGBA"). + + Returns: + A torch.Tensor representing the image (1, H, W, C). + + Raises: + PIL.UnidentifiedImageError: If the image data cannot be identified. + ValueError: If the specified mode is invalid. + """ + image = Image.open(image_bytesio) + image = image.convert(mode) + image_array = np.array(image).astype(np.float32) / 255.0 + return torch.from_numpy(image_array).unsqueeze(0) + + +def download_url_to_image_tensor(url: str, timeout: int = None) -> torch.Tensor: + """Downloads an image from a URL and returns a [B, H, W, C] tensor.""" + image_bytesio = download_url_to_bytesio(url, timeout) + return bytesio_to_image_tensor(image_bytesio) + +def process_image_response(response: requests.Response) -> torch.Tensor: + """Uses content from a Response object and converts it to a torch.Tensor""" + return bytesio_to_image_tensor(BytesIO(response.content)) + + +def _tensor_to_pil(image: torch.Tensor, total_pixels: int = 2048 * 2048) -> Image.Image: + """Converts a single torch.Tensor image [H, W, C] to a PIL Image, optionally downscaling.""" + if len(image.shape) > 3: + image = image[0] + # TODO: remove alpha if not allowed and present + input_tensor = image.cpu() + input_tensor = downscale_image_tensor( + input_tensor.unsqueeze(0), total_pixels=total_pixels + ).squeeze() + image_np = (input_tensor.numpy() * 255).astype(np.uint8) + img = Image.fromarray(image_np) + return img + + +def _pil_to_bytesio(img: Image.Image, mime_type: str = "image/png") -> BytesIO: + """Converts a PIL Image to a BytesIO object.""" + if not mime_type: + mime_type = "image/png" + + img_byte_arr = io.BytesIO() + # Derive PIL format from MIME type (e.g., 'image/png' -> 'PNG') + pil_format = mime_type.split("/")[-1].upper() + if pil_format == "JPG": + pil_format = "JPEG" + img.save(img_byte_arr, format=pil_format) + img_byte_arr.seek(0) + return img_byte_arr + + +def tensor_to_bytesio( + image: torch.Tensor, + name: Optional[str] = None, + total_pixels: int = 2048 * 2048, + mime_type: str = "image/png", +) -> BytesIO: + """Converts a torch.Tensor image to a named BytesIO object. + + Args: + image: Input torch.Tensor image. + name: Optional filename for the BytesIO object. + total_pixels: Maximum total pixels for potential downscaling. + mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp', 'video/mp4'). + + Returns: + Named BytesIO object containing the image data. + """ + if not mime_type: + mime_type = "image/png" + + pil_image = _tensor_to_pil(image, total_pixels=total_pixels) + img_binary = _pil_to_bytesio(pil_image, mime_type=mime_type) + img_binary.name = ( + f"{name if name else uuid.uuid4()}.{mimetype_to_extension(mime_type)}" + ) + return img_binary + + +def tensor_to_base64_string( + image_tensor: torch.Tensor, + total_pixels: int = 2048 * 2048, + mime_type: str = "image/png", +) -> str: + """Convert [B, H, W, C] or [H, W, C] tensor to a base64 string. + + Args: + image_tensor: Input torch.Tensor image. + total_pixels: Maximum total pixels for potential downscaling. + mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp', 'video/mp4'). + + Returns: + Base64 encoded string of the image. + """ + pil_image = _tensor_to_pil(image_tensor, total_pixels=total_pixels) + img_byte_arr = _pil_to_bytesio(pil_image, mime_type=mime_type) + img_bytes = img_byte_arr.getvalue() + # Encode bytes to base64 string + base64_encoded_string = base64.b64encode(img_bytes).decode("utf-8") + return base64_encoded_string + + +def tensor_to_data_uri( + image_tensor: torch.Tensor, + total_pixels: int = 2048 * 2048, + mime_type: str = "image/png", +) -> str: + """Converts a tensor image to a Data URI string. + + Args: + image_tensor: Input torch.Tensor image. + total_pixels: Maximum total pixels for potential downscaling. + mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp'). + + Returns: + Data URI string (e.g., 'data:image/png;base64,...'). + """ + base64_string = tensor_to_base64_string(image_tensor, total_pixels, mime_type) + return f"data:{mime_type};base64,{base64_string}" + + +def upload_file_to_comfyapi( + file_bytes_io: BytesIO, + filename: str, + upload_mime_type: str, + auth_token: Optional[str] = None, +) -> str: + """ + Uploads a single file to ComfyUI API and returns its download URL. + + Args: + file_bytes_io: BytesIO object containing the file data. + filename: The filename of the file. + upload_mime_type: MIME type of the file. + auth_token: Optional authentication token. + + Returns: + The download URL for the uploaded file. + """ + request_object = UploadRequest(file_name=filename, content_type=upload_mime_type) + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/customers/storage", + method=HttpMethod.POST, + request_model=UploadRequest, + response_model=UploadResponse, + ), + request=request_object, + auth_token=auth_token, + ) + + response: UploadResponse = operation.execute() + upload_response = ApiClient.upload_file( + response.upload_url, file_bytes_io, content_type=upload_mime_type + ) + upload_response.raise_for_status() + + return response.download_url + + +def upload_video_to_comfyapi( + video: VideoInput, + auth_token: Optional[str] = None, + container: VideoContainer = VideoContainer.MP4, + codec: VideoCodec = VideoCodec.H264, + max_duration: Optional[int] = None, +) -> str: + """ + Uploads a single video to ComfyUI API and returns its download URL. + Uses the specified container and codec for saving the video before upload. + + Args: + video: VideoInput object (Comfy VIDEO type). + auth_token: Optional authentication token. + container: The video container format to use (default: MP4). + codec: The video codec to use (default: H264). + max_duration: Optional maximum duration of the video in seconds. If the video is longer than this, an error will be raised. + + Returns: + The download URL for the uploaded video file. + """ + if max_duration is not None: + try: + actual_duration = video.duration_seconds + if actual_duration is not None and actual_duration > max_duration: + raise ValueError( + f"Video duration ({actual_duration:.2f}s) exceeds the maximum allowed ({max_duration}s)." + ) + except Exception as e: + logging.error(f"Error getting video duration: {e}") + raise ValueError(f"Could not verify video duration from source: {e}") from e + + upload_mime_type = f"video/{container.value.lower()}" + filename = f"uploaded_video.{container.value.lower()}" + + # Convert VideoInput to BytesIO using specified container/codec + video_bytes_io = io.BytesIO() + video.save_to(video_bytes_io, format=container, codec=codec) + video_bytes_io.seek(0) + + return upload_file_to_comfyapi( + video_bytes_io, filename, upload_mime_type, auth_token + ) + + +def audio_tensor_to_contiguous_ndarray(waveform: torch.Tensor) -> np.ndarray: + """ + Prepares audio waveform for av library by converting to a contiguous numpy array. + + Args: + waveform: a tensor of shape (1, channels, samples) derived from a Comfy `AUDIO` type. + + Returns: + Contiguous numpy array of the audio waveform. If the audio was batched, + the first item is taken. + """ + if waveform.ndim != 3 or waveform.shape[0] != 1: + raise ValueError("Expected waveform tensor shape (1, channels, samples)") + + # If batch is > 1, take first item + if waveform.shape[0] > 1: + waveform = waveform[0] + + # Prepare for av: remove batch dim, move to CPU, make contiguous, convert to numpy array + audio_data_np = waveform.squeeze(0).cpu().contiguous().numpy() + if audio_data_np.dtype != np.float32: + audio_data_np = audio_data_np.astype(np.float32) + + return audio_data_np + + +def audio_ndarray_to_bytesio( + audio_data_np: np.ndarray, + sample_rate: int, + container_format: str = "mp4", + codec_name: str = "aac", +) -> BytesIO: + """ + Encodes a numpy array of audio data into a BytesIO object. + """ + audio_bytes_io = io.BytesIO() + with av.open(audio_bytes_io, mode="w", format=container_format) as output_container: + audio_stream = output_container.add_stream(codec_name, rate=sample_rate) + frame = av.AudioFrame.from_ndarray( + audio_data_np, + format="fltp", + layout="stereo" if audio_data_np.shape[0] > 1 else "mono", + ) + frame.sample_rate = sample_rate + frame.pts = 0 + + for packet in audio_stream.encode(frame): + output_container.mux(packet) + + # Flush stream + for packet in audio_stream.encode(None): + output_container.mux(packet) + + audio_bytes_io.seek(0) + return audio_bytes_io + + +def upload_audio_to_comfyapi( + audio: AudioInput, + auth_token: Optional[str] = None, + container_format: str = "mp4", + codec_name: str = "aac", + mime_type: str = "audio/mp4", + filename: str = "uploaded_audio.mp4", +) -> str: + """ + Uploads a single audio input to ComfyUI API and returns its download URL. + Encodes the raw waveform into the specified format before uploading. + + Args: + audio: a Comfy `AUDIO` type (contains waveform tensor and sample_rate) + auth_token: Optional authentication token. + + Returns: + The download URL for the uploaded audio file. + """ + sample_rate: int = audio["sample_rate"] + waveform: torch.Tensor = audio["waveform"] + audio_data_np = audio_tensor_to_contiguous_ndarray(waveform) + audio_bytes_io = audio_ndarray_to_bytesio( + audio_data_np, sample_rate, container_format, codec_name + ) + + return upload_file_to_comfyapi(audio_bytes_io, filename, mime_type, auth_token) + + +def upload_images_to_comfyapi( + image: torch.Tensor, max_images=8, auth_token=None, mime_type: Optional[str] = None +) -> list[str]: + """ + Uploads images to ComfyUI API and returns download URLs. + To upload multiple images, stack them in the batch dimension first. + + Args: + image: Input torch.Tensor image. + max_images: Maximum number of images to upload. + auth_token: Optional authentication token. + mime_type: Optional MIME type for the image. + """ + # if batch, try to upload each file if max_images is greater than 0 + idx_image = 0 + download_urls: list[str] = [] + is_batch = len(image.shape) > 3 + batch_length = 1 + if is_batch: + batch_length = image.shape[0] + while True: + curr_image = image + if len(image.shape) > 3: + curr_image = image[idx_image] + # get BytesIO version of image + img_binary = tensor_to_bytesio(curr_image, mime_type=mime_type) + # first, request upload/download urls from comfy API + if not mime_type: + request_object = UploadRequest(file_name=img_binary.name) + else: + request_object = UploadRequest( + file_name=img_binary.name, content_type=mime_type + ) + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/customers/storage", + method=HttpMethod.POST, + request_model=UploadRequest, + response_model=UploadResponse, + ), + request=request_object, + auth_token=auth_token, + ) + response = operation.execute() + + upload_response = ApiClient.upload_file( + response.upload_url, img_binary, content_type=mime_type + ) + # verify success + try: + upload_response.raise_for_status() + except requests.exceptions.HTTPError as e: + raise ValueError(f"Could not upload one or more images: {e}") from e + # add download_url to list + download_urls.append(response.download_url) + + idx_image += 1 + # stop uploading additional files if done + if is_batch and max_images > 0: + if idx_image >= max_images: + break + if idx_image >= batch_length: + break + return download_urls + + +def resize_mask_to_image(mask: torch.Tensor, image: torch.Tensor, + upscale_method="nearest-exact", crop="disabled", + allow_gradient=True, add_channel_dim=False): + """ + Resize mask to be the same dimensions as an image, while maintaining proper format for API calls. + """ + _, H, W, _ = image.shape + mask = mask.unsqueeze(-1) + mask = mask.movedim(-1,1) + mask = common_upscale(mask, width=W, height=H, upscale_method=upscale_method, crop=crop) + mask = mask.movedim(1,-1) + if not add_channel_dim: + mask = mask.squeeze(-1) + if not allow_gradient: + mask = (mask > 0.5).float() + return mask + + +def validate_string(string: str, strip_whitespace=True, field_name="prompt", min_length=None, max_length=None): + if strip_whitespace: + string = string.strip() + if min_length and len(string) < min_length: + raise Exception(f"Field '{field_name}' cannot be shorter than {min_length} characters; was {len(string)} characters long.") + if max_length and len(string) > max_length: + raise Exception(f" Field '{field_name} cannot be longer than {max_length} characters; was {len(string)} characters long.") + if not string: + raise Exception(f"Field '{field_name}' cannot be empty.") diff --git a/comfy_api_nodes/apis/PixverseController.py b/comfy_api_nodes/apis/PixverseController.py index 29a3ab33b..310c0f546 100644 --- a/comfy_api_nodes/apis/PixverseController.py +++ b/comfy_api_nodes/apis/PixverseController.py @@ -1,6 +1,6 @@ # generated by datamodel-codegen: -# filename: https://api.comfy.org/openapi -# timestamp: 2025-04-23T15:56:33+00:00 +# filename: filtered-openapi.yaml +# timestamp: 2025-04-29T23:44:54+00:00 from __future__ import annotations diff --git a/comfy_api_nodes/apis/PixverseDto.py b/comfy_api_nodes/apis/PixverseDto.py index 399512214..323c38e96 100644 --- a/comfy_api_nodes/apis/PixverseDto.py +++ b/comfy_api_nodes/apis/PixverseDto.py @@ -1,12 +1,12 @@ # generated by datamodel-codegen: -# filename: https://api.comfy.org/openapi -# timestamp: 2025-04-23T15:56:33+00:00 +# filename: filtered-openapi.yaml +# timestamp: 2025-04-29T23:44:54+00:00 from __future__ import annotations from typing import Optional -from pydantic import BaseModel, Field, constr +from pydantic import BaseModel, Field class V2OpenAPII2VResp(BaseModel): @@ -30,10 +30,10 @@ class V2OpenAPIT2VReq(BaseModel): description='Motion mode (normal, fast, --fast only available when duration=5; --quality=1080p does not support fast)', examples=['normal'], ) - negative_prompt: Optional[constr(max_length=2048)] = Field( - None, description='Negative prompt\n' + negative_prompt: Optional[str] = Field( + None, description='Negative prompt\n', max_length=2048 ) - prompt: constr(max_length=2048) = Field(..., description='Prompt') + prompt: str = Field(..., description='Prompt', max_length=2048) quality: str = Field( ..., description='Video quality ("360p"(Turbo model), "540p", "720p", "1080p")', diff --git a/comfy_api_nodes/apis/__init__.py b/comfy_api_nodes/apis/__init__.py index e7ea9b332..aa1c4ce0b 100644 --- a/comfy_api_nodes/apis/__init__.py +++ b/comfy_api_nodes/apis/__init__.py @@ -1,127 +1,455 @@ # generated by datamodel-codegen: -# filename: https://api.comfy.org/openapi -# timestamp: 2025-04-23T15:56:33+00:00 +# filename: filtered-openapi.yaml +# timestamp: 2025-05-04T04:12:39+00:00 from __future__ import annotations from datetime import datetime from enum import Enum -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Literal, Optional, Union +from uuid import UUID -from pydantic import AnyUrl, BaseModel, Field, confloat, conint - -class Customer(BaseModel): - createdAt: Optional[datetime] = Field( - None, description='The date and time the user was created' - ) - email: Optional[str] = Field(None, description='The email address for this user') - id: str = Field(..., description='The firebase UID of the user') - name: Optional[str] = Field(None, description='The name for this user') - updatedAt: Optional[datetime] = Field( - None, description='The date and time the user was last updated' - ) +from pydantic import AnyUrl, BaseModel, Field, RootModel, StrictBytes -class Error(BaseModel): - details: Optional[List[str]] = Field( +class PersonalAccessToken(BaseModel): + id: Optional[UUID] = Field(None, description='Unique identifier for the GitCommit') + name: Optional[str] = Field( None, - description='Optional detailed information about the error or hints for resolving it.', + description='Required. The name of the token. Can be a simple description.', ) - message: Optional[str] = Field( - None, description='A clear and concise description of the error.' + description: Optional[str] = Field( + None, + description="Optional. A more detailed description of the token's intended use.", ) + createdAt: Optional[datetime] = Field( + None, description='[Output Only]The date and time the token was created.' + ) + token: Optional[str] = Field( + None, + description='[Output Only]. The personal access token. Only returned during creation.', + ) + + +class GitCommitSummary(BaseModel): + commit_hash: Optional[str] = Field(None, description='The hash of the commit') + commit_name: Optional[str] = Field(None, description='The name of the commit') + branch_name: Optional[str] = Field( + None, description='The branch where the commit was made' + ) + author: Optional[str] = Field(None, description='The author of the commit') + timestamp: Optional[datetime] = Field( + None, description='The timestamp when the commit was made' + ) + status_summary: Optional[Dict[str, str]] = Field( + None, description='A map of operating system to status pairs' + ) + + +class User(BaseModel): + id: Optional[str] = Field(None, description='The unique id for this user.') + email: Optional[str] = Field(None, description='The email address for this user.') + name: Optional[str] = Field(None, description='The name for this user.') + isApproved: Optional[bool] = Field( + None, description='Indicates if the user is approved.' + ) + isAdmin: Optional[bool] = Field( + None, description='Indicates if the user has admin privileges.' + ) + + +class PublisherUser(BaseModel): + id: Optional[str] = Field(None, description='The unique id for this user.') + email: Optional[str] = Field(None, description='The email address for this user.') + name: Optional[str] = Field(None, description='The name for this user.') class ErrorResponse(BaseModel): error: str message: str + +class StorageFile(BaseModel): + id: Optional[UUID] = Field( + None, description='Unique identifier for the storage file' + ) + file_path: Optional[str] = Field(None, description='Path to the file in storage') + public_url: Optional[str] = Field(None, description='Public URL') + + +class PublisherMember(BaseModel): + id: Optional[str] = Field( + None, description='The unique identifier for the publisher member.' + ) + user: Optional[PublisherUser] = Field( + None, description='The user associated with this publisher member.' + ) + role: Optional[str] = Field( + None, description='The role of the user in the publisher.' + ) + + +class ComfyNode(BaseModel): + comfy_node_name: Optional[str] = Field( + None, description='Unique identifier for the node' + ) + category: Optional[str] = Field( + None, + description='UI category where the node is listed, used for grouping nodes.', + ) + description: Optional[str] = Field( + None, description="Brief description of the node's functionality or purpose." + ) + input_types: Optional[str] = Field(None, description='Defines input parameters') + deprecated: Optional[bool] = Field( + None, + description='Indicates if the node is deprecated. Deprecated nodes are hidden in the UI.', + ) + experimental: Optional[bool] = Field( + None, + description='Indicates if the node is experimental, subject to changes or removal.', + ) + output_is_list: Optional[List[bool]] = Field( + None, description='Boolean values indicating if each output is a list.' + ) + return_names: Optional[str] = Field( + None, description='Names of the outputs for clarity in workflows.' + ) + return_types: Optional[str] = Field( + None, description='Specifies the types of outputs produced by the node.' + ) + function: Optional[str] = Field( + None, description='Name of the entry-point function to execute the node.' + ) + + +class ComfyNodeCloudBuildInfo(BaseModel): + project_id: Optional[str] = None + project_number: Optional[str] = None + location: Optional[str] = None + build_id: Optional[str] = None + + +class Error(BaseModel): + message: Optional[str] = Field( + None, description='A clear and concise description of the error.' + ) + details: Optional[List[str]] = Field( + None, + description='Optional detailed information about the error or hints for resolving it.', + ) + + +class NodeVersionUpdateRequest(BaseModel): + changelog: Optional[str] = Field( + None, description='The changelog describing the version changes.' + ) + deprecated: Optional[bool] = Field( + None, description='Whether the version is deprecated.' + ) + + +class NodeStatus(str, Enum): + NodeStatusActive = 'NodeStatusActive' + NodeStatusDeleted = 'NodeStatusDeleted' + NodeStatusBanned = 'NodeStatusBanned' + + +class NodeVersionStatus(str, Enum): + NodeVersionStatusActive = 'NodeVersionStatusActive' + NodeVersionStatusDeleted = 'NodeVersionStatusDeleted' + NodeVersionStatusBanned = 'NodeVersionStatusBanned' + NodeVersionStatusPending = 'NodeVersionStatusPending' + NodeVersionStatusFlagged = 'NodeVersionStatusFlagged' + + +class PublisherStatus(str, Enum): + PublisherStatusActive = 'PublisherStatusActive' + PublisherStatusBanned = 'PublisherStatusBanned' + + +class WorkflowRunStatus(str, Enum): + WorkflowRunStatusStarted = 'WorkflowRunStatusStarted' + WorkflowRunStatusFailed = 'WorkflowRunStatusFailed' + WorkflowRunStatusCompleted = 'WorkflowRunStatusCompleted' + + +class MachineStats(BaseModel): + machine_name: Optional[str] = Field(None, description='Name of the machine.') + os_version: Optional[str] = Field( + None, description='The operating system version. eg. Ubuntu Linux 20.04' + ) + gpu_type: Optional[str] = Field( + None, description='The GPU type. eg. NVIDIA Tesla K80' + ) + cpu_capacity: Optional[str] = Field(None, description='Total CPU on the machine.') + initial_cpu: Optional[str] = Field( + None, description='Initial CPU available before the job starts.' + ) + memory_capacity: Optional[str] = Field( + None, description='Total memory on the machine.' + ) + initial_ram: Optional[str] = Field( + None, description='Initial RAM available before the job starts.' + ) + vram_time_series: Optional[Dict[str, Any]] = Field( + None, description='Time series of VRAM usage.' + ) + disk_capacity: Optional[str] = Field( + None, description='Total disk capacity on the machine.' + ) + initial_disk: Optional[str] = Field( + None, description='Initial disk available before the job starts.' + ) + pip_freeze: Optional[str] = Field(None, description='The pip freeze output') + + +class Customer(BaseModel): + id: str = Field(..., description='The firebase UID of the user') + email: Optional[str] = Field(None, description='The email address for this user') + name: Optional[str] = Field(None, description='The name for this user') + createdAt: Optional[datetime] = Field( + None, description='The date and time the user was created' + ) + updatedAt: Optional[datetime] = Field( + None, description='The date and time the user was last updated' + ) + + +class MagicPrompt(str, Enum): + ON = 'ON' + OFF = 'OFF' + + +class ColorPalette(BaseModel): + name: str = Field(..., description='Name of the color palette', examples=['PASTEL']) + + +class StyleCode(RootModel[str]): + root: str = Field(..., pattern='^[0-9A-Fa-f]{8}$') + + +class StyleType(str, Enum): + GENERAL = 'GENERAL' + + +class IdeogramColorPalette1(BaseModel): + name: str = Field(..., description='Name of the preset color palette') + + +class Member(BaseModel): + color: Optional[str] = Field( + None, description='Hexadecimal color code', pattern='^#[0-9A-Fa-f]{6}$' + ) + weight: Optional[float] = Field( + None, description='Optional weight for the color (0-1)', ge=0.0, le=1.0 + ) + + +class IdeogramColorPalette2(BaseModel): + members: List[Member] = Field( + ..., description='Array of color definitions with optional weights' + ) + + +class IdeogramColorPalette( + RootModel[Union[IdeogramColorPalette1, IdeogramColorPalette2]] +): + root: Union[IdeogramColorPalette1, IdeogramColorPalette2] = Field( + ..., + description='A color palette specification that can either use a preset name or explicit color definitions with weights', + ) + + class ImageRequest(BaseModel): + prompt: str = Field( + ..., description='Required. The prompt to use to generate the image.' + ) aspect_ratio: Optional[str] = Field( None, description="Optional. The aspect ratio (e.g., 'ASPECT_16_9', 'ASPECT_1_1'). Cannot be used with resolution. Defaults to 'ASPECT_1_1' if unspecified.", ) - color_palette: Optional[Dict[str, Any]] = Field( - None, description='Optional. Color palette object. Only for V_2, V_2_TURBO.' - ) + model: str = Field(..., description="The model used (e.g., 'V_2', 'V_2A_TURBO')") magic_prompt_option: Optional[str] = Field( None, description="Optional. MagicPrompt usage ('AUTO', 'ON', 'OFF')." ) - model: str = Field(..., description="The model used (e.g., 'V_2', 'V_2A_TURBO')") - negative_prompt: Optional[str] = Field( + seed: Optional[int] = Field( None, - description='Optional. Description of what to exclude. Only for V_1, V_1_TURBO, V_2, V_2_TURBO.', - ) - num_images: Optional[conint(ge=1, le=8)] = Field( - 1, description='Optional. Number of images to generate (1-8). Defaults to 1.' - ) - prompt: str = Field( - ..., description='Required. The prompt to use to generate the image.' - ) - resolution: Optional[str] = Field( - None, - description="Optional. Resolution (e.g., 'RESOLUTION_1024_1024'). Only for model V_2. Cannot be used with aspect_ratio.", - ) - seed: Optional[conint(ge=0, le=2147483647)] = Field( - None, description='Optional. A number between 0 and 2147483647.' + description='Optional. A number between 0 and 2147483647.', + ge=0, + le=2147483647, ) style_type: Optional[str] = Field( None, description="Optional. Style type ('AUTO', 'GENERAL', 'REALISTIC', 'DESIGN', 'RENDER_3D', 'ANIME'). Only for models V_2 and above.", ) + negative_prompt: Optional[str] = Field( + None, + description='Optional. Description of what to exclude. Only for V_1, V_1_TURBO, V_2, V_2_TURBO.', + ) + num_images: Optional[int] = Field( + 1, + description='Optional. Number of images to generate (1-8). Defaults to 1.', + ge=1, + le=8, + ) + resolution: Optional[str] = Field( + None, + description="Optional. Resolution (e.g., 'RESOLUTION_1024_1024'). Only for model V_2. Cannot be used with aspect_ratio.", + ) + color_palette: Optional[Dict[str, Any]] = Field( + None, description='Optional. Color palette object. Only for V_2, V_2_TURBO.' + ) + + +class IdeogramGenerateRequest(BaseModel): + image_request: ImageRequest = Field( + ..., description='The image generation request parameters.' + ) class Datum(BaseModel): - is_image_safe: Optional[bool] = Field( - None, description='Indicates whether the image is considered safe.' - ) prompt: Optional[str] = Field( None, description='The prompt used to generate this image.' ) resolution: Optional[str] = Field( None, description="The resolution of the generated image (e.g., '1024x1024')." ) + is_image_safe: Optional[bool] = Field( + None, description='Indicates whether the image is considered safe.' + ) seed: Optional[int] = Field( None, description='The seed value used for this generation.' ) + url: Optional[str] = Field(None, description='URL to the generated image.') style_type: Optional[str] = Field( None, description="The style type used for generation (e.g., 'REALISTIC', 'ANIME').", ) - url: Optional[str] = Field(None, description='URL to the generated image.') -class Code(Enum): - int_1100 = 1100 - int_1101 = 1101 - int_1102 = 1102 - int_1103 = 1103 +class IdeogramGenerateResponse(BaseModel): + created: Optional[datetime] = Field( + None, description='Timestamp when the generation was created.' + ) + data: Optional[List[Datum]] = Field( + None, description='Array of generated image information.' + ) -class Code1(Enum): - int_1000 = 1000 - int_1001 = 1001 - int_1002 = 1002 - int_1003 = 1003 - int_1004 = 1004 +class RenderingSpeed1(str, Enum): + TURBO = 'TURBO' + DEFAULT = 'DEFAULT' + QUALITY = 'QUALITY' -class AspectRatio(str, Enum): +class MagicPrompt1(str, Enum): + AUTO = 'AUTO' + ON = 'ON' + OFF = 'OFF' + + +class StyleType1(str, Enum): + AUTO = 'AUTO' + GENERAL = 'GENERAL' + REALISTIC = 'REALISTIC' + DESIGN = 'DESIGN' + + +class IdeogramV3RemixRequest(BaseModel): + image: Optional[StrictBytes] = None + prompt: str + image_weight: Optional[int] = Field(50, ge=1, le=100) + seed: Optional[int] = Field(None, ge=0, le=2147483647) + resolution: Optional[str] = None + aspect_ratio: Optional[str] = None + rendering_speed: Optional[RenderingSpeed1] = None + magic_prompt: Optional[MagicPrompt1] = None + negative_prompt: Optional[str] = None + num_images: Optional[int] = Field(None, ge=1, le=8) + color_palette: Optional[Dict[str, Any]] = None + style_codes: Optional[List[str]] = None + style_type: Optional[StyleType1] = None + style_reference_images: Optional[List[StrictBytes]] = None + + +class Datum1(BaseModel): + prompt: Optional[str] = None + resolution: Optional[str] = None + is_image_safe: Optional[bool] = None + seed: Optional[int] = None + url: Optional[str] = None + style_type: Optional[str] = None + + +class IdeogramV3IdeogramResponse(BaseModel): + created: Optional[datetime] = None + data: Optional[List[Datum1]] = None + + +class IdeogramV3ReframeRequest(BaseModel): + image: Optional[StrictBytes] = None + resolution: str + num_images: Optional[int] = Field(None, ge=1, le=8) + seed: Optional[int] = Field(None, ge=0, le=2147483647) + rendering_speed: Optional[RenderingSpeed1] = None + color_palette: Optional[Dict[str, Any]] = None + style_codes: Optional[List[str]] = None + style_reference_images: Optional[List[StrictBytes]] = None + + +class IdeogramV3ReplaceBackgroundRequest(BaseModel): + image: Optional[StrictBytes] = None + prompt: str + magic_prompt: Optional[MagicPrompt1] = None + num_images: Optional[int] = Field(None, ge=1, le=8) + seed: Optional[int] = Field(None, ge=0, le=2147483647) + rendering_speed: Optional[RenderingSpeed1] = None + color_palette: Optional[Dict[str, Any]] = None + style_codes: Optional[List[str]] = None + style_reference_images: Optional[List[StrictBytes]] = None + + +class KlingTaskStatus(str, Enum): + submitted = 'submitted' + processing = 'processing' + succeed = 'succeed' + failed = 'failed' + + +class KlingVideoGenModelName(str, Enum): + kling_v1 = 'kling-v1' + kling_v1_5 = 'kling-v1-5' + kling_v1_6 = 'kling-v1-6' + kling_v2_master = 'kling-v2-master' + + +class KlingVideoGenMode(str, Enum): + std = 'std' + pro = 'pro' + + +class KlingVideoGenAspectRatio(str, Enum): field_16_9 = '16:9' field_9_16 = '9:16' field_1_1 = '1:1' -class Config(BaseModel): - horizontal: Optional[confloat(ge=-10.0, le=10.0)] = None - pan: Optional[confloat(ge=-10.0, le=10.0)] = None - roll: Optional[confloat(ge=-10.0, le=10.0)] = None - tilt: Optional[confloat(ge=-10.0, le=10.0)] = None - vertical: Optional[confloat(ge=-10.0, le=10.0)] = None - zoom: Optional[confloat(ge=-10.0, le=10.0)] = None +class KlingVideoGenDuration(str, Enum): + field_5 = '5' + field_10 = '10' -class Type(str, Enum): +class KlingVideoGenCfgScale(RootModel[float]): + root: float = Field( + ..., + description="Flexibility in video generation. The higher the value, the lower the model's degree of flexibility, and the stronger the relevance to the user's prompt.", + ge=0.0, + le=1.0, + ) + + +class KlingCameraControlType(str, Enum): simple = 'simple' down_back = 'down_back' forward_up = 'forward_up' @@ -129,52 +457,99 @@ class Type(str, Enum): left_turn_forward = 'left_turn_forward' -class CameraControl(BaseModel): - config: Optional[Config] = None - type: Optional[Type] = Field(None, description='Predefined camera movements type') +class KlingCameraConfig(BaseModel): + horizontal: Optional[float] = Field( + None, + description="Controls camera's movement along horizontal axis (x-axis). Negative indicates left, positive indicates right.", + ge=-10.0, + le=10.0, + ) + vertical: Optional[float] = Field( + None, + description="Controls camera's movement along vertical axis (y-axis). Negative indicates downward, positive indicates upward.", + ge=-10.0, + le=10.0, + ) + pan: Optional[float] = Field( + None, + description="Controls camera's rotation in vertical plane (x-axis). Negative indicates downward rotation, positive indicates upward rotation.", + ge=-10.0, + le=10.0, + ) + tilt: Optional[float] = Field( + None, + description="Controls camera's rotation in horizontal plane (y-axis). Negative indicates left rotation, positive indicates right rotation.", + ge=-10.0, + le=10.0, + ) + roll: Optional[float] = Field( + None, + description="Controls camera's rolling amount (z-axis). Negative indicates counterclockwise, positive indicates clockwise.", + ge=-10.0, + le=10.0, + ) + zoom: Optional[float] = Field( + None, + description="Controls change in camera's focal length. Negative indicates narrower field of view, positive indicates wider field of view.", + ge=-10.0, + le=10.0, + ) -class Duration(str, Enum): - field_5 = 5 - field_10 = 10 - - -class Mode(str, Enum): - std = 'std' - pro = 'pro' - - -class TaskInfo(BaseModel): - external_task_id: Optional[str] = None - - -class Video(BaseModel): - duration: Optional[str] = Field(None, description='Total video duration') +class KlingVideoResult(BaseModel): id: Optional[str] = Field(None, description='Generated video ID') url: Optional[AnyUrl] = Field(None, description='URL for generated video') + duration: Optional[str] = Field(None, description='Total video duration') -class TaskResult(BaseModel): - videos: Optional[List[Video]] = None +class KlingAudioUploadType(str, Enum): + file = 'file' + url = 'url' -class TaskStatus(str, Enum): - submitted = 'submitted' - processing = 'processing' - succeed = 'succeed' - failed = 'failed' +class KlingLipSyncMode(str, Enum): + text2video = 'text2video' + audio2video = 'audio2video' -class Data(BaseModel): - created_at: Optional[int] = Field(None, description='Task creation time') - task_id: Optional[str] = Field(None, description='Task ID') - task_info: Optional[TaskInfo] = None - task_result: Optional[TaskResult] = None - task_status: Optional[TaskStatus] = None - updated_at: Optional[int] = Field(None, description='Task update time') +class KlingLipSyncVoiceLanguage(str, Enum): + zh = 'zh' + en = 'en' -class AspectRatio1(str, Enum): +class KlingDualCharacterEffectsScene(str, Enum): + hug = 'hug' + kiss = 'kiss' + heart_gesture = 'heart_gesture' + + +class KlingSingleImageEffectsScene(str, Enum): + bloombloom = 'bloombloom' + dizzydizzy = 'dizzydizzy' + fuzzyfuzzy = 'fuzzyfuzzy' + squish = 'squish' + expansion = 'expansion' + + +class KlingCharacterEffectModelName(str, Enum): + kling_v1 = 'kling-v1' + kling_v1_5 = 'kling-v1-5' + kling_v1_6 = 'kling-v1-6' + + +class KlingSingleImageEffectModelName(str, Enum): + kling_v1_6 = 'kling-v1-6' + + +class KlingSingleImageEffectDuration(str, Enum): + field_5 = '5' + + +class KlingDualCharacterImages(RootModel[List[str]]): + root: List[str] = Field(..., max_length=2, min_length=2) + + +class KlingImageGenAspectRatio(str, Enum): field_16_9 = '16:9' field_9_16 = '9:16' field_1_1 = '1:1' @@ -185,63 +560,289 @@ class AspectRatio1(str, Enum): field_21_9 = '21:9' -class ImageReference(str, Enum): +class KlingImageGenImageReferenceType(str, Enum): subject = 'subject' face = 'face' -class Image(BaseModel): +class KlingImageGenModelName(str, Enum): + kling_v1 = 'kling-v1' + kling_v1_5 = 'kling-v1-5' + kling_v2 = 'kling-v2' + + +class KlingImageResult(BaseModel): index: Optional[int] = Field(None, description='Image Number (0-9)') url: Optional[AnyUrl] = Field(None, description='URL for generated image') -class TaskResult1(BaseModel): - images: Optional[List[Image]] = None +class KlingVirtualTryOnModelName(str, Enum): + kolors_virtual_try_on_v1 = 'kolors-virtual-try-on-v1' + kolors_virtual_try_on_v1_5 = 'kolors-virtual-try-on-v1-5' + + +class TaskInfo(BaseModel): + external_task_id: Optional[str] = None + + +class TaskResult(BaseModel): + videos: Optional[List[KlingVideoResult]] = None + + +class Data(BaseModel): + task_id: Optional[str] = Field(None, description='Task ID') + task_status: Optional[KlingTaskStatus] = None + task_info: Optional[TaskInfo] = None + created_at: Optional[int] = Field(None, description='Task creation time') + updated_at: Optional[int] = Field(None, description='Task update time') + task_result: Optional[TaskResult] = None + + +class KlingText2VideoResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + data: Optional[Data] = None + + +class Trajectory(BaseModel): + x: Optional[int] = Field( + None, + description='The horizontal coordinate of trajectory point. Based on bottom-left corner of image as origin (0,0).', + ) + y: Optional[int] = Field( + None, + description='The vertical coordinate of trajectory point. Based on bottom-left corner of image as origin (0,0).', + ) + + +class DynamicMask(BaseModel): + mask: Optional[AnyUrl] = Field( + None, + description='Dynamic Brush Application Area (Mask image created by users using the motion brush). The aspect ratio must match the input image.', + ) + trajectories: Optional[List[Trajectory]] = None class Data1(BaseModel): - created_at: Optional[int] = Field(None, description='Task creation time') task_id: Optional[str] = Field(None, description='Task ID') - task_result: Optional[TaskResult1] = None - task_status: Optional[TaskStatus] = None - task_status_msg: Optional[str] = Field(None, description='Task status information') + task_status: Optional[KlingTaskStatus] = None + task_info: Optional[TaskInfo] = None + created_at: Optional[int] = Field(None, description='Task creation time') updated_at: Optional[int] = Field(None, description='Task update time') + task_result: Optional[TaskResult] = None -class AspectRatio2(str, Enum): - field_16_9 = '16:9' - field_9_16 = '9:16' - field_1_1 = '1:1' +class KlingImage2VideoResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + data: Optional[Data1] = None -class CameraControl1(BaseModel): - config: Optional[Config] = None - type: Optional[Type] = Field(None, description='Predefined camera movements type') - - -class ModelName2(str, Enum): - kling_v1 = 'kling-v1' - kling_v1_6 = 'kling-v1-6' - - -class TaskResult2(BaseModel): - videos: Optional[List[Video]] = None +class KlingVideoExtendRequest(BaseModel): + video_id: Optional[str] = Field( + None, + description='The ID of the video to be extended. Supports videos generated by text-to-video, image-to-video, and previous video extension operations. Cannot exceed 3 minutes total duration after extension.', + ) + prompt: Optional[str] = Field( + None, + description='Positive text prompt for guiding the video extension', + max_length=2500, + ) + negative_prompt: Optional[str] = Field( + None, + description='Negative text prompt for elements to avoid in the extended video', + max_length=2500, + ) + cfg_scale: Optional[KlingVideoGenCfgScale] = Field( + default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) + ) + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback notification address. Server will notify when the task status changes.', + ) class Data2(BaseModel): - created_at: Optional[int] = Field(None, description='Task creation time') task_id: Optional[str] = Field(None, description='Task ID') + task_status: Optional[KlingTaskStatus] = None task_info: Optional[TaskInfo] = None - task_result: Optional[TaskResult2] = None - task_status: Optional[TaskStatus] = None + created_at: Optional[int] = Field(None, description='Task creation time') updated_at: Optional[int] = Field(None, description='Task update time') + task_result: Optional[TaskResult] = None -class Code2(Enum): - int_1200 = 1200 - int_1201 = 1201 - int_1202 = 1202 - int_1203 = 1203 +class KlingVideoExtendResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + data: Optional[Data2] = None + + +class KlingLipSyncInputObject(BaseModel): + video_id: Optional[str] = Field( + None, + description='The ID of the video generated by Kling AI. Only supports 5-second and 10-second videos generated within the last 30 days.', + ) + video_url: Optional[str] = Field( + None, + description='Get link for uploaded video. Video files support .mp4/.mov, file size does not exceed 100MB, video length between 2-10s.', + ) + mode: KlingLipSyncMode + text: Optional[str] = Field( + None, + description='Text Content for Lip-Sync Video Generation. Required when mode is text2video. Maximum length is 120 characters.', + ) + voice_id: Optional[str] = Field( + None, + description='Voice ID. Required when mode is text2video. The system offers a variety of voice options to choose from.', + ) + voice_language: Optional[KlingLipSyncVoiceLanguage] = 'en' + voice_speed: Optional[float] = Field( + 1, + description='Speech Rate. Valid range: 0.8~2.0, accurate to one decimal place.', + ge=0.8, + le=2.0, + ) + audio_type: Optional[KlingAudioUploadType] = None + audio_file: Optional[str] = Field( + None, + description='Local Path of Audio File. Supported formats: .mp3/.wav/.m4a/.aac, maximum file size of 5MB. Base64 code.', + ) + audio_url: Optional[str] = Field( + None, + description='Audio File Download URL. Supported formats: .mp3/.wav/.m4a/.aac, maximum file size of 5MB.', + ) + + +class KlingLipSyncRequest(BaseModel): + input: KlingLipSyncInputObject + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback notification address. Server will notify when the task status changes.', + ) + + +class Data3(BaseModel): + task_id: Optional[str] = Field(None, description='Task ID') + task_status: Optional[KlingTaskStatus] = None + task_info: Optional[TaskInfo] = None + created_at: Optional[int] = Field(None, description='Task creation time') + updated_at: Optional[int] = Field(None, description='Task update time') + task_result: Optional[TaskResult] = None + + +class KlingLipSyncResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + data: Optional[Data3] = None + + +class KlingSingleImageEffectInput(BaseModel): + model_name: KlingSingleImageEffectModelName + image: str = Field( + ..., + description='Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1.', + ) + duration: KlingSingleImageEffectDuration + + +class KlingDualCharacterEffectInput(BaseModel): + model_name: Optional[KlingCharacterEffectModelName] = 'kling-v1' + mode: Optional[KlingVideoGenMode] = 'std' + images: KlingDualCharacterImages + duration: KlingVideoGenDuration + + +class Data4(BaseModel): + task_id: Optional[str] = Field(None, description='Task ID') + task_status: Optional[KlingTaskStatus] = None + task_info: Optional[TaskInfo] = None + created_at: Optional[int] = Field(None, description='Task creation time') + updated_at: Optional[int] = Field(None, description='Task update time') + task_result: Optional[TaskResult] = None + + +class KlingVideoEffectsResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + data: Optional[Data4] = None + + +class KlingImageGenerationsRequest(BaseModel): + model_name: Optional[KlingImageGenModelName] = 'kling-v1' + prompt: str = Field(..., description='Positive text prompt', max_length=500) + negative_prompt: Optional[str] = Field( + None, description='Negative text prompt', max_length=200 + ) + image: Optional[str] = Field( + None, description='Reference Image - Base64 encoded string or image URL' + ) + image_reference: Optional[KlingImageGenImageReferenceType] = None + image_fidelity: Optional[float] = Field( + 0.5, description='Reference intensity for user-uploaded images', ge=0.0, le=1.0 + ) + human_fidelity: Optional[float] = Field( + 0.45, description='Subject reference similarity', ge=0.0, le=1.0 + ) + n: Optional[int] = Field(1, description='Number of generated images', ge=1, le=9) + aspect_ratio: Optional[KlingImageGenAspectRatio] = '16:9' + callback_url: Optional[AnyUrl] = Field( + None, description='The callback notification address' + ) + + +class TaskResult5(BaseModel): + images: Optional[List[KlingImageResult]] = None + + +class Data5(BaseModel): + task_id: Optional[str] = Field(None, description='Task ID') + task_status: Optional[KlingTaskStatus] = None + task_status_msg: Optional[str] = Field(None, description='Task status information') + created_at: Optional[int] = Field(None, description='Task creation time') + updated_at: Optional[int] = Field(None, description='Task update time') + task_result: Optional[TaskResult5] = None + + +class KlingImageGenerationsResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + data: Optional[Data5] = None + + +class KlingVirtualTryOnRequest(BaseModel): + model_name: Optional[KlingVirtualTryOnModelName] = 'kolors-virtual-try-on-v1' + human_image: str = Field( + ..., description='Reference human image - Base64 encoded string or image URL' + ) + cloth_image: Optional[str] = Field( + None, + description='Reference clothing image - Base64 encoded string or image URL', + ) + callback_url: Optional[AnyUrl] = Field( + None, description='The callback notification address' + ) + + +class Data6(BaseModel): + task_id: Optional[str] = Field(None, description='Task ID') + task_status: Optional[KlingTaskStatus] = None + task_status_msg: Optional[str] = Field(None, description='Task status information') + created_at: Optional[int] = Field(None, description='Task creation time') + updated_at: Optional[int] = Field(None, description='Task update time') + task_result: Optional[TaskResult5] = None + + +class KlingVirtualTryOnResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + data: Optional[Data6] = None class ResourcePackType(str, Enum): @@ -257,87 +858,1140 @@ class Status(str, Enum): class ResourcePackSubscribeInfo(BaseModel): + resource_pack_name: Optional[str] = Field(None, description='Resource package name') + resource_pack_id: Optional[str] = Field(None, description='Resource package ID') + resource_pack_type: Optional[ResourcePackType] = Field( + None, + description='Resource package type (decreasing_total=decreasing total, constant_period=constant periodicity)', + ) + total_quantity: Optional[float] = Field(None, description='Total quantity') + remaining_quantity: Optional[float] = Field( + None, description='Remaining quantity (updated with a 12-hour delay)' + ) + purchase_time: Optional[int] = Field( + None, description='Purchase time, Unix timestamp in ms' + ) effective_time: Optional[int] = Field( None, description='Effective time, Unix timestamp in ms' ) invalid_time: Optional[int] = Field( None, description='Expiration time, Unix timestamp in ms' ) - purchase_time: Optional[int] = Field( - None, description='Purchase time, Unix timestamp in ms' - ) - remaining_quantity: Optional[float] = Field( - None, description='Remaining quantity (updated with a 12-hour delay)' - ) - resource_pack_id: Optional[str] = Field(None, description='Resource package ID') - resource_pack_name: Optional[str] = Field(None, description='Resource package name') - resource_pack_type: Optional[ResourcePackType] = Field( - None, - description='Resource package type (decreasing_total=decreasing total, constant_period=constant periodicity)', - ) status: Optional[Status] = Field(None, description='Resource Package Status') - total_quantity: Optional[float] = Field(None, description='Total quantity') - -class Background(str, Enum): - transparent = 'transparent' - opaque = 'opaque' -class Moderation(str, Enum): - low = 'low' - auto = 'auto' +class Data7(BaseModel): + code: Optional[int] = Field(None, description='Error code; 0 indicates success') + msg: Optional[str] = Field(None, description='Error information') + resource_pack_subscribe_infos: Optional[List[ResourcePackSubscribeInfo]] = Field( + None, description='Resource package list' + ) + + +class KlingResourcePackageResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code; 0 indicates success') + message: Optional[str] = Field(None, description='Error information') + request_id: Optional[str] = Field( + None, + description='Request ID, generated by the system, used to track requests and troubleshoot problems', + ) + data: Optional[Data7] = None + + +class Object(str, Enum): + event = 'event' + + +class Type(str, Enum): + payment_intent_succeeded = 'payment_intent.succeeded' + + +class StripeRequestInfo(BaseModel): + id: Optional[str] = None + idempotency_key: Optional[str] = None + + +class Object1(str, Enum): + payment_intent = 'payment_intent' + + +class StripeAmountDetails(BaseModel): + tip: Optional[Dict[str, Any]] = None + + +class Object2(str, Enum): + charge = 'charge' + + +class StripeAddress(BaseModel): + city: Optional[str] = None + country: Optional[str] = None + line1: Optional[str] = None + line2: Optional[str] = None + postal_code: Optional[str] = None + state: Optional[str] = None + + +class StripeOutcome(BaseModel): + advice_code: Optional[Any] = None + network_advice_code: Optional[Any] = None + network_decline_code: Optional[Any] = None + network_status: Optional[str] = None + reason: Optional[Any] = None + risk_level: Optional[str] = None + risk_score: Optional[int] = None + seller_message: Optional[str] = None + type: Optional[str] = None + + +class Checks(BaseModel): + address_line1_check: Optional[Any] = None + address_postal_code_check: Optional[Any] = None + cvc_check: Optional[str] = None + + +class ExtendedAuthorization(BaseModel): + status: Optional[str] = None + + +class IncrementalAuthorization(BaseModel): + status: Optional[str] = None + + +class Multicapture(BaseModel): + status: Optional[str] = None + + +class NetworkToken(BaseModel): + used: Optional[bool] = None + + +class Overcapture(BaseModel): + maximum_amount_capturable: Optional[int] = None + status: Optional[str] = None + + +class StripeCardDetails(BaseModel): + amount_authorized: Optional[int] = None + authorization_code: Optional[Any] = None + brand: Optional[str] = None + checks: Optional[Checks] = None + country: Optional[str] = None + exp_month: Optional[int] = None + exp_year: Optional[int] = None + extended_authorization: Optional[ExtendedAuthorization] = None + fingerprint: Optional[str] = None + funding: Optional[str] = None + incremental_authorization: Optional[IncrementalAuthorization] = None + installments: Optional[Any] = None + last4: Optional[str] = None + mandate: Optional[Any] = None + multicapture: Optional[Multicapture] = None + network: Optional[str] = None + network_token: Optional[NetworkToken] = None + network_transaction_id: Optional[str] = None + overcapture: Optional[Overcapture] = None + regulated_status: Optional[str] = None + three_d_secure: Optional[Any] = None + wallet: Optional[Any] = None + + +class StripeRefundList(BaseModel): + object: Optional[str] = None + data: Optional[List[Dict[str, Any]]] = None + has_more: Optional[bool] = None + total_count: Optional[int] = None + url: Optional[str] = None + + +class Card(BaseModel): + installments: Optional[Any] = None + mandate_options: Optional[Any] = None + network: Optional[Any] = None + request_three_d_secure: Optional[str] = None + + +class StripePaymentMethodOptions(BaseModel): + card: Optional[Card] = None + + +class StripeShipping(BaseModel): + address: Optional[StripeAddress] = None + carrier: Optional[str] = None + name: Optional[str] = None + phone: Optional[str] = None + tracking_number: Optional[str] = None + + +class Model(str, Enum): + T2V_01_Director = 'T2V-01-Director' + I2V_01_Director = 'I2V-01-Director' + S2V_01 = 'S2V-01' + I2V_01 = 'I2V-01' + I2V_01_live = 'I2V-01-live' + T2V_01 = 'T2V-01' + + +class SubjectReferenceItem(BaseModel): + image: Optional[str] = Field( + None, description='URL or base64 encoding of the subject reference image.' + ) + mask: Optional[str] = Field( + None, + description='URL or base64 encoding of the mask for the subject reference image.', + ) + + +class MinimaxVideoGenerationRequest(BaseModel): + model: Model = Field( + ..., + description='Required. ID of model. Options: T2V-01-Director, I2V-01-Director, S2V-01, I2V-01, I2V-01-live, T2V-01', + ) + prompt: Optional[str] = Field( + None, + description='Description of the video. Should be less than 2000 characters. Supports camera movement instructions in [brackets].', + max_length=2000, + ) + prompt_optimizer: Optional[bool] = Field( + True, + description='If true (default), the model will automatically optimize the prompt. Set to false for more precise control.', + ) + first_frame_image: Optional[str] = Field( + None, + description='URL or base64 encoding of the first frame image. Required when model is I2V-01, I2V-01-Director, or I2V-01-live.', + ) + subject_reference: Optional[List[SubjectReferenceItem]] = Field( + None, + description='Only available when model is S2V-01. The model will generate a video based on the subject uploaded through this parameter.', + ) + callback_url: Optional[str] = Field( + None, + description='Optional. URL to receive real-time status updates about the video generation task.', + ) + + +class MinimaxBaseResponse(BaseModel): + status_code: int = Field( + ..., + description='Status code. 0 indicates success, other values indicate errors.', + ) + status_msg: str = Field( + ..., description='Specific error details or success message.' + ) + + +class MinimaxVideoGenerationResponse(BaseModel): + task_id: str = Field( + ..., description='The task ID for the asynchronous video generation task.' + ) + base_resp: MinimaxBaseResponse + + +class File(BaseModel): + file_id: Optional[int] = Field(None, description='Unique identifier for the file') + bytes: Optional[int] = Field(None, description='File size in bytes') + created_at: Optional[int] = Field( + None, description='Unix timestamp when the file was created, in seconds' + ) + filename: Optional[str] = Field(None, description='The name of the file') + purpose: Optional[str] = Field(None, description='The purpose of using the file') + download_url: Optional[str] = Field( + None, description='The URL to download the video' + ) + + +class MinimaxFileRetrieveResponse(BaseModel): + file: File + base_resp: MinimaxBaseResponse + + +class Status1(str, Enum): + Queueing = 'Queueing' + Preparing = 'Preparing' + Processing = 'Processing' + Success = 'Success' + Fail = 'Fail' + + +class MinimaxTaskResultResponse(BaseModel): + task_id: str = Field(..., description='The task ID being queried.') + status: Status1 = Field( + ..., + description="Task status: 'Queueing' (in queue), 'Preparing' (task is preparing), 'Processing' (generating), 'Success' (task completed successfully), or 'Fail' (task failed).", + ) + file_id: Optional[str] = Field( + None, + description='After the task status changes to Success, this field returns the file ID corresponding to the generated video.', + ) + base_resp: MinimaxBaseResponse class OutputFormat(str, Enum): - png = 'png' - webp = 'webp' jpeg = 'jpeg' + png = 'png' + + +class BFLFluxPro11GenerateRequest(BaseModel): + prompt: str = Field(..., description='The main text prompt for image generation') + image_prompt: Optional[str] = Field(None, description='Optional image prompt') + width: int = Field(..., description='Width of the generated image') + height: int = Field(..., description='Height of the generated image') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to use prompt upsampling' + ) + seed: Optional[int] = Field(None, description='Random seed for reproducibility') + safety_tolerance: Optional[int] = Field(None, description='Safety tolerance level') + output_format: Optional[OutputFormat] = Field( + None, description='Output image format' + ) + webhook_url: Optional[str] = Field( + None, description='Optional webhook URL for async processing' + ) + webhook_secret: Optional[str] = Field( + None, description='Optional webhook secret for async processing' + ) + + +class BFLFluxPro11GenerateResponse(BaseModel): + id: str = Field(..., description='Job ID for tracking') + polling_url: str = Field(..., description='URL to poll for results') + + +class BFLFluxProGenerateRequest(BaseModel): + prompt: str = Field(..., description='The text prompt for image generation.') + negative_prompt: Optional[str] = Field( + None, description='The negative prompt for image generation.' + ) + width: int = Field( + ..., description='The width of the image to generate.', ge=64, le=2048 + ) + height: int = Field( + ..., description='The height of the image to generate.', ge=64, le=2048 + ) + num_inference_steps: Optional[int] = Field( + None, description='The number of inference steps.', ge=1, le=100 + ) + guidance_scale: Optional[float] = Field( + None, description='The guidance scale for generation.', ge=1.0, le=20.0 + ) + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + num_images: Optional[int] = Field( + None, description='The number of images to generate.', ge=1, le=4 + ) + + +class BFLFluxProGenerateResponse(BaseModel): + id: str = Field(..., description='The unique identifier for the generation task.') + polling_url: str = Field(..., description='URL to poll for the generation result.') + + +class Steps(RootModel[int]): + root: int = Field( + ..., + description='Number of steps for the image generation process', + examples=[50], + ge=15, + le=50, + title='Steps', + ) + + +class Guidance(RootModel[float]): + root: float = Field( + ..., + description='Guidance strength for the image generation process', + ge=1.5, + le=100.0, + title='Guidance', + ) + + +class WebhookUrl(RootModel[AnyUrl]): + root: AnyUrl = Field( + ..., description='URL to receive webhook notifications', title='Webhook Url' + ) + + +class BFLAsyncResponse(BaseModel): + id: str = Field(..., title='Id') + polling_url: str = Field(..., title='Polling Url') + + +class BFLAsyncWebhookResponse(BaseModel): + id: str = Field(..., title='Id') + status: str = Field(..., title='Status') + webhook_url: str = Field(..., title='Webhook Url') + + +class Top(RootModel[int]): + root: int = Field( + ..., + description='Number of pixels to expand at the top of the image', + ge=0, + le=2048, + title='Top', + ) + + +class Bottom(RootModel[int]): + root: int = Field( + ..., + description='Number of pixels to expand at the bottom of the image', + ge=0, + le=2048, + title='Bottom', + ) + + +class Left(RootModel[int]): + root: int = Field( + ..., + description='Number of pixels to expand on the left side of the image', + ge=0, + le=2048, + title='Left', + ) + + +class Right(RootModel[int]): + root: int = Field( + ..., + description='Number of pixels to expand on the right side of the image', + ge=0, + le=2048, + title='Right', + ) + + +class CannyLowThreshold(RootModel[int]): + root: int = Field( + ..., + description='Low threshold for Canny edge detection', + ge=0, + le=500, + title='Canny Low Threshold', + ) + + +class CannyHighThreshold(RootModel[int]): + root: int = Field( + ..., + description='High threshold for Canny edge detection', + ge=0, + le=500, + title='Canny High Threshold', + ) + + +class Steps2(RootModel[int]): + root: int = Field( + ..., + description='Number of steps for the image generation process', + ge=15, + le=50, + title='Steps', + ) + + +class Guidance2(RootModel[float]): + root: float = Field( + ..., + description='Guidance strength for the image generation process', + ge=1.0, + le=100.0, + title='Guidance', + ) + + +class BFLOutputFormat(str, Enum): + jpeg = 'jpeg' + png = 'png' + + +class BFLValidationError(BaseModel): + loc: List[Union[str, int]] = Field(..., title='Location') + msg: str = Field(..., title='Message') + type: str = Field(..., title='Error Type') + + +class Datum2(BaseModel): + image_id: Optional[str] = Field( + None, description='Unique identifier for the generated image' + ) + url: Optional[str] = Field(None, description='URL to access the generated image') + + +class RecraftImageGenerationResponse(BaseModel): + created: int = Field( + ..., description='Unix timestamp when the generation was created' + ) + credits: int = Field(..., description='Number of credits used for the generation') + data: List[Datum2] = Field(..., description='Array of generated image information') + + +class RecraftImageFeatures(BaseModel): + nsfw_score: Optional[float] = None + + +class RecraftTextLayoutItem(BaseModel): + bbox: List[List[float]] + text: str + + +class RecraftImageColor(BaseModel): + rgb: Optional[List[int]] = None + std: Optional[List[float]] = None + weight: Optional[float] = None + + +class RecraftImageStyle(str, Enum): + digital_illustration = 'digital_illustration' + icon = 'icon' + realistic_image = 'realistic_image' + vector_illustration = 'vector_illustration' + + +class RecraftImageSubStyle(str, Enum): + field_2d_art_poster = '2d_art_poster' + field_3d = '3d' + field_80s = '80s' + glow = 'glow' + grain = 'grain' + hand_drawn = 'hand_drawn' + infantile_sketch = 'infantile_sketch' + kawaii = 'kawaii' + pixel_art = 'pixel_art' + psychedelic = 'psychedelic' + seamless = 'seamless' + voxel = 'voxel' + watercolor = 'watercolor' + broken_line = 'broken_line' + colored_outline = 'colored_outline' + colored_shapes = 'colored_shapes' + colored_shapes_gradient = 'colored_shapes_gradient' + doodle_fill = 'doodle_fill' + doodle_offset_fill = 'doodle_offset_fill' + offset_fill = 'offset_fill' + outline = 'outline' + outline_gradient = 'outline_gradient' + uneven_fill = 'uneven_fill' + field_70s = '70s' + cartoon = 'cartoon' + doodle_line_art = 'doodle_line_art' + engraving = 'engraving' + flat_2 = 'flat_2' + kawaii_1 = 'kawaii' + line_art = 'line_art' + linocut = 'linocut' + seamless_1 = 'seamless' + b_and_w = 'b_and_w' + enterprise = 'enterprise' + hard_flash = 'hard_flash' + hdr = 'hdr' + motion_blur = 'motion_blur' + natural_light = 'natural_light' + studio_portrait = 'studio_portrait' + line_circuit = 'line_circuit' + field_2d_art_poster_2 = '2d_art_poster_2' + engraving_color = 'engraving_color' + flat_air_art = 'flat_air_art' + hand_drawn_outline = 'hand_drawn_outline' + handmade_3d = 'handmade_3d' + stickers_drawings = 'stickers_drawings' + plastic = 'plastic' + pictogram = 'pictogram' + + +class RecraftTransformModel(str, Enum): + refm1 = 'refm1' + recraft20b = 'recraft20b' + recraftv2 = 'recraftv2' + recraftv3 = 'recraftv3' + flux1_1pro = 'flux1_1pro' + flux1dev = 'flux1dev' + imagen3 = 'imagen3' + hidream_i1_dev = 'hidream_i1_dev' + + +class RecraftImageFormat(str, Enum): + webp = 'webp' + png = 'png' + + +class RecraftResponseFormat(str, Enum): + url = 'url' + b64_json = 'b64_json' + + +class RecraftImage(BaseModel): + b64_json: Optional[str] = None + features: Optional[RecraftImageFeatures] = None + image_id: UUID + revised_prompt: Optional[str] = None + url: Optional[str] = None + + +class RecraftUserControls(BaseModel): + artistic_level: Optional[int] = None + background_color: Optional[RecraftImageColor] = None + colors: Optional[List[RecraftImageColor]] = None + no_text: Optional[bool] = None + + +class RecraftTextLayout(RootModel[List[RecraftTextLayoutItem]]): + root: List[RecraftTextLayoutItem] + + +class RecraftProcessImageRequest(BaseModel): + image: StrictBytes + image_format: Optional[RecraftImageFormat] = None + response_format: Optional[RecraftResponseFormat] = None + + +class RecraftProcessImageResponse(BaseModel): + created: int + credits: int + image: RecraftImage + + +class RecraftImageToImageRequest(BaseModel): + block_nsfw: Optional[bool] = None + calculate_features: Optional[bool] = None + controls: Optional[RecraftUserControls] = None + image: StrictBytes + image_format: Optional[RecraftImageFormat] = None + model: Optional[RecraftTransformModel] = None + n: Optional[int] = None + negative_prompt: Optional[str] = None + prompt: str + random_seed: Optional[int] = None + response_format: Optional[RecraftResponseFormat] = None + strength: float + style: Optional[RecraftImageStyle] = None + style_id: Optional[UUID] = None + substyle: Optional[RecraftImageSubStyle] = None + text_layout: Optional[RecraftTextLayout] = None + + +class RecraftGenerateImageResponse(BaseModel): + created: int + credits: int + data: List[RecraftImage] + + +class RecraftTransformImageWithMaskRequest(BaseModel): + block_nsfw: Optional[bool] = None + calculate_features: Optional[bool] = None + image: StrictBytes + image_format: Optional[RecraftImageFormat] = None + mask: StrictBytes + model: Optional[RecraftTransformModel] = None + n: Optional[int] = None + negative_prompt: Optional[str] = None + prompt: str + random_seed: Optional[int] = None + response_format: Optional[RecraftResponseFormat] = None + style: Optional[RecraftImageStyle] = None + style_id: Optional[UUID] = None + substyle: Optional[RecraftImageSubStyle] = None + text_layout: Optional[RecraftTextLayout] = None + + +class KlingErrorResponse(BaseModel): + code: int = Field( + ..., + description='- 1000: Authentication failed\n- 1001: Authorization is empty\n- 1002: Authorization is invalid\n- 1003: Authorization is not yet valid\n- 1004: Authorization has expired\n- 1100: Account exception\n- 1101: Account in arrears (postpaid scenario)\n- 1102: Resource pack depleted or expired (prepaid scenario)\n- 1103: Unauthorized access to requested resource\n- 1200: Invalid request parameters\n- 1201: Invalid parameters\n- 1202: Invalid request method\n- 1203: Requested resource does not exist\n- 1300: Trigger platform strategy\n- 1301: Trigger content security policy\n- 1302: API request too frequent\n- 1303: Concurrency/QPS exceeds limit\n- 1304: Trigger IP whitelist policy\n- 5000: Internal server error\n- 5001: Service temporarily unavailable\n- 5002: Server internal timeout\n', + ) + message: str = Field(..., description='Human-readable error message') + request_id: str = Field( + ..., description='Request ID for tracking and troubleshooting' + ) + + +class LumaAspectRatio(str, Enum): + field_1_1 = '1:1' + field_16_9 = '16:9' + field_9_16 = '9:16' + field_4_3 = '4:3' + field_3_4 = '3:4' + field_21_9 = '21:9' + field_9_21 = '9:21' + + +class LumaVideoModel(str, Enum): + ray_2 = 'ray-2' + ray_flash_2 = 'ray-flash-2' + ray_1_6 = 'ray-1-6' + + +class LumaVideoModelOutputResolution1(str, Enum): + field_540p = '540p' + field_720p = '720p' + field_1080p = '1080p' + field_4k = '4k' + + +class LumaVideoModelOutputResolution( + RootModel[Union[LumaVideoModelOutputResolution1, str]] +): + root: Union[LumaVideoModelOutputResolution1, str] + + +class LumaVideoModelOutputDuration1(str, Enum): + field_5s = '5s' + field_9s = '9s' + + +class LumaVideoModelOutputDuration( + RootModel[Union[LumaVideoModelOutputDuration1, str]] +): + root: Union[LumaVideoModelOutputDuration1, str] + + +class LumaImageModel(str, Enum): + photon_1 = 'photon-1' + photon_flash_1 = 'photon-flash-1' + + +class LumaImageRef(BaseModel): + url: Optional[AnyUrl] = Field(None, description='The URL of the image reference') + weight: Optional[float] = Field( + None, description='The weight of the image reference' + ) + + +class LumaImageIdentity(BaseModel): + images: Optional[List[AnyUrl]] = Field( + None, description='The URLs of the image identity' + ) + + +class LumaModifyImageRef(BaseModel): + url: Optional[AnyUrl] = Field(None, description='The URL of the image reference') + weight: Optional[float] = Field( + None, description='The weight of the modify image reference' + ) + + +class Type1(str, Enum): + generation = 'generation' + + +class LumaGenerationReference(BaseModel): + type: Literal['generation'] + id: UUID = Field(..., description='The ID of the generation') + + +class Type2(str, Enum): + image = 'image' + + +class LumaImageReference(BaseModel): + type: Literal['image'] + url: AnyUrl = Field(..., description='The URL of the image') + + +class LumaKeyframe(RootModel[Union[LumaGenerationReference, LumaImageReference]]): + root: Union[LumaGenerationReference, LumaImageReference] = Field( + ..., + description='A keyframe can be either a Generation reference, an Image, or a Video', + discriminator='type', + ) + + +class LumaGenerationType(str, Enum): + video = 'video' + image = 'image' + + +class LumaState(str, Enum): + queued = 'queued' + dreaming = 'dreaming' + completed = 'completed' + failed = 'failed' + + +class LumaAssets(BaseModel): + video: Optional[AnyUrl] = Field(None, description='The URL of the video') + image: Optional[AnyUrl] = Field(None, description='The URL of the image') + progress_video: Optional[AnyUrl] = Field( + None, description='The URL of the progress video' + ) + + +class GenerationType(str, Enum): + video = 'video' + + +class GenerationType1(str, Enum): + image = 'image' + + +class CharacterRef(BaseModel): + identity0: Optional[LumaImageIdentity] = None + + +class LumaImageGenerationRequest(BaseModel): + generation_type: Optional[GenerationType1] = 'image' + model: Optional[LumaImageModel] = 'photon-1' + prompt: Optional[str] = Field(None, description='The prompt of the generation') + aspect_ratio: Optional[LumaAspectRatio] = '16:9' + callback_url: Optional[AnyUrl] = Field( + None, description='The callback URL for the generation' + ) + image_ref: Optional[List[LumaImageRef]] = None + style_ref: Optional[List[LumaImageRef]] = None + character_ref: Optional[CharacterRef] = None + modify_image_ref: Optional[LumaModifyImageRef] = None + + +class GenerationType2(str, Enum): + upscale_video = 'upscale_video' + + +class LumaUpscaleVideoGenerationRequest(BaseModel): + generation_type: Optional[GenerationType2] = 'upscale_video' + resolution: Optional[LumaVideoModelOutputResolution] = None + callback_url: Optional[AnyUrl] = Field( + None, description='The callback URL for the upscale' + ) + + +class GenerationType3(str, Enum): + add_audio = 'add_audio' + + +class LumaAudioGenerationRequest(BaseModel): + generation_type: Optional[GenerationType3] = 'add_audio' + prompt: Optional[str] = Field(None, description='The prompt of the audio') + negative_prompt: Optional[str] = Field( + None, description='The negative prompt of the audio' + ) + callback_url: Optional[AnyUrl] = Field( + None, description='The callback URL for the audio' + ) + + +class LumaError(BaseModel): + detail: Optional[str] = Field(None, description='The error message') + + +class AspectRatio(str, Enum): + field_16_9 = '16:9' + field_4_3 = '4:3' + field_1_1 = '1:1' + field_3_4 = '3:4' + field_9_16 = '9:16' + + +class Duration(int, Enum): + integer_5 = 5 + integer_8 = 8 + + +class Model1(str, Enum): + v3_5 = 'v3.5' + + +class MotionMode(str, Enum): + normal = 'normal' + fast = 'fast' class Quality(str, Enum): - low = 'low' - medium = 'medium' - high = 'high' + field_360p = '360p' + field_540p = '540p' + field_720p = '720p' + field_1080p = '1080p' -class OpenAIImageEditRequest(BaseModel): - background: Optional[str] = Field( - None, description='Background transparency', examples=['opaque'] - ) - model: str = Field( - ..., description='The model to use for image editing', examples=['gpt-image-1'] - ) - moderation: Optional[Moderation] = Field( - None, description='Content moderation setting', examples=['auto'] - ) - n: Optional[int] = Field( - None, description='The number of images to generate', examples=[1] - ) - output_compression: Optional[int] = Field( - None, description='Compression level for JPEG or WebP (0-100)', examples=[100] - ) - output_format: Optional[OutputFormat] = Field( - None, description='Format of the output image', examples=['png'] - ) - prompt: str = Field( - ..., - description='A text description of the desired edit', - examples=['Give the rocketship rainbow coloring'], - ) - quality: Optional[str] = Field( - None, description='The quality of the edited image', examples=['low'] - ) - size: Optional[str] = Field( - None, description='Size of the output image', examples=['1024x1024'] - ) - user: Optional[str] = Field( +class Style(str, Enum): + anime = 'anime' + field_3d_animation = '3d_animation' + clay = 'clay' + comic = 'comic' + cyberpunk = 'cyberpunk' + + +class PixverseTextVideoRequest(BaseModel): + aspect_ratio: AspectRatio + duration: Duration + model: Model1 + motion_mode: Optional[MotionMode] = None + negative_prompt: Optional[str] = None + prompt: str + quality: Quality + seed: Optional[int] = None + style: Optional[Style] = None + template_id: Optional[int] = None + water_mark: Optional[bool] = None + + +class Resp(BaseModel): + video_id: Optional[int] = None + + +class PixverseVideoResponse(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp_1: Optional[Resp] = Field(None, alias='Resp') + + +class Resp1(BaseModel): + img_id: Optional[int] = None + + +class PixverseImageUploadResponse(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp: Optional[Resp1] = None + + +class PixverseImageVideoRequest(BaseModel): + img_id: int + model: Model1 + prompt: str + duration: Duration + quality: Quality + motion_mode: Optional[MotionMode] = None + seed: Optional[int] = None + style: Optional[Style] = None + template_id: Optional[int] = None + water_mark: Optional[bool] = None + + +class PixverseTransitionVideoRequest(BaseModel): + first_frame_img: int + last_frame_img: int + model: Model1 + duration: Duration + quality: Quality + motion_mode: MotionMode + seed: int + prompt: str + style: Optional[Style] = None + template_id: Optional[int] = None + water_mark: Optional[bool] = None + + +class Status2(int, Enum): + integer_1 = 1 + integer_5 = 5 + integer_6 = 6 + integer_7 = 7 + integer_8 = 8 + + +class Resp2(BaseModel): + create_time: Optional[str] = None + id: Optional[int] = None + modify_time: Optional[str] = None + negative_prompt: Optional[str] = None + outputHeight: Optional[int] = None + outputWidth: Optional[int] = None + prompt: Optional[str] = None + resolution_ratio: Optional[int] = None + seed: Optional[int] = None + size: Optional[int] = None + status: Optional[Status2] = Field( None, - description='A unique identifier for end-user monitoring', - examples=['user-1234'], + description='Video generation status codes:\n* 1 - Generation successful\n* 5 - Generating\n* 6 - Deleted\n* 7 - Contents moderation failed\n* 8 - Generation failed\n', + ) + style: Optional[str] = None + url: Optional[str] = None + + +class PixverseVideoResultResponse(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp: Optional[Resp2] = None + + +class Image(BaseModel): + bytesBase64Encoded: str + gcsUri: Optional[str] = None + mimeType: Optional[str] = None + + +class Image1(BaseModel): + bytesBase64Encoded: Optional[str] = None + gcsUri: str + mimeType: Optional[str] = None + + +class Instance(BaseModel): + prompt: str = Field(..., description='Text description of the video') + image: Optional[Union[Image, Image1]] = Field( + None, description='Optional image to guide video generation' ) -class Quality1(str, Enum): +class PersonGeneration(str, Enum): + ALLOW = 'ALLOW' + BLOCK = 'BLOCK' + + +class Parameters(BaseModel): + aspectRatio: Optional[str] = Field(None, examples=['16:9']) + negativePrompt: Optional[str] = None + personGeneration: Optional[PersonGeneration] = None + sampleCount: Optional[int] = None + seed: Optional[int] = None + storageUri: Optional[str] = Field( + None, description='Optional Cloud Storage URI to upload the video' + ) + durationSeconds: Optional[int] = None + enhancePrompt: Optional[bool] = None + + +class Veo2GenVidRequest(BaseModel): + instances: Optional[List[Instance]] = None + parameters: Optional[Parameters] = None + + +class Veo2GenVidResponse(BaseModel): + name: str = Field( + ..., + description='Operation resource name', + examples=[ + 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8' + ], + ) + + +class Veo2GenVidPollRequest(BaseModel): + operationName: str = Field( + ..., + description='Full operation name (from predict response)', + examples=[ + 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID' + ], + ) + + +class Video(BaseModel): + gcsUri: Optional[str] = Field(None, description='Cloud Storage URI of the video') + bytesBase64Encoded: Optional[str] = Field( + None, description='Base64-encoded video content' + ) + mimeType: Optional[str] = Field(None, description='Video MIME type') + + +class Response(BaseModel): + field_type: Optional[str] = Field( + None, + alias='@type', + examples=[ + 'type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse' + ], + ) + raiMediaFilteredCount: Optional[int] = Field( + None, description='Count of media filtered by responsible AI policies' + ) + raiMediaFilteredReasons: Optional[List[str]] = Field( + None, description='Reasons why media was filtered by responsible AI policies' + ) + videos: Optional[List[Video]] = None + + +class Error1(BaseModel): + code: Optional[int] = Field(None, description='Error code') + message: Optional[str] = Field(None, description='Error message') + + +class Veo2GenVidPollResponse(BaseModel): + name: Optional[str] = None + done: Optional[bool] = None + response: Optional[Response] = Field( + None, description='The actual prediction response if done is true' + ) + error: Optional[Error1] = Field( + None, description='Error details if operation failed' + ) + + +class RunwayImageToVideoResponse(BaseModel): + id: Optional[str] = Field(None, description='Task ID') + + +class RunwayTaskStatusEnum(str, Enum): + SUCCEEDED = 'SUCCEEDED' + RUNNING = 'RUNNING' + FAILED = 'FAILED' + PENDING = 'PENDING' + CANCELLED = 'CANCELLED' + THROTTLED = 'THROTTLED' + + +class RunwayModelEnum(str, Enum): + gen4_turbo = 'gen4_turbo' + gen3a_turbo = 'gen3a_turbo' + + +class Position(str, Enum): + first = 'first' + last = 'last' + + +class RunwayPromptImageDetailedObject(BaseModel): + uri: str = Field( + ..., description='A HTTPS URL or data URI containing an encoded image.' + ) + position: Position = Field( + ..., + description="The position of the image in the output video. 'last' is currently supported for gen3a_turbo only.", + ) + + +class RunwayDurationEnum(int, Enum): + integer_5 = 5 + integer_10 = 10 + + +class RunwayAspectRatioEnum(str, Enum): + field_1280_720 = '1280:720' + field_720_1280 = '720:1280' + field_1104_832 = '1104:832' + field_832_1104 = '832:1104' + field_960_960 = '960:960' + field_1584_672 = '1584:672' + field_1280_768 = '1280:768' + field_768_1280 = '768:1280' + + +class RunwayPromptImageObject( + RootModel[Union[str, List[RunwayPromptImageDetailedObject]]] +): + root: Union[str, List[RunwayPromptImageDetailedObject]] = Field( + ..., + description='Image(s) to use for the video generation. Can be a single URI or an array of image objects with positions.', + ) + + +class Datum3(BaseModel): + b64_json: Optional[str] = Field(None, description='Base64 encoded image data') + url: Optional[str] = Field(None, description='URL of the image') + revised_prompt: Optional[str] = Field(None, description='Revised prompt') + + +class InputTokensDetails(BaseModel): + text_tokens: Optional[int] = None + image_tokens: Optional[int] = None + + +class Usage(BaseModel): + input_tokens: Optional[int] = None + input_tokens_details: Optional[InputTokensDetails] = None + output_tokens: Optional[int] = None + total_tokens: Optional[int] = None + + +class OpenAIImageGenerationResponse(BaseModel): + data: Optional[List[Datum3]] = None + usage: Optional[Usage] = None + + +class Quality3(str, Enum): low = 'low' medium = 'medium' high = 'high' @@ -345,54 +1999,70 @@ class Quality1(str, Enum): hd = 'hd' +class OutputFormat1(str, Enum): + png = 'png' + webp = 'webp' + jpeg = 'jpeg' + + +class Moderation(str, Enum): + low = 'low' + auto = 'auto' + + +class Background(str, Enum): + transparent = 'transparent' + opaque = 'opaque' + + class ResponseFormat(str, Enum): url = 'url' b64_json = 'b64_json' -class Style(str, Enum): +class Style3(str, Enum): vivid = 'vivid' natural = 'natural' class OpenAIImageGenerationRequest(BaseModel): - background: Optional[Background] = Field( - None, description='Background transparency', examples=['opaque'] - ) model: Optional[str] = Field( None, description='The model to use for image generation', examples=['dall-e-3'] ) - moderation: Optional[Moderation] = Field( - None, description='Content moderation setting', examples=['auto'] - ) - n: Optional[int] = Field( - None, - description='The number of images to generate (1-10). Only 1 supported for dall-e-3.', - examples=[1], - ) - output_compression: Optional[int] = Field( - None, description='Compression level for JPEG or WebP (0-100)', examples=[100] - ) - output_format: Optional[OutputFormat] = Field( - None, description='Format of the output image', examples=['png'] - ) prompt: str = Field( ..., description='A text description of the desired image', examples=['Draw a rocket in front of a blackhole in deep space'], ) - quality: Optional[Quality1] = Field( - None, description='The quality of the generated image', examples=['high'] + n: Optional[int] = Field( + None, + description='The number of images to generate (1-10). Only 1 supported for dall-e-3.', + examples=[1], ) - response_format: Optional[ResponseFormat] = Field( - None, description='Response format of image data', examples=['b64_json'] + quality: Optional[Quality3] = Field( + None, description='The quality of the generated image', examples=['high'] ) size: Optional[str] = Field( None, description='Size of the image (e.g., 1024x1024, 1536x1024, auto)', examples=['1024x1536'], ) - style: Optional[Style] = Field( + output_format: Optional[OutputFormat1] = Field( + None, description='Format of the output image', examples=['png'] + ) + output_compression: Optional[int] = Field( + None, description='Compression level for JPEG or WebP (0-100)', examples=[100] + ) + moderation: Optional[Moderation] = Field( + None, description='Content moderation setting', examples=['auto'] + ) + background: Optional[Background] = Field( + None, description='Background transparency', examples=['opaque'] + ) + response_format: Optional[ResponseFormat] = Field( + None, description='Response format of image data', examples=['b64_json'] + ) + style: Optional[Style3] = Field( None, description='Style of the image (only for dall-e-3)', examples=['vivid'] ) user: Optional[str] = Field( @@ -402,21 +2072,1758 @@ class OpenAIImageGenerationRequest(BaseModel): ) -class Datum1(BaseModel): - b64_json: Optional[str] = Field(None, description='Base64 encoded image data') - revised_prompt: Optional[str] = Field(None, description='Revised prompt') - url: Optional[str] = Field(None, description='URL of the image') +class OpenAIImageEditRequest(BaseModel): + model: str = Field( + ..., description='The model to use for image editing', examples=['gpt-image-1'] + ) + prompt: str = Field( + ..., + description='A text description of the desired edit', + examples=['Give the rocketship rainbow coloring'], + ) + n: Optional[int] = Field( + None, description='The number of images to generate', examples=[1] + ) + quality: Optional[str] = Field( + None, description='The quality of the edited image', examples=['low'] + ) + size: Optional[str] = Field( + None, description='Size of the output image', examples=['1024x1024'] + ) + output_format: Optional[OutputFormat1] = Field( + None, description='Format of the output image', examples=['png'] + ) + output_compression: Optional[int] = Field( + None, description='Compression level for JPEG or WebP (0-100)', examples=[100] + ) + moderation: Optional[Moderation] = Field( + None, description='Content moderation setting', examples=['auto'] + ) + background: Optional[str] = Field( + None, description='Background transparency', examples=['opaque'] + ) + user: Optional[str] = Field( + None, + description='A unique identifier for end-user monitoring', + examples=['user-1234'], + ) -class OpenAIImageGenerationResponse(BaseModel): - data: Optional[List[Datum1]] = None -class User(BaseModel): - email: Optional[str] = Field(None, description='The email address for this user.') - id: Optional[str] = Field(None, description='The unique id for this user.') - isAdmin: Optional[bool] = Field( - None, description='Indicates if the user has admin privileges.' +class CustomerStorageResourceResponse(BaseModel): + download_url: Optional[str] = Field( + None, + description='The signed URL to use for downloading the file from the specified path', ) - isApproved: Optional[bool] = Field( - None, description='Indicates if the user is approved.' + upload_url: Optional[str] = Field( + None, + description='The signed URL to use for uploading the file to the specified path', ) - name: Optional[str] = Field(None, description='The name for this user.') + expires_at: Optional[datetime] = Field( + None, description='When the signed URL will expire' + ) + existing_file: Optional[bool] = Field( + None, description='Whether an existing file with the same hash was found' + ) + + +class Pikaffect(str, Enum): + Cake_ify = 'Cake-ify' + Crumble = 'Crumble' + Crush = 'Crush' + Decapitate = 'Decapitate' + Deflate = 'Deflate' + Dissolve = 'Dissolve' + Explode = 'Explode' + Eye_pop = 'Eye-pop' + Inflate = 'Inflate' + Levitate = 'Levitate' + Melt = 'Melt' + Peel = 'Peel' + Poke = 'Poke' + Squish = 'Squish' + Ta_da = 'Ta-da' + Tear = 'Tear' + + +class PikaBodyGeneratePikaffectsGeneratePikaffectsPost(BaseModel): + image: Optional[StrictBytes] = Field(None, title='Image') + pikaffect: Optional[Pikaffect] = Field(None, title='Pikaffect') + promptText: Optional[str] = Field(None, title='Prompttext') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + seed: Optional[int] = Field(None, title='Seed') + + +class PikaGenerateResponse(BaseModel): + video_id: str = Field(..., title='Video Id') + + +class PikaBodyGeneratePikadditionsGeneratePikadditionsPost(BaseModel): + video: Optional[StrictBytes] = Field(None, title='Video') + image: Optional[StrictBytes] = Field(None, title='Image') + promptText: Optional[str] = Field(None, title='Prompttext') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + seed: Optional[int] = Field(None, title='Seed') + + +class PikaBodyGeneratePikaswapsGeneratePikaswapsPost(BaseModel): + video: Optional[StrictBytes] = Field(None, title='Video') + image: Optional[StrictBytes] = Field(None, title='Image') + promptText: Optional[str] = Field(None, title='Prompttext') + modifyRegionMask: Optional[StrictBytes] = Field( + None, + description='A mask image that specifies the region to modify, where the mask is white and the background is black', + title='Modifyregionmask', + ) + modifyRegionRoi: Optional[str] = Field( + None, + description='Plaintext description of the object / region to modify', + title='Modifyregionroi', + ) + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + seed: Optional[int] = Field(None, title='Seed') + + +class IngredientsMode(str, Enum): + creative = 'creative' + precise = 'precise' + + +class AspectRatio1(RootModel[float]): + root: float = Field( + ..., + description='Aspect ratio (width / height)', + ge=0.4, + le=2.5, + title='Aspectratio', + ) + + +class PikaBodyGenerate22C2vGenerate22PikascenesPost(BaseModel): + images: Optional[List[StrictBytes]] = Field(None, title='Images') + ingredientsMode: IngredientsMode = Field(..., title='Ingredientsmode') + promptText: Optional[str] = Field(None, title='Prompttext') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + seed: Optional[int] = Field(None, title='Seed') + resolution: Optional[str] = Field('1080p', title='Resolution') + duration: Optional[int] = Field(5, title='Duration') + aspectRatio: Optional[AspectRatio1] = Field( + None, description='Aspect ratio (width / height)', title='Aspectratio' + ) + + +class PikaStatusEnum(str, Enum): + queued = 'queued' + started = 'started' + finished = 'finished' + + +class PikaValidationError(BaseModel): + loc: List[Union[str, int]] = Field(..., title='Location') + msg: str = Field(..., title='Message') + type: str = Field(..., title='Error Type') + + +class PikaResolutionEnum(str, Enum): + field_1080p = '1080p' + field_720p = '720p' + + +class PikaDurationEnum(int, Enum): + integer_5 = 5 + integer_10 = 10 + + +class RgbItem(RootModel[int]): + root: int = Field(..., ge=0, le=255) + + +class RGBColor(BaseModel): + rgb: List[RgbItem] = Field(..., max_length=3, min_length=3) + + +class StabilityStabilityClientID(RootModel[str]): + root: str = Field( + ..., + description='The name of your application, used to help us communicate app-specific debugging or moderation issues to you.', + examples=['my-awesome-app'], + max_length=256, + ) + + +class StabilityStabilityClientUserID(RootModel[str]): + root: str = Field( + ..., + description='A unique identifier for your end user. Used to help us communicate user-specific debugging or moderation issues to you. Feel free to obfuscate this value to protect user privacy.', + examples=['DiscordUser#9999'], + max_length=256, + ) + + +class StabilityStabilityClientVersion(RootModel[str]): + root: str = Field( + ..., + description='The version of your application, used to help us communicate version-specific debugging or moderation issues to you.', + examples=['1.2.1'], + max_length=256, + ) + + +class Name(str, Enum): + content_moderation = 'content_moderation' + + +class StabilityContentModerationResponse(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new) you file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: Name = Field( + ..., + description='Our content moderation system has flagged some part of your request and subsequently denied it. You were not charged for this request. While this may at times be frustrating, it is necessary to maintain the integrity of our platform and ensure a safe experience for all users. If you would like to provide feedback, please use the [Support Form](https://kb.stability.ai/knowledge-base/kb-tickets/new).', + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class RenderingSpeed(str, Enum): + BALANCED = 'BALANCED' + TURBO = 'TURBO' + QUALITY = 'QUALITY' + + +class StabilityCreativity(RootModel[float]): + root: float = Field( + ..., + description='Controls the likelihood of creating additional details not heavily conditioned by the init image.', + ge=0.2, + le=0.5, + ) + + +class StabilityGenerationID(RootModel[str]): + root: str = Field( + ..., + description='The `id` of a generation, typically used for async generations, that can be used to check the status of the generation or retrieve the result.', + examples=['a6dc6c6e20acda010fe14d71f180658f2896ed9b4ec25aa99a6ff06c796987c4'], + max_length=64, + min_length=64, + ) + + +class Mode(str, Enum): + text_to_image = 'text-to-image' + image_to_image = 'image-to-image' + + +class AspectRatio2(str, Enum): + field_21_9 = '21:9' + field_16_9 = '16:9' + field_3_2 = '3:2' + field_5_4 = '5:4' + field_1_1 = '1:1' + field_4_5 = '4:5' + field_2_3 = '2:3' + field_9_16 = '9:16' + field_9_21 = '9:21' + + +class Model4(str, Enum): + sd3_5_large = 'sd3.5-large' + sd3_5_large_turbo = 'sd3.5-large-turbo' + sd3_5_medium = 'sd3.5-medium' + + +class OutputFormat3(str, Enum): + png = 'png' + jpeg = 'jpeg' + + +class StylePreset(str, Enum): + enhance = 'enhance' + anime = 'anime' + photographic = 'photographic' + digital_art = 'digital-art' + comic_book = 'comic-book' + fantasy_art = 'fantasy-art' + line_art = 'line-art' + analog_film = 'analog-film' + neon_punk = 'neon-punk' + isometric = 'isometric' + low_poly = 'low-poly' + origami = 'origami' + modeling_compound = 'modeling-compound' + cinematic = 'cinematic' + field_3d_model = '3d-model' + pixel_art = 'pixel-art' + tile_texture = 'tile-texture' + + +class StabilityImageGenrationSD3Request(BaseModel): + prompt: str = Field( + ..., + description='What you wish to see in the output image. A strong, descriptive prompt that clearly defines\nelements, colors, and subjects will lead to better results.', + max_length=10000, + min_length=1, + ) + mode: Optional[Mode] = Field( + 'text-to-image', + description='Controls whether this is a text-to-image or image-to-image generation, which affects which parameters are required:\n- **text-to-image** requires only the `prompt` parameter\n- **image-to-image** requires the `prompt`, `image`, and `strength` parameters', + title='GenerationMode', + ) + image: Optional[StrictBytes] = Field( + None, + description='The image to use as the starting point for the generation.\n\nSupported formats:\n\n\n\n - jpeg\n - png\n - webp\n\nSupported dimensions:\n\n\n\n - Every side must be at least 64 pixels\n\n> **Important:** This parameter is only valid for **image-to-image** requests.', + ) + strength: Optional[float] = Field( + None, + description='Sometimes referred to as _denoising_, this parameter controls how much influence the\n`image` parameter has on the generated image. A value of 0 would yield an image that\nis identical to the input. A value of 1 would be as if you passed in no image at all.\n\n> **Important:** This parameter is only valid for **image-to-image** requests.', + ge=0.0, + le=1.0, + ) + aspect_ratio: Optional[AspectRatio2] = Field( + '1:1', + description='Controls the aspect ratio of the generated image. Defaults to 1:1.\n\n> **Important:** This parameter is only valid for **text-to-image** requests.', + ) + model: Optional[Model4] = Field( + 'sd3.5-large', + description='The model to use for generation.\n\n- `sd3.5-large` requires 6.5 credits per generation\n- `sd3.5-large-turbo` requires 4 credits per generation\n- `sd3.5-medium` requires 3.5 credits per generation\n- As of the April 17, 2025, `sd3-large`, `sd3-large-turbo` and `sd3-medium`\n\n\n\n are re-routed to their `sd3.5-[model version]` equivalent, at the same price.', + ) + seed: Optional[float] = Field( + 0, + description="A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass `0` to use a random seed.)", + ge=0.0, + le=4294967294.0, + ) + output_format: Optional[OutputFormat3] = Field( + 'png', description='Dictates the `content-type` of the generated image.' + ) + style_preset: Optional[StylePreset] = Field( + None, description='Guides the image model towards a particular style.' + ) + negative_prompt: Optional[str] = Field( + None, + description='Keywords of what you **do not** wish to see in the output image.\nThis is an advanced feature.', + max_length=10000, + ) + cfg_scale: Optional[float] = Field( + None, + description='How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt). The _Large_ and _Medium_ models use a default of `4`. The _Turbo_ model uses a default of `1`.', + ge=1.0, + le=10.0, + ) + + +class FinishReason(str, Enum): + SUCCESS = 'SUCCESS' + CONTENT_FILTERED = 'CONTENT_FILTERED' + + +class StabilityImageGenrationSD3Response200(BaseModel): + image: str = Field( + ..., + description='The generated image, encoded to base64.', + examples=['AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1...'], + ) + seed: Optional[float] = Field( + 0, + description='The seed used as random noise for this generation.', + examples=[343940597], + ge=0.0, + le=4294967294.0, + ) + finish_reason: FinishReason = Field( + ..., + description='The reason the generation finished.\n\n- `SUCCESS` = successful generation.\n- `CONTENT_FILTERED` = successful generation, however the output violated our content moderation\npolicy and has been blurred as a result.', + examples=['SUCCESS'], + ) + + +class StabilityImageGenrationSD3Response400(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationSD3Response413(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationSD3Response422(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationSD3Response429(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationSD3Response500(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class OutputFormat4(str, Enum): + jpeg = 'jpeg' + png = 'png' + webp = 'webp' + + +class StabilityImageGenrationUpscaleConservativeRequest(BaseModel): + image: StrictBytes = Field( + ..., + description='The image you wish to upscale.\n\nSupported Formats:\n- jpeg\n- png\n- webp\n\nValidation Rules:\n- Every side must be at least 64 pixels\n- Total pixel count must be between 4,096 and 9,437,184 pixels\n- The aspect ratio must be between 1:2.5 and 2.5:1', + examples=['./some/image.png'], + ) + prompt: str = Field( + ..., + description="What you wish to see in the output image. A strong, descriptive prompt that clearly defines\nelements, colors, and subjects will lead to better results.\n\nTo control the weight of a given word use the format `(word:weight)`,\nwhere `word` is the word you'd like to control the weight of and `weight`\nis a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`\nwould convey a sky that was blue and green, but more green than blue.", + max_length=10000, + min_length=1, + ) + negative_prompt: Optional[str] = Field( + None, + description='A blurb of text describing what you **do not** wish to see in the output image.\nThis is an advanced feature.', + max_length=10000, + ) + seed: Optional[float] = Field( + 0, + description="A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass `0` to use a random seed.)", + ge=0.0, + le=4294967294.0, + ) + output_format: Optional[OutputFormat4] = Field( + 'png', description='Dictates the `content-type` of the generated image.' + ) + creativity: Optional[StabilityCreativity] = Field( + default_factory=lambda: StabilityCreativity.model_validate(0.35) + ) + + +class StabilityImageGenrationUpscaleConservativeResponse200(BaseModel): + image: str = Field( + ..., + description='The generated image, encoded to base64.', + examples=['AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1...'], + ) + seed: Optional[float] = Field( + 0, + description='The seed used as random noise for this generation.', + examples=[343940597], + ge=0.0, + le=4294967294.0, + ) + finish_reason: FinishReason = Field( + ..., + description='The reason the generation finished.\n\n- `SUCCESS` = successful generation.\n- `CONTENT_FILTERED` = successful generation, however the output violated our content moderation\npolicy and has been blurred as a result.', + examples=['SUCCESS'], + ) + + +class StabilityImageGenrationUpscaleConservativeResponse400(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleConservativeResponse413(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleConservativeResponse422(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleConservativeResponse429(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleConservativeResponse500(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleCreativeRequest(BaseModel): + image: StrictBytes = Field( + ..., + description='The image you wish to upscale.\n\nSupported Formats:\n- jpeg\n- png\n- webp\n\nValidation Rules:\n- Every side must be at least 64 pixels\n- Total pixel count must be between 4,096 and 1,048,576 pixels', + examples=['./some/image.png'], + ) + prompt: str = Field( + ..., + description="What you wish to see in the output image. A strong, descriptive prompt that clearly defines\nelements, colors, and subjects will lead to better results.\n\nTo control the weight of a given word use the format `(word:weight)`,\nwhere `word` is the word you'd like to control the weight of and `weight`\nis a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`\nwould convey a sky that was blue and green, but more green than blue.", + max_length=10000, + min_length=1, + ) + negative_prompt: Optional[str] = Field( + None, + description='A blurb of text describing what you **do not** wish to see in the output image.\nThis is an advanced feature.', + max_length=10000, + ) + output_format: Optional[OutputFormat4] = Field( + 'png', description='Dictates the `content-type` of the generated image.' + ) + seed: Optional[float] = Field( + 0, + description="A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass `0` to use a random seed.)", + ge=0.0, + le=4294967294.0, + ) + creativity: Optional[float] = Field( + 0.3, + description='Indicates how creative the model should be when upscaling an image.\nHigher values will result in more details being added to the image during upscaling.', + ge=0.1, + le=0.5, + ) + style_preset: Optional[StylePreset] = Field( + None, description='Guides the image model towards a particular style.' + ) + + +class StabilityImageGenrationUpscaleCreativeResponse200(BaseModel): + id: StabilityGenerationID + + +class StabilityImageGenrationUpscaleCreativeResponse400(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleCreativeResponse413(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleCreativeResponse422(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleCreativeResponse429(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleCreativeResponse500(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleFastRequest(BaseModel): + image: StrictBytes = Field( + ..., + description='The image you wish to upscale.\n\nSupported Formats:\n- jpeg\n- png\n- webp\n\nValidation Rules:\n- Width must be between 32 and 1,536 pixels\n- Height must be between 32 and 1,536 pixels\n- Total pixel count must be between 1,024 and 1,048,576 pixels', + examples=['./some/image.png'], + ) + output_format: Optional[OutputFormat4] = Field( + 'png', description='Dictates the `content-type` of the generated image.' + ) + + +class StabilityImageGenrationUpscaleFastResponse200(BaseModel): + image: str = Field( + ..., + description='The generated image, encoded to base64.', + examples=['AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1...'], + ) + seed: Optional[float] = Field( + 0, + description='The seed used as random noise for this generation.', + examples=[343940597], + ge=0.0, + le=4294967294.0, + ) + finish_reason: FinishReason = Field( + ..., + description='The reason the generation finished.\n\n- `SUCCESS` = successful generation.\n- `CONTENT_FILTERED` = successful generation, however the output violated our content moderation\npolicy and has been blurred as a result.', + examples=['SUCCESS'], + ) + + +class StabilityImageGenrationUpscaleFastResponse400(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleFastResponse413(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleFastResponse422(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleFastResponse429(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleFastResponse500(BaseModel): + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + + +class ActionJobResult(BaseModel): + id: Optional[UUID] = Field(None, description='Unique identifier for the job result') + workflow_name: Optional[str] = Field(None, description='Name of the workflow') + operating_system: Optional[str] = Field(None, description='Operating system used') + python_version: Optional[str] = Field(None, description='PyTorch version used') + pytorch_version: Optional[str] = Field(None, description='PyTorch version used') + action_run_id: Optional[str] = Field( + None, description='Identifier of the run this result belongs to' + ) + action_job_id: Optional[str] = Field( + None, description='Identifier of the job this result belongs to' + ) + cuda_version: Optional[str] = Field(None, description='CUDA version used') + branch_name: Optional[str] = Field( + None, description='Name of the relevant git branch' + ) + commit_hash: Optional[str] = Field(None, description='The hash of the commit') + commit_id: Optional[str] = Field(None, description='The ID of the commit') + commit_time: Optional[int] = Field( + None, description='The Unix timestamp when the commit was made' + ) + commit_message: Optional[str] = Field(None, description='The message of the commit') + comfy_run_flags: Optional[str] = Field( + None, description='The comfy run flags. E.g. `--low-vram`' + ) + git_repo: Optional[str] = Field(None, description='The repository name') + pr_number: Optional[str] = Field(None, description='The pull request number') + start_time: Optional[int] = Field( + None, description='The start time of the job as a Unix timestamp.' + ) + end_time: Optional[int] = Field( + None, description='The end time of the job as a Unix timestamp.' + ) + avg_vram: Optional[int] = Field( + None, description='The average VRAM used by the job' + ) + peak_vram: Optional[int] = Field(None, description='The peak VRAM used by the job') + job_trigger_user: Optional[str] = Field( + None, description='The user who triggered the job.' + ) + author: Optional[str] = Field(None, description='The author of the commit') + machine_stats: Optional[MachineStats] = None + status: Optional[WorkflowRunStatus] = None + storage_file: Optional[StorageFile] = None + + +class Publisher(BaseModel): + name: Optional[str] = None + id: Optional[str] = Field( + None, + description="The unique identifier for the publisher. It's akin to a username. Should be lowercase.", + ) + description: Optional[str] = None + website: Optional[str] = None + support: Optional[str] = None + source_code_repo: Optional[str] = None + logo: Optional[str] = Field(None, description="URL to the publisher's logo.") + createdAt: Optional[datetime] = Field( + None, description='The date and time the publisher was created.' + ) + members: Optional[List[PublisherMember]] = Field( + None, description='A list of members in the publisher.' + ) + status: Optional[PublisherStatus] = Field( + None, description='The status of the publisher.' + ) + + +class NodeVersion(BaseModel): + id: Optional[str] = None + version: Optional[str] = Field( + None, + description='The version identifier, following semantic versioning. Must be unique for the node.', + ) + createdAt: Optional[datetime] = Field( + None, description='The date and time the version was created.' + ) + changelog: Optional[str] = Field( + None, description='Summary of changes made in this version' + ) + dependencies: Optional[List[str]] = Field( + None, description='A list of pip dependencies required by the node.' + ) + downloadUrl: Optional[str] = Field( + None, description='[Output Only] URL to download this version of the node' + ) + deprecated: Optional[bool] = Field( + None, description='Indicates if this version is deprecated.' + ) + status: Optional[NodeVersionStatus] = Field( + None, description='The status of the node version.' + ) + status_reason: Optional[str] = Field( + None, description='The reason for the status change.' + ) + node_id: Optional[str] = Field( + None, description='The unique identifier of the node.' + ) + comfy_node_extract_status: Optional[str] = Field( + None, description='The status of comfy node extraction process.' + ) + + +class IdeogramV3Request(BaseModel): + prompt: str = Field(..., description='The text prompt for image generation') + seed: Optional[int] = Field( + None, description='Seed value for reproducible generation' + ) + resolution: Optional[str] = Field( + None, description='Image resolution in format WxH', examples=['1280x800'] + ) + aspect_ratio: Optional[str] = Field( + None, description='Aspect ratio in format WxH', examples=['1x3'] + ) + rendering_speed: RenderingSpeed + magic_prompt: Optional[MagicPrompt] = Field( + None, description='Whether to enable magic prompt enhancement' + ) + negative_prompt: Optional[str] = Field( + None, description='Text prompt specifying what to avoid in the generation' + ) + num_images: Optional[int] = Field( + None, description='Number of images to generate', ge=1 + ) + color_palette: Optional[ColorPalette] = None + style_codes: Optional[List[StyleCode]] = Field( + None, description='Array of style codes in hexadecimal format' + ) + style_type: Optional[StyleType] = Field( + None, description='The type of style to apply' + ) + style_reference_images: Optional[List[str]] = Field( + None, description='Array of reference image URLs or identifiers' + ) + + +class IdeogramV3EditRequest(BaseModel): + image: Optional[StrictBytes] = Field( + None, + description='The image being edited (max size 10MB); only JPEG, WebP and PNG formats are supported at this time.', + ) + mask: Optional[StrictBytes] = Field( + None, + description='A black and white image of the same size as the image being edited (max size 10MB). Black regions in the mask should match up with the regions of the image that you would like to edit; only JPEG, WebP and PNG formats are supported at this time.', + ) + prompt: str = Field( + ..., description='The prompt used to describe the edited result.' + ) + magic_prompt: Optional[str] = Field( + None, + description='Determine if MagicPrompt should be used in generating the request or not.', + ) + num_images: Optional[int] = Field( + None, description='The number of images to generate.' + ) + seed: Optional[int] = Field( + None, description='Random seed. Set for reproducible generation.' + ) + rendering_speed: RenderingSpeed + color_palette: Optional[IdeogramColorPalette] = Field( + None, + description='A color palette for generation, must EITHER be specified via one of the presets (name) or explicitly via hexadecimal representations of the color with optional weights (members). Not supported by V_1, V_1_TURBO, V_2A and V_2A_TURBO models.', + ) + style_codes: Optional[List[StyleCode]] = Field( + None, + description='A list of 8 character hexadecimal codes representing the style of the image. Cannot be used in conjunction with style_reference_images or style_type.', + ) + style_reference_images: Optional[List[StrictBytes]] = Field( + None, + description='A set of images to use as style references (maximum total size 10MB across all style references). The images should be in JPEG, PNG or WebP format.', + ) + + +class KlingCameraControl(BaseModel): + type: Optional[KlingCameraControlType] = None + config: Optional[KlingCameraConfig] = None + + +class KlingText2VideoRequest(BaseModel): + model_name: Optional[KlingVideoGenModelName] = 'kling-v2-master' + prompt: Optional[str] = Field( + None, description='Positive text prompt', max_length=2500 + ) + negative_prompt: Optional[str] = Field( + None, description='Negative text prompt', max_length=2500 + ) + cfg_scale: Optional[KlingVideoGenCfgScale] = Field( + default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) + ) + mode: Optional[KlingVideoGenMode] = 'std' + camera_control: Optional[KlingCameraControl] = None + aspect_ratio: Optional[KlingVideoGenAspectRatio] = '16:9' + duration: Optional[KlingVideoGenDuration] = '5' + callback_url: Optional[AnyUrl] = Field( + None, description='The callback notification address' + ) + external_task_id: Optional[str] = Field(None, description='Customized Task ID') + + +class KlingImage2VideoRequest(BaseModel): + model_name: Optional[KlingVideoGenModelName] = 'kling-v2-master' + image: Optional[str] = Field( + None, + description='Reference Image - URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1. Base64 should not include data:image prefix.', + ) + image_tail: Optional[str] = Field( + None, + description='Reference Image - End frame control. URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px. Base64 should not include data:image prefix.', + ) + prompt: Optional[str] = Field( + None, description='Positive text prompt', max_length=2500 + ) + negative_prompt: Optional[str] = Field( + None, description='Negative text prompt', max_length=2500 + ) + cfg_scale: Optional[KlingVideoGenCfgScale] = Field( + default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) + ) + mode: Optional[KlingVideoGenMode] = 'std' + static_mask: Optional[str] = Field( + None, + description='Static Brush Application Area (Mask image created by users using the motion brush). The aspect ratio must match the input image.', + ) + dynamic_masks: Optional[List[DynamicMask]] = Field( + None, + description='Dynamic Brush Configuration List (up to 6 groups). For 5-second videos, trajectory length must not exceed 77 coordinates.', + ) + camera_control: Optional[KlingCameraControl] = None + aspect_ratio: Optional[KlingVideoGenAspectRatio] = '16:9' + duration: Optional[KlingVideoGenDuration] = '5' + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback notification address. Server will notify when the task status changes.', + ) + external_task_id: Optional[str] = Field( + None, + description='Customized Task ID. Must be unique within a single user account.', + ) + + +class KlingVideoEffectsInput( + RootModel[Union[KlingSingleImageEffectInput, KlingDualCharacterEffectInput]] +): + root: Union[KlingSingleImageEffectInput, KlingDualCharacterEffectInput] + + +class StripeBillingDetails(BaseModel): + address: Optional[StripeAddress] = None + email: Optional[str] = None + name: Optional[str] = None + phone: Optional[str] = None + tax_id: Optional[Any] = None + + +class StripePaymentMethodDetails(BaseModel): + card: Optional[StripeCardDetails] = None + type: Optional[str] = None + + +class BFLFluxProFillInputs(BaseModel): + image: str = Field( + ..., + description='A Base64-encoded string representing the image you wish to modify. Can contain alpha mask if desired.', + title='Image', + ) + mask: Optional[str] = Field( + None, + description='A Base64-encoded string representing a mask for the areas you want to modify in the image. The mask should be the same dimensions as the image and in black and white. Black areas (0%) indicate no modification, while white areas (100%) specify areas for inpainting. Optional if you provide an alpha mask in the original image. Validation: The endpoint verifies that the dimensions of the mask match the original image.', + title='Mask', + ) + prompt: Optional[str] = Field( + '', + description='The description of the changes you want to make. This text guides the inpainting process, allowing you to specify features, styles, or modifications for the masked area.', + examples=['ein fantastisches bild'], + title='Prompt', + ) + steps: Optional[Steps] = Field( + default_factory=lambda: Steps.model_validate(50), + description='Number of steps for the image generation process', + examples=[50], + title='Steps', + ) + prompt_upsampling: Optional[bool] = Field( + False, + description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation', + title='Prompt Upsampling', + ) + seed: Optional[int] = Field( + None, description='Optional seed for reproducibility', title='Seed' + ) + guidance: Optional[Guidance] = Field( + default_factory=lambda: Guidance.model_validate(60), + description='Guidance strength for the image generation process', + title='Guidance', + ) + output_format: Optional[BFLOutputFormat] = Field( + 'jpeg', + description="Output format for the generated image. Can be 'jpeg' or 'png'.", + ) + safety_tolerance: Optional[int] = Field( + 2, + description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', + examples=[2], + ge=0, + le=6, + title='Safety Tolerance', + ) + webhook_url: Optional[WebhookUrl] = Field( + None, description='URL to receive webhook notifications', title='Webhook Url' + ) + webhook_secret: Optional[str] = Field( + None, + description='Optional secret for webhook signature verification', + title='Webhook Secret', + ) + + +class BFLHTTPValidationError(BaseModel): + detail: Optional[List[BFLValidationError]] = Field(None, title='Detail') + + +class BFLFluxProExpandInputs(BaseModel): + image: str = Field( + ..., + description='A Base64-encoded string representing the image you wish to expand.', + title='Image', + ) + top: Optional[Top] = Field( + 0, description='Number of pixels to expand at the top of the image', title='Top' + ) + bottom: Optional[Bottom] = Field( + 0, + description='Number of pixels to expand at the bottom of the image', + title='Bottom', + ) + left: Optional[Left] = Field( + 0, + description='Number of pixels to expand on the left side of the image', + title='Left', + ) + right: Optional[Right] = Field( + 0, + description='Number of pixels to expand on the right side of the image', + title='Right', + ) + prompt: Optional[str] = Field( + '', + description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.', + examples=['ein fantastisches bild'], + title='Prompt', + ) + steps: Optional[Steps] = Field( + default_factory=lambda: Steps.model_validate(50), + description='Number of steps for the image generation process', + examples=[50], + title='Steps', + ) + prompt_upsampling: Optional[bool] = Field( + False, + description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation', + title='Prompt Upsampling', + ) + seed: Optional[int] = Field( + None, description='Optional seed for reproducibility', title='Seed' + ) + guidance: Optional[Guidance] = Field( + default_factory=lambda: Guidance.model_validate(60), + description='Guidance strength for the image generation process', + title='Guidance', + ) + output_format: Optional[BFLOutputFormat] = Field( + 'jpeg', + description="Output format for the generated image. Can be 'jpeg' or 'png'.", + ) + safety_tolerance: Optional[int] = Field( + 2, + description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', + examples=[2], + ge=0, + le=6, + title='Safety Tolerance', + ) + webhook_url: Optional[WebhookUrl] = Field( + None, description='URL to receive webhook notifications', title='Webhook Url' + ) + webhook_secret: Optional[str] = Field( + None, + description='Optional secret for webhook signature verification', + title='Webhook Secret', + ) + + +class BFLCannyInputs(BaseModel): + prompt: str = Field( + ..., + description='Text prompt for image generation', + examples=['ein fantastisches bild'], + title='Prompt', + ) + control_image: Optional[str] = Field( + None, + description='Base64 encoded image to use as control input if no preprocessed image is provided', + title='Control Image', + ) + preprocessed_image: Optional[str] = Field( + None, + description='Optional pre-processed image that will bypass the control preprocessing step', + title='Preprocessed Image', + ) + canny_low_threshold: Optional[CannyLowThreshold] = Field( + default_factory=lambda: CannyLowThreshold.model_validate(50), + description='Low threshold for Canny edge detection', + title='Canny Low Threshold', + ) + canny_high_threshold: Optional[CannyHighThreshold] = Field( + default_factory=lambda: CannyHighThreshold.model_validate(200), + description='High threshold for Canny edge detection', + title='Canny High Threshold', + ) + prompt_upsampling: Optional[bool] = Field( + False, + description='Whether to perform upsampling on the prompt', + title='Prompt Upsampling', + ) + seed: Optional[int] = Field( + None, + description='Optional seed for reproducibility', + examples=[42], + title='Seed', + ) + steps: Optional[Steps2] = Field( + default_factory=lambda: Steps2.model_validate(50), + description='Number of steps for the image generation process', + title='Steps', + ) + output_format: Optional[BFLOutputFormat] = Field( + 'jpeg', + description="Output format for the generated image. Can be 'jpeg' or 'png'.", + ) + guidance: Optional[Guidance2] = Field( + default_factory=lambda: Guidance2.model_validate(30), + description='Guidance strength for the image generation process', + title='Guidance', + ) + safety_tolerance: Optional[int] = Field( + 2, + description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', + ge=0, + le=6, + title='Safety Tolerance', + ) + webhook_url: Optional[WebhookUrl] = Field( + None, description='URL to receive webhook notifications', title='Webhook Url' + ) + webhook_secret: Optional[str] = Field( + None, + description='Optional secret for webhook signature verification', + title='Webhook Secret', + ) + + +class BFLDepthInputs(BaseModel): + prompt: str = Field( + ..., + description='Text prompt for image generation', + examples=['ein fantastisches bild'], + title='Prompt', + ) + control_image: Optional[str] = Field( + None, + description='Base64 encoded image to use as control input', + title='Control Image', + ) + preprocessed_image: Optional[str] = Field( + None, + description='Optional pre-processed image that will bypass the control preprocessing step', + title='Preprocessed Image', + ) + prompt_upsampling: Optional[bool] = Field( + False, + description='Whether to perform upsampling on the prompt', + title='Prompt Upsampling', + ) + seed: Optional[int] = Field( + None, + description='Optional seed for reproducibility', + examples=[42], + title='Seed', + ) + steps: Optional[Steps2] = Field( + default_factory=lambda: Steps2.model_validate(50), + description='Number of steps for the image generation process', + title='Steps', + ) + output_format: Optional[BFLOutputFormat] = Field( + 'jpeg', + description="Output format for the generated image. Can be 'jpeg' or 'png'.", + ) + guidance: Optional[Guidance2] = Field( + default_factory=lambda: Guidance2.model_validate(15), + description='Guidance strength for the image generation process', + title='Guidance', + ) + safety_tolerance: Optional[int] = Field( + 2, + description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', + ge=0, + le=6, + title='Safety Tolerance', + ) + webhook_url: Optional[WebhookUrl] = Field( + None, description='URL to receive webhook notifications', title='Webhook Url' + ) + webhook_secret: Optional[str] = Field( + None, + description='Optional secret for webhook signature verification', + title='Webhook Secret', + ) + + +class Controls(BaseModel): + artistic_level: Optional[int] = Field( + None, + description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity.', + ge=0, + le=5, + ) + colors: Optional[List[RGBColor]] = Field( + None, description='An array of preferable colors' + ) + background_color: Optional[RGBColor] = Field( + None, description='Use given color as a desired background color' + ) + no_text: Optional[bool] = Field(None, description='Do not embed text layouts') + + +class RecraftImageGenerationRequest(BaseModel): + prompt: str = Field( + ..., description='The text prompt describing the image to generate' + ) + model: str = Field( + ..., description='The model to use for generation (e.g., "recraftv3")' + ) + style: Optional[str] = Field( + None, + description='The style to apply to the generated image (e.g., "digital_illustration")', + ) + style_id: Optional[str] = Field( + None, + description='The style ID to apply to the generated image (e.g., "123e4567-e89b-12d3-a456-426614174000"). If style_id is provided, style should not be provided.', + ) + size: str = Field( + ..., description='The size of the generated image (e.g., "1024x1024")' + ) + controls: Optional[Controls] = Field( + None, description='The controls for the generated image' + ) + n: int = Field(..., description='The number of images to generate', ge=1, le=4) + + +class LumaKeyframes(BaseModel): + frame0: Optional[LumaKeyframe] = None + frame1: Optional[LumaKeyframe] = None + + +class LumaGenerationRequest(BaseModel): + generation_type: Optional[GenerationType] = 'video' + prompt: str = Field(..., description='The prompt of the generation') + aspect_ratio: LumaAspectRatio + loop: Optional[bool] = Field(None, description='Whether to loop the video') + keyframes: Optional[LumaKeyframes] = None + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback URL of the generation, a POST request with Generation object will be sent to the callback URL when the generation is dreaming, completed, or failed', + ) + model: LumaVideoModel + resolution: LumaVideoModelOutputResolution + duration: LumaVideoModelOutputDuration + + +class LumaGeneration(BaseModel): + id: Optional[UUID] = Field(None, description='The ID of the generation') + generation_type: Optional[LumaGenerationType] = None + state: Optional[LumaState] = None + failure_reason: Optional[str] = Field( + None, description='The reason for the state of the generation' + ) + created_at: Optional[datetime] = Field( + None, description='The date and time when the generation was created' + ) + assets: Optional[LumaAssets] = None + model: Optional[str] = Field(None, description='The model used for the generation') + request: Optional[ + Union[ + LumaGenerationRequest, + LumaImageGenerationRequest, + LumaUpscaleVideoGenerationRequest, + LumaAudioGenerationRequest, + ] + ] = Field(None, description='The request of the generation') + + +class RunwayImageToVideoRequest(BaseModel): + promptImage: RunwayPromptImageObject + seed: int = Field( + ..., description='Random seed for generation', ge=0, le=4294967295 + ) + model: RunwayModelEnum = Field(..., description='Model to use for generation') + promptText: Optional[str] = Field( + None, description='Text prompt for the generation', max_length=1000 + ) + duration: RunwayDurationEnum = Field( + ..., description='The number of seconds of duration for the output video.' + ) + ratio: RunwayAspectRatioEnum = Field( + ..., + description='The resolution (aspect ratio) of the output video. Allowable values depend on the selected model. 1280:768 and 768:1280 are only supported for gen3a_turbo.', + ) + + +class RunwayTaskStatusResponse(BaseModel): + id: Optional[str] = Field(None, description='Task ID') + status: Optional[RunwayTaskStatusEnum] = Field(None, description='Task status') + createdAt: Optional[datetime] = Field(None, description='Task creation timestamp') + output: Optional[List[str]] = Field(None, description='Array of output video URLs') + + +class PikaHTTPValidationError(BaseModel): + detail: Optional[List[PikaValidationError]] = Field(None, title='Detail') + + +class PikaBodyGenerate22T2vGenerate22T2vPost(BaseModel): + promptText: str = Field(..., title='Prompttext') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + seed: Optional[int] = Field(None, title='Seed') + resolution: Optional[PikaResolutionEnum] = Field('1080p', title='Resolution') + duration: Optional[PikaDurationEnum] = Field(5, title='Duration') + aspectRatio: Optional[float] = Field( + 1.7777777777777777, + description='Aspect ratio (width / height)', + ge=0.4, + le=2.5, + title='Aspectratio', + ) + + +class PikaBodyGenerate22I2vGenerate22I2vPost(BaseModel): + image: Optional[StrictBytes] = Field(None, title='Image') + promptText: Optional[str] = Field(None, title='Prompttext') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + seed: Optional[int] = Field(None, title='Seed') + resolution: Optional[PikaResolutionEnum] = Field('1080p', title='Resolution') + duration: Optional[PikaDurationEnum] = Field(5, title='Duration') + + +class PikaBodyGenerate22KeyframeGenerate22PikaframesPost(BaseModel): + keyFrames: Optional[List[StrictBytes]] = Field( + None, description='Array of keyframe images', title='Keyframes' + ) + promptText: str = Field(..., title='Prompttext') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + seed: Optional[int] = Field(None, title='Seed') + resolution: Optional[PikaResolutionEnum] = Field('1080p', title='Resolution') + duration: Optional[int] = Field(None, ge=5, le=10, title='Duration') + + +class PikaVideoResponse(BaseModel): + id: str = Field(..., title='Id') + status: PikaStatusEnum = Field( + ..., description='The status of the video', title='Status' + ) + url: Optional[str] = Field(None, title='Url') + progress: Optional[int] = Field(None, title='Progress') + + +class Node(BaseModel): + id: Optional[str] = Field(None, description='The unique identifier of the node.') + name: Optional[str] = Field(None, description='The display name of the node.') + category: Optional[str] = Field(None, description='The category of the node.') + description: Optional[str] = None + author: Optional[str] = None + license: Optional[str] = Field( + None, description="The path to the LICENSE file in the node's repository." + ) + icon: Optional[str] = Field(None, description="URL to the node's icon.") + repository: Optional[str] = Field(None, description="URL to the node's repository.") + tags: Optional[List[str]] = None + latest_version: Optional[NodeVersion] = Field( + None, description='The latest version of the node.' + ) + rating: Optional[float] = Field(None, description='The average rating of the node.') + downloads: Optional[int] = Field( + None, description='The number of downloads of the node.' + ) + publisher: Optional[Publisher] = Field( + None, description='The publisher of the node.' + ) + status: Optional[NodeStatus] = Field(None, description='The status of the node.') + status_detail: Optional[str] = Field( + None, description='The status detail of the node.' + ) + translations: Optional[Dict[str, Dict[str, Any]]] = None + + +class KlingVideoEffectsRequest(BaseModel): + effect_scene: Union[KlingDualCharacterEffectsScene, KlingSingleImageEffectsScene] + input: KlingVideoEffectsInput + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback notification address for the result of this task.', + ) + external_task_id: Optional[str] = Field( + None, + description='Customized Task ID. Must be unique within a single user account.', + ) + + +class StripeCharge(BaseModel): + id: Optional[str] = None + object: Optional[Object2] = None + amount: Optional[int] = None + amount_captured: Optional[int] = None + amount_refunded: Optional[int] = None + application: Optional[str] = None + application_fee: Optional[str] = None + application_fee_amount: Optional[int] = None + balance_transaction: Optional[str] = None + billing_details: Optional[StripeBillingDetails] = None + calculated_statement_descriptor: Optional[str] = None + captured: Optional[bool] = None + created: Optional[int] = None + currency: Optional[str] = None + customer: Optional[str] = None + description: Optional[str] = None + destination: Optional[Any] = None + dispute: Optional[Any] = None + disputed: Optional[bool] = None + failure_balance_transaction: Optional[Any] = None + failure_code: Optional[Any] = None + failure_message: Optional[Any] = None + fraud_details: Optional[Dict[str, Any]] = None + invoice: Optional[Any] = None + livemode: Optional[bool] = None + metadata: Optional[Dict[str, Any]] = None + on_behalf_of: Optional[Any] = None + order: Optional[Any] = None + outcome: Optional[StripeOutcome] = None + paid: Optional[bool] = None + payment_intent: Optional[str] = None + payment_method: Optional[str] = None + payment_method_details: Optional[StripePaymentMethodDetails] = None + radar_options: Optional[Dict[str, Any]] = None + receipt_email: Optional[str] = None + receipt_number: Optional[str] = None + receipt_url: Optional[str] = None + refunded: Optional[bool] = None + refunds: Optional[StripeRefundList] = None + review: Optional[Any] = None + shipping: Optional[StripeShipping] = None + source: Optional[Any] = None + source_transfer: Optional[Any] = None + statement_descriptor: Optional[Any] = None + statement_descriptor_suffix: Optional[Any] = None + status: Optional[str] = None + transfer_data: Optional[Any] = None + transfer_group: Optional[Any] = None + + +class StripeChargeList(BaseModel): + object: Optional[str] = None + data: Optional[List[StripeCharge]] = None + has_more: Optional[bool] = None + total_count: Optional[int] = None + url: Optional[str] = None + + +class StripePaymentIntent(BaseModel): + id: Optional[str] = None + object: Optional[Object1] = None + amount: Optional[int] = None + amount_capturable: Optional[int] = None + amount_details: Optional[StripeAmountDetails] = None + amount_received: Optional[int] = None + application: Optional[str] = None + application_fee_amount: Optional[int] = None + automatic_payment_methods: Optional[Any] = None + canceled_at: Optional[int] = None + cancellation_reason: Optional[str] = None + capture_method: Optional[str] = None + charges: Optional[StripeChargeList] = None + client_secret: Optional[str] = None + confirmation_method: Optional[str] = None + created: Optional[int] = None + currency: Optional[str] = None + customer: Optional[str] = None + description: Optional[str] = None + invoice: Optional[str] = None + last_payment_error: Optional[Any] = None + latest_charge: Optional[str] = None + livemode: Optional[bool] = None + metadata: Optional[Dict[str, Any]] = None + next_action: Optional[Any] = None + on_behalf_of: Optional[Any] = None + payment_method: Optional[str] = None + payment_method_configuration_details: Optional[Any] = None + payment_method_options: Optional[StripePaymentMethodOptions] = None + payment_method_types: Optional[List[str]] = None + processing: Optional[Any] = None + receipt_email: Optional[str] = None + review: Optional[Any] = None + setup_future_usage: Optional[Any] = None + shipping: Optional[StripeShipping] = None + source: Optional[Any] = None + statement_descriptor: Optional[Any] = None + statement_descriptor_suffix: Optional[Any] = None + status: Optional[str] = None + transfer_data: Optional[Any] = None + transfer_group: Optional[Any] = None + + +class Data8(BaseModel): + object: Optional[StripePaymentIntent] = None + + +class StripeEvent(BaseModel): + id: str + object: Object + api_version: Optional[str] = None + created: Optional[int] = None + data: Data8 + livemode: Optional[bool] = None + pending_webhooks: Optional[int] = None + request: Optional[StripeRequestInfo] = None + type: Type diff --git a/comfy_api_nodes/apis/bfl_api.py b/comfy_api_nodes/apis/bfl_api.py new file mode 100644 index 000000000..c189038fb --- /dev/null +++ b/comfy_api_nodes/apis/bfl_api.py @@ -0,0 +1,156 @@ +from __future__ import annotations + +from enum import Enum +from typing import Any, Dict, Optional + +from pydantic import BaseModel, Field, confloat, conint + + +class BFLOutputFormat(str, Enum): + png = 'png' + jpeg = 'jpeg' + + +class BFLFluxExpandImageRequest(BaseModel): + prompt: str = Field(..., description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + top: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the top of the image') + bottom: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the bottom of the image') + left: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the left side of the image') + right: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the right side of the image') + steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') + guidance: confloat(ge=1.5, le=100) = Field(..., description='Guidance strength for the image generation process') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + image: str = Field(None, description='A Base64-encoded string representing the image you wish to expand') + + +class BFLFluxFillImageRequest(BaseModel): + prompt: str = Field(..., description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') + guidance: confloat(ge=1.5, le=100) = Field(..., description='Guidance strength for the image generation process') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + image: str = Field(None, description='A Base64-encoded string representing the image you wish to modify. Can contain alpha mask if desired.') + mask: str = Field(None, description='A Base64-encoded string representing the mask of the areas you with to modify.') + + +class BFLFluxCannyImageRequest(BaseModel): + prompt: str = Field(..., description='Text prompt for image generation') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + canny_low_threshold: Optional[int] = Field(None, description='Low threshold for Canny edge detection') + canny_high_threshold: Optional[int] = Field(None, description='High threshold for Canny edge detection') + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') + guidance: confloat(ge=1, le=100) = Field(..., description='Guidance strength for the image generation process') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + control_image: Optional[str] = Field(None, description='Base64 encoded image to use as control input if no preprocessed image is provided') + preprocessed_image: Optional[str] = Field(None, description='Optional pre-processed image that will bypass the control preprocessing step') + + +class BFLFluxDepthImageRequest(BaseModel): + prompt: str = Field(..., description='Text prompt for image generation') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') + guidance: confloat(ge=1, le=100) = Field(..., description='Guidance strength for the image generation process') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + control_image: Optional[str] = Field(None, description='Base64 encoded image to use as control input if no preprocessed image is provided') + preprocessed_image: Optional[str] = Field(None, description='Optional pre-processed image that will bypass the control preprocessing step') + + +class BFLFluxProGenerateRequest(BaseModel): + prompt: str = Field(..., description='The text prompt for image generation.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + width: conint(ge=256, le=1440) = Field(1024, description='Width of the generated image in pixels. Must be a multiple of 32.') + height: conint(ge=256, le=1440) = Field(768, description='Height of the generated image in pixels. Must be a multiple of 32.') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + image_prompt: Optional[str] = Field(None, description='Optional image to remix in base64 format') + # image_prompt_strength: Optional[confloat(ge=0.0, le=1.0)] = Field( + # None, description='Blend between the prompt and the image prompt.' + # ) + + +class BFLFluxProUltraGenerateRequest(BaseModel): + prompt: str = Field(..., description='The text prompt for image generation.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + raw: Optional[bool] = Field(None, description='Generate less processed, more natural-looking images.') + image_prompt: Optional[str] = Field(None, description='Optional image to remix in base64 format') + image_prompt_strength: Optional[confloat(ge=0.0, le=1.0)] = Field( + None, description='Blend between the prompt and the image prompt.' + ) + + +class BFLFluxProGenerateResponse(BaseModel): + id: str = Field(..., description='The unique identifier for the generation task.') + polling_url: str = Field(..., description='URL to poll for the generation result.') + + +class BFLStatus(str, Enum): + task_not_found = "Task not found" + pending = "Pending" + request_moderated = "Request Moderated" + content_moderated = "Content Moderated" + ready = "Ready" + error = "Error" + + +class BFLFluxProStatusResponse(BaseModel): + id: str = Field(..., description="The unique identifier for the generation task.") + status: BFLStatus = Field(..., description="The status of the task.") + result: Optional[Dict[str, Any]] = Field( + None, description="The result of the task (null if not completed)." + ) + progress: confloat(ge=0.0, le=1.0) = Field( + ..., description="The progress of the task (0.0 to 1.0)." + ) + details: Optional[Dict[str, Any]] = Field( + None, description="Additional details about the task (null if not available)." + ) diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py index d3cd9ad2f..929e386d4 100644 --- a/comfy_api_nodes/apis/client.py +++ b/comfy_api_nodes/apis/client.py @@ -1,5 +1,3 @@ -import logging - """ API Client Framework for api.comfy.org. @@ -46,24 +44,71 @@ operation = ApiOperation( ) user_profile = operation.execute(client=api_client) # Returns immediately with the result + +# Example 2: Asynchronous API Operation with Polling +# ------------------------------------------------- +# For an API that starts a task and requires polling for completion: + +# 1. Define the endpoints (initial request and polling) +generate_image_endpoint = ApiEndpoint( + path="/v1/images/generate", + method=HttpMethod.POST, + request_model=ImageGenerationRequest, + response_model=TaskCreatedResponse, + query_params=None +) + +check_task_endpoint = ApiEndpoint( + path="/v1/tasks/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=ImageGenerationResult, + query_params=None +) + +# 2. Create the request object +request = ImageGenerationRequest( + prompt="a beautiful sunset over mountains", + width=1024, + height=1024, + num_images=1 +) + +# 3. Create and execute the polling operation +operation = PollingOperation( + initial_endpoint=generate_image_endpoint, + initial_request=request, + poll_endpoint=check_task_endpoint, + task_id_field="task_id", + status_field="status", + completed_statuses=["completed"], + failed_statuses=["failed", "error"] +) + +# This will make the initial request and then poll until completion +result = operation.execute(client=api_client) # Returns the final ImageGenerationResult when done """ -from typing import ( - Dict, - Type, - Optional, - Any, - TypeVar, - Generic, -) -from pydantic import BaseModel +from __future__ import annotations +import logging +import time +import io +from typing import Dict, Type, Optional, Any, TypeVar, Generic, Callable from enum import Enum import json import requests from urllib.parse import urljoin +from pydantic import BaseModel, Field + +from comfy.cli_args import args +from comfy import utils T = TypeVar("T", bound=BaseModel) R = TypeVar("R", bound=BaseModel) +P = TypeVar("P", bound=BaseModel) # For poll response + +PROGRESS_BAR_MAX = 100 + class EmptyRequest(BaseModel): """Base class for empty request bodies. @@ -72,6 +117,19 @@ class EmptyRequest(BaseModel): pass +class UploadRequest(BaseModel): + file_name: str = Field(..., description="Filename to upload") + content_type: str | None = Field( + None, + description="Mime type of the file. For example: image/png, image/jpeg, video/mp4, etc.", + ) + + +class UploadResponse(BaseModel): + download_url: str = Field(..., description="URL to GET uploaded file") + upload_url: str = Field(..., description="URL to PUT file to upload") + + class HttpMethod(str, Enum): GET = "GET" POST = "POST" @@ -89,7 +147,7 @@ class ApiClient: self, base_url: str, api_key: Optional[str] = None, - timeout: float = 30.0, + timeout: float = 3600.0, verify_ssl: bool = True, ): self.base_url = base_url @@ -97,6 +155,48 @@ class ApiClient: self.timeout = timeout self.verify_ssl = verify_ssl + def _create_json_payload_args( + self, + data: Optional[Dict[str, Any]] = None, + headers: Optional[Dict[str, str]] = None, + ) -> Dict[str, Any]: + return { + "json": data, + "headers": headers, + } + + def _create_form_data_args( + self, + data: Dict[str, Any], + files: Dict[str, Any], + headers: Optional[Dict[str, str]] = None, + multipart_parser = None, + ) -> Dict[str, Any]: + if headers and "Content-Type" in headers: + del headers["Content-Type"] + + if multipart_parser: + data = multipart_parser(data) + + return { + "data": data, + "files": files, + "headers": headers, + } + + def _create_urlencoded_form_data_args( + self, + data: Dict[str, Any], + headers: Optional[Dict[str, str]] = None, + ) -> Dict[str, Any]: + headers = headers or {} + headers["Content-Type"] = "application/x-www-form-urlencoded" + + return { + "data": data, + "headers": headers, + } + def get_headers(self) -> Dict[str, str]: """Get headers for API requests, including authentication if available""" headers = {"Content-Type": "application/json", "Accept": "application/json"} @@ -111,9 +211,11 @@ class ApiClient: method: str, path: str, params: Optional[Dict[str, Any]] = None, - json: Optional[Dict[str, Any]] = None, + data: Optional[Dict[str, Any]] = None, files: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None, + content_type: str = "application/json", + multipart_parser: Callable = None, ) -> Dict[str, Any]: """ Make an HTTP request to the API @@ -122,9 +224,10 @@ class ApiClient: method: HTTP method (GET, POST, etc.) path: API endpoint path (will be joined with base_url) params: Query parameters - json: JSON body data + data: body data files: Files to upload headers: Additional headers + content_type: Content type of the request. Defaults to application/json. Returns: Parsed JSON response @@ -146,34 +249,26 @@ class ApiClient: logging.debug(f"[DEBUG] Request Headers: {request_headers}") logging.debug(f"[DEBUG] Files: {files}") logging.debug(f"[DEBUG] Params: {params}") - logging.debug(f"[DEBUG] Json: {json}") + logging.debug(f"[DEBUG] Data: {data}") + + if content_type == "application/x-www-form-urlencoded": + payload_args = self._create_urlencoded_form_data_args(data, request_headers) + elif content_type == "multipart/form-data": + payload_args = self._create_form_data_args( + data, files, request_headers, multipart_parser + ) + else: + payload_args = self._create_json_payload_args(data, request_headers) try: - # If files are present, use data parameter instead of json - if files: - form_data = {} - if json: - form_data.update(json) - response = requests.request( - method=method, - url=url, - params=params, - data=form_data, # Use data instead of json - files=files, - headers=request_headers, - timeout=self.timeout, - verify=self.verify_ssl, - ) - else: - response = requests.request( - method=method, - url=url, - params=params, - json=json, - headers=request_headers, - timeout=self.timeout, - verify=self.verify_ssl, - ) + response = requests.request( + method=method, + url=url, + params=params, + timeout=self.timeout, + verify=self.verify_ssl, + **payload_args, + ) # Raise exception for error status codes response.raise_for_status() @@ -203,7 +298,9 @@ class ApiClient: error_message = f"API Error: {error_json}" except Exception as json_error: # If we can't parse the JSON, fall back to the original error message - logging.debug(f"[DEBUG] Failed to parse error response: {str(json_error)}") + logging.debug( + f"[DEBUG] Failed to parse error response: {str(json_error)}" + ) logging.debug(f"[DEBUG] API Error: {error_message} (Status: {status_code})") if hasattr(e, "response") and e.response.content: @@ -229,6 +326,32 @@ class ApiClient: raise Exception("Unauthorized: Please login first to use this node.") return auth_token + @staticmethod + def upload_file( + upload_url: str, + file: io.BytesIO | str, + content_type: str | None = None, + ): + """Upload a file to the API. Make sure the file has a filename equal to what the url expects. + + Args: + upload_url: The URL to upload to + file: Either a file path string, BytesIO object, or tuple of (file_path, filename) + mime_type: Optional mime type to set for the upload + """ + headers = {} + if content_type: + headers["Content-Type"] = content_type + + if isinstance(file, io.BytesIO): + file.seek(0) # Ensure we're at the start of the file + data = file.read() + return requests.put(upload_url, data=data, headers=headers) + elif isinstance(file, str): + with open(file, "rb") as f: + data = f.read() + return requests.put(upload_url, data=data, headers=headers) + class ApiEndpoint(Generic[T, R]): """Defines an API endpoint with its request and response types""" @@ -267,27 +390,29 @@ class SynchronousOperation(Generic[T, R]): endpoint: ApiEndpoint[T, R], request: T, files: Optional[Dict[str, Any]] = None, - api_base: str = "https://api.comfy.org", + api_base: str | None = None, auth_token: Optional[str] = None, timeout: float = 604800.0, verify_ssl: bool = True, + content_type: str = "application/json", + multipart_parser: Callable = None, ): self.endpoint = endpoint self.request = request self.response = None self.error = None - self.api_base = api_base + self.api_base: str = api_base or args.comfy_api_base self.auth_token = auth_token self.timeout = timeout self.verify_ssl = verify_ssl self.files = files + self.content_type = content_type + self.multipart_parser = multipart_parser def execute(self, client: Optional[ApiClient] = None) -> R: """Execute the API operation using the provided client or create one""" try: # Create client if not provided if client is None: - if self.api_base is None: - raise ValueError("Either client or api_base must be provided") client = ApiClient( base_url=self.api_base, api_key=self.auth_token, @@ -296,14 +421,25 @@ class SynchronousOperation(Generic[T, R]): ) # Convert request model to dict, but use None for EmptyRequest - request_dict = None if isinstance(self.request, EmptyRequest) else self.request.model_dump(exclude_none=True) + request_dict = ( + None + if isinstance(self.request, EmptyRequest) + else self.request.model_dump(exclude_none=True) + ) if request_dict: for key, value in request_dict.items(): if isinstance(value, Enum): request_dict[key] = value.value + if request_dict: + for key, value in request_dict.items(): + if isinstance(value, Enum): + request_dict[key] = value.value + # Debug log for request - logging.debug(f"[DEBUG] API Request: {self.endpoint.method.value} {self.endpoint.path}") + logging.debug( + f"[DEBUG] API Request: {self.endpoint.method.value} {self.endpoint.path}" + ) logging.debug(f"[DEBUG] Request Data: {json.dumps(request_dict, indent=2)}") logging.debug(f"[DEBUG] Query Params: {self.endpoint.query_params}") @@ -311,9 +447,11 @@ class SynchronousOperation(Generic[T, R]): resp = client.request( method=self.endpoint.method.value, path=self.endpoint.path, - json=request_dict, + data=request_dict, params=self.endpoint.query_params, files=self.files, + content_type=self.content_type, + multipart_parser=self.multipart_parser ) # Debug log for response @@ -327,7 +465,7 @@ class SynchronousOperation(Generic[T, R]): return self._parse_response(resp) except Exception as e: - logging.debug(f"[DEBUG] API Exception: {str(e)}") + logging.error(f"[DEBUG] API Exception: {str(e)}") raise Exception(str(e)) def _parse_response(self, resp): @@ -339,3 +477,140 @@ class SynchronousOperation(Generic[T, R]): self.response = self.endpoint.response_model.model_validate(resp) logging.debug(f"[DEBUG] Parsed Response: {self.response}") return self.response + + +class TaskStatus(str, Enum): + """Enum for task status values""" + + COMPLETED = "completed" + FAILED = "failed" + PENDING = "pending" + + +class PollingOperation(Generic[T, R]): + """ + Represents an asynchronous API operation that requires polling for completion. + """ + + def __init__( + self, + poll_endpoint: ApiEndpoint[EmptyRequest, R], + completed_statuses: list, + failed_statuses: list, + status_extractor: Callable[[R], str], + progress_extractor: Callable[[R], float] = None, + request: Optional[T] = None, + api_base: str | None = None, + auth_token: Optional[str] = None, + poll_interval: float = 5.0, + ): + self.poll_endpoint = poll_endpoint + self.request = request + self.api_base: str = api_base or args.comfy_api_base + self.auth_token = auth_token + self.poll_interval = poll_interval + + # Polling configuration + self.status_extractor = status_extractor or ( + lambda x: getattr(x, "status", None) + ) + self.progress_extractor = progress_extractor + self.completed_statuses = completed_statuses + self.failed_statuses = failed_statuses + + # For storing response data + self.final_response = None + self.error = None + + def execute(self, client: Optional[ApiClient] = None) -> R: + """Execute the polling operation using the provided client. If failed, raise an exception.""" + try: + if client is None: + client = ApiClient( + base_url=self.api_base, + api_key=self.auth_token, + ) + return self._poll_until_complete(client) + except Exception as e: + raise Exception(f"Error during polling: {str(e)}") + + def _check_task_status(self, response: R) -> TaskStatus: + """Check task status using the status extractor function""" + try: + status = self.status_extractor(response) + if status in self.completed_statuses: + return TaskStatus.COMPLETED + elif status in self.failed_statuses: + return TaskStatus.FAILED + return TaskStatus.PENDING + except Exception as e: + logging.error(f"Error extracting status: {e}") + return TaskStatus.PENDING + + def _poll_until_complete(self, client: ApiClient) -> R: + """Poll until the task is complete""" + poll_count = 0 + if self.progress_extractor: + progress = utils.ProgressBar(PROGRESS_BAR_MAX) + + while True: + try: + poll_count += 1 + logging.debug(f"[DEBUG] Polling attempt #{poll_count}") + + request_dict = ( + self.request.model_dump(exclude_none=True) + if self.request is not None + else None + ) + + if poll_count == 1: + logging.debug( + f"[DEBUG] Poll Request: {self.poll_endpoint.method.value} {self.poll_endpoint.path}" + ) + logging.debug( + f"[DEBUG] Poll Request Data: {json.dumps(request_dict, indent=2) if request_dict else 'None'}" + ) + + # Query task status + resp = client.request( + method=self.poll_endpoint.method.value, + path=self.poll_endpoint.path, + params=self.poll_endpoint.query_params, + data=request_dict, + ) + + # Parse response + response_obj = self.poll_endpoint.response_model.model_validate(resp) + # Check if task is complete + status = self._check_task_status(response_obj) + logging.debug(f"[DEBUG] Task Status: {status}") + + # If progress extractor is provided, extract progress + if self.progress_extractor: + new_progress = self.progress_extractor(response_obj) + if new_progress is not None: + progress.update_absolute(new_progress, total=PROGRESS_BAR_MAX) + + if status == TaskStatus.COMPLETED: + logging.debug("[DEBUG] Task completed successfully") + self.final_response = response_obj + if self.progress_extractor: + progress.update(100) + return self.final_response + elif status == TaskStatus.FAILED: + message = f"Task failed: {json.dumps(resp)}" + logging.error(f"[DEBUG] {message}") + raise Exception(message) + else: + logging.debug("[DEBUG] Task still pending, continuing to poll...") + + # Wait before polling again + logging.debug( + f"[DEBUG] Waiting {self.poll_interval} seconds before next poll" + ) + time.sleep(self.poll_interval) + + except Exception as e: + logging.error(f"[DEBUG] Polling error: {str(e)}") + raise Exception(f"Error while polling: {str(e)}") diff --git a/comfy_api_nodes/apis/luma_api.py b/comfy_api_nodes/apis/luma_api.py new file mode 100644 index 000000000..632c4ab96 --- /dev/null +++ b/comfy_api_nodes/apis/luma_api.py @@ -0,0 +1,253 @@ +from __future__ import annotations + + +import torch + +from enum import Enum +from typing import Optional, Union + +from pydantic import BaseModel, Field, confloat + + + +class LumaIO: + LUMA_REF = "LUMA_REF" + LUMA_CONCEPTS = "LUMA_CONCEPTS" + + +class LumaReference: + def __init__(self, image: torch.Tensor, weight: float): + self.image = image + self.weight = weight + + def create_api_model(self, download_url: str): + return LumaImageRef(url=download_url, weight=self.weight) + +class LumaReferenceChain: + def __init__(self, first_ref: LumaReference=None): + self.refs: list[LumaReference] = [] + if first_ref: + self.refs.append(first_ref) + + def add(self, luma_ref: LumaReference=None): + self.refs.append(luma_ref) + + def create_api_model(self, download_urls: list[str], max_refs=4): + if len(self.refs) == 0: + return None + api_refs: list[LumaImageRef] = [] + for ref, url in zip(self.refs, download_urls): + api_ref = LumaImageRef(url=url, weight=ref.weight) + api_refs.append(api_ref) + return api_refs + + def clone(self): + c = LumaReferenceChain() + for ref in self.refs: + c.add(ref) + return c + + +class LumaConcept: + def __init__(self, key: str): + self.key = key + + +class LumaConceptChain: + def __init__(self, str_list: list[str] = None): + self.concepts: list[LumaConcept] = [] + if str_list is not None: + for c in str_list: + if c != "None": + self.add(LumaConcept(key=c)) + + def add(self, concept: LumaConcept): + self.concepts.append(concept) + + def create_api_model(self): + if len(self.concepts) == 0: + return None + api_concepts: list[LumaConceptObject] = [] + for concept in self.concepts: + if concept.key == "None": + continue + api_concepts.append(LumaConceptObject(key=concept.key)) + if len(api_concepts) == 0: + return None + return api_concepts + + def clone(self): + c = LumaConceptChain() + for concept in self.concepts: + c.add(concept) + return c + + def clone_and_merge(self, other: LumaConceptChain): + c = self.clone() + for concept in other.concepts: + c.add(concept) + return c + + +def get_luma_concepts(include_none=False): + concepts = [] + if include_none: + concepts.append("None") + return concepts + [ + "truck_left", + "pan_right", + "pedestal_down", + "low_angle", + "pedestal_up", + "selfie", + "pan_left", + "roll_right", + "zoom_in", + "over_the_shoulder", + "orbit_right", + "orbit_left", + "static", + "tiny_planet", + "high_angle", + "bolt_cam", + "dolly_zoom", + "overhead", + "zoom_out", + "handheld", + "roll_left", + "pov", + "aerial_drone", + "push_in", + "crane_down", + "truck_right", + "tilt_down", + "elevator_doors", + "tilt_up", + "ground_level", + "pull_out", + "aerial", + "crane_up", + "eye_level" + ] + + +class LumaImageModel(str, Enum): + photon_1 = "photon-1" + photon_flash_1 = "photon-flash-1" + + +class LumaVideoModel(str, Enum): + ray_2 = "ray-2" + ray_flash_2 = "ray-flash-2" + ray_1_6 = "ray-1-6" + + +class LumaAspectRatio(str, Enum): + ratio_1_1 = "1:1" + ratio_16_9 = "16:9" + ratio_9_16 = "9:16" + ratio_4_3 = "4:3" + ratio_3_4 = "3:4" + ratio_21_9 = "21:9" + ratio_9_21 = "9:21" + + +class LumaVideoOutputResolution(str, Enum): + res_540p = "540p" + res_720p = "720p" + res_1080p = "1080p" + res_4k = "4k" + + +class LumaVideoModelOutputDuration(str, Enum): + dur_5s = "5s" + dur_9s = "9s" + + +class LumaGenerationType(str, Enum): + video = 'video' + image = 'image' + + +class LumaState(str, Enum): + queued = "queued" + dreaming = "dreaming" + completed = "completed" + failed = "failed" + + +class LumaAssets(BaseModel): + video: Optional[str] = Field(None, description='The URL of the video') + image: Optional[str] = Field(None, description='The URL of the image') + progress_video: Optional[str] = Field(None, description='The URL of the progress video') + + +class LumaImageRef(BaseModel): + '''Used for image gen''' + url: str = Field(..., description='The URL of the image reference') + weight: confloat(ge=0.0, le=1.0) = Field(..., description='The weight of the image reference') + + +class LumaImageReference(BaseModel): + '''Used for video gen''' + type: Optional[str] = Field('image', description='Input type, defaults to image') + url: str = Field(..., description='The URL of the image') + + +class LumaModifyImageRef(BaseModel): + url: str = Field(..., description='The URL of the image reference') + weight: confloat(ge=0.0, le=1.0) = Field(..., description='The weight of the image reference') + + +class LumaCharacterRef(BaseModel): + identity0: LumaImageIdentity = Field(..., description='The image identity object') + + +class LumaImageIdentity(BaseModel): + images: list[str] = Field(..., description='The URLs of the image identity') + + +class LumaGenerationReference(BaseModel): + type: str = Field('generation', description='Input type, defaults to generation') + id: str = Field(..., description='The ID of the generation') + + +class LumaKeyframes(BaseModel): + frame0: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='') + frame1: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='') + + +class LumaConceptObject(BaseModel): + key: str = Field(..., description='Camera Concept name') + + +class LumaImageGenerationRequest(BaseModel): + prompt: str = Field(..., description='The prompt of the generation') + model: LumaImageModel = Field(LumaImageModel.photon_1, description='The image model used for the generation') + aspect_ratio: Optional[LumaAspectRatio] = Field(LumaAspectRatio.ratio_16_9, description='The aspect ratio of the generation') + image_ref: Optional[list[LumaImageRef]] = Field(None, description='List of image reference objects') + style_ref: Optional[list[LumaImageRef]] = Field(None, description='List of style reference objects') + character_ref: Optional[LumaCharacterRef] = Field(None, description='The image identity object') + modify_image_ref: Optional[LumaModifyImageRef] = Field(None, description='The modify image reference object') + + +class LumaGenerationRequest(BaseModel): + prompt: str = Field(..., description='The prompt of the generation') + model: LumaVideoModel = Field(LumaVideoModel.ray_2, description='The video model used for the generation') + duration: Optional[LumaVideoModelOutputDuration] = Field(None, description='The duration of the generation') + aspect_ratio: Optional[LumaAspectRatio] = Field(None, description='The aspect ratio of the generation') + resolution: Optional[LumaVideoOutputResolution] = Field(None, description='The resolution of the generation') + loop: Optional[bool] = Field(None, description='Whether to loop the video') + keyframes: Optional[LumaKeyframes] = Field(None, description='The keyframes of the generation') + concepts: Optional[list[LumaConceptObject]] = Field(None, description='Camera Concepts to apply to generation') + + +class LumaGeneration(BaseModel): + id: str = Field(..., description='The ID of the generation') + generation_type: LumaGenerationType = Field(..., description='Generation type, image or video') + state: LumaState = Field(..., description='The state of the generation') + failure_reason: Optional[str] = Field(None, description='The reason for the state of the generation') + created_at: str = Field(..., description='The date and time when the generation was created') + assets: Optional[LumaAssets] = Field(None, description='The assets of the generation') + model: str = Field(..., description='The model used for the generation') + request: Union[LumaGenerationRequest, LumaImageGenerationRequest] = Field(..., description="The request used for the generation") diff --git a/comfy_api_nodes/apis/pixverse_api.py b/comfy_api_nodes/apis/pixverse_api.py new file mode 100644 index 000000000..9bb29c383 --- /dev/null +++ b/comfy_api_nodes/apis/pixverse_api.py @@ -0,0 +1,146 @@ +from __future__ import annotations + +from enum import Enum +from typing import Optional + +from pydantic import BaseModel, Field + + +pixverse_templates = { + "Microwave": 324641385496960, + "Suit Swagger": 328545151283968, + "Anything, Robot": 313358700761536, + "Subject 3 Fever": 327828816843648, + "kiss kiss": 315446315336768, +} + + +class PixverseIO: + TEMPLATE = "PIXVERSE_TEMPLATE" + + +class PixverseStatus(int, Enum): + successful = 1 + generating = 5 + deleted = 6 + contents_moderation = 7 + failed = 8 + + +class PixverseAspectRatio(str, Enum): + ratio_16_9 = "16:9" + ratio_4_3 = "4:3" + ratio_1_1 = "1:1" + ratio_3_4 = "3:4" + ratio_9_16 = "9:16" + + +class PixverseQuality(str, Enum): + res_360p = "360p" + res_540p = "540p" + res_720p = "720p" + res_1080p = "1080p" + + +class PixverseDuration(int, Enum): + dur_5 = 5 + dur_8 = 8 + + +class PixverseMotionMode(str, Enum): + normal = "normal" + fast = "fast" + + +class PixverseStyle(str, Enum): + anime = "anime" + animation_3d = "3d_animation" + clay = "clay" + comic = "comic" + cyberpunk = "cyberpunk" + + +# NOTE: forgoing descriptions for now in return for dev speed +class PixverseTextVideoRequest(BaseModel): + aspect_ratio: PixverseAspectRatio = Field(...) + quality: PixverseQuality = Field(...) + duration: PixverseDuration = Field(...) + model: Optional[str] = Field("v3.5") + motion_mode: Optional[PixverseMotionMode] = Field(PixverseMotionMode.normal) + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + style: Optional[str] = Field(None) + template_id: Optional[int] = Field(None) + water_mark: Optional[bool] = Field(None) + + +class PixverseImageVideoRequest(BaseModel): + quality: PixverseQuality = Field(...) + duration: PixverseDuration = Field(...) + img_id: int = Field(...) + model: Optional[str] = Field("v3.5") + motion_mode: Optional[PixverseMotionMode] = Field(PixverseMotionMode.normal) + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + style: Optional[str] = Field(None) + template_id: Optional[int] = Field(None) + water_mark: Optional[bool] = Field(None) + + +class PixverseTransitionVideoRequest(BaseModel): + quality: PixverseQuality = Field(...) + duration: PixverseDuration = Field(...) + first_frame_img: int = Field(...) + last_frame_img: int = Field(...) + model: Optional[str] = Field("v3.5") + motion_mode: Optional[PixverseMotionMode] = Field(PixverseMotionMode.normal) + prompt: str = Field(...) + # negative_prompt: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + # style: Optional[str] = Field(None) + # template_id: Optional[int] = Field(None) + # water_mark: Optional[bool] = Field(None) + + +class PixverseImageUploadResponse(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp: Optional[PixverseImgIdResponseObject] = Field(None, alias='Resp') + + +class PixverseImgIdResponseObject(BaseModel): + img_id: Optional[int] = None + + +class PixverseVideoResponse(BaseModel): + ErrCode: Optional[int] = Field(None) + ErrMsg: Optional[str] = Field(None) + Resp: Optional[PixverseVideoIdResponseObject] = Field(None) + + +class PixverseVideoIdResponseObject(BaseModel): + video_id: int = Field(..., description='Video_id') + + +class PixverseGenerationStatusResponse(BaseModel): + ErrCode: Optional[int] = Field(None) + ErrMsg: Optional[str] = Field(None) + Resp: Optional[PixverseGenerationStatusResponseObject] = Field(None) + + +class PixverseGenerationStatusResponseObject(BaseModel): + create_time: Optional[str] = Field(None) + id: Optional[int] = Field(None) + modify_time: Optional[str] = Field(None) + negative_prompt: Optional[str] = Field(None) + outputHeight: Optional[int] = Field(None) + outputWidth: Optional[int] = Field(None) + prompt: Optional[str] = Field(None) + resolution_ratio: Optional[int] = Field(None) + seed: Optional[int] = Field(None) + size: Optional[int] = Field(None) + status: Optional[int] = Field(None) + style: Optional[str] = Field(None) + url: Optional[str] = Field(None) diff --git a/comfy_api_nodes/apis/recraft_api.py b/comfy_api_nodes/apis/recraft_api.py new file mode 100644 index 000000000..c0ec9d0c8 --- /dev/null +++ b/comfy_api_nodes/apis/recraft_api.py @@ -0,0 +1,263 @@ +from __future__ import annotations + + + +from enum import Enum +from typing import Optional + +from pydantic import BaseModel, Field, conint, confloat + + +class RecraftColor: + def __init__(self, r: int, g: int, b: int): + self.color = [r, g, b] + + def create_api_model(self): + return RecraftColorObject(rgb=self.color) + + +class RecraftColorChain: + def __init__(self): + self.colors: list[RecraftColor] = [] + + def get_first(self): + if len(self.colors) > 0: + return self.colors[0] + return None + + def add(self, color: RecraftColor): + self.colors.append(color) + + def create_api_model(self): + if not self.colors: + return None + colors_api = [x.create_api_model() for x in self.colors] + return colors_api + + def clone(self): + c = RecraftColorChain() + for color in self.colors: + c.add(color) + return c + + def clone_and_merge(self, other: RecraftColorChain): + c = self.clone() + for color in other.colors: + c.add(color) + return c + + +class RecraftControls: + def __init__(self, colors: RecraftColorChain=None, background_color: RecraftColorChain=None, + artistic_level: int=None, no_text: bool=None): + self.colors = colors + self.background_color = background_color + self.artistic_level = artistic_level + self.no_text = no_text + + def create_api_model(self): + if self.colors is None and self.background_color is None and self.artistic_level is None and self.no_text is None: + return None + colors_api = None + background_color_api = None + if self.colors: + colors_api = self.colors.create_api_model() + if self.background_color: + first_background = self.background_color.get_first() + background_color_api = first_background.create_api_model() if first_background else None + + return RecraftControlsObject(colors=colors_api, background_color=background_color_api, + artistic_level=self.artistic_level, no_text=self.no_text) + + +class RecraftStyle: + def __init__(self, style: str=None, substyle: str=None, style_id: str=None): + self.style = style + if substyle == "None": + substyle = None + self.substyle = substyle + self.style_id = style_id + + +class RecraftIO: + STYLEV3 = "RECRAFT_V3_STYLE" + SVG = "SVG" # TODO: if acceptable, move into ComfyUI's typing class + COLOR = "RECRAFT_COLOR" + CONTROLS = "RECRAFT_CONTROLS" + + +class RecraftStyleV3(str, Enum): + #any = 'any' NOTE: this does not work for some reason... why? + realistic_image = 'realistic_image' + digital_illustration = 'digital_illustration' + vector_illustration = 'vector_illustration' + logo_raster = 'logo_raster' + + +def get_v3_substyles(style_v3: str, include_none=True) -> list[str]: + substyles: list[str] = [] + if include_none: + substyles.append("None") + return substyles + dict_recraft_substyles_v3.get(style_v3, []) + + +dict_recraft_substyles_v3 = { + RecraftStyleV3.realistic_image: [ + "b_and_w", + "enterprise", + "evening_light", + "faded_nostalgia", + "forest_life", + "hard_flash", + "hdr", + "motion_blur", + "mystic_naturalism", + "natural_light", + "natural_tones", + "organic_calm", + "real_life_glow", + "retro_realism", + "retro_snapshot", + "studio_portrait", + "urban_drama", + "village_realism", + "warm_folk" + ], + RecraftStyleV3.digital_illustration: [ + "2d_art_poster", + "2d_art_poster_2", + "antiquarian", + "bold_fantasy", + "child_book", + "child_books", + "cover", + "crosshatch", + "digital_engraving", + "engraving_color", + "expressionism", + "freehand_details", + "grain", + "grain_20", + "graphic_intensity", + "hand_drawn", + "hand_drawn_outline", + "handmade_3d", + "hard_comics", + "infantile_sketch", + "long_shadow", + "modern_folk", + "multicolor", + "neon_calm", + "noir", + "nostalgic_pastel", + "outline_details", + "pastel_gradient", + "pastel_sketch", + "pixel_art", + "plastic", + "pop_art", + "pop_renaissance", + "seamless", + "street_art", + "tablet_sketch", + "urban_glow", + "urban_sketching", + "vanilla_dreams", + "young_adult_book", + "young_adult_book_2" + ], + RecraftStyleV3.vector_illustration: [ + "bold_stroke", + "chemistry", + "colored_stencil", + "contour_pop_art", + "cosmics", + "cutout", + "depressive", + "editorial", + "emotional_flat", + "engraving", + "infographical", + "line_art", + "line_circuit", + "linocut", + "marker_outline", + "mosaic", + "naivector", + "roundish_flat", + "seamless", + "segmented_colors", + "sharp_contrast", + "thin", + "vector_photo", + "vivid_shapes" + ], + RecraftStyleV3.logo_raster: [ + "emblem_graffiti", + "emblem_pop_art", + "emblem_punk", + "emblem_stamp", + "emblem_vintage" + ], +} + + +class RecraftModel(str, Enum): + recraftv3 = 'recraftv3' + recraftv2 = 'recraftv2' + + +class RecraftImageSize(str, Enum): + res_1024x1024 = '1024x1024' + res_1365x1024 = '1365x1024' + res_1024x1365 = '1024x1365' + res_1536x1024 = '1536x1024' + res_1024x1536 = '1024x1536' + res_1820x1024 = '1820x1024' + res_1024x1820 = '1024x1820' + res_1024x2048 = '1024x2048' + res_2048x1024 = '2048x1024' + res_1434x1024 = '1434x1024' + res_1024x1434 = '1024x1434' + res_1024x1280 = '1024x1280' + res_1280x1024 = '1280x1024' + res_1024x1707 = '1024x1707' + res_1707x1024 = '1707x1024' + + +class RecraftColorObject(BaseModel): + rgb: list[int] = Field(..., description='An array of 3 integer values in range of 0...255 defining RGB Color Model') + + +class RecraftControlsObject(BaseModel): + colors: Optional[list[RecraftColorObject]] = Field(None, description='An array of preferable colors') + background_color: Optional[RecraftColorObject] = Field(None, description='Use given color as a desired background color') + no_text: Optional[bool] = Field(None, description='Do not embed text layouts') + artistic_level: Optional[conint(ge=0, le=5)] = Field(None, description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity. The value should be in range [0..5].') + + +class RecraftImageGenerationRequest(BaseModel): + prompt: str = Field(..., description='The text prompt describing the image to generate') + size: Optional[RecraftImageSize] = Field(None, description='The size of the generated image (e.g., "1024x1024")') + n: conint(ge=1, le=6) = Field(..., description='The number of images to generate') + negative_prompt: Optional[str] = Field(None, description='A text description of undesired elements on an image') + model: Optional[RecraftModel] = Field(RecraftModel.recraftv3, description='The model to use for generation (e.g., "recraftv3")') + style: Optional[str] = Field(None, description='The style to apply to the generated image (e.g., "digital_illustration")') + substyle: Optional[str] = Field(None, description='The substyle to apply to the generated image, depending on the style input') + controls: Optional[RecraftControlsObject] = Field(None, description='A set of custom parameters to tweak generation process') + style_id: Optional[str] = Field(None, description='Use a previously uploaded style as a reference; UUID') + strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None, description='Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity') + random_seed: Optional[int] = Field(None, description="Seed for video generation") + # text_layout + + +class RecraftReturnedObject(BaseModel): + image_id: str = Field(..., description='Unique identifier for the generated image') + url: str = Field(..., description='URL to access the generated image') + + +class RecraftImageGenerationResponse(BaseModel): + created: int = Field(..., description='Unix timestamp when the generation was created') + credits: int = Field(..., description='Number of credits used for the generation') + data: Optional[list[RecraftReturnedObject]] = Field(None, description='Array of generated image information') + image: Optional[RecraftReturnedObject] = Field(None, description='Single generated image') diff --git a/comfy_api_nodes/apis/stability_api.py b/comfy_api_nodes/apis/stability_api.py new file mode 100644 index 000000000..47c87daec --- /dev/null +++ b/comfy_api_nodes/apis/stability_api.py @@ -0,0 +1,127 @@ +from __future__ import annotations + +from enum import Enum +from typing import Optional + +from pydantic import BaseModel, Field, confloat + + +class StabilityFormat(str, Enum): + png = 'png' + jpeg = 'jpeg' + webp = 'webp' + + +class StabilityAspectRatio(str, Enum): + ratio_1_1 = "1:1" + ratio_16_9 = "16:9" + ratio_9_16 = "9:16" + ratio_3_2 = "3:2" + ratio_2_3 = "2:3" + ratio_5_4 = "5:4" + ratio_4_5 = "4:5" + ratio_21_9 = "21:9" + ratio_9_21 = "9:21" + + +def get_stability_style_presets(include_none=True): + presets = [] + if include_none: + presets.append("None") + return presets + [x.value for x in StabilityStylePreset] + + +class StabilityStylePreset(str, Enum): + _3d_model = "3d-model" + analog_film = "analog-film" + anime = "anime" + cinematic = "cinematic" + comic_book = "comic-book" + digital_art = "digital-art" + enhance = "enhance" + fantasy_art = "fantasy-art" + isometric = "isometric" + line_art = "line-art" + low_poly = "low-poly" + modeling_compound = "modeling-compound" + neon_punk = "neon-punk" + origami = "origami" + photographic = "photographic" + pixel_art = "pixel-art" + tile_texture = "tile-texture" + + +class Stability_SD3_5_Model(str, Enum): + sd3_5_large = "sd3.5-large" + # sd3_5_large_turbo = "sd3.5-large-turbo" + sd3_5_medium = "sd3.5-medium" + + +class Stability_SD3_5_GenerationMode(str, Enum): + text_to_image = "text-to-image" + image_to_image = "image-to-image" + + +class StabilityStable3_5Request(BaseModel): + model: str = Field(...) + mode: str = Field(...) + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + aspect_ratio: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + output_format: Optional[str] = Field(StabilityFormat.png.value) + image: Optional[str] = Field(None) + style_preset: Optional[str] = Field(None) + cfg_scale: float = Field(...) + strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None) + + +class StabilityUpscaleConservativeRequest(BaseModel): + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + output_format: Optional[str] = Field(StabilityFormat.png.value) + image: Optional[str] = Field(None) + creativity: Optional[confloat(ge=0.2, le=0.5)] = Field(None) + + +class StabilityUpscaleCreativeRequest(BaseModel): + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + output_format: Optional[str] = Field(StabilityFormat.png.value) + image: Optional[str] = Field(None) + creativity: Optional[confloat(ge=0.1, le=0.5)] = Field(None) + style_preset: Optional[str] = Field(None) + + +class StabilityStableUltraRequest(BaseModel): + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + aspect_ratio: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + output_format: Optional[str] = Field(StabilityFormat.png.value) + image: Optional[str] = Field(None) + style_preset: Optional[str] = Field(None) + strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None) + + +class StabilityStableUltraResponse(BaseModel): + image: Optional[str] = Field(None) + finish_reason: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + + +class StabilityResultsGetResponse(BaseModel): + image: Optional[str] = Field(None) + finish_reason: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + id: Optional[str] = Field(None) + name: Optional[str] = Field(None) + errors: Optional[list[str]] = Field(None) + status: Optional[str] = Field(None) + result: Optional[str] = Field(None) + + +class StabilityAsyncResponse(BaseModel): + id: Optional[str] = Field(None) diff --git a/comfy_api_nodes/mapper_utils.py b/comfy_api_nodes/mapper_utils.py new file mode 100644 index 000000000..6fab8f4bb --- /dev/null +++ b/comfy_api_nodes/mapper_utils.py @@ -0,0 +1,116 @@ +from enum import Enum + +from pydantic.fields import FieldInfo +from pydantic import BaseModel +from pydantic_core import PydanticUndefined + +from comfy.comfy_types.node_typing import IO, InputTypeOptions + +NodeInput = tuple[IO, InputTypeOptions] + + +def _create_base_config(field_info: FieldInfo) -> InputTypeOptions: + config = {} + if hasattr(field_info, "default") and field_info.default is not PydanticUndefined: + config["default"] = field_info.default + if hasattr(field_info, "description") and field_info.description is not None: + config["tooltip"] = field_info.description + return config + + +def _get_number_constraints_config(field_info: FieldInfo) -> dict: + config = {} + if hasattr(field_info, "metadata"): + metadata = field_info.metadata + for constraint in metadata: + if hasattr(constraint, "ge"): + config["min"] = constraint.ge + if hasattr(constraint, "le"): + config["max"] = constraint.le + if hasattr(constraint, "multiple_of"): + config["step"] = constraint.multiple_of + return config + + +def _model_field_to_image_input(field_info: FieldInfo, **kwargs) -> NodeInput: + return IO.IMAGE, { + **_create_base_config(field_info), + **kwargs, + } + + +def _model_field_to_string_input(field_info: FieldInfo, **kwargs) -> NodeInput: + return IO.STRING, { + **_create_base_config(field_info), + **kwargs, + } + + +def _model_field_to_float_input(field_info: FieldInfo, **kwargs) -> NodeInput: + return IO.FLOAT, { + **_create_base_config(field_info), + **_get_number_constraints_config(field_info), + **kwargs, + } + + +def _model_field_to_int_input(field_info: FieldInfo, **kwargs) -> NodeInput: + return IO.INT, { + **_create_base_config(field_info), + **_get_number_constraints_config(field_info), + **kwargs, + } + + +def _model_field_to_combo_input( + field_info: FieldInfo, enum_type: type[Enum] = None, **kwargs +) -> NodeInput: + combo_config = {} + if enum_type is not None: + combo_config["options"] = [option.value for option in enum_type] + combo_config = { + **combo_config, + **_create_base_config(field_info), + **kwargs, + } + return IO.COMBO, combo_config + + +def model_field_to_node_input( + input_type: IO, base_model: type[BaseModel], field_name: str, **kwargs +) -> NodeInput: + """ + Maps a field from a Pydantic model to a Comfy node input. + + Args: + input_type: The type of the input. + base_model: The Pydantic model to map the field from. + field_name: The name of the field to map. + **kwargs: Additional key/values to include in the input options. + + Note: + For combo inputs, pass an `Enum` to the `enum_type` keyword argument to populate the options automatically. + + Example: + >>> model_field_to_node_input(IO.STRING, MyModel, "my_field", multiline=True) + >>> model_field_to_node_input(IO.COMBO, MyModel, "my_field", enum_type=MyEnum) + >>> model_field_to_node_input(IO.FLOAT, MyModel, "my_field", slider=True) + """ + field_info: FieldInfo = base_model.model_fields[field_name] + result: NodeInput + + if input_type == IO.IMAGE: + result = _model_field_to_image_input(field_info, **kwargs) + elif input_type == IO.STRING: + result = _model_field_to_string_input(field_info, **kwargs) + elif input_type == IO.FLOAT: + result = _model_field_to_float_input(field_info, **kwargs) + elif input_type == IO.INT: + result = _model_field_to_int_input(field_info, **kwargs) + elif input_type == IO.COMBO: + result = _model_field_to_combo_input(field_info, **kwargs) + else: + message = f"Invalid input type: {input_type}" + raise ValueError(message) + + return result diff --git a/comfy_api_nodes/nodes_api.py b/comfy_api_nodes/nodes_api.py deleted file mode 100644 index a977bb9b7..000000000 --- a/comfy_api_nodes/nodes_api.py +++ /dev/null @@ -1,449 +0,0 @@ -import base64 -import io -import math -from inspect import cleandoc - -import numpy as np -import requests -import torch -from PIL import Image - -from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict -from comfy.utils import common_upscale -from comfy_api_nodes.apis import ( - OpenAIImageEditRequest, - OpenAIImageGenerationRequest, - OpenAIImageGenerationResponse, -) -from comfy_api_nodes.apis.client import ApiEndpoint, HttpMethod, SynchronousOperation - - -def downscale_input(image): - samples = image.movedim(-1,1) - #downscaling input images to roughly the same size as the outputs - total = int(1536 * 1024) - scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) - if scale_by >= 1: - return image - width = round(samples.shape[3] * scale_by) - height = round(samples.shape[2] * scale_by) - - s = common_upscale(samples, width, height, "lanczos", "disabled") - s = s.movedim(1,-1) - return s - -def validate_and_cast_response(response): - # validate raw JSON response - data = response.data - if not data or len(data) == 0: - raise Exception("No images returned from API endpoint") - - # Initialize list to store image tensors - image_tensors = [] - - # Process each image in the data array - for image_data in data: - image_url = image_data.url - b64_data = image_data.b64_json - - if not image_url and not b64_data: - raise Exception("No image was generated in the response") - - if b64_data: - img_data = base64.b64decode(b64_data) - img = Image.open(io.BytesIO(img_data)) - - elif image_url: - img_response = requests.get(image_url) - if img_response.status_code != 200: - raise Exception("Failed to download the image") - img = Image.open(io.BytesIO(img_response.content)) - - img = img.convert("RGBA") - - # Convert to numpy array, normalize to float32 between 0 and 1 - img_array = np.array(img).astype(np.float32) / 255.0 - img_tensor = torch.from_numpy(img_array) - - # Add to list of tensors - image_tensors.append(img_tensor) - - return torch.stack(image_tensors, dim=0) - -class OpenAIDalle2(ComfyNodeABC): - """ - Generates images synchronously via OpenAI's DALL·E 2 endpoint. - - Uses the proxy at /proxy/openai/images/generations. Returned URLs are short‑lived, - so download or cache results if you need to keep them. - """ - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "prompt": (IO.STRING, { - "multiline": True, - "default": "", - "tooltip": "Text prompt for DALL·E", - }), - }, - "optional": { - "seed": (IO.INT, { - "default": 0, - "min": 0, - "max": 2**31-1, - "step": 1, - "display": "number", - "tooltip": "not implemented yet in backend", - }), - "size": (IO.COMBO, { - "options": ["256x256", "512x512", "1024x1024"], - "default": "1024x1024", - "tooltip": "Image size", - }), - "n": (IO.INT, { - "default": 1, - "min": 1, - "max": 8, - "step": 1, - "display": "number", - "tooltip": "How many images to generate", - }), - "image": (IO.IMAGE, { - "default": None, - "tooltip": "Optional reference image for image editing.", - }), - "mask": (IO.MASK, { - "default": None, - "tooltip": "Optional mask for inpainting (white areas will be replaced)", - }), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG" - } - } - - RETURN_TYPES = (IO.IMAGE,) - FUNCTION = "api_call" - CATEGORY = "api node" - DESCRIPTION = cleandoc(__doc__ or "") - API_NODE = True - - def api_call(self, prompt, seed=0, image=None, mask=None, n=1, size="1024x1024", auth_token=None): - model = "dall-e-2" - path = "/proxy/openai/images/generations" - request_class = OpenAIImageGenerationRequest - img_binary = None - - if image is not None and mask is not None: - path = "/proxy/openai/images/edits" - request_class = OpenAIImageEditRequest - - input_tensor = image.squeeze().cpu() - height, width, channels = input_tensor.shape - rgba_tensor = torch.ones(height, width, 4, device="cpu") - rgba_tensor[:, :, :channels] = input_tensor - - if mask.shape[1:] != image.shape[1:-1]: - raise Exception("Mask and Image must be the same size") - rgba_tensor[:,:,3] = (1-mask.squeeze().cpu()) - - rgba_tensor = downscale_input(rgba_tensor.unsqueeze(0)).squeeze() - - image_np = (rgba_tensor.numpy() * 255).astype(np.uint8) - img = Image.fromarray(image_np) - img_byte_arr = io.BytesIO() - img.save(img_byte_arr, format='PNG') - img_byte_arr.seek(0) - img_binary = img_byte_arr#.getvalue() - img_binary.name = "image.png" - elif image is not None or mask is not None: - raise Exception("Dall-E 2 image editing requires an image AND a mask") - - # Build the operation - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=path, - method=HttpMethod.POST, - request_model=request_class, - response_model=OpenAIImageGenerationResponse - ), - request=request_class( - model=model, - prompt=prompt, - n=n, - size=size, - seed=seed, - ), - files={ - "image": img_binary, - } if img_binary else None, - auth_token=auth_token - ) - - response = operation.execute() - - img_tensor = validate_and_cast_response(response) - return (img_tensor,) - -class OpenAIDalle3(ComfyNodeABC): - """ - Generates images synchronously via OpenAI's DALL·E 3 endpoint. - - Uses the proxy at /proxy/openai/images/generations. Returned URLs are short‑lived, - so download or cache results if you need to keep them. - """ - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "prompt": (IO.STRING, { - "multiline": True, - "default": "", - "tooltip": "Text prompt for DALL·E", - }), - }, - "optional": { - "seed": (IO.INT, { - "default": 0, - "min": 0, - "max": 2**31-1, - "step": 1, - "display": "number", - "tooltip": "not implemented yet in backend", - }), - "quality" : (IO.COMBO, { - "options": ["standard","hd"], - "default": "standard", - "tooltip": "Image quality", - }), - "style": (IO.COMBO, { - "options": ["natural","vivid"], - "default": "natural", - "tooltip": "Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images.", - }), - "size": (IO.COMBO, { - "options": ["1024x1024", "1024x1792", "1792x1024"], - "default": "1024x1024", - "tooltip": "Image size", - }), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG" - } - } - - RETURN_TYPES = (IO.IMAGE,) - FUNCTION = "api_call" - CATEGORY = "api node" - DESCRIPTION = cleandoc(__doc__ or "") - API_NODE = True - - def api_call(self, prompt, seed=0, style="natural", quality="standard", size="1024x1024", auth_token=None): - model = "dall-e-3" - - # build the operation - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/openai/images/generations", - method=HttpMethod.POST, - request_model=OpenAIImageGenerationRequest, - response_model=OpenAIImageGenerationResponse - ), - request=OpenAIImageGenerationRequest( - model=model, - prompt=prompt, - quality=quality, - size=size, - style=style, - seed=seed, - ), - auth_token=auth_token - ) - - response = operation.execute() - - img_tensor = validate_and_cast_response(response) - return (img_tensor,) - -class OpenAIGPTImage1(ComfyNodeABC): - """ - Generates images synchronously via OpenAI's GPT Image 1 endpoint. - - Uses the proxy at /proxy/openai/images/generations. Returned URLs are short‑lived, - so download or cache results if you need to keep them. - """ - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "prompt": (IO.STRING, { - "multiline": True, - "default": "", - "tooltip": "Text prompt for GPT Image 1", - }), - }, - "optional": { - "seed": (IO.INT, { - "default": 0, - "min": 0, - "max": 2**31-1, - "step": 1, - "display": "number", - "tooltip": "not implemented yet in backend", - }), - "quality": (IO.COMBO, { - "options": ["low","medium","high"], - "default": "low", - "tooltip": "Image quality, affects cost and generation time.", - }), - "background": (IO.COMBO, { - "options": ["opaque","transparent"], - "default": "opaque", - "tooltip": "Return image with or without background", - }), - "size": (IO.COMBO, { - "options": ["auto", "1024x1024", "1024x1536", "1536x1024"], - "default": "auto", - "tooltip": "Image size", - }), - "n": (IO.INT, { - "default": 1, - "min": 1, - "max": 8, - "step": 1, - "display": "number", - "tooltip": "How many images to generate", - }), - "image": (IO.IMAGE, { - "default": None, - "tooltip": "Optional reference image for image editing.", - }), - "mask": (IO.MASK, { - "default": None, - "tooltip": "Optional mask for inpainting (white areas will be replaced)", - }), - "moderation": (IO.COMBO, { - "options": ["low","auto"], - "default": "low", - "tooltip": "Moderation level", - }), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG" - } - } - - RETURN_TYPES = (IO.IMAGE,) - FUNCTION = "api_call" - CATEGORY = "api node" - DESCRIPTION = cleandoc(__doc__ or "") - API_NODE = True - - def api_call(self, prompt, seed=0, quality="low", background="opaque", image=None, mask=None, n=1, size="1024x1024", auth_token=None, moderation="low"): - model = "gpt-image-1" - path = "/proxy/openai/images/generations" - request_class = OpenAIImageGenerationRequest - img_binaries = [] - mask_binary = None - files = [] - - if image is not None: - path = "/proxy/openai/images/edits" - request_class = OpenAIImageEditRequest - - batch_size = image.shape[0] - - - for i in range(batch_size): - single_image = image[i:i+1] - scaled_image = downscale_input(single_image).squeeze() - - image_np = (scaled_image.numpy() * 255).astype(np.uint8) - img = Image.fromarray(image_np) - img_byte_arr = io.BytesIO() - img.save(img_byte_arr, format='PNG') - img_byte_arr.seek(0) - img_binary = img_byte_arr - img_binary.name = f"image_{i}.png" - - img_binaries.append(img_binary) - if batch_size == 1: - files.append(("image", img_binary)) - else: - files.append(("image[]", img_binary)) - - if mask is not None: - if image.shape[0] != 1: - raise Exception("Cannot use a mask with multiple image") - if image is None: - raise Exception("Cannot use a mask without an input image") - if mask.shape[1:] != image.shape[1:-1]: - raise Exception("Mask and Image must be the same size") - batch, height, width = mask.shape - rgba_mask = torch.zeros(height, width, 4, device="cpu") - rgba_mask[:,:,3] = (1-mask.squeeze().cpu()) - - scaled_mask = downscale_input(rgba_mask.unsqueeze(0)).squeeze() - - mask_np = (scaled_mask.numpy() * 255).astype(np.uint8) - mask_img = Image.fromarray(mask_np) - mask_img_byte_arr = io.BytesIO() - mask_img.save(mask_img_byte_arr, format='PNG') - mask_img_byte_arr.seek(0) - mask_binary = mask_img_byte_arr - mask_binary.name = "mask.png" - files.append(("mask", mask_binary)) - - - # Build the operation - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=path, - method=HttpMethod.POST, - request_model=request_class, - response_model=OpenAIImageGenerationResponse - ), - request=request_class( - model=model, - prompt=prompt, - quality=quality, - background=background, - n=n, - seed=seed, - size=size, - moderation=moderation, - ), - files=files if files else None, - auth_token=auth_token - ) - - response = operation.execute() - - img_tensor = validate_and_cast_response(response) - return (img_tensor,) - - -# A dictionary that contains all nodes you want to export with their names -# NOTE: names should be globally unique -NODE_CLASS_MAPPINGS = { - "OpenAIDalle2": OpenAIDalle2, - "OpenAIDalle3": OpenAIDalle3, - "OpenAIGPTImage1": OpenAIGPTImage1, -} - -# A dictionary that contains the friendly/humanly readable titles for the nodes -NODE_DISPLAY_NAME_MAPPINGS = { - "OpenAIDalle2": "OpenAI DALL·E 2", - "OpenAIDalle3": "OpenAI DALL·E 3", - "OpenAIGPTImage1": "OpenAI GPT Image 1", -} diff --git a/comfy_api_nodes/nodes_bfl.py b/comfy_api_nodes/nodes_bfl.py new file mode 100644 index 000000000..122a6ddf8 --- /dev/null +++ b/comfy_api_nodes/nodes_bfl.py @@ -0,0 +1,906 @@ +import io +from inspect import cleandoc +from comfy.comfy_types.node_typing import IO, ComfyNodeABC +from comfy_api_nodes.apis.bfl_api import ( + BFLStatus, + BFLFluxExpandImageRequest, + BFLFluxFillImageRequest, + BFLFluxCannyImageRequest, + BFLFluxDepthImageRequest, + BFLFluxProGenerateRequest, + BFLFluxProUltraGenerateRequest, + BFLFluxProGenerateResponse, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, +) +from comfy_api_nodes.apinode_utils import ( + downscale_image_tensor, + validate_aspect_ratio, + process_image_response, + resize_mask_to_image, + validate_string, +) + +import numpy as np +from PIL import Image +import requests +import torch +import base64 +import time + + +def convert_mask_to_image(mask: torch.Tensor): + """ + Make mask have the expected amount of dims (4) and channels (3) to be recognized as an image. + """ + mask = mask.unsqueeze(-1) + mask = torch.cat([mask]*3, dim=-1) + return mask + + +def handle_bfl_synchronous_operation( + operation: SynchronousOperation, timeout_bfl_calls=360 +): + response_api: BFLFluxProGenerateResponse = operation.execute() + return _poll_until_generated( + response_api.polling_url, timeout=timeout_bfl_calls + ) + +def _poll_until_generated(polling_url: str, timeout=360): + # used bfl-comfy-nodes to verify code implementation: + # https://github.com/black-forest-labs/bfl-comfy-nodes/tree/main + start_time = time.time() + retries_404 = 0 + max_retries_404 = 5 + retry_404_seconds = 2 + retry_202_seconds = 2 + retry_pending_seconds = 1 + request = requests.Request(method=HttpMethod.GET, url=polling_url) + # NOTE: should True loop be replaced with checking if workflow has been interrupted? + while True: + response = requests.Session().send(request.prepare()) + if response.status_code == 200: + result = response.json() + if result["status"] == BFLStatus.ready: + img_url = result["result"]["sample"] + img_response = requests.get(img_url) + return process_image_response(img_response) + elif result["status"] in [ + BFLStatus.request_moderated, + BFLStatus.content_moderated, + ]: + status = result["status"] + raise Exception( + f"BFL API did not return an image due to: {status}." + ) + elif result["status"] == BFLStatus.error: + raise Exception(f"BFL API encountered an error: {result}.") + elif result["status"] == BFLStatus.pending: + time.sleep(retry_pending_seconds) + continue + elif response.status_code == 404: + if retries_404 < max_retries_404: + retries_404 += 1 + time.sleep(retry_404_seconds) + continue + raise Exception( + f"BFL API could not find task after {max_retries_404} tries." + ) + elif response.status_code == 202: + time.sleep(retry_202_seconds) + elif time.time() - start_time > timeout: + raise Exception( + f"BFL API experienced a timeout; could not return request under {timeout} seconds." + ) + else: + raise Exception(f"BFL API encountered an error: {response.json()}") + +def convert_image_to_base64(image: torch.Tensor): + scaled_image = downscale_image_tensor(image, total_pixels=2048 * 2048) + # remove batch dimension if present + if len(scaled_image.shape) > 3: + scaled_image = scaled_image[0] + image_np = (scaled_image.numpy() * 255).astype(np.uint8) + img = Image.fromarray(image_np) + img_byte_arr = io.BytesIO() + img.save(img_byte_arr, format="PNG") + return base64.b64encode(img_byte_arr.getvalue()).decode() + + +class FluxProUltraImageNode(ComfyNodeABC): + """ + Generates images using Flux Pro 1.1 Ultra via api based on prompt and resolution. + """ + + MINIMUM_RATIO = 1 / 4 + MAXIMUM_RATIO = 4 / 1 + MINIMUM_RATIO_STR = "1:4" + MAXIMUM_RATIO_STR = "4:1" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "prompt_upsampling": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + "aspect_ratio": ( + IO.STRING, + { + "default": "16:9", + "tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.", + }, + ), + "raw": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "When True, generate less processed, more natural-looking images.", + }, + ), + }, + "optional": { + "image_prompt": (IO.IMAGE,), + "image_prompt_strength": ( + IO.FLOAT, + { + "default": 0.1, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Blend between the prompt and the image prompt.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + @classmethod + def VALIDATE_INPUTS(cls, aspect_ratio: str): + try: + validate_aspect_ratio( + aspect_ratio, + minimum_ratio=cls.MINIMUM_RATIO, + maximum_ratio=cls.MAXIMUM_RATIO, + minimum_ratio_str=cls.MINIMUM_RATIO_STR, + maximum_ratio_str=cls.MAXIMUM_RATIO_STR, + ) + except Exception as e: + return str(e) + return True + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/BFL" + + def api_call( + self, + prompt: str, + aspect_ratio: str, + prompt_upsampling=False, + raw=False, + seed=0, + image_prompt=None, + image_prompt_strength=0.1, + auth_token=None, + **kwargs, + ): + if image_prompt is None: + validate_string(prompt, strip_whitespace=False) + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/bfl/flux-pro-1.1-ultra/generate", + method=HttpMethod.POST, + request_model=BFLFluxProUltraGenerateRequest, + response_model=BFLFluxProGenerateResponse, + ), + request=BFLFluxProUltraGenerateRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + seed=seed, + aspect_ratio=validate_aspect_ratio( + aspect_ratio, + minimum_ratio=self.MINIMUM_RATIO, + maximum_ratio=self.MAXIMUM_RATIO, + minimum_ratio_str=self.MINIMUM_RATIO_STR, + maximum_ratio_str=self.MAXIMUM_RATIO_STR, + ), + raw=raw, + image_prompt=( + image_prompt + if image_prompt is None + else convert_image_to_base64(image_prompt) + ), + image_prompt_strength=( + None if image_prompt is None else round(image_prompt_strength, 2) + ), + ), + auth_token=auth_token, + ) + output_image = handle_bfl_synchronous_operation(operation) + return (output_image,) + + + +class FluxProImageNode(ComfyNodeABC): + """ + Generates images synchronously based on prompt and resolution. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "prompt_upsampling": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + }, + ), + "width": ( + IO.INT, + { + "default": 1024, + "min": 256, + "max": 1440, + "step": 32, + }, + ), + "height": ( + IO.INT, + { + "default": 768, + "min": 256, + "max": 1440, + "step": 32, + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": { + "image_prompt": (IO.IMAGE,), + # "image_prompt_strength": ( + # IO.FLOAT, + # { + # "default": 0.1, + # "min": 0.0, + # "max": 1.0, + # "step": 0.01, + # "tooltip": "Blend between the prompt and the image prompt.", + # }, + # ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/BFL" + + def api_call( + self, + prompt: str, + prompt_upsampling, + width: int, + height: int, + seed=0, + image_prompt=None, + # image_prompt_strength=0.1, + auth_token=None, + **kwargs, + ): + image_prompt = ( + image_prompt + if image_prompt is None + else convert_image_to_base64(image_prompt) + ) + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/bfl/flux-pro-1.1/generate", + method=HttpMethod.POST, + request_model=BFLFluxProGenerateRequest, + response_model=BFLFluxProGenerateResponse, + ), + request=BFLFluxProGenerateRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + width=width, + height=height, + seed=seed, + image_prompt=image_prompt, + ), + auth_token=auth_token, + ) + output_image = handle_bfl_synchronous_operation(operation) + return (output_image,) + + +class FluxProExpandNode(ComfyNodeABC): + """ + Outpaints image based on prompt. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "prompt_upsampling": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + }, + ), + "top": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2048, + "tooltip": "Number of pixels to expand at the top of the image" + }, + ), + "bottom": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2048, + "tooltip": "Number of pixels to expand at the bottom of the image" + }, + ), + "left": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2048, + "tooltip": "Number of pixels to expand at the left side of the image" + }, + ), + "right": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2048, + "tooltip": "Number of pixels to expand at the right side of the image" + }, + ), + "guidance": ( + IO.FLOAT, + { + "default": 60, + "min": 1.5, + "max": 100, + "tooltip": "Guidance strength for the image generation process" + }, + ), + "steps": ( + IO.INT, + { + "default": 50, + "min": 15, + "max": 50, + "tooltip": "Number of steps for the image generation process" + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": { + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/BFL" + + def api_call( + self, + image: torch.Tensor, + prompt: str, + prompt_upsampling: bool, + top: int, + bottom: int, + left: int, + right: int, + steps: int, + guidance: float, + seed=0, + auth_token=None, + **kwargs, + ): + image = convert_image_to_base64(image) + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/bfl/flux-pro-1.0-expand/generate", + method=HttpMethod.POST, + request_model=BFLFluxExpandImageRequest, + response_model=BFLFluxProGenerateResponse, + ), + request=BFLFluxExpandImageRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + top=top, + bottom=bottom, + left=left, + right=right, + steps=steps, + guidance=guidance, + seed=seed, + image=image, + ), + auth_token=auth_token, + ) + output_image = handle_bfl_synchronous_operation(operation) + return (output_image,) + + + +class FluxProFillNode(ComfyNodeABC): + """ + Inpaints image based on mask and prompt. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + "mask": (IO.MASK,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "prompt_upsampling": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + }, + ), + "guidance": ( + IO.FLOAT, + { + "default": 60, + "min": 1.5, + "max": 100, + "tooltip": "Guidance strength for the image generation process" + }, + ), + "steps": ( + IO.INT, + { + "default": 50, + "min": 15, + "max": 50, + "tooltip": "Number of steps for the image generation process" + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": { + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/BFL" + + def api_call( + self, + image: torch.Tensor, + mask: torch.Tensor, + prompt: str, + prompt_upsampling: bool, + steps: int, + guidance: float, + seed=0, + auth_token=None, + **kwargs, + ): + # prepare mask + mask = resize_mask_to_image(mask, image) + mask = convert_image_to_base64(convert_mask_to_image(mask)) + # make sure image will have alpha channel removed + image = convert_image_to_base64(image[:,:,:,:3]) + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/bfl/flux-pro-1.0-fill/generate", + method=HttpMethod.POST, + request_model=BFLFluxFillImageRequest, + response_model=BFLFluxProGenerateResponse, + ), + request=BFLFluxFillImageRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + steps=steps, + guidance=guidance, + seed=seed, + image=image, + mask=mask, + ), + auth_token=auth_token, + ) + output_image = handle_bfl_synchronous_operation(operation) + return (output_image,) + + +class FluxProCannyNode(ComfyNodeABC): + """ + Generate image using a control image (canny). + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "control_image": (IO.IMAGE,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "prompt_upsampling": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + }, + ), + "canny_low_threshold": ( + IO.FLOAT, + { + "default": 0.1, + "min": 0.01, + "max": 0.99, + "step": 0.01, + "tooltip": "Low threshold for Canny edge detection; ignored if skip_processing is True" + }, + ), + "canny_high_threshold": ( + IO.FLOAT, + { + "default": 0.4, + "min": 0.01, + "max": 0.99, + "step": 0.01, + "tooltip": "High threshold for Canny edge detection; ignored if skip_processing is True" + }, + ), + "skip_preprocessing": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to skip preprocessing; set to True if control_image already is canny-fied, False if it is a raw image.", + }, + ), + "guidance": ( + IO.FLOAT, + { + "default": 30, + "min": 1, + "max": 100, + "tooltip": "Guidance strength for the image generation process" + }, + ), + "steps": ( + IO.INT, + { + "default": 50, + "min": 15, + "max": 50, + "tooltip": "Number of steps for the image generation process" + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": { + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/BFL" + + def api_call( + self, + control_image: torch.Tensor, + prompt: str, + prompt_upsampling: bool, + canny_low_threshold: float, + canny_high_threshold: float, + skip_preprocessing: bool, + steps: int, + guidance: float, + seed=0, + auth_token=None, + **kwargs, + ): + control_image = convert_image_to_base64(control_image[:,:,:,:3]) + preprocessed_image = None + + # scale canny threshold between 0-500, to match BFL's API + def scale_value(value: float, min_val=0, max_val=500): + return min_val + value * (max_val - min_val) + canny_low_threshold = int(round(scale_value(canny_low_threshold))) + canny_high_threshold = int(round(scale_value(canny_high_threshold))) + + + if skip_preprocessing: + preprocessed_image = control_image + control_image = None + canny_low_threshold = None + canny_high_threshold = None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/bfl/flux-pro-1.0-canny/generate", + method=HttpMethod.POST, + request_model=BFLFluxCannyImageRequest, + response_model=BFLFluxProGenerateResponse, + ), + request=BFLFluxCannyImageRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + steps=steps, + guidance=guidance, + seed=seed, + control_image=control_image, + canny_low_threshold=canny_low_threshold, + canny_high_threshold=canny_high_threshold, + preprocessed_image=preprocessed_image, + ), + auth_token=auth_token, + ) + output_image = handle_bfl_synchronous_operation(operation) + return (output_image,) + + +class FluxProDepthNode(ComfyNodeABC): + """ + Generate image using a control image (depth). + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "control_image": (IO.IMAGE,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "prompt_upsampling": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + }, + ), + "skip_preprocessing": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to skip preprocessing; set to True if control_image already is depth-ified, False if it is a raw image.", + }, + ), + "guidance": ( + IO.FLOAT, + { + "default": 15, + "min": 1, + "max": 100, + "tooltip": "Guidance strength for the image generation process" + }, + ), + "steps": ( + IO.INT, + { + "default": 50, + "min": 15, + "max": 50, + "tooltip": "Number of steps for the image generation process" + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": { + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/BFL" + + def api_call( + self, + control_image: torch.Tensor, + prompt: str, + prompt_upsampling: bool, + skip_preprocessing: bool, + steps: int, + guidance: float, + seed=0, + auth_token=None, + **kwargs, + ): + control_image = convert_image_to_base64(control_image[:,:,:,:3]) + preprocessed_image = None + + if skip_preprocessing: + preprocessed_image = control_image + control_image = None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/bfl/flux-pro-1.0-depth/generate", + method=HttpMethod.POST, + request_model=BFLFluxDepthImageRequest, + response_model=BFLFluxProGenerateResponse, + ), + request=BFLFluxDepthImageRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + steps=steps, + guidance=guidance, + seed=seed, + control_image=control_image, + preprocessed_image=preprocessed_image, + ), + auth_token=auth_token, + ) + output_image = handle_bfl_synchronous_operation(operation) + return (output_image,) + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "FluxProUltraImageNode": FluxProUltraImageNode, + # "FluxProImageNode": FluxProImageNode, + "FluxProExpandNode": FluxProExpandNode, + "FluxProFillNode": FluxProFillNode, + "FluxProCannyNode": FluxProCannyNode, + "FluxProDepthNode": FluxProDepthNode, +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image", + # "FluxProImageNode": "Flux 1.1 [pro] Image", + "FluxProExpandNode": "Flux.1 Expand Image", + "FluxProFillNode": "Flux.1 Fill Image", + "FluxProCannyNode": "Flux.1 Canny Control Image", + "FluxProDepthNode": "Flux.1 Depth Control Image", +} diff --git a/comfy_api_nodes/nodes_ideogram.py b/comfy_api_nodes/nodes_ideogram.py new file mode 100644 index 000000000..45c021f4a --- /dev/null +++ b/comfy_api_nodes/nodes_ideogram.py @@ -0,0 +1,777 @@ +from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict +from inspect import cleandoc +from PIL import Image +import numpy as np +import io +import torch +from comfy_api_nodes.apis import ( + IdeogramGenerateRequest, + IdeogramGenerateResponse, + ImageRequest, + IdeogramV3Request, + IdeogramV3EditRequest, +) + +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, +) + +from comfy_api_nodes.apinode_utils import ( + download_url_to_bytesio, + bytesio_to_image_tensor, + resize_mask_to_image, +) + +V1_V1_RES_MAP = { + "Auto":"AUTO", + "512 x 1536":"RESOLUTION_512_1536", + "576 x 1408":"RESOLUTION_576_1408", + "576 x 1472":"RESOLUTION_576_1472", + "576 x 1536":"RESOLUTION_576_1536", + "640 x 1024":"RESOLUTION_640_1024", + "640 x 1344":"RESOLUTION_640_1344", + "640 x 1408":"RESOLUTION_640_1408", + "640 x 1472":"RESOLUTION_640_1472", + "640 x 1536":"RESOLUTION_640_1536", + "704 x 1152":"RESOLUTION_704_1152", + "704 x 1216":"RESOLUTION_704_1216", + "704 x 1280":"RESOLUTION_704_1280", + "704 x 1344":"RESOLUTION_704_1344", + "704 x 1408":"RESOLUTION_704_1408", + "704 x 1472":"RESOLUTION_704_1472", + "720 x 1280":"RESOLUTION_720_1280", + "736 x 1312":"RESOLUTION_736_1312", + "768 x 1024":"RESOLUTION_768_1024", + "768 x 1088":"RESOLUTION_768_1088", + "768 x 1152":"RESOLUTION_768_1152", + "768 x 1216":"RESOLUTION_768_1216", + "768 x 1232":"RESOLUTION_768_1232", + "768 x 1280":"RESOLUTION_768_1280", + "768 x 1344":"RESOLUTION_768_1344", + "832 x 960":"RESOLUTION_832_960", + "832 x 1024":"RESOLUTION_832_1024", + "832 x 1088":"RESOLUTION_832_1088", + "832 x 1152":"RESOLUTION_832_1152", + "832 x 1216":"RESOLUTION_832_1216", + "832 x 1248":"RESOLUTION_832_1248", + "864 x 1152":"RESOLUTION_864_1152", + "896 x 960":"RESOLUTION_896_960", + "896 x 1024":"RESOLUTION_896_1024", + "896 x 1088":"RESOLUTION_896_1088", + "896 x 1120":"RESOLUTION_896_1120", + "896 x 1152":"RESOLUTION_896_1152", + "960 x 832":"RESOLUTION_960_832", + "960 x 896":"RESOLUTION_960_896", + "960 x 1024":"RESOLUTION_960_1024", + "960 x 1088":"RESOLUTION_960_1088", + "1024 x 640":"RESOLUTION_1024_640", + "1024 x 768":"RESOLUTION_1024_768", + "1024 x 832":"RESOLUTION_1024_832", + "1024 x 896":"RESOLUTION_1024_896", + "1024 x 960":"RESOLUTION_1024_960", + "1024 x 1024":"RESOLUTION_1024_1024", + "1088 x 768":"RESOLUTION_1088_768", + "1088 x 832":"RESOLUTION_1088_832", + "1088 x 896":"RESOLUTION_1088_896", + "1088 x 960":"RESOLUTION_1088_960", + "1120 x 896":"RESOLUTION_1120_896", + "1152 x 704":"RESOLUTION_1152_704", + "1152 x 768":"RESOLUTION_1152_768", + "1152 x 832":"RESOLUTION_1152_832", + "1152 x 864":"RESOLUTION_1152_864", + "1152 x 896":"RESOLUTION_1152_896", + "1216 x 704":"RESOLUTION_1216_704", + "1216 x 768":"RESOLUTION_1216_768", + "1216 x 832":"RESOLUTION_1216_832", + "1232 x 768":"RESOLUTION_1232_768", + "1248 x 832":"RESOLUTION_1248_832", + "1280 x 704":"RESOLUTION_1280_704", + "1280 x 720":"RESOLUTION_1280_720", + "1280 x 768":"RESOLUTION_1280_768", + "1280 x 800":"RESOLUTION_1280_800", + "1312 x 736":"RESOLUTION_1312_736", + "1344 x 640":"RESOLUTION_1344_640", + "1344 x 704":"RESOLUTION_1344_704", + "1344 x 768":"RESOLUTION_1344_768", + "1408 x 576":"RESOLUTION_1408_576", + "1408 x 640":"RESOLUTION_1408_640", + "1408 x 704":"RESOLUTION_1408_704", + "1472 x 576":"RESOLUTION_1472_576", + "1472 x 640":"RESOLUTION_1472_640", + "1472 x 704":"RESOLUTION_1472_704", + "1536 x 512":"RESOLUTION_1536_512", + "1536 x 576":"RESOLUTION_1536_576", + "1536 x 640":"RESOLUTION_1536_640", +} + +V1_V2_RATIO_MAP = { + "1:1":"ASPECT_1_1", + "4:3":"ASPECT_4_3", + "3:4":"ASPECT_3_4", + "16:9":"ASPECT_16_9", + "9:16":"ASPECT_9_16", + "2:1":"ASPECT_2_1", + "1:2":"ASPECT_1_2", + "3:2":"ASPECT_3_2", + "2:3":"ASPECT_2_3", + "4:5":"ASPECT_4_5", + "5:4":"ASPECT_5_4", +} + +V3_RATIO_MAP = { + "1:3":"1x3", + "3:1":"3x1", + "1:2":"1x2", + "2:1":"2x1", + "9:16":"9x16", + "16:9":"16x9", + "10:16":"10x16", + "16:10":"16x10", + "2:3":"2x3", + "3:2":"3x2", + "3:4":"3x4", + "4:3":"4x3", + "4:5":"4x5", + "5:4":"5x4", + "1:1":"1x1", +} + +V3_RESOLUTIONS= [ + "Auto", + "512x1536", + "576x1408", + "576x1472", + "576x1536", + "640x1344", + "640x1408", + "640x1472", + "640x1536", + "704x1152", + "704x1216", + "704x1280", + "704x1344", + "704x1408", + "704x1472", + "736x1312", + "768x1088", + "768x1216", + "768x1280", + "768x1344", + "800x1280", + "832x960", + "832x1024", + "832x1088", + "832x1152", + "832x1216", + "832x1248", + "864x1152", + "896x960", + "896x1024", + "896x1088", + "896x1120", + "896x1152", + "960x832", + "960x896", + "960x1024", + "960x1088", + "1024x832", + "1024x896", + "1024x960", + "1024x1024", + "1088x768", + "1088x832", + "1088x896", + "1088x960", + "1120x896", + "1152x704", + "1152x832", + "1152x864", + "1152x896", + "1216x704", + "1216x768", + "1216x832", + "1248x832", + "1280x704", + "1280x768", + "1280x800", + "1312x736", + "1344x640", + "1344x704", + "1344x768", + "1408x576", + "1408x640", + "1408x704", + "1472x576", + "1472x640", + "1472x704", + "1536x512", + "1536x576", + "1536x640" +] + +def download_and_process_images(image_urls): + """Helper function to download and process multiple images from URLs""" + + # Initialize list to store image tensors + image_tensors = [] + + for image_url in image_urls: + # Using functions from apinode_utils.py to handle downloading and processing + image_bytesio = download_url_to_bytesio(image_url) # Download image content to BytesIO + img_tensor = bytesio_to_image_tensor(image_bytesio, mode="RGB") # Convert to torch.Tensor with RGB mode + image_tensors.append(img_tensor) + + # Stack tensors to match (N, width, height, channels) + if image_tensors: + stacked_tensors = torch.cat(image_tensors, dim=0) + else: + raise Exception("No valid images were processed") + + return stacked_tensors + + +class IdeogramV1(ComfyNodeABC): + """ + Generates images synchronously using the Ideogram V1 model. + + Images links are available for a limited period of time; if you would like to keep the image, you must download it. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "turbo": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to use turbo mode (faster generation, potentially lower quality)", + } + ), + }, + "optional": { + "aspect_ratio": ( + IO.COMBO, + { + "options": list(V1_V2_RATIO_MAP.keys()), + "default": "1:1", + "tooltip": "The aspect ratio for image generation.", + }, + ), + "magic_prompt_option": ( + IO.COMBO, + { + "options": ["AUTO", "ON", "OFF"], + "default": "AUTO", + "tooltip": "Determine if MagicPrompt should be used in generation", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2147483647, + "step": 1, + "control_after_generate": True, + "display": "number", + }, + ), + "negative_prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Description of what to exclude from the image", + }, + ), + "num_images": ( + IO.INT, + {"default": 1, "min": 1, "max": 8, "step": 1, "display": "number"}, + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node/image/Ideogram/v1" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + def api_call( + self, + prompt, + turbo=False, + aspect_ratio="1:1", + magic_prompt_option="AUTO", + seed=0, + negative_prompt="", + num_images=1, + auth_token=None, + ): + # Determine the model based on turbo setting + aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None) + model = "V_1_TURBO" if turbo else "V_1" + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/ideogram/generate", + method=HttpMethod.POST, + request_model=IdeogramGenerateRequest, + response_model=IdeogramGenerateResponse, + ), + request=IdeogramGenerateRequest( + image_request=ImageRequest( + prompt=prompt, + model=model, + num_images=num_images, + seed=seed, + aspect_ratio=aspect_ratio if aspect_ratio != "ASPECT_1_1" else None, + magic_prompt_option=( + magic_prompt_option if magic_prompt_option != "AUTO" else None + ), + negative_prompt=negative_prompt if negative_prompt else None, + ) + ), + auth_token=auth_token, + ) + + response = operation.execute() + + if not response.data or len(response.data) == 0: + raise Exception("No images were generated in the response") + + image_urls = [image_data.url for image_data in response.data if image_data.url] + + if not image_urls: + raise Exception("No image URLs were generated in the response") + + return (download_and_process_images(image_urls),) + + +class IdeogramV2(ComfyNodeABC): + """ + Generates images synchronously using the Ideogram V2 model. + + Images links are available for a limited period of time; if you would like to keep the image, you must download it. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "turbo": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to use turbo mode (faster generation, potentially lower quality)", + } + ), + }, + "optional": { + "aspect_ratio": ( + IO.COMBO, + { + "options": list(V1_V2_RATIO_MAP.keys()), + "default": "1:1", + "tooltip": "The aspect ratio for image generation. Ignored if resolution is not set to AUTO.", + }, + ), + "resolution": ( + IO.COMBO, + { + "options": list(V1_V1_RES_MAP.keys()), + "default": "Auto", + "tooltip": "The resolution for image generation. If not set to AUTO, this overrides the aspect_ratio setting.", + }, + ), + "magic_prompt_option": ( + IO.COMBO, + { + "options": ["AUTO", "ON", "OFF"], + "default": "AUTO", + "tooltip": "Determine if MagicPrompt should be used in generation", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2147483647, + "step": 1, + "control_after_generate": True, + "display": "number", + }, + ), + "style_type": ( + IO.COMBO, + { + "options": ["AUTO", "GENERAL", "REALISTIC", "DESIGN", "RENDER_3D", "ANIME"], + "default": "NONE", + "tooltip": "Style type for generation (V2 only)", + }, + ), + "negative_prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Description of what to exclude from the image", + }, + ), + "num_images": ( + IO.INT, + {"default": 1, "min": 1, "max": 8, "step": 1, "display": "number"}, + ), + #"color_palette": ( + # IO.STRING, + # { + # "multiline": False, + # "default": "", + # "tooltip": "Color palette preset name or hex colors with weights", + # }, + #), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node/image/Ideogram/v2" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + def api_call( + self, + prompt, + turbo=False, + aspect_ratio="1:1", + resolution="Auto", + magic_prompt_option="AUTO", + seed=0, + style_type="NONE", + negative_prompt="", + num_images=1, + color_palette="", + auth_token=None, + ): + aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None) + resolution = V1_V1_RES_MAP.get(resolution, None) + # Determine the model based on turbo setting + model = "V_2_TURBO" if turbo else "V_2" + + # Handle resolution vs aspect_ratio logic + # If resolution is not AUTO, it overrides aspect_ratio + final_resolution = None + final_aspect_ratio = None + + if resolution != "AUTO": + final_resolution = resolution + else: + final_aspect_ratio = aspect_ratio if aspect_ratio != "ASPECT_1_1" else None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/ideogram/generate", + method=HttpMethod.POST, + request_model=IdeogramGenerateRequest, + response_model=IdeogramGenerateResponse, + ), + request=IdeogramGenerateRequest( + image_request=ImageRequest( + prompt=prompt, + model=model, + num_images=num_images, + seed=seed, + aspect_ratio=final_aspect_ratio, + resolution=final_resolution, + magic_prompt_option=( + magic_prompt_option if magic_prompt_option != "AUTO" else None + ), + style_type=style_type if style_type != "NONE" else None, + negative_prompt=negative_prompt if negative_prompt else None, + color_palette=color_palette if color_palette else None, + ) + ), + auth_token=auth_token, + ) + + response = operation.execute() + + if not response.data or len(response.data) == 0: + raise Exception("No images were generated in the response") + + image_urls = [image_data.url for image_data in response.data if image_data.url] + + if not image_urls: + raise Exception("No image URLs were generated in the response") + + return (download_and_process_images(image_urls),) + +class IdeogramV3(ComfyNodeABC): + """ + Generates images synchronously using the Ideogram V3 model. + + Supports both regular image generation from text prompts and image editing with mask. + Images links are available for a limited period of time; if you would like to keep the image, you must download it. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation or editing", + }, + ), + }, + "optional": { + "image": ( + IO.IMAGE, + { + "default": None, + "tooltip": "Optional reference image for image editing.", + }, + ), + "mask": ( + IO.MASK, + { + "default": None, + "tooltip": "Optional mask for inpainting (white areas will be replaced)", + }, + ), + "aspect_ratio": ( + IO.COMBO, + { + "options": list(V3_RATIO_MAP.keys()), + "default": "1:1", + "tooltip": "The aspect ratio for image generation. Ignored if resolution is not set to Auto.", + }, + ), + "resolution": ( + IO.COMBO, + { + "options": V3_RESOLUTIONS, + "default": "Auto", + "tooltip": "The resolution for image generation. If not set to Auto, this overrides the aspect_ratio setting.", + }, + ), + "magic_prompt_option": ( + IO.COMBO, + { + "options": ["AUTO", "ON", "OFF"], + "default": "AUTO", + "tooltip": "Determine if MagicPrompt should be used in generation", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2147483647, + "step": 1, + "control_after_generate": True, + "display": "number", + }, + ), + "num_images": ( + IO.INT, + {"default": 1, "min": 1, "max": 8, "step": 1, "display": "number"}, + ), + "rendering_speed": ( + IO.COMBO, + { + "options": ["BALANCED", "TURBO", "QUALITY"], + "default": "BALANCED", + "tooltip": "Controls the trade-off between generation speed and quality", + }, + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node/image/Ideogram/v3" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + def api_call( + self, + prompt, + image=None, + mask=None, + resolution="Auto", + aspect_ratio="1:1", + magic_prompt_option="AUTO", + seed=0, + num_images=1, + rendering_speed="BALANCED", + auth_token=None, + ): + # Check if both image and mask are provided for editing mode + if image is not None and mask is not None: + # Edit mode + path = "/proxy/ideogram/ideogram-v3/edit" + + # Process image and mask + input_tensor = image.squeeze().cpu() + # Resize mask to match image dimension + mask = resize_mask_to_image(mask, image, allow_gradient=False) + # Invert mask, as Ideogram API will edit black areas instead of white areas (opposite of convention). + mask = 1.0 - mask + + # Validate mask dimensions match image + if mask.shape[1:] != image.shape[1:-1]: + raise Exception("Mask and Image must be the same size") + + # Process image + img_np = (input_tensor.numpy() * 255).astype(np.uint8) + img = Image.fromarray(img_np) + img_byte_arr = io.BytesIO() + img.save(img_byte_arr, format="PNG") + img_byte_arr.seek(0) + img_binary = img_byte_arr + img_binary.name = "image.png" + + # Process mask - white areas will be replaced + mask_np = (mask.squeeze().cpu().numpy() * 255).astype(np.uint8) + mask_img = Image.fromarray(mask_np) + mask_byte_arr = io.BytesIO() + mask_img.save(mask_byte_arr, format="PNG") + mask_byte_arr.seek(0) + mask_binary = mask_byte_arr + mask_binary.name = "mask.png" + + # Create edit request + edit_request = IdeogramV3EditRequest( + prompt=prompt, + rendering_speed=rendering_speed, + ) + + # Add optional parameters + if magic_prompt_option != "AUTO": + edit_request.magic_prompt = magic_prompt_option + if seed != 0: + edit_request.seed = seed + if num_images > 1: + edit_request.num_images = num_images + + # Execute the operation for edit mode + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=IdeogramV3EditRequest, + response_model=IdeogramGenerateResponse, + ), + request=edit_request, + files={ + "image": img_binary, + "mask": mask_binary, + }, + content_type="multipart/form-data", + auth_token=auth_token, + ) + + elif image is not None or mask is not None: + # If only one of image or mask is provided, raise an error + raise Exception("Ideogram V3 image editing requires both an image AND a mask") + else: + # Generation mode + path = "/proxy/ideogram/ideogram-v3/generate" + + # Create generation request + gen_request = IdeogramV3Request( + prompt=prompt, + rendering_speed=rendering_speed, + ) + + # Handle resolution vs aspect ratio + if resolution != "Auto": + gen_request.resolution = resolution + elif aspect_ratio != "1:1": + v3_aspect = V3_RATIO_MAP.get(aspect_ratio) + if v3_aspect: + gen_request.aspect_ratio = v3_aspect + + # Add optional parameters + if magic_prompt_option != "AUTO": + gen_request.magic_prompt = magic_prompt_option + if seed != 0: + gen_request.seed = seed + if num_images > 1: + gen_request.num_images = num_images + + # Execute the operation for generation mode + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=IdeogramV3Request, + response_model=IdeogramGenerateResponse, + ), + request=gen_request, + auth_token=auth_token, + ) + + # Execute the operation and process response + response = operation.execute() + + if not response.data or len(response.data) == 0: + raise Exception("No images were generated in the response") + + image_urls = [image_data.url for image_data in response.data if image_data.url] + + if not image_urls: + raise Exception("No image URLs were generated in the response") + + return (download_and_process_images(image_urls),) + + +NODE_CLASS_MAPPINGS = { + "IdeogramV1": IdeogramV1, + "IdeogramV2": IdeogramV2, + "IdeogramV3": IdeogramV3, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "IdeogramV1": "Ideogram V1", + "IdeogramV2": "Ideogram V2", + "IdeogramV3": "Ideogram V3", +} + diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py new file mode 100644 index 000000000..9aa8df58b --- /dev/null +++ b/comfy_api_nodes/nodes_kling.py @@ -0,0 +1,1563 @@ +"""Kling API Nodes + +For source of truth on the allowed permutations of request fields, please reference: +- [Compatibility Table](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap) +""" + +from __future__ import annotations +from typing import Optional, TypeVar, Any +import math +import logging + +import torch + +from comfy_api_nodes.apis import ( + KlingTaskStatus, + KlingCameraControl, + KlingCameraConfig, + KlingCameraControlType, + KlingVideoGenDuration, + KlingVideoGenMode, + KlingVideoGenAspectRatio, + KlingVideoGenModelName, + KlingText2VideoRequest, + KlingText2VideoResponse, + KlingImage2VideoRequest, + KlingImage2VideoResponse, + KlingVideoExtendRequest, + KlingVideoExtendResponse, + KlingLipSyncVoiceLanguage, + KlingLipSyncInputObject, + KlingLipSyncRequest, + KlingLipSyncResponse, + KlingVirtualTryOnModelName, + KlingVirtualTryOnRequest, + KlingVirtualTryOnResponse, + KlingVideoResult, + KlingImageResult, + KlingImageGenerationsRequest, + KlingImageGenerationsResponse, + KlingImageGenImageReferenceType, + KlingImageGenModelName, + KlingImageGenAspectRatio, + KlingVideoEffectsRequest, + KlingVideoEffectsResponse, + KlingDualCharacterEffectsScene, + KlingSingleImageEffectsScene, + KlingDualCharacterEffectInput, + KlingSingleImageEffectInput, + KlingCharacterEffectModelName, + KlingSingleImageEffectModelName, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + tensor_to_base64_string, + download_url_to_video_output, + upload_video_to_comfyapi, + upload_audio_to_comfyapi, + download_url_to_image_tensor, +) +from comfy_api_nodes.mapper_utils import model_field_to_node_input +from comfy_api.input.basic_types import AudioInput +from comfy_api.input.video_types import VideoInput +from comfy_api.input_impl import VideoFromFile +from comfy.comfy_types.node_typing import IO, InputTypeOptions, ComfyNodeABC + +KLING_API_VERSION = "v1" +PATH_TEXT_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/text2video" +PATH_IMAGE_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/image2video" +PATH_VIDEO_EXTEND = f"/proxy/kling/{KLING_API_VERSION}/videos/video-extend" +PATH_LIP_SYNC = f"/proxy/kling/{KLING_API_VERSION}/videos/lip-sync" +PATH_VIDEO_EFFECTS = f"/proxy/kling/{KLING_API_VERSION}/videos/effects" +PATH_CHARACTER_IMAGE = f"/proxy/kling/{KLING_API_VERSION}/images/generations" +PATH_VIRTUAL_TRY_ON = f"/proxy/kling/{KLING_API_VERSION}/images/kolors-virtual-try-on" +PATH_IMAGE_GENERATIONS = f"/proxy/kling/{KLING_API_VERSION}/images/generations" + + +MAX_PROMPT_LENGTH_T2V = 2500 +MAX_PROMPT_LENGTH_I2V = 500 +MAX_PROMPT_LENGTH_IMAGE_GEN = 500 +MAX_NEGATIVE_PROMPT_LENGTH_IMAGE_GEN = 200 +MAX_PROMPT_LENGTH_LIP_SYNC = 120 + +R = TypeVar("R") + + +class KlingApiError(Exception): + """Base exception for Kling API errors.""" + + pass + + +def poll_until_finished(auth_token: str, api_endpoint: ApiEndpoint[Any, R]) -> R: + """Polls the Kling API endpoint until the task reaches a terminal state, then returns the response.""" + return PollingOperation( + poll_endpoint=api_endpoint, + completed_statuses=[ + KlingTaskStatus.succeed.value, + ], + failed_statuses=[KlingTaskStatus.failed.value], + status_extractor=lambda response: ( + response.data.task_status.value + if response.data and response.data.task_status + else None + ), + auth_token=auth_token, + ).execute() + + +def is_valid_camera_control_configs(configs: list[float]) -> bool: + """Verifies that at least one camera control configuration is non-zero.""" + return any(not math.isclose(value, 0.0) for value in configs) + + +def is_valid_prompt(prompt: str) -> bool: + """Verifies that the prompt is not empty.""" + return bool(prompt) + + +def is_valid_task_creation_response(response: KlingText2VideoResponse) -> bool: + """Verifies that the initial response contains a task ID.""" + return bool(response.data.task_id) + + +def is_valid_video_response(response: KlingText2VideoResponse) -> bool: + """Verifies that the response contains a task result with at least one video.""" + return ( + response.data is not None + and response.data.task_result is not None + and response.data.task_result.videos is not None + and len(response.data.task_result.videos) > 0 + ) + + +def is_valid_image_response(response: KlingVirtualTryOnResponse) -> bool: + """Verifies that the response contains a task result with at least one image.""" + return ( + response.data is not None + and response.data.task_result is not None + and response.data.task_result.images is not None + and len(response.data.task_result.images) > 0 + ) + + +def validate_prompts(prompt: str, negative_prompt: str, max_length: int) -> bool: + """Verifies that the positive prompt is not empty and that neither promt is too long.""" + if not prompt: + raise ValueError("Positive prompt is empty") + if len(prompt) > max_length: + raise ValueError(f"Positive prompt is too long: {len(prompt)} characters") + if negative_prompt and len(negative_prompt) > max_length: + raise ValueError( + f"Negative prompt is too long: {len(negative_prompt)} characters" + ) + return True + + +def validate_task_creation_response(response) -> None: + """Validates that the Kling task creation request was successful.""" + if not is_valid_task_creation_response(response): + error_msg = f"Kling initial request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}" + logging.error(error_msg) + raise KlingApiError(error_msg) + + +def validate_video_result_response(response) -> None: + """Validates that the Kling task result contains a video.""" + if not is_valid_video_response(response): + error_msg = f"Kling task {response.data.task_id} succeeded but no video data found in response." + logging.error(f"Error: {error_msg}.\nResponse: {response}") + raise KlingApiError(error_msg) + + +def validate_image_result_response(response) -> None: + """Validates that the Kling task result contains an image.""" + if not is_valid_image_response(response): + error_msg = f"Kling task {response.data.task_id} succeeded but no image data found in response." + logging.error(f"Error: {error_msg}.\nResponse: {response}") + raise KlingApiError(error_msg) + + +def get_camera_control_input_config( + tooltip: str, default: float = 0.0 +) -> tuple[IO, InputTypeOptions]: + """Returns common InputTypeOptions for Kling camera control configurations.""" + input_config = { + "default": default, + "min": -10.0, + "max": 10.0, + "step": 0.25, + "display": "slider", + "tooltip": tooltip, + } + return IO.FLOAT, input_config + + +def get_video_from_response(response) -> KlingVideoResult: + """Returns the first video object from the Kling video generation task result.""" + video = response.data.task_result.videos[0] + logging.info( + "Kling task %s succeeded. Video URL: %s", response.data.task_id, video.url + ) + return video + + +def get_images_from_response(response) -> list[KlingImageResult]: + images = response.data.task_result.images + logging.info("Kling task %s succeeded. Images: %s", response.data.task_id, images) + return images + + +def video_result_to_node_output( + video: KlingVideoResult, +) -> tuple[VideoFromFile, str, str]: + """Converts a KlingVideoResult to a tuple of (VideoFromFile, str, str) to be used as a ComfyUI node output.""" + return ( + download_url_to_video_output(video.url), + str(video.id), + str(video.duration), + ) + + +def image_result_to_node_output( + images: list[KlingImageResult], +) -> torch.Tensor: + """ + Converts a KlingImageResult to a tuple containing a [B, H, W, C] tensor. + If multiple images are returned, they will be stacked along the batch dimension. + """ + if len(images) == 1: + return download_url_to_image_tensor(images[0].url) + else: + return torch.cat([download_url_to_image_tensor(image.url) for image in images]) + + +class KlingNodeBase(ComfyNodeABC): + """Base class for Kling nodes.""" + + FUNCTION = "api_call" + CATEGORY = "api node/video/Kling" + API_NODE = True + + +class KlingCameraControls(KlingNodeBase): + """Kling Camera Controls Node""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "camera_control_type": model_field_to_node_input( + IO.COMBO, + KlingCameraControl, + "type", + enum_type=KlingCameraControlType, + ), + "horizontal_movement": get_camera_control_input_config( + "Controls camera's movement along horizontal axis (x-axis). Negative indicates left, positive indicates right" + ), + "vertical_movement": get_camera_control_input_config( + "Controls camera's movement along vertical axis (y-axis). Negative indicates downward, positive indicates upward." + ), + "pan": get_camera_control_input_config( + "Controls camera's rotation in vertical plane (x-axis). Negative indicates downward rotation, positive indicates upward rotation.", + default=0.5, + ), + "tilt": get_camera_control_input_config( + "Controls camera's rotation in horizontal plane (y-axis). Negative indicates left rotation, positive indicates right rotation.", + ), + "roll": get_camera_control_input_config( + "Controls camera's rolling amount (z-axis). Negative indicates counterclockwise, positive indicates clockwise.", + ), + "zoom": get_camera_control_input_config( + "Controls change in camera's focal length. Negative indicates narrower field of view, positive indicates wider field of view.", + ), + } + } + + DESCRIPTION = "Allows specifying configuration options for Kling Camera Controls and motion control effects." + RETURN_TYPES = ("CAMERA_CONTROL",) + RETURN_NAMES = ("camera_control",) + FUNCTION = "main" + + @classmethod + def VALIDATE_INPUTS( + cls, + horizontal_movement: float, + vertical_movement: float, + pan: float, + tilt: float, + roll: float, + zoom: float, + ) -> bool | str: + if not is_valid_camera_control_configs( + [ + horizontal_movement, + vertical_movement, + pan, + tilt, + roll, + zoom, + ] + ): + return "Invalid camera control configs: at least one of the values must be non-zero" + return True + + def main( + self, + camera_control_type: str, + horizontal_movement: float, + vertical_movement: float, + pan: float, + tilt: float, + roll: float, + zoom: float, + ) -> tuple[KlingCameraControl]: + return ( + KlingCameraControl( + type=KlingCameraControlType(camera_control_type), + config=KlingCameraConfig( + horizontal=horizontal_movement, + vertical=vertical_movement, + pan=pan, + roll=roll, + tilt=tilt, + zoom=zoom, + ), + ), + ) + + +class KlingTextToVideoNode(KlingNodeBase): + """Kling Text to Video Node""" + + @staticmethod + def get_mode_string_mapping() -> dict[str, tuple[str, str, str]]: + """ + Returns a mapping of mode strings to their corresponding (mode, duration, model_name) tuples. + Only includes config combos that support the `image_tail` request field. + + See: [Kling API Docs Capability Map](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap) + """ + return { + "standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"), + "standard mode / 10s duration / kling-v1": ("std", "10", "kling-v1"), + "pro mode / 5s duration / kling-v1": ("pro", "5", "kling-v1"), + "pro mode / 10s duration / kling-v1": ("pro", "10", "kling-v1"), + "standard mode / 5s duration / kling-v1-6": ("std", "5", "kling-v1-6"), + "standard mode / 10s duration / kling-v1-6": ("std", "10", "kling-v1-6"), + "pro mode / 5s duration / kling-v2-master": ("pro", "5", "kling-v2-master"), + "pro mode / 10s duration / kling-v2-master": ("pro", "10", "kling-v2-master"), + "standard mode / 5s duration / kling-v2-master": ("std", "5", "kling-v2-master"), + "standard mode / 10s duration / kling-v2-master": ("std", "10", "kling-v2-master"), + } + + @classmethod + def INPUT_TYPES(s): + modes = list(KlingTextToVideoNode.get_mode_string_mapping().keys()) + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, KlingText2VideoRequest, "prompt", multiline=True + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, KlingText2VideoRequest, "negative_prompt", multiline=True + ), + "cfg_scale": model_field_to_node_input( + IO.FLOAT, + KlingText2VideoRequest, + "cfg_scale", + default=1.0, + min=0.0, + max=1.0, + ), + "aspect_ratio": model_field_to_node_input( + IO.COMBO, + KlingText2VideoRequest, + "aspect_ratio", + enum_type=KlingVideoGenAspectRatio, + ), + "mode": ( + modes, + { + "default": modes[4], + "tooltip": "The configuration to use for the video generation following the format: mode / duration / model_name.", + }, + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + RETURN_TYPES = ("VIDEO", "STRING", "STRING") + RETURN_NAMES = ("VIDEO", "video_id", "duration") + DESCRIPTION = "Kling Text to Video Node" + + def get_response(self, task_id: str, auth_token: str) -> KlingText2VideoResponse: + return poll_until_finished( + auth_token, + ApiEndpoint( + path=f"{PATH_TEXT_TO_VIDEO}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=KlingText2VideoResponse, + ), + ) + + def api_call( + self, + prompt: str, + negative_prompt: str, + cfg_scale: float, + mode: str, + aspect_ratio: str, + camera_control: Optional[KlingCameraControl] = None, + model_name: Optional[str] = None, + duration: Optional[str] = None, + auth_token: Optional[str] = None, + ) -> tuple[VideoFromFile, str, str]: + validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V) + if model_name is None: + mode, duration, model_name = self.get_mode_string_mapping()[mode] + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_TEXT_TO_VIDEO, + method=HttpMethod.POST, + request_model=KlingText2VideoRequest, + response_model=KlingText2VideoResponse, + ), + request=KlingText2VideoRequest( + prompt=prompt if prompt else None, + negative_prompt=negative_prompt if negative_prompt else None, + duration=KlingVideoGenDuration(duration), + mode=KlingVideoGenMode(mode), + model_name=KlingVideoGenModelName(model_name), + cfg_scale=cfg_scale, + aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), + camera_control=camera_control, + ), + auth_token=auth_token, + ) + + task_creation_response = initial_operation.execute() + validate_task_creation_response(task_creation_response) + + task_id = task_creation_response.data.task_id + final_response = self.get_response(task_id, auth_token) + validate_video_result_response(final_response) + + video = get_video_from_response(final_response) + return video_result_to_node_output(video) + + +class KlingCameraControlT2VNode(KlingTextToVideoNode): + """ + Kling Text to Video Camera Control Node. This node is a text to video node, but it supports controlling the camera. + Duration, mode, and model_name request fields are hard-coded because camera control is only supported in pro mode with the kling-v1-5 model at 5s duration as of 2025-05-02. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, KlingText2VideoRequest, "prompt", multiline=True + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + KlingText2VideoRequest, + "negative_prompt", + multiline=True, + ), + "cfg_scale": model_field_to_node_input( + IO.FLOAT, + KlingText2VideoRequest, + "cfg_scale", + default=0.75, + min=0.0, + max=1.0, + ), + "aspect_ratio": model_field_to_node_input( + IO.COMBO, + KlingText2VideoRequest, + "aspect_ratio", + enum_type=KlingVideoGenAspectRatio, + ), + "camera_control": ( + "CAMERA_CONTROL", + { + "tooltip": "Can be created using the Kling Camera Controls node. Controls the camera movement and motion during the video generation.", + }, + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + DESCRIPTION = "Transform text into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original text." + + def api_call( + self, + prompt: str, + negative_prompt: str, + cfg_scale: float, + aspect_ratio: str, + camera_control: Optional[KlingCameraControl] = None, + auth_token: Optional[str] = None, + ): + return super().api_call( + model_name=KlingVideoGenModelName.kling_v1, + cfg_scale=cfg_scale, + mode=KlingVideoGenMode.std, + aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), + duration=KlingVideoGenDuration.field_5, + prompt=prompt, + negative_prompt=negative_prompt, + camera_control=camera_control, + auth_token=auth_token, + ) + + +class KlingImage2VideoNode(KlingNodeBase): + """Kling Image to Video Node""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "start_frame": model_field_to_node_input( + IO.IMAGE, KlingImage2VideoRequest, "image" + ), + "prompt": model_field_to_node_input( + IO.STRING, KlingImage2VideoRequest, "prompt", multiline=True + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + KlingImage2VideoRequest, + "negative_prompt", + multiline=True, + ), + "model_name": model_field_to_node_input( + IO.COMBO, + KlingImage2VideoRequest, + "model_name", + enum_type=KlingVideoGenModelName, + ), + "cfg_scale": model_field_to_node_input( + IO.FLOAT, + KlingImage2VideoRequest, + "cfg_scale", + default=0.8, + min=0.0, + max=1.0, + ), + "mode": model_field_to_node_input( + IO.COMBO, + KlingImage2VideoRequest, + "mode", + enum_type=KlingVideoGenMode, + ), + "aspect_ratio": model_field_to_node_input( + IO.COMBO, + KlingImage2VideoRequest, + "aspect_ratio", + enum_type=KlingVideoGenAspectRatio, + ), + "duration": model_field_to_node_input( + IO.COMBO, + KlingImage2VideoRequest, + "duration", + enum_type=KlingVideoGenDuration, + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + RETURN_TYPES = ("VIDEO", "STRING", "STRING") + RETURN_NAMES = ("VIDEO", "video_id", "duration") + DESCRIPTION = "Kling Image to Video Node" + + def get_response(self, task_id: str, auth_token: str) -> KlingImage2VideoResponse: + return poll_until_finished( + auth_token, + ApiEndpoint( + path=f"{PATH_IMAGE_TO_VIDEO}/{task_id}", + method=HttpMethod.GET, + request_model=KlingImage2VideoRequest, + response_model=KlingImage2VideoResponse, + ), + ) + + def api_call( + self, + start_frame: torch.Tensor, + prompt: str, + negative_prompt: str, + model_name: str, + cfg_scale: float, + mode: str, + aspect_ratio: str, + duration: str, + camera_control: Optional[KlingCameraControl] = None, + end_frame: Optional[torch.Tensor] = None, + auth_token: Optional[str] = None, + ) -> tuple[VideoFromFile]: + validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_I2V) + + if camera_control is not None: + # Camera control type for image 2 video is always simple + camera_control.type = KlingCameraControlType.simple + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_IMAGE_TO_VIDEO, + method=HttpMethod.POST, + request_model=KlingImage2VideoRequest, + response_model=KlingImage2VideoResponse, + ), + request=KlingImage2VideoRequest( + model_name=KlingVideoGenModelName(model_name), + image=tensor_to_base64_string(start_frame), + image_tail=( + tensor_to_base64_string(end_frame) + if end_frame is not None + else None + ), + prompt=prompt, + negative_prompt=negative_prompt if negative_prompt else None, + cfg_scale=cfg_scale, + mode=KlingVideoGenMode(mode), + aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), + duration=KlingVideoGenDuration(duration), + camera_control=camera_control, + ), + auth_token=auth_token, + ) + + task_creation_response = initial_operation.execute() + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = self.get_response(task_id, auth_token) + validate_video_result_response(final_response) + + video = get_video_from_response(final_response) + return video_result_to_node_output(video) + + +class KlingCameraControlI2VNode(KlingImage2VideoNode): + """ + Kling Image to Video Camera Control Node. This node is a image to video node, but it supports controlling the camera. + Duration, mode, and model_name request fields are hard-coded because camera control is only supported in pro mode with the kling-v1-5 model at 5s duration as of 2025-05-02. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "start_frame": model_field_to_node_input( + IO.IMAGE, KlingImage2VideoRequest, "image" + ), + "prompt": model_field_to_node_input( + IO.STRING, KlingImage2VideoRequest, "prompt", multiline=True + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + KlingImage2VideoRequest, + "negative_prompt", + multiline=True, + ), + "cfg_scale": model_field_to_node_input( + IO.FLOAT, + KlingImage2VideoRequest, + "cfg_scale", + default=0.75, + min=0.0, + max=1.0, + ), + "aspect_ratio": model_field_to_node_input( + IO.COMBO, + KlingImage2VideoRequest, + "aspect_ratio", + enum_type=KlingVideoGenAspectRatio, + ), + "camera_control": ( + "CAMERA_CONTROL", + { + "tooltip": "Can be created using the Kling Camera Controls node. Controls the camera movement and motion during the video generation.", + }, + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + DESCRIPTION = "Transform still images into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original image." + + def api_call( + self, + start_frame: torch.Tensor, + prompt: str, + negative_prompt: str, + cfg_scale: float, + aspect_ratio: str, + camera_control: KlingCameraControl, + auth_token: Optional[str] = None, + ): + return super().api_call( + model_name=KlingVideoGenModelName.kling_v1_5, + start_frame=start_frame, + cfg_scale=cfg_scale, + mode=KlingVideoGenMode.pro, + aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), + duration=KlingVideoGenDuration.field_5, + prompt=prompt, + negative_prompt=negative_prompt, + camera_control=camera_control, + auth_token=auth_token, + ) + + +class KlingStartEndFrameNode(KlingImage2VideoNode): + """ + Kling First Last Frame Node. This node allows creation of a video from a first and last frame. It calls the normal image to video endpoint, but only allows the subset of input options that support the `image_tail` request field. + """ + + @staticmethod + def get_mode_string_mapping() -> dict[str, tuple[str, str, str]]: + """ + Returns a mapping of mode strings to their corresponding (mode, duration, model_name) tuples. + Only includes config combos that support the `image_tail` request field. + + See: [Kling API Docs Capability Map](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap) + """ + return { + "standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"), + "pro mode / 5s duration / kling-v1": ("pro", "5", "kling-v1"), + "pro mode / 5s duration / kling-v1-5": ("pro", "5", "kling-v1-5"), + "pro mode / 10s duration / kling-v1-5": ("pro", "10", "kling-v1-5"), + "pro mode / 5s duration / kling-v1-6": ("pro", "5", "kling-v1-6"), + "pro mode / 10s duration / kling-v1-6": ("pro", "10", "kling-v1-6"), + } + + @classmethod + def INPUT_TYPES(s): + modes = list(KlingStartEndFrameNode.get_mode_string_mapping().keys()) + return { + "required": { + "start_frame": model_field_to_node_input( + IO.IMAGE, KlingImage2VideoRequest, "image" + ), + "end_frame": model_field_to_node_input( + IO.IMAGE, KlingImage2VideoRequest, "image_tail" + ), + "prompt": model_field_to_node_input( + IO.STRING, KlingImage2VideoRequest, "prompt", multiline=True + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + KlingImage2VideoRequest, + "negative_prompt", + multiline=True, + ), + "cfg_scale": model_field_to_node_input( + IO.FLOAT, + KlingImage2VideoRequest, + "cfg_scale", + default=0.5, + min=0.0, + max=1.0, + ), + "aspect_ratio": model_field_to_node_input( + IO.COMBO, + KlingImage2VideoRequest, + "aspect_ratio", + enum_type=KlingVideoGenAspectRatio, + ), + "mode": ( + modes, + { + "default": modes[2], + "tooltip": "The configuration to use for the video generation following the format: mode / duration / model_name.", + }, + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + DESCRIPTION = "Generate a video sequence that transitions between your provided start and end images. The node creates all frames in between, producing a smooth transformation from the first frame to the last." + + def api_call( + self, + start_frame: torch.Tensor, + end_frame: torch.Tensor, + prompt: str, + negative_prompt: str, + cfg_scale: float, + aspect_ratio: str, + mode: str, + auth_token: Optional[str] = None, + ): + mode, duration, model_name = KlingStartEndFrameNode.get_mode_string_mapping()[ + mode + ] + return super().api_call( + prompt=prompt, + negative_prompt=negative_prompt, + model_name=model_name, + start_frame=start_frame, + cfg_scale=cfg_scale, + mode=mode, + aspect_ratio=aspect_ratio, + duration=duration, + end_frame=end_frame, + auth_token=auth_token, + ) + + +class KlingVideoExtendNode(KlingNodeBase): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, KlingVideoExtendRequest, "prompt", multiline=True + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + KlingVideoExtendRequest, + "negative_prompt", + multiline=True, + ), + "cfg_scale": model_field_to_node_input( + IO.FLOAT, + KlingVideoExtendRequest, + "cfg_scale", + default=0.5, + min=0.0, + max=1.0, + ), + "video_id": model_field_to_node_input( + IO.STRING, KlingVideoExtendRequest, "video_id", forceInput=True + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + RETURN_TYPES = ("VIDEO", "STRING", "STRING") + RETURN_NAMES = ("VIDEO", "video_id", "duration") + DESCRIPTION = "Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes." + + def get_response(self, task_id: str, auth_token: str) -> KlingVideoExtendResponse: + return poll_until_finished( + auth_token, + ApiEndpoint( + path=f"{PATH_VIDEO_EXTEND}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=KlingVideoExtendResponse, + ), + ) + + def api_call( + self, + prompt: str, + negative_prompt: str, + cfg_scale: float, + video_id: str, + auth_token: Optional[str] = None, + ) -> tuple[VideoFromFile, str, str]: + validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V) + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_VIDEO_EXTEND, + method=HttpMethod.POST, + request_model=KlingVideoExtendRequest, + response_model=KlingVideoExtendResponse, + ), + request=KlingVideoExtendRequest( + prompt=prompt if prompt else None, + negative_prompt=negative_prompt if negative_prompt else None, + cfg_scale=cfg_scale, + video_id=video_id, + ), + auth_token=auth_token, + ) + + task_creation_response = initial_operation.execute() + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = self.get_response(task_id, auth_token) + validate_video_result_response(final_response) + + video = get_video_from_response(final_response) + return video_result_to_node_output(video) + + +class KlingVideoEffectsBase(KlingNodeBase): + """Kling Video Effects Base""" + + RETURN_TYPES = ("VIDEO", "STRING", "STRING") + RETURN_NAMES = ("VIDEO", "video_id", "duration") + + def get_response(self, task_id: str, auth_token: str) -> KlingVideoEffectsResponse: + return poll_until_finished( + auth_token, + ApiEndpoint( + path=f"{PATH_VIDEO_EFFECTS}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=KlingVideoEffectsResponse, + ), + ) + + def api_call( + self, + dual_character: bool, + effect_scene: KlingDualCharacterEffectsScene | KlingSingleImageEffectsScene, + model_name: str, + duration: KlingVideoGenDuration, + image_1: torch.Tensor, + image_2: Optional[torch.Tensor] = None, + mode: Optional[KlingVideoGenMode] = None, + auth_token: Optional[str] = None, + ): + if dual_character: + request_input_field = KlingDualCharacterEffectInput( + model_name=model_name, + mode=mode, + images=[ + tensor_to_base64_string(image_1), + tensor_to_base64_string(image_2), + ], + duration=duration, + ) + else: + request_input_field = KlingSingleImageEffectInput( + model_name=model_name, + image=tensor_to_base64_string(image_1), + duration=duration, + ) + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_VIDEO_EFFECTS, + method=HttpMethod.POST, + request_model=KlingVideoEffectsRequest, + response_model=KlingVideoEffectsResponse, + ), + request=KlingVideoEffectsRequest( + effect_scene=effect_scene, + input=request_input_field, + ), + auth_token=auth_token, + ) + + task_creation_response = initial_operation.execute() + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = self.get_response(task_id, auth_token) + validate_video_result_response(final_response) + + video = get_video_from_response(final_response) + return video_result_to_node_output(video) + + +class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase): + """Kling Dual Character Video Effect Node""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image_left": (IO.IMAGE, {"tooltip": "Left side image"}), + "image_right": (IO.IMAGE, {"tooltip": "Right side image"}), + "effect_scene": model_field_to_node_input( + IO.COMBO, + KlingVideoEffectsRequest, + "effect_scene", + enum_type=KlingDualCharacterEffectsScene, + ), + "model_name": model_field_to_node_input( + IO.COMBO, + KlingDualCharacterEffectInput, + "model_name", + enum_type=KlingCharacterEffectModelName, + ), + "mode": model_field_to_node_input( + IO.COMBO, + KlingDualCharacterEffectInput, + "mode", + enum_type=KlingVideoGenMode, + ), + "duration": model_field_to_node_input( + IO.COMBO, + KlingDualCharacterEffectInput, + "duration", + enum_type=KlingVideoGenDuration, + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + DESCRIPTION = "Achieve different special effects when generating a video based on the effect_scene. First image will be positioned on left side, second on right side of the composite." + RETURN_TYPES = ("VIDEO", "STRING") + RETURN_NAMES = ("VIDEO", "duration") + + def api_call( + self, + image_left: torch.Tensor, + image_right: torch.Tensor, + effect_scene: KlingDualCharacterEffectsScene, + model_name: KlingCharacterEffectModelName, + mode: KlingVideoGenMode, + duration: KlingVideoGenDuration, + auth_token: Optional[str] = None, + ): + video, _, duration = super().api_call( + dual_character=True, + effect_scene=effect_scene, + model_name=model_name, + mode=mode, + duration=duration, + image_1=image_left, + image_2=image_right, + auth_token=auth_token, + ) + return video, duration + +class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase): + """Kling Single Image Video Effect Node""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ( + IO.IMAGE, + { + "tooltip": " Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1" + }, + ), + "effect_scene": model_field_to_node_input( + IO.COMBO, + KlingVideoEffectsRequest, + "effect_scene", + enum_type=KlingSingleImageEffectsScene, + ), + "model_name": model_field_to_node_input( + IO.COMBO, + KlingSingleImageEffectInput, + "model_name", + enum_type=KlingSingleImageEffectModelName, + ), + "duration": model_field_to_node_input( + IO.COMBO, + KlingSingleImageEffectInput, + "duration", + enum_type=KlingVideoGenDuration, + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + DESCRIPTION = "Achieve different special effects when generating a video based on the effect_scene." + + def api_call( + self, + image: torch.Tensor, + effect_scene: KlingSingleImageEffectsScene, + model_name: KlingSingleImageEffectModelName, + duration: KlingVideoGenDuration, + auth_token: Optional[str] = None, + ): + return super().api_call( + dual_character=False, + effect_scene=effect_scene, + model_name=model_name, + duration=duration, + image_1=image, + auth_token=auth_token, + ) + + +class KlingLipSyncBase(KlingNodeBase): + """Kling Lip Sync Base""" + + RETURN_TYPES = ("VIDEO", "STRING", "STRING") + RETURN_NAMES = ("VIDEO", "video_id", "duration") + + def validate_text(self, text: str): + if not text: + raise ValueError("Text is required") + if len(text) > MAX_PROMPT_LENGTH_LIP_SYNC: + raise ValueError( + f"Text is too long. Maximum length is {MAX_PROMPT_LENGTH_LIP_SYNC} characters." + ) + + def get_response(self, task_id: str, auth_token: str) -> KlingLipSyncResponse: + """Polls the Kling API endpoint until the task reaches a terminal state.""" + return poll_until_finished( + auth_token, + ApiEndpoint( + path=f"{PATH_LIP_SYNC}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=KlingLipSyncResponse, + ), + ) + + def api_call( + self, + video: VideoInput, + audio: Optional[AudioInput] = None, + voice_language: Optional[str] = None, + mode: Optional[str] = None, + text: Optional[str] = None, + voice_speed: Optional[float] = None, + voice_id: Optional[str] = None, + auth_token: Optional[str] = None, + ) -> tuple[VideoFromFile, str, str]: + if text: + self.validate_text(text) + + # Upload video to Comfy API and get download URL + video_url = upload_video_to_comfyapi(video, auth_token) + logging.info("Uploaded video to Comfy API. URL: %s", video_url) + + # Upload the audio file to Comfy API and get download URL + if audio: + audio_url = upload_audio_to_comfyapi(audio, auth_token) + logging.info("Uploaded audio to Comfy API. URL: %s", audio_url) + else: + audio_url = None + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_LIP_SYNC, + method=HttpMethod.POST, + request_model=KlingLipSyncRequest, + response_model=KlingLipSyncResponse, + ), + request=KlingLipSyncRequest( + input=KlingLipSyncInputObject( + video_url=video_url, + mode=mode, + text=text, + voice_language=voice_language, + voice_speed=voice_speed, + audio_type="url", + audio_url=audio_url, + voice_id=voice_id, + ), + ), + auth_token=auth_token, + ) + + task_creation_response = initial_operation.execute() + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = self.get_response(task_id, auth_token) + validate_video_result_response(final_response) + + video = get_video_from_response(final_response) + return video_result_to_node_output(video) + + +class KlingLipSyncAudioToVideoNode(KlingLipSyncBase): + """Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file.""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "video": (IO.VIDEO, {}), + "audio": (IO.AUDIO, {}), + "voice_language": model_field_to_node_input( + IO.COMBO, + KlingLipSyncInputObject, + "voice_language", + enum_type=KlingLipSyncVoiceLanguage, + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + DESCRIPTION = "Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file." + + def api_call( + self, + video: VideoInput, + audio: AudioInput, + voice_language: str, + auth_token: Optional[str] = None, + ): + return super().api_call( + video=video, + audio=audio, + voice_language=voice_language, + mode="audio2video", + auth_token=auth_token, + ) + + +class KlingLipSyncTextToVideoNode(KlingLipSyncBase): + """Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt.""" + + @staticmethod + def get_voice_config() -> dict[str, tuple[str, str]]: + return { + # English voices + "Melody": ("girlfriend_4_speech02", "en"), + "Sunny": ("genshin_vindi2", "en"), + "Sage": ("zhinen_xuesheng", "en"), + "Ace": ("AOT", "en"), + "Blossom": ("ai_shatang", "en"), + "Peppy": ("genshin_klee2", "en"), + "Dove": ("genshin_kirara", "en"), + "Shine": ("ai_kaiya", "en"), + "Anchor": ("oversea_male1", "en"), + "Lyric": ("ai_chenjiahao_712", "en"), + "Tender": ("chat1_female_new-3", "en"), + "Siren": ("chat_0407_5-1", "en"), + "Zippy": ("cartoon-boy-07", "en"), + "Bud": ("uk_boy1", "en"), + "Sprite": ("cartoon-girl-01", "en"), + "Candy": ("PeppaPig_platform", "en"), + "Beacon": ("ai_huangzhong_712", "en"), + "Rock": ("ai_huangyaoshi_712", "en"), + "Titan": ("ai_laoguowang_712", "en"), + "Grace": ("chengshu_jiejie", "en"), + "Helen": ("you_pingjing", "en"), + "Lore": ("calm_story1", "en"), + "Crag": ("uk_man2", "en"), + "Prattle": ("laopopo_speech02", "en"), + "Hearth": ("heainainai_speech02", "en"), + "The Reader": ("reader_en_m-v1", "en"), + "Commercial Lady": ("commercial_lady_en_f-v1", "en"), + # Chinese voices + "阳光少年": ("genshin_vindi2", "zh"), + "懂事小弟": ("zhinen_xuesheng", "zh"), + "运动少年": ("tiyuxi_xuedi", "zh"), + "青春少女": ("ai_shatang", "zh"), + "温柔小妹": ("genshin_klee2", "zh"), + "元气少女": ("genshin_kirara", "zh"), + "阳光男生": ("ai_kaiya", "zh"), + "幽默小哥": ("tiexin_nanyou", "zh"), + "文艺小哥": ("ai_chenjiahao_712", "zh"), + "甜美邻家": ("girlfriend_1_speech02", "zh"), + "温柔姐姐": ("chat1_female_new-3", "zh"), + "职场女青": ("girlfriend_2_speech02", "zh"), + "活泼男童": ("cartoon-boy-07", "zh"), + "俏皮女童": ("cartoon-girl-01", "zh"), + "稳重老爸": ("ai_huangyaoshi_712", "zh"), + "温柔妈妈": ("you_pingjing", "zh"), + "严肃上司": ("ai_laoguowang_712", "zh"), + "优雅贵妇": ("chengshu_jiejie", "zh"), + "慈祥爷爷": ("zhuxi_speech02", "zh"), + "唠叨爷爷": ("uk_oldman3", "zh"), + "唠叨奶奶": ("laopopo_speech02", "zh"), + "和蔼奶奶": ("heainainai_speech02", "zh"), + "东北老铁": ("dongbeilaotie_speech02", "zh"), + "重庆小伙": ("chongqingxiaohuo_speech02", "zh"), + "四川妹子": ("chuanmeizi_speech02", "zh"), + "潮汕大叔": ("chaoshandashu_speech02", "zh"), + "台湾男生": ("ai_taiwan_man2_speech02", "zh"), + "西安掌柜": ("xianzhanggui_speech02", "zh"), + "天津姐姐": ("tianjinjiejie_speech02", "zh"), + "新闻播报男": ("diyinnansang_DB_CN_M_04-v2", "zh"), + "译制片男": ("yizhipiannan-v1", "zh"), + "撒娇女友": ("tianmeixuemei-v1", "zh"), + "刀片烟嗓": ("daopianyansang-v1", "zh"), + "乖巧正太": ("mengwa-v1", "zh"), + } + + @classmethod + def INPUT_TYPES(s): + voice_options = list(s.get_voice_config().keys()) + return { + "required": { + "video": (IO.VIDEO, {}), + "text": model_field_to_node_input( + IO.STRING, KlingLipSyncInputObject, "text", multiline=True + ), + "voice": (voice_options, {"default": voice_options[0]}), + "voice_speed": model_field_to_node_input( + IO.FLOAT, KlingLipSyncInputObject, "voice_speed", slider=True + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + DESCRIPTION = "Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt." + + def api_call( + self, + video: VideoInput, + text: str, + voice: str, + voice_speed: float, + auth_token: Optional[str] = None, + ): + voice_id, voice_language = KlingLipSyncTextToVideoNode.get_voice_config()[voice] + return super().api_call( + video=video, + text=text, + voice_language=voice_language, + voice_id=voice_id, + voice_speed=voice_speed, + mode="text2video", + auth_token=auth_token, + ) + + +class KlingImageGenerationBase(KlingNodeBase): + """Kling Image Generation Base Node.""" + + RETURN_TYPES = ("IMAGE",) + CATEGORY = "api node/image/Kling" + + def validate_prompt(self, prompt: str, negative_prompt: Optional[str] = None): + if not prompt or len(prompt) > MAX_PROMPT_LENGTH_IMAGE_GEN: + raise ValueError( + f"Prompt must be less than {MAX_PROMPT_LENGTH_IMAGE_GEN} characters" + ) + if negative_prompt and len(negative_prompt) > MAX_PROMPT_LENGTH_IMAGE_GEN: + raise ValueError( + f"Negative prompt must be less than {MAX_PROMPT_LENGTH_IMAGE_GEN} characters" + ) + + +class KlingVirtualTryOnNode(KlingImageGenerationBase): + """Kling Virtual Try On Node.""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "human_image": (IO.IMAGE, {}), + "cloth_image": (IO.IMAGE, {}), + "model_name": model_field_to_node_input( + IO.COMBO, + KlingVirtualTryOnRequest, + "model_name", + enum_type=KlingVirtualTryOnModelName, + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + DESCRIPTION = "Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human." + + def get_response( + self, task_id: str, auth_token: Optional[str] = None + ) -> KlingVirtualTryOnResponse: + return poll_until_finished( + auth_token, + ApiEndpoint( + path=f"{PATH_VIRTUAL_TRY_ON}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=KlingVirtualTryOnResponse, + ), + ) + + def api_call( + self, + human_image: torch.Tensor, + cloth_image: torch.Tensor, + model_name: KlingVirtualTryOnModelName, + auth_token: Optional[str] = None, + ): + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_VIRTUAL_TRY_ON, + method=HttpMethod.POST, + request_model=KlingVirtualTryOnRequest, + response_model=KlingVirtualTryOnResponse, + ), + request=KlingVirtualTryOnRequest( + human_image=tensor_to_base64_string(human_image), + cloth_image=tensor_to_base64_string(cloth_image), + model_name=model_name, + ), + auth_token=auth_token, + ) + + task_creation_response = initial_operation.execute() + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = self.get_response(task_id, auth_token) + validate_image_result_response(final_response) + + images = get_images_from_response(final_response) + return (image_result_to_node_output(images),) + + +class KlingImageGenerationNode(KlingImageGenerationBase): + """Kling Image Generation Node. Generate an image from a text prompt with an optional reference image.""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, + KlingImageGenerationsRequest, + "prompt", + multiline=True, + max_length=MAX_PROMPT_LENGTH_IMAGE_GEN, + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + KlingImageGenerationsRequest, + "negative_prompt", + multiline=True, + ), + "image_type": model_field_to_node_input( + IO.COMBO, + KlingImageGenerationsRequest, + "image_reference", + enum_type=KlingImageGenImageReferenceType, + ), + "image_fidelity": model_field_to_node_input( + IO.FLOAT, + KlingImageGenerationsRequest, + "image_fidelity", + slider=True, + step=0.01, + ), + "human_fidelity": model_field_to_node_input( + IO.FLOAT, + KlingImageGenerationsRequest, + "human_fidelity", + slider=True, + step=0.01, + ), + "model_name": model_field_to_node_input( + IO.COMBO, + KlingImageGenerationsRequest, + "model_name", + enum_type=KlingImageGenModelName, + ), + "aspect_ratio": model_field_to_node_input( + IO.COMBO, + KlingImageGenerationsRequest, + "aspect_ratio", + enum_type=KlingImageGenAspectRatio, + ), + "n": model_field_to_node_input( + IO.INT, + KlingImageGenerationsRequest, + "n", + ), + }, + "optional": { + "image": (IO.IMAGE, {}), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + DESCRIPTION = "Kling Image Generation Node. Generate an image from a text prompt with an optional reference image." + + def get_response( + self, task_id: str, auth_token: Optional[str] = None + ) -> KlingImageGenerationsResponse: + return poll_until_finished( + auth_token, + ApiEndpoint( + path=f"{PATH_IMAGE_GENERATIONS}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=KlingImageGenerationsResponse, + ), + ) + + def api_call( + self, + model_name: KlingImageGenModelName, + prompt: str, + negative_prompt: str, + image_type: KlingImageGenImageReferenceType, + image_fidelity: float, + human_fidelity: float, + n: int, + aspect_ratio: KlingImageGenAspectRatio, + image: Optional[torch.Tensor] = None, + auth_token: Optional[str] = None, + ): + self.validate_prompt(prompt, negative_prompt) + + if image is not None: + image = tensor_to_base64_string(image) + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_IMAGE_GENERATIONS, + method=HttpMethod.POST, + request_model=KlingImageGenerationsRequest, + response_model=KlingImageGenerationsResponse, + ), + request=KlingImageGenerationsRequest( + model_name=model_name, + prompt=prompt, + negative_prompt=negative_prompt, + image=image, + image_reference=image_type, + image_fidelity=image_fidelity, + human_fidelity=human_fidelity, + n=n, + aspect_ratio=aspect_ratio, + ), + auth_token=auth_token, + ) + + task_creation_response = initial_operation.execute() + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = self.get_response(task_id, auth_token) + validate_image_result_response(final_response) + + images = get_images_from_response(final_response) + return (image_result_to_node_output(images),) + + +NODE_CLASS_MAPPINGS = { + "KlingCameraControls": KlingCameraControls, + "KlingTextToVideoNode": KlingTextToVideoNode, + "KlingImage2VideoNode": KlingImage2VideoNode, + "KlingCameraControlI2VNode": KlingCameraControlI2VNode, + "KlingCameraControlT2VNode": KlingCameraControlT2VNode, + "KlingStartEndFrameNode": KlingStartEndFrameNode, + "KlingVideoExtendNode": KlingVideoExtendNode, + "KlingLipSyncAudioToVideoNode": KlingLipSyncAudioToVideoNode, + "KlingLipSyncTextToVideoNode": KlingLipSyncTextToVideoNode, + "KlingVirtualTryOnNode": KlingVirtualTryOnNode, + "KlingImageGenerationNode": KlingImageGenerationNode, + "KlingSingleImageVideoEffectNode": KlingSingleImageVideoEffectNode, + "KlingDualCharacterVideoEffectNode": KlingDualCharacterVideoEffectNode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "KlingCameraControls": "Kling Camera Controls", + "KlingTextToVideoNode": "Kling Text to Video", + "KlingImage2VideoNode": "Kling Image to Video", + "KlingCameraControlI2VNode": "Kling Image to Video (Camera Control)", + "KlingCameraControlT2VNode": "Kling Text to Video (Camera Control)", + "KlingStartEndFrameNode": "Kling Start-End Frame to Video", + "KlingVideoExtendNode": "Kling Video Extend", + "KlingLipSyncAudioToVideoNode": "Kling Lip Sync Video with Audio", + "KlingLipSyncTextToVideoNode": "Kling Lip Sync Video with Text", + "KlingVirtualTryOnNode": "Kling Virtual Try On", + "KlingImageGenerationNode": "Kling Image Generation", + "KlingSingleImageVideoEffectNode": "Kling Video Effects", + "KlingDualCharacterVideoEffectNode": "Kling Dual Character Video Effects", +} diff --git a/comfy_api_nodes/nodes_luma.py b/comfy_api_nodes/nodes_luma.py new file mode 100644 index 000000000..0f0d9aa80 --- /dev/null +++ b/comfy_api_nodes/nodes_luma.py @@ -0,0 +1,702 @@ +from inspect import cleandoc +from comfy.comfy_types.node_typing import IO, ComfyNodeABC +from comfy_api.input_impl.video_types import VideoFromFile +from comfy_api_nodes.apis.luma_api import ( + LumaImageModel, + LumaVideoModel, + LumaVideoOutputResolution, + LumaVideoModelOutputDuration, + LumaAspectRatio, + LumaState, + LumaImageGenerationRequest, + LumaGenerationRequest, + LumaGeneration, + LumaCharacterRef, + LumaModifyImageRef, + LumaImageIdentity, + LumaReference, + LumaReferenceChain, + LumaImageReference, + LumaKeyframes, + LumaConceptChain, + LumaIO, + get_luma_concepts, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + upload_images_to_comfyapi, + process_image_response, + validate_string, +) + +import requests +import torch +from io import BytesIO + + +class LumaReferenceNode(ComfyNodeABC): + """ + Holds an image and weight for use with Luma Generate Image node. + """ + + RETURN_TYPES = (LumaIO.LUMA_REF,) + RETURN_NAMES = ("luma_ref",) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "create_luma_reference" + CATEGORY = "api node/image/Luma" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ( + IO.IMAGE, + { + "tooltip": "Image to use as reference.", + }, + ), + "weight": ( + IO.FLOAT, + { + "default": 1.0, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Weight of image reference.", + }, + ), + }, + "optional": {"luma_ref": (LumaIO.LUMA_REF,)}, + } + + def create_luma_reference( + self, image: torch.Tensor, weight: float, luma_ref: LumaReferenceChain = None + ): + if luma_ref is not None: + luma_ref = luma_ref.clone() + else: + luma_ref = LumaReferenceChain() + luma_ref.add(LumaReference(image=image, weight=round(weight, 2))) + return (luma_ref,) + + +class LumaConceptsNode(ComfyNodeABC): + """ + Holds one or more Camera Concepts for use with Luma Text to Video and Luma Image to Video nodes. + """ + + RETURN_TYPES = (LumaIO.LUMA_CONCEPTS,) + RETURN_NAMES = ("luma_concepts",) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "create_concepts" + CATEGORY = "api node/video/Luma" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "concept1": (get_luma_concepts(include_none=True),), + "concept2": (get_luma_concepts(include_none=True),), + "concept3": (get_luma_concepts(include_none=True),), + "concept4": (get_luma_concepts(include_none=True),), + }, + "optional": { + "luma_concepts": ( + LumaIO.LUMA_CONCEPTS, + { + "tooltip": "Optional Camera Concepts to add to the ones chosen here." + }, + ), + }, + } + + def create_concepts( + self, + concept1: str, + concept2: str, + concept3: str, + concept4: str, + luma_concepts: LumaConceptChain = None, + ): + chain = LumaConceptChain(str_list=[concept1, concept2, concept3, concept4]) + if luma_concepts is not None: + chain = luma_concepts.clone_and_merge(chain) + return (chain,) + + +class LumaImageGenerationNode(ComfyNodeABC): + """ + Generates images synchronously based on prompt and aspect ratio. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Luma" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "model": ([model.value for model in LumaImageModel],), + "aspect_ratio": ( + [ratio.value for ratio in LumaAspectRatio], + { + "default": LumaAspectRatio.ratio_16_9, + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + "style_image_weight": ( + IO.FLOAT, + { + "default": 1.0, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Weight of style image. Ignored if no style_image provided.", + }, + ), + }, + "optional": { + "image_luma_ref": ( + LumaIO.LUMA_REF, + { + "tooltip": "Luma Reference node connection to influence generation with input images; up to 4 images can be considered." + }, + ), + "style_image": ( + IO.IMAGE, + {"tooltip": "Style reference image; only 1 image will be used."}, + ), + "character_image": ( + IO.IMAGE, + { + "tooltip": "Character reference images; can be a batch of multiple, up to 4 images can be considered." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call( + self, + prompt: str, + model: str, + aspect_ratio: str, + seed, + style_image_weight: float, + image_luma_ref: LumaReferenceChain = None, + style_image: torch.Tensor = None, + character_image: torch.Tensor = None, + auth_token=None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=True, min_length=3) + # handle image_luma_ref + api_image_ref = None + if image_luma_ref is not None: + api_image_ref = self._convert_luma_refs( + image_luma_ref, max_refs=4, auth_token=auth_token + ) + # handle style_luma_ref + api_style_ref = None + if style_image is not None: + api_style_ref = self._convert_style_image( + style_image, weight=style_image_weight, auth_token=auth_token + ) + # handle character_ref images + character_ref = None + if character_image is not None: + download_urls = upload_images_to_comfyapi( + character_image, max_images=4, auth_token=auth_token + ) + character_ref = LumaCharacterRef( + identity0=LumaImageIdentity(images=download_urls) + ) + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/luma/generations/image", + method=HttpMethod.POST, + request_model=LumaImageGenerationRequest, + response_model=LumaGeneration, + ), + request=LumaImageGenerationRequest( + prompt=prompt, + model=model, + aspect_ratio=aspect_ratio, + image_ref=api_image_ref, + style_ref=api_style_ref, + character_ref=character_ref, + ), + auth_token=auth_token, + ) + response_api: LumaGeneration = operation.execute() + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/luma/generations/{response_api.id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=LumaGeneration, + ), + completed_statuses=[LumaState.completed], + failed_statuses=[LumaState.failed], + status_extractor=lambda x: x.state, + auth_token=auth_token, + ) + response_poll = operation.execute() + + img_response = requests.get(response_poll.assets.image) + img = process_image_response(img_response) + return (img,) + + def _convert_luma_refs( + self, luma_ref: LumaReferenceChain, max_refs: int, auth_token=None + ): + luma_urls = [] + ref_count = 0 + for ref in luma_ref.refs: + download_urls = upload_images_to_comfyapi( + ref.image, max_images=1, auth_token=auth_token + ) + luma_urls.append(download_urls[0]) + ref_count += 1 + if ref_count >= max_refs: + break + return luma_ref.create_api_model(download_urls=luma_urls, max_refs=max_refs) + + def _convert_style_image( + self, style_image: torch.Tensor, weight: float, auth_token=None + ): + chain = LumaReferenceChain( + first_ref=LumaReference(image=style_image, weight=weight) + ) + return self._convert_luma_refs(chain, max_refs=1, auth_token=auth_token) + + +class LumaImageModifyNode(ComfyNodeABC): + """ + Modifies images synchronously based on prompt and aspect ratio. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Luma" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "image_weight": ( + IO.FLOAT, + { + "default": 0.1, + "min": 0.0, + "max": 0.98, + "step": 0.01, + "tooltip": "Weight of the image; the closer to 1.0, the less the image will be modified.", + }, + ), + "model": ([model.value for model in LumaImageModel],), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": {}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call( + self, + prompt: str, + model: str, + image: torch.Tensor, + image_weight: float, + seed, + auth_token=None, + **kwargs, + ): + # first, upload image + download_urls = upload_images_to_comfyapi( + image, max_images=1, auth_token=auth_token + ) + image_url = download_urls[0] + # next, make Luma call with download url provided + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/luma/generations/image", + method=HttpMethod.POST, + request_model=LumaImageGenerationRequest, + response_model=LumaGeneration, + ), + request=LumaImageGenerationRequest( + prompt=prompt, + model=model, + modify_image_ref=LumaModifyImageRef( + url=image_url, weight=round(max(min(1.0-image_weight, 0.98), 0.0), 2) + ), + ), + auth_token=auth_token, + ) + response_api: LumaGeneration = operation.execute() + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/luma/generations/{response_api.id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=LumaGeneration, + ), + completed_statuses=[LumaState.completed], + failed_statuses=[LumaState.failed], + status_extractor=lambda x: x.state, + auth_token=auth_token, + ) + response_poll = operation.execute() + + img_response = requests.get(response_poll.assets.image) + img = process_image_response(img_response) + return (img,) + + +class LumaTextToVideoGenerationNode(ComfyNodeABC): + """ + Generates videos synchronously based on prompt and output_size. + """ + + RETURN_TYPES = (IO.VIDEO,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/video/Luma" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the video generation", + }, + ), + "model": ([model.value for model in LumaVideoModel],), + "aspect_ratio": ( + [ratio.value for ratio in LumaAspectRatio], + { + "default": LumaAspectRatio.ratio_16_9, + }, + ), + "resolution": ( + [resolution.value for resolution in LumaVideoOutputResolution], + { + "default": LumaVideoOutputResolution.res_540p, + }, + ), + "duration": ([dur.value for dur in LumaVideoModelOutputDuration],), + "loop": ( + IO.BOOLEAN, + { + "default": False, + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "luma_concepts": ( + LumaIO.LUMA_CONCEPTS, + { + "tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call( + self, + prompt: str, + model: str, + aspect_ratio: str, + resolution: str, + duration: str, + loop: bool, + seed, + luma_concepts: LumaConceptChain = None, + auth_token=None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False, min_length=3) + duration = duration if model != LumaVideoModel.ray_1_6 else None + resolution = resolution if model != LumaVideoModel.ray_1_6 else None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/luma/generations", + method=HttpMethod.POST, + request_model=LumaGenerationRequest, + response_model=LumaGeneration, + ), + request=LumaGenerationRequest( + prompt=prompt, + model=model, + resolution=resolution, + aspect_ratio=aspect_ratio, + duration=duration, + loop=loop, + concepts=luma_concepts.create_api_model() if luma_concepts else None, + ), + auth_token=auth_token, + ) + response_api: LumaGeneration = operation.execute() + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/luma/generations/{response_api.id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=LumaGeneration, + ), + completed_statuses=[LumaState.completed], + failed_statuses=[LumaState.failed], + status_extractor=lambda x: x.state, + auth_token=auth_token, + ) + response_poll = operation.execute() + + vid_response = requests.get(response_poll.assets.video) + return (VideoFromFile(BytesIO(vid_response.content)),) + + +class LumaImageToVideoGenerationNode(ComfyNodeABC): + """ + Generates videos synchronously based on prompt, input images, and output_size. + """ + + RETURN_TYPES = (IO.VIDEO,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/video/Luma" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the video generation", + }, + ), + "model": ([model.value for model in LumaVideoModel],), + # "aspect_ratio": ([ratio.value for ratio in LumaAspectRatio], { + # "default": LumaAspectRatio.ratio_16_9, + # }), + "resolution": ( + [resolution.value for resolution in LumaVideoOutputResolution], + { + "default": LumaVideoOutputResolution.res_540p, + }, + ), + "duration": ([dur.value for dur in LumaVideoModelOutputDuration],), + "loop": ( + IO.BOOLEAN, + { + "default": False, + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "first_image": ( + IO.IMAGE, + {"tooltip": "First frame of generated video."}, + ), + "last_image": (IO.IMAGE, {"tooltip": "Last frame of generated video."}), + "luma_concepts": ( + LumaIO.LUMA_CONCEPTS, + { + "tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call( + self, + prompt: str, + model: str, + resolution: str, + duration: str, + loop: bool, + seed, + first_image: torch.Tensor = None, + last_image: torch.Tensor = None, + luma_concepts: LumaConceptChain = None, + auth_token=None, + **kwargs, + ): + if first_image is None and last_image is None: + raise Exception( + "At least one of first_image and last_image requires an input." + ) + keyframes = self._convert_to_keyframes(first_image, last_image, auth_token) + duration = duration if model != LumaVideoModel.ray_1_6 else None + resolution = resolution if model != LumaVideoModel.ray_1_6 else None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/luma/generations", + method=HttpMethod.POST, + request_model=LumaGenerationRequest, + response_model=LumaGeneration, + ), + request=LumaGenerationRequest( + prompt=prompt, + model=model, + aspect_ratio=LumaAspectRatio.ratio_16_9, # ignored, but still needed by the API for some reason + resolution=resolution, + duration=duration, + loop=loop, + keyframes=keyframes, + concepts=luma_concepts.create_api_model() if luma_concepts else None, + ), + auth_token=auth_token, + ) + response_api: LumaGeneration = operation.execute() + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/luma/generations/{response_api.id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=LumaGeneration, + ), + completed_statuses=[LumaState.completed], + failed_statuses=[LumaState.failed], + status_extractor=lambda x: x.state, + auth_token=auth_token, + ) + response_poll = operation.execute() + + vid_response = requests.get(response_poll.assets.video) + return (VideoFromFile(BytesIO(vid_response.content)),) + + def _convert_to_keyframes( + self, + first_image: torch.Tensor = None, + last_image: torch.Tensor = None, + auth_token=None, + ): + if first_image is None and last_image is None: + return None + frame0 = None + frame1 = None + if first_image is not None: + download_urls = upload_images_to_comfyapi( + first_image, max_images=1, auth_token=auth_token + ) + frame0 = LumaImageReference(type="image", url=download_urls[0]) + if last_image is not None: + download_urls = upload_images_to_comfyapi( + last_image, max_images=1, auth_token=auth_token + ) + frame1 = LumaImageReference(type="image", url=download_urls[0]) + return LumaKeyframes(frame0=frame0, frame1=frame1) + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "LumaImageNode": LumaImageGenerationNode, + "LumaImageModifyNode": LumaImageModifyNode, + "LumaVideoNode": LumaTextToVideoGenerationNode, + "LumaImageToVideoNode": LumaImageToVideoGenerationNode, + "LumaReferenceNode": LumaReferenceNode, + "LumaConceptsNode": LumaConceptsNode, +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "LumaImageNode": "Luma Text to Image", + "LumaImageModifyNode": "Luma Image to Image", + "LumaVideoNode": "Luma Text to Video", + "LumaImageToVideoNode": "Luma Image to Video", + "LumaReferenceNode": "Luma Reference", + "LumaConceptsNode": "Luma Concepts", +} diff --git a/comfy_api_nodes/nodes_minimax.py b/comfy_api_nodes/nodes_minimax.py new file mode 100644 index 000000000..cacda22c6 --- /dev/null +++ b/comfy_api_nodes/nodes_minimax.py @@ -0,0 +1,306 @@ +from comfy.comfy_types.node_typing import IO +from comfy_api.input_impl.video_types import VideoFromFile +from comfy_api_nodes.apis import ( + MinimaxVideoGenerationRequest, + MinimaxVideoGenerationResponse, + MinimaxFileRetrieveResponse, + MinimaxTaskResultResponse, + SubjectReferenceItem, + Model +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + download_url_to_bytesio, + upload_images_to_comfyapi, + validate_string, +) + +import torch +import logging + + +class MinimaxTextToVideoNode: + """ + Generates videos synchronously based on a prompt, and optional parameters using MiniMax's API. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt_text": ( + "STRING", + { + "multiline": True, + "default": "", + "tooltip": "Text prompt to guide the video generation", + }, + ), + "model": ( + [ + "T2V-01", + "T2V-01-Director", + ], + { + "default": "T2V-01", + "tooltip": "Model to use for video generation", + }, + ), + }, + "optional": { + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + RETURN_TYPES = ("VIDEO",) + DESCRIPTION = "Generates videos from prompts using MiniMax's API" + FUNCTION = "generate_video" + CATEGORY = "api node/video/MiniMax" + API_NODE = True + OUTPUT_NODE = True + + def generate_video( + self, + prompt_text, + seed=0, + model="T2V-01", + image: torch.Tensor=None, # used for ImageToVideo + subject: torch.Tensor=None, # used for SubjectToVideo + auth_token=None, + ): + ''' + Function used between MiniMax nodes - supports T2V, I2V, and S2V, based on provided arguments. + ''' + if image is None: + validate_string(prompt_text, field_name="prompt_text") + # upload image, if passed in + image_url = None + if image is not None: + image_url = upload_images_to_comfyapi(image, max_images=1, auth_token=auth_token)[0] + + # TODO: figure out how to deal with subject properly, API returns invalid params when using S2V-01 model + subject_reference = None + if subject is not None: + subject_url = upload_images_to_comfyapi(subject, max_images=1, auth_token=auth_token)[0] + subject_reference = [SubjectReferenceItem(image=subject_url)] + + + video_generate_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/minimax/video_generation", + method=HttpMethod.POST, + request_model=MinimaxVideoGenerationRequest, + response_model=MinimaxVideoGenerationResponse, + ), + request=MinimaxVideoGenerationRequest( + model=Model(model), + prompt=prompt_text, + callback_url=None, + first_frame_image=image_url, + subject_reference=subject_reference, + prompt_optimizer=None, + ), + auth_token=auth_token, + ) + response = video_generate_operation.execute() + + task_id = response.task_id + if not task_id: + raise Exception(f"MiniMax generation failed: {response.base_resp}") + + video_generate_operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path="/proxy/minimax/query/video_generation", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=MinimaxTaskResultResponse, + query_params={"task_id": task_id}, + ), + completed_statuses=["Success"], + failed_statuses=["Fail"], + status_extractor=lambda x: x.status.value, + auth_token=auth_token, + ) + task_result = video_generate_operation.execute() + + file_id = task_result.file_id + if file_id is None: + raise Exception("Request was not successful. Missing file ID.") + file_retrieve_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/minimax/files/retrieve", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=MinimaxFileRetrieveResponse, + query_params={"file_id": int(file_id)}, + ), + request=EmptyRequest(), + auth_token=auth_token, + ) + file_result = file_retrieve_operation.execute() + + file_url = file_result.file.download_url + if file_url is None: + raise Exception( + f"No video was found in the response. Full response: {file_result.model_dump()}" + ) + logging.info(f"Generated video URL: {file_url}") + + video_io = download_url_to_bytesio(file_url) + if video_io is None: + error_msg = f"Failed to download video from {file_url}" + logging.error(error_msg) + raise Exception(error_msg) + return (VideoFromFile(video_io),) + + +class MinimaxImageToVideoNode(MinimaxTextToVideoNode): + """ + Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ( + IO.IMAGE, + { + "tooltip": "Image to use as first frame of video generation" + }, + ), + "prompt_text": ( + "STRING", + { + "multiline": True, + "default": "", + "tooltip": "Text prompt to guide the video generation", + }, + ), + "model": ( + [ + "I2V-01-Director", + "I2V-01", + "I2V-01-live", + ], + { + "default": "I2V-01", + "tooltip": "Model to use for video generation", + }, + ), + }, + "optional": { + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + RETURN_TYPES = ("VIDEO",) + DESCRIPTION = "Generates videos from an image and prompts using MiniMax's API" + FUNCTION = "generate_video" + CATEGORY = "api node/video/MiniMax" + API_NODE = True + OUTPUT_NODE = True + + +class MinimaxSubjectToVideoNode(MinimaxTextToVideoNode): + """ + Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "subject": ( + IO.IMAGE, + { + "tooltip": "Image of subject to reference video generation" + }, + ), + "prompt_text": ( + "STRING", + { + "multiline": True, + "default": "", + "tooltip": "Text prompt to guide the video generation", + }, + ), + "model": ( + [ + "S2V-01", + ], + { + "default": "S2V-01", + "tooltip": "Model to use for video generation", + }, + ), + }, + "optional": { + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + RETURN_TYPES = ("VIDEO",) + DESCRIPTION = "Generates videos from an image and prompts using MiniMax's API" + FUNCTION = "generate_video" + CATEGORY = "api node/video/MiniMax" + API_NODE = True + OUTPUT_NODE = True + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "MinimaxTextToVideoNode": MinimaxTextToVideoNode, + "MinimaxImageToVideoNode": MinimaxImageToVideoNode, + # "MinimaxSubjectToVideoNode": MinimaxSubjectToVideoNode, +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "MinimaxTextToVideoNode": "MiniMax Text to Video", + "MinimaxImageToVideoNode": "MiniMax Image to Video", + "MinimaxSubjectToVideoNode": "MiniMax Subject to Video", +} diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py new file mode 100644 index 000000000..c18c65d7a --- /dev/null +++ b/comfy_api_nodes/nodes_openai.py @@ -0,0 +1,487 @@ +import io +from inspect import cleandoc +import numpy as np +import torch +from PIL import Image + +from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict + + +from comfy_api_nodes.apis import ( + OpenAIImageGenerationRequest, + OpenAIImageEditRequest, + OpenAIImageGenerationResponse, +) + +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, +) + +from comfy_api_nodes.apinode_utils import ( + downscale_image_tensor, + validate_and_cast_response, + validate_string, +) + +class OpenAIDalle2(ComfyNodeABC): + """ + Generates images synchronously via OpenAI's DALL·E 2 endpoint. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text prompt for DALL·E", + }, + ), + }, + "optional": { + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2**31 - 1, + "step": 1, + "display": "number", + "control_after_generate": True, + "tooltip": "not implemented yet in backend", + }, + ), + "size": ( + IO.COMBO, + { + "options": ["256x256", "512x512", "1024x1024"], + "default": "1024x1024", + "tooltip": "Image size", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 8, + "step": 1, + "display": "number", + "tooltip": "How many images to generate", + }, + ), + "image": ( + IO.IMAGE, + { + "default": None, + "tooltip": "Optional reference image for image editing.", + }, + ), + "mask": ( + IO.MASK, + { + "default": None, + "tooltip": "Optional mask for inpainting (white areas will be replaced)", + }, + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node/image/OpenAI" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + def api_call( + self, + prompt, + seed=0, + image=None, + mask=None, + n=1, + size="1024x1024", + auth_token=None, + ): + validate_string(prompt, strip_whitespace=False) + model = "dall-e-2" + path = "/proxy/openai/images/generations" + content_type = "application/json" + request_class = OpenAIImageGenerationRequest + img_binary = None + + if image is not None and mask is not None: + path = "/proxy/openai/images/edits" + content_type = "multipart/form-data" + request_class = OpenAIImageEditRequest + + input_tensor = image.squeeze().cpu() + height, width, channels = input_tensor.shape + rgba_tensor = torch.ones(height, width, 4, device="cpu") + rgba_tensor[:, :, :channels] = input_tensor + + if mask.shape[1:] != image.shape[1:-1]: + raise Exception("Mask and Image must be the same size") + rgba_tensor[:, :, 3] = 1 - mask.squeeze().cpu() + + rgba_tensor = downscale_image_tensor(rgba_tensor.unsqueeze(0)).squeeze() + + image_np = (rgba_tensor.numpy() * 255).astype(np.uint8) + img = Image.fromarray(image_np) + img_byte_arr = io.BytesIO() + img.save(img_byte_arr, format="PNG") + img_byte_arr.seek(0) + img_binary = img_byte_arr # .getvalue() + img_binary.name = "image.png" + elif image is not None or mask is not None: + raise Exception("Dall-E 2 image editing requires an image AND a mask") + + # Build the operation + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=request_class, + response_model=OpenAIImageGenerationResponse, + ), + request=request_class( + model=model, + prompt=prompt, + n=n, + size=size, + seed=seed, + ), + files=( + { + "image": img_binary, + } + if img_binary + else None + ), + content_type=content_type, + auth_token=auth_token, + ) + + response = operation.execute() + + img_tensor = validate_and_cast_response(response) + return (img_tensor,) + + +class OpenAIDalle3(ComfyNodeABC): + """ + Generates images synchronously via OpenAI's DALL·E 3 endpoint. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text prompt for DALL·E", + }, + ), + }, + "optional": { + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2**31 - 1, + "step": 1, + "display": "number", + "control_after_generate": True, + "tooltip": "not implemented yet in backend", + }, + ), + "quality": ( + IO.COMBO, + { + "options": ["standard", "hd"], + "default": "standard", + "tooltip": "Image quality", + }, + ), + "style": ( + IO.COMBO, + { + "options": ["natural", "vivid"], + "default": "natural", + "tooltip": "Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images.", + }, + ), + "size": ( + IO.COMBO, + { + "options": ["1024x1024", "1024x1792", "1792x1024"], + "default": "1024x1024", + "tooltip": "Image size", + }, + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node/image/OpenAI" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + def api_call( + self, + prompt, + seed=0, + style="natural", + quality="standard", + size="1024x1024", + auth_token=None, + ): + validate_string(prompt, strip_whitespace=False) + model = "dall-e-3" + + # build the operation + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/openai/images/generations", + method=HttpMethod.POST, + request_model=OpenAIImageGenerationRequest, + response_model=OpenAIImageGenerationResponse, + ), + request=OpenAIImageGenerationRequest( + model=model, + prompt=prompt, + quality=quality, + size=size, + style=style, + seed=seed, + ), + auth_token=auth_token, + ) + + response = operation.execute() + + img_tensor = validate_and_cast_response(response) + return (img_tensor,) + + +class OpenAIGPTImage1(ComfyNodeABC): + """ + Generates images synchronously via OpenAI's GPT Image 1 endpoint. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text prompt for GPT Image 1", + }, + ), + }, + "optional": { + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2**31 - 1, + "step": 1, + "display": "number", + "control_after_generate": True, + "tooltip": "not implemented yet in backend", + }, + ), + "quality": ( + IO.COMBO, + { + "options": ["low", "medium", "high"], + "default": "low", + "tooltip": "Image quality, affects cost and generation time.", + }, + ), + "background": ( + IO.COMBO, + { + "options": ["opaque", "transparent"], + "default": "opaque", + "tooltip": "Return image with or without background", + }, + ), + "size": ( + IO.COMBO, + { + "options": ["auto", "1024x1024", "1024x1536", "1536x1024"], + "default": "auto", + "tooltip": "Image size", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 8, + "step": 1, + "display": "number", + "tooltip": "How many images to generate", + }, + ), + "image": ( + IO.IMAGE, + { + "default": None, + "tooltip": "Optional reference image for image editing.", + }, + ), + "mask": ( + IO.MASK, + { + "default": None, + "tooltip": "Optional mask for inpainting (white areas will be replaced)", + }, + ), + }, + "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node/image/OpenAI" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + def api_call( + self, + prompt, + seed=0, + quality="low", + background="opaque", + image=None, + mask=None, + n=1, + size="1024x1024", + auth_token=None, + ): + validate_string(prompt, strip_whitespace=False) + model = "gpt-image-1" + path = "/proxy/openai/images/generations" + content_type="application/json" + request_class = OpenAIImageGenerationRequest + img_binaries = [] + mask_binary = None + files = [] + + if image is not None: + path = "/proxy/openai/images/edits" + request_class = OpenAIImageEditRequest + content_type ="multipart/form-data" + + batch_size = image.shape[0] + + for i in range(batch_size): + single_image = image[i : i + 1] + scaled_image = downscale_image_tensor(single_image).squeeze() + + image_np = (scaled_image.numpy() * 255).astype(np.uint8) + img = Image.fromarray(image_np) + img_byte_arr = io.BytesIO() + img.save(img_byte_arr, format="PNG") + img_byte_arr.seek(0) + img_binary = img_byte_arr + img_binary.name = f"image_{i}.png" + + img_binaries.append(img_binary) + if batch_size == 1: + files.append(("image", img_binary)) + else: + files.append(("image[]", img_binary)) + + if mask is not None: + if image is None: + raise Exception("Cannot use a mask without an input image") + if image.shape[0] != 1: + raise Exception("Cannot use a mask with multiple image") + if mask.shape[1:] != image.shape[1:-1]: + raise Exception("Mask and Image must be the same size") + batch, height, width = mask.shape + rgba_mask = torch.zeros(height, width, 4, device="cpu") + rgba_mask[:, :, 3] = 1 - mask.squeeze().cpu() + + scaled_mask = downscale_image_tensor(rgba_mask.unsqueeze(0)).squeeze() + + mask_np = (scaled_mask.numpy() * 255).astype(np.uint8) + mask_img = Image.fromarray(mask_np) + mask_img_byte_arr = io.BytesIO() + mask_img.save(mask_img_byte_arr, format="PNG") + mask_img_byte_arr.seek(0) + mask_binary = mask_img_byte_arr + mask_binary.name = "mask.png" + files.append(("mask", mask_binary)) + + # Build the operation + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=request_class, + response_model=OpenAIImageGenerationResponse, + ), + request=request_class( + model=model, + prompt=prompt, + quality=quality, + background=background, + n=n, + seed=seed, + size=size, + ), + files=files if files else None, + content_type=content_type, + auth_token=auth_token, + ) + + response = operation.execute() + + img_tensor = validate_and_cast_response(response) + return (img_tensor,) + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "OpenAIDalle2": OpenAIDalle2, + "OpenAIDalle3": OpenAIDalle3, + "OpenAIGPTImage1": OpenAIGPTImage1, +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "OpenAIDalle2": "OpenAI DALL·E 2", + "OpenAIDalle3": "OpenAI DALL·E 3", + "OpenAIGPTImage1": "OpenAI GPT Image 1", +} diff --git a/comfy_api_nodes/nodes_pika.py b/comfy_api_nodes/nodes_pika.py new file mode 100644 index 000000000..ba4e8457d --- /dev/null +++ b/comfy_api_nodes/nodes_pika.py @@ -0,0 +1,749 @@ +""" +Pika x ComfyUI API Nodes + +Pika API docs: https://pika-827374fb.mintlify.app/api-reference +""" + +import io +from typing import Optional, TypeVar +import logging +import torch +import numpy as np +from comfy_api_nodes.apis import ( + PikaBodyGenerate22T2vGenerate22T2vPost, + PikaGenerateResponse, + PikaBodyGenerate22I2vGenerate22I2vPost, + PikaVideoResponse, + PikaBodyGenerate22C2vGenerate22PikascenesPost, + IngredientsMode, + PikaDurationEnum, + PikaResolutionEnum, + PikaBodyGeneratePikaffectsGeneratePikaffectsPost, + PikaBodyGeneratePikadditionsGeneratePikadditionsPost, + PikaBodyGeneratePikaswapsGeneratePikaswapsPost, + PikaBodyGenerate22KeyframeGenerate22PikaframesPost, + Pikaffect, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + tensor_to_bytesio, + download_url_to_video_output, +) +from comfy_api_nodes.mapper_utils import model_field_to_node_input +from comfy_api.input_impl.video_types import VideoInput, VideoContainer, VideoCodec +from comfy_api.input_impl import VideoFromFile +from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeOptions + +R = TypeVar("R") + +PATH_PIKADDITIONS = "/proxy/pika/generate/pikadditions" +PATH_PIKASWAPS = "/proxy/pika/generate/pikaswaps" +PATH_PIKAFFECTS = "/proxy/pika/generate/pikaffects" + +PIKA_API_VERSION = "2.2" +PATH_TEXT_TO_VIDEO = f"/proxy/pika/generate/{PIKA_API_VERSION}/t2v" +PATH_IMAGE_TO_VIDEO = f"/proxy/pika/generate/{PIKA_API_VERSION}/i2v" +PATH_PIKAFRAMES = f"/proxy/pika/generate/{PIKA_API_VERSION}/pikaframes" +PATH_PIKASCENES = f"/proxy/pika/generate/{PIKA_API_VERSION}/pikascenes" + +PATH_VIDEO_GET = "/proxy/pika/videos" + + +class PikaApiError(Exception): + """Exception for Pika API errors.""" + + pass + + +def is_valid_video_response(response: PikaVideoResponse) -> bool: + """Check if the video response is valid.""" + return hasattr(response, "url") and response.url is not None + + +def is_valid_initial_response(response: PikaGenerateResponse) -> bool: + """Check if the initial response is valid.""" + return hasattr(response, "video_id") and response.video_id is not None + + +class PikaNodeBase(ComfyNodeABC): + """Base class for Pika nodes.""" + + @classmethod + def get_base_inputs_types( + cls, request_model + ) -> dict[str, tuple[IO, InputTypeOptions]]: + """Get the base required inputs types common to all Pika nodes.""" + return { + "prompt_text": model_field_to_node_input( + IO.STRING, + request_model, + "promptText", + multiline=True, + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + request_model, + "negativePrompt", + multiline=True, + ), + "seed": model_field_to_node_input( + IO.INT, + request_model, + "seed", + min=0, + max=0xFFFFFFFF, + control_after_generate=True, + ), + "resolution": model_field_to_node_input( + IO.COMBO, + request_model, + "resolution", + enum_type=PikaResolutionEnum, + ), + "duration": model_field_to_node_input( + IO.COMBO, + request_model, + "duration", + enum_type=PikaDurationEnum, + ), + } + + CATEGORY = "api node/video/Pika" + API_NODE = True + FUNCTION = "api_call" + RETURN_TYPES = ("VIDEO",) + + def poll_for_task_status( + self, task_id: str, auth_token: str + ) -> PikaGenerateResponse: + polling_operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"{PATH_VIDEO_GET}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=PikaVideoResponse, + ), + completed_statuses=[ + "finished", + ], + failed_statuses=["failed", "cancelled"], + status_extractor=lambda response: ( + response.status.value if response.status else None + ), + progress_extractor=lambda response: ( + response.progress if hasattr(response, "progress") else None + ), + auth_token=auth_token, + ) + return polling_operation.execute() + + def execute_task( + self, + initial_operation: SynchronousOperation[R, PikaGenerateResponse], + auth_token: Optional[str] = None, + ) -> tuple[VideoFromFile]: + """Executes the initial operation then polls for the task status until it is completed. + + Args: + initial_operation: The initial operation to execute. + auth_token: The authentication token to use for the API call. + + Returns: + A tuple containing the video file as a VIDEO output. + """ + initial_response = initial_operation.execute() + if not is_valid_initial_response(initial_response): + error_msg = f"Pika initial request failed. Code: {initial_response.code}, Message: {initial_response.message}, Data: {initial_response.data}" + logging.error(error_msg) + raise PikaApiError(error_msg) + + task_id = initial_response.video_id + final_response = self.poll_for_task_status(task_id, auth_token) + if not is_valid_video_response(final_response): + error_msg = ( + f"Pika task {task_id} succeeded but no video data found in response." + ) + logging.error(error_msg) + raise PikaApiError(error_msg) + + video_url = str(final_response.url) + logging.info("Pika task %s succeeded. Video URL: %s", task_id, video_url) + + return (download_url_to_video_output(video_url),) + + +class PikaImageToVideoV2_2(PikaNodeBase): + """Pika 2.2 Image to Video Node.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ( + IO.IMAGE, + {"tooltip": "The image to convert to video"}, + ), + **cls.get_base_inputs_types(PikaBodyGenerate22I2vGenerate22I2vPost), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + DESCRIPTION = "Sends an image and prompt to the Pika API v2.2 to generate a video." + + def api_call( + self, + image: torch.Tensor, + prompt_text: str, + negative_prompt: str, + seed: int, + resolution: str, + duration: int, + auth_token: Optional[str] = None, + ) -> tuple[VideoFromFile]: + # Convert image to BytesIO + image_bytes_io = tensor_to_bytesio(image) + image_bytes_io.seek(0) + + pika_files = {"image": ("image.png", image_bytes_io, "image/png")} + + # Prepare non-file data + pika_request_data = PikaBodyGenerate22I2vGenerate22I2vPost( + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + resolution=resolution, + duration=duration, + ) + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_IMAGE_TO_VIDEO, + method=HttpMethod.POST, + request_model=PikaBodyGenerate22I2vGenerate22I2vPost, + response_model=PikaGenerateResponse, + ), + request=pika_request_data, + files=pika_files, + content_type="multipart/form-data", + auth_token=auth_token, + ) + + return self.execute_task(initial_operation, auth_token) + + +class PikaTextToVideoNodeV2_2(PikaNodeBase): + """Pika Text2Video v2.2 Node.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + **cls.get_base_inputs_types(PikaBodyGenerate22T2vGenerate22T2vPost), + "aspect_ratio": model_field_to_node_input( + IO.FLOAT, + PikaBodyGenerate22T2vGenerate22T2vPost, + "aspectRatio", + step=0.001, + min=0.4, + max=2.5, + default=1.7777777777777777, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + DESCRIPTION = "Sends a text prompt to the Pika API v2.2 to generate a video." + + def api_call( + self, + prompt_text: str, + negative_prompt: str, + seed: int, + resolution: str, + duration: int, + aspect_ratio: float, + auth_token: Optional[str] = None, + ) -> tuple[VideoFromFile]: + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_TEXT_TO_VIDEO, + method=HttpMethod.POST, + request_model=PikaBodyGenerate22T2vGenerate22T2vPost, + response_model=PikaGenerateResponse, + ), + request=PikaBodyGenerate22T2vGenerate22T2vPost( + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + resolution=resolution, + duration=duration, + aspectRatio=aspect_ratio, + ), + auth_token=auth_token, + content_type="application/x-www-form-urlencoded", + ) + + return self.execute_task(initial_operation, auth_token) + + +class PikaScenesV2_2(PikaNodeBase): + """PikaScenes v2.2 Node.""" + + @classmethod + def INPUT_TYPES(cls): + image_ingredient_input = ( + IO.IMAGE, + {"tooltip": "Image that will be used as ingredient to create a video."}, + ) + return { + "required": { + **cls.get_base_inputs_types( + PikaBodyGenerate22C2vGenerate22PikascenesPost, + ), + "ingredients_mode": model_field_to_node_input( + IO.COMBO, + PikaBodyGenerate22C2vGenerate22PikascenesPost, + "ingredientsMode", + enum_type=IngredientsMode, + default="creative", + ), + "aspect_ratio": model_field_to_node_input( + IO.FLOAT, + PikaBodyGenerate22C2vGenerate22PikascenesPost, + "aspectRatio", + step=0.001, + min=0.4, + max=2.5, + default=1.7777777777777777, + ), + }, + "optional": { + "image_ingredient_1": image_ingredient_input, + "image_ingredient_2": image_ingredient_input, + "image_ingredient_3": image_ingredient_input, + "image_ingredient_4": image_ingredient_input, + "image_ingredient_5": image_ingredient_input, + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + DESCRIPTION = "Combine your images to create a video with the objects in them. Upload multiple images as ingredients and generate a high-quality video that incorporates all of them." + + def api_call( + self, + prompt_text: str, + negative_prompt: str, + seed: int, + resolution: str, + duration: int, + ingredients_mode: str, + aspect_ratio: float, + image_ingredient_1: Optional[torch.Tensor] = None, + image_ingredient_2: Optional[torch.Tensor] = None, + image_ingredient_3: Optional[torch.Tensor] = None, + image_ingredient_4: Optional[torch.Tensor] = None, + image_ingredient_5: Optional[torch.Tensor] = None, + auth_token: Optional[str] = None, + ) -> tuple[VideoFromFile]: + # Convert all passed images to BytesIO + all_image_bytes_io = [] + for image in [ + image_ingredient_1, + image_ingredient_2, + image_ingredient_3, + image_ingredient_4, + image_ingredient_5, + ]: + if image is not None: + image_bytes_io = tensor_to_bytesio(image) + image_bytes_io.seek(0) + all_image_bytes_io.append(image_bytes_io) + + pika_files = [ + ("images", (f"image_{i}.png", image_bytes_io, "image/png")) + for i, image_bytes_io in enumerate(all_image_bytes_io) + ] + + pika_request_data = PikaBodyGenerate22C2vGenerate22PikascenesPost( + ingredientsMode=ingredients_mode, + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + resolution=resolution, + duration=duration, + aspectRatio=aspect_ratio, + ) + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_PIKASCENES, + method=HttpMethod.POST, + request_model=PikaBodyGenerate22C2vGenerate22PikascenesPost, + response_model=PikaGenerateResponse, + ), + request=pika_request_data, + files=pika_files, + content_type="multipart/form-data", + auth_token=auth_token, + ) + + return self.execute_task(initial_operation, auth_token) + + +class PikAdditionsNode(PikaNodeBase): + """Pika Pikadditions Node. Add an image into a video.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "video": (IO.VIDEO, {"tooltip": "The video to add an image to."}), + "image": (IO.IMAGE, {"tooltip": "The image to add to the video."}), + "prompt_text": model_field_to_node_input( + IO.STRING, + PikaBodyGeneratePikadditionsGeneratePikadditionsPost, + "promptText", + multiline=True, + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + PikaBodyGeneratePikadditionsGeneratePikadditionsPost, + "negativePrompt", + multiline=True, + ), + "seed": model_field_to_node_input( + IO.INT, + PikaBodyGeneratePikadditionsGeneratePikadditionsPost, + "seed", + min=0, + max=0xFFFFFFFF, + control_after_generate=True, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + DESCRIPTION = "Add any object or image into your video. Upload a video and specify what you’d like to add to create a seamlessly integrated result." + + def api_call( + self, + video: VideoInput, + image: torch.Tensor, + prompt_text: str, + negative_prompt: str, + seed: int, + auth_token: Optional[str] = None, + ) -> tuple[VideoFromFile]: + # Convert video to BytesIO + video_bytes_io = io.BytesIO() + video.save_to(video_bytes_io, format=VideoContainer.MP4, codec=VideoCodec.H264) + video_bytes_io.seek(0) + + # Convert image to BytesIO + image_bytes_io = tensor_to_bytesio(image) + image_bytes_io.seek(0) + + pika_files = [ + ("video", ("video.mp4", video_bytes_io, "video/mp4")), + ("image", ("image.png", image_bytes_io, "image/png")), + ] + + # Prepare non-file data + pika_request_data = PikaBodyGeneratePikadditionsGeneratePikadditionsPost( + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + ) + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_PIKADDITIONS, + method=HttpMethod.POST, + request_model=PikaBodyGeneratePikadditionsGeneratePikadditionsPost, + response_model=PikaGenerateResponse, + ), + request=pika_request_data, + files=pika_files, + content_type="multipart/form-data", + auth_token=auth_token, + ) + + return self.execute_task(initial_operation, auth_token) + + +class PikaSwapsNode(PikaNodeBase): + """Pika Pikaswaps Node.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "video": (IO.VIDEO, {"tooltip": "The video to swap an object in."}), + "image": ( + IO.IMAGE, + { + "tooltip": "The image used to replace the masked object in the video." + }, + ), + "mask": ( + IO.MASK, + {"tooltip": "Use the mask to define areas in the video to replace"}, + ), + "prompt_text": model_field_to_node_input( + IO.STRING, + PikaBodyGeneratePikaswapsGeneratePikaswapsPost, + "promptText", + multiline=True, + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + PikaBodyGeneratePikaswapsGeneratePikaswapsPost, + "negativePrompt", + multiline=True, + ), + "seed": model_field_to_node_input( + IO.INT, + PikaBodyGeneratePikaswapsGeneratePikaswapsPost, + "seed", + min=0, + max=0xFFFFFFFF, + control_after_generate=True, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + DESCRIPTION = "Swap out any object or region of your video with a new image or object. Define areas to replace either with a mask or coordinates." + RETURN_TYPES = ("VIDEO",) + + def api_call( + self, + video: VideoInput, + image: torch.Tensor, + mask: torch.Tensor, + prompt_text: str, + negative_prompt: str, + seed: int, + auth_token: Optional[str] = None, + ) -> tuple[VideoFromFile]: + # Convert video to BytesIO + video_bytes_io = io.BytesIO() + video.save_to(video_bytes_io, format=VideoContainer.MP4, codec=VideoCodec.H264) + video_bytes_io.seek(0) + + # Convert mask to binary mask with three channels + mask = torch.round(mask) + mask = mask.repeat(1, 3, 1, 1) + + # Convert 3-channel binary mask to BytesIO + mask_bytes_io = io.BytesIO() + mask_bytes_io.write(mask.numpy().astype(np.uint8)) + mask_bytes_io.seek(0) + + # Convert image to BytesIO + image_bytes_io = tensor_to_bytesio(image) + image_bytes_io.seek(0) + + pika_files = [ + ("video", ("video.mp4", video_bytes_io, "video/mp4")), + ("image", ("image.png", image_bytes_io, "image/png")), + ("modifyRegionMask", ("mask.png", mask_bytes_io, "image/png")), + ] + + # Prepare non-file data + pika_request_data = PikaBodyGeneratePikaswapsGeneratePikaswapsPost( + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + ) + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_PIKADDITIONS, + method=HttpMethod.POST, + request_model=PikaBodyGeneratePikadditionsGeneratePikadditionsPost, + response_model=PikaGenerateResponse, + ), + request=pika_request_data, + files=pika_files, + content_type="multipart/form-data", + auth_token=auth_token, + ) + + return self.execute_task(initial_operation, auth_token) + + +class PikaffectsNode(PikaNodeBase): + """Pika Pikaffects Node.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ( + IO.IMAGE, + {"tooltip": "The reference image to apply the Pikaffect to."}, + ), + "pikaffect": model_field_to_node_input( + IO.COMBO, + PikaBodyGeneratePikaffectsGeneratePikaffectsPost, + "pikaffect", + enum_type=Pikaffect, + default="Cake-ify", + ), + "prompt_text": model_field_to_node_input( + IO.STRING, + PikaBodyGeneratePikaffectsGeneratePikaffectsPost, + "promptText", + multiline=True, + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + PikaBodyGeneratePikaffectsGeneratePikaffectsPost, + "negativePrompt", + multiline=True, + ), + "seed": model_field_to_node_input( + IO.INT, + PikaBodyGeneratePikaffectsGeneratePikaffectsPost, + "seed", + min=0, + max=0xFFFFFFFF, + control_after_generate=True, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + DESCRIPTION = "Generate a video with a specific Pikaffect. Supported Pikaffects: Cake-ify, Crumble, Crush, Decapitate, Deflate, Dissolve, Explode, Eye-pop, Inflate, Levitate, Melt, Peel, Poke, Squish, Ta-da, Tear" + + def api_call( + self, + image: torch.Tensor, + pikaffect: str, + prompt_text: str, + negative_prompt: str, + seed: int, + auth_token: Optional[str] = None, + ) -> tuple[VideoFromFile]: + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_PIKAFFECTS, + method=HttpMethod.POST, + request_model=PikaBodyGeneratePikaffectsGeneratePikaffectsPost, + response_model=PikaGenerateResponse, + ), + request=PikaBodyGeneratePikaffectsGeneratePikaffectsPost( + pikaffect=pikaffect, + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + ), + files={"image": ("image.png", tensor_to_bytesio(image), "image/png")}, + content_type="multipart/form-data", + auth_token=auth_token, + ) + + return self.execute_task(initial_operation, auth_token) + + +class PikaStartEndFrameNode2_2(PikaNodeBase): + """PikaFrames v2.2 Node.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_start": (IO.IMAGE, {"tooltip": "The first image to combine."}), + "image_end": (IO.IMAGE, {"tooltip": "The last image to combine."}), + **cls.get_base_inputs_types( + PikaBodyGenerate22KeyframeGenerate22PikaframesPost + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + DESCRIPTION = "Generate a video by combining your first and last frame. Upload two images to define the start and end points, and let the AI create a smooth transition between them." + + def api_call( + self, + image_start: torch.Tensor, + image_end: torch.Tensor, + prompt_text: str, + negative_prompt: str, + seed: int, + resolution: str, + duration: int, + auth_token: Optional[str] = None, + ) -> tuple[VideoFromFile]: + + pika_files = [ + ( + "keyFrames", + ("image_start.png", tensor_to_bytesio(image_start), "image/png"), + ), + ("keyFrames", ("image_end.png", tensor_to_bytesio(image_end), "image/png")), + ] + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_PIKAFRAMES, + method=HttpMethod.POST, + request_model=PikaBodyGenerate22KeyframeGenerate22PikaframesPost, + response_model=PikaGenerateResponse, + ), + request=PikaBodyGenerate22KeyframeGenerate22PikaframesPost( + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + resolution=resolution, + duration=duration, + ), + files=pika_files, + content_type="multipart/form-data", + auth_token=auth_token, + ) + + return self.execute_task(initial_operation, auth_token) + + +NODE_CLASS_MAPPINGS = { + "PikaImageToVideoNode2_2": PikaImageToVideoV2_2, + "PikaTextToVideoNode2_2": PikaTextToVideoNodeV2_2, + "PikaScenesV2_2": PikaScenesV2_2, + "Pikadditions": PikAdditionsNode, + "Pikaswaps": PikaSwapsNode, + "Pikaffects": PikaffectsNode, + "PikaStartEndFrameNode2_2": PikaStartEndFrameNode2_2, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PikaImageToVideoNode2_2": "Pika Image to Video", + "PikaTextToVideoNode2_2": "Pika Text to Video", + "PikaScenesV2_2": "Pika Scenes (Video Image Composition)", + "Pikadditions": "Pikadditions (Video Object Insertion)", + "Pikaswaps": "Pika Swaps (Video Object Replacement)", + "Pikaffects": "Pikaffects (Video Effects)", + "PikaStartEndFrameNode2_2": "Pika Start and End Frame to Video", +} diff --git a/comfy_api_nodes/nodes_pixverse.py b/comfy_api_nodes/nodes_pixverse.py new file mode 100644 index 000000000..dbb90c1dd --- /dev/null +++ b/comfy_api_nodes/nodes_pixverse.py @@ -0,0 +1,492 @@ +from inspect import cleandoc + +from comfy_api_nodes.apis.pixverse_api import ( + PixverseTextVideoRequest, + PixverseImageVideoRequest, + PixverseTransitionVideoRequest, + PixverseImageUploadResponse, + PixverseVideoResponse, + PixverseGenerationStatusResponse, + PixverseAspectRatio, + PixverseQuality, + PixverseDuration, + PixverseMotionMode, + PixverseStatus, + PixverseIO, + pixverse_templates, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + tensor_to_bytesio, + validate_string, +) +from comfy.comfy_types.node_typing import IO, ComfyNodeABC +from comfy_api.input_impl import VideoFromFile + +import torch +import requests +from io import BytesIO + + +def upload_image_to_pixverse(image: torch.Tensor, auth_token=None): + # first, upload image to Pixverse and get image id to use in actual generation call + files = { + "image": tensor_to_bytesio(image) + } + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/pixverse/image/upload", + method=HttpMethod.POST, + request_model=EmptyRequest, + response_model=PixverseImageUploadResponse, + ), + request=EmptyRequest(), + files=files, + content_type="multipart/form-data", + auth_token=auth_token, + ) + response_upload: PixverseImageUploadResponse = operation.execute() + + if response_upload.Resp is None: + raise Exception(f"PixVerse image upload request failed: '{response_upload.ErrMsg}'") + + return response_upload.Resp.img_id + + +class PixverseTemplateNode: + """ + Select template for PixVerse Video generation. + """ + + RETURN_TYPES = (PixverseIO.TEMPLATE,) + RETURN_NAMES = ("pixverse_template",) + FUNCTION = "create_template" + CATEGORY = "api node/video/PixVerse" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "template": (list(pixverse_templates.keys()), ), + } + } + + def create_template(self, template: str): + template_id = pixverse_templates.get(template, None) + if template_id is None: + raise Exception(f"Template '{template}' is not recognized.") + # just return the integer + return (template_id,) + + +class PixverseTextToVideoNode(ComfyNodeABC): + """ + Generates videos synchronously based on prompt and output_size. + """ + + RETURN_TYPES = (IO.VIDEO,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/video/PixVerse" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the video generation", + }, + ), + "aspect_ratio": ( + [ratio.value for ratio in PixverseAspectRatio], + ), + "quality": ( + [resolution.value for resolution in PixverseQuality], + { + "default": PixverseQuality.res_540p, + }, + ), + "duration_seconds": ([dur.value for dur in PixverseDuration],), + "motion_mode": ([mode.value for mode in PixverseMotionMode],), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2147483647, + "control_after_generate": True, + "tooltip": "Seed for video generation.", + }, + ), + }, + "optional": { + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + "pixverse_template": ( + PixverseIO.TEMPLATE, + { + "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node." + } + ) + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call( + self, + prompt: str, + aspect_ratio: str, + quality: str, + duration_seconds: int, + motion_mode: str, + seed, + negative_prompt: str=None, + pixverse_template: int=None, + auth_token=None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False) + # 1080p is limited to 5 seconds duration + # only normal motion_mode supported for 1080p or for non-5 second duration + if quality == PixverseQuality.res_1080p: + motion_mode = PixverseMotionMode.normal + duration_seconds = PixverseDuration.dur_5 + elif duration_seconds != PixverseDuration.dur_5: + motion_mode = PixverseMotionMode.normal + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/pixverse/video/text/generate", + method=HttpMethod.POST, + request_model=PixverseTextVideoRequest, + response_model=PixverseVideoResponse, + ), + request=PixverseTextVideoRequest( + prompt=prompt, + aspect_ratio=aspect_ratio, + quality=quality, + duration=duration_seconds, + motion_mode=motion_mode, + negative_prompt=negative_prompt if negative_prompt else None, + template_id=pixverse_template, + seed=seed, + ), + auth_token=auth_token, + ) + response_api = operation.execute() + + if response_api.Resp is None: + raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'") + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=PixverseGenerationStatusResponse, + ), + completed_statuses=[PixverseStatus.successful], + failed_statuses=[PixverseStatus.contents_moderation, PixverseStatus.failed, PixverseStatus.deleted], + status_extractor=lambda x: x.Resp.status, + auth_token=auth_token, + ) + response_poll = operation.execute() + + vid_response = requests.get(response_poll.Resp.url) + return (VideoFromFile(BytesIO(vid_response.content)),) + + +class PixverseImageToVideoNode(ComfyNodeABC): + """ + Generates videos synchronously based on prompt and output_size. + """ + + RETURN_TYPES = (IO.VIDEO,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/video/PixVerse" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ( + IO.IMAGE, + ), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the video generation", + }, + ), + "quality": ( + [resolution.value for resolution in PixverseQuality], + { + "default": PixverseQuality.res_540p, + }, + ), + "duration_seconds": ([dur.value for dur in PixverseDuration],), + "motion_mode": ([mode.value for mode in PixverseMotionMode],), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2147483647, + "control_after_generate": True, + "tooltip": "Seed for video generation.", + }, + ), + }, + "optional": { + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + "pixverse_template": ( + PixverseIO.TEMPLATE, + { + "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node." + } + ) + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call( + self, + image: torch.Tensor, + prompt: str, + quality: str, + duration_seconds: int, + motion_mode: str, + seed, + negative_prompt: str=None, + pixverse_template: int=None, + auth_token=None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False) + img_id = upload_image_to_pixverse(image, auth_token=auth_token) + + # 1080p is limited to 5 seconds duration + # only normal motion_mode supported for 1080p or for non-5 second duration + if quality == PixverseQuality.res_1080p: + motion_mode = PixverseMotionMode.normal + duration_seconds = PixverseDuration.dur_5 + elif duration_seconds != PixverseDuration.dur_5: + motion_mode = PixverseMotionMode.normal + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/pixverse/video/img/generate", + method=HttpMethod.POST, + request_model=PixverseImageVideoRequest, + response_model=PixverseVideoResponse, + ), + request=PixverseImageVideoRequest( + img_id=img_id, + prompt=prompt, + quality=quality, + duration=duration_seconds, + motion_mode=motion_mode, + negative_prompt=negative_prompt if negative_prompt else None, + template_id=pixverse_template, + seed=seed, + ), + auth_token=auth_token, + ) + response_api = operation.execute() + + if response_api.Resp is None: + raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'") + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=PixverseGenerationStatusResponse, + ), + completed_statuses=[PixverseStatus.successful], + failed_statuses=[PixverseStatus.contents_moderation, PixverseStatus.failed, PixverseStatus.deleted], + status_extractor=lambda x: x.Resp.status, + auth_token=auth_token, + ) + response_poll = operation.execute() + + vid_response = requests.get(response_poll.Resp.url) + return (VideoFromFile(BytesIO(vid_response.content)),) + + +class PixverseTransitionVideoNode(ComfyNodeABC): + """ + Generates videos synchronously based on prompt and output_size. + """ + + RETURN_TYPES = (IO.VIDEO,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/video/PixVerse" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "first_frame": ( + IO.IMAGE, + ), + "last_frame": ( + IO.IMAGE, + ), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the video generation", + }, + ), + "quality": ( + [resolution.value for resolution in PixverseQuality], + { + "default": PixverseQuality.res_540p, + }, + ), + "duration_seconds": ([dur.value for dur in PixverseDuration],), + "motion_mode": ([mode.value for mode in PixverseMotionMode],), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2147483647, + "control_after_generate": True, + "tooltip": "Seed for video generation.", + }, + ), + }, + "optional": { + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call( + self, + first_frame: torch.Tensor, + last_frame: torch.Tensor, + prompt: str, + quality: str, + duration_seconds: int, + motion_mode: str, + seed, + negative_prompt: str=None, + auth_token=None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False) + first_frame_id = upload_image_to_pixverse(first_frame, auth_token=auth_token) + last_frame_id = upload_image_to_pixverse(last_frame, auth_token=auth_token) + + # 1080p is limited to 5 seconds duration + # only normal motion_mode supported for 1080p or for non-5 second duration + if quality == PixverseQuality.res_1080p: + motion_mode = PixverseMotionMode.normal + duration_seconds = PixverseDuration.dur_5 + elif duration_seconds != PixverseDuration.dur_5: + motion_mode = PixverseMotionMode.normal + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/pixverse/video/transition/generate", + method=HttpMethod.POST, + request_model=PixverseTransitionVideoRequest, + response_model=PixverseVideoResponse, + ), + request=PixverseTransitionVideoRequest( + first_frame_img=first_frame_id, + last_frame_img=last_frame_id, + prompt=prompt, + quality=quality, + duration=duration_seconds, + motion_mode=motion_mode, + negative_prompt=negative_prompt if negative_prompt else None, + seed=seed, + ), + auth_token=auth_token, + ) + response_api = operation.execute() + + if response_api.Resp is None: + raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'") + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=PixverseGenerationStatusResponse, + ), + completed_statuses=[PixverseStatus.successful], + failed_statuses=[PixverseStatus.contents_moderation, PixverseStatus.failed, PixverseStatus.deleted], + status_extractor=lambda x: x.Resp.status, + auth_token=auth_token, + ) + response_poll = operation.execute() + + vid_response = requests.get(response_poll.Resp.url) + return (VideoFromFile(BytesIO(vid_response.content)),) + + +NODE_CLASS_MAPPINGS = { + "PixverseTextToVideoNode": PixverseTextToVideoNode, + "PixverseImageToVideoNode": PixverseImageToVideoNode, + "PixverseTransitionVideoNode": PixverseTransitionVideoNode, + "PixverseTemplateNode": PixverseTemplateNode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PixverseTextToVideoNode": "PixVerse Text to Video", + "PixverseImageToVideoNode": "PixVerse Image to Video", + "PixverseTransitionVideoNode": "PixVerse Transition Video", + "PixverseTemplateNode": "PixVerse Template", +} diff --git a/comfy_api_nodes/nodes_recraft.py b/comfy_api_nodes/nodes_recraft.py new file mode 100644 index 000000000..994f377d1 --- /dev/null +++ b/comfy_api_nodes/nodes_recraft.py @@ -0,0 +1,1217 @@ +from __future__ import annotations +from inspect import cleandoc +from comfy.utils import ProgressBar +from comfy.comfy_types.node_typing import IO +from comfy_api_nodes.apis.recraft_api import ( + RecraftImageGenerationRequest, + RecraftImageGenerationResponse, + RecraftImageSize, + RecraftModel, + RecraftStyle, + RecraftStyleV3, + RecraftColor, + RecraftColorChain, + RecraftControls, + RecraftIO, + get_v3_substyles, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + bytesio_to_image_tensor, + download_url_to_bytesio, + tensor_to_bytesio, + resize_mask_to_image, + validate_string, +) +import folder_paths +import json +import os +import torch +from io import BytesIO +from PIL import UnidentifiedImageError + + +def handle_recraft_file_request( + image: torch.Tensor, + path: str, + mask: torch.Tensor=None, + total_pixels=4096*4096, + timeout=1024, + request=None, + auth_token=None + ) -> list[BytesIO]: + """ + Handle sending common Recraft file-only request to get back file bytes. + """ + if request is None: + request = EmptyRequest() + + files = { + 'image': tensor_to_bytesio(image, total_pixels=total_pixels).read() + } + if mask is not None: + files['mask'] = tensor_to_bytesio(mask, total_pixels=total_pixels).read() + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=type(request), + response_model=RecraftImageGenerationResponse, + ), + request=request, + files=files, + content_type="multipart/form-data", + auth_token=auth_token, + multipart_parser=recraft_multipart_parser, + ) + response: RecraftImageGenerationResponse = operation.execute() + all_bytesio = [] + if response.image is not None: + all_bytesio.append(download_url_to_bytesio(response.image.url, timeout=timeout)) + else: + for data in response.data: + all_bytesio.append(download_url_to_bytesio(data.url, timeout=timeout)) + + return all_bytesio + + +def recraft_multipart_parser(data, parent_key=None, formatter: callable=None, converted_to_check: list[list]=None, is_list=False) -> dict: + """ + Formats data such that multipart/form-data will work with requests library + when both files and data are present. + + The OpenAI client that Recraft uses has a bizarre way of serializing lists: + + It does NOT keep track of indeces of each list, so for background_color, that must be serialized as: + 'background_color[rgb][]' = [0, 0, 255] + where the array is assigned to a key that has '[]' at the end, to signal it's an array. + + This has the consequence of nested lists having the exact same key, forcing arrays to merge; all colors inputs fall under the same key: + if 1 color -> 'controls[colors][][rgb][]' = [0, 0, 255] + if 2 colors -> 'controls[colors][][rgb][]' = [0, 0, 255, 255, 0, 0] + if 3 colors -> 'controls[colors][][rgb][]' = [0, 0, 255, 255, 0, 0, 0, 255, 0] + etc. + Whoever made this serialization up at OpenAI added the constraint that lists must be of uniform length on objects of same 'type'. + """ + # Modification of a function that handled a different type of multipart parsing, big ups: + # https://gist.github.com/kazqvaizer/4cebebe5db654a414132809f9f88067b + + def handle_converted_lists(data, parent_key, lists_to_check=tuple[list]): + # if list already exists exists, just extend list with data + for check_list in lists_to_check: + for conv_tuple in check_list: + if conv_tuple[0] == parent_key and type(conv_tuple[1]) is list: + conv_tuple[1].append(formatter(data)) + return True + return False + + if converted_to_check is None: + converted_to_check = [] + + + if formatter is None: + formatter = lambda v: v # Multipart representation of value + + if type(data) is not dict: + # if list already exists exists, just extend list with data + added = handle_converted_lists(data, parent_key, converted_to_check) + if added: + return {} + # otherwise if is_list, create new list with data + if is_list: + return {parent_key: [formatter(data)]} + # return new key with data + return {parent_key: formatter(data)} + + converted = [] + next_check = [converted] + next_check.extend(converted_to_check) + + for key, value in data.items(): + current_key = key if parent_key is None else f"{parent_key}[{key}]" + if type(value) is dict: + converted.extend(recraft_multipart_parser(value, current_key, formatter, next_check).items()) + elif type(value) is list: + for ind, list_value in enumerate(value): + iter_key = f"{current_key}[]" + converted.extend(recraft_multipart_parser(list_value, iter_key, formatter, next_check, is_list=True).items()) + else: + converted.append((current_key, formatter(value))) + + return dict(converted) + + +class handle_recraft_image_output: + """ + Catch an exception related to receiving SVG data instead of image, when Infinite Style Library style_id is in use. + """ + def __init__(self): + pass + + def __enter__(self): + pass + + def __exit__(self, exc_type, exc_val, exc_tb): + if exc_type is not None and exc_type is UnidentifiedImageError: + raise Exception("Received output data was not an image; likely an SVG. If you used style_id, make sure it is not a Vector art style.") + + +class SVG: + """ + Stores SVG representations via a list of BytesIO objects. + """ + def __init__(self, data: list[BytesIO]): + self.data = data + + def combine(self, other: SVG): + return SVG(self.data + other.data) + + @staticmethod + def combine_all(svgs: list[SVG]): + all_svgs = [] + for svg in svgs: + all_svgs.extend(svg.data) + return SVG(all_svgs) + + +class SaveSVGNode: + """ + Save SVG files on disk. + """ + + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + RETURN_TYPES = () + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "save_svg" + CATEGORY = "api node/image/Recraft" + OUTPUT_NODE = True + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "svg": (RecraftIO.SVG,), + "filename_prefix": ("STRING", {"default": "svg/ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}) + }, + "hidden": { + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO" + } + } + + def save_svg(self, svg: SVG, filename_prefix="svg/ComfyUI", prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + results = list() + + # Prepare metadata JSON + metadata_dict = {} + if prompt is not None: + metadata_dict["prompt"] = prompt + if extra_pnginfo is not None: + metadata_dict.update(extra_pnginfo) + + # Convert metadata to JSON string + metadata_json = json.dumps(metadata_dict, indent=2) if metadata_dict else None + + for batch_number, svg_bytes in enumerate(svg.data): + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.svg" + + # Read SVG content + svg_bytes.seek(0) + svg_content = svg_bytes.read().decode('utf-8') + + # Inject metadata if available + if metadata_json: + # Create metadata element with CDATA section + metadata_element = f""" + + +""" + # Insert metadata after opening svg tag using regex + import re + svg_content = re.sub(r'(]*>)', r'\1\n' + metadata_element, svg_content) + + # Write the modified SVG to file + with open(os.path.join(full_output_folder, file), 'wb') as svg_file: + svg_file.write(svg_content.encode('utf-8')) + + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + return { "ui": { "images": results } } + + +class RecraftColorRGBNode: + """ + Create Recraft Color by choosing specific RGB values. + """ + + RETURN_TYPES = (RecraftIO.COLOR,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + RETURN_NAMES = ("recraft_color",) + FUNCTION = "create_color" + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "r": (IO.INT, { + "default": 0, + "min": 0, + "max": 255, + "tooltip": "Red value of color." + }), + "g": (IO.INT, { + "default": 0, + "min": 0, + "max": 255, + "tooltip": "Green value of color." + }), + "b": (IO.INT, { + "default": 0, + "min": 0, + "max": 255, + "tooltip": "Blue value of color." + }), + }, + "optional": { + "recraft_color": (RecraftIO.COLOR,), + } + } + + def create_color(self, r: int, g: int, b: int, recraft_color: RecraftColorChain=None): + recraft_color = recraft_color.clone() if recraft_color else RecraftColorChain() + recraft_color.add(RecraftColor(r, g, b)) + return (recraft_color, ) + + +class RecraftControlsNode: + """ + Create Recraft Controls for customizing Recraft generation. + """ + + RETURN_TYPES = (RecraftIO.CONTROLS,) + RETURN_NAMES = ("recraft_controls",) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "create_controls" + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + }, + "optional": { + "colors": (RecraftIO.COLOR,), + "background_color": (RecraftIO.COLOR,), + } + } + + def create_controls(self, colors: RecraftColorChain=None, background_color: RecraftColorChain=None): + return (RecraftControls(colors=colors, background_color=background_color), ) + + +class RecraftStyleV3RealisticImageNode: + """ + Select realistic_image style and optional substyle. + """ + + RETURN_TYPES = (RecraftIO.STYLEV3,) + RETURN_NAMES = ("recraft_style",) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "create_style" + CATEGORY = "api node/image/Recraft" + + RECRAFT_STYLE = RecraftStyleV3.realistic_image + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "substyle": (get_v3_substyles(s.RECRAFT_STYLE),), + } + } + + def create_style(self, substyle: str): + if substyle == "None": + substyle = None + return (RecraftStyle(self.RECRAFT_STYLE, substyle),) + + +class RecraftStyleV3DigitalIllustrationNode(RecraftStyleV3RealisticImageNode): + """ + Select digital_illustration style and optional substyle. + """ + + RECRAFT_STYLE = RecraftStyleV3.digital_illustration + + +class RecraftStyleV3VectorIllustrationNode(RecraftStyleV3RealisticImageNode): + """ + Select vector_illustration style and optional substyle. + """ + + RECRAFT_STYLE = RecraftStyleV3.vector_illustration + + +class RecraftStyleV3LogoRasterNode(RecraftStyleV3RealisticImageNode): + """ + Select vector_illustration style and optional substyle. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "substyle": (get_v3_substyles(s.RECRAFT_STYLE, include_none=False),), + } + } + + RECRAFT_STYLE = RecraftStyleV3.logo_raster + + +class RecraftStyleInfiniteStyleLibrary: + """ + Select style based on preexisting UUID from Recraft's Infinite Style Library. + """ + + RETURN_TYPES = (RecraftIO.STYLEV3,) + RETURN_NAMES = ("recraft_style",) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "create_style" + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "style_id": (IO.STRING, { + "default": "", + "tooltip": "UUID of style from Infinite Style Library.", + }) + } + } + + def create_style(self, style_id: str): + if not style_id: + raise Exception("The style_id input cannot be empty.") + return (RecraftStyle(style_id=style_id),) + + +class RecraftTextToImageNode: + """ + Generates images synchronously based on prompt and resolution. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation.", + }, + ), + "size": ( + [res.value for res in RecraftImageSize], + { + "default": RecraftImageSize.res_1024x1024, + "tooltip": "The size of the generated image.", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 6, + "tooltip": "The number of images to generate.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "recraft_style": (RecraftIO.STYLEV3,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + "recraft_controls": ( + RecraftIO.CONTROLS, + { + "tooltip": "Optional additional controls over the generation via the Recraft Controls node." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call( + self, + prompt: str, + size: str, + n: int, + seed, + recraft_style: RecraftStyle = None, + negative_prompt: str = None, + recraft_controls: RecraftControls = None, + auth_token=None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False, max_length=1000) + default_style = RecraftStyle(RecraftStyleV3.realistic_image) + if recraft_style is None: + recraft_style = default_style + + controls_api = None + if recraft_controls: + controls_api = recraft_controls.create_api_model() + + if not negative_prompt: + negative_prompt = None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/recraft/image_generation", + method=HttpMethod.POST, + request_model=RecraftImageGenerationRequest, + response_model=RecraftImageGenerationResponse, + ), + request=RecraftImageGenerationRequest( + prompt=prompt, + negative_prompt=negative_prompt, + model=RecraftModel.recraftv3, + size=size, + n=n, + style=recraft_style.style, + substyle=recraft_style.substyle, + style_id=recraft_style.style_id, + controls=controls_api, + ), + auth_token=auth_token, + ) + response: RecraftImageGenerationResponse = operation.execute() + images = [] + for data in response.data: + with handle_recraft_image_output(): + image = bytesio_to_image_tensor( + download_url_to_bytesio(data.url, timeout=1024) + ) + if len(image.shape) < 4: + image = image.unsqueeze(0) + images.append(image) + output_image = torch.cat(images, dim=0) + + return (output_image,) + + +class RecraftImageToImageNode: + """ + Modify image based on prompt and strength. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation.", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 6, + "tooltip": "The number of images to generate.", + }, + ), + "strength": ( + IO.FLOAT, + { + "default": 0.5, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity." + } + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "recraft_style": (RecraftIO.STYLEV3,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + "recraft_controls": ( + RecraftIO.CONTROLS, + { + "tooltip": "Optional additional controls over the generation via the Recraft Controls node." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call( + self, + image: torch.Tensor, + prompt: str, + n: int, + strength: float, + seed, + auth_token=None, + recraft_style: RecraftStyle = None, + negative_prompt: str = None, + recraft_controls: RecraftControls = None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False, max_length=1000) + default_style = RecraftStyle(RecraftStyleV3.realistic_image) + if recraft_style is None: + recraft_style = default_style + + controls_api = None + if recraft_controls: + controls_api = recraft_controls.create_api_model() + + if not negative_prompt: + negative_prompt = None + + request = RecraftImageGenerationRequest( + prompt=prompt, + negative_prompt=negative_prompt, + model=RecraftModel.recraftv3, + n=n, + strength=round(strength, 2), + style=recraft_style.style, + substyle=recraft_style.substyle, + style_id=recraft_style.style_id, + controls=controls_api, + ) + + images = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = handle_recraft_file_request( + image=image[i], + path="/proxy/recraft/images/imageToImage", + request=request, + auth_token=auth_token, + ) + with handle_recraft_image_output(): + images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) + pbar.update(1) + + images_tensor = torch.cat(images, dim=0) + return (images_tensor, ) + + +class RecraftImageInpaintingNode: + """ + Modify image based on prompt and mask. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + "mask": (IO.MASK, ), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation.", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 6, + "tooltip": "The number of images to generate.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "recraft_style": (RecraftIO.STYLEV3,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call( + self, + image: torch.Tensor, + mask: torch.Tensor, + prompt: str, + n: int, + seed, + auth_token=None, + recraft_style: RecraftStyle = None, + negative_prompt: str = None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False, max_length=1000) + default_style = RecraftStyle(RecraftStyleV3.realistic_image) + if recraft_style is None: + recraft_style = default_style + + if not negative_prompt: + negative_prompt = None + + request = RecraftImageGenerationRequest( + prompt=prompt, + negative_prompt=negative_prompt, + model=RecraftModel.recraftv3, + n=n, + style=recraft_style.style, + substyle=recraft_style.substyle, + style_id=recraft_style.style_id, + ) + + # prepare mask tensor + mask = resize_mask_to_image(mask, image, allow_gradient=False, add_channel_dim=True) + + images = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = handle_recraft_file_request( + image=image[i], + mask=mask[i:i+1], + path="/proxy/recraft/images/inpaint", + request=request, + auth_token=auth_token, + ) + with handle_recraft_image_output(): + images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) + pbar.update(1) + + images_tensor = torch.cat(images, dim=0) + return (images_tensor, ) + + +class RecraftTextToVectorNode: + """ + Generates SVG synchronously based on prompt and resolution. + """ + + RETURN_TYPES = (RecraftIO.SVG,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation.", + }, + ), + "substyle": (get_v3_substyles(RecraftStyleV3.vector_illustration),), + "size": ( + [res.value for res in RecraftImageSize], + { + "default": RecraftImageSize.res_1024x1024, + "tooltip": "The size of the generated image.", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 6, + "tooltip": "The number of images to generate.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + "recraft_controls": ( + RecraftIO.CONTROLS, + { + "tooltip": "Optional additional controls over the generation via the Recraft Controls node." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call( + self, + prompt: str, + substyle: str, + size: str, + n: int, + seed, + negative_prompt: str = None, + recraft_controls: RecraftControls = None, + auth_token=None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False, max_length=1000) + # create RecraftStyle so strings will be formatted properly (i.e. "None" will become None) + recraft_style = RecraftStyle(RecraftStyleV3.vector_illustration, substyle=substyle) + + controls_api = None + if recraft_controls: + controls_api = recraft_controls.create_api_model() + + if not negative_prompt: + negative_prompt = None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/recraft/image_generation", + method=HttpMethod.POST, + request_model=RecraftImageGenerationRequest, + response_model=RecraftImageGenerationResponse, + ), + request=RecraftImageGenerationRequest( + prompt=prompt, + negative_prompt=negative_prompt, + model=RecraftModel.recraftv3, + size=size, + n=n, + style=recraft_style.style, + substyle=recraft_style.substyle, + controls=controls_api, + ), + auth_token=auth_token, + ) + response: RecraftImageGenerationResponse = operation.execute() + svg_data = [] + for data in response.data: + svg_data.append(download_url_to_bytesio(data.url, timeout=1024)) + + return (SVG(svg_data),) + + +class RecraftVectorizeImageNode: + """ + Generates SVG synchronously from an input image. + """ + + RETURN_TYPES = (RecraftIO.SVG,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + }, + "optional": { + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call( + self, + image: torch.Tensor, + auth_token=None, + **kwargs, + ): + svgs = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = handle_recraft_file_request( + image=image[i], + path="/proxy/recraft/images/vectorize", + auth_token=auth_token, + ) + svgs.append(SVG(sub_bytes)) + pbar.update(1) + + return (SVG.combine_all(svgs), ) + + +class RecraftReplaceBackgroundNode: + """ + Replace background on image, based on provided prompt. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation.", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 6, + "tooltip": "The number of images to generate.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "recraft_style": (RecraftIO.STYLEV3,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call( + self, + image: torch.Tensor, + prompt: str, + n: int, + seed, + auth_token=None, + recraft_style: RecraftStyle = None, + negative_prompt: str = None, + **kwargs, + ): + default_style = RecraftStyle(RecraftStyleV3.realistic_image) + if recraft_style is None: + recraft_style = default_style + + if not negative_prompt: + negative_prompt = None + + request = RecraftImageGenerationRequest( + prompt=prompt, + negative_prompt=negative_prompt, + model=RecraftModel.recraftv3, + n=n, + style=recraft_style.style, + substyle=recraft_style.substyle, + style_id=recraft_style.style_id, + ) + + images = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = handle_recraft_file_request( + image=image[i], + path="/proxy/recraft/images/replaceBackground", + request=request, + auth_token=auth_token, + ) + images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) + pbar.update(1) + + images_tensor = torch.cat(images, dim=0) + return (images_tensor, ) + + +class RecraftRemoveBackgroundNode: + """ + Remove background from image, and return processed image and mask. + """ + + RETURN_TYPES = (IO.IMAGE, IO.MASK) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + }, + "optional": { + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call( + self, + image: torch.Tensor, + auth_token=None, + **kwargs, + ): + images = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = handle_recraft_file_request( + image=image[i], + path="/proxy/recraft/images/removeBackground", + auth_token=auth_token, + ) + images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) + pbar.update(1) + + images_tensor = torch.cat(images, dim=0) + # use alpha channel as masks, in B,H,W format + masks_tensor = images_tensor[:,:,:,-1:].squeeze(-1) + return (images_tensor, masks_tensor) + + +class RecraftCrispUpscaleNode: + """ + Upscale image synchronously. + Enhances a given raster image using ‘crisp upscale’ tool, increasing image resolution, making the image sharper and cleaner. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + RECRAFT_PATH = "/proxy/recraft/images/crispUpscale" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + }, + "optional": { + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call( + self, + image: torch.Tensor, + auth_token=None, + **kwargs, + ): + images = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = handle_recraft_file_request( + image=image[i], + path=self.RECRAFT_PATH, + auth_token=auth_token, + ) + images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) + pbar.update(1) + + images_tensor = torch.cat(images, dim=0) + return (images_tensor,) + + +class RecraftCreativeUpscaleNode(RecraftCrispUpscaleNode): + """ + Upscale image synchronously. + Enhances a given raster image using ‘creative upscale’ tool, boosting resolution with a focus on refining small details and faces. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + RECRAFT_PATH = "/proxy/recraft/images/creativeUpscale" + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "RecraftTextToImageNode": RecraftTextToImageNode, + "RecraftImageToImageNode": RecraftImageToImageNode, + "RecraftImageInpaintingNode": RecraftImageInpaintingNode, + "RecraftTextToVectorNode": RecraftTextToVectorNode, + "RecraftVectorizeImageNode": RecraftVectorizeImageNode, + "RecraftRemoveBackgroundNode": RecraftRemoveBackgroundNode, + "RecraftReplaceBackgroundNode": RecraftReplaceBackgroundNode, + "RecraftCrispUpscaleNode": RecraftCrispUpscaleNode, + "RecraftCreativeUpscaleNode": RecraftCreativeUpscaleNode, + "RecraftStyleV3RealisticImage": RecraftStyleV3RealisticImageNode, + "RecraftStyleV3DigitalIllustration": RecraftStyleV3DigitalIllustrationNode, + "RecraftStyleV3LogoRaster": RecraftStyleV3LogoRasterNode, + "RecraftStyleV3InfiniteStyleLibrary": RecraftStyleInfiniteStyleLibrary, + "RecraftColorRGB": RecraftColorRGBNode, + "RecraftControls": RecraftControlsNode, + "SaveSVG": SaveSVGNode, +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "RecraftTextToImageNode": "Recraft Text to Image", + "RecraftImageToImageNode": "Recraft Image to Image", + "RecraftImageInpaintingNode": "Recraft Image Inpainting", + "RecraftTextToVectorNode": "Recraft Text to Vector", + "RecraftVectorizeImageNode": "Recraft Vectorize Image", + "RecraftRemoveBackgroundNode": "Recraft Remove Background", + "RecraftReplaceBackgroundNode": "Recraft Replace Background", + "RecraftCrispUpscaleNode": "Recraft Crisp Upscale Image", + "RecraftCreativeUpscaleNode": "Recraft Creative Upscale Image", + "RecraftStyleV3RealisticImage": "Recraft Style - Realistic Image", + "RecraftStyleV3DigitalIllustration": "Recraft Style - Digital Illustration", + "RecraftStyleV3LogoRaster": "Recraft Style - Logo Raster", + "RecraftStyleV3InfiniteStyleLibrary": "Recraft Style - Infinite Style Library", + "RecraftColorRGB": "Recraft Color RGB", + "RecraftControls": "Recraft Controls", + "SaveSVG": "Save SVG", +} diff --git a/comfy_api_nodes/nodes_stability.py b/comfy_api_nodes/nodes_stability.py new file mode 100644 index 000000000..52fe2417c --- /dev/null +++ b/comfy_api_nodes/nodes_stability.py @@ -0,0 +1,609 @@ +from inspect import cleandoc +from comfy.comfy_types.node_typing import IO +from comfy_api_nodes.apis.stability_api import ( + StabilityUpscaleConservativeRequest, + StabilityUpscaleCreativeRequest, + StabilityAsyncResponse, + StabilityResultsGetResponse, + StabilityStable3_5Request, + StabilityStableUltraRequest, + StabilityStableUltraResponse, + StabilityAspectRatio, + Stability_SD3_5_Model, + Stability_SD3_5_GenerationMode, + get_stability_style_presets, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + bytesio_to_image_tensor, + tensor_to_bytesio, + validate_string, +) + +import torch +import base64 +from io import BytesIO +from enum import Enum + + +class StabilityPollStatus(str, Enum): + finished = "finished" + in_progress = "in_progress" + failed = "failed" + + +def get_async_dummy_status(x: StabilityResultsGetResponse): + if x.name is not None or x.errors is not None: + return StabilityPollStatus.failed + elif x.finish_reason is not None: + return StabilityPollStatus.finished + return StabilityPollStatus.in_progress + + +class StabilityStableImageUltraNode: + """ + Generates images synchronously based on prompt and resolution. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Stability AI" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines" + + "What you wish to see in the output image. A strong, descriptive prompt that clearly defines" + + "elements, colors, and subjects will lead to better results. " + + "To control the weight of a given word use the format `(word:weight)`," + + "where `word` is the word you'd like to control the weight of and `weight`" + + "is a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`" + + "would convey a sky that was blue and green, but more green than blue." + }, + ), + "aspect_ratio": ([x.value for x in StabilityAspectRatio], + { + "default": StabilityAspectRatio.ratio_1_1, + "tooltip": "Aspect ratio of generated image.", + }, + ), + "style_preset": (get_stability_style_presets(), + { + "tooltip": "Optional desired style of generated image.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 4294967294, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": { + "image": (IO.IMAGE,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "A blurb of text describing what you do not wish to see in the output image. This is an advanced feature." + }, + ), + "image_denoise": ( + IO.FLOAT, + { + "default": 0.5, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call(self, prompt: str, aspect_ratio: str, style_preset: str, seed: int, + negative_prompt: str=None, image: torch.Tensor = None, image_denoise: float=None, + auth_token=None): + validate_string(prompt, strip_whitespace=False) + # prepare image binary if image present + image_binary = None + if image is not None: + image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read() + else: + image_denoise = None + + if not negative_prompt: + negative_prompt = None + if style_preset == "None": + style_preset = None + + files = { + "image": image_binary + } + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/stable-image/generate/ultra", + method=HttpMethod.POST, + request_model=StabilityStableUltraRequest, + response_model=StabilityStableUltraResponse, + ), + request=StabilityStableUltraRequest( + prompt=prompt, + negative_prompt=negative_prompt, + aspect_ratio=aspect_ratio, + seed=seed, + strength=image_denoise, + style_preset=style_preset, + ), + files=files, + content_type="multipart/form-data", + auth_token=auth_token, + ) + response_api = operation.execute() + + if response_api.finish_reason != "SUCCESS": + raise Exception(f"Stable Image Ultra generation failed: {response_api.finish_reason}.") + + image_data = base64.b64decode(response_api.image) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + + return (returned_image,) + + +class StabilityStableImageSD_3_5Node: + """ + Generates images synchronously based on prompt and resolution. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Stability AI" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results." + }, + ), + "model": ([x.value for x in Stability_SD3_5_Model],), + "aspect_ratio": ([x.value for x in StabilityAspectRatio], + { + "default": StabilityAspectRatio.ratio_1_1, + "tooltip": "Aspect ratio of generated image.", + }, + ), + "style_preset": (get_stability_style_presets(), + { + "tooltip": "Optional desired style of generated image.", + }, + ), + "cfg_scale": ( + IO.FLOAT, + { + "default": 4.0, + "min": 1.0, + "max": 10.0, + "step": 0.1, + "tooltip": "How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt)", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 4294967294, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": { + "image": (IO.IMAGE,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "Keywords of what you do not wish to see in the output image. This is an advanced feature." + }, + ), + "image_denoise": ( + IO.FLOAT, + { + "default": 0.5, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call(self, model: str, prompt: str, aspect_ratio: str, style_preset: str, seed: int, cfg_scale: float, + negative_prompt: str=None, image: torch.Tensor = None, image_denoise: float=None, + auth_token=None): + validate_string(prompt, strip_whitespace=False) + # prepare image binary if image present + image_binary = None + mode = Stability_SD3_5_GenerationMode.text_to_image + if image is not None: + image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read() + mode = Stability_SD3_5_GenerationMode.image_to_image + aspect_ratio = None + else: + image_denoise = None + + if not negative_prompt: + negative_prompt = None + if style_preset == "None": + style_preset = None + + files = { + "image": image_binary + } + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/stable-image/generate/sd3", + method=HttpMethod.POST, + request_model=StabilityStable3_5Request, + response_model=StabilityStableUltraResponse, + ), + request=StabilityStable3_5Request( + prompt=prompt, + negative_prompt=negative_prompt, + aspect_ratio=aspect_ratio, + seed=seed, + strength=image_denoise, + style_preset=style_preset, + cfg_scale=cfg_scale, + model=model, + mode=mode, + ), + files=files, + content_type="multipart/form-data", + auth_token=auth_token, + ) + response_api = operation.execute() + + if response_api.finish_reason != "SUCCESS": + raise Exception(f"Stable Diffusion 3.5 Image generation failed: {response_api.finish_reason}.") + + image_data = base64.b64decode(response_api.image) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + + return (returned_image,) + + +class StabilityUpscaleConservativeNode: + """ + Upscale image with minimal alterations to 4K resolution. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Stability AI" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results." + }, + ), + "creativity": ( + IO.FLOAT, + { + "default": 0.35, + "min": 0.2, + "max": 0.5, + "step": 0.01, + "tooltip": "Controls the likelihood of creating additional details not heavily conditioned by the init image.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 4294967294, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": { + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "Keywords of what you do not wish to see in the output image. This is an advanced feature." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call(self, image: torch.Tensor, prompt: str, creativity: float, seed: int, negative_prompt: str=None, + auth_token=None): + validate_string(prompt, strip_whitespace=False) + image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read() + + if not negative_prompt: + negative_prompt = None + + files = { + "image": image_binary + } + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/stable-image/upscale/conservative", + method=HttpMethod.POST, + request_model=StabilityUpscaleConservativeRequest, + response_model=StabilityStableUltraResponse, + ), + request=StabilityUpscaleConservativeRequest( + prompt=prompt, + negative_prompt=negative_prompt, + creativity=round(creativity,2), + seed=seed, + ), + files=files, + content_type="multipart/form-data", + auth_token=auth_token, + ) + response_api = operation.execute() + + if response_api.finish_reason != "SUCCESS": + raise Exception(f"Stability Upscale Conservative generation failed: {response_api.finish_reason}.") + + image_data = base64.b64decode(response_api.image) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + + return (returned_image,) + + +class StabilityUpscaleCreativeNode: + """ + Upscale image with minimal alterations to 4K resolution. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Stability AI" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results." + }, + ), + "creativity": ( + IO.FLOAT, + { + "default": 0.3, + "min": 0.1, + "max": 0.5, + "step": 0.01, + "tooltip": "Controls the likelihood of creating additional details not heavily conditioned by the init image.", + }, + ), + "style_preset": (get_stability_style_presets(), + { + "tooltip": "Optional desired style of generated image.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 4294967294, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": { + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "Keywords of what you do not wish to see in the output image. This is an advanced feature." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call(self, image: torch.Tensor, prompt: str, creativity: float, style_preset: str, seed: int, negative_prompt: str=None, + auth_token=None): + validate_string(prompt, strip_whitespace=False) + image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read() + + if not negative_prompt: + negative_prompt = None + if style_preset == "None": + style_preset = None + + files = { + "image": image_binary + } + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/stable-image/upscale/creative", + method=HttpMethod.POST, + request_model=StabilityUpscaleCreativeRequest, + response_model=StabilityAsyncResponse, + ), + request=StabilityUpscaleCreativeRequest( + prompt=prompt, + negative_prompt=negative_prompt, + creativity=round(creativity,2), + style_preset=style_preset, + seed=seed, + ), + files=files, + content_type="multipart/form-data", + auth_token=auth_token, + ) + response_api = operation.execute() + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/stability/v2beta/results/{response_api.id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=StabilityResultsGetResponse, + ), + poll_interval=3, + completed_statuses=[StabilityPollStatus.finished], + failed_statuses=[StabilityPollStatus.failed], + status_extractor=lambda x: get_async_dummy_status(x), + auth_token=auth_token, + ) + response_poll: StabilityResultsGetResponse = operation.execute() + + if response_poll.finish_reason != "SUCCESS": + raise Exception(f"Stability Upscale Creative generation failed: {response_poll.finish_reason}.") + + image_data = base64.b64decode(response_poll.result) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + + return (returned_image,) + + +class StabilityUpscaleFastNode: + """ + Quickly upscales an image via Stability API call to 4x its original size; intended for upscaling low-quality/compressed images. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Stability AI" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + }, + "optional": { + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + def api_call(self, image: torch.Tensor, + auth_token=None): + image_binary = tensor_to_bytesio(image, total_pixels=4096*4096).read() + + files = { + "image": image_binary + } + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/stable-image/upscale/fast", + method=HttpMethod.POST, + request_model=EmptyRequest, + response_model=StabilityStableUltraResponse, + ), + request=EmptyRequest(), + files=files, + content_type="multipart/form-data", + auth_token=auth_token, + ) + response_api = operation.execute() + + if response_api.finish_reason != "SUCCESS": + raise Exception(f"Stability Upscale Fast failed: {response_api.finish_reason}.") + + image_data = base64.b64decode(response_api.image) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + + return (returned_image,) + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "StabilityStableImageUltraNode": StabilityStableImageUltraNode, + "StabilityStableImageSD_3_5Node": StabilityStableImageSD_3_5Node, + "StabilityUpscaleConservativeNode": StabilityUpscaleConservativeNode, + "StabilityUpscaleCreativeNode": StabilityUpscaleCreativeNode, + "StabilityUpscaleFastNode": StabilityUpscaleFastNode, +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "StabilityStableImageUltraNode": "Stability AI Stable Image Ultra", + "StabilityStableImageSD_3_5Node": "Stability AI Stable Diffusion 3.5 Image", + "StabilityUpscaleConservativeNode": "Stability AI Upscale Conservative", + "StabilityUpscaleCreativeNode": "Stability AI Upscale Creative", + "StabilityUpscaleFastNode": "Stability AI Upscale Fast", +} diff --git a/comfy_api_nodes/nodes_veo2.py b/comfy_api_nodes/nodes_veo2.py new file mode 100644 index 000000000..9233944b5 --- /dev/null +++ b/comfy_api_nodes/nodes_veo2.py @@ -0,0 +1,283 @@ +import io +import logging +import base64 +import requests +import torch + +from comfy.comfy_types.node_typing import IO, ComfyNodeABC +from comfy_api.input_impl.video_types import VideoFromFile +from comfy_api_nodes.apis import ( + Veo2GenVidRequest, + Veo2GenVidResponse, + Veo2GenVidPollRequest, + Veo2GenVidPollResponse +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, +) + +from comfy_api_nodes.apinode_utils import ( + downscale_image_tensor, + tensor_to_base64_string +) + +def convert_image_to_base64(image: torch.Tensor): + if image is None: + return None + + scaled_image = downscale_image_tensor(image, total_pixels=2048*2048) + return tensor_to_base64_string(scaled_image) + +class VeoVideoGenerationNode(ComfyNodeABC): + """ + Generates videos from text prompts using Google's Veo API. + + This node can create videos from text descriptions and optional image inputs, + with control over parameters like aspect ratio, duration, and more. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text description of the video", + }, + ), + "aspect_ratio": ( + IO.COMBO, + { + "options": ["16:9", "9:16"], + "default": "16:9", + "tooltip": "Aspect ratio of the output video", + }, + ), + }, + "optional": { + "negative_prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Negative text prompt to guide what to avoid in the video", + }, + ), + "duration_seconds": ( + IO.INT, + { + "default": 5, + "min": 5, + "max": 8, + "step": 1, + "display": "number", + "tooltip": "Duration of the output video in seconds", + }, + ), + "enhance_prompt": ( + IO.BOOLEAN, + { + "default": True, + "tooltip": "Whether to enhance the prompt with AI assistance", + } + ), + "person_generation": ( + IO.COMBO, + { + "options": ["ALLOW", "BLOCK"], + "default": "ALLOW", + "tooltip": "Whether to allow generating people in the video", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFF, + "step": 1, + "display": "number", + "control_after_generate": True, + "tooltip": "Seed for video generation (0 for random)", + }, + ), + "image": (IO.IMAGE, { + "default": None, + "tooltip": "Optional reference image to guide video generation", + }), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + }, + } + + RETURN_TYPES = (IO.VIDEO,) + FUNCTION = "generate_video" + CATEGORY = "api node/video/Veo" + DESCRIPTION = "Generates videos from text prompts using Google's Veo API" + API_NODE = True + + def generate_video( + self, + prompt, + aspect_ratio="16:9", + negative_prompt="", + duration_seconds=5, + enhance_prompt=True, + person_generation="ALLOW", + seed=0, + image=None, + auth_token=None, + ): + # Prepare the instances for the request + instances = [] + + instance = { + "prompt": prompt + } + + # Add image if provided + if image is not None: + image_base64 = convert_image_to_base64(image) + if image_base64: + instance["image"] = { + "bytesBase64Encoded": image_base64, + "mimeType": "image/png" + } + + instances.append(instance) + + # Create parameters dictionary + parameters = { + "aspectRatio": aspect_ratio, + "personGeneration": person_generation, + "durationSeconds": duration_seconds, + "enhancePrompt": enhance_prompt, + } + + # Add optional parameters if provided + if negative_prompt: + parameters["negativePrompt"] = negative_prompt + if seed > 0: + parameters["seed"] = seed + + # Initial request to start video generation + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/veo/generate", + method=HttpMethod.POST, + request_model=Veo2GenVidRequest, + response_model=Veo2GenVidResponse + ), + request=Veo2GenVidRequest( + instances=instances, + parameters=parameters + ), + auth_token=auth_token + ) + + initial_response = initial_operation.execute() + operation_name = initial_response.name + + logging.info(f"Veo generation started with operation name: {operation_name}") + + # Define status extractor function + def status_extractor(response): + # Only return "completed" if the operation is done, regardless of success or failure + # We'll check for errors after polling completes + return "completed" if response.done else "pending" + + # Define progress extractor function + def progress_extractor(response): + # Could be enhanced if the API provides progress information + return None + + # Define the polling operation + poll_operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path="/proxy/veo/poll", + method=HttpMethod.POST, + request_model=Veo2GenVidPollRequest, + response_model=Veo2GenVidPollResponse + ), + completed_statuses=["completed"], + failed_statuses=[], # No failed statuses, we'll handle errors after polling + status_extractor=status_extractor, + progress_extractor=progress_extractor, + request=Veo2GenVidPollRequest( + operationName=operation_name + ), + auth_token=auth_token, + poll_interval=5.0 + ) + + # Execute the polling operation + poll_response = poll_operation.execute() + + # Now check for errors in the final response + # Check for error in poll response + if hasattr(poll_response, 'error') and poll_response.error: + error_message = f"Veo API error: {poll_response.error.message} (code: {poll_response.error.code})" + logging.error(error_message) + raise Exception(error_message) + + # Check for RAI filtered content + if (hasattr(poll_response.response, 'raiMediaFilteredCount') and + poll_response.response.raiMediaFilteredCount > 0): + + # Extract reason message if available + if (hasattr(poll_response.response, 'raiMediaFilteredReasons') and + poll_response.response.raiMediaFilteredReasons): + reason = poll_response.response.raiMediaFilteredReasons[0] + error_message = f"Content filtered by Google's Responsible AI practices: {reason} ({poll_response.response.raiMediaFilteredCount} videos filtered.)" + else: + error_message = f"Content filtered by Google's Responsible AI practices ({poll_response.response.raiMediaFilteredCount} videos filtered.)" + + logging.error(error_message) + raise Exception(error_message) + + # Extract video data + video_data = None + if poll_response.response and hasattr(poll_response.response, 'videos') and poll_response.response.videos and len(poll_response.response.videos) > 0: + video = poll_response.response.videos[0] + + # Check if video is provided as base64 or URL + if hasattr(video, 'bytesBase64Encoded') and video.bytesBase64Encoded: + # Decode base64 string to bytes + video_data = base64.b64decode(video.bytesBase64Encoded) + elif hasattr(video, 'gcsUri') and video.gcsUri: + # Download from URL + video_url = video.gcsUri + video_response = requests.get(video_url) + video_data = video_response.content + else: + raise Exception("Video returned but no data or URL was provided") + else: + raise Exception("Video generation completed but no video was returned") + + if not video_data: + raise Exception("No video data was returned") + + logging.info("Video generation completed successfully") + + # Convert video data to BytesIO object + video_io = io.BytesIO(video_data) + + # Return VideoFromFile object + return (VideoFromFile(video_io),) + + +# Register the node +NODE_CLASS_MAPPINGS = { + "VeoVideoGenerationNode": VeoVideoGenerationNode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "VeoVideoGenerationNode": "Google Veo2 Video Generation", +} diff --git a/comfy_api_nodes/redocly-dev.yaml b/comfy_api_nodes/redocly-dev.yaml new file mode 100644 index 000000000..d9e3cab70 --- /dev/null +++ b/comfy_api_nodes/redocly-dev.yaml @@ -0,0 +1,10 @@ +# This file is used to filter the Comfy Org OpenAPI spec for schemas related to API Nodes. +# This is used for development purposes to generate stubs for unreleased API endpoints. +apis: + filter: + root: openapi.yaml + decorators: + filter-in: + property: tags + value: ['API Nodes'] + matchStrategy: all diff --git a/comfy_api_nodes/redocly.yaml b/comfy_api_nodes/redocly.yaml new file mode 100644 index 000000000..d102345b1 --- /dev/null +++ b/comfy_api_nodes/redocly.yaml @@ -0,0 +1,10 @@ +# This file is used to filter the Comfy Org OpenAPI spec for schemas related to API Nodes. + +apis: + filter: + root: openapi.yaml + decorators: + filter-in: + property: tags + value: ['API Nodes', 'Released'] + matchStrategy: all diff --git a/comfy_extras/nodes_primitive.py b/comfy_extras/nodes_primitive.py index 184b990c3..1f93f87a7 100644 --- a/comfy_extras/nodes_primitive.py +++ b/comfy_extras/nodes_primitive.py @@ -21,6 +21,21 @@ class String(ComfyNodeABC): return (value,) +class StringMultiline(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": {"value": (IO.STRING, {"multiline": True,},)}, + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/primitive" + + def execute(self, value: str) -> tuple[str]: + return (value,) + + class Int(ComfyNodeABC): @classmethod def INPUT_TYPES(cls) -> InputTypeDict: @@ -68,6 +83,7 @@ class Boolean(ComfyNodeABC): NODE_CLASS_MAPPINGS = { "PrimitiveString": String, + "PrimitiveStringMultiline": StringMultiline, "PrimitiveInt": Int, "PrimitiveFloat": Float, "PrimitiveBoolean": Boolean, @@ -75,6 +91,7 @@ NODE_CLASS_MAPPINGS = { NODE_DISPLAY_NAME_MAPPINGS = { "PrimitiveString": "String", + "PrimitiveStringMultiline": "String (Multiline)", "PrimitiveInt": "Int", "PrimitiveFloat": "Float", "PrimitiveBoolean": "Boolean", diff --git a/nodes.py b/nodes.py index 92b8ca6ae..d31e0774d 100644 --- a/nodes.py +++ b/nodes.py @@ -2263,7 +2263,17 @@ def init_builtin_extra_nodes(): api_nodes_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_api_nodes") api_nodes_files = [ - "nodes_api.py", + "nodes_ideogram.py", + "nodes_openai.py", + "nodes_minimax.py", + "nodes_veo2.py", + "nodes_kling.py", + "nodes_bfl.py", + "nodes_luma.py", + "nodes_recraft.py", + "nodes_pixverse.py", + "nodes_stability.py", + "nodes_pika.py", ] import_failed = [] diff --git a/requirements.txt b/requirements.txt index 05ceba00a..29cf0e2ac 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ -comfyui-frontend-package==1.18.6 -comfyui-workflow-templates==0.1.3 +comfyui-frontend-package==1.18.9 +comfyui-workflow-templates==0.1.11 torch torchsde torchvision diff --git a/tests-unit/comfy_api_nodes_test/mapper_utils_test.py b/tests-unit/comfy_api_nodes_test/mapper_utils_test.py new file mode 100644 index 000000000..69488f691 --- /dev/null +++ b/tests-unit/comfy_api_nodes_test/mapper_utils_test.py @@ -0,0 +1,297 @@ +from typing import Optional +from enum import Enum + +from pydantic import BaseModel, Field + +from comfy.comfy_types.node_typing import IO +from comfy_api_nodes.mapper_utils import model_field_to_node_input + + +def test_model_field_to_float_input(): + """Tests mapping a float field with constraints.""" + + class ModelWithFloatField(BaseModel): + cfg_scale: Optional[float] = Field( + default=0.5, + description="Flexibility in video generation", + ge=0.0, + le=1.0, + multiple_of=0.001, + ) + + expected_output = ( + IO.FLOAT, + { + "default": 0.5, + "tooltip": "Flexibility in video generation", + "min": 0.0, + "max": 1.0, + "step": 0.001, + }, + ) + + actual_output = model_field_to_node_input( + IO.FLOAT, ModelWithFloatField, "cfg_scale" + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_float_input_no_constraints(): + """Tests mapping a float field with no constraints.""" + + class ModelWithFloatField(BaseModel): + cfg_scale: Optional[float] = Field(default=0.5) + + expected_output = ( + IO.FLOAT, + { + "default": 0.5, + }, + ) + + actual_output = model_field_to_node_input( + IO.FLOAT, ModelWithFloatField, "cfg_scale" + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_int_input(): + """Tests mapping an int field with constraints.""" + + class ModelWithIntField(BaseModel): + num_frames: Optional[int] = Field( + default=10, + description="Number of frames to generate", + ge=1, + le=100, + multiple_of=1, + ) + + expected_output = ( + IO.INT, + { + "default": 10, + "tooltip": "Number of frames to generate", + "min": 1, + "max": 100, + "step": 1, + }, + ) + + actual_output = model_field_to_node_input(IO.INT, ModelWithIntField, "num_frames") + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_string_input(): + """Tests mapping a string field.""" + + class ModelWithStringField(BaseModel): + prompt: Optional[str] = Field( + default="A beautiful sunset over a calm ocean", + description="A prompt for the video generation", + ) + + expected_output = ( + IO.STRING, + { + "default": "A beautiful sunset over a calm ocean", + "tooltip": "A prompt for the video generation", + }, + ) + + actual_output = model_field_to_node_input(IO.STRING, ModelWithStringField, "prompt") + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_string_input_multiline(): + """Tests mapping a string field.""" + + class ModelWithStringField(BaseModel): + prompt: Optional[str] = Field( + default="A beautiful sunset over a calm ocean", + description="A prompt for the video generation", + ) + + expected_output = ( + IO.STRING, + { + "default": "A beautiful sunset over a calm ocean", + "tooltip": "A prompt for the video generation", + "multiline": True, + }, + ) + + actual_output = model_field_to_node_input( + IO.STRING, ModelWithStringField, "prompt", multiline=True + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_combo_input(): + """Tests mapping a combo field.""" + + class MockEnum(str, Enum): + option_1 = "option 1" + option_2 = "option 2" + option_3 = "option 3" + + class ModelWithComboField(BaseModel): + model_name: Optional[MockEnum] = Field("option 1", description="Model Name") + + expected_output = ( + IO.COMBO, + { + "options": ["option 1", "option 2", "option 3"], + "default": "option 1", + "tooltip": "Model Name", + }, + ) + + actual_output = model_field_to_node_input( + IO.COMBO, ModelWithComboField, "model_name", enum_type=MockEnum + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_combo_input_no_options(): + """Tests mapping a combo field with no options.""" + + class ModelWithComboField(BaseModel): + model_name: Optional[str] = Field(description="Model Name") + + expected_output = ( + IO.COMBO, + { + "tooltip": "Model Name", + }, + ) + + actual_output = model_field_to_node_input( + IO.COMBO, ModelWithComboField, "model_name" + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_image_input(): + """Tests mapping an image field.""" + + class ModelWithImageField(BaseModel): + image: Optional[str] = Field( + default=None, + description="An image for the video generation", + ) + + expected_output = ( + IO.IMAGE, + { + "default": None, + "tooltip": "An image for the video generation", + }, + ) + + actual_output = model_field_to_node_input(IO.IMAGE, ModelWithImageField, "image") + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_node_input_no_description(): + """Tests mapping a field with no description.""" + + class ModelWithNoDescriptionField(BaseModel): + field: Optional[str] = Field(default="default value") + + expected_output = ( + IO.STRING, + { + "default": "default value", + }, + ) + + actual_output = model_field_to_node_input( + IO.STRING, ModelWithNoDescriptionField, "field" + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_node_input_no_default(): + """Tests mapping a field with no default.""" + + class ModelWithNoDefaultField(BaseModel): + field: Optional[str] = Field(description="A field with no default") + + expected_output = ( + IO.STRING, + { + "tooltip": "A field with no default", + }, + ) + + actual_output = model_field_to_node_input( + IO.STRING, ModelWithNoDefaultField, "field" + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_node_input_no_metadata(): + """Tests mapping a field with no metadata or properties defined on the schema.""" + + class ModelWithNoMetadataField(BaseModel): + field: Optional[str] = Field() + + expected_output = ( + IO.STRING, + {}, + ) + + actual_output = model_field_to_node_input( + IO.STRING, ModelWithNoMetadataField, "field" + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_node_input_default_is_none(): + """ + Tests mapping a field with a default of `None`. + I.e., the default field should be included as the schema explicitly sets it to `None`. + """ + + class ModelWithNoneDefaultField(BaseModel): + field: Optional[str] = Field( + default=None, description="A field with a default of None" + ) + + expected_output = ( + IO.STRING, + { + "default": None, + "tooltip": "A field with a default of None", + }, + ) + + actual_output = model_field_to_node_input( + IO.STRING, ModelWithNoneDefaultField, "field" + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] From 094e9ef126fb60a6f5eeb7e1e30669b9328e7349 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 6 May 2025 01:53:53 -0700 Subject: [PATCH 072/193] Add a way to disable api nodes: --disable-api-nodes (#7960) --- comfy/cli_args.py | 1 + main.py | 2 +- nodes.py | 30 +++++++++++++++++++++++++----- 3 files changed, 27 insertions(+), 6 deletions(-) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index ef5ab6277..97b348f0d 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -148,6 +148,7 @@ parser.add_argument("--windows-standalone-build", action="store_true", help="Win parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.") parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.") +parser.add_argument("--disable-api-nodes", action="store_true", help="Disable loading all api nodes.") parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.") diff --git a/main.py b/main.py index 5c21542b3..221e48e41 100644 --- a/main.py +++ b/main.py @@ -270,7 +270,7 @@ def start_comfyui(asyncio_loop=None): q = execution.PromptQueue(prompt_server) hook_breaker_ac10a0.save_functions() - nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes) + nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes, init_api_nodes=not args.disable_api_nodes) hook_breaker_ac10a0.restore_functions() cuda_malloc_warning() diff --git a/nodes.py b/nodes.py index d31e0774d..3c3617562 100644 --- a/nodes.py +++ b/nodes.py @@ -2261,6 +2261,15 @@ def init_builtin_extra_nodes(): "nodes_preview_any.py", ] + import_failed = [] + for node_file in extras_files: + if not load_custom_node(os.path.join(extras_dir, node_file), module_parent="comfy_extras"): + import_failed.append(node_file) + + return import_failed + + +def init_builtin_api_nodes(): api_nodes_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_api_nodes") api_nodes_files = [ "nodes_ideogram.py", @@ -2277,10 +2286,6 @@ def init_builtin_extra_nodes(): ] import_failed = [] - for node_file in extras_files: - if not load_custom_node(os.path.join(extras_dir, node_file), module_parent="comfy_extras"): - import_failed.append(node_file) - for node_file in api_nodes_files: if not load_custom_node(os.path.join(api_nodes_dir, node_file), module_parent="comfy_api_nodes"): import_failed.append(node_file) @@ -2288,14 +2293,29 @@ def init_builtin_extra_nodes(): return import_failed -def init_extra_nodes(init_custom_nodes=True): +def init_extra_nodes(init_custom_nodes=True, init_api_nodes=True): import_failed = init_builtin_extra_nodes() + import_failed_api = [] + if init_api_nodes: + import_failed_api = init_builtin_api_nodes() + if init_custom_nodes: init_external_custom_nodes() else: logging.info("Skipping loading of custom nodes") + if len(import_failed_api) > 0: + logging.warning("WARNING: some comfy_api_nodes/ nodes did not import correctly. This may be because they are missing some dependencies.\n") + for node in import_failed_api: + logging.warning("IMPORT FAILED: {}".format(node)) + logging.warning("\nThis issue might be caused by new missing dependencies added the last time you updated ComfyUI.") + if args.windows_standalone_build: + logging.warning("Please run the update script: update/update_comfyui.bat") + else: + logging.warning("Please do a: pip install -r requirements.txt") + logging.warning("") + if len(import_failed) > 0: logging.warning("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n") for node in import_failed: From 0cf2e46b1725a5d0d6cb7b177a524026ca00f5a4 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 6 May 2025 07:39:54 -0400 Subject: [PATCH 073/193] ComfyUI version 0.3.32 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 8d2068de7..61573aead 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.31" +__version__ = "0.3.32" diff --git a/pyproject.toml b/pyproject.toml index 8b549a0b6..878e7c66a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.31" +version = "0.3.32" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9" From a4e679765ec6a7adc6dda326c7bab915e2914bd6 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 6 May 2025 06:00:01 -0700 Subject: [PATCH 074/193] Change chroma to use Flux shift. (#7961) --- comfy/model_base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index 3d33086d8..045df1317 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -1111,7 +1111,7 @@ class HiDream(BaseModel): return out class Chroma(Flux): - def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + def __init__(self, model_config, model_type=ModelType.FLUX, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.chroma.model.Chroma) def extra_conds(self, **kwargs): From 271c9c5b9eb027c682161ed7848bbf640253f973 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 6 May 2025 06:52:37 -0700 Subject: [PATCH 075/193] Better mem estimation for the LTXV 13B model. (#7963) --- comfy/supported_models.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index d5210cfac..a1dea2343 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -785,6 +785,10 @@ class LTXV(supported_models_base.BASE): vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] + def __init__(self, unet_config): + super().__init__(unet_config) + self.memory_usage_factor = (unet_config.get("cross_attention_dim", 2048) / 2048) * 5.5 + def get_model(self, state_dict, prefix="", device=None): out = model_base.LTXV(self, device=device) return out From 16417b40d9411c6e3a63949aa0f3582be25b28db Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 7 May 2025 05:33:34 -0700 Subject: [PATCH 076/193] Initial ACE-Step model implementation. (#7972) --- comfy/latent_formats.py | 4 + comfy/ldm/ace/attention.py | 768 + comfy/ldm/ace/lyric_encoder.py | 1067 ++ comfy/ldm/ace/model.py | 381 + comfy/ldm/ace/vae/autoencoder_dc.py | 644 + comfy/ldm/ace/vae/music_dcae_pipeline.py | 104 + comfy/ldm/ace/vae/music_log_mel.py | 108 + comfy/ldm/ace/vae/music_vocoder.py | 542 + comfy/model_base.py | 19 + comfy/model_detection.py | 25 + comfy/sd.py | 25 +- comfy/supported_models.py | 31 +- comfy/text_encoders/ace.py | 145 + .../ace_lyrics_tokenizer/vocab.json | 15535 ++++++++++++++++ comfy/text_encoders/ace_text_cleaners.py | 270 + comfy/text_encoders/umt5_config_base.json | 22 + comfy_extras/nodes_ace.py | 46 + nodes.py | 6 +- 18 files changed, 19738 insertions(+), 4 deletions(-) create mode 100644 comfy/ldm/ace/attention.py create mode 100644 comfy/ldm/ace/lyric_encoder.py create mode 100644 comfy/ldm/ace/model.py create mode 100644 comfy/ldm/ace/vae/autoencoder_dc.py create mode 100644 comfy/ldm/ace/vae/music_dcae_pipeline.py create mode 100755 comfy/ldm/ace/vae/music_log_mel.py create mode 100755 comfy/ldm/ace/vae/music_vocoder.py create mode 100644 comfy/text_encoders/ace.py create mode 100644 comfy/text_encoders/ace_lyrics_tokenizer/vocab.json create mode 100644 comfy/text_encoders/ace_text_cleaners.py create mode 100644 comfy/text_encoders/umt5_config_base.json create mode 100644 comfy_extras/nodes_ace.py diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 556c39512..82d9f9bb8 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -466,3 +466,7 @@ class Hunyuan3Dv2mini(LatentFormat): latent_channels = 64 latent_dimensions = 1 scale_factor = 1.0188137142395404 + +class ACEAudio(LatentFormat): + latent_channels = 8 + latent_dimensions = 2 diff --git a/comfy/ldm/ace/attention.py b/comfy/ldm/ace/attention.py new file mode 100644 index 000000000..631d13647 --- /dev/null +++ b/comfy/ldm/ace/attention.py @@ -0,0 +1,768 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/attention.py +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Tuple, Union, Optional + +import torch +import torch.nn.functional as F +from torch import nn + +import comfy.model_management + +class Attention(nn.Module): + def __init__( + self, + query_dim: int, + cross_attention_dim: Optional[int] = None, + heads: int = 8, + kv_heads: Optional[int] = None, + dim_head: int = 64, + dropout: float = 0.0, + bias: bool = False, + qk_norm: Optional[str] = None, + added_kv_proj_dim: Optional[int] = None, + added_proj_bias: Optional[bool] = True, + out_bias: bool = True, + scale_qk: bool = True, + only_cross_attention: bool = False, + eps: float = 1e-5, + rescale_output_factor: float = 1.0, + residual_connection: bool = False, + processor=None, + out_dim: int = None, + out_context_dim: int = None, + context_pre_only=None, + pre_only=False, + elementwise_affine: bool = True, + is_causal: bool = False, + dtype=None, device=None, operations=None + ): + super().__init__() + + self.inner_dim = out_dim if out_dim is not None else dim_head * heads + self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads + self.query_dim = query_dim + self.use_bias = bias + self.is_cross_attention = cross_attention_dim is not None + self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim + self.rescale_output_factor = rescale_output_factor + self.residual_connection = residual_connection + self.dropout = dropout + self.fused_projections = False + self.out_dim = out_dim if out_dim is not None else query_dim + self.out_context_dim = out_context_dim if out_context_dim is not None else query_dim + self.context_pre_only = context_pre_only + self.pre_only = pre_only + self.is_causal = is_causal + + self.scale_qk = scale_qk + self.scale = dim_head**-0.5 if self.scale_qk else 1.0 + + self.heads = out_dim // dim_head if out_dim is not None else heads + # for slice_size > 0 the attention score computation + # is split across the batch axis to save memory + # You can set slice_size with `set_attention_slice` + self.sliceable_head_dim = heads + + self.added_kv_proj_dim = added_kv_proj_dim + self.only_cross_attention = only_cross_attention + + if self.added_kv_proj_dim is None and self.only_cross_attention: + raise ValueError( + "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`." + ) + + self.group_norm = None + self.spatial_norm = None + + self.norm_q = None + self.norm_k = None + + self.norm_cross = None + self.to_q = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device) + + if not self.only_cross_attention: + # only relevant for the `AddedKVProcessor` classes + self.to_k = operations.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) + self.to_v = operations.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) + else: + self.to_k = None + self.to_v = None + + self.added_proj_bias = added_proj_bias + if self.added_kv_proj_dim is not None: + self.add_k_proj = operations.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias, dtype=dtype, device=device) + self.add_v_proj = operations.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias, dtype=dtype, device=device) + if self.context_pre_only is not None: + self.add_q_proj = operations.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias, dtype=dtype, device=device) + else: + self.add_q_proj = None + self.add_k_proj = None + self.add_v_proj = None + + if not self.pre_only: + self.to_out = nn.ModuleList([]) + self.to_out.append(operations.Linear(self.inner_dim, self.out_dim, bias=out_bias, dtype=dtype, device=device)) + self.to_out.append(nn.Dropout(dropout)) + else: + self.to_out = None + + if self.context_pre_only is not None and not self.context_pre_only: + self.to_add_out = operations.Linear(self.inner_dim, self.out_context_dim, bias=out_bias, dtype=dtype, device=device) + else: + self.to_add_out = None + + self.norm_added_q = None + self.norm_added_k = None + self.processor = processor + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + **cross_attention_kwargs, + ) -> torch.Tensor: + return self.processor( + self, + hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + + +class CustomLiteLAProcessor2_0: + """Attention processor used typically in processing the SD3-like self-attention projections. add rms norm for query and key and apply RoPE""" + + def __init__(self): + self.kernel_func = nn.ReLU(inplace=False) + self.eps = 1e-15 + self.pad_val = 1.0 + + def apply_rotary_emb( + self, + x: torch.Tensor, + freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings + to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are + reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting + tensors contain rotary embeddings and are returned as real tensors. + + Args: + x (`torch.Tensor`): + Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply + freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],) + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. + """ + cos, sin = freqs_cis # [S, D] + cos = cos[None, None] + sin = sin[None, None] + cos, sin = cos.to(x.device), sin.to(x.device) + + x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] + x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) + out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype) + + return out + + def __call__( + self, + attn: Attention, + hidden_states: torch.FloatTensor, + encoder_hidden_states: torch.FloatTensor = None, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + *args, + **kwargs, + ) -> torch.FloatTensor: + hidden_states_len = hidden_states.shape[1] + + input_ndim = hidden_states.ndim + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + if encoder_hidden_states is not None: + context_input_ndim = encoder_hidden_states.ndim + if context_input_ndim == 4: + batch_size, channel, height, width = encoder_hidden_states.shape + encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size = hidden_states.shape[0] + + # `sample` projections. + dtype = hidden_states.dtype + query = attn.to_q(hidden_states) + key = attn.to_k(hidden_states) + value = attn.to_v(hidden_states) + + # `context` projections. + has_encoder_hidden_state_proj = hasattr(attn, "add_q_proj") and hasattr(attn, "add_k_proj") and hasattr(attn, "add_v_proj") + if encoder_hidden_states is not None and has_encoder_hidden_state_proj: + encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states) + encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) + encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) + + # attention + if not attn.is_cross_attention: + query = torch.cat([query, encoder_hidden_states_query_proj], dim=1) + key = torch.cat([key, encoder_hidden_states_key_proj], dim=1) + value = torch.cat([value, encoder_hidden_states_value_proj], dim=1) + else: + query = hidden_states + key = encoder_hidden_states + value = encoder_hidden_states + + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + + query = query.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1) + key = key.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1).transpose(-1, -2) + value = value.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1) + + # RoPE需要 [B, H, S, D] 输入 + # 此时 query是 [B, H, D, S], 需要转成 [B, H, S, D] 才能应用RoPE + query = query.permute(0, 1, 3, 2) # [B, H, S, D] (从 [B, H, D, S]) + + # Apply query and key normalization if needed + if attn.norm_q is not None: + query = attn.norm_q(query) + if attn.norm_k is not None: + key = attn.norm_k(key) + + # Apply RoPE if needed + if rotary_freqs_cis is not None: + query = self.apply_rotary_emb(query, rotary_freqs_cis) + if not attn.is_cross_attention: + key = self.apply_rotary_emb(key, rotary_freqs_cis) + elif rotary_freqs_cis_cross is not None and has_encoder_hidden_state_proj: + key = self.apply_rotary_emb(key, rotary_freqs_cis_cross) + + # 此时 query是 [B, H, S, D]，需要还原成 [B, H, D, S] + query = query.permute(0, 1, 3, 2) # [B, H, D, S] + + if attention_mask is not None: + # attention_mask: [B, S] -> [B, 1, S, 1] + attention_mask = attention_mask[:, None, :, None].to(key.dtype) # [B, 1, S, 1] + query = query * attention_mask.permute(0, 1, 3, 2) # [B, H, S, D] * [B, 1, S, 1] + if not attn.is_cross_attention: + key = key * attention_mask # key: [B, h, S, D] 与 mask [B, 1, S, 1] 相乘 + value = value * attention_mask.permute(0, 1, 3, 2) # 如果 value 是 [B, h, D, S]，那么需调整mask以匹配S维度 + + if attn.is_cross_attention and encoder_attention_mask is not None and has_encoder_hidden_state_proj: + encoder_attention_mask = encoder_attention_mask[:, None, :, None].to(key.dtype) # [B, 1, S_enc, 1] + # 此时 key: [B, h, S_enc, D], value: [B, h, D, S_enc] + key = key * encoder_attention_mask # [B, h, S_enc, D] * [B, 1, S_enc, 1] + value = value * encoder_attention_mask.permute(0, 1, 3, 2) # [B, h, D, S_enc] * [B, 1, 1, S_enc] + + query = self.kernel_func(query) + key = self.kernel_func(key) + + query, key, value = query.float(), key.float(), value.float() + + value = F.pad(value, (0, 0, 0, 1), mode="constant", value=self.pad_val) + + vk = torch.matmul(value, key) + + hidden_states = torch.matmul(vk, query) + + if hidden_states.dtype in [torch.float16, torch.bfloat16]: + hidden_states = hidden_states.float() + + hidden_states = hidden_states[:, :, :-1] / (hidden_states[:, :, -1:] + self.eps) + + hidden_states = hidden_states.view(batch_size, attn.heads * head_dim, -1).permute(0, 2, 1) + + hidden_states = hidden_states.to(dtype) + if encoder_hidden_states is not None: + encoder_hidden_states = encoder_hidden_states.to(dtype) + + # Split the attention outputs. + if encoder_hidden_states is not None and not attn.is_cross_attention and has_encoder_hidden_state_proj: + hidden_states, encoder_hidden_states = ( + hidden_states[:, : hidden_states_len], + hidden_states[:, hidden_states_len:], + ) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + if encoder_hidden_states is not None and not attn.context_pre_only and not attn.is_cross_attention and hasattr(attn, "to_add_out"): + encoder_hidden_states = attn.to_add_out(encoder_hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + if encoder_hidden_states is not None and context_input_ndim == 4: + encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if torch.get_autocast_gpu_dtype() == torch.float16: + hidden_states = hidden_states.clip(-65504, 65504) + if encoder_hidden_states is not None: + encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504) + + return hidden_states, encoder_hidden_states + + +class CustomerAttnProcessor2_0: + r""" + Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). + """ + + def __init__(self): + if not hasattr(F, "scaled_dot_product_attention"): + raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") + + def apply_rotary_emb( + self, + x: torch.Tensor, + freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings + to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are + reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting + tensors contain rotary embeddings and are returned as real tensors. + + Args: + x (`torch.Tensor`): + Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply + freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],) + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. + """ + cos, sin = freqs_cis # [S, D] + cos = cos[None, None] + sin = sin[None, None] + cos, sin = cos.to(x.device), sin.to(x.device) + + x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] + x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) + out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype) + + return out + + def __call__( + self, + attn: Attention, + hidden_states: torch.FloatTensor, + encoder_hidden_states: torch.FloatTensor = None, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + *args, + **kwargs, + ) -> torch.Tensor: + + residual = hidden_states + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + + has_encoder_hidden_state_proj = hasattr(attn, "add_q_proj") and hasattr(attn, "add_k_proj") and hasattr(attn, "add_v_proj") + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + if attn.norm_q is not None: + query = attn.norm_q(query) + if attn.norm_k is not None: + key = attn.norm_k(key) + + # Apply RoPE if needed + if rotary_freqs_cis is not None: + query = self.apply_rotary_emb(query, rotary_freqs_cis) + if not attn.is_cross_attention: + key = self.apply_rotary_emb(key, rotary_freqs_cis) + elif rotary_freqs_cis_cross is not None and has_encoder_hidden_state_proj: + key = self.apply_rotary_emb(key, rotary_freqs_cis_cross) + + if attn.is_cross_attention and encoder_attention_mask is not None and has_encoder_hidden_state_proj: + # attention_mask: N x S1 + # encoder_attention_mask: N x S2 + # cross attention 整合attention_mask和encoder_attention_mask + combined_mask = attention_mask[:, :, None] * encoder_attention_mask[:, None, :] + attention_mask = torch.where(combined_mask == 1, 0.0, -torch.inf) + attention_mask = attention_mask[:, None, :, :].expand(-1, attn.heads, -1, -1).to(query.dtype) + + elif not attn.is_cross_attention and attention_mask is not None: + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + # scaled_dot_product_attention expects attention_mask shape to be + # (batch, heads, source_length, target_length) + attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) + + # the output of sdp = (batch, num_heads, seq_len, head_dim) + # TODO: add support for attn.scale when we move to Torch 2.1 + hidden_states = F.scaled_dot_product_attention( + query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False + ) + + hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) + hidden_states = hidden_states.to(query.dtype) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + +def val2list(x: list or tuple or any, repeat_time=1) -> list: # type: ignore + """Repeat `val` for `repeat_time` times and return the list or val if list/tuple.""" + if isinstance(x, (list, tuple)): + return list(x) + return [x for _ in range(repeat_time)] + + +def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1) -> tuple: # type: ignore + """Return tuple with min_len by repeating element at idx_repeat.""" + # convert to list first + x = val2list(x) + + # repeat elements if necessary + if len(x) > 0: + x[idx_repeat:idx_repeat] = [x[idx_repeat] for _ in range(min_len - len(x))] + + return tuple(x) + + +def t2i_modulate(x, shift, scale): + return x * (1 + scale) + shift + + +def get_same_padding(kernel_size: Union[int, Tuple[int, ...]]) -> Union[int, Tuple[int, ...]]: + if isinstance(kernel_size, tuple): + return tuple([get_same_padding(ks) for ks in kernel_size]) + else: + assert kernel_size % 2 > 0, f"kernel size {kernel_size} should be odd number" + return kernel_size // 2 + +class ConvLayer(nn.Module): + def __init__( + self, + in_dim: int, + out_dim: int, + kernel_size=3, + stride=1, + dilation=1, + groups=1, + padding: Union[int, None] = None, + use_bias=False, + norm=None, + act=None, + dtype=None, device=None, operations=None + ): + super().__init__() + if padding is None: + padding = get_same_padding(kernel_size) + padding *= dilation + + self.in_dim = in_dim + self.out_dim = out_dim + self.kernel_size = kernel_size + self.stride = stride + self.dilation = dilation + self.groups = groups + self.padding = padding + self.use_bias = use_bias + + self.conv = operations.Conv1d( + in_dim, + out_dim, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=use_bias, + device=device, + dtype=dtype + ) + if norm is not None: + self.norm = operations.RMSNorm(out_dim, elementwise_affine=False, dtype=dtype, device=device) + else: + self.norm = None + if act is not None: + self.act = nn.SiLU(inplace=True) + else: + self.act = None + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.conv(x) + if self.norm: + x = self.norm(x) + if self.act: + x = self.act(x) + return x + + +class GLUMBConv(nn.Module): + def __init__( + self, + in_features: int, + hidden_features: int, + out_feature=None, + kernel_size=3, + stride=1, + padding: Union[int, None] = None, + use_bias=False, + norm=(None, None, None), + act=("silu", "silu", None), + dilation=1, + dtype=None, device=None, operations=None + ): + out_feature = out_feature or in_features + super().__init__() + use_bias = val2tuple(use_bias, 3) + norm = val2tuple(norm, 3) + act = val2tuple(act, 3) + + self.glu_act = nn.SiLU(inplace=False) + self.inverted_conv = ConvLayer( + in_features, + hidden_features * 2, + 1, + use_bias=use_bias[0], + norm=norm[0], + act=act[0], + dtype=dtype, + device=device, + operations=operations, + ) + self.depth_conv = ConvLayer( + hidden_features * 2, + hidden_features * 2, + kernel_size, + stride=stride, + groups=hidden_features * 2, + padding=padding, + use_bias=use_bias[1], + norm=norm[1], + act=None, + dilation=dilation, + dtype=dtype, + device=device, + operations=operations, + ) + self.point_conv = ConvLayer( + hidden_features, + out_feature, + 1, + use_bias=use_bias[2], + norm=norm[2], + act=act[2], + dtype=dtype, + device=device, + operations=operations, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = x.transpose(1, 2) + x = self.inverted_conv(x) + x = self.depth_conv(x) + + x, gate = torch.chunk(x, 2, dim=1) + gate = self.glu_act(gate) + x = x * gate + + x = self.point_conv(x) + x = x.transpose(1, 2) + + return x + + +class LinearTransformerBlock(nn.Module): + """ + A Sana block with global shared adaptive layer norm (adaLN-single) conditioning. + """ + def __init__( + self, + dim, + num_attention_heads, + attention_head_dim, + use_adaln_single=True, + cross_attention_dim=None, + added_kv_proj_dim=None, + context_pre_only=False, + mlp_ratio=4.0, + add_cross_attention=False, + add_cross_attention_dim=None, + qk_norm=None, + dtype=None, device=None, operations=None + ): + super().__init__() + + self.norm1 = operations.RMSNorm(dim, elementwise_affine=False, eps=1e-6) + self.attn = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim, + added_kv_proj_dim=added_kv_proj_dim, + dim_head=attention_head_dim, + heads=num_attention_heads, + out_dim=dim, + bias=True, + qk_norm=qk_norm, + processor=CustomLiteLAProcessor2_0(), + dtype=dtype, + device=device, + operations=operations, + ) + + self.add_cross_attention = add_cross_attention + self.context_pre_only = context_pre_only + + if add_cross_attention and add_cross_attention_dim is not None: + self.cross_attn = Attention( + query_dim=dim, + cross_attention_dim=add_cross_attention_dim, + added_kv_proj_dim=add_cross_attention_dim, + dim_head=attention_head_dim, + heads=num_attention_heads, + out_dim=dim, + context_pre_only=context_pre_only, + bias=True, + qk_norm=qk_norm, + processor=CustomerAttnProcessor2_0(), + dtype=dtype, + device=device, + operations=operations, + ) + + self.norm2 = operations.RMSNorm(dim, 1e-06, elementwise_affine=False) + + self.ff = GLUMBConv( + in_features=dim, + hidden_features=int(dim * mlp_ratio), + use_bias=(True, True, False), + norm=(None, None, None), + act=("silu", "silu", None), + dtype=dtype, + device=device, + operations=operations, + ) + self.use_adaln_single = use_adaln_single + if use_adaln_single: + self.scale_shift_table = nn.Parameter(torch.empty(6, dim, dtype=dtype, device=device)) + + def forward( + self, + hidden_states: torch.FloatTensor, + encoder_hidden_states: torch.FloatTensor = None, + attention_mask: torch.FloatTensor = None, + encoder_attention_mask: torch.FloatTensor = None, + rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + temb: torch.FloatTensor = None, + ): + + N = hidden_states.shape[0] + + # step 1: AdaLN single + if self.use_adaln_single: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + comfy.model_management.cast_to(self.scale_shift_table[None], dtype=temb.dtype, device=temb.device) + temb.reshape(N, 6, -1) + ).chunk(6, dim=1) + + norm_hidden_states = self.norm1(hidden_states) + if self.use_adaln_single: + norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa + + # step 2: attention + if not self.add_cross_attention: + attn_output, encoder_hidden_states = self.attn( + hidden_states=norm_hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + rotary_freqs_cis=rotary_freqs_cis, + rotary_freqs_cis_cross=rotary_freqs_cis_cross, + ) + else: + attn_output, _ = self.attn( + hidden_states=norm_hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=None, + encoder_attention_mask=None, + rotary_freqs_cis=rotary_freqs_cis, + rotary_freqs_cis_cross=None, + ) + + if self.use_adaln_single: + attn_output = gate_msa * attn_output + hidden_states = attn_output + hidden_states + + if self.add_cross_attention: + attn_output = self.cross_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + rotary_freqs_cis=rotary_freqs_cis, + rotary_freqs_cis_cross=rotary_freqs_cis_cross, + ) + hidden_states = attn_output + hidden_states + + # step 3: add norm + norm_hidden_states = self.norm2(hidden_states) + if self.use_adaln_single: + norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp + + # step 4: feed forward + ff_output = self.ff(norm_hidden_states) + if self.use_adaln_single: + ff_output = gate_mlp * ff_output + + hidden_states = hidden_states + ff_output + + return hidden_states diff --git a/comfy/ldm/ace/lyric_encoder.py b/comfy/ldm/ace/lyric_encoder.py new file mode 100644 index 000000000..ff4359b26 --- /dev/null +++ b/comfy/ldm/ace/lyric_encoder.py @@ -0,0 +1,1067 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/lyrics_utils/lyric_encoder.py +from typing import Optional, Tuple, Union +import math +import torch +from torch import nn + +import comfy.model_management + +class ConvolutionModule(nn.Module): + """ConvolutionModule in Conformer model.""" + + def __init__(self, + channels: int, + kernel_size: int = 15, + activation: nn.Module = nn.ReLU(), + norm: str = "batch_norm", + causal: bool = False, + bias: bool = True, + dtype=None, device=None, operations=None): + """Construct an ConvolutionModule object. + Args: + channels (int): The number of channels of conv layers. + kernel_size (int): Kernel size of conv layers. + causal (int): Whether use causal convolution or not + """ + super().__init__() + + self.pointwise_conv1 = operations.Conv1d( + channels, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + dtype=dtype, device=device + ) + # self.lorder is used to distinguish if it's a causal convolution, + # if self.lorder > 0: it's a causal convolution, the input will be + # padded with self.lorder frames on the left in forward. + # else: it's a symmetrical convolution + if causal: + padding = 0 + self.lorder = kernel_size - 1 + else: + # kernel_size should be an odd number for none causal convolution + assert (kernel_size - 1) % 2 == 0 + padding = (kernel_size - 1) // 2 + self.lorder = 0 + self.depthwise_conv = operations.Conv1d( + channels, + channels, + kernel_size, + stride=1, + padding=padding, + groups=channels, + bias=bias, + dtype=dtype, device=device + ) + + assert norm in ['batch_norm', 'layer_norm'] + if norm == "batch_norm": + self.use_layer_norm = False + self.norm = nn.BatchNorm1d(channels) + else: + self.use_layer_norm = True + self.norm = operations.LayerNorm(channels, dtype=dtype, device=device) + + self.pointwise_conv2 = operations.Conv1d( + channels, + channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + dtype=dtype, device=device + ) + self.activation = activation + + def forward( + self, + x: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + cache: torch.Tensor = torch.zeros((0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute convolution module. + Args: + x (torch.Tensor): Input tensor (#batch, time, channels). + mask_pad (torch.Tensor): used for batch padding (#batch, 1, time), + (0, 0, 0) means fake mask. + cache (torch.Tensor): left context cache, it is only + used in causal convolution (#batch, channels, cache_t), + (0, 0, 0) meas fake cache. + Returns: + torch.Tensor: Output tensor (#batch, time, channels). + """ + # exchange the temporal dimension and the feature dimension + x = x.transpose(1, 2) # (#batch, channels, time) + + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + x.masked_fill_(~mask_pad, 0.0) + + if self.lorder > 0: + if cache.size(2) == 0: # cache_t == 0 + x = nn.functional.pad(x, (self.lorder, 0), 'constant', 0.0) + else: + assert cache.size(0) == x.size(0) # equal batch + assert cache.size(1) == x.size(1) # equal channel + x = torch.cat((cache, x), dim=2) + assert (x.size(2) > self.lorder) + new_cache = x[:, :, -self.lorder:] + else: + # It's better we just return None if no cache is required, + # However, for JIT export, here we just fake one tensor instead of + # None. + new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channel, dim) + x = nn.functional.glu(x, dim=1) # (batch, channel, dim) + + # 1D Depthwise Conv + x = self.depthwise_conv(x) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.activation(self.norm(x)) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.pointwise_conv2(x) + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + x.masked_fill_(~mask_pad, 0.0) + + return x.transpose(1, 2), new_cache + +class PositionwiseFeedForward(torch.nn.Module): + """Positionwise feed forward layer. + + FeedForward are appied on each position of the sequence. + The output dim is same with the input dim. + + Args: + idim (int): Input dimenstion. + hidden_units (int): The number of hidden units. + dropout_rate (float): Dropout rate. + activation (torch.nn.Module): Activation function + """ + + def __init__( + self, + idim: int, + hidden_units: int, + dropout_rate: float, + activation: torch.nn.Module = torch.nn.ReLU(), + dtype=None, device=None, operations=None + ): + """Construct a PositionwiseFeedForward object.""" + super(PositionwiseFeedForward, self).__init__() + self.w_1 = operations.Linear(idim, hidden_units, dtype=dtype, device=device) + self.activation = activation + self.dropout = torch.nn.Dropout(dropout_rate) + self.w_2 = operations.Linear(hidden_units, idim, dtype=dtype, device=device) + + def forward(self, xs: torch.Tensor) -> torch.Tensor: + """Forward function. + + Args: + xs: input tensor (B, L, D) + Returns: + output tensor, (B, L, D) + """ + return self.w_2(self.dropout(self.activation(self.w_1(xs)))) + +class Swish(torch.nn.Module): + """Construct an Swish object.""" + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Return Swish activation function.""" + return x * torch.sigmoid(x) + +class MultiHeadedAttention(nn.Module): + """Multi-Head Attention layer. + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, + n_head: int, + n_feat: int, + dropout_rate: float, + key_bias: bool = True, + dtype=None, device=None, operations=None): + """Construct an MultiHeadedAttention object.""" + super().__init__() + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.h = n_head + self.linear_q = operations.Linear(n_feat, n_feat, dtype=dtype, device=device) + self.linear_k = operations.Linear(n_feat, n_feat, bias=key_bias, dtype=dtype, device=device) + self.linear_v = operations.Linear(n_feat, n_feat, dtype=dtype, device=device) + self.linear_out = operations.Linear(n_feat, n_feat, dtype=dtype, device=device) + self.dropout = nn.Dropout(p=dropout_rate) + + def forward_qkv( + self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Transform query, key and value. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + + Returns: + torch.Tensor: Transformed query tensor, size + (#batch, n_head, time1, d_k). + torch.Tensor: Transformed key tensor, size + (#batch, n_head, time2, d_k). + torch.Tensor: Transformed value tensor, size + (#batch, n_head, time2, d_k). + + """ + n_batch = query.size(0) + q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) + k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) + v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) + q = q.transpose(1, 2) # (batch, head, time1, d_k) + k = k.transpose(1, 2) # (batch, head, time2, d_k) + v = v.transpose(1, 2) # (batch, head, time2, d_k) + return q, k, v + + def forward_attention( + self, + value: torch.Tensor, + scores: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool) + ) -> torch.Tensor: + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value, size + (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score, size + (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask, size (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + + if mask is not None and mask.size(2) > 0: # time2 > 0 + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + # For last chunk, time2 might be larger than scores.size(-1) + mask = mask[:, :, :, :scores.size(-1)] # (batch, 1, *, time2) + scores = scores.masked_fill(mask, -float('inf')) + attn = torch.softmax(scores, dim=-1).masked_fill( + mask, 0.0) # (batch, head, time1, time2) + + else: + attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = (x.transpose(1, 2).contiguous().view(n_batch, -1, + self.h * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)) + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + 1.When applying cross attention between decoder and encoder, + the batch padding mask for input is in (#batch, 1, T) shape. + 2.When applying self attention of encoder, + the mask is in (#batch, T, T) shape. + 3.When applying self attention of decoder, + the mask is in (#batch, L, L) shape. + 4.If the different position in decoder see different block + of the encoder, such as Mocha, the passed in mask could be + in (#batch, L, T) shape. But there is no such case in current + CosyVoice. + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + + """ + q, k, v = self.forward_qkv(query, key, value) + if cache.size(0) > 0: + key_cache, value_cache = torch.split(cache, + cache.size(-1) // 2, + dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + new_cache = torch.cat((k, v), dim=-1) + + scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) + return self.forward_attention(v, scores, mask), new_cache + + +class RelPositionMultiHeadedAttention(MultiHeadedAttention): + """Multi-Head Attention layer with relative position encoding. + Paper: https://arxiv.org/abs/1901.02860 + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + """ + + def __init__(self, + n_head: int, + n_feat: int, + dropout_rate: float, + key_bias: bool = True, + dtype=None, device=None, operations=None): + """Construct an RelPositionMultiHeadedAttention object.""" + super().__init__(n_head, n_feat, dropout_rate, key_bias, dtype=dtype, device=device, operations=operations) + # linear transformation for positional encoding + self.linear_pos = operations.Linear(n_feat, n_feat, bias=False, dtype=dtype, device=device) + # these two learnable bias are used in matrix c and matrix d + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + self.pos_bias_u = nn.Parameter(torch.empty(self.h, self.d_k, dtype=dtype, device=device)) + self.pos_bias_v = nn.Parameter(torch.empty(self.h, self.d_k, dtype=dtype, device=device)) + # torch.nn.init.xavier_uniform_(self.pos_bias_u) + # torch.nn.init.xavier_uniform_(self.pos_bias_v) + + def rel_shift(self, x: torch.Tensor) -> torch.Tensor: + """Compute relative positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1). + time1 means the length of query vector. + + Returns: + torch.Tensor: Output tensor. + + """ + zero_pad = torch.zeros((x.size()[0], x.size()[1], x.size()[2], 1), + device=x.device, + dtype=x.dtype) + x_padded = torch.cat([zero_pad, x], dim=-1) + + x_padded = x_padded.view(x.size()[0], + x.size()[1], + x.size(3) + 1, x.size(2)) + x = x_padded[:, :, 1:].view_as(x)[ + :, :, :, : x.size(-1) // 2 + 1 + ] # only keep the positions from 0 to time2 + return x + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)) + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute 'Scaled Dot Product Attention' with rel. positional encoding. + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): Positional embedding tensor + (#batch, time2, size). + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + """ + q, k, v = self.forward_qkv(query, key, value) + q = q.transpose(1, 2) # (batch, time1, head, d_k) + + if cache.size(0) > 0: + key_cache, value_cache = torch.split(cache, + cache.size(-1) // 2, + dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's + # non-trivial to calculate `next_cache_start` here. + new_cache = torch.cat((k, v), dim=-1) + + n_batch_pos = pos_emb.size(0) + p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) + p = p.transpose(1, 2) # (batch, head, time1, d_k) + + # (batch, head, time1, d_k) + q_with_bias_u = (q + comfy.model_management.cast_to(self.pos_bias_u, dtype=q.dtype, device=q.device)).transpose(1, 2) + # (batch, head, time1, d_k) + q_with_bias_v = (q + comfy.model_management.cast_to(self.pos_bias_v, dtype=q.dtype, device=q.device)).transpose(1, 2) + + # compute attention score + # first compute matrix a and matrix c + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + # (batch, head, time1, time2) + matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) + + # compute matrix b and matrix d + # (batch, head, time1, time2) + matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + # NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used + if matrix_ac.shape != matrix_bd.shape: + matrix_bd = self.rel_shift(matrix_bd) + + scores = (matrix_ac + matrix_bd) / math.sqrt( + self.d_k) # (batch, head, time1, time2) + + return self.forward_attention(v, scores, mask), new_cache + + + +def subsequent_mask( + size: int, + device: torch.device = torch.device("cpu"), +) -> torch.Tensor: + """Create mask for subsequent steps (size, size). + + This mask is used only in decoder which works in an auto-regressive mode. + This means the current step could only do attention with its left steps. + + In encoder, fully attention is used when streaming is not necessary and + the sequence is not long. In this case, no attention mask is needed. + + When streaming is need, chunk-based attention is used in encoder. See + subsequent_chunk_mask for the chunk-based attention mask. + + Args: + size (int): size of mask + str device (str): "cpu" or "cuda" or torch.Tensor.device + dtype (torch.device): result dtype + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_mask(3) + [[1, 0, 0], + [1, 1, 0], + [1, 1, 1]] + """ + arange = torch.arange(size, device=device) + mask = arange.expand(size, size) + arange = arange.unsqueeze(-1) + mask = mask <= arange + return mask + + +def subsequent_chunk_mask( + size: int, + chunk_size: int, + num_left_chunks: int = -1, + device: torch.device = torch.device("cpu"), + ) -> torch.Tensor: + """Create mask for subsequent steps (size, size) with chunk size, + this is for streaming encoder + + Args: + size (int): size of mask + chunk_size (int): size of chunk + num_left_chunks (int): number of left chunks + <0: use full chunk + >=0: use num_left_chunks + device (torch.device): "cpu" or "cuda" or torch.Tensor.device + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_chunk_mask(4, 2) + [[1, 1, 0, 0], + [1, 1, 0, 0], + [1, 1, 1, 1], + [1, 1, 1, 1]] + """ + ret = torch.zeros(size, size, device=device, dtype=torch.bool) + for i in range(size): + if num_left_chunks < 0: + start = 0 + else: + start = max((i // chunk_size - num_left_chunks) * chunk_size, 0) + ending = min((i // chunk_size + 1) * chunk_size, size) + ret[i, start:ending] = True + return ret + +def add_optional_chunk_mask(xs: torch.Tensor, + masks: torch.Tensor, + use_dynamic_chunk: bool, + use_dynamic_left_chunk: bool, + decoding_chunk_size: int, + static_chunk_size: int, + num_decoding_left_chunks: int, + enable_full_context: bool = True): + """ Apply optional mask for encoder. + + Args: + xs (torch.Tensor): padded input, (B, L, D), L for max length + mask (torch.Tensor): mask for xs, (B, 1, L) + use_dynamic_chunk (bool): whether to use dynamic chunk or not + use_dynamic_left_chunk (bool): whether to use dynamic left chunk for + training. + decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's + 0: default for training, use random dynamic chunk. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + static_chunk_size (int): chunk size for static chunk training/decoding + if it's greater than 0, if use_dynamic_chunk is true, + this parameter will be ignored + num_decoding_left_chunks: number of left chunks, this is for decoding, + the chunk size is decoding_chunk_size. + >=0: use num_decoding_left_chunks + <0: use all left chunks + enable_full_context (bool): + True: chunk size is either [1, 25] or full context(max_len) + False: chunk size ~ U[1, 25] + + Returns: + torch.Tensor: chunk mask of the input xs. + """ + # Whether to use chunk mask or not + if use_dynamic_chunk: + max_len = xs.size(1) + if decoding_chunk_size < 0: + chunk_size = max_len + num_left_chunks = -1 + elif decoding_chunk_size > 0: + chunk_size = decoding_chunk_size + num_left_chunks = num_decoding_left_chunks + else: + # chunk size is either [1, 25] or full context(max_len). + # Since we use 4 times subsampling and allow up to 1s(100 frames) + # delay, the maximum frame is 100 / 4 = 25. + chunk_size = torch.randint(1, max_len, (1, )).item() + num_left_chunks = -1 + if chunk_size > max_len // 2 and enable_full_context: + chunk_size = max_len + else: + chunk_size = chunk_size % 25 + 1 + if use_dynamic_left_chunk: + max_left_chunks = (max_len - 1) // chunk_size + num_left_chunks = torch.randint(0, max_left_chunks, + (1, )).item() + chunk_masks = subsequent_chunk_mask(xs.size(1), chunk_size, + num_left_chunks, + xs.device) # (L, L) + chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) + chunk_masks = masks & chunk_masks # (B, L, L) + elif static_chunk_size > 0: + num_left_chunks = num_decoding_left_chunks + chunk_masks = subsequent_chunk_mask(xs.size(1), static_chunk_size, + num_left_chunks, + xs.device) # (L, L) + chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) + chunk_masks = masks & chunk_masks # (B, L, L) + else: + chunk_masks = masks + return chunk_masks + + +class ConformerEncoderLayer(nn.Module): + """Encoder layer module. + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` + instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward` instance can be used as the argument. + feed_forward_macaron (torch.nn.Module): Additional feed-forward module + instance. + `PositionwiseFeedForward` instance can be used as the argument. + conv_module (torch.nn.Module): Convolution module instance. + `ConvlutionModule` instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): + True: use layer_norm before each sub-block. + False: use layer_norm after each sub-block. + """ + + def __init__( + self, + size: int, + self_attn: torch.nn.Module, + feed_forward: Optional[nn.Module] = None, + feed_forward_macaron: Optional[nn.Module] = None, + conv_module: Optional[nn.Module] = None, + dropout_rate: float = 0.1, + normalize_before: bool = True, + dtype=None, device=None, operations=None + ): + """Construct an EncoderLayer object.""" + super().__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.feed_forward_macaron = feed_forward_macaron + self.conv_module = conv_module + self.norm_ff = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the FNN module + self.norm_mha = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the MHA module + if feed_forward_macaron is not None: + self.norm_ff_macaron = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) + self.ff_scale = 0.5 + else: + self.ff_scale = 1.0 + if self.conv_module is not None: + self.norm_conv = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the CNN module + self.norm_final = operations.LayerNorm( + size, eps=1e-5, dtype=dtype, device=device) # for the final output of the block + self.dropout = nn.Dropout(dropout_rate) + self.size = size + self.normalize_before = normalize_before + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute encoded features. + + Args: + x (torch.Tensor): (#batch, time, size) + mask (torch.Tensor): Mask tensor for the input (#batch, time，time), + (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): positional encoding, must not be None + for ConformerEncoderLayer. + mask_pad (torch.Tensor): batch padding mask used for conv module. + (#batch, 1，time), (0, 0, 0) means fake mask. + att_cache (torch.Tensor): Cache tensor of the KEY & VALUE + (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. + cnn_cache (torch.Tensor): Convolution cache in conformer layer + (#batch=1, size, cache_t2) + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time, time). + torch.Tensor: att_cache tensor, + (#batch=1, head, cache_t1 + time, d_k * 2). + torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2). + """ + + # whether to use macaron style + if self.feed_forward_macaron is not None: + residual = x + if self.normalize_before: + x = self.norm_ff_macaron(x) + x = residual + self.ff_scale * self.dropout( + self.feed_forward_macaron(x)) + if not self.normalize_before: + x = self.norm_ff_macaron(x) + + # multi-headed self-attention module + residual = x + if self.normalize_before: + x = self.norm_mha(x) + x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, + att_cache) + x = residual + self.dropout(x_att) + if not self.normalize_before: + x = self.norm_mha(x) + + # convolution module + # Fake new cnn cache here, and then change it in conv_module + new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + if self.conv_module is not None: + residual = x + if self.normalize_before: + x = self.norm_conv(x) + x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache) + x = residual + self.dropout(x) + + if not self.normalize_before: + x = self.norm_conv(x) + + # feed forward module + residual = x + if self.normalize_before: + x = self.norm_ff(x) + + x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm_ff(x) + + if self.conv_module is not None: + x = self.norm_final(x) + + return x, mask, new_att_cache, new_cnn_cache + + + +class EspnetRelPositionalEncoding(torch.nn.Module): + """Relative positional encoding module (new implementation). + + Details can be found in https://github.com/espnet/espnet/pull/2816. + + See : Appendix B in https://arxiv.org/abs/1901.02860 + + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + + """ + + def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000): + """Construct an PositionalEncoding object.""" + super(EspnetRelPositionalEncoding, self).__init__() + self.d_model = d_model + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x: torch.Tensor): + """Reset the positional encodings.""" + if self.pe is not None: + # self.pe contains both positive and negative parts + # the length of self.pe is 2 * input_len - 1 + if self.pe.size(1) >= x.size(1) * 2 - 1: + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + # Suppose `i` means to the position of query vecotr and `j` means the + # position of key vector. We use position relative positions when keys + # are to the left (i>j) and negative relative positions otherwise (i Tuple[torch.Tensor, torch.Tensor]: + """Add positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + + """ + self.extend_pe(x) + x = x * self.xscale + pos_emb = self.position_encoding(size=x.size(1), offset=offset) + return self.dropout(x), self.dropout(pos_emb) + + def position_encoding(self, + offset: Union[int, torch.Tensor], + size: int) -> torch.Tensor: + """ For getting encoding in a streaming fashion + + Attention!!!!! + we apply dropout only once at the whole utterance level in a none + streaming way, but will call this function several times with + increasing input size in a streaming scenario, so the dropout will + be applied several times. + + Args: + offset (int or torch.tensor): start offset + size (int): required size of position encoding + + Returns: + torch.Tensor: Corresponding encoding + """ + pos_emb = self.pe[ + :, + self.pe.size(1) // 2 - size + 1: self.pe.size(1) // 2 + size, + ] + return pos_emb + + + +class LinearEmbed(torch.nn.Module): + """Linear transform the input without subsampling + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module, dtype=None, device=None, operations=None): + """Construct an linear object.""" + super().__init__() + self.out = torch.nn.Sequential( + operations.Linear(idim, odim, dtype=dtype, device=device), + operations.LayerNorm(odim, eps=1e-5, dtype=dtype, device=device), + torch.nn.Dropout(dropout_rate), + ) + self.pos_enc = pos_enc_class #rel_pos_espnet + + def position_encoding(self, offset: Union[int, torch.Tensor], + size: int) -> torch.Tensor: + return self.pos_enc.position_encoding(offset, size) + + def forward( + self, + x: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Input x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: linear input tensor (#batch, time', odim), + where time' = time . + torch.Tensor: linear input mask (#batch, 1, time'), + where time' = time . + + """ + x = self.out(x) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb + + +ATTENTION_CLASSES = { + "selfattn": MultiHeadedAttention, + "rel_selfattn": RelPositionMultiHeadedAttention, +} + +ACTIVATION_CLASSES = { + "hardtanh": torch.nn.Hardtanh, + "tanh": torch.nn.Tanh, + "relu": torch.nn.ReLU, + "selu": torch.nn.SELU, + "swish": getattr(torch.nn, "SiLU", Swish), + "gelu": torch.nn.GELU, +} + + +def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor: + """Make mask tensor containing indices of padded part. + + See description of make_non_pad_mask. + + Args: + lengths (torch.Tensor): Batch of lengths (B,). + Returns: + torch.Tensor: Mask tensor containing indices of padded part. + + Examples: + >>> lengths = [5, 3, 2] + >>> make_pad_mask(lengths) + masks = [[0, 0, 0, 0 ,0], + [0, 0, 0, 1, 1], + [0, 0, 1, 1, 1]] + """ + batch_size = lengths.size(0) + max_len = max_len if max_len > 0 else lengths.max().item() + seq_range = torch.arange(0, + max_len, + dtype=torch.int64, + device=lengths.device) + seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len) + seq_length_expand = lengths.unsqueeze(-1) + mask = seq_range_expand >= seq_length_expand + return mask + +#https://github.com/FunAudioLLM/CosyVoice/blob/main/examples/magicdata-read/cosyvoice/conf/cosyvoice.yaml +class ConformerEncoder(torch.nn.Module): + """Conformer encoder module.""" + + def __init__( + self, + input_size: int, + output_size: int = 1024, + attention_heads: int = 16, + linear_units: int = 4096, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = 'linear', + pos_enc_layer_type: str = 'rel_pos_espnet', + normalize_before: bool = True, + static_chunk_size: int = 1, # 1: causal_mask; 0: full_mask + use_dynamic_chunk: bool = False, + use_dynamic_left_chunk: bool = False, + positionwise_conv_kernel_size: int = 1, + macaron_style: bool =False, + selfattention_layer_type: str = "rel_selfattn", + activation_type: str = "swish", + use_cnn_module: bool = False, + cnn_module_kernel: int = 15, + causal: bool = False, + cnn_module_norm: str = "batch_norm", + key_bias: bool = True, + dtype=None, device=None, operations=None + ): + """Construct ConformerEncoder + + Args: + input_size to use_dynamic_chunk, see in BaseEncoder + positionwise_conv_kernel_size (int): Kernel size of positionwise + conv1d layer. + macaron_style (bool): Whether to use macaron style for + positionwise layer. + selfattention_layer_type (str): Encoder attention layer type, + the parameter has no effect now, it's just for configure + compatibility. #'rel_selfattn' + activation_type (str): Encoder activation function type. + use_cnn_module (bool): Whether to use convolution module. + cnn_module_kernel (int): Kernel size of convolution module. + causal (bool): whether to use causal convolution or not. + key_bias: whether use bias in attention.linear_k, False for whisper models. + """ + super().__init__() + self.output_size = output_size + self.embed = LinearEmbed(input_size, output_size, dropout_rate, + EspnetRelPositionalEncoding(output_size, positional_dropout_rate), dtype=dtype, device=device, operations=operations) + self.normalize_before = normalize_before + self.after_norm = operations.LayerNorm(output_size, eps=1e-5, dtype=dtype, device=device) + self.use_dynamic_chunk = use_dynamic_chunk + + self.static_chunk_size = static_chunk_size + self.use_dynamic_chunk = use_dynamic_chunk + self.use_dynamic_left_chunk = use_dynamic_left_chunk + activation = ACTIVATION_CLASSES[activation_type]() + + # self-attention module definition + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + key_bias, + ) + # feed-forward module definition + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + activation, + ) + # convolution module definition + convolution_layer_args = (output_size, cnn_module_kernel, activation, + cnn_module_norm, causal) + + self.encoders = torch.nn.ModuleList([ + ConformerEncoderLayer( + output_size, + RelPositionMultiHeadedAttention( + *encoder_selfattn_layer_args, dtype=dtype, device=device, operations=operations), + PositionwiseFeedForward(*positionwise_layer_args, dtype=dtype, device=device, operations=operations), + PositionwiseFeedForward( + *positionwise_layer_args, dtype=dtype, device=device, operations=operations) if macaron_style else None, + ConvolutionModule( + *convolution_layer_args, dtype=dtype, device=device, operations=operations) if use_cnn_module else None, + dropout_rate, + normalize_before, dtype=dtype, device=device, operations=operations + ) for _ in range(num_blocks) + ]) + + def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor) -> torch.Tensor: + for layer in self.encoders: + xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) + return xs + + def forward( + self, + xs: torch.Tensor, + pad_mask: torch.Tensor, + decoding_chunk_size: int = 0, + num_decoding_left_chunks: int = -1, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Embed positions in tensor. + + Args: + xs: padded input tensor (B, T, D) + xs_lens: input length (B) + decoding_chunk_size: decoding chunk size for dynamic chunk + 0: default for training, use random dynamic chunk. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + num_decoding_left_chunks: number of left chunks, this is for decoding, + the chunk size is decoding_chunk_size. + >=0: use num_decoding_left_chunks + <0: use all left chunks + Returns: + encoder output tensor xs, and subsampled masks + xs: padded output tensor (B, T' ~= T/subsample_rate, D) + masks: torch.Tensor batch padding mask after subsample + (B, 1, T' ~= T/subsample_rate) + NOTE(xcsong): + We pass the `__call__` method of the modules instead of `forward` to the + checkpointing API because `__call__` attaches all the hooks of the module. + https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2 + """ + masks = None + if pad_mask is not None: + masks = pad_mask.to(torch.bool).unsqueeze(1) # (B, 1, T) + xs, pos_emb = self.embed(xs) + mask_pad = masks # (B, 1, T/subsample_rate) + chunk_masks = add_optional_chunk_mask(xs, masks, + self.use_dynamic_chunk, + self.use_dynamic_left_chunk, + decoding_chunk_size, + self.static_chunk_size, + num_decoding_left_chunks) + + xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad) + if self.normalize_before: + xs = self.after_norm(xs) + # Here we assume the mask is not changed in encoder layers, so just + # return the masks before encoder layers, and the masks will be used + # for cross attention with decoder later + return xs, masks + diff --git a/comfy/ldm/ace/model.py b/comfy/ldm/ace/model.py new file mode 100644 index 000000000..e5883df90 --- /dev/null +++ b/comfy/ldm/ace/model.py @@ -0,0 +1,381 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/ace_step_transformer.py + +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Optional, List, Union + +import torch +from torch import nn + +import comfy.model_management + +from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps +from .attention import LinearTransformerBlock, t2i_modulate +from .lyric_encoder import ConformerEncoder as LyricEncoder + + +def cross_norm(hidden_states, controlnet_input): + # input N x T x c + mean_hidden_states, std_hidden_states = hidden_states.mean(dim=(1,2), keepdim=True), hidden_states.std(dim=(1,2), keepdim=True) + mean_controlnet_input, std_controlnet_input = controlnet_input.mean(dim=(1,2), keepdim=True), controlnet_input.std(dim=(1,2), keepdim=True) + controlnet_input = (controlnet_input - mean_controlnet_input) * (std_hidden_states / (std_controlnet_input + 1e-12)) + mean_hidden_states + return controlnet_input + + +# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Qwen2 +class Qwen2RotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, dtype=None, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device=device).float() / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32 + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) + + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:seq_len].to(dtype=x.dtype), + self.sin_cached[:seq_len].to(dtype=x.dtype), + ) + + +class T2IFinalLayer(nn.Module): + """ + The final layer of Sana. + """ + + def __init__(self, hidden_size, patch_size=[16, 1], out_channels=256, dtype=None, device=None, operations=None): + super().__init__() + self.norm_final = operations.RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, patch_size[0] * patch_size[1] * out_channels, bias=True, dtype=dtype, device=device) + self.scale_shift_table = nn.Parameter(torch.empty(2, hidden_size, dtype=dtype, device=device)) + self.out_channels = out_channels + self.patch_size = patch_size + + def unpatchfy( + self, + hidden_states: torch.Tensor, + width: int, + ): + # 4 unpatchify + new_height, new_width = 1, hidden_states.size(1) + hidden_states = hidden_states.reshape( + shape=(hidden_states.shape[0], new_height, new_width, self.patch_size[0], self.patch_size[1], self.out_channels) + ).contiguous() + hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) + output = hidden_states.reshape( + shape=(hidden_states.shape[0], self.out_channels, new_height * self.patch_size[0], new_width * self.patch_size[1]) + ).contiguous() + if width > new_width: + output = torch.nn.functional.pad(output, (0, width - new_width, 0, 0), 'constant', 0) + elif width < new_width: + output = output[:, :, :, :width] + return output + + def forward(self, x, t, output_length): + shift, scale = (comfy.model_management.cast_to(self.scale_shift_table[None], device=t.device, dtype=t.dtype) + t[:, None]).chunk(2, dim=1) + x = t2i_modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + # unpatchify + output = self.unpatchfy(x, output_length) + return output + + +class PatchEmbed(nn.Module): + """2D Image to Patch Embedding""" + + def __init__( + self, + height=16, + width=4096, + patch_size=(16, 1), + in_channels=8, + embed_dim=1152, + bias=True, + dtype=None, device=None, operations=None + ): + super().__init__() + patch_size_h, patch_size_w = patch_size + self.early_conv_layers = nn.Sequential( + operations.Conv2d(in_channels, in_channels*256, kernel_size=patch_size, stride=patch_size, padding=0, bias=bias, dtype=dtype, device=device), + operations.GroupNorm(num_groups=32, num_channels=in_channels*256, eps=1e-6, affine=True, dtype=dtype, device=device), + operations.Conv2d(in_channels*256, embed_dim, kernel_size=1, stride=1, padding=0, bias=bias, dtype=dtype, device=device) + ) + self.patch_size = patch_size + self.height, self.width = height // patch_size_h, width // patch_size_w + self.base_size = self.width + + def forward(self, latent): + # early convolutions, N x C x H x W -> N x 256 * sqrt(patch_size) x H/patch_size x W/patch_size + latent = self.early_conv_layers(latent) + latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC + return latent + + +class ACEStepTransformer2DModel(nn.Module): + # _supports_gradient_checkpointing = True + + def __init__( + self, + in_channels: Optional[int] = 8, + num_layers: int = 28, + inner_dim: int = 1536, + attention_head_dim: int = 64, + num_attention_heads: int = 24, + mlp_ratio: float = 4.0, + out_channels: int = 8, + max_position: int = 32768, + rope_theta: float = 1000000.0, + speaker_embedding_dim: int = 512, + text_embedding_dim: int = 768, + ssl_encoder_depths: List[int] = [9, 9], + ssl_names: List[str] = ["mert", "m-hubert"], + ssl_latent_dims: List[int] = [1024, 768], + lyric_encoder_vocab_size: int = 6681, + lyric_hidden_size: int = 1024, + patch_size: List[int] = [16, 1], + max_height: int = 16, + max_width: int = 4096, + audio_model=None, + dtype=None, device=None, operations=None + + ): + super().__init__() + + self.dtype = dtype + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + self.inner_dim = inner_dim + self.out_channels = out_channels + self.max_position = max_position + self.patch_size = patch_size + + self.rope_theta = rope_theta + + self.rotary_emb = Qwen2RotaryEmbedding( + dim=self.attention_head_dim, + max_position_embeddings=self.max_position, + base=self.rope_theta, + dtype=dtype, + device=device, + ) + + # 2. Define input layers + self.in_channels = in_channels + + self.num_layers = num_layers + # 3. Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + LinearTransformerBlock( + dim=self.inner_dim, + num_attention_heads=self.num_attention_heads, + attention_head_dim=attention_head_dim, + mlp_ratio=mlp_ratio, + add_cross_attention=True, + add_cross_attention_dim=self.inner_dim, + dtype=dtype, + device=device, + operations=operations, + ) + for i in range(self.num_layers) + ] + ) + + self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) + self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=self.inner_dim, dtype=dtype, device=device, operations=operations) + self.t_block = nn.Sequential(nn.SiLU(), operations.Linear(self.inner_dim, 6 * self.inner_dim, bias=True, dtype=dtype, device=device)) + + # speaker + self.speaker_embedder = operations.Linear(speaker_embedding_dim, self.inner_dim, dtype=dtype, device=device) + + # genre + self.genre_embedder = operations.Linear(text_embedding_dim, self.inner_dim, dtype=dtype, device=device) + + # lyric + self.lyric_embs = operations.Embedding(lyric_encoder_vocab_size, lyric_hidden_size, dtype=dtype, device=device) + self.lyric_encoder = LyricEncoder(input_size=lyric_hidden_size, static_chunk_size=0, dtype=dtype, device=device, operations=operations) + self.lyric_proj = operations.Linear(lyric_hidden_size, self.inner_dim, dtype=dtype, device=device) + + projector_dim = 2 * self.inner_dim + + self.projectors = nn.ModuleList([ + nn.Sequential( + operations.Linear(self.inner_dim, projector_dim, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(projector_dim, projector_dim, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(projector_dim, ssl_dim, dtype=dtype, device=device), + ) for ssl_dim in ssl_latent_dims + ]) + + self.proj_in = PatchEmbed( + height=max_height, + width=max_width, + patch_size=patch_size, + embed_dim=self.inner_dim, + bias=True, + dtype=dtype, + device=device, + operations=operations, + ) + + self.final_layer = T2IFinalLayer(self.inner_dim, patch_size=patch_size, out_channels=out_channels, dtype=dtype, device=device, operations=operations) + + def forward_lyric_encoder( + self, + lyric_token_idx: Optional[torch.LongTensor] = None, + lyric_mask: Optional[torch.LongTensor] = None, + out_dtype=None, + ): + # N x T x D + lyric_embs = self.lyric_embs(lyric_token_idx, out_dtype=out_dtype) + prompt_prenet_out, _mask = self.lyric_encoder(lyric_embs, lyric_mask, decoding_chunk_size=1, num_decoding_left_chunks=-1) + prompt_prenet_out = self.lyric_proj(prompt_prenet_out) + return prompt_prenet_out + + def encode( + self, + encoder_text_hidden_states: Optional[torch.Tensor] = None, + text_attention_mask: Optional[torch.LongTensor] = None, + speaker_embeds: Optional[torch.FloatTensor] = None, + lyric_token_idx: Optional[torch.LongTensor] = None, + lyric_mask: Optional[torch.LongTensor] = None, + ): + + bs = encoder_text_hidden_states.shape[0] + device = encoder_text_hidden_states.device + + # speaker embedding + encoder_spk_hidden_states = self.speaker_embedder(speaker_embeds).unsqueeze(1) + + # genre embedding + encoder_text_hidden_states = self.genre_embedder(encoder_text_hidden_states) + + # lyric + encoder_lyric_hidden_states = self.forward_lyric_encoder( + lyric_token_idx=lyric_token_idx, + lyric_mask=lyric_mask, + out_dtype=encoder_text_hidden_states.dtype, + ) + + encoder_hidden_states = torch.cat([encoder_spk_hidden_states, encoder_text_hidden_states, encoder_lyric_hidden_states], dim=1) + + encoder_hidden_mask = None + if text_attention_mask is not None: + speaker_mask = torch.ones(bs, 1, device=device) + encoder_hidden_mask = torch.cat([speaker_mask, text_attention_mask, lyric_mask], dim=1) + + return encoder_hidden_states, encoder_hidden_mask + + def decode( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + encoder_hidden_states: torch.Tensor, + encoder_hidden_mask: torch.Tensor, + timestep: Optional[torch.Tensor], + output_length: int = 0, + block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, + controlnet_scale: Union[float, torch.Tensor] = 1.0, + return_dict: bool = True, + ): + embedded_timestep = self.timestep_embedder(self.time_proj(timestep).to(dtype=hidden_states.dtype)) + temb = self.t_block(embedded_timestep) + + hidden_states = self.proj_in(hidden_states) + + # controlnet logic + if block_controlnet_hidden_states is not None: + control_condi = cross_norm(hidden_states, block_controlnet_hidden_states) + hidden_states = hidden_states + control_condi * controlnet_scale + + # inner_hidden_states = [] + + rotary_freqs_cis = self.rotary_emb(hidden_states, seq_len=hidden_states.shape[1]) + encoder_rotary_freqs_cis = self.rotary_emb(encoder_hidden_states, seq_len=encoder_hidden_states.shape[1]) + + for index_block, block in enumerate(self.transformer_blocks): + hidden_states = block( + hidden_states=hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_hidden_mask, + rotary_freqs_cis=rotary_freqs_cis, + rotary_freqs_cis_cross=encoder_rotary_freqs_cis, + temb=temb, + ) + + output = self.final_layer(hidden_states, embedded_timestep, output_length) + return output + + def forward( + self, + x, + timestep, + attention_mask=None, + context: Optional[torch.Tensor] = None, + text_attention_mask: Optional[torch.LongTensor] = None, + speaker_embeds: Optional[torch.FloatTensor] = None, + lyric_token_idx: Optional[torch.LongTensor] = None, + lyric_mask: Optional[torch.LongTensor] = None, + block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, + controlnet_scale: Union[float, torch.Tensor] = 1.0, + **kwargs + ): + hidden_states = x + encoder_text_hidden_states = context + encoder_hidden_states, encoder_hidden_mask = self.encode( + encoder_text_hidden_states=encoder_text_hidden_states, + text_attention_mask=text_attention_mask, + speaker_embeds=speaker_embeds, + lyric_token_idx=lyric_token_idx, + lyric_mask=lyric_mask, + ) + + output_length = hidden_states.shape[-1] + + output = self.decode( + hidden_states=hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_hidden_mask=encoder_hidden_mask, + timestep=timestep, + output_length=output_length, + block_controlnet_hidden_states=block_controlnet_hidden_states, + controlnet_scale=controlnet_scale, + ) + + return output diff --git a/comfy/ldm/ace/vae/autoencoder_dc.py b/comfy/ldm/ace/vae/autoencoder_dc.py new file mode 100644 index 000000000..e7b1d4801 --- /dev/null +++ b/comfy/ldm/ace/vae/autoencoder_dc.py @@ -0,0 +1,644 @@ +# Rewritten from diffusers +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import Tuple, Union + +import comfy.model_management +import comfy.ops +ops = comfy.ops.disable_weight_init + + +class RMSNorm(ops.RMSNorm): + def __init__(self, dim, eps=1e-5, elementwise_affine=True, bias=False): + super().__init__(dim, eps=eps, elementwise_affine=elementwise_affine) + if elementwise_affine: + self.bias = nn.Parameter(torch.empty(dim)) if bias else None + + def forward(self, x): + x = super().forward(x) + if self.elementwise_affine: + if self.bias is not None: + x = x + comfy.model_management.cast_to(self.bias, dtype=x.dtype, device=x.device) + return x + + +def get_normalization(norm_type, num_features, num_groups=32, eps=1e-5): + if norm_type == "batch_norm": + return nn.BatchNorm2d(num_features) + elif norm_type == "group_norm": + return ops.GroupNorm(num_groups, num_features) + elif norm_type == "layer_norm": + return ops.LayerNorm(num_features) + elif norm_type == "rms_norm": + return RMSNorm(num_features, eps=eps, elementwise_affine=True, bias=True) + else: + raise ValueError(f"Unknown normalization type: {norm_type}") + + +def get_activation(activation_type): + if activation_type == "relu": + return nn.ReLU() + elif activation_type == "relu6": + return nn.ReLU6() + elif activation_type == "silu": + return nn.SiLU() + elif activation_type == "leaky_relu": + return nn.LeakyReLU(0.2) + else: + raise ValueError(f"Unknown activation type: {activation_type}") + + +class ResBlock(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + norm_type: str = "batch_norm", + act_fn: str = "relu6", + ) -> None: + super().__init__() + + self.norm_type = norm_type + self.nonlinearity = get_activation(act_fn) if act_fn is not None else nn.Identity() + self.conv1 = ops.Conv2d(in_channels, in_channels, 3, 1, 1) + self.conv2 = ops.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False) + self.norm = get_normalization(norm_type, out_channels) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + residual = hidden_states + hidden_states = self.conv1(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + hidden_states = self.conv2(hidden_states) + + if self.norm_type == "rms_norm": + # move channel to the last dimension so we apply RMSnorm across channel dimension + hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1) + else: + hidden_states = self.norm(hidden_states) + + return hidden_states + residual + +class SanaMultiscaleAttentionProjection(nn.Module): + def __init__( + self, + in_channels: int, + num_attention_heads: int, + kernel_size: int, + ) -> None: + super().__init__() + + channels = 3 * in_channels + self.proj_in = ops.Conv2d( + channels, + channels, + kernel_size, + padding=kernel_size // 2, + groups=channels, + bias=False, + ) + self.proj_out = ops.Conv2d(channels, channels, 1, 1, 0, groups=3 * num_attention_heads, bias=False) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.proj_in(hidden_states) + hidden_states = self.proj_out(hidden_states) + return hidden_states + +class SanaMultiscaleLinearAttention(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + num_attention_heads: int = None, + attention_head_dim: int = 8, + mult: float = 1.0, + norm_type: str = "batch_norm", + kernel_sizes: tuple = (5,), + eps: float = 1e-15, + residual_connection: bool = False, + ): + super().__init__() + + self.eps = eps + self.attention_head_dim = attention_head_dim + self.norm_type = norm_type + self.residual_connection = residual_connection + + num_attention_heads = ( + int(in_channels // attention_head_dim * mult) + if num_attention_heads is None + else num_attention_heads + ) + inner_dim = num_attention_heads * attention_head_dim + + self.to_q = ops.Linear(in_channels, inner_dim, bias=False) + self.to_k = ops.Linear(in_channels, inner_dim, bias=False) + self.to_v = ops.Linear(in_channels, inner_dim, bias=False) + + self.to_qkv_multiscale = nn.ModuleList() + for kernel_size in kernel_sizes: + self.to_qkv_multiscale.append( + SanaMultiscaleAttentionProjection(inner_dim, num_attention_heads, kernel_size) + ) + + self.nonlinearity = nn.ReLU() + self.to_out = ops.Linear(inner_dim * (1 + len(kernel_sizes)), out_channels, bias=False) + self.norm_out = get_normalization(norm_type, out_channels) + + def apply_linear_attention(self, query, key, value): + value = F.pad(value, (0, 0, 0, 1), mode="constant", value=1) + scores = torch.matmul(value, key.transpose(-1, -2)) + hidden_states = torch.matmul(scores, query) + + hidden_states = hidden_states.to(dtype=torch.float32) + hidden_states = hidden_states[:, :, :-1] / (hidden_states[:, :, -1:] + self.eps) + return hidden_states + + def apply_quadratic_attention(self, query, key, value): + scores = torch.matmul(key.transpose(-1, -2), query) + scores = scores.to(dtype=torch.float32) + scores = scores / (torch.sum(scores, dim=2, keepdim=True) + self.eps) + hidden_states = torch.matmul(value, scores.to(value.dtype)) + return hidden_states + + def forward(self, hidden_states): + height, width = hidden_states.shape[-2:] + if height * width > self.attention_head_dim: + use_linear_attention = True + else: + use_linear_attention = False + + residual = hidden_states + + batch_size, _, height, width = list(hidden_states.size()) + original_dtype = hidden_states.dtype + + hidden_states = hidden_states.movedim(1, -1) + query = self.to_q(hidden_states) + key = self.to_k(hidden_states) + value = self.to_v(hidden_states) + hidden_states = torch.cat([query, key, value], dim=3) + hidden_states = hidden_states.movedim(-1, 1) + + multi_scale_qkv = [hidden_states] + for block in self.to_qkv_multiscale: + multi_scale_qkv.append(block(hidden_states)) + + hidden_states = torch.cat(multi_scale_qkv, dim=1) + + if use_linear_attention: + # for linear attention upcast hidden_states to float32 + hidden_states = hidden_states.to(dtype=torch.float32) + + hidden_states = hidden_states.reshape(batch_size, -1, 3 * self.attention_head_dim, height * width) + + query, key, value = hidden_states.chunk(3, dim=2) + query = self.nonlinearity(query) + key = self.nonlinearity(key) + + if use_linear_attention: + hidden_states = self.apply_linear_attention(query, key, value) + hidden_states = hidden_states.to(dtype=original_dtype) + else: + hidden_states = self.apply_quadratic_attention(query, key, value) + + hidden_states = torch.reshape(hidden_states, (batch_size, -1, height, width)) + hidden_states = self.to_out(hidden_states.movedim(1, -1)).movedim(-1, 1) + + if self.norm_type == "rms_norm": + hidden_states = self.norm_out(hidden_states.movedim(1, -1)).movedim(-1, 1) + else: + hidden_states = self.norm_out(hidden_states) + + if self.residual_connection: + hidden_states = hidden_states + residual + + return hidden_states + + +class EfficientViTBlock(nn.Module): + def __init__( + self, + in_channels: int, + mult: float = 1.0, + attention_head_dim: int = 32, + qkv_multiscales: tuple = (5,), + norm_type: str = "batch_norm", + ) -> None: + super().__init__() + + self.attn = SanaMultiscaleLinearAttention( + in_channels=in_channels, + out_channels=in_channels, + mult=mult, + attention_head_dim=attention_head_dim, + norm_type=norm_type, + kernel_sizes=qkv_multiscales, + residual_connection=True, + ) + + self.conv_out = GLUMBConv( + in_channels=in_channels, + out_channels=in_channels, + norm_type="rms_norm", + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.attn(x) + x = self.conv_out(x) + return x + + +class GLUMBConv(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + expand_ratio: float = 4, + norm_type: str = None, + residual_connection: bool = True, + ) -> None: + super().__init__() + + hidden_channels = int(expand_ratio * in_channels) + self.norm_type = norm_type + self.residual_connection = residual_connection + + self.nonlinearity = nn.SiLU() + self.conv_inverted = ops.Conv2d(in_channels, hidden_channels * 2, 1, 1, 0) + self.conv_depth = ops.Conv2d(hidden_channels * 2, hidden_channels * 2, 3, 1, 1, groups=hidden_channels * 2) + self.conv_point = ops.Conv2d(hidden_channels, out_channels, 1, 1, 0, bias=False) + + self.norm = None + if norm_type == "rms_norm": + self.norm = RMSNorm(out_channels, eps=1e-5, elementwise_affine=True, bias=True) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + if self.residual_connection: + residual = hidden_states + + hidden_states = self.conv_inverted(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.conv_depth(hidden_states) + hidden_states, gate = torch.chunk(hidden_states, 2, dim=1) + hidden_states = hidden_states * self.nonlinearity(gate) + + hidden_states = self.conv_point(hidden_states) + + if self.norm_type == "rms_norm": + # move channel to the last dimension so we apply RMSnorm across channel dimension + hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1) + + if self.residual_connection: + hidden_states = hidden_states + residual + + return hidden_states + + +def get_block( + block_type: str, + in_channels: int, + out_channels: int, + attention_head_dim: int, + norm_type: str, + act_fn: str, + qkv_mutliscales: tuple = (), +): + if block_type == "ResBlock": + block = ResBlock(in_channels, out_channels, norm_type, act_fn) + elif block_type == "EfficientViTBlock": + block = EfficientViTBlock( + in_channels, + attention_head_dim=attention_head_dim, + norm_type=norm_type, + qkv_multiscales=qkv_mutliscales + ) + else: + raise ValueError(f"Block with {block_type=} is not supported.") + + return block + + +class DCDownBlock2d(nn.Module): + def __init__(self, in_channels: int, out_channels: int, downsample: bool = False, shortcut: bool = True) -> None: + super().__init__() + + self.downsample = downsample + self.factor = 2 + self.stride = 1 if downsample else 2 + self.group_size = in_channels * self.factor**2 // out_channels + self.shortcut = shortcut + + out_ratio = self.factor**2 + if downsample: + assert out_channels % out_ratio == 0 + out_channels = out_channels // out_ratio + + self.conv = ops.Conv2d( + in_channels, + out_channels, + kernel_size=3, + stride=self.stride, + padding=1, + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + x = self.conv(hidden_states) + if self.downsample: + x = F.pixel_unshuffle(x, self.factor) + + if self.shortcut: + y = F.pixel_unshuffle(hidden_states, self.factor) + y = y.unflatten(1, (-1, self.group_size)) + y = y.mean(dim=2) + hidden_states = x + y + else: + hidden_states = x + + return hidden_states + + +class DCUpBlock2d(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + interpolate: bool = False, + shortcut: bool = True, + interpolation_mode: str = "nearest", + ) -> None: + super().__init__() + + self.interpolate = interpolate + self.interpolation_mode = interpolation_mode + self.shortcut = shortcut + self.factor = 2 + self.repeats = out_channels * self.factor**2 // in_channels + + out_ratio = self.factor**2 + if not interpolate: + out_channels = out_channels * out_ratio + + self.conv = ops.Conv2d(in_channels, out_channels, 3, 1, 1) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + if self.interpolate: + x = F.interpolate(hidden_states, scale_factor=self.factor, mode=self.interpolation_mode) + x = self.conv(x) + else: + x = self.conv(hidden_states) + x = F.pixel_shuffle(x, self.factor) + + if self.shortcut: + y = hidden_states.repeat_interleave(self.repeats, dim=1, output_size=hidden_states.shape[1] * self.repeats) + y = F.pixel_shuffle(y, self.factor) + hidden_states = x + y + else: + hidden_states = x + + return hidden_states + + +class Encoder(nn.Module): + def __init__( + self, + in_channels: int, + latent_channels: int, + attention_head_dim: int = 32, + block_type: str or tuple = "ResBlock", + block_out_channels: tuple = (128, 256, 512, 512, 1024, 1024), + layers_per_block: tuple = (2, 2, 2, 2, 2, 2), + qkv_multiscales: tuple = ((), (), (), (5,), (5,), (5,)), + downsample_block_type: str = "pixel_unshuffle", + out_shortcut: bool = True, + ): + super().__init__() + + num_blocks = len(block_out_channels) + + if isinstance(block_type, str): + block_type = (block_type,) * num_blocks + + if layers_per_block[0] > 0: + self.conv_in = ops.Conv2d( + in_channels, + block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1], + kernel_size=3, + stride=1, + padding=1, + ) + else: + self.conv_in = DCDownBlock2d( + in_channels=in_channels, + out_channels=block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1], + downsample=downsample_block_type == "pixel_unshuffle", + shortcut=False, + ) + + down_blocks = [] + for i, (out_channel, num_layers) in enumerate(zip(block_out_channels, layers_per_block)): + down_block_list = [] + + for _ in range(num_layers): + block = get_block( + block_type[i], + out_channel, + out_channel, + attention_head_dim=attention_head_dim, + norm_type="rms_norm", + act_fn="silu", + qkv_mutliscales=qkv_multiscales[i], + ) + down_block_list.append(block) + + if i < num_blocks - 1 and num_layers > 0: + downsample_block = DCDownBlock2d( + in_channels=out_channel, + out_channels=block_out_channels[i + 1], + downsample=downsample_block_type == "pixel_unshuffle", + shortcut=True, + ) + down_block_list.append(downsample_block) + + down_blocks.append(nn.Sequential(*down_block_list)) + + self.down_blocks = nn.ModuleList(down_blocks) + + self.conv_out = ops.Conv2d(block_out_channels[-1], latent_channels, 3, 1, 1) + + self.out_shortcut = out_shortcut + if out_shortcut: + self.out_shortcut_average_group_size = block_out_channels[-1] // latent_channels + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.conv_in(hidden_states) + for down_block in self.down_blocks: + hidden_states = down_block(hidden_states) + + if self.out_shortcut: + x = hidden_states.unflatten(1, (-1, self.out_shortcut_average_group_size)) + x = x.mean(dim=2) + hidden_states = self.conv_out(hidden_states) + x + else: + hidden_states = self.conv_out(hidden_states) + + return hidden_states + + +class Decoder(nn.Module): + def __init__( + self, + in_channels: int, + latent_channels: int, + attention_head_dim: int = 32, + block_type: str or tuple = "ResBlock", + block_out_channels: tuple = (128, 256, 512, 512, 1024, 1024), + layers_per_block: tuple = (2, 2, 2, 2, 2, 2), + qkv_multiscales: tuple = ((), (), (), (5,), (5,), (5,)), + norm_type: str or tuple = "rms_norm", + act_fn: str or tuple = "silu", + upsample_block_type: str = "pixel_shuffle", + in_shortcut: bool = True, + ): + super().__init__() + + num_blocks = len(block_out_channels) + + if isinstance(block_type, str): + block_type = (block_type,) * num_blocks + if isinstance(norm_type, str): + norm_type = (norm_type,) * num_blocks + if isinstance(act_fn, str): + act_fn = (act_fn,) * num_blocks + + self.conv_in = ops.Conv2d(latent_channels, block_out_channels[-1], 3, 1, 1) + + self.in_shortcut = in_shortcut + if in_shortcut: + self.in_shortcut_repeats = block_out_channels[-1] // latent_channels + + up_blocks = [] + for i, (out_channel, num_layers) in reversed(list(enumerate(zip(block_out_channels, layers_per_block)))): + up_block_list = [] + + if i < num_blocks - 1 and num_layers > 0: + upsample_block = DCUpBlock2d( + block_out_channels[i + 1], + out_channel, + interpolate=upsample_block_type == "interpolate", + shortcut=True, + ) + up_block_list.append(upsample_block) + + for _ in range(num_layers): + block = get_block( + block_type[i], + out_channel, + out_channel, + attention_head_dim=attention_head_dim, + norm_type=norm_type[i], + act_fn=act_fn[i], + qkv_mutliscales=qkv_multiscales[i], + ) + up_block_list.append(block) + + up_blocks.insert(0, nn.Sequential(*up_block_list)) + + self.up_blocks = nn.ModuleList(up_blocks) + + channels = block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1] + + self.norm_out = RMSNorm(channels, 1e-5, elementwise_affine=True, bias=True) + self.conv_act = nn.ReLU() + self.conv_out = None + + if layers_per_block[0] > 0: + self.conv_out = ops.Conv2d(channels, in_channels, 3, 1, 1) + else: + self.conv_out = DCUpBlock2d( + channels, in_channels, interpolate=upsample_block_type == "interpolate", shortcut=False + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + if self.in_shortcut: + x = hidden_states.repeat_interleave( + self.in_shortcut_repeats, dim=1, output_size=hidden_states.shape[1] * self.in_shortcut_repeats + ) + hidden_states = self.conv_in(hidden_states) + x + else: + hidden_states = self.conv_in(hidden_states) + + for up_block in reversed(self.up_blocks): + hidden_states = up_block(hidden_states) + + hidden_states = self.norm_out(hidden_states.movedim(1, -1)).movedim(-1, 1) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + return hidden_states + + +class AutoencoderDC(nn.Module): + def __init__( + self, + in_channels: int = 2, + latent_channels: int = 8, + attention_head_dim: int = 32, + encoder_block_types: Union[str, Tuple[str]] = ["ResBlock", "ResBlock", "ResBlock", "EfficientViTBlock"], + decoder_block_types: Union[str, Tuple[str]] = ["ResBlock", "ResBlock", "ResBlock", "EfficientViTBlock"], + encoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024), + decoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024), + encoder_layers_per_block: Tuple[int] = (2, 2, 3, 3), + decoder_layers_per_block: Tuple[int] = (3, 3, 3, 3), + encoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (5,), (5,)), + decoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (5,), (5,)), + upsample_block_type: str = "interpolate", + downsample_block_type: str = "Conv", + decoder_norm_types: Union[str, Tuple[str]] = "rms_norm", + decoder_act_fns: Union[str, Tuple[str]] = "silu", + scaling_factor: float = 0.41407, + ) -> None: + super().__init__() + + self.encoder = Encoder( + in_channels=in_channels, + latent_channels=latent_channels, + attention_head_dim=attention_head_dim, + block_type=encoder_block_types, + block_out_channels=encoder_block_out_channels, + layers_per_block=encoder_layers_per_block, + qkv_multiscales=encoder_qkv_multiscales, + downsample_block_type=downsample_block_type, + ) + + self.decoder = Decoder( + in_channels=in_channels, + latent_channels=latent_channels, + attention_head_dim=attention_head_dim, + block_type=decoder_block_types, + block_out_channels=decoder_block_out_channels, + layers_per_block=decoder_layers_per_block, + qkv_multiscales=decoder_qkv_multiscales, + norm_type=decoder_norm_types, + act_fn=decoder_act_fns, + upsample_block_type=upsample_block_type, + ) + + self.scaling_factor = scaling_factor + self.spatial_compression_ratio = 2 ** (len(encoder_block_out_channels) - 1) + + def encode(self, x: torch.Tensor) -> torch.Tensor: + """Internal encoding function.""" + encoded = self.encoder(x) + return encoded * self.scaling_factor + + def decode(self, z: torch.Tensor) -> torch.Tensor: + # Scale the latents back + z = z / self.scaling_factor + decoded = self.decoder(z) + return decoded + + def forward(self, x: torch.Tensor) -> torch.Tensor: + z = self.encode(x) + return self.decode(z) + diff --git a/comfy/ldm/ace/vae/music_dcae_pipeline.py b/comfy/ldm/ace/vae/music_dcae_pipeline.py new file mode 100644 index 000000000..3188bc770 --- /dev/null +++ b/comfy/ldm/ace/vae/music_dcae_pipeline.py @@ -0,0 +1,104 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_dcae_pipeline.py +import torch +from .autoencoder_dc import AutoencoderDC +import torchaudio +import torchvision.transforms as transforms +from .music_vocoder import ADaMoSHiFiGANV1 + + +class MusicDCAE(torch.nn.Module): + def __init__(self, source_sample_rate=None, dcae_config={}, vocoder_config={}): + super(MusicDCAE, self).__init__() + + self.dcae = AutoencoderDC(**dcae_config) + self.vocoder = ADaMoSHiFiGANV1(**vocoder_config) + + if source_sample_rate is None: + self.source_sample_rate = 48000 + else: + self.source_sample_rate = source_sample_rate + + # self.resampler = torchaudio.transforms.Resample(source_sample_rate, 44100) + + self.transform = transforms.Compose([ + transforms.Normalize(0.5, 0.5), + ]) + self.min_mel_value = -11.0 + self.max_mel_value = 3.0 + self.audio_chunk_size = int(round((1024 * 512 / 44100 * 48000))) + self.mel_chunk_size = 1024 + self.time_dimention_multiple = 8 + self.latent_chunk_size = self.mel_chunk_size // self.time_dimention_multiple + self.scale_factor = 0.1786 + self.shift_factor = -1.9091 + + def load_audio(self, audio_path): + audio, sr = torchaudio.load(audio_path) + return audio, sr + + def forward_mel(self, audios): + mels = [] + for i in range(len(audios)): + image = self.vocoder.mel_transform(audios[i]) + mels.append(image) + mels = torch.stack(mels) + return mels + + @torch.no_grad() + def encode(self, audios, audio_lengths=None, sr=None): + if audio_lengths is None: + audio_lengths = torch.tensor([audios.shape[2]] * audios.shape[0]) + audio_lengths = audio_lengths.to(audios.device) + + if sr is None: + sr = self.source_sample_rate + + if sr != 44100: + audios = torchaudio.functional.resample(audios, sr, 44100) + + max_audio_len = audios.shape[-1] + if max_audio_len % (8 * 512) != 0: + audios = torch.nn.functional.pad(audios, (0, 8 * 512 - max_audio_len % (8 * 512))) + + mels = self.forward_mel(audios) + mels = (mels - self.min_mel_value) / (self.max_mel_value - self.min_mel_value) + mels = self.transform(mels) + latents = [] + for mel in mels: + latent = self.dcae.encoder(mel.unsqueeze(0)) + latents.append(latent) + latents = torch.cat(latents, dim=0) + # latent_lengths = (audio_lengths / sr * 44100 / 512 / self.time_dimention_multiple).long() + latents = (latents - self.shift_factor) * self.scale_factor + return latents + # return latents, latent_lengths + + @torch.no_grad() + def decode(self, latents, audio_lengths=None, sr=None): + latents = latents / self.scale_factor + self.shift_factor + + pred_wavs = [] + + for latent in latents: + mels = self.dcae.decoder(latent.unsqueeze(0)) + mels = mels * 0.5 + 0.5 + mels = mels * (self.max_mel_value - self.min_mel_value) + self.min_mel_value + wav = self.vocoder.decode(mels[0]).squeeze(1) + + if sr is not None: + # resampler = torchaudio.transforms.Resample(44100, sr).to(latents.device).to(latents.dtype) + wav = torchaudio.functional.resample(wav, 44100, sr) + # wav = resampler(wav) + else: + sr = 44100 + pred_wavs.append(wav) + + if audio_lengths is not None: + pred_wavs = [wav[:, :length].cpu() for wav, length in zip(pred_wavs, audio_lengths)] + return torch.stack(pred_wavs) + # return sr, pred_wavs + + def forward(self, audios, audio_lengths=None, sr=None): + latents, latent_lengths = self.encode(audios=audios, audio_lengths=audio_lengths, sr=sr) + sr, pred_wavs = self.decode(latents=latents, audio_lengths=audio_lengths, sr=sr) + return sr, pred_wavs, latents, latent_lengths diff --git a/comfy/ldm/ace/vae/music_log_mel.py b/comfy/ldm/ace/vae/music_log_mel.py new file mode 100755 index 000000000..d73d3f8e8 --- /dev/null +++ b/comfy/ldm/ace/vae/music_log_mel.py @@ -0,0 +1,108 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_log_mel.py +import torch +import torch.nn as nn +from torch import Tensor +from torchaudio.transforms import MelScale +import comfy.model_management + +class LinearSpectrogram(nn.Module): + def __init__( + self, + n_fft=2048, + win_length=2048, + hop_length=512, + center=False, + mode="pow2_sqrt", + ): + super().__init__() + + self.n_fft = n_fft + self.win_length = win_length + self.hop_length = hop_length + self.center = center + self.mode = mode + + self.register_buffer("window", torch.hann_window(win_length)) + + def forward(self, y: Tensor) -> Tensor: + if y.ndim == 3: + y = y.squeeze(1) + + y = torch.nn.functional.pad( + y.unsqueeze(1), + ( + (self.win_length - self.hop_length) // 2, + (self.win_length - self.hop_length + 1) // 2, + ), + mode="reflect", + ).squeeze(1) + dtype = y.dtype + spec = torch.stft( + y.float(), + self.n_fft, + hop_length=self.hop_length, + win_length=self.win_length, + window=comfy.model_management.cast_to(self.window, dtype=torch.float32, device=y.device), + center=self.center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + spec = torch.view_as_real(spec) + + if self.mode == "pow2_sqrt": + spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) + spec = spec.to(dtype) + return spec + + +class LogMelSpectrogram(nn.Module): + def __init__( + self, + sample_rate=44100, + n_fft=2048, + win_length=2048, + hop_length=512, + n_mels=128, + center=False, + f_min=0.0, + f_max=None, + ): + super().__init__() + + self.sample_rate = sample_rate + self.n_fft = n_fft + self.win_length = win_length + self.hop_length = hop_length + self.center = center + self.n_mels = n_mels + self.f_min = f_min + self.f_max = f_max or sample_rate // 2 + + self.spectrogram = LinearSpectrogram(n_fft, win_length, hop_length, center) + self.mel_scale = MelScale( + self.n_mels, + self.sample_rate, + self.f_min, + self.f_max, + self.n_fft // 2 + 1, + "slaney", + "slaney", + ) + + def compress(self, x: Tensor) -> Tensor: + return torch.log(torch.clamp(x, min=1e-5)) + + def decompress(self, x: Tensor) -> Tensor: + return torch.exp(x) + + def forward(self, x: Tensor, return_linear: bool = False) -> Tensor: + linear = self.spectrogram(x) + x = self.mel_scale(linear) + x = self.compress(x) + # print(x.shape) + if return_linear: + return x, self.compress(linear) + + return x diff --git a/comfy/ldm/ace/vae/music_vocoder.py b/comfy/ldm/ace/vae/music_vocoder.py new file mode 100755 index 000000000..dc7c867da --- /dev/null +++ b/comfy/ldm/ace/vae/music_vocoder.py @@ -0,0 +1,542 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_vocoder.py +import torch +from torch import nn + +from functools import partial +from math import prod +from typing import Callable, Tuple, List + +import numpy as np +import torch.nn.functional as F +from torch.nn.utils import weight_norm +from torch.nn.utils.parametrize import remove_parametrizations as remove_weight_norm +# from diffusers.models.modeling_utils import ModelMixin +# from diffusers.loaders import FromOriginalModelMixin +# from diffusers.configuration_utils import ConfigMixin, register_to_config + +from .music_log_mel import LogMelSpectrogram + +import comfy.model_management +import comfy.ops +ops = comfy.ops.disable_weight_init + + +def drop_path( + x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True +): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + + """ # noqa: E501 + + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0 and scale_by_keep: + random_tensor.div_(keep_prob) + return x * random_tensor + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" # noqa: E501 + + def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) + + def extra_repr(self): + return f"drop_prob={round(self.drop_prob,3):0.3f}" + + +class LayerNorm(nn.Module): + r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with + shape (batch_size, height, width, channels) while channels_first corresponds to inputs + with shape (batch_size, channels, height, width). + """ # noqa: E501 + + def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"): + super().__init__() + self.weight = nn.Parameter(torch.ones(normalized_shape)) + self.bias = nn.Parameter(torch.zeros(normalized_shape)) + self.eps = eps + self.data_format = data_format + if self.data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError + self.normalized_shape = (normalized_shape,) + + def forward(self, x): + if self.data_format == "channels_last": + return F.layer_norm( + x, self.normalized_shape, comfy.model_management.cast_to(self.weight, dtype=x.dtype, device=x.device), comfy.model_management.cast_to(self.bias, dtype=x.dtype, device=x.device), self.eps + ) + elif self.data_format == "channels_first": + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = comfy.model_management.cast_to(self.weight[:, None], dtype=x.dtype, device=x.device) * x + comfy.model_management.cast_to(self.bias[:, None], dtype=x.dtype, device=x.device) + return x + + +class ConvNeXtBlock(nn.Module): + r"""ConvNeXt Block. There are two equivalent implementations: + (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W) + (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back + We use (2) as we find it slightly faster in PyTorch + + Args: + dim (int): Number of input channels. + drop_path (float): Stochastic depth rate. Default: 0.0 + layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0. + kernel_size (int): Kernel size for depthwise conv. Default: 7. + dilation (int): Dilation for depthwise conv. Default: 1. + """ # noqa: E501 + + def __init__( + self, + dim: int, + drop_path: float = 0.0, + layer_scale_init_value: float = 1e-6, + mlp_ratio: float = 4.0, + kernel_size: int = 7, + dilation: int = 1, + ): + super().__init__() + + self.dwconv = ops.Conv1d( + dim, + dim, + kernel_size=kernel_size, + padding=int(dilation * (kernel_size - 1) / 2), + groups=dim, + ) # depthwise conv + self.norm = LayerNorm(dim, eps=1e-6) + self.pwconv1 = ops.Linear( + dim, int(mlp_ratio * dim) + ) # pointwise/1x1 convs, implemented with linear layers + self.act = nn.GELU() + self.pwconv2 = ops.Linear(int(mlp_ratio * dim), dim) + self.gamma = ( + nn.Parameter(torch.empty((dim)), requires_grad=False) + if layer_scale_init_value > 0 + else None + ) + self.drop_path = DropPath( + drop_path) if drop_path > 0.0 else nn.Identity() + + def forward(self, x, apply_residual: bool = True): + input = x + + x = self.dwconv(x) + x = x.permute(0, 2, 1) # (N, C, L) -> (N, L, C) + x = self.norm(x) + x = self.pwconv1(x) + x = self.act(x) + x = self.pwconv2(x) + + if self.gamma is not None: + x = comfy.model_management.cast_to(self.gamma, dtype=x.dtype, device=x.device) * x + + x = x.permute(0, 2, 1) # (N, L, C) -> (N, C, L) + x = self.drop_path(x) + + if apply_residual: + x = input + x + + return x + + +class ParallelConvNeXtBlock(nn.Module): + def __init__(self, kernel_sizes: List[int], *args, **kwargs): + super().__init__() + self.blocks = nn.ModuleList( + [ + ConvNeXtBlock(kernel_size=kernel_size, *args, **kwargs) + for kernel_size in kernel_sizes + ] + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return torch.stack( + [block(x, apply_residual=False) for block in self.blocks] + [x], + dim=1, + ).sum(dim=1) + + +class ConvNeXtEncoder(nn.Module): + def __init__( + self, + input_channels=3, + depths=[3, 3, 9, 3], + dims=[96, 192, 384, 768], + drop_path_rate=0.0, + layer_scale_init_value=1e-6, + kernel_sizes: Tuple[int] = (7,), + ): + super().__init__() + assert len(depths) == len(dims) + + self.channel_layers = nn.ModuleList() + stem = nn.Sequential( + ops.Conv1d( + input_channels, + dims[0], + kernel_size=7, + padding=3, + padding_mode="replicate", + ), + LayerNorm(dims[0], eps=1e-6, data_format="channels_first"), + ) + self.channel_layers.append(stem) + + for i in range(len(depths) - 1): + mid_layer = nn.Sequential( + LayerNorm(dims[i], eps=1e-6, data_format="channels_first"), + ops.Conv1d(dims[i], dims[i + 1], kernel_size=1), + ) + self.channel_layers.append(mid_layer) + + block_fn = ( + partial(ConvNeXtBlock, kernel_size=kernel_sizes[0]) + if len(kernel_sizes) == 1 + else partial(ParallelConvNeXtBlock, kernel_sizes=kernel_sizes) + ) + + self.stages = nn.ModuleList() + drop_path_rates = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] + + cur = 0 + for i in range(len(depths)): + stage = nn.Sequential( + *[ + block_fn( + dim=dims[i], + drop_path=drop_path_rates[cur + j], + layer_scale_init_value=layer_scale_init_value, + ) + for j in range(depths[i]) + ] + ) + self.stages.append(stage) + cur += depths[i] + + self.norm = LayerNorm(dims[-1], eps=1e-6, data_format="channels_first") + + def forward( + self, + x: torch.Tensor, + ) -> torch.Tensor: + for channel_layer, stage in zip(self.channel_layers, self.stages): + x = channel_layer(x) + x = stage(x) + + return self.norm(x) + + +def get_padding(kernel_size, dilation=1): + return (kernel_size * dilation - dilation) // 2 + + +class ResBlock1(torch.nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): + super().__init__() + + self.convs1 = nn.ModuleList( + [ + weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + + self.convs2 = nn.ModuleList( + [ + weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.silu(x) + xt = c1(xt) + xt = F.silu(xt) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for conv in self.convs1: + remove_weight_norm(conv) + for conv in self.convs2: + remove_weight_norm(conv) + + +class HiFiGANGenerator(nn.Module): + def __init__( + self, + *, + hop_length: int = 512, + upsample_rates: Tuple[int] = (8, 8, 2, 2, 2), + upsample_kernel_sizes: Tuple[int] = (16, 16, 8, 2, 2), + resblock_kernel_sizes: Tuple[int] = (3, 7, 11), + resblock_dilation_sizes: Tuple[Tuple[int]] = ( + (1, 3, 5), (1, 3, 5), (1, 3, 5)), + num_mels: int = 128, + upsample_initial_channel: int = 512, + use_template: bool = True, + pre_conv_kernel_size: int = 7, + post_conv_kernel_size: int = 7, + post_activation: Callable = partial(nn.SiLU, inplace=True), + ): + super().__init__() + + assert ( + prod(upsample_rates) == hop_length + ), f"hop_length must be {prod(upsample_rates)}" + + self.conv_pre = weight_norm( + ops.Conv1d( + num_mels, + upsample_initial_channel, + pre_conv_kernel_size, + 1, + padding=get_padding(pre_conv_kernel_size), + ) + ) + + self.num_upsamples = len(upsample_rates) + self.num_kernels = len(resblock_kernel_sizes) + + self.noise_convs = nn.ModuleList() + self.use_template = use_template + self.ups = nn.ModuleList() + + for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): + c_cur = upsample_initial_channel // (2 ** (i + 1)) + self.ups.append( + weight_norm( + ops.ConvTranspose1d( + upsample_initial_channel // (2**i), + upsample_initial_channel // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + if not use_template: + continue + + if i + 1 < len(upsample_rates): + stride_f0 = np.prod(upsample_rates[i + 1:]) + self.noise_convs.append( + ops.Conv1d( + 1, + c_cur, + kernel_size=stride_f0 * 2, + stride=stride_f0, + padding=stride_f0 // 2, + ) + ) + else: + self.noise_convs.append(ops.Conv1d(1, c_cur, kernel_size=1)) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = upsample_initial_channel // (2 ** (i + 1)) + for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes): + self.resblocks.append(ResBlock1(ch, k, d)) + + self.activation_post = post_activation() + self.conv_post = weight_norm( + ops.Conv1d( + ch, + 1, + post_conv_kernel_size, + 1, + padding=get_padding(post_conv_kernel_size), + ) + ) + + def forward(self, x, template=None): + x = self.conv_pre(x) + + for i in range(self.num_upsamples): + x = F.silu(x, inplace=True) + x = self.ups[i](x) + + if self.use_template: + x = x + self.noise_convs[i](template) + + xs = None + + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + + x = xs / self.num_kernels + + x = self.activation_post(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + for up in self.ups: + remove_weight_norm(up) + for block in self.resblocks: + block.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class ADaMoSHiFiGANV1(nn.Module): + def __init__( + self, + input_channels: int = 128, + depths: List[int] = [3, 3, 9, 3], + dims: List[int] = [128, 256, 384, 512], + drop_path_rate: float = 0.0, + kernel_sizes: Tuple[int] = (7,), + upsample_rates: Tuple[int] = (4, 4, 2, 2, 2, 2, 2), + upsample_kernel_sizes: Tuple[int] = (8, 8, 4, 4, 4, 4, 4), + resblock_kernel_sizes: Tuple[int] = (3, 7, 11, 13), + resblock_dilation_sizes: Tuple[Tuple[int]] = ( + (1, 3, 5), (1, 3, 5), (1, 3, 5), (1, 3, 5)), + num_mels: int = 512, + upsample_initial_channel: int = 1024, + use_template: bool = False, + pre_conv_kernel_size: int = 13, + post_conv_kernel_size: int = 13, + sampling_rate: int = 44100, + n_fft: int = 2048, + win_length: int = 2048, + hop_length: int = 512, + f_min: int = 40, + f_max: int = 16000, + n_mels: int = 128, + ): + super().__init__() + + self.backbone = ConvNeXtEncoder( + input_channels=input_channels, + depths=depths, + dims=dims, + drop_path_rate=drop_path_rate, + kernel_sizes=kernel_sizes, + ) + + self.head = HiFiGANGenerator( + hop_length=hop_length, + upsample_rates=upsample_rates, + upsample_kernel_sizes=upsample_kernel_sizes, + resblock_kernel_sizes=resblock_kernel_sizes, + resblock_dilation_sizes=resblock_dilation_sizes, + num_mels=num_mels, + upsample_initial_channel=upsample_initial_channel, + use_template=use_template, + pre_conv_kernel_size=pre_conv_kernel_size, + post_conv_kernel_size=post_conv_kernel_size, + ) + self.sampling_rate = sampling_rate + self.mel_transform = LogMelSpectrogram( + sample_rate=sampling_rate, + n_fft=n_fft, + win_length=win_length, + hop_length=hop_length, + f_min=f_min, + f_max=f_max, + n_mels=n_mels, + ) + self.eval() + + @torch.no_grad() + def decode(self, mel): + y = self.backbone(mel) + y = self.head(y) + return y + + @torch.no_grad() + def encode(self, x): + return self.mel_transform(x) + + def forward(self, mel): + y = self.backbone(mel) + y = self.head(y) + return y diff --git a/comfy/model_base.py b/comfy/model_base.py index 045df1317..6408005b6 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -39,6 +39,7 @@ import comfy.ldm.wan.model import comfy.ldm.hunyuan3d.model import comfy.ldm.hidream.model import comfy.ldm.chroma.model +import comfy.ldm.ace.model import comfy.model_management import comfy.patcher_extension @@ -1121,3 +1122,21 @@ class Chroma(Flux): if guidance is not None: out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) return out + +class ACEStep(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ace.model.ACEStepTransformer2DModel) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + noise = kwargs.get("noise", None) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + conditioning_lyrics = kwargs.get("conditioning_lyrics", None) + if cross_attn is not None: + out['lyric_token_idx'] = comfy.conds.CONDRegular(conditioning_lyrics) + out['speaker_embeds'] = comfy.conds.CONDRegular(torch.zeros(noise.shape[0], 512, device=noise.device, dtype=noise.dtype)) + return out diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 9254843ea..ff4c29d7e 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -226,6 +226,31 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config.update(json.loads(metadata["config"]).get("transformer", {})) return dit_config + if '{}genre_embedder.weight'.format(key_prefix) in state_dict_keys: #ACE-Step model + dit_config = {} + dit_config["audio_model"] = "ace" + dit_config["attention_head_dim"] = 128 + dit_config["in_channels"] = 8 + dit_config["inner_dim"] = 2560 + dit_config["max_height"] = 16 + dit_config["max_position"] = 32768 + dit_config["max_width"] = 32768 + dit_config["mlp_ratio"] = 2.5 + dit_config["num_attention_heads"] = 20 + dit_config["num_layers"] = 24 + dit_config["out_channels"] = 8 + dit_config["patch_size"] = [16, 1] + dit_config["rope_theta"] = 1000000.0 + dit_config["speaker_embedding_dim"] = 512 + dit_config["text_embedding_dim"] = 768 + + dit_config["ssl_encoder_depths"] = [8, 8] + dit_config["ssl_latent_dims"] = [1024, 768] + dit_config["ssl_names"] = ["mert", "m-hubert"] + dit_config["lyric_encoder_vocab_size"] = 6693 + dit_config["lyric_hidden_size"] = 1024 + return dit_config + if '{}t_block.1.weight'.format(key_prefix) in state_dict_keys: # PixArt patch_size = 2 dit_config = {} diff --git a/comfy/sd.py b/comfy/sd.py index da9b36d0e..50af243ba 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -15,6 +15,7 @@ import comfy.ldm.lightricks.vae.causal_video_autoencoder import comfy.ldm.cosmos.vae import comfy.ldm.wan.vae import comfy.ldm.hunyuan3d.vae +import comfy.ldm.ace.vae.music_dcae_pipeline import yaml import math @@ -42,6 +43,7 @@ import comfy.text_encoders.cosmos import comfy.text_encoders.lumina2 import comfy.text_encoders.wan import comfy.text_encoders.hidream +import comfy.text_encoders.ace import comfy.model_patcher import comfy.lora @@ -437,6 +439,19 @@ class VAE: ddconfig = {"embed_dim": 64, "num_freqs": 8, "include_pi": False, "heads": 16, "width": 1024, "num_decoder_layers": 16, "qkv_bias": False, "qk_norm": True, "geo_decoder_mlp_expand_ratio": mlp_expand, "geo_decoder_downsample_ratio": downsample_ratio, "geo_decoder_ln_post": ln_post} self.first_stage_model = comfy.ldm.hunyuan3d.vae.ShapeVAE(**ddconfig) self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] + elif "vocoder.backbone.channel_layers.0.0.bias" in sd: #Ace Step Audio + self.first_stage_model = comfy.ldm.ace.vae.music_dcae_pipeline.MusicDCAE(source_sample_rate=44100) + self.memory_used_encode = lambda shape, dtype: (shape[2] * 300) * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 72000) * model_management.dtype_size(dtype) + self.latent_channels = 8 + self.output_channels = 2 + # self.upscale_ratio = 2048 + # self.downscale_ratio = 2048 + self.latent_dim = 2 + self.process_output = lambda audio: audio + self.process_input = lambda audio: audio + self.working_dtypes = [torch.bfloat16, torch.float32] + self.disable_offload = True else: logging.warning("WARNING: No VAE weights detected, VAE not initalized.") self.first_stage_model = None @@ -715,6 +730,7 @@ class CLIPType(Enum): WAN = 13 HIDREAM = 14 CHROMA = 15 + ACE = 16 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}): @@ -840,8 +856,13 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer elif te_model == TEModel.T5_BASE: - clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model - clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer + if clip_type == CLIPType.ACE or "spiece_model" in clip_data[0]: + clip_target.clip = comfy.text_encoders.ace.AceT5Model + clip_target.tokenizer = comfy.text_encoders.ace.AceT5Tokenizer + tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) + else: + clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model + clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer elif te_model == TEModel.GEMMA_2_2B: clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer diff --git a/comfy/supported_models.py b/comfy/supported_models.py index a1dea2343..fef25eb24 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -17,6 +17,7 @@ import comfy.text_encoders.hunyuan_video import comfy.text_encoders.cosmos import comfy.text_encoders.lumina2 import comfy.text_encoders.wan +import comfy.text_encoders.ace from . import supported_models_base from . import latent_formats @@ -1100,6 +1101,34 @@ class Chroma(supported_models_base.BASE): t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.pixart_te(**t5_detect)) -models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma] +class ACEStep(supported_models_base.BASE): + unet_config = { + "audio_model": "ace", + } + + unet_extra_config = { + } + + sampling_settings = { + "shift": 3.0, + } + + latent_format = comfy.latent_formats.ACEAudio + + memory_usage_factor = 0.5 + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.ACEStep(self, device=device) + return out + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(comfy.text_encoders.ace.AceT5Tokenizer, comfy.text_encoders.ace.AceT5Model) + +models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep] models += [SVD_img2vid] diff --git a/comfy/text_encoders/ace.py b/comfy/text_encoders/ace.py new file mode 100644 index 000000000..b6fe451bd --- /dev/null +++ b/comfy/text_encoders/ace.py @@ -0,0 +1,145 @@ +from comfy import sd1_clip +from .spiece_tokenizer import SPieceTokenizer +import comfy.text_encoders.t5 +import os +import re +import torch +import logging + +from tokenizers import Tokenizer +from .ace_text_cleaners import multilingual_cleaners + +SUPPORT_LANGUAGES = { + "en": 259, "de": 260, "fr": 262, "es": 284, "it": 285, + "pt": 286, "pl": 294, "tr": 295, "ru": 267, "cs": 293, + "nl": 297, "ar": 5022, "zh": 5023, "ja": 5412, "hu": 5753, + "ko": 6152, "hi": 6680 +} + +structure_pattern = re.compile(r"\[.*?\]") + +DEFAULT_VOCAB_FILE = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "ace_lyrics_tokenizer"), "vocab.json") + + +class VoiceBpeTokenizer: + def __init__(self, vocab_file=DEFAULT_VOCAB_FILE): + self.tokenizer = None + if vocab_file is not None: + self.tokenizer = Tokenizer.from_file(vocab_file) + + def preprocess_text(self, txt, lang): + txt = multilingual_cleaners(txt, lang) + return txt + + def encode(self, txt, lang='en'): + # lang = lang.split("-")[0] # remove the region + # self.check_input_length(txt, lang) + txt = self.preprocess_text(txt, lang) + lang = "zh-cn" if lang == "zh" else lang + txt = f"[{lang}]{txt}" + txt = txt.replace(" ", "[SPACE]") + return self.tokenizer.encode(txt).ids + + def get_lang(self, line): + if line.startswith("[") and line[3:4] == ']': + lang = line[1:3].lower() + if lang in SUPPORT_LANGUAGES: + return lang, line[4:] + return "en", line + + def __call__(self, string): + lines = string.split("\n") + lyric_token_idx = [261] + for line in lines: + line = line.strip() + if not line: + lyric_token_idx += [2] + continue + + lang, line = self.get_lang(line) + + if lang not in SUPPORT_LANGUAGES: + lang = "en" + if "zh" in lang: + lang = "zh" + if "spa" in lang: + lang = "es" + + try: + if structure_pattern.match(line): + token_idx = self.encode(line, "en") + else: + token_idx = self.encode(line, lang) + lyric_token_idx = lyric_token_idx + token_idx + [2] + except Exception as e: + logging.warning("tokenize error {} for line {} major_language {}".format(e, line, lang)) + return {"input_ids": lyric_token_idx} + + @staticmethod + def from_pretrained(path, **kwargs): + return VoiceBpeTokenizer(path, **kwargs) + + def get_vocab(self): + return {} + + +class UMT5BaseModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "umt5_config_base.json") + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=False, model_options=model_options) + +class UMT5BaseTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer = tokenizer_data.get("spiece_model", None) + super().__init__(tokenizer, pad_with_end=False, embedding_size=768, embedding_key='umt5base', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=0, tokenizer_data=tokenizer_data) + + def state_dict(self): + return {"spiece_model": self.tokenizer.serialize_model()} + +class LyricsTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "ace_lyrics_tokenizer"), "vocab.json") + super().__init__(tokenizer, pad_with_end=False, embedding_size=1024, embedding_key='lyrics', tokenizer_class=VoiceBpeTokenizer, has_start_token=True, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=2, has_end_token=False, tokenizer_data=tokenizer_data) + +class AceT5Tokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}): + self.voicebpe = LyricsTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.umt5base = UMT5BaseTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): + out = {} + out["lyrics"] = self.voicebpe.tokenize_with_weights(kwargs.get("lyrics", ""), return_word_ids, **kwargs) + out["umt5base"] = self.umt5base.tokenize_with_weights(text, return_word_ids, **kwargs) + return out + + def untokenize(self, token_weight_pair): + return self.umt5base.untokenize(token_weight_pair) + + def state_dict(self): + return self.umt5base.state_dict() + +class AceT5Model(torch.nn.Module): + def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): + super().__init__() + self.umt5base = UMT5BaseModel(device=device, dtype=dtype, model_options=model_options) + self.dtypes = set() + if dtype is not None: + self.dtypes.add(dtype) + + def set_clip_options(self, options): + self.umt5base.set_clip_options(options) + + def reset_clip_options(self): + self.umt5base.reset_clip_options() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_umt5base = token_weight_pairs["umt5base"] + token_weight_pairs_lyrics = token_weight_pairs["lyrics"] + + t5_out, t5_pooled = self.umt5base.encode_token_weights(token_weight_pairs_umt5base) + + lyrics_embeds = torch.tensor(list(map(lambda a: a[0], token_weight_pairs_lyrics[0]))).unsqueeze(0) + return t5_out, None, {"conditioning_lyrics": lyrics_embeds} + + def load_sd(self, sd): + return self.umt5base.load_sd(sd) diff --git a/comfy/text_encoders/ace_lyrics_tokenizer/vocab.json b/comfy/text_encoders/ace_lyrics_tokenizer/vocab.json new file mode 100644 index 000000000..519ed340c --- /dev/null +++ b/comfy/text_encoders/ace_lyrics_tokenizer/vocab.json @@ -0,0 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3674, + "рова": 3675, + "ций": 3676, + "нам": 3677, + "этой": 3678, + "жения": 3679, + "необходи": 3680, + "меня": 3681, + "было": 3682, + "сили": 3683, + "фи": 3684, + "вя": 3685, + "шь": 3686, + "этого": 3687, + "они": 3688, + "органи": 3689, + "безо": 3690, + "проб": 3691, + "име": 3692, + "реш": 3693, + "би": 3694, + "безопас": 3695, + "ются": 3696, + "оста": 3697, + "енно": 3698, + "год": 3699, + "ела": 3700, + "представ": 3701, + "ться": 3702, + "слово": 3703, + "организа": 3704, + "должны": 3705, + "этом": 3706, + "бла": 3707, + "че": 3708, + "чу": 3709, + "благо": 3710, + "этому": 3711, + "врем": 3712, + "спе": 3713, + "ном": 3714, + "ений": 3715, + "спо": 3716, + "нас": 3717, + "нет": 3718, + "зу": 3719, + "вед": 3720, + "еще": 3721, + "сказа": 3722, + "сей": 3723, + "ерен": 3724, + "дан": 3725, + "сам": 3726, + "еля": 3727, + "ран": 3728, + "зыва": 3729, + "является": 3730, + "будет": 3731, + "ктив": 3732, + "тре": 3733, + "деле": 3734, + "мот": 3735, + "конферен": 3736, + "лась": 3737, + "час": 3738, + "сторо": 3739, + "кого": 3740, + "ез": 3741, + "ней": 3742, + "ос": 3743, + "лись": 3744, + "разору": 3745, + "пере": 3746, + "сси": 3747, + "ными": 3748, + "проц": 3749, + "голо": 3750, + "чело": 3751, + "боле": 3752, + "челове": 3753, + "сер": 3754, + "пл": 3755, + "чет": 3756, + "стран": 3757, + "пя": 3758, + "был": 3759, + "кла": 3760, + "тов": 3761, + "жд": 3762, + "дела": 3763, + "ера": 3764, + "уже": 3765, + "совет": 3766, + "ген": 3767, + "безопасности": 3768, + "ца": 3769, + "седа": 3770, + "поз": 3771, + "ответ": 3772, + "проблем": 3773, + "нако": 3774, + "тем": 3775, + "доста": 3776, + "пы": 3777, + "ща": 3778, + "вой": 3779, + "сущест": 3780, + "необходимо": 3781, + "быть": 3782, + "может": 3783, + "дем": 3784, + "чтобы": 3785, + "ек": 3786, + "чер": 3787, + "усили": 3788, + "рес": 3789, + "руд": 3790, + "единенных": 3791, + "доб": 3792, + "дости": 3793, + "ствен": 3794, + "ядер": 3795, + "годня": 3796, + "каза": 3797, + "сегодня": 3798, + 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3860, + "всех": 3861, + "свя": 3862, + "более": 3863, + "сов": 3864, + "когда": 3865, + "вот": 3866, + "кре": 3867, + "кры": 3868, + "поэтому": 3869, + "воль": 3870, + "ой": 3871, + "генера": 3872, + "чем": 3873, + "лы": 3874, + "полити": 3875, + "вен": 3876, + "конференции": 3877, + "процес": 3878, + "бя": 3879, + "ите": 3880, + "отно": 3881, + "развити": 3882, + "аф": 3883, + "ющ": 3884, + "вно": 3885, + "мир": 3886, + "нии": 3887, + "кая": 3888, + "ас": 3889, + "ительно": 3890, + "вто": 3891, + "ением": 3892, + "генераль": 3893, + "прот": 3894, + "всем": 3895, + "самбле": 3896, + "ассамбле": 3897, + "ом": 3898, + "зд": 3899, + "смот": 3900, + "реги": 3901, + "чего": 3902, + "однако": 3903, + "усилия": 3904, + "действи": 3905, + "чно": 3906, + "уча": 3907, + "образ": 3908, + "вос": 3909, + "эта": 3910, + "перего": 3911, + "говор": 3912, + "вам": 3913, + "моло": 3914, + "время": 3915, + "дь": 3916, + "хотел": 3917, + "гру": 3918, + "заявл": 3919, + "предоста": 3920, + "поль": 3921, + 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3994, + "ۚ": 3995, + "ۛ": 3996, + "—": 3997, + "☭": 3998, + "ﺃ": 3999, + "ﻻ": 4000, + "ال": 4001, + "َا": 4002, + "وَ": 4003, + "َّ": 4004, + "ِي": 4005, + "أَ": 4006, + "لَ": 4007, + "نَ": 4008, + "الْ": 4009, + "هُ": 4010, + "ُو": 4011, + "ما": 4012, + "نْ": 4013, + "من": 4014, + "عَ": 4015, + "نا": 4016, + "لا": 4017, + "مَ": 4018, + "تَ": 4019, + "فَ": 4020, + "أن": 4021, + "لي": 4022, + "مِ": 4023, + "ان": 4024, + "في": 4025, + "رَ": 4026, + "يَ": 4027, + "هِ": 4028, + "مْ": 4029, + "قَ": 4030, + "بِ": 4031, + "لى": 4032, + "ين": 4033, + "إِ": 4034, + "لِ": 4035, + "وا": 4036, + "كَ": 4037, + "ها": 4038, + "ًا": 4039, + "مُ": 4040, + "ون": 4041, + "الم": 4042, + "بَ": 4043, + "يا": 4044, + "ذا": 4045, + "سا": 4046, + "الل": 4047, + "مي": 4048, + "يْ": 4049, + "را": 4050, + "ري": 4051, + "لك": 4052, + "مَا": 4053, + "نَّ": 4054, + "لم": 4055, + "إن": 4056, + "ست": 4057, + "وم": 4058, + "َّا": 4059, + "لَا": 4060, + "هم": 4061, + "ِّ": 4062, + "كُ": 4063, + "كان": 4064, + "سَ": 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4337, + "الْحَ": 4338, + "وَقَالَ": 4339, + "به": 4340, + "ةٍ": 4341, + "سأ": 4342, + "رٌ": 4343, + "بال": 4344, + "مة": 4345, + "شْ": 4346, + "وت": 4347, + "عند": 4348, + "فس": 4349, + "بَعْ": 4350, + "هر": 4351, + "قط": 4352, + "أح": 4353, + "إنه": 4354, + "وع": 4355, + "فت": 4356, + "غا": 4357, + "هناك": 4358, + "بت": 4359, + "مِنَ": 4360, + "سر": 4361, + "ذَلِكَ": 4362, + "رس": 4363, + "حدث": 4364, + "غْ": 4365, + "ِّي": 4366, + "الإ": 4367, + "وَيَ": 4368, + "جل": 4369, + "است": 4370, + "قِي": 4371, + "عب": 4372, + "وس": 4373, + "يش": 4374, + "الَّذِينَ": 4375, + "تاب": 4376, + "دِي": 4377, + "جب": 4378, + "كون": 4379, + "بن": 4380, + "الث": 4381, + "لَيْ": 4382, + "بعد": 4383, + "وَالْ": 4384, + "فَأَ": 4385, + "عم": 4386, + "هُم": 4387, + "تن": 4388, + "ذْ": 4389, + "أص": 4390, + "أين": 4391, + "رَبِّ": 4392, + "الذين": 4393, + "إِن": 4394, + "بين": 4395, + "جُ": 4396, + "عَلَيْهِ": 4397, + "حَا": 4398, + "لو": 4399, + "ستط": 4400, + "ظر": 4401, + "لَمْ": 4402, + "ءِ": 4403, + 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rai", + "la r", + "c et", + "regar d", + "l ant", + "de m", + "s om", + "mom ent", + "il les", + "p le", + "p s", + "b es", + "m ère", + "c l", + "s our", + "y s", + "tr op", + "en ne", + "jus qu", + "av aient", + "av ais", + "jeu ne", + "de puis", + "person ne", + "f it", + "cer t", + "j o", + "g es", + "ou i", + "r est", + "sem b", + "c ap", + "m at", + "m u", + "lon g", + "fr an", + "f aut", + "it i", + "b li", + "che v", + "pr i", + "ent e", + "ain si", + "ch am", + "l ors", + "c as", + "d o", + "il i", + "b é", + "n os", + "an ge", + "su i", + "r it", + "cr o", + "gu e", + "d e", + "e n", + "e s", + "o s", + "l a", + "e r", + "q u", + "a r", + "a n", + "o n", + "qu e", + "a s", + "o r", + "e l", + "d o", + "a l", + "c i", + "u n", + "r e", + "a b", + "i n", + "t e", + "t o", + "s e", + "d i", + "t r", + "d a", + "c on", + "t a", + "s u", + "m i", + "c o", + "t i", + "l e", + "l os", + "n o", + "l o", + "í a", + "c u", + "c a", + "s i", + "v i", + "m e", + "p or", + "m o", + "p ar", + "r a", + "r i", + "la s", + "c h", + "r o", + "m a", + "p er", + "ó n", + "m en", + "de s", + "un a", + "m p", + "s o", + "ab a", + "p u", + "d os", + "t u", + "g u", + "er a", + "de l", + "h a", + "m u", + "l i", + "en t", + "m b", + "h ab", + "es t", + "g o", + "p a", + "r es", + "par a", + "p o", + "á s", + "m os", + "tr a", + "t en", + "an do", + "p i", + "qu i", + "b i", + "m an", + "co mo", + "v e", + "m ás", + "j o", + "ci ón", + "i s", + "t an", + "v o", + "da d", + "c e", + "a do", + "v er", + "f u", + "ci a", + "c er", + "p e", + "c as", + "c ar", + "men te", + "n i", + "su s", + "t ar", + "n a", + "f i", + "t er", + "z a", + "p ro", + "tr o", + "s a", + "l u", + "b a", + "per o", + "s er", + "c es", + "d as", + "d u", + "s in", + "e mp", + "m ar", + "l la", + "e x", + "á n", + "c or", + "i a", + "v a", + "r an", + "ch o", + "g a", + "y o", + "t os", + "c os", + "mi s", + "l es", + "t es", + "v en", + "h o", + "y a", + "en te", + "on es", + "hab ía", + "n u", + "u s", + "p as", + "h i", + "n os", + "es ta", + "la n", + "m as", + "t or", + "l le", + "h e", + "s on", + "b re", + "p re", + "ab an", + "d or", + "í an", + "i r", + "t as", + "é n", + "r u", + "en do", + "a que", + "er o", + "i o", + "qu é", + "m in", + "c ab", + "j a", + "de r", + "t al", + "é s", + "se ñ", + "or a", + "to do", + "la r", + "d on", + "g ar", + "s al", + "p r", + "cu ando", + "j e", + "h u", + "g un", + "b u", + "g i", + "d ar", + "n e", + "r as", + "de n", + "es to", + "par e", + "p en", + "é l", + "tr as", + "c an", + "b o", + "j os", + "mi en", + "pu e", + "c re", + "co mp", + "p on", + "d ía", + "tr os", + "s ab", + "so bre", + "es e", + "mb re", + "er on", + "a ñ", + "m or", + "f or", + "i do", + "por que", + "el la", + "p ri", + "g ran", + "f a", + "c en", + "di s", + "c ri", + "mu y", + "ch a", + "c al", + "es te", + "h as", + "c ó", + "g ra", + "r os", + "p os", + "o b", + "al l", + "aque l", + "j u", + "p res", + "m er", + "di jo", + "c ía", + "ent re", + "z o", + "ci ones", + "bi en", + "mb i", + "el o", + "t ó", + "in a", + "to dos", + "g en", + "ti en", + "est aba", + "de ci", + "ci o", + "h er", + "ñ o", + "l or", + "nu es", + "me di", + "l en", + "vi da", + "f e", + "al i", + "m on", + "c la", + "d re", + "pu es", + "al es", + "vo l", + "m í", + "r ar", + "b le", + "ci on", + "has ta", + "señ or", + "con o", + "a h", + "di os", + "s en", + "es a", + "ú n", + "v ar", + "s an", + "gu i", + "a c", + "o tros", + "ta do", + "bu en", + "ñ a", + "ti emp", + "ha cer", + "j er", + "f er", + "v u", + "f in", + "an a", + "as í", + "an tes", + "t in", + "ve z", + "mien to", + "j ar", + "la b", + "ch e", + "cas a", + "d r", + "es o", + "e go", + "di ó", + "an te", + "est á", + "m al", + "en cia", + "el i", + "í as", + "tiemp o", + "z ar", + "v an", + "m un", + "er ta", + "ta mbi", + "s í", + "b ar", + "a un", + "al e", + "mis mo", + "ent es", + "vi s", + "man o", + "el e", + "na da", + "se gu", + "me j", + "er ra", + "ab le", + "b e", + "ti r", + "un o", + "don de", + "to da", + "des de", + "r en", + "tambi én", + "cu er", + "per son", + "ho mbre", + "o tro", + "li b", + "tr ar", + "cu al", + "ha y", + "a u", + "ca da", + "t aba", + "i mp", + "men to", + "ten ía", + "qu er", + "er an", + "si emp", + "siemp re", + "er to", + "qu í", + "g os", + "pu és", + "el los", + "des pués", + "nu e", + "g an", + "l lo", + "in ter", + "có mo", + "tr i", + "ah ora", + "us te", + "tr aba", + "la do", + "in o", + "po co", + "er te", + "mu jer", + "i m", + "qui er", + "al gun", + "fu e", + "o jos", + "ent on", + "v os", + "es per", + "mu ch", + "o tra", + "a z", + "a d", + "in g", + "e za", + "a quí", + "ci as", + "gu a", + "mu cho", + "deci r", + "es ti", + "i dad", + "al go", + "e z", + "o cu", + "enton ces", + "di do", + "ent os", + "g ri", + "da do", + "i os", + "so l", + "dos e", + "uste d", + "qui en", + "a mi", + "un to", + "f r", + "mi r", + "mej or", + "b as", + "so lo", + "pre gun", + "tu r", + "al g", + "p la", + "to das", + "par te", + "e mb", + "c to", + "mun do", + "tien e", + "tan te", + "pa lab", + "tr an", + "aque lla", + "ci os", + "aun que", + "a y", + "cu en", + "ten er", + "f un", + "res pon", + "all í", + "x i", + "h an", + "pen s", + "con tra", + "tu ra", + "v al", + "di o", + "tr es", + "t re", + "tan to", + "ca min", + "m ó", + "es p", + "a da", + "í o", + "in s", + "ha cia", + "de j", + "est ar", + "i ón", + "g as", + "b er", + "v as", + "no che", + "é r", + "añ os", + "pa dre", + "gu s", + "á r", + "sin o", + "man os", + "ci do", + "es tu", + "a de", + "hu bi", + "vi r", + "b ri", + "ra z", + "ch i", + "pue de", + "men os", + "hab i", + "ho mb", + "ne ces", + "ma y", + "er os", + "r ía", + "he cho", + "es cu", + "l ti", + "án do", + "b us", + "cos as", + "t ú", + "es pa", + "re ci", + "c tor", + "pri m", + "di a", + "de se", + "mien tras", + "h or", + "fu er", + "i da", + "pos i", + "lan te", + "t on", + "an o", + "est as", + "p li", + "ch ar", + "lu ego", + "si ón", + "ci n", + "ti erra", + "m es", + "gu ar", + "ca do", + "en con", + "pr en", + "may or", + "f al", + "e r", + "o n", + "a n", + "t o", + "d i", + "r e", + "l a", + "i n", + "e n", + "a l", + "t a", + "c h", + "e l", + "r i", + "c o", + "t i", + "t e", + "s i", + "r a", + "u n", + "l e", + "l i", + "ch e", + "r o", + "c i", + "c a", + "s e", + "q u", + "m a", + "p o", + "s o", + "i l", + "d o", + "e s", + "v a", + "p er", + "l o", + "c on", + "d el", + "p a", + "m o", + "s a", + "p i", + "d a", + "m i", + "g i", + "s u", + "d e", + "v i", + "z i", + "m e", + "g li", + "n o", + "m en", + "v o", + "t u", + "n on", + "v e", + "t to", + "s t", + "on e", + "an o", + "ch i", + "er a", + "er e", + "f a", + "c e", + "z a", + "un a", + "b i", + "p re", + "s ta", + "o r", + "a r", + "f i", + "on o", + "t ra", + "n a", + "n el", + "n e", + "p ro", + "t ro", + "al e", + "v er", + "n i", + "c u", + "t ti", + "men te", + "del la", + "t er", + "zi one", + "g u", + "p e", + "t ta", + "an do", + "t à", + "al i", + "u o", + "qu el", + "co m", + "s en", + "co me", + "b a", + "al la", + "p ri", + "d u", + "qu es", + "l u", + "on i", + "g gi", + "pa r", + "s si", + "v en", + "in a", + "g a", + "pi ù", + "ci a", + "i m", + "co r", + "m an", + "in o", + "in i", + "t en", + "r an", + "b b", + "g o", + "s to", + "t re", + "a ve", + "a v", + "s ono", + "er i", + "a c", + "s se", + "er o", + "h a", + "s c", + "su l", + "f or", + "v ano", + "po r", + "s ti", + "su o", + "c chi", + "t an", + "z za", + "an che", + "p u", + "i o", + "t te", + "vo l", + "es s", + "s ci", + "co l", + "r u", + "p en", + "f u", + "al l", + "s so", + "s te", + "se m", + "s sa", + "d en", + "a d", + "t ri", + "de i", + "in e", + "ave va", + "men to", + "z z", + "a mo", + "g no", + "f o", + "un o", + "su a", + "g en", + "ri a", + "g e", + "st ra", + "s ì", + "c er", + "ch é", + "b u", + "a p", + "c en", + "d al", + "on a", + "s pe", + "g ni", + "b o", + "t t", + "del le", + "ques to", + "nel la", + "f f", + "d ere", + "an no", + "del l", + "un i", + "bb e", + "an ti", + "g ra", + "s p", + "en e", + "gi o", + "u to", + "qu al", + "gli a", + "qu ando", + "tu tto", + "c an", + "gli o", + "zi oni", + "ca m", + "h o", + "es so", + "s s", + "mo l", + "a t", + "lo ro", + "per ché", + "co sa", + "du e", + "po i", + "ca r", + "s co", + "ci o", + "to r", + "c co", + "c re", + "a m", + "g na", + "te m", + "pri ma", + "lu i", + "co sì", + "qu e", + "gu ar", + "ess ere", + "an i", + "con o", + "b ra", + "al le", + "m on", + "ri o", + "an co", + "cu i", + "s pi", + "vi a", + "g ran", + "gi or", + "a i", + "bi le", + "u l", + "ggi o", + "f e", + "an te", + "ma i", + "ta re", + "in ter", + "in di", + "re bbe", + "sen za", + "so lo", + "zi o", + "e d", + "en te", + "tu tti", + "sta to", + "zi a", + "d alla", + "tu ra", + "mi a", + "vi ta", + "quel la", + "qu a", + "ma r", + "do ve", + "g h", + "al lo", + "sem pre", + "zz o", + "si a", + "mo r", + "do po", + "por ta", + "d re", + "c cia", + "er ano", + "an ni", + "di o", + "chi a", + "en za", + "pro pri", + "qu i", + "m u", + "m b", + "an da", + "c ca", + "o cchi", + "ques ta", + "f fi", + "le i", + "par te", + "d on", + "r on", + "mi o", + "tan to", + "ri s", + "o gni", + "di s", + "r in", + "fa r", + "men ti", + "t el", + "anco ra", + "f ra", + "fa tto", + "man i", + "sen ti", + "p ra", + "tem po", + "es si", + "b bi", + "f in", + "a re", + "la re", + "per s", + "f on", + "b el", + "so r", + "d er", + "pre n", + "an za", + "di re", + "pi e", + "o ra", + "ver so", + "se gu", + "al tro", + "ta to", + "ca to", + "a to", + "vol ta", + "c c", + "fa re", + "pa re", + "ci ò", + "li b", + "bi li", + "n uo", + "s er", + "quel lo", + "co lo", + "p po", + "ca sa", + "tro va", + "o re", + "f er", + "r ono", + "d es", + "mol to", + "al mente", + "s ca", + "vo le", + "t ali", + "sul la", + "s ce", + "men o", + "an to", + "p un", + "s tu", + "ca pi", + "so l", + "gi u", + "m ini", + "m ano", + "z e", + "pi a", + "par ti", + "s al", + "la vo", + "ver o", + "r si", + "al tri", + "es ti", + "s cia", + "suo i", + "gli e", + "so tto", + "b ene", + "sc ri", + "t ale", + "de gli", + "n u", + "al c", + "uo mo", + "p el", + "f re", + "po te", + "es sa", + "s cu", + "si gno", + "el e", + "st ro", + "u ti", + "di a", + "si one", + "g re", + "f ini", + "ar ri", + "l un", + "c ri", + "e si", + "pa ssa", + "r à", + "men tre", + "an d", + "h anno", + "el o", + "u sci", + "gi a", + "gi à", + "di e", + "m ina", + "b e", + "ti ca", + "gior no", + "t in", + "es se", + "mo do", + "c al", + "s pa", + "propri o", + "l en", + "o ri", + "con tro", + "st ru", + "di ven", + "di sse", + "ra to", + "no i", + "v ere", + "pu ò", + "di ce", + "s an", + "es a", + "c ci", + "se con", + "re n", + "c cio", + "qual che", + "tu tta", + "g g", + "mon do", + "for ma", + "p li", + "m ma", + "pen sa", + "de va", + "tu r", + "fo sse", + "so pra", + "ta mente", + "n ess", + "qu anto", + "ra ga", + "un que", + "ca re", + "st re", + "gran de", + "pi cco", + "guar da", + "b en", + "nel l", + "a ff", + "po ssi", + "pre sen", + "r ò", + "pa ro", + "tu a", + "v in", + "an e", + "a s", + "ste sso", + "da v", + "ne i", + "nel le", + "gh i", + "pi o", + "ta r", + "an a", + "la to", + "si d", + "f ine", + "f uo", + "m er", + "z o", + "qua si", + "ul ti", + "i to", + "su e", + "si e", + "f il", + "allo ra", + "m in", + "ven i", + "t ano", + "el lo", + "d e", + "r a", + "e s", + "d o", + "e n", + "q u", + "c o", + "a s", + "o s", + "e r", + "a r", + "s e", + "qu e", + "a n", + "i n", + "i s", + "t o", + "ã o", + "t e", + "d a", + "m a", + "e l", + "t a", + "o r", + "i a", + "r e", + "e m", + "a l", + "co m", + "p a", + "o u", + "c a", + "u m", + "r o", + "v a", + "t i", + "s o", + "m en", + "n ão", + "h a", + "co n", + "m e", + "r i", + "pa ra", + "p o", + "d i", + "s a", + "v o", + "u ma", + "c i", + "n a", + "p or", + "n o", + "g u", + "s u", + "h o", + "an do", + "t ra", + "e i", + "v i", + "e u", + "i m", + "do s", + "el e", + "r es", + "m o", + "en t", + "f i", + "l a", + "e ra", + "l e", + "de s", 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"chce sz", + "ni k", + "p ę", + "chy ba", + "cią g", + "ją cy", + "wo j", + "na sze", + "mnie j", + "wię cej", + "z wy", + "o sta", + "f e", + "wa ż", + "h o", + "se r", + "śmie r", + "wie r", + "dz ą", + "za ś", + "gdy by", + "ja ki", + "wo l", + "wi n", + "d ą", + "ści a", + "roz ma", + "wa l", + "pa nie", + "sta r", + "ka z", + "je żeli", + "d em", + "w ra", + "ko ń", + "sie bie", + "zno wu", + "p ró", + "cz em", + "st wa", + "i sto", + "pó ł", + "d ał", + "ko bie", + "ała m", + "wy ch", + "ce sa", + "ni ch", + "za wsze", + "dzi ć", + "te ż", + "le pie", + "pro szę", + "k re", + "t wa", + "o t", + "ł ą", + "ch u", + "c ą", + "p rz", + "ł e", + "sze dł", + "od powie", + "my śli", + "ś wią", + "e n", + "e r", + "d e", + "a n", + "e t", + "i j", + "i n", + "e l", + "a a", + "s t", + "o r", + "g e", + "i s", + "a t", + "i e", + "c h", + "o n", + "e en", + "h et", + "i t", + "v er", + "aa r", + "a l", + "o or", + "g en", + "v an", + "o p", + "d en", + "h e", + "o m", + "t e", + "w e", + 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"a p", + "we e", + "a d", + "he eft", + "w aar", + "i cht", + "d an", + "er en", + "n e", + "w el", + "w at", + "w il", + "a cht", + "aa g", + "ge b", + "c on", + "z o", + "k e", + "b et", + "h ij", + "d ig", + "k un", + "u w", + "d t", + "d oor", + "t ij", + "a m", + "an g", + "on d", + "er s", + "is ch", + "ge en", + "i ge", + "ge v", + "ve el", + "n u", + "m a", + "on s", + "o f", + "b l", + "n aar", + "g ro", + "p l", + "an der", + "at en", + "kun nen", + "e cht", + "h ier", + "g oe", + "an t", + "u s", + "t wee", + "on t", + "de lijk", + "el e", + "u ur", + "al le", + "t oe", + "me er", + "i st", + "n a", + "n ie", + "on ze", + "l o", + "i m", + "p en", + "h ad", + "tij d", + "h oe", + "to t", + "z ou", + "a k", + "aa k", + "a men", + "d r", + "w oor", + "s e", + "wor dt", + "o t", + "gel ijk", + "g aan", + "i c", + "g er", + "k er", + "el d", + "e m", + "h ou", + "de l", + "z en", + "z el", + "te gen", + "b o", + "kom en", + "c om", + "i gen", + "e it", + "wer k", + "goe d", + "z al", + "z ij", + "sl ag", + "e s", + "z ien", + "a st", + "echt er", + "it ie", + "t ie", + "el ijk", + "m is", + "isch e", + "bel an", + "h aar", + "i ch", + "b er", + "h an", + "v r", + "al e", + "c i", + "gr ijk", + "in d", + "do en", + "l and", + "belan grijk", + "p un", + "op en", + "ct ie", + "zel f", + "m ij", + "it eit", + "ste m", + "me e", + "ar en", + "al l", + "b r", + "re cht", + "d ien", + "h u", + "g aat", + "pro b", + "m oe", + "p er", + "a u", + "ul len", + "z ich", + "daar om", + "or m", + "k l", + "v o", + "en t", + "st aat", + "z it", + "du i", + "n at", + "du s", + "d s", + "ver slag", + "kel ijk", + "prob le", + "w et", + "ge m", + "c r", + "i on", + "p r", + "sch ap", + "g d", + "h un", + "z a", + "er d", + "z et", + "st aan", + "st r", + "m aal", + "in der", + "e id", + "st en", + "p ar", + "k ken", + "ge d", + "z ullen", + "re s", + "men sen", + "j aar", + "re gel", + "ie der", + "vol gen", + "ge ven", + "e ven", + "l u", + "bl ij", + "i ë", + "k o", + "u we", 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et", + "ar t", + "st ie", + "st aten", + "g gen", + "re ken", + "e in", + "al en", + "m ing", + "mo gelijk", + "gro te", + "al tijd", + "z or", + "en kel", + "w ik", + "pol itie", + "e igen", + "el k", + "han del", + "g t", + "k we", + "m aat", + "el en", + "i p", + "v rij", + "s om", + "je s", + "aa m", + "hu is", + "v al", + "we er", + "lid staten", + "k ing", + "k le", + "be d", + "gev al", + "stel l", + "a i", + "wik kel", + "kwe stie", + "t al", + "ste e", + "a b", + "h el", + "kom st", + "p as", + "s s", + "it u", + "i den", + "eer d", + "m in", + "c e", + "p o", + "twee de", + "proble em", + "w aren", + "us sen", + "sn el", + "t ig", + "ge w", + "j u", + "ul t", + "ne men", + "com mis", + "versch il", + "k on", + "z oek", + "k rij", + "gr aag", + "den k", + "l anden", + "re den", + "be sl", + "oe g", + "bet er", + "he den", + "m ag", + "p e", + "bo ven", + "a c", + "con t", + "f d", + "h ele", + "k r", + "v ier", + "w in", + "ge z", + "k w", + "m il", + "v or", + "he m", + "ra 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r", + "ve l", + "sk u", + "v še", + "t ní", + "do b", + "by la", + "č ní", + "ja k", + "v u", + "je ho", + "b ý", + "vá ní", + "ný ch", + "po u", + "te n", + "t ři", + "v z", + "st ře", + "d va", + "h le", + "č á", + "no sti", + "c k", + "v š", + "vo u", + "s u", + "h e", + "h ra", + "je n", + "s y", + "da l", + "po z", + "s lo", + "te l", + "d ru", + "de n", + "vš ak", + "g i", + "k dy", + "by lo", + "bu de", + "st ra", + "j ší", + "m é", + "me n", + "vý ch", + "ní m", + "s m", + "ko li", + "r ů", + "t ra", + "mů že", + "ne ní", + "ho d", + "b í", + "do u", + "sk a", + "t ý", + "st ě", + "u je", + "s á", + "pě t", + "ne s", + "k rá", + "to m", + "st ví", + "v ně", + "se d", + "s vé", + "p í", + "z o", + "mu sí", + "u ž", + "tí m", + "jí cí", + "jed no", + "t r", + "ča s", + "e v", + "č ty", + "sk ý", + "ni c", + "ev ro", + "to ho", + "h y", + "k ter", + "r ní", + "st í", + "s vě", + "pa k", + "vše ch", + "k ů", + "n g", + "á d", + "chá zí", + "a ni", + "a r", + "jed na", + "bý t", + "t ro", + "k ra", + "pr vní", + "m no", + "ské ho", + "p á", + "p la", + "le m", + "ne bo", + "ke m", + "st ro", + "s la", + "né ho", + "z de", + "dal ší", + "ř a", + "čty ři", + "h rá", + "dru h", + "l ně", + "v la", + "sk ých", + "š ko", + "pů so", + "pro to", + "v ů", + "sk á", + "ve n", + "še st", + "d ně", + "je ště", + "me zi", + "te k", + "s ko", + "ch a", + "ně koli", + "be z", + "g ra", + "ji ž", + "č ně", + "j á", + "s lu", + "z ná", + "ve r", + "sed m", + "k ro", + "ta m", + "a no", + "v lá", + "o sm", + "byl y", + "vá m", + "ck ý", + "te ch", + "dě ji", + "vel mi", + "le ži", + "va la", + "l ý", + "t vo", + "spo le", + "ch u", + "stu p", + "mo ž", + "evro p", + "g e", + "sta l", + "j de", + "ch y", + "ro di", + "je jí", + "po li", + "de vět", + "s me", + "a ž", + "té to", + "re m", + "d é", + "f or", + "u ni", + "f o", + "ten to", + "a u", + "ka ž", + "nu la", + "na d", + "by ch", + "mo c", + "sto u", + "e x", + "le n", + "k do", + "z d", + "pra co", + "to mu", + "ný m", + "ži vo", + "ze m", + "f e", + "f u", + "ná sle", + "j o", + "sk y", + "ji ch", + "h á", + "mě l", + "dě la", + "j sme", + "p re", + "ni ce", + "ste j", + "ne m", + "st ní", + "he m", + "ná ro", + "z u", + "b li", + "ni t", + "pa r", + "a l", + "poz ději", + "ta ko", + "n ce", + "če r", + "ší m", + "ně co", + "vá l", + "ře j", + "krá t", + "á lní", + "u r", + ". .", + "a si", + "kter é", + "sta v", + "ma jí", + "my s", + "do bě", + "s ně", + "ce n", + "z y", + "z ku", + "t ů", + "ch od", + "s pě", + "je jich", + "sou čas", + "d r", + "va li", + "ri e", + "k te", + "pr ů", + "ze ní", + "pa t", + "a n", + "po tře", + "de m", + "d nes", + "ze mí", + "sa mo", + "zna m", + "b ra", + "má m", + "te dy", + "g o", + "hla vní", + "pou ží", + "b ní", + "ve de", + "le p", + "je k", + "pra v", + "poli ti", + "d ne", + "je m", + "le t", + "če ní", + "pro b", + "ne ž", + "dě l", + "fi l", + "č o", + "cí ch", + "st é", + "d lou", + "h i", + "a by", + "to u", + "několi k", + "d la", + "vy u", + "vi t", + "ho u", + "ck ých", + "no vé", + "či n", + "st y", + "dě lá", + "k ý", + "ob la", + "pod le", + "ra n", + "dů leži", + "ta to", + "po ku", + "ko ne", + "d ý", + "d vě", + "ž ád", + "nou t", + "t ku", + "t vr", + "cké ho", + "ro v", + "r é", + "te le", + "p sa", + "s vět", + "ti vní", + "do sta", + "te m", + "še l", + "druh é", + "s kou", + "ž o", + "jed ná", + "vý znam", + "prob lé", + "pu bli", + "vá n", + "od po", + "pod po", + "d le", + "ja ké", + "še ní", + "ví m", + "bě hem", + "na chází", + "s lou", + "pou ze", + "o tá", + "p lo", + "to vé", + "vět ši", + "ko mi", + "va jí", + "ty to", + "zá pa", + "z mě", + "mo h", + "ví ce", + "spole č", + "au to", + "pro ti", + "st ru", + "dě t", + "chá ze", + "že l", + "с т", + "е н", + "н о", + "н а", + "п р", + "т о", + "п о", + "р а", + "г о", + "к о", + "н е", + "в о", + "в а", + "е т", + "е р", + "н и", + "е л", + "и т", + "н ы", + "з а", + "р о", + "ен и", + "к а", + "л и", + "е м", + "д а", + "о б", + "л а", + "д о", + "с я", + "т ь", + "о т", + "л о", + "л ь", + "е д", + "с о", + "м и", + "р е", + "м о", + "ц и", + "пр о", + "т а", + "э то", + "к и", + "р у", + "пр и", + "т и", + "с е", + "ст а", + "в ы", + "м ы", + "в и", + "б ы", + "м а", + "е с", + "л я", + "ст и", + "л е", + "ч то", + "м е", + "р и", + "ч а", + "о д", + "е й", + "ел ь", + "ени я", + "г а", + "н у", + "с и", + "п а", + "ра з", + "б о", + "ст о", + "с у", + "с а", + "д у", + "е го", + "е ст", + "и н", + "ит ь", + "и з", + "ж е", + "м у", + "п ер", + "по д", + "ени е", + "с ь", + "к у", + "пр ед", + "но го", + "ны х", + "в ер", + "т е", + "но й", + "ци и", + "д е", + "р ы", + "д ел", + "л ю", + "в е", + "о н", + "м ен", + "г и", + "н я", + "б у", + "пр а", + "в се", + "ет ся", + "ст ь", + "ж а", + "до л", + "ж и", + "б е", + "ко н", + "с л", + "ш и", + "д и", + "ст в", + "с ко", + "ны е", + "ч и", + "ю т", + "д ер", + "ст ра", + "т ы", + "х од", + "щ и", + "з о", + "з на", + "но сти", + "ч ес", + "в ля", + "ва ть", + "о р", + "по л", + "в ет", + "та к", + "ш а", + "т у", + "с во", + "пр е", + "о на", + "ит ель", + "ны й", + "с ло", + "ка к", + "в л", + "но сть", + "х о", + "мо ж", + "п е", + "д ля", + "ни я", + "но е", + "ра с", + "дол ж", + "да р", + "т ель", + "с ка", + "п у", + "ст во", + "ко то", + "ра б", + "е е", + "ро д", + "э ти", + "с об", + "о ру", + "ж ен", + "ны м", + "ит и", + "ни е", + "ко м", + "д ет", + "ст у", + "г у", + "п и", + "ме ж", + "ени ю", + "т ер", + "раб от", + "во з", + "ци я", + "ко й", + "щ ест", + "г ра", + "з и", + "р я", + "меж ду", + "ст ва", + "в с", + "ел о", + "ш е", + "м ер", + "б а", + "з ы", + "л у", + "а ль", + "д ей", + "г ла", + "на род", + "к ти", + "пред ста", + "л ся", + "я вля", + "с ки", + "но в", + "ед ин", + "ро в", + "и с", + "ни ма", + "р ем", + "ход и", + "так же", + "д ру", + "а ть", + "сл ед", + "го во", + "на я", + "ю щи", + "ен ь", + "кото ры", + "х от", + "в у", + "и х", + "ем у", + "ч ит", + "ва ж", + "ор га", + "чес ки", + "щ е", + "к е", + "х а", + "по с", + "то м", + "бо ль", + "м не", + "па с", + "об ъ", + "пра в", + "кон ф", + "сл у", + "под дер", + "ст ви", + "на ш", + "ль ко", + "сто я", + "ну ю", + "л ем", + "ен ных", + "к ра", + "д ы", + "между народ", + "г да", + "не об", + "го су", + "ств у", + "ени и", + "госу дар", + "к то", + "и м", + "ч ест", + "р ет", + "во про", + "л ен", + "ел и", + "ро ва", + "ци й", + "на м", + "это й", + "ж ения", + "необ ходи", + "мен я", + "бы ло", + "си ли", + "ф и", + "в я", + "ш ь", + "это го", + "о ни", + "орга ни", + "бе зо", + "пр об", + "и ме", + "ре ш", + "б и", + "безо пас", + "ют ся", + "о ста", + "ен но", + "го д", + "ел а", + "предста в", + "ть ся", + "сло во", + "органи за", + "долж ны", + "это м", + "б ла", + "ч е", + "ч у", + "бла го", + "это му", + "в рем", + "с пе", + "но м", + "ени й", + "с по", + "на с", + "не т", + "з у", + "в ед", + "е ще", + "ска за", + "се й", + "ер ен", + "да н", + "са м", + "ел я", + "ра н", + "зы ва", + "явля ется", + "бу дет", + "кти в", + "т ре", + "дел е", + "м от", + "конф ерен", + "ла сь", + "ча с", + "сто ро", + "ко го", + "е з", + "не й", + "о с", + "ли сь", + "раз ору", + "пер е", + "с си", + "ны ми", + "про ц", + "го ло", + "ч ело", + "бо ле", + "чело ве", + "с ер", + "п л", + "ч ет", + "стра н", + "п я", + "бы л", + "к ла", + "то в", + "ж д", + "дел а", + "е ра", + "у же", + "со вет", + "г ен", + "безопас ности", + "ц а", + "се да", + "по з", + "от вет", + "проб лем", + "на ко", + "т ем", + "до ста", + "п ы", + "щ а", + "во й", + "су щест", + "необходи мо", + "бы ть", + "мож ет", + "д ем", + "что бы", + "е к", + "ч ер", + "у сили", + "ре с", + "ру д", + "един енных", + "д об", + "до сти", + "ств ен", + "я дер", + "год ня", + "ка за", + "се годня", + "сей час", + "то лько", + "во д", + "ес ь", + "м ного", + "бу ду", + "е в", + "ест ь", + "т ри", + "об щест", + ". .", + "я вл", + "вы сту", + "р ед", + "с чит", + "с ит", + "деле га", + "ло ж", + "это т", + "ф ор", + "к лю", + "воз мож", + "ва ния", + "б ли", + "и ли", + "в з", + "на ций", + "ско го", + "при ня", + "п ла", + "о ч", + "ить ся", + "ст е", + "на ши", + "которы е", + "а р", + "име ет", + "с от", + "зна ч", + "пер ь", + "след у", + "ен ы", + "та ки", + "объ единенных", + "ст ро", + "те перь", + "б ле", + "благо дар", + "раз в", + "а н", + "жи ва", + "оч ень", + "я т", + "бе з", + "об ес", + "г ро", + "ло сь", + "с ы", + "организа ции", + "ч лен", + "то го", + "она ль", + "ж да", + "все х", + "с вя", + "боле е", + "со в", + "ко гда", + "во т", + "к ре", + "к ры", + "по этому", + "во ль", + "о й", + "ген ера", + "ч ем", + "л ы", + "пол ити", + "в ен", + "конферен ции", + "проц ес", + "б я", + "ит е", + "от но", + "разв ити", + "а ф", + "ю щ", + "в но", + "ми р", + "ни и", + "ка я", + "а с", + "итель но", + "в то", + "ени ем", + "генера ль", + "пр от", + "вс ем", + "сам бле", + "ас самбле", + "о м", + "з д", + "с мот", + "ре ги", + "ч его", + "од нако", + "усили я", + "дей стви", + "ч но", + "у ча", + "об раз", + "во с", + "э та", + "пер его", + "гово р", + "ва м", + "мо ло", + "врем я", + "д ь", + "хот ел", + "г ру", + "за явл", + "пре доста", + "по ль", + "не е", + "ре зо", + "перего во", + "резо лю", + "к рет", + "поддер ж", + "обес пе", + "не го", + "представ ит", + "на де", + "к ри", + "ч ь", + "про ек", + "л ет", + "дру ги", + "ا ل", + "َ ا", + "و َ", + "ّ َ", + "ِ ي", + "أ َ", + "ل َ", + "ن َ", + "ال ْ", + "ه ُ", + "ُ و", + "م ا", + "ن ْ", + "م ن", + "ع َ", + "ن ا", + "ل ا", + "م َ", + "ت َ", + "ف َ", + "أ ن", + "ل ي", + "م ِ", + "ا ن", + "ف ي", + "ر َ", + "ي َ", + "ه ِ", + "م ْ", + "ق َ", + "ب ِ", + "ل ى", + "ي ن", + "إ ِ", + "ل ِ", + "و ا", + "ك َ", + "ه ا", + "ً ا", + "م ُ", + "و ن", + "ال م", + "ب َ", + "ي ا", + "ذ ا", + "س ا", + "ال ل", + "م ي", + "ي ْ", + "ر ا", + "ر ي", + "ل ك", + "م َا", + "ن َّ", + "ل م", + "إ ن", + "س ت", + "و م", + "ّ َا", + "ل َا", + "ه م", + "ّ ِ", + "ك ُ", + "ك ان", + "س َ", + "ب ا", + "د ي", + "ح َ", + "ع ْ", + "ب ي", + "ال أ", + "و ل", + "ف ِي", + "ر ِ", + "د ا", + "مِ نْ", + "ُو نَ", + "و ْ", + "ه َا", + "ّ ُ", + "ال س", + "ال َ", + "ن ي", + "ل ْ", + "ت ُ", + "ه ل", + "ر ة", + "د َ", + "س ْ", + "ت ِ", + "ن َا", + "ر ْ", + "الل َّ", + "سا مي", + "ك ن", + "ك ل", + "ه َ", + "عَ لَ", + "ع لى", + "م ع", + "إ لى", + "ق د", + "ال ر", + "ُو ا", + "ي ر", + "ع ن", + "ي ُ", + "ن ِ", + "ب ْ", + "ال ح", + "هُ مْ", + "ق ا", + "ذ ه", + "ال ت", + "ِي نَ", + "ج َ", + "ه ذا", + "ع د", + "ال ع", + "د ْ", + "قَ الَ", + "ر ُ", + "ي م", + "ي ة", + "ن ُ", + "خ َ", + "ر ب", + "ال ك", + "و َا", + "أ نا", + "ة ِ", + "ال ن", + "ح د", + "ع ِ", + "ت ا", + "ه و", + "ف ا", + "ع ا", + "ال ش", + "ل ُ", + "ي ت", + "ذ َا", + "ي ع", + "ال ذ", + "ح ْ", + "ال ص", + "إِ نَّ", + "ج ا", + "ع لي", + "ك َا", + "ب ُ", + "ت ع", + "و ق", + "م ل", + "ل َّ", + "ي د", + "أ خ", + "ر ف", + "ت ي", + "ال ِ", + "ّ ا", + "ذ لك", + "أَ نْ", + "س ِ", + "ت وم", + "م ر", + "مَ نْ", + "ب ل", + "ال ق", + "الل ه", + "ِي َ", + "ك م", + "ذ َ", + "ع ل", + "ح ب", + "س ي", + "ع ُ", + "ال ج", + "ال د", + "ش َ", + "ت ك", + "ف ْ", + "ص َ", + "ل ل", + "د ِ", + "ب ر", + "ف ِ", + "ت ه", + "أ ع", + "ت ْ", + "ق ْ", + "الْ أَ", + "ئ ِ", + "عَ نْ", + "و ر", + "ح ا", + "ال َّ", + "م ت", + "ف ر", + "د ُ", + "ه نا", + "وَ أَ", + "ت ب", + "ة ُ", + "أ ي", + "س ب", + "ري د", + "و ج", + "كُ مْ", + "ح ِ", + "ك ْ", + "د ر", + "َا ء", + "ه ذه", + "ال ط", + "الْ مُ", + "د ة", + "ق ل", + "غ َ", + "ي وم", + "الَّ ذ", + "ك ر", + "ت ر", + "ك ِ", + "ك ي", + "عَلَ ى", + "رَ ب", + "ع ة", + "ق ُ", + "ج ْ", + "ف ض", + "ل ة", + "ه ْ", + "ر َا", + "وَ لَ", + "الْ مَ", + "أَ نَّ", + "ي َا", + "أ ُ", + "ش ي", + "اللَّ هُ", + "لَ ى", + "ق ِ", + "أ ت", + "عَلَ يْ", + "اللَّ هِ", + "ال ب", + "ض َ", + "ة ً", + "ق ي", + "ا ر", + "ب د", + "خ ْ", + "سْ تَ", + "ط َ", + "قَ دْ", + "ذه ب", + "أ م", + "ما ذا", + "وَ إِ", + "ة ٌ", + "و نَ", + "لي لى", + "و لا", + "ح ُ", + "ه ي", + "ص ل", + "ال خ", + "و د", + "لي س", + "ل دي", + "ق ال", + "كَا نَ", + "م َّ", + "ح ي", + "ت م", + "ل ن", + "وَ لَا", + "ب ع", + "يم كن", + "س ُ", + "ة َ", + "ح ت", + "ر ًا", + "ك ا", + "ش ا", + "هِ مْ", + "لَ هُ", + "ز َ", + "دا ً", + "م س", + "ك ث", + "الْ عَ", + "ج ِ", + "ص ْ", + "ف َا", + "ل ه", + "و ي", + "ع َا", + "هُ وَ", + "ب ِي", + "ب َا", + "أ س", + "ث َ", + "ل ِي", + "ر ض", + "الر َّ", + "لِ كَ", + "ت َّ", + "ف ُ", + "ق ة", + "ف عل", + "مِ ن", + "ال آ", + "ث ُ", + "س م", + "م َّا", + "بِ هِ", + "ت ق", + "خ ر", + "ل قد", + "خ ل", + "ش ر", + "أن ت", + "ل َّا", + "س ن", + "الس َّ", + "الذ ي", + "س َا", + "و ما", + "ز ل", + "و ب", + "أ ْ", + "إ ذا", + "ر ِي", + "ح ة", + "ن ِي", + "الْ حَ", + "وَ قَالَ", + "ب ه", + "ة ٍ", + "س أ", + "ر ٌ", + "ب ال", + "م ة", + "ش ْ", + "و ت", + "عن د", + "ف س", + "بَ عْ", + "ه ر", + "ق ط", + "أ ح", + "إن ه", + "و ع", + "ف ت", + "غ ا", + "هنا ك", + "ب ت", + "مِ نَ", + "س ر", + "ذَ لِكَ", + "ر س", + "حد ث", + "غ ْ", + "ّ ِي", + "ال إ", + "وَ يَ", + "ج ل", + "ا ست", + "ق ِي", + "ع ب", + "و س", + "ي ش", + "الَّذ ِينَ", + "تا ب", + "د ِي", + "ج ب", + "ك ون", + "ب ن", + "ال ث", + "لَ يْ", + "ب عد", + "وَ الْ", + "فَ أَ", + "ع م", + "هُ م", + "ت ن", + "ذ ْ", + "أ ص", + "أ ين", + "رَب ِّ", + "الذ ين", + "إِ ن", + "ب ين", + "ج ُ", + "عَلَيْ هِ", + "ح َا", + "ل و", + "ست ط", + "ظ ر", + "لَ مْ", + "ء ِ", + "كُ ل", + "ط ل", + "ت َا", + "ض ُ", + "كن ت", + "ل ًا", + "م ٌ", + "ق بل", + "ـ ـ", + "ذ ِ", + "قَ وْ", + "ص ِ", + "م ًا", + "كان ت", + "ص ا", + "ي ق", + "ال ف", + "ال نا", + "م ٍ", + "إِ نْ", + "ال نَّ", + "ج د", + "وَ مَا", + "ت ت", + "ب ح", + "م كان", + "كي ف", + "ّ ة", + "ال ا", + "ج َا", + "أ و", + "سا عد", + "ض ِ", + "إ لا", + "را ً", + "ق َا", + "ر أ", + "ع ت", + "أ حد", + "ه د", + "ض ا", + "ط ر", + "أ ق", + "ما ء", + "د َّ", + "ال با", + "م ُو", + "أَ وْ", + "ط ا", + "ق ُو", + "خ ِ", + "ت ل", + "ستط يع", + "د َا", + "الن َّا", + "إ لَى", + "وَ تَ", + "هَ ذَا", + "ب ة", + "علي ك", + "ج ر", + "ال من", + "ز ا", + "ر ٍ", + "د ع", + "ّ ًا", + "س ة", + "ثُ مَّ", + "شي ء", + "ال غ", + "ت ح", + "ر ُونَ", + "ال يوم", + "م ِي", + "ن ُوا", + "أ ر", + "تُ مْ", + "ع ر", + "ي ف", + "أ ب", + "د ًا", + "ص َا", + "الت َّ", + "أ ريد", + "ال ز", + "يَ وْ", + "إ لي", + "ج ي", + "يَ عْ", + "فض ل", + "ال إن", + "أن ه", + "n g", + "i 4", + "a n", + "s h", + "z h", + "i 2", + "ng 1", + "u 4", + "i 1", + "ng 2", + "d e", + "j i", + "a o", + "x i", + "u 3", + "de 5", + "e 4", + "i 3", + "ng 4", + "an 4", + "e n", + "u o", + "sh i4", + "an 2", + "u 2", + "c h", + "u 1", + "ng 3", + "a 1", + "an 1", + "e 2", + "a 4", + "e i4", + "o ng1", + "a i4", + "ao 4", + "h u", + "a ng1", + "l i", + "y o", + "an 3", + "w ei4", + "uo 2", + "n 1", + "en 2", + "ao 3", + "e 1", + "y u", + "q i", + "e ng2", + "zh o", + "a ng3", + "a ng4", + "a ng2", + "uo 4", + "m i", + "g e4", + "y i1", + "g uo2", + "e r", + "b i", + "a 3", + "h e2", + "e 3", + "y i2", + "d i4", + "zh ong1", + "b u4", + "g u", + "a i2", + "n 2", + "z ai4", + "sh i2", + "e ng1", + "r en2", + "o ng2", + "xi an4", + "y i", + "x u", + "n 4", + "l i4", + "en 4", + "y u2", + "e i2", + "yi2 ge4", + "o u4", + "e i3", + "d i", + "u i4", + "a 2", + "yo u3", + "ao 1", + "d a4", + "ch eng2", + "en 1", + "e ng4", + "y i4", + "s i1", + "zh i4", + "ji a1", + "yu an2", + "n i", + "t a1", + "de5 yi2ge4", + "k e1", + "sh u3", + "x i1", + "j i2", + "ao 2", + "t i", + "o u3", + "o ng4", + "xi a4", + "a i1", + "g ong1", + "zh i1", + "en 3", + "w ei2", + "j u", + "xu e2", + "q u1", + "zho u1", + "er 3", + "mi ng2", + "zho ng3", + "l i3", + "w u4", + "y i3", + "uo 1", + "e 5", + "j i4", + "xi ng2", + "ji an4", + "hu a4", + "y u3", + "uo 3", + "j i1", + "a i3", + "z uo4", + "h ou4", + "hu i4", + "e i1", + "ni an2", + "q i2", + "p i", + "d ao4", + "sh eng1", + "de 2", + "d ai4", + "u an2", + "zh e4", + "zh eng4", + "b en3", + "sh ang4", + "zh u3", + "b ei4", + "y e4", + "ch u1", + "zh an4", + "l e5", + "l ai2", + "sh i3", + "n an2", + "r en4", + "yo u2", + "k e4", + "b a1", + "f u4", + "d ui4", + "y a4", + "m ei3", + "z i4", + "xi n1", + "ji ng1", + "zh u", + "n 3", + "yo ng4", + "m u4", + "ji ao4", + "y e3", + "ji n4", + "bi an4", + "l u4", + "q i1", + "sh e4", + "xi ang1", + "o ng3", + "sh u4", + "d ong4", + "s uo3", + "gu an1", + "s an1", + "b o", + "t e4", + "d uo1", + "f u2", + "mi n2", + "l a1", + "zh i2", + "zh en4", + "o u1", + "w u3", + "m a3", + "i 5", + "z i5", + "j u4", + "er 4", + "y ao4", + "xia4 de5yi2ge4", + "s i4", + "t u2", + "sh an1", + "z ui4", + "ch u", + "yi n1", + "er 2", + "t ong2", + "d ong1", + "y u4", + "y an2", + "qi an2", + "shu3 xia4de5yi2ge4", + "ju n1", + "k e3", + "w en2", + "f a3", + "l uo2", + "zh u4", + "x i4", + "k ou3", + "b ei3", + "ji an1", + "f a1", + "di an4", + "ji ang1", + "wei4 yu2", + "xi ang4", + "zh i3", + "e ng3", + "f ang1", + "l an2", + "sh u", + "r i4", + "li an2", + "sh ou3", + "m o", + "qi u2", + "ji n1", + "h uo4", + "shu3xia4de5yi2ge4 zhong3", + "f en1", + "n ei4", + "g ai1", + "mei3 guo2", + "u n2", + "g e2", + "b ao3", + "qi ng1", + "g ao1", + "t ai2", + "d u", + "xi ao3", + "ji e2", + "ti an1", + "ch ang2", + "q uan2", + "li e4", + "h ai3", + "f ei1", + "t i3", + "ju e2", + "o u2", + "c i3", + "z u2", + "n i2", + "bi ao3", + "zhong1 guo2", + "d u4", + "yu e4", + "xi ng4", + "sh eng4", + "ch e1", + "d an1", + "ji e1", + "li n2", + "pi ng2", + "f u3", + "g u3", + "ji e4", + "w o", + "v 3", + "sh eng3", + "n a4", + "yu an4", + "zh ang3", + "gu an3", + "d ao3", + "z u3", + "di ng4", + "di an3", + "c eng2", + "ren2 kou3", + "t ai4", + "t ong1", + "g uo4", + "n eng2", + "ch ang3", + "hu a2", + "li u2", + "yi ng1", + "xi ao4", + "c i4", + "bian4 hua4", + "li ang3", + "g ong4", + "zho ng4", + "de5 yi1", + "s e4", + "k ai1", + "w ang2", + "ji u4", + "sh i1", + "sh ou4", + "m ei2", + "k u", + "s u", + "f eng1", + "z e2", + "tu2 shi4", + "t i2", + "q i4", + "ji u3", + "sh en1", + "zh e3", + "ren2kou3 bian4hua4", + "ren2kou3bian4hua4 tu2shi4", + "di4 qu1", + "y ang2", + "m en", + "men 5", + "l ong2", + "bi ng4", + "ch an3", + "zh u1", + "w ei3", + "w ai4", + "xi ng1", + "bo 1", + "b i3", + "t ang2", + "hu a1", + "bo 2", + "shu i3", + "sh u1", + "d ou1", + "s ai4", + "ch ao2", + "b i4", + "li ng2", + "l ei4", + "da4 xue2", + "f en4", + "shu3 de5", + "m u3", + "ji ao1", + "d ang1", + "ch eng1", + "t ong3", + "n v3", + "q i3", + "y an3", + "mi an4", + "l uo4", + "ji ng4", + "g e1", + "r u4", + "d an4", + "ri4 ben3", + "p u3", + "yu n4", + "hu ang2", + "wo 3", + "l v", + "h ai2", + "shi4 yi1", + "xi e1", + "yi ng3", + "w u2", + "sh en2", + "w ang3", + "gu ang3", + "li u4", + "s u4", + "shi4 zhen4", + "c an1", + "c ao3", + "xi a2", + "k a3", + "d a2", + "h u4", + "b an4", + "d ang3", + "h u2", + "z ong3", + "de ng3", + "de5yi2ge4 shi4zhen4", + "ch uan2", + "mo 4", + "zh ang1", + "b an1", + "mo 2", + "ch a2", + "c e4", + "zhu3 yao4", + "t ou2", + "j u2", + "shi4 wei4yu2", + "s a4", + "u n1", + "ke3 yi3", + "d u1", + "h an4", + "li ang4", + "sh a1", + "ji a3", + "z i1", + "lv 4", + "f u1", + "xi an1", + "x u4", + "gu ang1", + "m eng2", + "b ao4", + "yo u4", + "r ong2", + "zhi1 yi1", + "w ei1", + "m ao2", + "guo2 jia1", + "c ong2", + "g ou4", + "ti e3", + "zh en1", + "d u2", + "bi an1", + "c i2", + "q u3", + "f an4", + "xi ang3", + "m en2", + "j u1", + "h ong2", + "z i3", + "ta1 men5", + "ji 3", + "z ong1", + "zhou1 de5yi2ge4shi4zhen4", + "t uan2", + "ji ng3", + "gong1 si1", + "xi e4", + "l i2", + "li4 shi3", + "b ao1", + "g ang3", + "gu i1", + "zh eng1", + "zhi2 wu4", + "ta1 de5", + "pi n3", + "zhu an1", + "ch ong2", + "shi3 yong4", + "w a3", + "sh uo1", + "chu an1", + "l ei2", + "w an1", + "h uo2", + "q u", + "s u1", + "z ao3", + "g ai3", + "q u4", + "g u4", + "l u", + "x i2", + "h ang2", + "yi ng4", + "c un1", + "g en1", + "yi ng2", + "ti ng2", + "cheng2 shi4", + "ji ang3", + "li ng3", + "l un2", + "bu4 fen4", + "de ng1", + "xu an3", + "dong4 wu4", + "de2 guo2", + "xi an3", + "f an3", + "zh e5", + "h an2", + "h ao4", + "m i4", + "r an2", + "qi n1", + "ti ao2", + "zh an3", + "h i", + "k a", + "n o", + "t e", + "s u", + "s hi", + "t a", + "t o", + "n a", + "w a", + "o u", + "r u", + "n i", + "k u", + "k i", + "g a", + "d e", + "k o", + "m a", + "r e", + "r a", + "m o", + "t su", + "w o", + "e n", + "r i", + "s a", + "d a", + "s e", + "j i", + "h a", + "c hi", + "k e", + "te ki", + "m i", + "y ou", + "s h", + "s o", + "y o", + "y a", + "na i", + "t te", + "a ru", + "b a", + "u u", + "t ta", + "ka i", + "ka n", + "shi te", + "m e", + "d o", + "mo no", + "se i", + "r o", + "ko to", + "ka ra", + "shi ta", + "b u", + "m u", + "c h", + "su ru", + "k ou", + "g o", + "ma su", + "ta i", + "f u", + "k en", + "i u", + "g en", + "wa re", + "shi n", + "z u", + "a i", + "o n", + "o ku", + "g i", + "d ou", + "n e", + "y uu", + "i ru", + "i te", + "ji ko", + "de su", + "j u", + "ra re", + "sh u", + "b e", + "sh ou", + "s ha", + "se kai", + "s ou", + "k you", + "ma shita", + "s en", + "na ra", + "sa n", + "ke i", + "i ta", + "a ri", + "i tsu", + "ko no", + "j ou", + "na ka", + "ch ou", + "so re", + "g u", + "na ru", + "ga ku", + "re ba", + "g e", + "h o", + "i n", + "hi to", + "sa i", + "na n", + "da i", + "tsu ku", + "shi ki", + "sa re", + "na ku", + "p p", + "bu n", + "ju n", + "so no", + "ka ku", + "z ai", + "b i", + "to u", + "wa ta", + "sh uu", + "i i", + "te i", + "ka re", + "y u", + "shi i", + "ma de", + "sh o", + "a n", + "ke reba", + "shi ka", + "i chi", + "ha n", + "de ki", + "ni n", + "ware ware", + "na kereba", + "o ite", + "h ou", + "ya ku", + "ra i", + "mu jun", + "l e", + "yo ku", + "bu tsu", + "o o", + "ko n", + "o mo", + "ga e", + "nara nai", + "ta chi", + "z en", + "ch uu", + "kan gae", + "ta ra", + "to ki", + "ko ro", + "mujun teki", + "z e", + "na ga", + "ji n", + "shi ma", + "te n", + "i ki", + "i ku", + "no u", + "i masu", + "r ou", + "h on", + "ka e", + "t to", + "ko re", + "ta n", + "ki ta", + "i s", + "da tta", + "ji tsu", + "ma e", + "i e", + "me i", + "da n", + "h e", + "to ku", + "dou itsu", + "ri tsu", + "k yuu", + "h you", + "rare ta", + "kei sei", + "k kan", + "rare ru", + "m ou", + "do ko", + "r you", + "da ke", + "naka tta", + "so ko", + "ta be", + "e r", + "ha na", + "c o", + "fu ku", + "p a", + "so n", + "ya su", + "ch o", + "wata ku", + "ya ma", + "z a", + "k yo", + "gen zai", + "b oku", + "a ta", + "j a", + "ka wa", + "ma sen", + "j uu", + "ro n", + "b o", + "na tte", + "wataku shi", + "yo tte", + "ma i", + "g ou", + "ha i", + "mo n", + "ba n", + "ji shin", + "c a", + "re te", + "n en", + "o ka", + "ka gaku", + "na tta", + "p o", + "ka ru", + "na ri", + "m en", + "ma ta", + "e i", + "ku ru", + "ga i", + "ka ri", + "sha kai", + "kou i", + "yo ri", + "se tsu", + "j o", + "re ru", + "to koro", + "ju tsu", + "i on", + "sa ku", + "tta i", + "c ha", + "nin gen", + "n u", + "c e", + "ta me", + "kan kyou", + "de n", + "o oku", + "i ma", + "wata shi", + "tsuku ru", + "su gi", + "b en", + "ji bun", + "shi tsu", + "ke ru", + "ki n", + "ki shi", + "shika shi", + "mo to", + "ma ri", + "i tte", + "de shita", + "n de", + "ari masu", + "te r", + "z ou", + "ko e", + "ze ttai", + "kkan teki", + "h en", + "re kishi", + "deki ru", + "tsu ka", + "l a", + "i tta", + "o i", + "ko butsu", + "mi ru", + "sh oku", + "shi masu", + "gi jutsu", + "g you", + "jou shiki", + "a tta", + "ho do", + "ko ko", + "tsuku rareta", + "z oku", + "hi tei", + "ko ku", + "rekishi teki", + "ke te", + "o ri", + "i mi", + "ka ko", + "naga ra", + "ka karu", + "shu tai", + "ha ji", + "ma n", + "ta ku", + "ra n", + "douitsu teki", + "z o", + "me te", + "re i", + "tsu u", + "sare te", + "gen jitsu", + "p e", + "s t", + "ba i", + "na wa", + "ji kan", + "wa ru", + "r t", + "a tsu", + "so ku", + "koui teki", + "a ra", + "u ma", + "a no", + "i de", + "ka ta", + "te tsu", + "ga wa", + "ke do", + "re ta", + "mi n", + "sa you", + "tte ru", + "to ri", + "p u", + "ki mi", + "b ou", + "mu ra", + "sare ru", + "ma chi", + "k ya", + "o sa", + "kon na", + "a ku", + "a l", + "sare ta", + "i pp", + "shi ku", + "u chi", + "hito tsu", + "ha tara", + "tachi ba", + "shi ro", + "ka tachi", + "to mo", + "e te", + "me ru", + "ni chi", + "da re", + "ka tta", + "e ru", + "su ki", + "a ge", + "oo ki", + "ma ru", + "mo ku", + "o ko", + "kangae rareru", + "o to", + "tan ni", + "ta da", + "tai teki", + "mo tte", + "ki nou", + "shi nai", + "k ki", + "u e", + "ta ri", + "l i", + "ra nai", + "k kou", + "mi rai", + "pp on", + "go to", + "hi n", + "hi tsu", + "te ru", + "mo chi", + "ka tsu", + "re n", + "n yuu", + "su i", + "zu ka", + "tsu ite", + "no mi", + "su gu", + "ku da", + "tetsu gaku", + "i ka", + "ron ri", + "o ki", + "ni ppon", + "p er", + "shi mashita", + "chi shiki", + "cho kkanteki", + "su ko", + "t ion", + "ku u", + "a na", + "a rou", + "ka tte", + "ku ri", + "i nai", + "hyou gen", + "i shiki", + "do ku", + "a tte", + "a tara", + "to n", + "wa ri", + "ka o", + "sei san", + "hana shi", + "s i", + "ka ke", + "na ji", + "su nawa", + "sunawa chi", + "u go", + "su u", + "ba ra", + "le v", + "hi ro", + "i wa", + "be tsu", + "yo i", + "se ru", + "shite ru", + "rare te", + "to shi", + "se ki", + "tai ritsu", + "wa kara", + "to kyo", + "k ka", + "k yoku", + "u n", + "i ro", + "mi te", + "sa ki", + "kan ji", + "mi ta", + "su be", + "r yoku", + "ma tta", + "kuda sai", + "omo i", + "ta no", + "ware ru", + "co m", + "hitsu you", + "ka shi", + "re nai", + "kan kei", + "a to", + "ga tte", + "o chi", + "mo tsu", + "in g", + "son zai", + "l l", + "o re", + "tai shite", + "a me", + "sei mei", + "ka no", + "gi ri", + "kangae ru", + "yu e", + "a sa", + "o naji", + "yo ru", + "ni ku", + "osa ka", + "suko shi", + "c k", + "ta ma", + "kano jo", + "ki te", + "mon dai", + "a mari", + "e ki", + "ko jin", + "ha ya", + "i t", + "de te", + "atara shii", + "a wa", + "ga kkou", + "tsu zu", + "shu kan", + "i mashita", + "mi na", + "ata e", + "da rou", + "hatara ku", + "ga ta", + "da chi", + "ma tsu", + "ari masen", + "sei butsu", + "mi tsu", + "he ya", + "yasu i", + "d i", + "de ni", + "no ko", + "ha ha", + "do mo", + "ka mi", + "su deni", + "na o", + "ra ku", + "i ke", + "a ki", + "me ta", + "l o", + "ko domo", + "so shite", + "ga me", + "ba kari", + "to te", + "ha tsu", + "mi se", + "moku teki", + "da kara", + "s z", + "e l", + "g y", + "e n", + "t t", + "e m", + "a n", + "a k", + "e r", + "a z", + "a l", + "e t", + "o l", + "e g", + "e k", + "m i", + "o n", + "é s", + "c s", + "a t", + "á r", + "h o", + "e z", + "á l", + "i s", + "á n", + "o r", + "a r", + "e gy", + "e s", + "é r", + "á t", + "o tt", + "e tt", + "m eg", + "t a", + "o k", + "o s", + "ho gy", + "n em", + "é g", + "n y", + "k i", + "é l", + "h a", + "á s", + "ü l", + "i n", + "mi n", + "n a", + "e d", + "o m", + "i k", + "k ö", + "m a", + "n i", + "v a", + "v ol", + "é t", + "b b", + "f el", + "i g", + "l e", + "r a", + "é n", + "t e", + "d e", + "a d", + "ó l", + "b e", + "on d", + "j a", + "r e", + "u l", + "b en", + "n ek", + "u t", + "vol t", + "b an", + "ö r", + "o g", + "a p", + "o d", + "á g", + "n k", + "é k", + "v al", + "k or", + "a m", + "i l", + "í t", + "á k", + "b a", + "u d", + "sz er", + "min d", + "o z", + "é p", + "el l", + "ér t", + "m ond", + "i t", + "sz t", + "n ak", + "a mi", + "n e", + "ő l", + "cs ak", + "n é", + "ma g", + "ol y", + "m er", + "ál l", + "án y", + "ö n", + "ö l", + "min t", + "m ár", + "ö tt", + "na gy", + "é sz", + "az t", + "el ő", + "t ud", + "o t", + "é ny", + "á z", + "m ég", + "kö z", + "el y", + "s ég", + "en t", + "s em", + "ta m", + "h et", + "h al", + "f i", + "a s", + "v an", + "ho z", + "v e", + "u k", + "k ez", + "á m", + "v el", + "b er", + "a j", + "u nk", + "i z", + "va gy", + "m os", + "sz em", + "em ber", + "f og", + "mer t", + "ü k", + "l en", + "ö s", + "e j", + "t al", + "h at", + "t ak", + "h i", + "m ás", + "s ág", + "ett e", + "l eg", + "ü nk", + "h át", + "sz a", + "on y", + "ez t", + "mind en", + "en d", + "ül t", + "h an", + "j ó", + "k is", + "á j", + "in t", + "ú gy", + "i d", + "mos t", + "ar t", + "í r", + "k er", + "i tt", + "a tt", + "el t", + "mond ta", + "k ell", + "l á", + "ak i", + "ál t", + "ér d", + "t ö", + "l an", + "v ár", + "h ol", + "t el", + "l át", + "ő k", + "v et", + "s e", + "ut án", + "k ét", + "na p", + "í v", + "ál y", + "v ég", + "ö k", + "i r", + "d ul", + "v is", + "né z", + "t er", + "á ban", + "k ül", + "ak kor", + "k ap", + "sz él", + "y en", + "ú j", + "i m", + "oly an", + "es en", + "k ed", + "h ely", + "t ör", + "b ól", + "el m", + "r á", + "ár a", + "r ó", + "l ó", + "vol na", + "t an", + "le het", + "e bb", + "t en", + "t ek", + "s ok", + "k al", + "f or", + "u g", + "ol t", + "k a", + "ek et", + "b or", + "f ej", + "g ond", + "a g", + "ak ar", + "f él", + "ú l", + "b el", + "ott a", + "mi t", + "val ami", + "j el", + "é d", + "ar c", + "u r", + "hal l", + "t i", + "f öl", + "á ba", + "ol g", + "ki r", + "ol d", + "m ar", + "k érd", + "j ár", + "ú r", + "sz e", + "z s", + "él et", + "j át", + "o v", + "u s", + "é z", + "v il", + "v er", + "ő r", + "á d", + "ö g", + "le sz", + "on t", + "b iz", + "k oz", + "á bb", + "kir ály", + "es t", + "a b", + "en g", + "ig az", + "b ar", + "ha j", + "d i", + "o b", + "k od", + "r ól", + "v ez", + "tö bb", + "sz ó", + "é ben", + "ö t", + "ny i", + "t á", + "sz ól", + "gond ol", + "eg ész", + "í gy", + "ő s", + "o bb", + "os an", + "b ől", + "a bb", + "c i", + "ő t", + "n ál", + "k ép", + "azt án", + "v i", + "t art", + "be szél", + "m en", + "elő tt", + "a szt", + "ma j", + "kö r", + "han g", + "í z", + "in cs", + "a i", + "é v", + "ó d", + "ó k", + "hoz z", + "t em", + "ok at", + "an y", + "nagy on", + "h áz", + "p er", + "p ed", + "ez te", + "et len", + "nek i", + "maj d", + "sz ony", + "án ak", + "fel é", + "egy szer", + "j e", + "ad t", + "gy er", + "ami kor", + "f oly", + "sz ak", + "ő d", + "h ú", + "á sz", + "am ely", + "h ar", + "ér e", + "il yen", + "od a", + "j ák", + "t ár", + "á val", + "l ak", + "t ó", + "m ent", + "gy an", + "él y", + "ú t", + "v ar", + "kez d", + "m ell", + "mi kor", + "h ez", + "val ó", + "k o", + "m es", + "szer et", + "r end", + "l et", + "vis sza", + "ig en", + "f ő", + "va s", + "as szony", + "r ől", + "ped ig", + "p i", + "sz ép", + "t ák", + "ö v", + "an i", + "vil ág", + "p en", + "mag a", + "t et", + "sz ik", + "é j", + "én t", + "j ött", + "s an", + "sz í", + "i de", + "g at", + "ett em", + "ul t", + "h ány", + "ás t", + "a hol", + "ők et", + "h ár", + "k el", + "n ő", + "cs i", + "tal ál", + "el te", + "lá tt", + "tör t", + "ha gy", + "e sz", + "s en", + "n él", + "p ar", + "v ál", + "k ut", + "l ány", + "ami t", + "s ő", + "ell en", + "mag át", + "in k", + "u gyan", + "kül ön", + "a sz", + "mind ig", + "l ép", + "tal án", + "u n", + "sz or", + "k e", + "il lan", + "n incs", + "z et", + "vagy ok", + "tel en", + "is mer", + "s or", + "is ten", + "ít ott", + "j obb", + "v es", + "dul t", + "j uk", + "sz en", + "r o", + "ö m", + "l ett", + "k ar", + "egy ik", + "b ár", + "sz i", + "sz ív", + "az on", + "e szt", + "föl d", + "kut y", + "p illan", + "f ér", + "k om", + "t ől", + "t ű", + "é be", + "t ött", + "bar át", + "í g", + "a hogy", + "e h", + "e p", + "s o", + "v en", + "jel ent", + "t at", + "sz eg", + "mint ha", + "f al", + "egy en", + "mi l", + "sza b", + "r i", + "é m", + "biz ony", + "j on", + "ör eg", + "d olg", + "cs ap", + "ti szt", + "áll t", + "an cs", + "id ő", + "k at", + "ü gy", + "mi ért", + "ó t", + "ü r", + "cs in", + "h az", + "b et", + "én ek", + "v ér", + "j ól", + "al att", + "m ely", + "l o", + "sem mi", + "ny ug", + "v ág", + "kö vet", + "ös sze", + "ma d", + "l i", + "a cs", + "fi ú", + "kö n", + "más ik", + "j ön", + "sz ám", + "g er", + "s ó", + "r ész", + "k ér", + "z el", + "é vel", + "e o", + "e u", + "a n", + "eu l", + "eu n", + "eo n", + "a e", + "d a", + "a l", + "s s", + "i n", + "i l", + "a g", + "an g", + "y eon", + "y eo", + "d o", + "c h", + "n g", + "j i", + "h an", + "g a", + "g o", + "u i", + "h ae", + "a m", + "u l", + "u n", + "g eo", + "s i", + "n eun", + "ss da", + "s eo", + "eon g", + "y o", + "i da", + "t t", + "k k", + "j eo", + "d eul", + "w a", + "eu m", + "g e", + "o n", + "o g", + "s al", + "m an", + "yeon g", + "geo s", + "h ag", + "an eun", + "j a", + "g i", + "s u", + "i ss", + "o l", + "d ae", + "eo b", + "h a", + "j u", + "eo l", + "g eu", + "j eong", + "s ae", + "do e", + "g eul", + "s eu", + "s in", + "eul o", + "b n", + "s ang", + "bn ida", + "h al", + "b o", + "han eun", + "m al", + "i m", + "m o", + "b u", + "jeo g", + "sae ng", + "in eun", + "an h", + "m a", + "sal am", + "j o", + "s a", + "eo m", + "n ae", + "w i", + "l o", + "g wa", + "yeo l", + "n a", + "e seo", + "y e", + "m yeon", + "tt ae", + "h w", + "j e", + "eob s", + "j ang", + "g u", + "g w", + "il eul", + "yeo g", + "j eon", + "si g", + "j ag", + "j in", + "y u", + "o e", + "s e", + "hag o", + "d eun", + "y a", + "m un", + "s eong", + "g ag", + "h am", + "d ang", + "b a", + "l eul", + "s il", + "do ng", + "kk a", + "b al", + "da l", + "han da", + "eo ssda", + "ae g", + "l i", + "ha ji", + "s eon", + "o ng", + "hae ssda", + "d e", + "i ssda", + "e ge", + "b un", + "m ul", + "ju ng", + "ji g", + "m u", + "iss neun", + "b i", + "g eun", + "seu bnida", + "w on", + "p p", + "d aneun", + "eo h", + "d eo", + "ga m", + "j al", + "hae ng", + "ag o", + "y ang", + "b ul", + "b ang", + "u m", + "s o", + "h i", + "j ae", + "si m", + "saeng gag", + "hag e", + "s og", + "eo ss", + "d an", + "ja sin", + "j il", + "eo g", + "g yeong", + "doe n", + "go ng", + "m i", + "ch i", + "d eu", + "d eon", + "hae ss", + "d u", + "n am", + "eun g", + "jo h", + "n al", + "m yeong", + "w o", + "eon a", + "i go", + "g yeol", + "y ag", + "gw an", + "ul i", + "yo ng", + "n o", + "l yeo", + "j og", + "eoh ge", + "ga t", + "b og", + "mo s", + "t ong", + "ch a", + "man h", + "jeo l", + "geo l", + "h oe", + "ag a", + "n aneun", + "g an", + "un eun", + "ch eol", + "ch e", + "do l", + "b on", + "b an", + "ba d", + "ch u", + "ham yeon", + "yeo ssda", + "i bnida", + "g ye", + "eo s", + "hw al", + "salam deul", + "ji man", + "dang sin", + "ji b", + "ttae mun", + "m ae", + "i b", + "e neun", + "eu g", + "jeo m", + "geul eon", + "h wa", + "a ssda", + "b eob", + "bu t", + "b ae", + "yeo ss", + "ch in", + "ch aeg", + "g eon", + "g ae", + "nae ga", + "i ga", + "m og", + "sig an", + "g il", + "h yeon", + "l yeog", + "gu g", + "p yeon", + "s an", + "w ae", + "j ul", + "s eul", + "deun g", + "haji man", + "eum yeon", + "p il", + "m ol", + "n eu", + "a ss", + "n yeon", + "t ae", + "h u", + "p yo", + "s ul", + "g ang", + "j ineun", + "b eon", + "ha da", + "seo l", + "si p", + "dal eun", + "a p", + "sal m", + "g yo", + "ch eon", + "hag i", + "in a", + "cheol eom", + "g al", + "il a", + "kka ji", + "anh neun", + "ha bnida", + "tt eon", + "n u", + "hae seo", + "doen da", + "s ol", + "tt al", + "l a", + "il o", + "seu b", + "b yeon", + "m yeo", + "b eol", + "s on", + "n un", + "j un", + "j am", + "j eung", + "tt o", + "e n", + "mo m", + "h o", + "ch im", + "hw ang", + "eun eun", + "jo ng", + "bo da", + "n ol", + "n eom", + "but eo", + "jig eum", + "eobs da", + "dae lo", + "i g", + "y ul", + "p yeong", + "seon eun", + "sal ang", + "seu t", + "h im", + "n an", + "h eom", + "h yang", + "p i", + "gw ang", + "eobs neun", + "hw ag", + "ge ss", + "jag i", + "il eon", + "wi hae", + "dae han", + "ga ji", + "m eog", + "j yeo", + "cha j", + "b yeong", + "eo d", + "g yeo", + "do n", + "eo ji", + "g ul", + "mo deun", + "j on", + "in saeng", + "geul ae", + "h ang", + "sa sil", + "si b", + "ch al", + "il ago", + "doe l", + "g eum", + "doe neun", + "b ol", + "ga jang", + "geul igo", + "e l", + "h yeong", + "haeng bog", + "ch ul", + "h on", + "ch ae", + "s am", + "m ang", + "in da", + "da m", + "w ol", + "ch oe", + "d ul", + "si jag", + "ch eong", + "il aneun", + "ul ineun", + "ae n", + "kk e", + "mun je", + "a do", + "t eu", + "g un", + "geun eun", + "b ge", + "ch eo", + "b aeg", + "ju g", + "t a", + "sang dae", + "geu geos", + "do g", + "eu s", + "deu s", + "ja b", + "h yeo", + "tt eohge", + "u g", + "ma j", + "ch il", + "s wi", + "j ileul", + "ch ang", + "g aneun", + "m ag", + "i ji", + "da go", + "m in", + "yo han", + "t eug", + "pp un", + "al eul", + "haeng dong", + "p o", + "m il", + "ch am", + "se sang", + "e do", + "p an", + "man deul", + "am yeon", + "a b", + "kk ae", + "b ag", + "i deul", + "p um", + "m eol", + "s un", + "n eul", + "ham kke", + "chu ng", + "da b", + "yu g", + "s ag", + "gwang ye", + "il eohge", + "bal o", + "neun de", + "ham yeo", + "go s", + "geul eoh", + "an ila", + "bang beob", + "da si", + "b yeol", + "g yeon", + "gam jeong", + "on eul", + "j aneun", + "yeo m", + "l ago", + "i gi", + "hw an", + "t eul", + "eo seo", + "si k", + "ch o", + "jag a", + "geul eom", + "geul eona", + "jeong do", + "g yeog", + "geul eohge", + "geu deul", + "eu t", + "im yeon", + "j jae", + "k eun", + "i sang", + "mal haessda", + "eu ge", + "no p", + "in gan", + "bo myeon", + "t aeg", + "seu s", + "d wi", + "s aneun", + "w an", + "anh go", + "t an", + "nu gu", + "su ng", + "da myeon", + "a deul", + "p eul", + "ttal a", + "d i", + "geos do", + "a ji", + "m eon", + "eum yeo", + "dol og", + "neun g", + "mo du", + "क े", + "ह ै", + "े ं", + "् र", + "ा र", + "न े", + "य ा", + "म ें", + "स े", + "क ी", + "क ा", + "ो ं", + "त ा", + "क र", + "स ्", + "क ि", + "क ो", + "र ्", + "न ा", + "क ्", + "ह ी", + "औ र", + "प र", + "त े", + "ह ो", + "प ्र", + "ा न", + "् य", + "ल ा", + "व ा", + "ल े", + "स ा", + "है ं", + "ल ि", + "ज ा", + "ह ा", + "भ ी", + "व ि", + "इ स", + "त ी", + "न ्", + "र ा", + "म ा", + "द े", + "द ि", + "ब ा", + "त ि", + "थ ा", + "न ि", + "क ार", + "ए क", + "ही ं", + "ह ु", + "ं ग", + "ै ं", + "न ी", + "स ी", + "अ प", + "त ्", + "न हीं", + "र ी", + "म े", + "म ु", + "ि त", + "त ो", + "प ा", + "ल ी", + "लि ए", + "ग ा", + "ल ्", + "र ह", + "र े", + "क् ष", + "म ैं", + "स म", + "उ स", + "ज ि", + "त ्र", + "म ि", + "च ा", + "ो ग", + "स ं", + "द ्", + "स ि", + "आ प", + "त ु", + "द ा", + "क ु", + "य ों", + "व े", + "ज ी", + "् या", + "उ न", + "ि क", + "य े", + "भ ा", + "् ट", + "ह म", + "स् ट", + "श ा", + "ड ़", + "ं द", + "ख ा", + "म ्", + "श ्", + "य ह", + "स क", + "प ू", + "कि या", + "अप ने", + "र ू", + "स ु", + "म ी", + "ह ि", + "ज ो", + "थ े", + "र ि", + "द ी", + "थ ी", + "ग ी", + "ल ोग", + "ग या", + "त र", + "न् ह", + "च ्", + "व ार", + "ब ी", + "प ्", + "द ो", + "ट ी", + "श ि", + "कर ने", + "ग े", + "ै से", + "इ न", + "ं ड", + "सा थ", + "प ु", + "ब े", + "ब ार", + "व ी", + "अ न", + "ह र", + "उ न्ह", + "हो ता", + "ज ब", + "कु छ", + "म ान", + "क ्र", + "ब ि", + "प ह", + "फ ि", + "स र", + "ार ी", + "र ो", + "द ू", + "क हा", + "त क", + "श न", + "ब ्", + "स् थ", + "व ह", + "बा द", + "ओ ं", + "ग ु", + "ज ्", + "्र े", + "ग र", + "रह े", + "व र्", + "ह ू", + "ार ्", + "प ी", + "ब हु", + "मु झ", + "्र ा", + "दि या", + "स ब", + "कर ते", + "अप नी", + "बहु त", + "क ह", + "ट े", + "हु ए", + "कि सी", + "र हा", + "ष ्ट", + "ज ़", + "ब ना", + "स ो", + "ड ि", + "को ई", + "व ्य", + "बा त", + "र ु", + "व ो", + "मुझ े", + "द् ध", + "च ार", + "मे रे", + "व र", + "्र ी", + "जा ता", + "न ों", + "प्र ा", + "दे ख", + "ट ा", + "क् या", + "अ ध", + "ल ग", + "ल ो", + "प ि", + "य ु", + "च े", + "जि स", + "ं त", + "ान ी", + "प ै", + "ज न", + "ार े", + "च ी", + "मि ल", + "द ु", + "दे श", + "च् छ", + "ष ्", + "स ू", + "ख े", + "च ु", + "ि या", + "ल गा", + "ब ु", + "उन के", + "ज् ञ", + "क्ष ा", + "त रह", + "्या दा", + "वा ले", + "पू र्", + "मैं ने", + "का म", + "रू प", + "हो ती", + "उ प", + "ज ान", + "प्र कार", + "भ ार", + "म न", + "हु आ", + "ट र", + "हू ँ", + "पर ि", + "पा स", + "अन ु", + "रा ज", + "लोग ों", + "अ ब", + "सम झ", + "ड ी", + "म ौ", + "श ु", + "च ि", + "प े", + "क ृ", + "सक ते", + "म ह", + "य ोग", + "द र्", + "उ से", + "ं ध", + "ड ा", + "जा ए", + "ब ो", + "ू ल", + "म ो", + "ों ने", + "ं स", + "तु म", + "पह ले", + "ब ता", + "त था", + "य ो", + "ग ई", + "उ त्", + "सक ता", + "क म", + "ज ्यादा", + "र ख", + "सम य", + "ार ा", + "अ गर", + "स् त", + "च ल", + "फि र", + "वार ा", + "कर ना", + "श ी", + "ग ए", + "ब न", + "ौ र", + "हो ने", + "चा ह", + "ख ु", + "हा ँ", + "उन्ह ें", + "उन्ह ोंने", + "छ ो", + "म् ह", + "प्र ति", + "नि क", + "व न", + "्य ू", + "र ही", + "तु म्ह", + "ज ैसे", + "ि यों", + "क् यों", + "ल ों", + "फ ़", + "ं त्र", + "हो ते", + "क् ति", + "त ्य", + "कर ्", + "क ई", + "व ं", + "कि न", + "प ो", + "कार ण", + "ड़ ी", + "भ ि", + "इस के", + "ब र", + "उस के", + "द् वारा", + "श े", + "क ॉ", + "दि न", + "न् न", + "ड़ ा", + "स् व", + "नि र्", + "मु ख", + "लि या", + "ट ि", + "ज्ञ ान", + "क् त", + "द ्र", + "ग ्", + "क् स", + "म ै", + "ग ो", + "ज े", + "ट ्र", + "म ार", + "त् व", + "ध ार", + "भा व", + "कर ता", + "ख ि", + "क ं", + "चा हि", + "य र", + "प् त", + "क ों", + "ं च", + "ज ु", + "म त", + "अ च्छ", + "हु ई", + "क भी", + "ले किन", + "भ ू", + "अप ना", + "दू स", + "चाहि ए", + "य ू", + "घ र", + "सब से", + "मे री", + "ना म", + "ढ ़", + "ं ट", + "ें गे", + "ब ै", + "फ ा", + "ए वं", + "य ी", + "ग ्र", + "क्ष े", + "आ ज", + "आप को", + "भा ग", + "ठ ा", + "क ै", + "भार त", + "उन की", + "प हु", + "स भी", + "ध ा", + "ण ा", + "स ान", + "हो गा", + "त ब", + "स ंग", + "प र्", + "अ व", + "त ना", + "ग ि", + "य न", + "स् था", + "च ित", + "ट ्", + "छ ा", + "जा ने", + "क्षे त्र", + "वा ली", + "पूर् ण", + "स मा", + "कार ी" + ] + } +} \ No newline at end of file diff --git a/comfy/text_encoders/ace_text_cleaners.py b/comfy/text_encoders/ace_text_cleaners.py new file mode 100644 index 000000000..ad3612e5f --- /dev/null +++ b/comfy/text_encoders/ace_text_cleaners.py @@ -0,0 +1,270 @@ +# basic text cleaners for the ACE step model +# I didn't copy the ones from the reference code because I didn't want to deal with the dependencies +# TODO: more languages than english? + +import re + +def number_to_text(num, ordinal=False): + """ + Convert a number (int or float) to its text representation. + + Args: + num: The number to convert + + Returns: + str: Text representation of the number + """ + + if not isinstance(num, (int, float)): + return "Input must be a number" + + # Handle special case of zero + if num == 0: + return "zero" + + # Handle negative numbers + negative = num < 0 + num = abs(num) + + # Handle floats + if isinstance(num, float): + # Split into integer and decimal parts + int_part = int(num) + + # Convert both parts + int_text = _int_to_text(int_part) + + # Handle decimal part (convert to string and remove '0.') + decimal_str = str(num).split('.')[1] + decimal_text = " point " + " ".join(_digit_to_text(int(digit)) for digit in decimal_str) + + result = int_text + decimal_text + else: + # Handle integers + result = _int_to_text(num) + + # Add 'negative' prefix for negative numbers + if negative: + result = "negative " + result + + return result + + +def _int_to_text(num): + """Helper function to convert an integer to text""" + + ones = ["", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", + "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", + "seventeen", "eighteen", "nineteen"] + + tens = ["", "", "twenty", "thirty", "forty", "fifty", "sixty", "seventy", "eighty", "ninety"] + + if num < 20: + return ones[num] + + if num < 100: + return tens[num // 10] + (" " + ones[num % 10] if num % 10 != 0 else "") + + if num < 1000: + return ones[num // 100] + " hundred" + (" " + _int_to_text(num % 100) if num % 100 != 0 else "") + + if num < 1000000: + return _int_to_text(num // 1000) + " thousand" + (" " + _int_to_text(num % 1000) if num % 1000 != 0 else "") + + if num < 1000000000: + return _int_to_text(num // 1000000) + " million" + (" " + _int_to_text(num % 1000000) if num % 1000000 != 0 else "") + + return _int_to_text(num // 1000000000) + " billion" + (" " + _int_to_text(num % 1000000000) if num % 1000000000 != 0 else "") + + +def _digit_to_text(digit): + """Convert a single digit to text""" + digits = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"] + return digits[digit] + + +_whitespace_re = re.compile(r"\s+") + + +# List of (regular expression, replacement) pairs for abbreviations: +_abbreviations = { + "en": [ + (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) + for x in [ + ("mrs", "misess"), + ("mr", "mister"), + ("dr", "doctor"), + ("st", "saint"), + ("co", "company"), + ("jr", "junior"), + ("maj", "major"), + ("gen", "general"), + ("drs", "doctors"), + ("rev", "reverend"), + ("lt", "lieutenant"), + ("hon", "honorable"), + ("sgt", "sergeant"), + ("capt", "captain"), + ("esq", "esquire"), + ("ltd", "limited"), + ("col", "colonel"), + ("ft", "fort"), + ] + ], +} + + +def expand_abbreviations_multilingual(text, lang="en"): + for regex, replacement in _abbreviations[lang]: + text = re.sub(regex, replacement, text) + return text + + +_symbols_multilingual = { + "en": [ + (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1]) + for x in [ + ("&", " and "), + ("@", " at "), + ("%", " percent "), + ("#", " hash "), + ("$", " dollar "), + ("£", " pound "), + ("°", " degree "), + ] + ], +} + + +def expand_symbols_multilingual(text, lang="en"): + for regex, replacement in _symbols_multilingual[lang]: + text = re.sub(regex, replacement, text) + text = text.replace(" ", " ") # Ensure there are no double spaces + return text.strip() + + +_ordinal_re = { + "en": re.compile(r"([0-9]+)(st|nd|rd|th)"), +} +_number_re = re.compile(r"[0-9]+") +_currency_re = { + "USD": re.compile(r"((\$[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+\$))"), + "GBP": re.compile(r"((£[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+£))"), + "EUR": re.compile(r"(([0-9\.\,]*[0-9]+€)|((€[0-9\.\,]*[0-9]+)))"), +} + +_comma_number_re = re.compile(r"\b\d{1,3}(,\d{3})*(\.\d+)?\b") +_dot_number_re = re.compile(r"\b\d{1,3}(.\d{3})*(\,\d+)?\b") +_decimal_number_re = re.compile(r"([0-9]+[.,][0-9]+)") + + +def _remove_commas(m): + text = m.group(0) + if "," in text: + text = text.replace(",", "") + return text + + +def _remove_dots(m): + text = m.group(0) + if "." in text: + text = text.replace(".", "") + return text + + +def _expand_decimal_point(m, lang="en"): + amount = m.group(1).replace(",", ".") + return number_to_text(float(amount)) + + +def _expand_currency(m, lang="en", currency="USD"): + amount = float((re.sub(r"[^\d.]", "", m.group(0).replace(",", ".")))) + full_amount = number_to_text(amount) + + and_equivalents = { + "en": ", ", + "es": " con ", + "fr": " et ", + "de": " und ", + "pt": " e ", + "it": " e ", + "pl": ", ", + "cs": ", ", + "ru": ", ", + "nl": ", ", + "ar": ", ", + "tr": ", ", + "hu": ", ", + "ko": ", ", + } + + if amount.is_integer(): + last_and = full_amount.rfind(and_equivalents[lang]) + if last_and != -1: + full_amount = full_amount[:last_and] + + return full_amount + + +def _expand_ordinal(m, lang="en"): + return number_to_text(int(m.group(1)), ordinal=True) + + +def _expand_number(m, lang="en"): + return number_to_text(int(m.group(0))) + + +def expand_numbers_multilingual(text, lang="en"): + if lang in ["en", "ru"]: + text = re.sub(_comma_number_re, _remove_commas, text) + else: + text = re.sub(_dot_number_re, _remove_dots, text) + try: + text = re.sub(_currency_re["GBP"], lambda m: _expand_currency(m, lang, "GBP"), text) + text = re.sub(_currency_re["USD"], lambda m: _expand_currency(m, lang, "USD"), text) + text = re.sub(_currency_re["EUR"], lambda m: _expand_currency(m, lang, "EUR"), text) + except: + pass + + text = re.sub(_decimal_number_re, lambda m: _expand_decimal_point(m, lang), text) + text = re.sub(_ordinal_re[lang], lambda m: _expand_ordinal(m, lang), text) + text = re.sub(_number_re, lambda m: _expand_number(m, lang), text) + return text + + +def lowercase(text): + return text.lower() + + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text) + + +def multilingual_cleaners(text, lang): + text = text.replace('"', "") + if lang == "tr": + text = text.replace("İ", "i") + text = text.replace("Ö", "ö") + text = text.replace("Ü", "ü") + text = lowercase(text) + try: + text = expand_numbers_multilingual(text, lang) + except: + pass + try: + text = expand_abbreviations_multilingual(text, lang) + except: + pass + try: + text = expand_symbols_multilingual(text, lang=lang) + except: + pass + text = collapse_whitespace(text) + return text + + +def basic_cleaners(text): + """Basic pipeline that lowercases and collapses whitespace without transliteration.""" + text = lowercase(text) + text = collapse_whitespace(text) + return text diff --git a/comfy/text_encoders/umt5_config_base.json b/comfy/text_encoders/umt5_config_base.json new file mode 100644 index 000000000..6b3618f07 --- /dev/null +++ b/comfy/text_encoders/umt5_config_base.json @@ -0,0 +1,22 @@ +{ + "d_ff": 2048, + "d_kv": 64, + "d_model": 768, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "gelu_pytorch_tanh", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": true, + "layer_norm_epsilon": 1e-06, + "model_type": "umt5", + "num_decoder_layers": 12, + "num_heads": 12, + "num_layers": 12, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 256384 +} diff --git a/comfy_extras/nodes_ace.py b/comfy_extras/nodes_ace.py new file mode 100644 index 000000000..36eb999d1 --- /dev/null +++ b/comfy_extras/nodes_ace.py @@ -0,0 +1,46 @@ +import torch +import comfy.model_management + + +class TextEncodeAceStepAudio: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "clip": ("CLIP", ), + "tags": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "lyrics": ("STRING", {"multiline": True, "dynamicPrompts": True}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "conditioning" + + def encode(self, clip, tags, lyrics): + tokens = clip.tokenize(tags, lyrics=lyrics) + return (clip.encode_from_tokens_scheduled(tokens), ) + + +class EmptyAceStepLatentAudio: + def __init__(self): + self.device = comfy.model_management.intermediate_device() + + @classmethod + def INPUT_TYPES(s): + return {"required": {"seconds": ("FLOAT", {"default": 120.0, "min": 1.0, "max": 1000.0, "step": 0.1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent/audio" + + def generate(self, seconds, batch_size): + length = int(seconds * 44100 / 512 / 8) + latent = torch.zeros([batch_size, 8, 16, length], device=self.device) + return ({"samples": latent, "type": "audio"}, ) + + +NODE_CLASS_MAPPINGS = { + "TextEncodeAceStepAudio": TextEncodeAceStepAudio, + "EmptyAceStepLatentAudio": EmptyAceStepLatentAudio, +} diff --git a/nodes.py b/nodes.py index 3c3617562..d2ffd5259 100644 --- a/nodes.py +++ b/nodes.py @@ -246,6 +246,9 @@ class ConditioningZeroOut: pooled_output = d.get("pooled_output", None) if pooled_output is not None: d["pooled_output"] = torch.zeros_like(pooled_output) + conditioning_lyrics = d.get("conditioning_lyrics", None) + if conditioning_lyrics is not None: + d["conditioning_lyrics"] = torch.zeros_like(conditioning_lyrics) n = [torch.zeros_like(t[0]), d] c.append(n) return (c, ) @@ -917,7 +920,7 @@ class CLIPLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ), - "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma"], ), + "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace"], ), }, "optional": { "device": (["default", "cpu"], {"advanced": True}), @@ -2259,6 +2262,7 @@ def init_builtin_extra_nodes(): "nodes_hidream.py", "nodes_fresca.py", "nodes_preview_any.py", + "nodes_ace.py", ] import_failed = [] From b9980592c4c629be4d46b707c65c14dc2a3da842 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 7 May 2025 14:27:16 -0700 Subject: [PATCH 077/193] Refuse to load api nodes on old pyav version. (#7981) --- comfy_api_nodes/canary.py | 10 ++++++++++ nodes.py | 3 +++ 2 files changed, 13 insertions(+) create mode 100644 comfy_api_nodes/canary.py diff --git a/comfy_api_nodes/canary.py b/comfy_api_nodes/canary.py new file mode 100644 index 000000000..4df7590b6 --- /dev/null +++ b/comfy_api_nodes/canary.py @@ -0,0 +1,10 @@ +import av + +ver = av.__version__.split(".") +if int(ver[0]) < 14: + raise Exception("INSTALL NEW VERSION OF PYAV TO USE API NODES.") + +if int(ver[0]) == 14 and int(ver[1]) < 2: + raise Exception("INSTALL NEW VERSION OF PYAV TO USE API NODES.") + +NODE_CLASS_MAPPINGS = {} diff --git a/nodes.py b/nodes.py index d2ffd5259..a1ddf2dd6 100644 --- a/nodes.py +++ b/nodes.py @@ -2289,6 +2289,9 @@ def init_builtin_api_nodes(): "nodes_pika.py", ] + if not load_custom_node(os.path.join(api_nodes_dir, "canary.py"), module_parent="comfy_api_nodes"): + return api_nodes_files + import_failed = [] for node_file in api_nodes_files: if not load_custom_node(os.path.join(api_nodes_dir, node_file), module_parent="comfy_api_nodes"): From cc33cd3422642445c994b104f0380821043024ec Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 7 May 2025 16:22:07 -0700 Subject: [PATCH 078/193] Experimental lyrics strength for ACE. (#7984) --- comfy/ldm/ace/model.py | 6 +++++- comfy/model_base.py | 1 + comfy_extras/nodes_ace.py | 9 ++++++--- 3 files changed, 12 insertions(+), 4 deletions(-) diff --git a/comfy/ldm/ace/model.py b/comfy/ldm/ace/model.py index e5883df90..12c524701 100644 --- a/comfy/ldm/ace/model.py +++ b/comfy/ldm/ace/model.py @@ -273,6 +273,7 @@ class ACEStepTransformer2DModel(nn.Module): speaker_embeds: Optional[torch.FloatTensor] = None, lyric_token_idx: Optional[torch.LongTensor] = None, lyric_mask: Optional[torch.LongTensor] = None, + lyrics_strength=1.0, ): bs = encoder_text_hidden_states.shape[0] @@ -291,6 +292,8 @@ class ACEStepTransformer2DModel(nn.Module): out_dtype=encoder_text_hidden_states.dtype, ) + encoder_lyric_hidden_states *= lyrics_strength + encoder_hidden_states = torch.cat([encoder_spk_hidden_states, encoder_text_hidden_states, encoder_lyric_hidden_states], dim=1) encoder_hidden_mask = None @@ -310,7 +313,6 @@ class ACEStepTransformer2DModel(nn.Module): output_length: int = 0, block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, controlnet_scale: Union[float, torch.Tensor] = 1.0, - return_dict: bool = True, ): embedded_timestep = self.timestep_embedder(self.time_proj(timestep).to(dtype=hidden_states.dtype)) temb = self.t_block(embedded_timestep) @@ -353,6 +355,7 @@ class ACEStepTransformer2DModel(nn.Module): lyric_mask: Optional[torch.LongTensor] = None, block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, controlnet_scale: Union[float, torch.Tensor] = 1.0, + lyrics_strength=1.0, **kwargs ): hidden_states = x @@ -363,6 +366,7 @@ class ACEStepTransformer2DModel(nn.Module): speaker_embeds=speaker_embeds, lyric_token_idx=lyric_token_idx, lyric_mask=lyric_mask, + lyrics_strength=lyrics_strength, ) output_length = hidden_states.shape[-1] diff --git a/comfy/model_base.py b/comfy/model_base.py index 6408005b6..6d27930dc 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -1139,4 +1139,5 @@ class ACEStep(BaseModel): if cross_attn is not None: out['lyric_token_idx'] = comfy.conds.CONDRegular(conditioning_lyrics) out['speaker_embeds'] = comfy.conds.CONDRegular(torch.zeros(noise.shape[0], 512, device=noise.device, dtype=noise.dtype)) + out['lyrics_strength'] = comfy.conds.CONDConstant(kwargs.get("lyrics_strength", 1.0)) return out diff --git a/comfy_extras/nodes_ace.py b/comfy_extras/nodes_ace.py index 36eb999d1..cbfec15a2 100644 --- a/comfy_extras/nodes_ace.py +++ b/comfy_extras/nodes_ace.py @@ -1,6 +1,6 @@ import torch import comfy.model_management - +import node_helpers class TextEncodeAceStepAudio: @classmethod @@ -9,15 +9,18 @@ class TextEncodeAceStepAudio: "clip": ("CLIP", ), "tags": ("STRING", {"multiline": True, "dynamicPrompts": True}), "lyrics": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "lyrics_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "encode" CATEGORY = "conditioning" - def encode(self, clip, tags, lyrics): + def encode(self, clip, tags, lyrics, lyrics_strength): tokens = clip.tokenize(tags, lyrics=lyrics) - return (clip.encode_from_tokens_scheduled(tokens), ) + conditioning = clip.encode_from_tokens_scheduled(tokens) + conditioning = node_helpers.conditioning_set_values(conditioning, {"lyrics_strength": lyrics_strength}) + return (conditioning, ) class EmptyAceStepLatentAudio: From 56b6ee6754f9ca7cf336394eacd17e24364090f1 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 7 May 2025 18:28:24 -0700 Subject: [PATCH 079/193] Detection code to make ltxv models without config work. (#7986) --- comfy/model_detection.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index ff4c29d7e..28c586389 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -222,6 +222,10 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv dit_config = {} dit_config["image_model"] = "ltxv" + dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.') + shape = state_dict['{}transformer_blocks.0.attn2.to_k.weight'.format(key_prefix)].shape + dit_config["attention_head_dim"] = shape[0] // 32 + dit_config["cross_attention_dim"] = shape[1] if metadata is not None and "config" in metadata: dit_config.update(json.loads(metadata["config"]).get("transformer", {})) return dit_config From fd08e39588b777552f88cb3800a73eb55e844ac5 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 7 May 2025 18:37:12 -0700 Subject: [PATCH 080/193] Make torchaudio not a hard requirement. (#7987) Some platforms can't install it apparently so if it's not there it should only break models that actually use it. --- comfy/ldm/ace/vae/music_dcae_pipeline.py | 7 ++++++- comfy/ldm/ace/vae/music_log_mel.py | 7 ++++++- 2 files changed, 12 insertions(+), 2 deletions(-) diff --git a/comfy/ldm/ace/vae/music_dcae_pipeline.py b/comfy/ldm/ace/vae/music_dcae_pipeline.py index 3188bc770..af81280eb 100644 --- a/comfy/ldm/ace/vae/music_dcae_pipeline.py +++ b/comfy/ldm/ace/vae/music_dcae_pipeline.py @@ -1,7 +1,12 @@ # Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_dcae_pipeline.py import torch from .autoencoder_dc import AutoencoderDC -import torchaudio +import logging +try: + import torchaudio +except: + logging.warning("torchaudio missing, ACE model will be broken") + import torchvision.transforms as transforms from .music_vocoder import ADaMoSHiFiGANV1 diff --git a/comfy/ldm/ace/vae/music_log_mel.py b/comfy/ldm/ace/vae/music_log_mel.py index d73d3f8e8..9c584eb7f 100755 --- a/comfy/ldm/ace/vae/music_log_mel.py +++ b/comfy/ldm/ace/vae/music_log_mel.py @@ -2,7 +2,12 @@ import torch import torch.nn as nn from torch import Tensor -from torchaudio.transforms import MelScale +import logging +try: + from torchaudio.transforms import MelScale +except: + logging.warning("torchaudio missing, ACE model will be broken") + import comfy.model_management class LinearSpectrogram(nn.Module): From c7c025b8d16f7f34b01409ead4dba4476cc64dae Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 7 May 2025 22:22:23 -0700 Subject: [PATCH 081/193] Adjust memory estimation code for ACE VAE. (#7990) --- comfy/sd.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/sd.py b/comfy/sd.py index 50af243ba..c6b6a3b19 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -442,7 +442,7 @@ class VAE: elif "vocoder.backbone.channel_layers.0.0.bias" in sd: #Ace Step Audio self.first_stage_model = comfy.ldm.ace.vae.music_dcae_pipeline.MusicDCAE(source_sample_rate=44100) self.memory_used_encode = lambda shape, dtype: (shape[2] * 300) * model_management.dtype_size(dtype) - self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 72000) * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 87000) * model_management.dtype_size(dtype) self.latent_channels = 8 self.output_channels = 2 # self.upscale_ratio = 2048 From 5d3cc85e13833aeb6ef9242cdae243083e30c6fc Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 8 May 2025 00:32:36 -0700 Subject: [PATCH 082/193] Make japanese hiragana and katakana characters work with ACE. (#7997) --- comfy/sd.py | 2 +- comfy/text_encoders/ace.py | 10 +- comfy/text_encoders/ace_text_cleaners.py | 125 +++++++++++++++++++++++ 3 files changed, 135 insertions(+), 2 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index c6b6a3b19..161d96f1e 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -441,7 +441,7 @@ class VAE: self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] elif "vocoder.backbone.channel_layers.0.0.bias" in sd: #Ace Step Audio self.first_stage_model = comfy.ldm.ace.vae.music_dcae_pipeline.MusicDCAE(source_sample_rate=44100) - self.memory_used_encode = lambda shape, dtype: (shape[2] * 300) * model_management.dtype_size(dtype) + self.memory_used_encode = lambda shape, dtype: (shape[2] * 330) * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 87000) * model_management.dtype_size(dtype) self.latent_channels = 8 self.output_channels = 2 diff --git a/comfy/text_encoders/ace.py b/comfy/text_encoders/ace.py index b6fe451bd..d650bb10d 100644 --- a/comfy/text_encoders/ace.py +++ b/comfy/text_encoders/ace.py @@ -7,7 +7,7 @@ import torch import logging from tokenizers import Tokenizer -from .ace_text_cleaners import multilingual_cleaners +from .ace_text_cleaners import multilingual_cleaners, japanese_to_romaji SUPPORT_LANGUAGES = { "en": 259, "de": 260, "fr": 262, "es": 284, "it": 285, @@ -65,6 +65,14 @@ class VoiceBpeTokenizer: if "spa" in lang: lang = "es" + try: + line_out = japanese_to_romaji(line) + if line_out != line: + lang = "ja" + line = line_out + except: + pass + try: if structure_pattern.match(line): token_idx = self.encode(line, "en") diff --git a/comfy/text_encoders/ace_text_cleaners.py b/comfy/text_encoders/ace_text_cleaners.py index ad3612e5f..cd31d8d8c 100644 --- a/comfy/text_encoders/ace_text_cleaners.py +++ b/comfy/text_encoders/ace_text_cleaners.py @@ -4,6 +4,131 @@ import re +def japanese_to_romaji(japanese_text): + """ + Convert Japanese hiragana and katakana to romaji (Latin alphabet representation). + + Args: + japanese_text (str): Text containing hiragana and/or katakana characters + + Returns: + str: The romaji (Latin alphabet) equivalent + """ + # Dictionary mapping kana characters to their romaji equivalents + kana_map = { + # Katakana characters + 'ア': 'a', 'イ': 'i', 'ウ': 'u', 'エ': 'e', 'オ': 'o', + 'カ': 'ka', 'キ': 'ki', 'ク': 'ku', 'ケ': 'ke', 'コ': 'ko', + 'サ': 'sa', 'シ': 'shi', 'ス': 'su', 'セ': 'se', 'ソ': 'so', + 'タ': 'ta', 'チ': 'chi', 'ツ': 'tsu', 'テ': 'te', 'ト': 'to', + 'ナ': 'na', 'ニ': 'ni', 'ヌ': 'nu', 'ネ': 'ne', 'ノ': 'no', + 'ハ': 'ha', 'ヒ': 'hi', 'フ': 'fu', 'ヘ': 'he', 'ホ': 'ho', + 'マ': 'ma', 'ミ': 'mi', 'ム': 'mu', 'メ': 'me', 'モ': 'mo', + 'ヤ': 'ya', 'ユ': 'yu', 'ヨ': 'yo', + 'ラ': 'ra', 'リ': 'ri', 'ル': 'ru', 'レ': 're', 'ロ': 'ro', + 'ワ': 'wa', 'ヲ': 'wo', 'ン': 'n', + + # Katakana voiced consonants + 'ガ': 'ga', 'ギ': 'gi', 'グ': 'gu', 'ゲ': 'ge', 'ゴ': 'go', + 'ザ': 'za', 'ジ': 'ji', 'ズ': 'zu', 'ゼ': 'ze', 'ゾ': 'zo', + 'ダ': 'da', 'ヂ': 'ji', 'ヅ': 'zu', 'デ': 'de', 'ド': 'do', + 'バ': 'ba', 'ビ': 'bi', 'ブ': 'bu', 'ベ': 'be', 'ボ': 'bo', + 'パ': 'pa', 'ピ': 'pi', 'プ': 'pu', 'ペ': 'pe', 'ポ': 'po', + + # Katakana combinations + 'キャ': 'kya', 'キュ': 'kyu', 'キョ': 'kyo', + 'シャ': 'sha', 'シュ': 'shu', 'ショ': 'sho', + 'チャ': 'cha', 'チュ': 'chu', 'チョ': 'cho', + 'ニャ': 'nya', 'ニュ': 'nyu', 'ニョ': 'nyo', + 'ヒャ': 'hya', 'ヒュ': 'hyu', 'ヒョ': 'hyo', + 'ミャ': 'mya', 'ミュ': 'myu', 'ミョ': 'myo', + 'リャ': 'rya', 'リュ': 'ryu', 'リョ': 'ryo', + 'ギャ': 'gya', 'ギュ': 'gyu', 'ギョ': 'gyo', + 'ジャ': 'ja', 'ジュ': 'ju', 'ジョ': 'jo', + 'ビャ': 'bya', 'ビュ': 'byu', 'ビョ': 'byo', + 'ピャ': 'pya', 'ピュ': 'pyu', 'ピョ': 'pyo', + + # Katakana small characters and special cases + 'ッ': '', # Small tsu (doubles the following consonant) + 'ャ': 'ya', 'ュ': 'yu', 'ョ': 'yo', + + # Katakana extras + 'ヴ': 'vu', 'ファ': 'fa', 'フィ': 'fi', 'フェ': 'fe', 'フォ': 'fo', + 'ウィ': 'wi', 'ウェ': 'we', 'ウォ': 'wo', + + # Hiragana characters + 'あ': 'a', 'い': 'i', 'う': 'u', 'え': 'e', 'お': 'o', + 'か': 'ka', 'き': 'ki', 'く': 'ku', 'け': 'ke', 'こ': 'ko', + 'さ': 'sa', 'し': 'shi', 'す': 'su', 'せ': 'se', 'そ': 'so', + 'た': 'ta', 'ち': 'chi', 'つ': 'tsu', 'て': 'te', 'と': 'to', + 'な': 'na', 'に': 'ni', 'ぬ': 'nu', 'ね': 'ne', 'の': 'no', + 'は': 'ha', 'ひ': 'hi', 'ふ': 'fu', 'へ': 'he', 'ほ': 'ho', + 'ま': 'ma', 'み': 'mi', 'む': 'mu', 'め': 'me', 'も': 'mo', + 'や': 'ya', 'ゆ': 'yu', 'よ': 'yo', + 'ら': 'ra', 'り': 'ri', 'る': 'ru', 'れ': 're', 'ろ': 'ro', + 'わ': 'wa', 'を': 'wo', 'ん': 'n', + + # Hiragana voiced consonants + 'が': 'ga', 'ぎ': 'gi', 'ぐ': 'gu', 'げ': 'ge', 'ご': 'go', + 'ざ': 'za', 'じ': 'ji', 'ず': 'zu', 'ぜ': 'ze', 'ぞ': 'zo', + 'だ': 'da', 'ぢ': 'ji', 'づ': 'zu', 'で': 'de', 'ど': 'do', + 'ば': 'ba', 'び': 'bi', 'ぶ': 'bu', 'べ': 'be', 'ぼ': 'bo', + 'ぱ': 'pa', 'ぴ': 'pi', 'ぷ': 'pu', 'ぺ': 'pe', 'ぽ': 'po', + + # Hiragana combinations + 'きゃ': 'kya', 'きゅ': 'kyu', 'きょ': 'kyo', + 'しゃ': 'sha', 'しゅ': 'shu', 'しょ': 'sho', + 'ちゃ': 'cha', 'ちゅ': 'chu', 'ちょ': 'cho', + 'にゃ': 'nya', 'にゅ': 'nyu', 'にょ': 'nyo', + 'ひゃ': 'hya', 'ひゅ': 'hyu', 'ひょ': 'hyo', + 'みゃ': 'mya', 'みゅ': 'myu', 'みょ': 'myo', + 'りゃ': 'rya', 'りゅ': 'ryu', 'りょ': 'ryo', + 'ぎゃ': 'gya', 'ぎゅ': 'gyu', 'ぎょ': 'gyo', + 'じゃ': 'ja', 'じゅ': 'ju', 'じょ': 'jo', + 'びゃ': 'bya', 'びゅ': 'byu', 'びょ': 'byo', + 'ぴゃ': 'pya', 'ぴゅ': 'pyu', 'ぴょ': 'pyo', + + # Hiragana small characters and special cases + 'っ': '', # Small tsu (doubles the following consonant) + 'ゃ': 'ya', 'ゅ': 'yu', 'ょ': 'yo', + + # Common punctuation and spaces + '　': ' ', # Japanese space + '、': ', ', '。': '. ', + } + + result = [] + i = 0 + + while i < len(japanese_text): + # Check for small tsu (doubling the following consonant) + if i < len(japanese_text) - 1 and (japanese_text[i] == 'っ' or japanese_text[i] == 'ッ'): + if i < len(japanese_text) - 1 and japanese_text[i+1] in kana_map: + next_romaji = kana_map[japanese_text[i+1]] + if next_romaji and next_romaji[0] not in 'aiueon': + result.append(next_romaji[0]) # Double the consonant + i += 1 + continue + + # Check for combinations with small ya, yu, yo + if i < len(japanese_text) - 1 and japanese_text[i+1] in ('ゃ', 'ゅ', 'ょ', 'ャ', 'ュ', 'ョ'): + combo = japanese_text[i:i+2] + if combo in kana_map: + result.append(kana_map[combo]) + i += 2 + continue + + # Regular character + if japanese_text[i] in kana_map: + result.append(kana_map[japanese_text[i]]) + else: + # If it's not in our map, keep it as is (might be kanji, romaji, etc.) + result.append(japanese_text[i]) + + i += 1 + + return ''.join(result) + def number_to_text(num, ordinal=False): """ Convert a number (int or float) to its text representation. From a692c3cca40f67ddceefaacf29dbf1bd38bdc293 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 8 May 2025 04:25:45 -0700 Subject: [PATCH 083/193] Make ACE VAE tiling work. (#8004) --- comfy/sd.py | 39 +++++++++++++++++++++++++++++++-------- 1 file changed, 31 insertions(+), 8 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index 161d96f1e..ee350d5b5 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -282,6 +282,7 @@ class VAE: self.downscale_index_formula = None self.upscale_index_formula = None + self.extra_1d_channel = None if config is None: if "decoder.mid.block_1.mix_factor" in sd: @@ -445,13 +446,14 @@ class VAE: self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 87000) * model_management.dtype_size(dtype) self.latent_channels = 8 self.output_channels = 2 - # self.upscale_ratio = 2048 - # self.downscale_ratio = 2048 + self.upscale_ratio = 4096 + self.downscale_ratio = 4096 self.latent_dim = 2 self.process_output = lambda audio: audio self.process_input = lambda audio: audio self.working_dtypes = [torch.bfloat16, torch.float32] self.disable_offload = True + self.extra_1d_channel = 16 else: logging.warning("WARNING: No VAE weights detected, VAE not initalized.") self.first_stage_model = None @@ -510,7 +512,13 @@ class VAE: return output def decode_tiled_1d(self, samples, tile_x=128, overlap=32): - decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() + if samples.ndim == 3: + decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() + else: + og_shape = samples.shape + samples = samples.reshape((og_shape[0], og_shape[1] * og_shape[2], -1)) + decode_fn = lambda a: self.first_stage_model.decode(a.reshape((-1, og_shape[1], og_shape[2], a.shape[-1])).to(self.vae_dtype).to(self.device)).float() + return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)) def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)): @@ -530,9 +538,24 @@ class VAE: samples /= 3.0 return samples - def encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048): - encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() - return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device) + def encode_tiled_1d(self, samples, tile_x=256 * 2048, overlap=64 * 2048): + if self.latent_dim == 1: + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() + out_channels = self.latent_channels + upscale_amount = 1 / self.downscale_ratio + else: + extra_channel_size = self.extra_1d_channel + out_channels = self.latent_channels * extra_channel_size + tile_x = tile_x // extra_channel_size + overlap = overlap // extra_channel_size + upscale_amount = 1 / self.downscale_ratio + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).reshape(1, out_channels, -1).float() + + out = comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=self.output_device) + if self.latent_dim == 1: + return out + else: + return out.reshape(samples.shape[0], self.latent_channels, extra_channel_size, -1) def encode_tiled_3d(self, samples, tile_t=9999, tile_x=512, tile_y=512, overlap=(1, 64, 64)): encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() @@ -557,7 +580,7 @@ class VAE: except model_management.OOM_EXCEPTION: logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") dims = samples_in.ndim - 2 - if dims == 1: + if dims == 1 or self.extra_1d_channel is not None: pixel_samples = self.decode_tiled_1d(samples_in) elif dims == 2: pixel_samples = self.decode_tiled_(samples_in) @@ -624,7 +647,7 @@ class VAE: tile = 256 overlap = tile // 4 samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap)) - elif self.latent_dim == 1: + elif self.latent_dim == 1 or self.extra_1d_channel is not None: samples = self.encode_tiled_1d(pixel_samples) else: samples = self.encode_tiled_(pixel_samples) From 02a1b01aad28470f06c8b4f95b90914413d3e4c8 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 8 May 2025 07:36:48 -0400 Subject: [PATCH 084/193] ComfyUI version 0.3.33 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 61573aead..5a73f76e4 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.32" +__version__ = "0.3.33" diff --git a/pyproject.toml b/pyproject.toml index 878e7c66a..e0be329de 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.32" +version = "0.3.33" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9" From 924d771e18000f4cb223575189daa6d2c6c5a9c1 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 8 May 2025 05:40:57 -0700 Subject: [PATCH 085/193] Add ACE Step to README. (#8005) --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 0f39cfce2..deee70c6b 100644 --- a/README.md +++ b/README.md @@ -69,9 +69,11 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith - [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/) - [Nvidia Cosmos](https://comfyanonymous.github.io/ComfyUI_examples/cosmos/) - [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/) +- Audio Models + - [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/) + - [ACE Step](https://comfyanonymous.github.io/ComfyUI_examples/audio/) - 3D Models - [Hunyuan3D 2.0](https://docs.comfy.org/tutorials/3d/hunyuan3D-2) -- [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/) - Asynchronous Queue system - Many optimizations: Only re-executes the parts of the workflow that changes between executions. - Smart memory management: can automatically run models on GPUs with as low as 1GB vram. From 8ab15c863c91bce1f9c3a32f947cb4ec659fd7fb Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 9 May 2025 01:52:47 -0700 Subject: [PATCH 086/193] Add --mmap-torch-files to enable use of mmap when loading ckpt/pt (#8021) --- comfy/cli_args.py | 2 ++ comfy/utils.py | 9 ++++++++- 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index 97b348f0d..de292d9b3 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -142,6 +142,8 @@ class PerformanceFeature(enum.Enum): parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: fp16_accumulation fp8_matrix_mult cublas_ops") +parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.") + parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.") parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.") parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).") diff --git a/comfy/utils.py b/comfy/utils.py index a826e41bf..561e1b858 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -28,6 +28,9 @@ import logging import itertools from torch.nn.functional import interpolate from einops import rearrange +from comfy.cli_args import args + +MMAP_TORCH_FILES = args.mmap_torch_files ALWAYS_SAFE_LOAD = False if hasattr(torch.serialization, "add_safe_globals"): # TODO: this was added in pytorch 2.4, the unsafe path should be removed once earlier versions are deprecated @@ -67,8 +70,12 @@ def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False): raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt/incomplete. Check the file size and make sure you have copied/downloaded it correctly.".format(message, ckpt)) raise e else: + torch_args = {} + if MMAP_TORCH_FILES: + torch_args["mmap"] = True + if safe_load or ALWAYS_SAFE_LOAD: - pl_sd = torch.load(ckpt, map_location=device, weights_only=True) + pl_sd = torch.load(ckpt, map_location=device, weights_only=True, **torch_args) else: pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle) if "global_step" in pl_sd: From 28f178a840aaa59971ecc6e0ce287bb40d275a89 Mon Sep 17 00:00:00 2001 From: thot experiment <94414189+thot-experiment@users.noreply.github.com> Date: Fri, 9 May 2025 10:46:34 -0700 Subject: [PATCH 087/193] move SVG to core (#7982) * move SVG to core * fix workflow embedding w/ unicode characters --- comfy_api_nodes/apis/recraft_api.py | 1 - comfy_api_nodes/nodes_recraft.py | 110 ++-------------------------- comfy_extras/nodes_images.py | 102 ++++++++++++++++++++++++++ 3 files changed, 107 insertions(+), 106 deletions(-) diff --git a/comfy_api_nodes/apis/recraft_api.py b/comfy_api_nodes/apis/recraft_api.py index c0ec9d0c8..c36d95f24 100644 --- a/comfy_api_nodes/apis/recraft_api.py +++ b/comfy_api_nodes/apis/recraft_api.py @@ -81,7 +81,6 @@ class RecraftStyle: class RecraftIO: STYLEV3 = "RECRAFT_V3_STYLE" - SVG = "SVG" # TODO: if acceptable, move into ComfyUI's typing class COLOR = "RECRAFT_COLOR" CONTROLS = "RECRAFT_CONTROLS" diff --git a/comfy_api_nodes/nodes_recraft.py b/comfy_api_nodes/nodes_recraft.py index 994f377d1..5c89d21e9 100644 --- a/comfy_api_nodes/nodes_recraft.py +++ b/comfy_api_nodes/nodes_recraft.py @@ -1,6 +1,7 @@ from __future__ import annotations from inspect import cleandoc from comfy.utils import ProgressBar +from comfy_extras.nodes_images import SVG # Added from comfy.comfy_types.node_typing import IO from comfy_api_nodes.apis.recraft_api import ( RecraftImageGenerationRequest, @@ -28,9 +29,6 @@ from comfy_api_nodes.apinode_utils import ( resize_mask_to_image, validate_string, ) -import folder_paths -import json -import os import torch from io import BytesIO from PIL import UnidentifiedImageError @@ -162,102 +160,6 @@ class handle_recraft_image_output: raise Exception("Received output data was not an image; likely an SVG. If you used style_id, make sure it is not a Vector art style.") -class SVG: - """ - Stores SVG representations via a list of BytesIO objects. - """ - def __init__(self, data: list[BytesIO]): - self.data = data - - def combine(self, other: SVG): - return SVG(self.data + other.data) - - @staticmethod - def combine_all(svgs: list[SVG]): - all_svgs = [] - for svg in svgs: - all_svgs.extend(svg.data) - return SVG(all_svgs) - - -class SaveSVGNode: - """ - Save SVG files on disk. - """ - - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - self.type = "output" - self.prefix_append = "" - - RETURN_TYPES = () - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "save_svg" - CATEGORY = "api node/image/Recraft" - OUTPUT_NODE = True - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "svg": (RecraftIO.SVG,), - "filename_prefix": ("STRING", {"default": "svg/ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}) - }, - "hidden": { - "prompt": "PROMPT", - "extra_pnginfo": "EXTRA_PNGINFO" - } - } - - def save_svg(self, svg: SVG, filename_prefix="svg/ComfyUI", prompt=None, extra_pnginfo=None): - filename_prefix += self.prefix_append - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) - results = list() - - # Prepare metadata JSON - metadata_dict = {} - if prompt is not None: - metadata_dict["prompt"] = prompt - if extra_pnginfo is not None: - metadata_dict.update(extra_pnginfo) - - # Convert metadata to JSON string - metadata_json = json.dumps(metadata_dict, indent=2) if metadata_dict else None - - for batch_number, svg_bytes in enumerate(svg.data): - filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) - file = f"{filename_with_batch_num}_{counter:05}_.svg" - - # Read SVG content - svg_bytes.seek(0) - svg_content = svg_bytes.read().decode('utf-8') - - # Inject metadata if available - if metadata_json: - # Create metadata element with CDATA section - metadata_element = f""" - - -""" - # Insert metadata after opening svg tag using regex - import re - svg_content = re.sub(r'(]*>)', r'\1\n' + metadata_element, svg_content) - - # Write the modified SVG to file - with open(os.path.join(full_output_folder, file), 'wb') as svg_file: - svg_file.write(svg_content.encode('utf-8')) - - results.append({ - "filename": file, - "subfolder": subfolder, - "type": self.type - }) - counter += 1 - return { "ui": { "images": results } } - - class RecraftColorRGBNode: """ Create Recraft Color by choosing specific RGB values. @@ -796,8 +698,8 @@ class RecraftTextToVectorNode: Generates SVG synchronously based on prompt and resolution. """ - RETURN_TYPES = (RecraftIO.SVG,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + RETURN_TYPES = ("SVG",) # Changed + DESCRIPTION = cleandoc(__doc__ or "") if 'cleandoc' in globals() else __doc__ # Keep cleandoc if other nodes use it FUNCTION = "api_call" API_NODE = True CATEGORY = "api node/image/Recraft" @@ -918,8 +820,8 @@ class RecraftVectorizeImageNode: Generates SVG synchronously from an input image. """ - RETURN_TYPES = (RecraftIO.SVG,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + RETURN_TYPES = ("SVG",) # Changed + DESCRIPTION = cleandoc(__doc__ or "") if 'cleandoc' in globals() else __doc__ # Keep cleandoc if other nodes use it FUNCTION = "api_call" API_NODE = True CATEGORY = "api node/image/Recraft" @@ -1193,7 +1095,6 @@ NODE_CLASS_MAPPINGS = { "RecraftStyleV3InfiniteStyleLibrary": RecraftStyleInfiniteStyleLibrary, "RecraftColorRGB": RecraftColorRGBNode, "RecraftControls": RecraftControlsNode, - "SaveSVG": SaveSVGNode, } # A dictionary that contains the friendly/humanly readable titles for the nodes @@ -1213,5 +1114,4 @@ NODE_DISPLAY_NAME_MAPPINGS = { "RecraftStyleV3InfiniteStyleLibrary": "Recraft Style - Infinite Style Library", "RecraftColorRGB": "Recraft Color RGB", "RecraftControls": "Recraft Controls", - "SaveSVG": "Save SVG", } diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py index e11a4583a..77c305619 100644 --- a/comfy_extras/nodes_images.py +++ b/comfy_extras/nodes_images.py @@ -10,6 +10,9 @@ from PIL.PngImagePlugin import PngInfo import numpy as np import json import os +import re +from io import BytesIO +from inspect import cleandoc from comfy.comfy_types import FileLocator @@ -190,10 +193,109 @@ class SaveAnimatedPNG: return { "ui": { "images": results, "animated": (True,)} } +class SVG: + """ + Stores SVG representations via a list of BytesIO objects. + """ + def __init__(self, data: list[BytesIO]): + self.data = data + + def combine(self, other: 'SVG') -> 'SVG': + return SVG(self.data + other.data) + + @staticmethod + def combine_all(svgs: list['SVG']) -> 'SVG': + all_svgs_list: list[BytesIO] = [] + for svg_item in svgs: + all_svgs_list.extend(svg_item.data) + return SVG(all_svgs_list) + +class SaveSVGNode: + """ + Save SVG files on disk. + """ + + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + RETURN_TYPES = () + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "save_svg" + CATEGORY = "image/save" # Changed + OUTPUT_NODE = True + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "svg": ("SVG",), # Changed + "filename_prefix": ("STRING", {"default": "svg/ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}) + }, + "hidden": { + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO" + } + } + + def save_svg(self, svg: SVG, filename_prefix="svg/ComfyUI", prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + results = list() + + # Prepare metadata JSON + metadata_dict = {} + if prompt is not None: + metadata_dict["prompt"] = prompt + if extra_pnginfo is not None: + metadata_dict.update(extra_pnginfo) + + # Convert metadata to JSON string + metadata_json = json.dumps(metadata_dict, indent=2) if metadata_dict else None + + for batch_number, svg_bytes in enumerate(svg.data): + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.svg" + + # Read SVG content + svg_bytes.seek(0) + svg_content = svg_bytes.read().decode('utf-8') + + # Inject metadata if available + if metadata_json: + # Create metadata element with CDATA section + metadata_element = f""" + + + """ + # Insert metadata after opening svg tag using regex with a replacement function + def replacement(match): + # match.group(1) contains the captured tag + return match.group(1) + '\n' + metadata_element + + # Apply the substitution + svg_content = re.sub(r'(]*>)', replacement, svg_content, flags=re.UNICODE) + + # Write the modified SVG to file + with open(os.path.join(full_output_folder, file), 'wb') as svg_file: + svg_file.write(svg_content.encode('utf-8')) + + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + return { "ui": { "images": results } } + NODE_CLASS_MAPPINGS = { "ImageCrop": ImageCrop, "RepeatImageBatch": RepeatImageBatch, "ImageFromBatch": ImageFromBatch, "SaveAnimatedWEBP": SaveAnimatedWEBP, "SaveAnimatedPNG": SaveAnimatedPNG, + "SaveSVGNode": SaveSVGNode, } From 42da274717ff75640e1fb50f88d5c117a9c50630 Mon Sep 17 00:00:00 2001 From: blepping <157360029+blepping@users.noreply.github.com> Date: Fri, 9 May 2025 11:51:02 -0600 Subject: [PATCH 088/193] Use normal ComfyUI attention in ACE-Steps model (#8023) * Use normal ComfyUI attention in ACE-Steps model * Let optimized_attention handle output reshape for ACE --- comfy/ldm/ace/attention.py | 15 ++++----------- 1 file changed, 4 insertions(+), 11 deletions(-) diff --git a/comfy/ldm/ace/attention.py b/comfy/ldm/ace/attention.py index 631d13647..f20a01669 100644 --- a/comfy/ldm/ace/attention.py +++ b/comfy/ldm/ace/attention.py @@ -19,6 +19,7 @@ import torch.nn.functional as F from torch import nn import comfy.model_management +from comfy.ldm.modules.attention import optimized_attention class Attention(nn.Module): def __init__( @@ -326,10 +327,6 @@ class CustomerAttnProcessor2_0: Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). """ - def __init__(self): - if not hasattr(F, "scaled_dot_product_attention"): - raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") - def apply_rotary_emb( self, x: torch.Tensor, @@ -435,13 +432,9 @@ class CustomerAttnProcessor2_0: attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - hidden_states = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - hidden_states = hidden_states.to(query.dtype) + hidden_states = optimized_attention( + query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True, + ).to(query.dtype) # linear proj hidden_states = attn.to_out[0](hidden_states) From ae60b150e577de470032840ed7194889686fa424 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Fri, 9 May 2025 17:02:45 -0700 Subject: [PATCH 089/193] update node tooltips and validation (#8036) --- comfy_api_nodes/nodes_ideogram.py | 13 +++--------- comfy_api_nodes/nodes_kling.py | 35 +++++++++++++++++++++++++++++-- 2 files changed, 36 insertions(+), 12 deletions(-) diff --git a/comfy_api_nodes/nodes_ideogram.py b/comfy_api_nodes/nodes_ideogram.py index 45c021f4a..0a16d74bf 100644 --- a/comfy_api_nodes/nodes_ideogram.py +++ b/comfy_api_nodes/nodes_ideogram.py @@ -234,9 +234,7 @@ def download_and_process_images(image_urls): class IdeogramV1(ComfyNodeABC): """ - Generates images synchronously using the Ideogram V1 model. - - Images links are available for a limited period of time; if you would like to keep the image, you must download it. + Generates images using the Ideogram V1 model. """ def __init__(self): @@ -365,9 +363,7 @@ class IdeogramV1(ComfyNodeABC): class IdeogramV2(ComfyNodeABC): """ - Generates images synchronously using the Ideogram V2 model. - - Images links are available for a limited period of time; if you would like to keep the image, you must download it. + Generates images using the Ideogram V2 model. """ def __init__(self): @@ -536,10 +532,7 @@ class IdeogramV2(ComfyNodeABC): class IdeogramV3(ComfyNodeABC): """ - Generates images synchronously using the Ideogram V3 model. - - Supports both regular image generation from text prompts and image editing with mask. - Images links are available for a limited period of time; if you would like to keep the image, you must download it. + Generates images using the Ideogram V3 model. Supports both regular image generation from text prompts and image editing with mask. """ def __init__(self): diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py index 9aa8df58b..c8d1704c1 100644 --- a/comfy_api_nodes/nodes_kling.py +++ b/comfy_api_nodes/nodes_kling.py @@ -184,6 +184,33 @@ def validate_image_result_response(response) -> None: raise KlingApiError(error_msg) +def validate_input_image(image: torch.Tensor) -> None: + """ + Validates the input image adheres to the expectations of the Kling API: + - The image resolution should not be less than 300*300px + - The aspect ratio of the image should be between 1:2.5 ~ 2.5:1 + + See: https://app.klingai.com/global/dev/document-api/apiReference/model/imageToVideo + """ + if len(image.shape) == 4: + height, width = image.shape[1], image.shape[2] + elif len(image.shape) == 3: + height, width = image.shape[0], image.shape[1] + else: + raise ValueError("Invalid image tensor shape.") + + # Ensure minimum resolution is met + if height < 300: + raise ValueError("Image height must be at least 300px") + if width < 300: + raise ValueError("Image width must be at least 300px") + + # Ensure aspect ratio is within acceptable range + aspect_ratio = width / height + if aspect_ratio < 1 / 2.5 or aspect_ratio > 2.5: + raise ValueError("Image aspect ratio must be between 1:2.5 and 2.5:1") + + def get_camera_control_input_config( tooltip: str, default: float = 0.0 ) -> tuple[IO, InputTypeOptions]: @@ -530,7 +557,10 @@ class KlingImage2VideoNode(KlingNodeBase): return { "required": { "start_frame": model_field_to_node_input( - IO.IMAGE, KlingImage2VideoRequest, "image" + IO.IMAGE, + KlingImage2VideoRequest, + "image", + tooltip="The reference image used to generate the video.", ), "prompt": model_field_to_node_input( IO.STRING, KlingImage2VideoRequest, "prompt", multiline=True @@ -607,9 +637,10 @@ class KlingImage2VideoNode(KlingNodeBase): auth_token: Optional[str] = None, ) -> tuple[VideoFromFile]: validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_I2V) + validate_input_image(start_frame) if camera_control is not None: - # Camera control type for image 2 video is always simple + # Camera control type for image 2 video is always `simple` camera_control.type = KlingCameraControlType.simple initial_operation = SynchronousOperation( From 1b3bf0a5dac887ec651df8e326bd260e17e56909 Mon Sep 17 00:00:00 2001 From: Pam <42671363+pamparamm@users.noreply.github.com> Date: Sat, 10 May 2025 05:14:13 +0500 Subject: [PATCH 090/193] Fix res_multistep_ancestral sampler (#8030) --- comfy/k_diffusion/sampling.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py index 77ef748e8..fbdf6f554 100644 --- a/comfy/k_diffusion/sampling.py +++ b/comfy/k_diffusion/sampling.py @@ -1277,6 +1277,7 @@ def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None phi1_fn = lambda t: torch.expm1(t) / t phi2_fn = lambda t: (phi1_fn(t) - 1.0) / t + old_sigma_down = None old_denoised = None uncond_denoised = None def post_cfg_function(args): @@ -1304,9 +1305,9 @@ def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None x = x + d * dt else: # Second order multistep method in https://arxiv.org/pdf/2308.02157 - t, t_next, t_prev = t_fn(sigmas[i]), t_fn(sigma_down), t_fn(sigmas[i - 1]) + t, t_old, t_next, t_prev = t_fn(sigmas[i]), t_fn(old_sigma_down), t_fn(sigma_down), t_fn(sigmas[i - 1]) h = t_next - t - c2 = (t_prev - t) / h + c2 = (t_prev - t_old) / h phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h) b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0) @@ -1326,6 +1327,7 @@ def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None old_denoised = uncond_denoised else: old_denoised = denoised + old_sigma_down = sigma_down return x @torch.no_grad() From d42613686f3db1bf55e6dec75434ed2649baa6bc Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 10 May 2025 04:52:56 -0700 Subject: [PATCH 091/193] Fix issue with fp8 ops on some models. (#8045) _scaled_mm errors when an input is non contiguous. --- comfy/ops.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy/ops.py b/comfy/ops.py index 032787915..431c8f89d 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -308,10 +308,10 @@ def fp8_linear(self, input): if scale_input is None: scale_input = torch.ones((), device=input.device, dtype=torch.float32) input = torch.clamp(input, min=-448, max=448, out=input) - input = input.reshape(-1, input_shape[2]).to(dtype) + input = input.reshape(-1, input_shape[2]).to(dtype).contiguous() else: scale_input = scale_input.to(input.device) - input = (input * (1.0 / scale_input).to(input_dtype)).reshape(-1, input_shape[2]).to(dtype) + input = (input * (1.0 / scale_input).to(input_dtype)).reshape(-1, input_shape[2]).to(dtype).contiguous() if bias is not None: o = torch._scaled_mm(input, w, out_dtype=input_dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight) From 235d3901fc3f97698e97ff52f61a7caa5f1f11ed Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Sat, 10 May 2025 17:40:02 -0700 Subject: [PATCH 092/193] Add method to stream text to node UI (#8018) * show text progress preview * include node id in message --- server.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/server.py b/server.py index f64ec27d4..cb1c6a8fd 100644 --- a/server.py +++ b/server.py @@ -32,12 +32,13 @@ from app.frontend_management import FrontendManager from app.user_manager import UserManager from app.model_manager import ModelFileManager from app.custom_node_manager import CustomNodeManager -from typing import Optional +from typing import Optional, Union from api_server.routes.internal.internal_routes import InternalRoutes class BinaryEventTypes: PREVIEW_IMAGE = 1 UNENCODED_PREVIEW_IMAGE = 2 + TEXT = 3 async def send_socket_catch_exception(function, message): try: @@ -878,3 +879,15 @@ class PromptServer(): logging.warning(traceback.format_exc()) return json_data + + def send_progress_text( + self, text: Union[bytes, bytearray, str], node_id: str, sid=None + ): + if isinstance(text, str): + text = text.encode("utf-8") + node_id_bytes = str(node_id).encode("utf-8") + + # Pack the node_id length as a 4-byte unsigned integer, followed by the node_id bytes + message = struct.pack(">I", len(node_id_bytes)) + node_id_bytes + text + + self.send_sync(BinaryEventTypes.TEXT, message, sid) From 3535909eb8581ba3e4461f3c9723d244c446f65e Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Sat, 10 May 2025 19:10:58 -0700 Subject: [PATCH 093/193] Add support for Comfy API keys (#8041) * Handle Comfy API key based authorizaton (#167) Co-authored-by: Jedrzej Kosinski * Bump frontend version to include API key features (#170) * bump templates version --------- Co-authored-by: Jedrzej Kosinski --- comfy_api_nodes/apinode_utils.py | 25 ++--- comfy_api_nodes/apis/client.py | 43 +++++--- comfy_api_nodes/nodes_bfl.py | 24 ++--- comfy_api_nodes/nodes_ideogram.py | 29 +++-- comfy_api_nodes/nodes_kling.py | 166 ++++++++++++++++++----------- comfy_api_nodes/nodes_luma.py | 50 ++++----- comfy_api_nodes/nodes_minimax.py | 15 +-- comfy_api_nodes/nodes_openai.py | 27 +++-- comfy_api_nodes/nodes_pika.py | 60 ++++++----- comfy_api_nodes/nodes_pixverse.py | 28 ++--- comfy_api_nodes/nodes_recraft.py | 36 +++---- comfy_api_nodes/nodes_stability.py | 27 +++-- comfy_api_nodes/nodes_veo2.py | 7 +- execution.py | 2 + requirements.txt | 4 +- 15 files changed, 319 insertions(+), 224 deletions(-) diff --git a/comfy_api_nodes/apinode_utils.py b/comfy_api_nodes/apinode_utils.py index bd3b8908b..e28d7d607 100644 --- a/comfy_api_nodes/apinode_utils.py +++ b/comfy_api_nodes/apinode_utils.py @@ -1,3 +1,4 @@ +from __future__ import annotations import io import logging from typing import Optional @@ -314,7 +315,7 @@ def upload_file_to_comfyapi( file_bytes_io: BytesIO, filename: str, upload_mime_type: str, - auth_token: Optional[str] = None, + auth_kwargs: Optional[dict[str,str]] = None, ) -> str: """ Uploads a single file to ComfyUI API and returns its download URL. @@ -323,7 +324,7 @@ def upload_file_to_comfyapi( file_bytes_io: BytesIO object containing the file data. filename: The filename of the file. upload_mime_type: MIME type of the file. - auth_token: Optional authentication token. + auth_kwargs: Optional authentication token(s). Returns: The download URL for the uploaded file. @@ -337,7 +338,7 @@ def upload_file_to_comfyapi( response_model=UploadResponse, ), request=request_object, - auth_token=auth_token, + auth_kwargs=auth_kwargs, ) response: UploadResponse = operation.execute() @@ -351,7 +352,7 @@ def upload_file_to_comfyapi( def upload_video_to_comfyapi( video: VideoInput, - auth_token: Optional[str] = None, + auth_kwargs: Optional[dict[str,str]] = None, container: VideoContainer = VideoContainer.MP4, codec: VideoCodec = VideoCodec.H264, max_duration: Optional[int] = None, @@ -362,7 +363,7 @@ def upload_video_to_comfyapi( Args: video: VideoInput object (Comfy VIDEO type). - auth_token: Optional authentication token. + auth_kwargs: Optional authentication token(s). container: The video container format to use (default: MP4). codec: The video codec to use (default: H264). max_duration: Optional maximum duration of the video in seconds. If the video is longer than this, an error will be raised. @@ -390,7 +391,7 @@ def upload_video_to_comfyapi( video_bytes_io.seek(0) return upload_file_to_comfyapi( - video_bytes_io, filename, upload_mime_type, auth_token + video_bytes_io, filename, upload_mime_type, auth_kwargs ) @@ -453,7 +454,7 @@ def audio_ndarray_to_bytesio( def upload_audio_to_comfyapi( audio: AudioInput, - auth_token: Optional[str] = None, + auth_kwargs: Optional[dict[str,str]] = None, container_format: str = "mp4", codec_name: str = "aac", mime_type: str = "audio/mp4", @@ -465,7 +466,7 @@ def upload_audio_to_comfyapi( Args: audio: a Comfy `AUDIO` type (contains waveform tensor and sample_rate) - auth_token: Optional authentication token. + auth_kwargs: Optional authentication token(s). Returns: The download URL for the uploaded audio file. @@ -477,11 +478,11 @@ def upload_audio_to_comfyapi( audio_data_np, sample_rate, container_format, codec_name ) - return upload_file_to_comfyapi(audio_bytes_io, filename, mime_type, auth_token) + return upload_file_to_comfyapi(audio_bytes_io, filename, mime_type, auth_kwargs) def upload_images_to_comfyapi( - image: torch.Tensor, max_images=8, auth_token=None, mime_type: Optional[str] = None + image: torch.Tensor, max_images=8, auth_kwargs: Optional[dict[str,str]] = None, mime_type: Optional[str] = None ) -> list[str]: """ Uploads images to ComfyUI API and returns download URLs. @@ -490,7 +491,7 @@ def upload_images_to_comfyapi( Args: image: Input torch.Tensor image. max_images: Maximum number of images to upload. - auth_token: Optional authentication token. + auth_kwargs: Optional authentication token(s). mime_type: Optional MIME type for the image. """ # if batch, try to upload each file if max_images is greater than 0 @@ -521,7 +522,7 @@ def upload_images_to_comfyapi( response_model=UploadResponse, ), request=request_object, - auth_token=auth_token, + auth_kwargs=auth_kwargs, ) response = operation.execute() diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py index 929e386d4..cff52714f 100644 --- a/comfy_api_nodes/apis/client.py +++ b/comfy_api_nodes/apis/client.py @@ -20,7 +20,8 @@ Usage Examples: # 1. Create the API client api_client = ApiClient( base_url="https://api.example.com", - api_key="your_api_key_here", + auth_token="your_auth_token_here", + comfy_api_key="your_comfy_api_key_here", timeout=30.0, verify_ssl=True ) @@ -146,12 +147,14 @@ class ApiClient: def __init__( self, base_url: str, - api_key: Optional[str] = None, + auth_token: Optional[str] = None, + comfy_api_key: Optional[str] = None, timeout: float = 3600.0, verify_ssl: bool = True, ): self.base_url = base_url - self.api_key = api_key + self.auth_token = auth_token + self.comfy_api_key = comfy_api_key self.timeout = timeout self.verify_ssl = verify_ssl @@ -201,8 +204,10 @@ class ApiClient: """Get headers for API requests, including authentication if available""" headers = {"Content-Type": "application/json", "Accept": "application/json"} - if self.api_key: - headers["Authorization"] = f"Bearer {self.api_key}" + if self.auth_token: + headers["Authorization"] = f"Bearer {self.auth_token}" + elif self.comfy_api_key: + headers["X-API-KEY"] = self.comfy_api_key return headers @@ -236,7 +241,7 @@ class ApiClient: requests.RequestException: If the request fails """ url = urljoin(self.base_url, path) - self.check_auth_token(self.api_key) + self.check_auth(self.auth_token, self.comfy_api_key) # Combine default headers with any provided headers request_headers = self.get_headers() if headers: @@ -320,11 +325,11 @@ class ApiClient: return response.json() return {} - def check_auth_token(self, auth_token): - """Verify that an auth token is present.""" - if auth_token is None: + def check_auth(self, auth_token, comfy_api_key): + """Verify that an auth token is present or comfy_api_key is present""" + if auth_token is None and comfy_api_key is None: raise Exception("Unauthorized: Please login first to use this node.") - return auth_token + return auth_token or comfy_api_key @staticmethod def upload_file( @@ -392,6 +397,8 @@ class SynchronousOperation(Generic[T, R]): files: Optional[Dict[str, Any]] = None, api_base: str | None = None, auth_token: Optional[str] = None, + comfy_api_key: Optional[str] = None, + auth_kwargs: Optional[Dict[str,str]] = None, timeout: float = 604800.0, verify_ssl: bool = True, content_type: str = "application/json", @@ -403,6 +410,10 @@ class SynchronousOperation(Generic[T, R]): self.error = None self.api_base: str = api_base or args.comfy_api_base self.auth_token = auth_token + self.comfy_api_key = comfy_api_key + if auth_kwargs is not None: + self.auth_token = auth_kwargs.get("auth_token", self.auth_token) + self.comfy_api_key = auth_kwargs.get("comfy_api_key", self.comfy_api_key) self.timeout = timeout self.verify_ssl = verify_ssl self.files = files @@ -415,7 +426,8 @@ class SynchronousOperation(Generic[T, R]): if client is None: client = ApiClient( base_url=self.api_base, - api_key=self.auth_token, + auth_token=self.auth_token, + comfy_api_key=self.comfy_api_key, timeout=self.timeout, verify_ssl=self.verify_ssl, ) @@ -502,12 +514,18 @@ class PollingOperation(Generic[T, R]): request: Optional[T] = None, api_base: str | None = None, auth_token: Optional[str] = None, + comfy_api_key: Optional[str] = None, + auth_kwargs: Optional[Dict[str,str]] = None, poll_interval: float = 5.0, ): self.poll_endpoint = poll_endpoint self.request = request self.api_base: str = api_base or args.comfy_api_base self.auth_token = auth_token + self.comfy_api_key = comfy_api_key + if auth_kwargs is not None: + self.auth_token = auth_kwargs.get("auth_token", self.auth_token) + self.comfy_api_key = auth_kwargs.get("comfy_api_key", self.comfy_api_key) self.poll_interval = poll_interval # Polling configuration @@ -528,7 +546,8 @@ class PollingOperation(Generic[T, R]): if client is None: client = ApiClient( base_url=self.api_base, - api_key=self.auth_token, + auth_token=self.auth_token, + comfy_api_key=self.comfy_api_key, ) return self._poll_until_complete(client) except Exception as e: diff --git a/comfy_api_nodes/nodes_bfl.py b/comfy_api_nodes/nodes_bfl.py index 122a6ddf8..66ef1b391 100644 --- a/comfy_api_nodes/nodes_bfl.py +++ b/comfy_api_nodes/nodes_bfl.py @@ -179,6 +179,7 @@ class FluxProUltraImageNode(ComfyNodeABC): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -211,7 +212,6 @@ class FluxProUltraImageNode(ComfyNodeABC): seed=0, image_prompt=None, image_prompt_strength=0.1, - auth_token=None, **kwargs, ): if image_prompt is None: @@ -244,7 +244,7 @@ class FluxProUltraImageNode(ComfyNodeABC): None if image_prompt is None else round(image_prompt_strength, 2) ), ), - auth_token=auth_token, + auth_kwargs=kwargs, ) output_image = handle_bfl_synchronous_operation(operation) return (output_image,) @@ -319,6 +319,7 @@ class FluxProImageNode(ComfyNodeABC): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -337,7 +338,6 @@ class FluxProImageNode(ComfyNodeABC): seed=0, image_prompt=None, # image_prompt_strength=0.1, - auth_token=None, **kwargs, ): image_prompt = ( @@ -361,7 +361,7 @@ class FluxProImageNode(ComfyNodeABC): seed=seed, image_prompt=image_prompt, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) output_image = handle_bfl_synchronous_operation(operation) return (output_image,) @@ -461,6 +461,7 @@ class FluxProExpandNode(ComfyNodeABC): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -482,7 +483,6 @@ class FluxProExpandNode(ComfyNodeABC): steps: int, guidance: float, seed=0, - auth_token=None, **kwargs, ): image = convert_image_to_base64(image) @@ -506,7 +506,7 @@ class FluxProExpandNode(ComfyNodeABC): seed=seed, image=image, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) output_image = handle_bfl_synchronous_operation(operation) return (output_image,) @@ -572,6 +572,7 @@ class FluxProFillNode(ComfyNodeABC): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -590,7 +591,6 @@ class FluxProFillNode(ComfyNodeABC): steps: int, guidance: float, seed=0, - auth_token=None, **kwargs, ): # prepare mask @@ -615,7 +615,7 @@ class FluxProFillNode(ComfyNodeABC): image=image, mask=mask, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) output_image = handle_bfl_synchronous_operation(operation) return (output_image,) @@ -706,6 +706,7 @@ class FluxProCannyNode(ComfyNodeABC): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -726,7 +727,6 @@ class FluxProCannyNode(ComfyNodeABC): steps: int, guidance: float, seed=0, - auth_token=None, **kwargs, ): control_image = convert_image_to_base64(control_image[:,:,:,:3]) @@ -763,7 +763,7 @@ class FluxProCannyNode(ComfyNodeABC): canny_high_threshold=canny_high_threshold, preprocessed_image=preprocessed_image, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) output_image = handle_bfl_synchronous_operation(operation) return (output_image,) @@ -834,6 +834,7 @@ class FluxProDepthNode(ComfyNodeABC): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -852,7 +853,6 @@ class FluxProDepthNode(ComfyNodeABC): steps: int, guidance: float, seed=0, - auth_token=None, **kwargs, ): control_image = convert_image_to_base64(control_image[:,:,:,:3]) @@ -878,7 +878,7 @@ class FluxProDepthNode(ComfyNodeABC): control_image=control_image, preprocessed_image=preprocessed_image, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) output_image = handle_bfl_synchronous_operation(operation) return (output_image,) diff --git a/comfy_api_nodes/nodes_ideogram.py b/comfy_api_nodes/nodes_ideogram.py index 0a16d74bf..d25468b17 100644 --- a/comfy_api_nodes/nodes_ideogram.py +++ b/comfy_api_nodes/nodes_ideogram.py @@ -301,7 +301,10 @@ class IdeogramV1(ComfyNodeABC): {"default": 1, "min": 1, "max": 8, "step": 1, "display": "number"}, ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } RETURN_TYPES = (IO.IMAGE,) @@ -319,7 +322,7 @@ class IdeogramV1(ComfyNodeABC): seed=0, negative_prompt="", num_images=1, - auth_token=None, + **kwargs, ): # Determine the model based on turbo setting aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None) @@ -345,7 +348,7 @@ class IdeogramV1(ComfyNodeABC): negative_prompt=negative_prompt if negative_prompt else None, ) ), - auth_token=auth_token, + auth_kwargs=kwargs, ) response = operation.execute() @@ -454,7 +457,10 @@ class IdeogramV2(ComfyNodeABC): # }, #), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } RETURN_TYPES = (IO.IMAGE,) @@ -475,7 +481,7 @@ class IdeogramV2(ComfyNodeABC): negative_prompt="", num_images=1, color_palette="", - auth_token=None, + **kwargs, ): aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None) resolution = V1_V1_RES_MAP.get(resolution, None) @@ -515,7 +521,7 @@ class IdeogramV2(ComfyNodeABC): color_palette=color_palette if color_palette else None, ) ), - auth_token=auth_token, + auth_kwargs=kwargs, ) response = operation.execute() @@ -614,7 +620,10 @@ class IdeogramV3(ComfyNodeABC): }, ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } RETURN_TYPES = (IO.IMAGE,) @@ -634,7 +643,7 @@ class IdeogramV3(ComfyNodeABC): seed=0, num_images=1, rendering_speed="BALANCED", - auth_token=None, + **kwargs, ): # Check if both image and mask are provided for editing mode if image is not None and mask is not None: @@ -698,7 +707,7 @@ class IdeogramV3(ComfyNodeABC): "mask": mask_binary, }, content_type="multipart/form-data", - auth_token=auth_token, + auth_kwargs=kwargs, ) elif image is not None or mask is not None: @@ -739,7 +748,7 @@ class IdeogramV3(ComfyNodeABC): response_model=IdeogramGenerateResponse, ), request=gen_request, - auth_token=auth_token, + auth_kwargs=kwargs, ) # Execute the operation and process response diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py index c8d1704c1..b2be83656 100644 --- a/comfy_api_nodes/nodes_kling.py +++ b/comfy_api_nodes/nodes_kling.py @@ -95,7 +95,7 @@ class KlingApiError(Exception): pass -def poll_until_finished(auth_token: str, api_endpoint: ApiEndpoint[Any, R]) -> R: +def poll_until_finished(auth_kwargs: dict[str,str], api_endpoint: ApiEndpoint[Any, R]) -> R: """Polls the Kling API endpoint until the task reaches a terminal state, then returns the response.""" return PollingOperation( poll_endpoint=api_endpoint, @@ -108,7 +108,7 @@ def poll_until_finished(auth_token: str, api_endpoint: ApiEndpoint[Any, R]) -> R if response.data and response.data.task_status else None ), - auth_token=auth_token, + auth_kwargs=auth_kwargs, ).execute() @@ -418,16 +418,19 @@ class KlingTextToVideoNode(KlingNodeBase): }, ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } RETURN_TYPES = ("VIDEO", "STRING", "STRING") RETURN_NAMES = ("VIDEO", "video_id", "duration") DESCRIPTION = "Kling Text to Video Node" - def get_response(self, task_id: str, auth_token: str) -> KlingText2VideoResponse: + def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingText2VideoResponse: return poll_until_finished( - auth_token, + auth_kwargs, ApiEndpoint( path=f"{PATH_TEXT_TO_VIDEO}/{task_id}", method=HttpMethod.GET, @@ -446,7 +449,7 @@ class KlingTextToVideoNode(KlingNodeBase): camera_control: Optional[KlingCameraControl] = None, model_name: Optional[str] = None, duration: Optional[str] = None, - auth_token: Optional[str] = None, + **kwargs, ) -> tuple[VideoFromFile, str, str]: validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V) if model_name is None: @@ -468,14 +471,14 @@ class KlingTextToVideoNode(KlingNodeBase): aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), camera_control=camera_control, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) task_creation_response = initial_operation.execute() validate_task_creation_response(task_creation_response) task_id = task_creation_response.data.task_id - final_response = self.get_response(task_id, auth_token) + final_response = self.get_response(task_id, auth_kwargs=kwargs) validate_video_result_response(final_response) video = get_video_from_response(final_response) @@ -522,7 +525,10 @@ class KlingCameraControlT2VNode(KlingTextToVideoNode): }, ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } DESCRIPTION = "Transform text into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original text." @@ -534,7 +540,7 @@ class KlingCameraControlT2VNode(KlingTextToVideoNode): cfg_scale: float, aspect_ratio: str, camera_control: Optional[KlingCameraControl] = None, - auth_token: Optional[str] = None, + **kwargs, ): return super().api_call( model_name=KlingVideoGenModelName.kling_v1, @@ -545,7 +551,7 @@ class KlingCameraControlT2VNode(KlingTextToVideoNode): prompt=prompt, negative_prompt=negative_prompt, camera_control=camera_control, - auth_token=auth_token, + **kwargs, ) @@ -604,16 +610,19 @@ class KlingImage2VideoNode(KlingNodeBase): enum_type=KlingVideoGenDuration, ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } RETURN_TYPES = ("VIDEO", "STRING", "STRING") RETURN_NAMES = ("VIDEO", "video_id", "duration") DESCRIPTION = "Kling Image to Video Node" - def get_response(self, task_id: str, auth_token: str) -> KlingImage2VideoResponse: + def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingImage2VideoResponse: return poll_until_finished( - auth_token, + auth_kwargs, ApiEndpoint( path=f"{PATH_IMAGE_TO_VIDEO}/{task_id}", method=HttpMethod.GET, @@ -634,7 +643,7 @@ class KlingImage2VideoNode(KlingNodeBase): duration: str, camera_control: Optional[KlingCameraControl] = None, end_frame: Optional[torch.Tensor] = None, - auth_token: Optional[str] = None, + **kwargs, ) -> tuple[VideoFromFile]: validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_I2V) validate_input_image(start_frame) @@ -666,14 +675,14 @@ class KlingImage2VideoNode(KlingNodeBase): duration=KlingVideoGenDuration(duration), camera_control=camera_control, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) task_creation_response = initial_operation.execute() validate_task_creation_response(task_creation_response) task_id = task_creation_response.data.task_id - final_response = self.get_response(task_id, auth_token) + final_response = self.get_response(task_id, auth_kwargs=kwargs) validate_video_result_response(final_response) video = get_video_from_response(final_response) @@ -723,7 +732,10 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode): }, ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } DESCRIPTION = "Transform still images into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original image." @@ -736,7 +748,7 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode): cfg_scale: float, aspect_ratio: str, camera_control: KlingCameraControl, - auth_token: Optional[str] = None, + **kwargs, ): return super().api_call( model_name=KlingVideoGenModelName.kling_v1_5, @@ -748,7 +760,7 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode): prompt=prompt, negative_prompt=negative_prompt, camera_control=camera_control, - auth_token=auth_token, + **kwargs, ) @@ -816,7 +828,10 @@ class KlingStartEndFrameNode(KlingImage2VideoNode): }, ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } DESCRIPTION = "Generate a video sequence that transitions between your provided start and end images. The node creates all frames in between, producing a smooth transformation from the first frame to the last." @@ -830,7 +845,7 @@ class KlingStartEndFrameNode(KlingImage2VideoNode): cfg_scale: float, aspect_ratio: str, mode: str, - auth_token: Optional[str] = None, + **kwargs, ): mode, duration, model_name = KlingStartEndFrameNode.get_mode_string_mapping()[ mode @@ -845,7 +860,7 @@ class KlingStartEndFrameNode(KlingImage2VideoNode): aspect_ratio=aspect_ratio, duration=duration, end_frame=end_frame, - auth_token=auth_token, + **kwargs, ) @@ -875,16 +890,19 @@ class KlingVideoExtendNode(KlingNodeBase): IO.STRING, KlingVideoExtendRequest, "video_id", forceInput=True ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } RETURN_TYPES = ("VIDEO", "STRING", "STRING") RETURN_NAMES = ("VIDEO", "video_id", "duration") DESCRIPTION = "Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes." - def get_response(self, task_id: str, auth_token: str) -> KlingVideoExtendResponse: + def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingVideoExtendResponse: return poll_until_finished( - auth_token, + auth_kwargs, ApiEndpoint( path=f"{PATH_VIDEO_EXTEND}/{task_id}", method=HttpMethod.GET, @@ -899,7 +917,7 @@ class KlingVideoExtendNode(KlingNodeBase): negative_prompt: str, cfg_scale: float, video_id: str, - auth_token: Optional[str] = None, + **kwargs, ) -> tuple[VideoFromFile, str, str]: validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V) initial_operation = SynchronousOperation( @@ -915,14 +933,14 @@ class KlingVideoExtendNode(KlingNodeBase): cfg_scale=cfg_scale, video_id=video_id, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) task_creation_response = initial_operation.execute() validate_task_creation_response(task_creation_response) task_id = task_creation_response.data.task_id - final_response = self.get_response(task_id, auth_token) + final_response = self.get_response(task_id, auth_kwargs=kwargs) validate_video_result_response(final_response) video = get_video_from_response(final_response) @@ -935,9 +953,9 @@ class KlingVideoEffectsBase(KlingNodeBase): RETURN_TYPES = ("VIDEO", "STRING", "STRING") RETURN_NAMES = ("VIDEO", "video_id", "duration") - def get_response(self, task_id: str, auth_token: str) -> KlingVideoEffectsResponse: + def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingVideoEffectsResponse: return poll_until_finished( - auth_token, + auth_kwargs, ApiEndpoint( path=f"{PATH_VIDEO_EFFECTS}/{task_id}", method=HttpMethod.GET, @@ -955,7 +973,7 @@ class KlingVideoEffectsBase(KlingNodeBase): image_1: torch.Tensor, image_2: Optional[torch.Tensor] = None, mode: Optional[KlingVideoGenMode] = None, - auth_token: Optional[str] = None, + **kwargs, ): if dual_character: request_input_field = KlingDualCharacterEffectInput( @@ -985,14 +1003,14 @@ class KlingVideoEffectsBase(KlingNodeBase): effect_scene=effect_scene, input=request_input_field, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) task_creation_response = initial_operation.execute() validate_task_creation_response(task_creation_response) task_id = task_creation_response.data.task_id - final_response = self.get_response(task_id, auth_token) + final_response = self.get_response(task_id, auth_kwargs=kwargs) validate_video_result_response(final_response) video = get_video_from_response(final_response) @@ -1033,7 +1051,10 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase): enum_type=KlingVideoGenDuration, ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } DESCRIPTION = "Achieve different special effects when generating a video based on the effect_scene. First image will be positioned on left side, second on right side of the composite." @@ -1048,7 +1069,7 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase): model_name: KlingCharacterEffectModelName, mode: KlingVideoGenMode, duration: KlingVideoGenDuration, - auth_token: Optional[str] = None, + **kwargs, ): video, _, duration = super().api_call( dual_character=True, @@ -1058,7 +1079,7 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase): duration=duration, image_1=image_left, image_2=image_right, - auth_token=auth_token, + **kwargs, ) return video, duration @@ -1094,7 +1115,10 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase): enum_type=KlingVideoGenDuration, ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } DESCRIPTION = "Achieve different special effects when generating a video based on the effect_scene." @@ -1105,7 +1129,7 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase): effect_scene: KlingSingleImageEffectsScene, model_name: KlingSingleImageEffectModelName, duration: KlingVideoGenDuration, - auth_token: Optional[str] = None, + **kwargs, ): return super().api_call( dual_character=False, @@ -1113,7 +1137,7 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase): model_name=model_name, duration=duration, image_1=image, - auth_token=auth_token, + **kwargs, ) @@ -1131,10 +1155,10 @@ class KlingLipSyncBase(KlingNodeBase): f"Text is too long. Maximum length is {MAX_PROMPT_LENGTH_LIP_SYNC} characters." ) - def get_response(self, task_id: str, auth_token: str) -> KlingLipSyncResponse: + def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingLipSyncResponse: """Polls the Kling API endpoint until the task reaches a terminal state.""" return poll_until_finished( - auth_token, + auth_kwargs, ApiEndpoint( path=f"{PATH_LIP_SYNC}/{task_id}", method=HttpMethod.GET, @@ -1152,18 +1176,18 @@ class KlingLipSyncBase(KlingNodeBase): text: Optional[str] = None, voice_speed: Optional[float] = None, voice_id: Optional[str] = None, - auth_token: Optional[str] = None, + **kwargs ) -> tuple[VideoFromFile, str, str]: if text: self.validate_text(text) # Upload video to Comfy API and get download URL - video_url = upload_video_to_comfyapi(video, auth_token) + video_url = upload_video_to_comfyapi(video, auth_kwargs=kwargs) logging.info("Uploaded video to Comfy API. URL: %s", video_url) # Upload the audio file to Comfy API and get download URL if audio: - audio_url = upload_audio_to_comfyapi(audio, auth_token) + audio_url = upload_audio_to_comfyapi(audio, auth_kwargs=kwargs) logging.info("Uploaded audio to Comfy API. URL: %s", audio_url) else: audio_url = None @@ -1187,14 +1211,14 @@ class KlingLipSyncBase(KlingNodeBase): voice_id=voice_id, ), ), - auth_token=auth_token, + auth_kwargs=kwargs, ) task_creation_response = initial_operation.execute() validate_task_creation_response(task_creation_response) task_id = task_creation_response.data.task_id - final_response = self.get_response(task_id, auth_token) + final_response = self.get_response(task_id, auth_kwargs=kwargs) validate_video_result_response(final_response) video = get_video_from_response(final_response) @@ -1217,7 +1241,10 @@ class KlingLipSyncAudioToVideoNode(KlingLipSyncBase): enum_type=KlingLipSyncVoiceLanguage, ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } DESCRIPTION = "Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file." @@ -1227,14 +1254,14 @@ class KlingLipSyncAudioToVideoNode(KlingLipSyncBase): video: VideoInput, audio: AudioInput, voice_language: str, - auth_token: Optional[str] = None, + **kwargs, ): return super().api_call( video=video, audio=audio, voice_language=voice_language, mode="audio2video", - auth_token=auth_token, + **kwargs, ) @@ -1323,7 +1350,10 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase): IO.FLOAT, KlingLipSyncInputObject, "voice_speed", slider=True ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } DESCRIPTION = "Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt." @@ -1334,7 +1364,7 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase): text: str, voice: str, voice_speed: float, - auth_token: Optional[str] = None, + **kwargs, ): voice_id, voice_language = KlingLipSyncTextToVideoNode.get_voice_config()[voice] return super().api_call( @@ -1344,7 +1374,7 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase): voice_id=voice_id, voice_speed=voice_speed, mode="text2video", - auth_token=auth_token, + **kwargs, ) @@ -1381,16 +1411,19 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase): enum_type=KlingVirtualTryOnModelName, ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } DESCRIPTION = "Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human." def get_response( - self, task_id: str, auth_token: Optional[str] = None + self, task_id: str, auth_kwargs: dict[str,str] = None ) -> KlingVirtualTryOnResponse: return poll_until_finished( - auth_token, + auth_kwargs, ApiEndpoint( path=f"{PATH_VIRTUAL_TRY_ON}/{task_id}", method=HttpMethod.GET, @@ -1404,7 +1437,7 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase): human_image: torch.Tensor, cloth_image: torch.Tensor, model_name: KlingVirtualTryOnModelName, - auth_token: Optional[str] = None, + **kwargs, ): initial_operation = SynchronousOperation( endpoint=ApiEndpoint( @@ -1418,14 +1451,14 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase): cloth_image=tensor_to_base64_string(cloth_image), model_name=model_name, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) task_creation_response = initial_operation.execute() validate_task_creation_response(task_creation_response) task_id = task_creation_response.data.task_id - final_response = self.get_response(task_id, auth_token) + final_response = self.get_response(task_id, auth_kwargs=kwargs) validate_image_result_response(final_response) images = get_images_from_response(final_response) @@ -1493,16 +1526,19 @@ class KlingImageGenerationNode(KlingImageGenerationBase): "optional": { "image": (IO.IMAGE, {}), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } DESCRIPTION = "Kling Image Generation Node. Generate an image from a text prompt with an optional reference image." def get_response( - self, task_id: str, auth_token: Optional[str] = None + self, task_id: str, auth_kwargs: Optional[dict[str,str]] = None ) -> KlingImageGenerationsResponse: return poll_until_finished( - auth_token, + auth_kwargs, ApiEndpoint( path=f"{PATH_IMAGE_GENERATIONS}/{task_id}", method=HttpMethod.GET, @@ -1522,7 +1558,7 @@ class KlingImageGenerationNode(KlingImageGenerationBase): n: int, aspect_ratio: KlingImageGenAspectRatio, image: Optional[torch.Tensor] = None, - auth_token: Optional[str] = None, + **kwargs, ): self.validate_prompt(prompt, negative_prompt) @@ -1547,14 +1583,14 @@ class KlingImageGenerationNode(KlingImageGenerationBase): n=n, aspect_ratio=aspect_ratio, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) task_creation_response = initial_operation.execute() validate_task_creation_response(task_creation_response) task_id = task_creation_response.data.task_id - final_response = self.get_response(task_id, auth_token) + final_response = self.get_response(task_id, auth_kwargs=kwargs) validate_image_result_response(final_response) images = get_images_from_response(final_response) diff --git a/comfy_api_nodes/nodes_luma.py b/comfy_api_nodes/nodes_luma.py index 0f0d9aa80..bd33a53e0 100644 --- a/comfy_api_nodes/nodes_luma.py +++ b/comfy_api_nodes/nodes_luma.py @@ -1,4 +1,6 @@ +from __future__ import annotations from inspect import cleandoc +from typing import Optional from comfy.comfy_types.node_typing import IO, ComfyNodeABC from comfy_api.input_impl.video_types import VideoFromFile from comfy_api_nodes.apis.luma_api import ( @@ -201,6 +203,7 @@ class LumaImageGenerationNode(ComfyNodeABC): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -214,7 +217,6 @@ class LumaImageGenerationNode(ComfyNodeABC): image_luma_ref: LumaReferenceChain = None, style_image: torch.Tensor = None, character_image: torch.Tensor = None, - auth_token=None, **kwargs, ): validate_string(prompt, strip_whitespace=True, min_length=3) @@ -222,19 +224,19 @@ class LumaImageGenerationNode(ComfyNodeABC): api_image_ref = None if image_luma_ref is not None: api_image_ref = self._convert_luma_refs( - image_luma_ref, max_refs=4, auth_token=auth_token + image_luma_ref, max_refs=4, auth_kwargs=kwargs, ) # handle style_luma_ref api_style_ref = None if style_image is not None: api_style_ref = self._convert_style_image( - style_image, weight=style_image_weight, auth_token=auth_token + style_image, weight=style_image_weight, auth_kwargs=kwargs, ) # handle character_ref images character_ref = None if character_image is not None: download_urls = upload_images_to_comfyapi( - character_image, max_images=4, auth_token=auth_token + character_image, max_images=4, auth_kwargs=kwargs, ) character_ref = LumaCharacterRef( identity0=LumaImageIdentity(images=download_urls) @@ -255,7 +257,7 @@ class LumaImageGenerationNode(ComfyNodeABC): style_ref=api_style_ref, character_ref=character_ref, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) response_api: LumaGeneration = operation.execute() @@ -269,7 +271,7 @@ class LumaImageGenerationNode(ComfyNodeABC): completed_statuses=[LumaState.completed], failed_statuses=[LumaState.failed], status_extractor=lambda x: x.state, - auth_token=auth_token, + auth_kwargs=kwargs, ) response_poll = operation.execute() @@ -278,13 +280,13 @@ class LumaImageGenerationNode(ComfyNodeABC): return (img,) def _convert_luma_refs( - self, luma_ref: LumaReferenceChain, max_refs: int, auth_token=None + self, luma_ref: LumaReferenceChain, max_refs: int, auth_kwargs: Optional[dict[str,str]] = None ): luma_urls = [] ref_count = 0 for ref in luma_ref.refs: download_urls = upload_images_to_comfyapi( - ref.image, max_images=1, auth_token=auth_token + ref.image, max_images=1, auth_kwargs=auth_kwargs ) luma_urls.append(download_urls[0]) ref_count += 1 @@ -293,12 +295,12 @@ class LumaImageGenerationNode(ComfyNodeABC): return luma_ref.create_api_model(download_urls=luma_urls, max_refs=max_refs) def _convert_style_image( - self, style_image: torch.Tensor, weight: float, auth_token=None + self, style_image: torch.Tensor, weight: float, auth_kwargs: Optional[dict[str,str]] = None ): chain = LumaReferenceChain( first_ref=LumaReference(image=style_image, weight=weight) ) - return self._convert_luma_refs(chain, max_refs=1, auth_token=auth_token) + return self._convert_luma_refs(chain, max_refs=1, auth_kwargs=auth_kwargs) class LumaImageModifyNode(ComfyNodeABC): @@ -350,6 +352,7 @@ class LumaImageModifyNode(ComfyNodeABC): "optional": {}, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -360,12 +363,11 @@ class LumaImageModifyNode(ComfyNodeABC): image: torch.Tensor, image_weight: float, seed, - auth_token=None, **kwargs, ): # first, upload image download_urls = upload_images_to_comfyapi( - image, max_images=1, auth_token=auth_token + image, max_images=1, auth_kwargs=kwargs, ) image_url = download_urls[0] # next, make Luma call with download url provided @@ -383,7 +385,7 @@ class LumaImageModifyNode(ComfyNodeABC): url=image_url, weight=round(max(min(1.0-image_weight, 0.98), 0.0), 2) ), ), - auth_token=auth_token, + auth_kwargs=kwargs, ) response_api: LumaGeneration = operation.execute() @@ -397,7 +399,7 @@ class LumaImageModifyNode(ComfyNodeABC): completed_statuses=[LumaState.completed], failed_statuses=[LumaState.failed], status_extractor=lambda x: x.state, - auth_token=auth_token, + auth_kwargs=kwargs, ) response_poll = operation.execute() @@ -470,6 +472,7 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -483,7 +486,6 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC): loop: bool, seed, luma_concepts: LumaConceptChain = None, - auth_token=None, **kwargs, ): validate_string(prompt, strip_whitespace=False, min_length=3) @@ -506,7 +508,7 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC): loop=loop, concepts=luma_concepts.create_api_model() if luma_concepts else None, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) response_api: LumaGeneration = operation.execute() @@ -520,7 +522,7 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC): completed_statuses=[LumaState.completed], failed_statuses=[LumaState.failed], status_extractor=lambda x: x.state, - auth_token=auth_token, + auth_kwargs=kwargs, ) response_poll = operation.execute() @@ -594,6 +596,7 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -608,14 +611,13 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC): first_image: torch.Tensor = None, last_image: torch.Tensor = None, luma_concepts: LumaConceptChain = None, - auth_token=None, **kwargs, ): if first_image is None and last_image is None: raise Exception( "At least one of first_image and last_image requires an input." ) - keyframes = self._convert_to_keyframes(first_image, last_image, auth_token) + keyframes = self._convert_to_keyframes(first_image, last_image, auth_kwargs=kwargs) duration = duration if model != LumaVideoModel.ray_1_6 else None resolution = resolution if model != LumaVideoModel.ray_1_6 else None @@ -636,7 +638,7 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC): keyframes=keyframes, concepts=luma_concepts.create_api_model() if luma_concepts else None, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) response_api: LumaGeneration = operation.execute() @@ -650,7 +652,7 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC): completed_statuses=[LumaState.completed], failed_statuses=[LumaState.failed], status_extractor=lambda x: x.state, - auth_token=auth_token, + auth_kwargs=kwargs, ) response_poll = operation.execute() @@ -661,7 +663,7 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC): self, first_image: torch.Tensor = None, last_image: torch.Tensor = None, - auth_token=None, + auth_kwargs: Optional[dict[str,str]] = None, ): if first_image is None and last_image is None: return None @@ -669,12 +671,12 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC): frame1 = None if first_image is not None: download_urls = upload_images_to_comfyapi( - first_image, max_images=1, auth_token=auth_token + first_image, max_images=1, auth_kwargs=auth_kwargs, ) frame0 = LumaImageReference(type="image", url=download_urls[0]) if last_image is not None: download_urls = upload_images_to_comfyapi( - last_image, max_images=1, auth_token=auth_token + last_image, max_images=1, auth_kwargs=auth_kwargs, ) frame1 = LumaImageReference(type="image", url=download_urls[0]) return LumaKeyframes(frame0=frame0, frame1=frame1) diff --git a/comfy_api_nodes/nodes_minimax.py b/comfy_api_nodes/nodes_minimax.py index cacda22c6..fd64aeb0b 100644 --- a/comfy_api_nodes/nodes_minimax.py +++ b/comfy_api_nodes/nodes_minimax.py @@ -67,6 +67,7 @@ class MinimaxTextToVideoNode: }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -84,7 +85,7 @@ class MinimaxTextToVideoNode: model="T2V-01", image: torch.Tensor=None, # used for ImageToVideo subject: torch.Tensor=None, # used for SubjectToVideo - auth_token=None, + **kwargs, ): ''' Function used between MiniMax nodes - supports T2V, I2V, and S2V, based on provided arguments. @@ -94,12 +95,12 @@ class MinimaxTextToVideoNode: # upload image, if passed in image_url = None if image is not None: - image_url = upload_images_to_comfyapi(image, max_images=1, auth_token=auth_token)[0] + image_url = upload_images_to_comfyapi(image, max_images=1, auth_kwargs=kwargs)[0] # TODO: figure out how to deal with subject properly, API returns invalid params when using S2V-01 model subject_reference = None if subject is not None: - subject_url = upload_images_to_comfyapi(subject, max_images=1, auth_token=auth_token)[0] + subject_url = upload_images_to_comfyapi(subject, max_images=1, auth_kwargs=kwargs)[0] subject_reference = [SubjectReferenceItem(image=subject_url)] @@ -118,7 +119,7 @@ class MinimaxTextToVideoNode: subject_reference=subject_reference, prompt_optimizer=None, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) response = video_generate_operation.execute() @@ -137,7 +138,7 @@ class MinimaxTextToVideoNode: completed_statuses=["Success"], failed_statuses=["Fail"], status_extractor=lambda x: x.status.value, - auth_token=auth_token, + auth_kwargs=kwargs, ) task_result = video_generate_operation.execute() @@ -153,7 +154,7 @@ class MinimaxTextToVideoNode: query_params={"file_id": int(file_id)}, ), request=EmptyRequest(), - auth_token=auth_token, + auth_kwargs=kwargs, ) file_result = file_retrieve_operation.execute() @@ -221,6 +222,7 @@ class MinimaxImageToVideoNode(MinimaxTextToVideoNode): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -279,6 +281,7 @@ class MinimaxSubjectToVideoNode(MinimaxTextToVideoNode): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py index c18c65d7a..c63908be2 100644 --- a/comfy_api_nodes/nodes_openai.py +++ b/comfy_api_nodes/nodes_openai.py @@ -93,7 +93,10 @@ class OpenAIDalle2(ComfyNodeABC): }, ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } RETURN_TYPES = (IO.IMAGE,) @@ -110,7 +113,7 @@ class OpenAIDalle2(ComfyNodeABC): mask=None, n=1, size="1024x1024", - auth_token=None, + **kwargs ): validate_string(prompt, strip_whitespace=False) model = "dall-e-2" @@ -168,7 +171,7 @@ class OpenAIDalle2(ComfyNodeABC): else None ), content_type=content_type, - auth_token=auth_token, + auth_kwargs=kwargs, ) response = operation.execute() @@ -236,7 +239,10 @@ class OpenAIDalle3(ComfyNodeABC): }, ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } RETURN_TYPES = (IO.IMAGE,) @@ -252,7 +258,7 @@ class OpenAIDalle3(ComfyNodeABC): style="natural", quality="standard", size="1024x1024", - auth_token=None, + **kwargs ): validate_string(prompt, strip_whitespace=False) model = "dall-e-3" @@ -273,7 +279,7 @@ class OpenAIDalle3(ComfyNodeABC): style=style, seed=seed, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) response = operation.execute() @@ -366,7 +372,10 @@ class OpenAIGPTImage1(ComfyNodeABC): }, ), }, - "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, } RETURN_TYPES = (IO.IMAGE,) @@ -385,7 +394,7 @@ class OpenAIGPTImage1(ComfyNodeABC): mask=None, n=1, size="1024x1024", - auth_token=None, + **kwargs ): validate_string(prompt, strip_whitespace=False) model = "gpt-image-1" @@ -462,7 +471,7 @@ class OpenAIGPTImage1(ComfyNodeABC): ), files=files if files else None, content_type=content_type, - auth_token=auth_token, + auth_kwargs=kwargs, ) response = operation.execute() diff --git a/comfy_api_nodes/nodes_pika.py b/comfy_api_nodes/nodes_pika.py index ba4e8457d..08ec9cf07 100644 --- a/comfy_api_nodes/nodes_pika.py +++ b/comfy_api_nodes/nodes_pika.py @@ -3,6 +3,7 @@ Pika x ComfyUI API Nodes Pika API docs: https://pika-827374fb.mintlify.app/api-reference """ +from __future__ import annotations import io from typing import Optional, TypeVar @@ -120,7 +121,7 @@ class PikaNodeBase(ComfyNodeABC): RETURN_TYPES = ("VIDEO",) def poll_for_task_status( - self, task_id: str, auth_token: str + self, task_id: str, auth_kwargs: Optional[dict[str,str]] = None ) -> PikaGenerateResponse: polling_operation = PollingOperation( poll_endpoint=ApiEndpoint( @@ -139,20 +140,20 @@ class PikaNodeBase(ComfyNodeABC): progress_extractor=lambda response: ( response.progress if hasattr(response, "progress") else None ), - auth_token=auth_token, + auth_kwargs=auth_kwargs, ) return polling_operation.execute() def execute_task( self, initial_operation: SynchronousOperation[R, PikaGenerateResponse], - auth_token: Optional[str] = None, + auth_kwargs: Optional[dict[str,str]] = None, ) -> tuple[VideoFromFile]: """Executes the initial operation then polls for the task status until it is completed. Args: initial_operation: The initial operation to execute. - auth_token: The authentication token to use for the API call. + auth_kwargs: The authentication token(s) to use for the API call. Returns: A tuple containing the video file as a VIDEO output. @@ -164,7 +165,7 @@ class PikaNodeBase(ComfyNodeABC): raise PikaApiError(error_msg) task_id = initial_response.video_id - final_response = self.poll_for_task_status(task_id, auth_token) + final_response = self.poll_for_task_status(task_id, auth_kwargs) if not is_valid_video_response(final_response): error_msg = ( f"Pika task {task_id} succeeded but no video data found in response." @@ -193,6 +194,7 @@ class PikaImageToVideoV2_2(PikaNodeBase): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -206,7 +208,7 @@ class PikaImageToVideoV2_2(PikaNodeBase): seed: int, resolution: str, duration: int, - auth_token: Optional[str] = None, + **kwargs ) -> tuple[VideoFromFile]: # Convert image to BytesIO image_bytes_io = tensor_to_bytesio(image) @@ -233,10 +235,10 @@ class PikaImageToVideoV2_2(PikaNodeBase): request=pika_request_data, files=pika_files, content_type="multipart/form-data", - auth_token=auth_token, + auth_kwargs=kwargs, ) - return self.execute_task(initial_operation, auth_token) + return self.execute_task(initial_operation, auth_kwargs=kwargs) class PikaTextToVideoNodeV2_2(PikaNodeBase): @@ -259,6 +261,7 @@ class PikaTextToVideoNodeV2_2(PikaNodeBase): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -272,7 +275,7 @@ class PikaTextToVideoNodeV2_2(PikaNodeBase): resolution: str, duration: int, aspect_ratio: float, - auth_token: Optional[str] = None, + **kwargs, ) -> tuple[VideoFromFile]: initial_operation = SynchronousOperation( endpoint=ApiEndpoint( @@ -289,11 +292,11 @@ class PikaTextToVideoNodeV2_2(PikaNodeBase): duration=duration, aspectRatio=aspect_ratio, ), - auth_token=auth_token, + auth_kwargs=kwargs, content_type="application/x-www-form-urlencoded", ) - return self.execute_task(initial_operation, auth_token) + return self.execute_task(initial_operation, auth_kwargs=kwargs) class PikaScenesV2_2(PikaNodeBase): @@ -336,6 +339,7 @@ class PikaScenesV2_2(PikaNodeBase): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -355,7 +359,7 @@ class PikaScenesV2_2(PikaNodeBase): image_ingredient_3: Optional[torch.Tensor] = None, image_ingredient_4: Optional[torch.Tensor] = None, image_ingredient_5: Optional[torch.Tensor] = None, - auth_token: Optional[str] = None, + **kwargs, ) -> tuple[VideoFromFile]: # Convert all passed images to BytesIO all_image_bytes_io = [] @@ -396,10 +400,10 @@ class PikaScenesV2_2(PikaNodeBase): request=pika_request_data, files=pika_files, content_type="multipart/form-data", - auth_token=auth_token, + auth_kwargs=kwargs, ) - return self.execute_task(initial_operation, auth_token) + return self.execute_task(initial_operation, auth_kwargs=kwargs) class PikAdditionsNode(PikaNodeBase): @@ -434,6 +438,7 @@ class PikAdditionsNode(PikaNodeBase): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -446,7 +451,7 @@ class PikAdditionsNode(PikaNodeBase): prompt_text: str, negative_prompt: str, seed: int, - auth_token: Optional[str] = None, + **kwargs, ) -> tuple[VideoFromFile]: # Convert video to BytesIO video_bytes_io = io.BytesIO() @@ -479,10 +484,10 @@ class PikAdditionsNode(PikaNodeBase): request=pika_request_data, files=pika_files, content_type="multipart/form-data", - auth_token=auth_token, + auth_kwargs=kwargs, ) - return self.execute_task(initial_operation, auth_token) + return self.execute_task(initial_operation, auth_kwargs=kwargs) class PikaSwapsNode(PikaNodeBase): @@ -526,6 +531,7 @@ class PikaSwapsNode(PikaNodeBase): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -540,7 +546,7 @@ class PikaSwapsNode(PikaNodeBase): prompt_text: str, negative_prompt: str, seed: int, - auth_token: Optional[str] = None, + **kwargs, ) -> tuple[VideoFromFile]: # Convert video to BytesIO video_bytes_io = io.BytesIO() @@ -583,10 +589,10 @@ class PikaSwapsNode(PikaNodeBase): request=pika_request_data, files=pika_files, content_type="multipart/form-data", - auth_token=auth_token, + auth_kwargs=kwargs, ) - return self.execute_task(initial_operation, auth_token) + return self.execute_task(initial_operation, auth_kwargs=kwargs) class PikaffectsNode(PikaNodeBase): @@ -630,6 +636,7 @@ class PikaffectsNode(PikaNodeBase): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -642,7 +649,7 @@ class PikaffectsNode(PikaNodeBase): prompt_text: str, negative_prompt: str, seed: int, - auth_token: Optional[str] = None, + **kwargs, ) -> tuple[VideoFromFile]: initial_operation = SynchronousOperation( @@ -660,10 +667,10 @@ class PikaffectsNode(PikaNodeBase): ), files={"image": ("image.png", tensor_to_bytesio(image), "image/png")}, content_type="multipart/form-data", - auth_token=auth_token, + auth_kwargs=kwargs, ) - return self.execute_task(initial_operation, auth_token) + return self.execute_task(initial_operation, auth_kwargs=kwargs) class PikaStartEndFrameNode2_2(PikaNodeBase): @@ -681,6 +688,7 @@ class PikaStartEndFrameNode2_2(PikaNodeBase): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -695,7 +703,7 @@ class PikaStartEndFrameNode2_2(PikaNodeBase): seed: int, resolution: str, duration: int, - auth_token: Optional[str] = None, + **kwargs, ) -> tuple[VideoFromFile]: pika_files = [ @@ -722,10 +730,10 @@ class PikaStartEndFrameNode2_2(PikaNodeBase): ), files=pika_files, content_type="multipart/form-data", - auth_token=auth_token, + auth_kwargs=kwargs, ) - return self.execute_task(initial_operation, auth_token) + return self.execute_task(initial_operation, auth_kwargs=kwargs) NODE_CLASS_MAPPINGS = { diff --git a/comfy_api_nodes/nodes_pixverse.py b/comfy_api_nodes/nodes_pixverse.py index dbb90c1dd..0c29e77c2 100644 --- a/comfy_api_nodes/nodes_pixverse.py +++ b/comfy_api_nodes/nodes_pixverse.py @@ -34,7 +34,7 @@ import requests from io import BytesIO -def upload_image_to_pixverse(image: torch.Tensor, auth_token=None): +def upload_image_to_pixverse(image: torch.Tensor, auth_kwargs=None): # first, upload image to Pixverse and get image id to use in actual generation call files = { "image": tensor_to_bytesio(image) @@ -49,7 +49,7 @@ def upload_image_to_pixverse(image: torch.Tensor, auth_token=None): request=EmptyRequest(), files=files, content_type="multipart/form-data", - auth_token=auth_token, + auth_kwargs=auth_kwargs, ) response_upload: PixverseImageUploadResponse = operation.execute() @@ -148,6 +148,7 @@ class PixverseTextToVideoNode(ComfyNodeABC): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -161,7 +162,6 @@ class PixverseTextToVideoNode(ComfyNodeABC): seed, negative_prompt: str=None, pixverse_template: int=None, - auth_token=None, **kwargs, ): validate_string(prompt, strip_whitespace=False) @@ -190,7 +190,7 @@ class PixverseTextToVideoNode(ComfyNodeABC): template_id=pixverse_template, seed=seed, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) response_api = operation.execute() @@ -207,7 +207,7 @@ class PixverseTextToVideoNode(ComfyNodeABC): completed_statuses=[PixverseStatus.successful], failed_statuses=[PixverseStatus.contents_moderation, PixverseStatus.failed, PixverseStatus.deleted], status_extractor=lambda x: x.Resp.status, - auth_token=auth_token, + auth_kwargs=kwargs, ) response_poll = operation.execute() @@ -278,6 +278,7 @@ class PixverseImageToVideoNode(ComfyNodeABC): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -291,11 +292,10 @@ class PixverseImageToVideoNode(ComfyNodeABC): seed, negative_prompt: str=None, pixverse_template: int=None, - auth_token=None, **kwargs, ): validate_string(prompt, strip_whitespace=False) - img_id = upload_image_to_pixverse(image, auth_token=auth_token) + img_id = upload_image_to_pixverse(image, auth_kwargs=kwargs) # 1080p is limited to 5 seconds duration # only normal motion_mode supported for 1080p or for non-5 second duration @@ -322,7 +322,7 @@ class PixverseImageToVideoNode(ComfyNodeABC): template_id=pixverse_template, seed=seed, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) response_api = operation.execute() @@ -339,7 +339,7 @@ class PixverseImageToVideoNode(ComfyNodeABC): completed_statuses=[PixverseStatus.successful], failed_statuses=[PixverseStatus.contents_moderation, PixverseStatus.failed, PixverseStatus.deleted], status_extractor=lambda x: x.Resp.status, - auth_token=auth_token, + auth_kwargs=kwargs, ) response_poll = operation.execute() @@ -407,6 +407,7 @@ class PixverseTransitionVideoNode(ComfyNodeABC): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -420,12 +421,11 @@ class PixverseTransitionVideoNode(ComfyNodeABC): motion_mode: str, seed, negative_prompt: str=None, - auth_token=None, **kwargs, ): validate_string(prompt, strip_whitespace=False) - first_frame_id = upload_image_to_pixverse(first_frame, auth_token=auth_token) - last_frame_id = upload_image_to_pixverse(last_frame, auth_token=auth_token) + first_frame_id = upload_image_to_pixverse(first_frame, auth_kwargs=kwargs) + last_frame_id = upload_image_to_pixverse(last_frame, auth_kwargs=kwargs) # 1080p is limited to 5 seconds duration # only normal motion_mode supported for 1080p or for non-5 second duration @@ -452,7 +452,7 @@ class PixverseTransitionVideoNode(ComfyNodeABC): negative_prompt=negative_prompt if negative_prompt else None, seed=seed, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) response_api = operation.execute() @@ -469,7 +469,7 @@ class PixverseTransitionVideoNode(ComfyNodeABC): completed_statuses=[PixverseStatus.successful], failed_statuses=[PixverseStatus.contents_moderation, PixverseStatus.failed, PixverseStatus.deleted], status_extractor=lambda x: x.Resp.status, - auth_token=auth_token, + auth_kwargs=kwargs, ) response_poll = operation.execute() diff --git a/comfy_api_nodes/nodes_recraft.py b/comfy_api_nodes/nodes_recraft.py index 5c89d21e9..767d93e3c 100644 --- a/comfy_api_nodes/nodes_recraft.py +++ b/comfy_api_nodes/nodes_recraft.py @@ -41,7 +41,7 @@ def handle_recraft_file_request( total_pixels=4096*4096, timeout=1024, request=None, - auth_token=None + auth_kwargs: dict[str,str] = None, ) -> list[BytesIO]: """ Handle sending common Recraft file-only request to get back file bytes. @@ -65,7 +65,7 @@ def handle_recraft_file_request( request=request, files=files, content_type="multipart/form-data", - auth_token=auth_token, + auth_kwargs=auth_kwargs, multipart_parser=recraft_multipart_parser, ) response: RecraftImageGenerationResponse = operation.execute() @@ -387,6 +387,7 @@ class RecraftTextToImageNode: }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -399,7 +400,6 @@ class RecraftTextToImageNode: recraft_style: RecraftStyle = None, negative_prompt: str = None, recraft_controls: RecraftControls = None, - auth_token=None, **kwargs, ): validate_string(prompt, strip_whitespace=False, max_length=1000) @@ -432,7 +432,7 @@ class RecraftTextToImageNode: style_id=recraft_style.style_id, controls=controls_api, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) response: RecraftImageGenerationResponse = operation.execute() images = [] @@ -522,6 +522,7 @@ class RecraftImageToImageNode: }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -532,7 +533,6 @@ class RecraftImageToImageNode: n: int, strength: float, seed, - auth_token=None, recraft_style: RecraftStyle = None, negative_prompt: str = None, recraft_controls: RecraftControls = None, @@ -570,7 +570,7 @@ class RecraftImageToImageNode: image=image[i], path="/proxy/recraft/images/imageToImage", request=request, - auth_token=auth_token, + auth_kwargs=kwargs, ) with handle_recraft_image_output(): images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) @@ -638,6 +638,7 @@ class RecraftImageInpaintingNode: }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -648,7 +649,6 @@ class RecraftImageInpaintingNode: prompt: str, n: int, seed, - auth_token=None, recraft_style: RecraftStyle = None, negative_prompt: str = None, **kwargs, @@ -683,7 +683,7 @@ class RecraftImageInpaintingNode: mask=mask[i:i+1], path="/proxy/recraft/images/inpaint", request=request, - auth_token=auth_token, + auth_kwargs=kwargs, ) with handle_recraft_image_output(): images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) @@ -762,6 +762,7 @@ class RecraftTextToVectorNode: }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -774,7 +775,6 @@ class RecraftTextToVectorNode: seed, negative_prompt: str = None, recraft_controls: RecraftControls = None, - auth_token=None, **kwargs, ): validate_string(prompt, strip_whitespace=False, max_length=1000) @@ -805,7 +805,7 @@ class RecraftTextToVectorNode: substyle=recraft_style.substyle, controls=controls_api, ), - auth_token=auth_token, + auth_kwargs=kwargs, ) response: RecraftImageGenerationResponse = operation.execute() svg_data = [] @@ -836,13 +836,13 @@ class RecraftVectorizeImageNode: }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } def api_call( self, image: torch.Tensor, - auth_token=None, **kwargs, ): svgs = [] @@ -852,7 +852,7 @@ class RecraftVectorizeImageNode: sub_bytes = handle_recraft_file_request( image=image[i], path="/proxy/recraft/images/vectorize", - auth_token=auth_token, + auth_kwargs=kwargs, ) svgs.append(SVG(sub_bytes)) pbar.update(1) @@ -917,6 +917,7 @@ class RecraftReplaceBackgroundNode: }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -926,7 +927,6 @@ class RecraftReplaceBackgroundNode: prompt: str, n: int, seed, - auth_token=None, recraft_style: RecraftStyle = None, negative_prompt: str = None, **kwargs, @@ -956,7 +956,7 @@ class RecraftReplaceBackgroundNode: image=image[i], path="/proxy/recraft/images/replaceBackground", request=request, - auth_token=auth_token, + auth_kwargs=kwargs, ) images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) pbar.update(1) @@ -986,13 +986,13 @@ class RecraftRemoveBackgroundNode: }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } def api_call( self, image: torch.Tensor, - auth_token=None, **kwargs, ): images = [] @@ -1002,7 +1002,7 @@ class RecraftRemoveBackgroundNode: sub_bytes = handle_recraft_file_request( image=image[i], path="/proxy/recraft/images/removeBackground", - auth_token=auth_token, + auth_kwargs=kwargs, ) images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) pbar.update(1) @@ -1037,13 +1037,13 @@ class RecraftCrispUpscaleNode: }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } def api_call( self, image: torch.Tensor, - auth_token=None, **kwargs, ): images = [] @@ -1053,7 +1053,7 @@ class RecraftCrispUpscaleNode: sub_bytes = handle_recraft_file_request( image=image[i], path=self.RECRAFT_PATH, - auth_token=auth_token, + auth_kwargs=kwargs, ) images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) pbar.update(1) diff --git a/comfy_api_nodes/nodes_stability.py b/comfy_api_nodes/nodes_stability.py index 52fe2417c..02e421678 100644 --- a/comfy_api_nodes/nodes_stability.py +++ b/comfy_api_nodes/nodes_stability.py @@ -120,12 +120,13 @@ class StabilityStableImageUltraNode: }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } def api_call(self, prompt: str, aspect_ratio: str, style_preset: str, seed: int, negative_prompt: str=None, image: torch.Tensor = None, image_denoise: float=None, - auth_token=None): + **kwargs): validate_string(prompt, strip_whitespace=False) # prepare image binary if image present image_binary = None @@ -160,7 +161,7 @@ class StabilityStableImageUltraNode: ), files=files, content_type="multipart/form-data", - auth_token=auth_token, + auth_kwargs=kwargs, ) response_api = operation.execute() @@ -252,12 +253,13 @@ class StabilityStableImageSD_3_5Node: }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } def api_call(self, model: str, prompt: str, aspect_ratio: str, style_preset: str, seed: int, cfg_scale: float, negative_prompt: str=None, image: torch.Tensor = None, image_denoise: float=None, - auth_token=None): + **kwargs): validate_string(prompt, strip_whitespace=False) # prepare image binary if image present image_binary = None @@ -298,7 +300,7 @@ class StabilityStableImageSD_3_5Node: ), files=files, content_type="multipart/form-data", - auth_token=auth_token, + auth_kwargs=kwargs, ) response_api = operation.execute() @@ -368,11 +370,12 @@ class StabilityUpscaleConservativeNode: }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } def api_call(self, image: torch.Tensor, prompt: str, creativity: float, seed: int, negative_prompt: str=None, - auth_token=None): + **kwargs): validate_string(prompt, strip_whitespace=False) image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read() @@ -398,7 +401,7 @@ class StabilityUpscaleConservativeNode: ), files=files, content_type="multipart/form-data", - auth_token=auth_token, + auth_kwargs=kwargs, ) response_api = operation.execute() @@ -473,11 +476,12 @@ class StabilityUpscaleCreativeNode: }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } def api_call(self, image: torch.Tensor, prompt: str, creativity: float, style_preset: str, seed: int, negative_prompt: str=None, - auth_token=None): + **kwargs): validate_string(prompt, strip_whitespace=False) image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read() @@ -506,7 +510,7 @@ class StabilityUpscaleCreativeNode: ), files=files, content_type="multipart/form-data", - auth_token=auth_token, + auth_kwargs=kwargs, ) response_api = operation.execute() @@ -521,7 +525,7 @@ class StabilityUpscaleCreativeNode: completed_statuses=[StabilityPollStatus.finished], failed_statuses=[StabilityPollStatus.failed], status_extractor=lambda x: get_async_dummy_status(x), - auth_token=auth_token, + auth_kwargs=kwargs, ) response_poll: StabilityResultsGetResponse = operation.execute() @@ -555,11 +559,12 @@ class StabilityUpscaleFastNode: }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } def api_call(self, image: torch.Tensor, - auth_token=None): + **kwargs): image_binary = tensor_to_bytesio(image, total_pixels=4096*4096).read() files = { @@ -576,7 +581,7 @@ class StabilityUpscaleFastNode: request=EmptyRequest(), files=files, content_type="multipart/form-data", - auth_token=auth_token, + auth_kwargs=kwargs, ) response_api = operation.execute() diff --git a/comfy_api_nodes/nodes_veo2.py b/comfy_api_nodes/nodes_veo2.py index 9233944b5..2740179c8 100644 --- a/comfy_api_nodes/nodes_veo2.py +++ b/comfy_api_nodes/nodes_veo2.py @@ -114,6 +114,7 @@ class VeoVideoGenerationNode(ComfyNodeABC): }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", }, } @@ -133,7 +134,7 @@ class VeoVideoGenerationNode(ComfyNodeABC): person_generation="ALLOW", seed=0, image=None, - auth_token=None, + **kwargs, ): # Prepare the instances for the request instances = [] @@ -179,7 +180,7 @@ class VeoVideoGenerationNode(ComfyNodeABC): instances=instances, parameters=parameters ), - auth_token=auth_token + auth_kwargs=kwargs, ) initial_response = initial_operation.execute() @@ -213,7 +214,7 @@ class VeoVideoGenerationNode(ComfyNodeABC): request=Veo2GenVidPollRequest( operationName=operation_name ), - auth_token=auth_token, + auth_kwargs=kwargs, poll_interval=5.0 ) diff --git a/execution.py b/execution.py index feb61ae82..e5d1c69d9 100644 --- a/execution.py +++ b/execution.py @@ -146,6 +146,8 @@ def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, e input_data_all[x] = [unique_id] if h[x] == "AUTH_TOKEN_COMFY_ORG": input_data_all[x] = [extra_data.get("auth_token_comfy_org", None)] + if h[x] == "API_KEY_COMFY_ORG": + input_data_all[x] = [extra_data.get("api_key_comfy_org", None)] return input_data_all, missing_keys map_node_over_list = None #Don't hook this please diff --git a/requirements.txt b/requirements.txt index 29cf0e2ac..01aab4ca2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ -comfyui-frontend-package==1.18.9 -comfyui-workflow-templates==0.1.11 +comfyui-frontend-package==1.18.10 +comfyui-workflow-templates==0.1.14 torch torchsde torchvision From 577de83ca9c99e997825439e017113456c4c78f7 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 11 May 2025 01:58:00 -0700 Subject: [PATCH 094/193] ACE VAE works in fp16. (#8055) --- comfy/sd.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/sd.py b/comfy/sd.py index ee350d5b5..e98a3aa87 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -451,7 +451,7 @@ class VAE: self.latent_dim = 2 self.process_output = lambda audio: audio self.process_input = lambda audio: audio - self.working_dtypes = [torch.bfloat16, torch.float32] + self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] self.disable_offload = True self.extra_1d_channel = 16 else: From 31e9e36c941bbedd29bb388a052911a65bed1bc4 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Mon, 12 May 2025 10:32:24 -0700 Subject: [PATCH 095/193] remove aspect ratio from kling request (#8062) --- comfy_api_nodes/nodes_kling.py | 1 - 1 file changed, 1 deletion(-) diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py index b2be83656..2d0fd8883 100644 --- a/comfy_api_nodes/nodes_kling.py +++ b/comfy_api_nodes/nodes_kling.py @@ -671,7 +671,6 @@ class KlingImage2VideoNode(KlingNodeBase): negative_prompt=negative_prompt if negative_prompt else None, cfg_scale=cfg_scale, mode=KlingVideoGenMode(mode), - aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), duration=KlingVideoGenDuration(duration), camera_control=camera_control, ), From 640c47e7deeb52978e84716613015cd8bfd93c5c Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 12 May 2025 11:32:01 -0700 Subject: [PATCH 096/193] Fix torch warning about deprecated function. (#8075) Drop support for torch versions below 2.2 on the audio VAEs. --- comfy/ldm/ace/vae/music_vocoder.py | 22 +++++++++------------- comfy/ldm/audio/autoencoder.py | 10 ++-------- 2 files changed, 11 insertions(+), 21 deletions(-) diff --git a/comfy/ldm/ace/vae/music_vocoder.py b/comfy/ldm/ace/vae/music_vocoder.py index dc7c867da..2f989fa86 100755 --- a/comfy/ldm/ace/vae/music_vocoder.py +++ b/comfy/ldm/ace/vae/music_vocoder.py @@ -8,11 +8,7 @@ from typing import Callable, Tuple, List import numpy as np import torch.nn.functional as F -from torch.nn.utils import weight_norm from torch.nn.utils.parametrize import remove_parametrizations as remove_weight_norm -# from diffusers.models.modeling_utils import ModelMixin -# from diffusers.loaders import FromOriginalModelMixin -# from diffusers.configuration_utils import ConfigMixin, register_to_config from .music_log_mel import LogMelSpectrogram @@ -259,7 +255,7 @@ class ResBlock1(torch.nn.Module): self.convs1 = nn.ModuleList( [ - weight_norm( + torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( channels, channels, @@ -269,7 +265,7 @@ class ResBlock1(torch.nn.Module): padding=get_padding(kernel_size, dilation[0]), ) ), - weight_norm( + torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( channels, channels, @@ -279,7 +275,7 @@ class ResBlock1(torch.nn.Module): padding=get_padding(kernel_size, dilation[1]), ) ), - weight_norm( + torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( channels, channels, @@ -294,7 +290,7 @@ class ResBlock1(torch.nn.Module): self.convs2 = nn.ModuleList( [ - weight_norm( + torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( channels, channels, @@ -304,7 +300,7 @@ class ResBlock1(torch.nn.Module): padding=get_padding(kernel_size, 1), ) ), - weight_norm( + torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( channels, channels, @@ -314,7 +310,7 @@ class ResBlock1(torch.nn.Module): padding=get_padding(kernel_size, 1), ) ), - weight_norm( + torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( channels, channels, @@ -366,7 +362,7 @@ class HiFiGANGenerator(nn.Module): prod(upsample_rates) == hop_length ), f"hop_length must be {prod(upsample_rates)}" - self.conv_pre = weight_norm( + self.conv_pre = torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( num_mels, upsample_initial_channel, @@ -386,7 +382,7 @@ class HiFiGANGenerator(nn.Module): for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): c_cur = upsample_initial_channel // (2 ** (i + 1)) self.ups.append( - weight_norm( + torch.nn.utils.parametrizations.weight_norm( ops.ConvTranspose1d( upsample_initial_channel // (2**i), upsample_initial_channel // (2 ** (i + 1)), @@ -421,7 +417,7 @@ class HiFiGANGenerator(nn.Module): self.resblocks.append(ResBlock1(ch, k, d)) self.activation_post = post_activation() - self.conv_post = weight_norm( + self.conv_post = torch.nn.utils.parametrizations.weight_norm( ops.Conv1d( ch, 1, diff --git a/comfy/ldm/audio/autoencoder.py b/comfy/ldm/audio/autoencoder.py index 9e7e7c876..78ed6ffa6 100644 --- a/comfy/ldm/audio/autoencoder.py +++ b/comfy/ldm/audio/autoencoder.py @@ -75,16 +75,10 @@ class SnakeBeta(nn.Module): return x def WNConv1d(*args, **kwargs): - try: - return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs)) - except: - return torch.nn.utils.weight_norm(ops.Conv1d(*args, **kwargs)) #support pytorch 2.1 and older + return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs)) def WNConvTranspose1d(*args, **kwargs): - try: - return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) - except: - return torch.nn.utils.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) #support pytorch 2.1 and older + return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module: if activation == "elu": From 158419f3a0017c2ce123484b14b6c527716d6ec8 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 12 May 2025 15:58:28 -0400 Subject: [PATCH 097/193] ComfyUI version 0.3.34 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 5a73f76e4..b740b378d 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.33" +__version__ = "0.3.34" diff --git a/pyproject.toml b/pyproject.toml index e0be329de..80061b39a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.33" +version = "0.3.34" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9" From b4abca828e3a76ef565a2f5f519028e689837fc6 Mon Sep 17 00:00:00 2001 From: thot experiment <94414189+thot-experiment@users.noreply.github.com> Date: Mon, 12 May 2025 13:00:01 -0700 Subject: [PATCH 098/193] add opus and mp3 to audio output node (#8019) * first pass at opus and mp3 as well as migrating flac to pyav * minor mp3 encoding fix * fix ruff * delete dead code * split out save audio to separate nodes per filetype * fix ruff --- comfy_extras/nodes_audio.py | 229 +++++++++++++++++++++++++----------- 1 file changed, 160 insertions(+), 69 deletions(-) diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py index 136ad6159..49af1eae4 100644 --- a/comfy_extras/nodes_audio.py +++ b/comfy_extras/nodes_audio.py @@ -1,5 +1,6 @@ from __future__ import annotations +import av import torchaudio import torch import comfy.model_management @@ -7,7 +8,6 @@ import folder_paths import os import io import json -import struct import random import hashlib import node_helpers @@ -90,60 +90,118 @@ class VAEDecodeAudio: return ({"waveform": audio, "sample_rate": 44100}, ) -def create_vorbis_comment_block(comment_dict, last_block): - vendor_string = b'ComfyUI' - vendor_length = len(vendor_string) +def save_audio(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None, quality="128k"): - comments = [] - for key, value in comment_dict.items(): - comment = f"{key}={value}".encode('utf-8') - comments.append(struct.pack('I', len(comment_data))[1:] + comment_data + # Opus supported sample rates + OPUS_RATES = [8000, 12000, 16000, 24000, 48000] - return comment_block + for (batch_number, waveform) in enumerate(audio["waveform"].cpu()): + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.{format}" + output_path = os.path.join(full_output_folder, file) -def insert_or_replace_vorbis_comment(flac_io, comment_dict): - if len(comment_dict) == 0: - return flac_io + # Use original sample rate initially + sample_rate = audio["sample_rate"] - flac_io.seek(4) + # Handle Opus sample rate requirements + if format == "opus": + if sample_rate > 48000: + sample_rate = 48000 + elif sample_rate not in OPUS_RATES: + # Find the next highest supported rate + for rate in sorted(OPUS_RATES): + if rate > sample_rate: + sample_rate = rate + break + if sample_rate not in OPUS_RATES: # Fallback if still not supported + sample_rate = 48000 - blocks = [] - last_block = False + # Resample if necessary + if sample_rate != audio["sample_rate"]: + waveform = torchaudio.functional.resample(waveform, audio["sample_rate"], sample_rate) - while not last_block: - header = flac_io.read(4) - last_block = (header[0] & 0x80) != 0 - block_type = header[0] & 0x7F - block_length = struct.unpack('>I', b'\x00' + header[1:])[0] - block_data = flac_io.read(block_length) + # Create in-memory WAV buffer + wav_buffer = io.BytesIO() + torchaudio.save(wav_buffer, waveform, sample_rate, format="WAV") + wav_buffer.seek(0) # Rewind for reading - if block_type == 4 or block_type == 1: - pass - else: - header = bytes([(header[0] & (~0x80))]) + header[1:] - blocks.append(header + block_data) + # Use PyAV to convert and add metadata + input_container = av.open(wav_buffer) - blocks.append(create_vorbis_comment_block(comment_dict, last_block=True)) + # Create output with specified format + output_buffer = io.BytesIO() + output_container = av.open(output_buffer, mode='w', format=format) - new_flac_io = io.BytesIO() - new_flac_io.write(b'fLaC') - for block in blocks: - new_flac_io.write(block) + # Set metadata on the container + for key, value in metadata.items(): + output_container.metadata[key] = value - new_flac_io.write(flac_io.read()) - return new_flac_io + # Set up the output stream with appropriate properties + input_container.streams.audio[0] + if format == "opus": + out_stream = output_container.add_stream("libopus", rate=sample_rate) + if quality == "64k": + out_stream.bit_rate = 64000 + elif quality == "96k": + out_stream.bit_rate = 96000 + elif quality == "128k": + out_stream.bit_rate = 128000 + elif quality == "192k": + out_stream.bit_rate = 192000 + elif quality == "320k": + out_stream.bit_rate = 320000 + elif format == "mp3": + out_stream = output_container.add_stream("libmp3lame", rate=sample_rate) + if quality == "V0": + #TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool + out_stream.codec_context.qscale = 1 + elif quality == "128k": + out_stream.bit_rate = 128000 + elif quality == "320k": + out_stream.bit_rate = 320000 + else: #format == "flac": + out_stream = output_container.add_stream("flac", rate=sample_rate) + # Copy frames from input to output + for frame in input_container.decode(audio=0): + frame.pts = None # Let PyAV handle timestamps + output_container.mux(out_stream.encode(frame)) + + # Flush encoder + output_container.mux(out_stream.encode(None)) + + # Close containers + output_container.close() + input_container.close() + + # Write the output to file + output_buffer.seek(0) + with open(output_path, 'wb') as f: + f.write(output_buffer.getbuffer()) + + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + return { "ui": { "audio": results } } + class SaveAudio: def __init__(self): self.output_dir = folder_paths.get_output_directory() @@ -153,50 +211,70 @@ class SaveAudio: @classmethod def INPUT_TYPES(s): return {"required": { "audio": ("AUDIO", ), - "filename_prefix": ("STRING", {"default": "audio/ComfyUI"})}, + "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}), + }, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } RETURN_TYPES = () - FUNCTION = "save_audio" + FUNCTION = "save_flac" OUTPUT_NODE = True CATEGORY = "audio" - def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): - filename_prefix += self.prefix_append - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) - results: list[FileLocator] = [] + def save_flac(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None): + return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo) - metadata = {} - if not args.disable_metadata: - if prompt is not None: - metadata["prompt"] = json.dumps(prompt) - if extra_pnginfo is not None: - for x in extra_pnginfo: - metadata[x] = json.dumps(extra_pnginfo[x]) +class SaveAudioMP3: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" - for (batch_number, waveform) in enumerate(audio["waveform"].cpu()): - filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) - file = f"{filename_with_batch_num}_{counter:05}_.flac" + @classmethod + def INPUT_TYPES(s): + return {"required": { "audio": ("AUDIO", ), + "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}), + "quality": (["V0", "128k", "320k"], {"default": "V0"}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } - buff = io.BytesIO() - torchaudio.save(buff, waveform, audio["sample_rate"], format="FLAC") + RETURN_TYPES = () + FUNCTION = "save_mp3" - buff = insert_or_replace_vorbis_comment(buff, metadata) + OUTPUT_NODE = True - with open(os.path.join(full_output_folder, file), 'wb') as f: - f.write(buff.getbuffer()) + CATEGORY = "audio" - results.append({ - "filename": file, - "subfolder": subfolder, - "type": self.type - }) - counter += 1 + def save_mp3(self, audio, filename_prefix="ComfyUI", format="mp3", prompt=None, extra_pnginfo=None, quality="128k"): + return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality) - return { "ui": { "audio": results } } +class SaveAudioOpus: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + @classmethod + def INPUT_TYPES(s): + return {"required": { "audio": ("AUDIO", ), + "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}), + "quality": (["64k", "96k", "128k", "192k", "320k"], {"default": "128k"}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_opus" + + OUTPUT_NODE = True + + CATEGORY = "audio" + + def save_opus(self, audio, filename_prefix="ComfyUI", format="opus", prompt=None, extra_pnginfo=None, quality="V3"): + return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality) class PreviewAudio(SaveAudio): def __init__(self): @@ -248,7 +326,20 @@ NODE_CLASS_MAPPINGS = { "VAEEncodeAudio": VAEEncodeAudio, "VAEDecodeAudio": VAEDecodeAudio, "SaveAudio": SaveAudio, + "SaveAudioMP3": SaveAudioMP3, + "SaveAudioOpus": SaveAudioOpus, "LoadAudio": LoadAudio, "PreviewAudio": PreviewAudio, "ConditioningStableAudio": ConditioningStableAudio, } + +NODE_DISPLAY_NAME_MAPPINGS = { + "EmptyLatentAudio": "Empty Latent Audio", + "VAEEncodeAudio": "VAE Encode Audio", + "VAEDecodeAudio": "VAE Decode Audio", + "PreviewAudio": "Preview Audio", + "LoadAudio": "Load Audio", + "SaveAudio": "Save Audio (FLAC)", + "SaveAudioMP3": "Save Audio (MP3)", + "SaveAudioOpus": "Save Audio (Opus)", +} From b7ed5f57bda02d7cad3f77abe6de68da6dedc4e4 Mon Sep 17 00:00:00 2001 From: Terry Jia Date: Mon, 12 May 2025 16:29:32 -0400 Subject: [PATCH 099/193] string node (#7952) --- comfy_extras/nodes_string.py | 322 +++++++++++++++++++++++++++++++++++ nodes.py | 1 + 2 files changed, 323 insertions(+) create mode 100644 comfy_extras/nodes_string.py diff --git a/comfy_extras/nodes_string.py b/comfy_extras/nodes_string.py new file mode 100644 index 000000000..a852326e5 --- /dev/null +++ b/comfy_extras/nodes_string.py @@ -0,0 +1,322 @@ +import re + +from comfy.comfy_types.node_typing import IO + +class StringConcatenate(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string_a": (IO.STRING, {"multiline": True}), + "string_b": (IO.STRING, {"multiline": True}) + } + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string_a, string_b, **kwargs): + return string_a + string_b, + +class StringSubstring(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "start": (IO.INT, {}), + "end": (IO.INT, {}), + } + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, start, end, **kwargs): + return string[start:end], + +class StringLength(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}) + } + } + + RETURN_TYPES = (IO.INT,) + RETURN_NAMES = ("length",) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, **kwargs): + length = len(string) + + return length, + +class CaseConverter(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "mode": (IO.COMBO, {"options": ["UPPERCASE", "lowercase", "Capitalize", "Title Case"]}) + } + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, mode, **kwargs): + if mode == "UPPERCASE": + result = string.upper() + elif mode == "lowercase": + result = string.lower() + elif mode == "Capitalize": + result = string.capitalize() + elif mode == "Title Case": + result = string.title() + else: + result = string + + return result, + + +class StringTrim(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "mode": (IO.COMBO, {"options": ["Both", "Left", "Right"]}) + } + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, mode, **kwargs): + if mode == "Both": + result = string.strip() + elif mode == "Left": + result = string.lstrip() + elif mode == "Right": + result = string.rstrip() + else: + result = string + + return result, + +class StringReplace(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "find": (IO.STRING, {"multiline": True}), + "replace": (IO.STRING, {"multiline": True}) + } + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, find, replace, **kwargs): + result = string.replace(find, replace) + return result, + + +class StringContains(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "substring": (IO.STRING, {"multiline": True}), + "case_sensitive": (IO.BOOLEAN, {"default": True}) + } + } + + RETURN_TYPES = (IO.BOOLEAN,) + RETURN_NAMES = ("contains",) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, substring, case_sensitive, **kwargs): + if case_sensitive: + contains = substring in string + else: + contains = substring.lower() in string.lower() + + return contains, + + +class StringCompare(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string_a": (IO.STRING, {"multiline": True}), + "string_b": (IO.STRING, {"multiline": True}), + "mode": (IO.COMBO, {"options": ["Starts With", "Ends With", "Equal"]}), + "case_sensitive": (IO.BOOLEAN, {"default": True}) + } + } + + RETURN_TYPES = (IO.BOOLEAN,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string_a, string_b, mode, case_sensitive, **kwargs): + if case_sensitive: + a = string_a + b = string_b + else: + a = string_a.lower() + b = string_b.lower() + + if mode == "Equal": + return a == b, + elif mode == "Starts With": + return a.startswith(b), + elif mode == "Ends With": + return a.endswith(b), + +class RegexMatch(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "regex_pattern": (IO.STRING, {"multiline": True}), + "case_insensitive": (IO.BOOLEAN, {"default": True}), + "multiline": (IO.BOOLEAN, {"default": False}), + "dotall": (IO.BOOLEAN, {"default": False}) + } + } + + RETURN_TYPES = (IO.BOOLEAN,) + RETURN_NAMES = ("matches",) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, regex_pattern, case_insensitive, multiline, dotall, **kwargs): + flags = 0 + + if case_insensitive: + flags |= re.IGNORECASE + if multiline: + flags |= re.MULTILINE + if dotall: + flags |= re.DOTALL + + try: + match = re.search(regex_pattern, string, flags) + result = match is not None + + except re.error: + result = False + + return result, + + +class RegexExtract(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "regex_pattern": (IO.STRING, {"multiline": True}), + "mode": (IO.COMBO, {"options": ["First Match", "All Matches", "First Group", "All Groups"]}), + "case_insensitive": (IO.BOOLEAN, {"default": True}), + "multiline": (IO.BOOLEAN, {"default": False}), + "dotall": (IO.BOOLEAN, {"default": False}), + "group_index": (IO.INT, {"default": 1, "min": 0, "max": 100}) + } + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, regex_pattern, mode, case_insensitive, multiline, dotall, group_index, **kwargs): + join_delimiter = "\n" + + flags = 0 + if case_insensitive: + flags |= re.IGNORECASE + if multiline: + flags |= re.MULTILINE + if dotall: + flags |= re.DOTALL + + try: + if mode == "First Match": + match = re.search(regex_pattern, string, flags) + if match: + result = match.group(0) + else: + result = "" + + elif mode == "All Matches": + matches = re.findall(regex_pattern, string, flags) + if matches: + if isinstance(matches[0], tuple): + result = join_delimiter.join([m[0] for m in matches]) + else: + result = join_delimiter.join(matches) + else: + result = "" + + elif mode == "First Group": + match = re.search(regex_pattern, string, flags) + if match and len(match.groups()) >= group_index: + result = match.group(group_index) + else: + result = "" + + elif mode == "All Groups": + matches = re.finditer(regex_pattern, string, flags) + results = [] + for match in matches: + if match.groups() and len(match.groups()) >= group_index: + results.append(match.group(group_index)) + result = join_delimiter.join(results) + else: + result = "" + + except re.error: + result = "" + + return result, + +NODE_CLASS_MAPPINGS = { + "StringConcatenate": StringConcatenate, + "StringSubstring": StringSubstring, + "StringLength": StringLength, + "CaseConverter": CaseConverter, + "StringTrim": StringTrim, + "StringReplace": StringReplace, + "StringContains": StringContains, + "StringCompare": StringCompare, + "RegexMatch": RegexMatch, + "RegexExtract": RegexExtract +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "StringConcatenate": "Concatenate", + "StringSubstring": "Substring", + "StringLength": "Length", + "CaseConverter": "Case Converter", + "StringTrim": "Trim", + "StringReplace": "Replace", + "StringContains": "Contains", + "StringCompare": "Compare", + "RegexMatch": "Regex Match", + "RegexExtract": "Regex Extract" +} diff --git a/nodes.py b/nodes.py index a1ddf2dd6..a26a138fa 100644 --- a/nodes.py +++ b/nodes.py @@ -2263,6 +2263,7 @@ def init_builtin_extra_nodes(): "nodes_fresca.py", "nodes_preview_any.py", "nodes_ace.py", + "nodes_string.py", ] import_failed = [] From f5cacaeb14acb07c9dde561297ec5e06f5c9c4c8 Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Mon, 12 May 2025 16:47:02 -0400 Subject: [PATCH 100/193] Update frontend to v1.19 (#8076) * Update frontend to v1.19 * Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 01aab4ca2..8f7a78984 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.18.10 +comfyui-frontend-package==1.19.9 comfyui-workflow-templates==0.1.14 torch torchsde From 9ad287ff20daca21e5ab30e82ceb3095fc32c154 Mon Sep 17 00:00:00 2001 From: Terry Jia Date: Mon, 12 May 2025 16:47:14 -0400 Subject: [PATCH 101/193] add support to record video as output for 3d node (#7927) * add support to record video as output for 3d node * source format * add support to record video for load3d animation node --- comfy_extras/nodes_load_3d.py | 30 ++++++++++++++++++++++++------ 1 file changed, 24 insertions(+), 6 deletions(-) diff --git a/comfy_extras/nodes_load_3d.py b/comfy_extras/nodes_load_3d.py index 53d892bc4..d5b4d9111 100644 --- a/comfy_extras/nodes_load_3d.py +++ b/comfy_extras/nodes_load_3d.py @@ -2,6 +2,10 @@ import nodes import folder_paths import os +from comfy.comfy_types import IO +from comfy_api.input_impl import VideoFromFile + + def normalize_path(path): return path.replace('\\', '/') @@ -21,8 +25,8 @@ class Load3D(): "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), }} - RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "IMAGE", "LOAD3D_CAMERA") - RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "lineart", "camera_info") + RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO) + RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "lineart", "camera_info", "recording_video") FUNCTION = "process" EXPERIMENTAL = True @@ -41,7 +45,14 @@ class Load3D(): normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path) lineart_image, ignore_mask3 = load_image_node.load_image(image=lineart_path) - return output_image, output_mask, model_file, normal_image, lineart_image, image['camera_info'] + video = None + + if image['recording'] != "": + recording_video_path = folder_paths.get_annotated_filepath(image['recording']) + + video = VideoFromFile(recording_video_path) + + return output_image, output_mask, model_file, normal_image, lineart_image, image['camera_info'], video class Load3DAnimation(): @classmethod @@ -59,8 +70,8 @@ class Load3DAnimation(): "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), }} - RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "LOAD3D_CAMERA") - RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "camera_info") + RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO) + RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "camera_info", "recording_video") FUNCTION = "process" EXPERIMENTAL = True @@ -77,7 +88,14 @@ class Load3DAnimation(): ignore_image, output_mask = load_image_node.load_image(image=mask_path) normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path) - return output_image, output_mask, model_file, normal_image, image['camera_info'] + video = None + + if image['recording'] != "": + recording_video_path = folder_paths.get_annotated_filepath(image['recording']) + + video = VideoFromFile(recording_video_path) + + return output_image, output_mask, model_file, normal_image, image['camera_info'], video class Preview3D(): @classmethod From 4136502b7a530df28e489f81c0c58b30612f9ece Mon Sep 17 00:00:00 2001 From: thot experiment <94414189+thot-experiment@users.noreply.github.com> Date: Mon, 12 May 2025 18:10:24 -0700 Subject: [PATCH 102/193] implement APG guidance (#8081) * first pass at impementing AGP * rename, cleanup code * fix ruff * fix modified cond to match ref impl better, support different cond arity --- comfy_extras/nodes_apg.py | 76 +++++++++++++++++++++++++++++++++++++++ nodes.py | 1 + 2 files changed, 77 insertions(+) create mode 100644 comfy_extras/nodes_apg.py diff --git a/comfy_extras/nodes_apg.py b/comfy_extras/nodes_apg.py new file mode 100644 index 000000000..1325985b2 --- /dev/null +++ b/comfy_extras/nodes_apg.py @@ -0,0 +1,76 @@ +import torch + +def project(v0, v1): + v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3]) + v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1 + v0_orthogonal = v0 - v0_parallel + return v0_parallel, v0_orthogonal + +class APG: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "eta": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "tooltip": "Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1."}), + "norm_threshold": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 50.0, "step": 0.1, "tooltip": "Normalize guidance vector to this value, normalization disable at a setting of 0."}), + "momentum": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip":"Controls a running average of guidance during diffusion, disabled at a setting of 0."}), + } + } + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + CATEGORY = "sampling/custom_sampling" + + def patch(self, model, eta, norm_threshold, momentum): + running_avg = 0 + prev_sigma = None + + def pre_cfg_function(args): + nonlocal running_avg, prev_sigma + + if len(args["conds_out"]) == 1: return args["conds_out"] + + cond = args["conds_out"][0] + uncond = args["conds_out"][1] + sigma = args["sigma"][0] + cond_scale = args["cond_scale"] + + if prev_sigma is not None and sigma > prev_sigma: + running_avg = 0 + prev_sigma = sigma + + guidance = cond - uncond + + if momentum > 0: + if not torch.is_tensor(running_avg): + running_avg = guidance + else: + running_avg = momentum * running_avg + guidance + guidance = running_avg + + if norm_threshold > 0: + guidance_norm = guidance.norm(p=2, dim=[-1, -2, -3], keepdim=True) + scale = torch.minimum( + torch.ones_like(guidance_norm), + norm_threshold / guidance_norm + ) + guidance = guidance * scale + + guidance_parallel, guidance_orthogonal = project(guidance, cond) + modified_guidance = guidance_orthogonal + eta * guidance_parallel + + modified_cond = (uncond + modified_guidance) + (cond - uncond) / cond_scale + + return [modified_cond, uncond] + args["conds_out"][2:] + + m = model.clone() + m.set_model_sampler_pre_cfg_function(pre_cfg_function) + return (m,) + +NODE_CLASS_MAPPINGS = { + "APG": APG, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "APG": "Adaptive Projected Guidance", +} diff --git a/nodes.py b/nodes.py index a26a138fa..54e3886a3 100644 --- a/nodes.py +++ b/nodes.py @@ -2261,6 +2261,7 @@ def init_builtin_extra_nodes(): "nodes_optimalsteps.py", "nodes_hidream.py", "nodes_fresca.py", + "nodes_apg.py", "nodes_preview_any.py", "nodes_ace.py", "nodes_string.py", From 2156ce9453a8508efec4c36c26e810a14a3ce473 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Mon, 12 May 2025 20:06:44 -0700 Subject: [PATCH 103/193] add comment about using api key in headless (#8082) --- script_examples/basic_api_example.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/script_examples/basic_api_example.py b/script_examples/basic_api_example.py index c916e6cb9..9128420c4 100644 --- a/script_examples/basic_api_example.py +++ b/script_examples/basic_api_example.py @@ -101,6 +101,14 @@ prompt_text = """ def queue_prompt(prompt): p = {"prompt": prompt} + + # If the workflow contains API nodes, you can add a Comfy API key to the `extra_data`` field of the payload. + # p["extra_data"] = { + # "api_key_comfy_org": "comfyui-87d01e28d*******************************************************" # replace with real key + # } + # See: https://docs.comfy.org/tutorials/api-nodes/overview + # Generate a key here: https://platform.comfy.org/login + data = json.dumps(p).encode('utf-8') req = request.Request("http://127.0.0.1:8188/prompt", data=data) request.urlopen(req) From 481732a0ed609ccd86cb3d2df00e3d14c9133280 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 13 May 2025 04:32:16 -0700 Subject: [PATCH 104/193] Support official ACE Step loras. (#8094) --- comfy/lora.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/comfy/lora.py b/comfy/lora.py index fff524be2..ef110c164 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -286,6 +286,12 @@ def model_lora_keys_unet(model, key_map={}): key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") key_map["lycoris_{}".format(key_lora)] = k #SimpleTuner lycoris format + if isinstance(model, comfy.model_base.ACEStep): + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): #Official ACE step lora format + key_lora = k[len("diffusion_model."):-len(".weight")] + key_map["{}".format(key_lora)] = k + return key_map From a814f2e8ccf171c967ee2dc095dd02f2a93dccde Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 13 May 2025 04:54:28 -0700 Subject: [PATCH 105/193] Fix issue with old pytorch RMSNorm. (#8095) --- comfy/rmsnorm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/rmsnorm.py b/comfy/rmsnorm.py index 9d82bee1a..66ae8321d 100644 --- a/comfy/rmsnorm.py +++ b/comfy/rmsnorm.py @@ -30,7 +30,7 @@ if RMSNorm is None: def __init__( self, normalized_shape, - eps=None, + eps=1e-6, elementwise_affine=True, device=None, dtype=None, From 8a7c894d5415c499817c1fcdcba26940755c03a4 Mon Sep 17 00:00:00 2001 From: thot experiment <94414189+thot-experiment@users.noreply.github.com> Date: Tue, 13 May 2025 10:50:32 -0700 Subject: [PATCH 106/193] fix negative momentum (#8100) --- comfy_extras/nodes_apg.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_apg.py b/comfy_extras/nodes_apg.py index 1325985b2..25b21b1b8 100644 --- a/comfy_extras/nodes_apg.py +++ b/comfy_extras/nodes_apg.py @@ -14,7 +14,7 @@ class APG: "model": ("MODEL",), "eta": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "tooltip": "Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1."}), "norm_threshold": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 50.0, "step": 0.1, "tooltip": "Normalize guidance vector to this value, normalization disable at a setting of 0."}), - "momentum": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip":"Controls a running average of guidance during diffusion, disabled at a setting of 0."}), + "momentum": ("FLOAT", {"default": 0.0, "min": -5.0, "max": 1.0, "step": 0.01, "tooltip":"Controls a running average of guidance during diffusion, disabled at a setting of 0."}), } } RETURN_TYPES = ("MODEL",) @@ -41,7 +41,7 @@ class APG: guidance = cond - uncond - if momentum > 0: + if momentum != 0: if not torch.is_tensor(running_avg): running_avg = guidance else: From 4a9014e201b71dfcf97d59d1086cb451cd40873f Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 13 May 2025 12:53:47 -0700 Subject: [PATCH 107/193] Hunyuan Custom initial untested implementation. (#8101) --- comfy/ldm/hunyuan_video/model.py | 21 ++++++++++++++++++--- comfy/model_base.py | 4 ++++ comfy_extras/nodes_hunyuan.py | 6 ++++-- 3 files changed, 26 insertions(+), 5 deletions(-) diff --git a/comfy/ldm/hunyuan_video/model.py b/comfy/ldm/hunyuan_video/model.py index 72af3d5bb..fbd8d4196 100644 --- a/comfy/ldm/hunyuan_video/model.py +++ b/comfy/ldm/hunyuan_video/model.py @@ -228,6 +228,7 @@ class HunyuanVideo(nn.Module): y: Tensor, guidance: Tensor = None, guiding_frame_index=None, + ref_latent=None, control=None, transformer_options={}, ) -> Tensor: @@ -238,6 +239,14 @@ class HunyuanVideo(nn.Module): img = self.img_in(img) vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype)) + if ref_latent is not None: + ref_latent_ids = self.img_ids(ref_latent) + ref_latent = self.img_in(ref_latent) + img = torch.cat([ref_latent, img], dim=-2) + ref_latent_ids[..., 0] = -1 + ref_latent_ids[..., 2] += (initial_shape[-1] // self.patch_size[-1]) + img_ids = torch.cat([ref_latent_ids, img_ids], dim=-2) + if guiding_frame_index is not None: token_replace_vec = self.time_in(timestep_embedding(guiding_frame_index, 256, time_factor=1.0)) vec_ = self.vector_in(y[:, :self.params.vec_in_dim]) @@ -313,6 +322,8 @@ class HunyuanVideo(nn.Module): img[:, : img_len] += add img = img[:, : img_len] + if ref_latent is not None: + img = img[:, ref_latent.shape[1]:] img = self.final_layer(img, vec, modulation_dims=modulation_dims) # (N, T, patch_size ** 2 * out_channels) @@ -324,7 +335,7 @@ class HunyuanVideo(nn.Module): img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3], initial_shape[4]) return img - def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, guiding_frame_index=None, control=None, transformer_options={}, **kwargs): + def img_ids(self, x): bs, c, t, h, w = x.shape patch_size = self.patch_size t_len = ((t + (patch_size[0] // 2)) // patch_size[0]) @@ -334,7 +345,11 @@ class HunyuanVideo(nn.Module): img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1) img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1) img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1) - img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=bs) + return repeat(img_ids, "t h w c -> b (t h w) c", b=bs) + + def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, control=None, transformer_options={}, **kwargs): + bs, c, t, h, w = x.shape + img_ids = self.img_ids(x) txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) - out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, guidance, guiding_frame_index, control, transformer_options) + out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, guidance, guiding_frame_index, ref_latent, control=control, transformer_options=transformer_options) return out diff --git a/comfy/model_base.py b/comfy/model_base.py index 6d27930dc..047861593 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -924,6 +924,10 @@ class HunyuanVideo(BaseModel): if guiding_frame_index is not None: out['guiding_frame_index'] = comfy.conds.CONDRegular(torch.FloatTensor([guiding_frame_index])) + ref_latent = kwargs.get("ref_latent", None) + if ref_latent is not None: + out['ref_latent'] = comfy.conds.CONDRegular(self.process_latent_in(ref_latent)) + return out def scale_latent_inpaint(self, latent_image, **kwargs): diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py index 504010ad0..d7278e7a7 100644 --- a/comfy_extras/nodes_hunyuan.py +++ b/comfy_extras/nodes_hunyuan.py @@ -77,7 +77,7 @@ class HunyuanImageToVideo: "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), "length": ("INT", {"default": 53, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - "guidance_type": (["v1 (concat)", "v2 (replace)"], ) + "guidance_type": (["v1 (concat)", "v2 (replace)", "custom"], ) }, "optional": {"start_image": ("IMAGE", ), }} @@ -101,10 +101,12 @@ class HunyuanImageToVideo: if guidance_type == "v1 (concat)": cond = {"concat_latent_image": concat_latent_image, "concat_mask": mask} - else: + elif guidance_type == "v2 (replace)": cond = {'guiding_frame_index': 0} latent[:, :, :concat_latent_image.shape[2]] = concat_latent_image out_latent["noise_mask"] = mask + elif guidance_type == "custom": + cond = {"ref_latent": concat_latent_image} positive = node_helpers.conditioning_set_values(positive, cond) From bab836d88d62ebbe3b3040f3a9fff05f79d5049d Mon Sep 17 00:00:00 2001 From: thot experiment <94414189+thot-experiment@users.noreply.github.com> Date: Tue, 13 May 2025 17:42:29 -0700 Subject: [PATCH 108/193] rework client.py to be more robust, add logging of api requests (#7988) * rework how errors are handled on the client side * add logging to /temp * fix ruff * fix rebase, stupid vscode gui --- comfy_api_nodes/apis/client.py | 543 ++++++++++++++++++++++--- comfy_api_nodes/apis/request_logger.py | 125 ++++++ 2 files changed, 622 insertions(+), 46 deletions(-) create mode 100644 comfy_api_nodes/apis/request_logger.py diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py index cff52714f..158d20deb 100644 --- a/comfy_api_nodes/apis/client.py +++ b/comfy_api_nodes/apis/client.py @@ -94,15 +94,18 @@ from __future__ import annotations import logging import time import io -from typing import Dict, Type, Optional, Any, TypeVar, Generic, Callable +import socket +from typing import Dict, Type, Optional, Any, TypeVar, Generic, Callable, Tuple from enum import Enum import json import requests -from urllib.parse import urljoin +from urllib.parse import urljoin, urlparse from pydantic import BaseModel, Field +import uuid # For generating unique operation IDs from comfy.cli_args import args from comfy import utils +from . import request_logger T = TypeVar("T", bound=BaseModel) R = TypeVar("R", bound=BaseModel) @@ -111,6 +114,21 @@ P = TypeVar("P", bound=BaseModel) # For poll response PROGRESS_BAR_MAX = 100 +class NetworkError(Exception): + """Base exception for network-related errors with diagnostic information.""" + pass + + +class LocalNetworkError(NetworkError): + """Exception raised when local network connectivity issues are detected.""" + pass + + +class ApiServerError(NetworkError): + """Exception raised when the API server is unreachable but internet is working.""" + pass + + class EmptyRequest(BaseModel): """Base class for empty request bodies. For GET requests, fields will be sent as query parameters.""" @@ -141,7 +159,7 @@ class HttpMethod(str, Enum): class ApiClient: """ - Client for making HTTP requests to an API with authentication and error handling. + Client for making HTTP requests to an API with authentication, error handling, and retry logic. """ def __init__( @@ -151,12 +169,26 @@ class ApiClient: comfy_api_key: Optional[str] = None, timeout: float = 3600.0, verify_ssl: bool = True, + max_retries: int = 3, + retry_delay: float = 1.0, + retry_backoff_factor: float = 2.0, + retry_status_codes: Optional[Tuple[int, ...]] = None, ): self.base_url = base_url self.auth_token = auth_token self.comfy_api_key = comfy_api_key self.timeout = timeout self.verify_ssl = verify_ssl + self.max_retries = max_retries + self.retry_delay = retry_delay + self.retry_backoff_factor = retry_backoff_factor + # Default retry status codes: 408 (Request Timeout), 429 (Too Many Requests), + # 500, 502, 503, 504 (Server Errors) + self.retry_status_codes = retry_status_codes or (408, 429, 500, 502, 503, 504) + + def _generate_operation_id(self, path: str) -> str: + """Generates a unique operation ID for logging.""" + return f"{path.strip('/').replace('/', '_')}_{uuid.uuid4().hex[:8]}" def _create_json_payload_args( self, @@ -211,6 +243,56 @@ class ApiClient: return headers + def _check_connectivity(self, target_url: str) -> Dict[str, bool]: + """ + Check connectivity to determine if network issues are local or server-related. + + Args: + target_url: URL to check connectivity to + + Returns: + Dictionary with connectivity status details + """ + results = { + "internet_accessible": False, + "api_accessible": False, + "is_local_issue": False, + "is_api_issue": False + } + + # First check basic internet connectivity using a reliable external site + try: + # Use a reliable external domain for checking basic connectivity + check_response = requests.get("https://www.google.com", + timeout=5.0, + verify=self.verify_ssl) + if check_response.status_code < 500: + results["internet_accessible"] = True + except (requests.RequestException, socket.error): + results["internet_accessible"] = False + results["is_local_issue"] = True + return results + + # Now check API server connectivity + try: + # Extract domain from the target URL to do a simpler health check + parsed_url = urlparse(target_url) + api_base = f"{parsed_url.scheme}://{parsed_url.netloc}" + + # Try to reach the API domain + api_response = requests.get(f"{api_base}/health", timeout=5.0, verify=self.verify_ssl) + if api_response.status_code < 500: + results["api_accessible"] = True + else: + results["api_accessible"] = False + results["is_api_issue"] = True + except requests.RequestException: + results["api_accessible"] = False + # If we can reach the internet but not the API, it's an API issue + results["is_api_issue"] = True + + return results + def request( self, method: str, @@ -221,9 +303,10 @@ class ApiClient: headers: Optional[Dict[str, str]] = None, content_type: str = "application/json", multipart_parser: Callable = None, + retry_count: int = 0, # Used internally for tracking retries ) -> Dict[str, Any]: """ - Make an HTTP request to the API + Make an HTTP request to the API with automatic retries for transient errors. Args: method: HTTP method (GET, POST, etc.) @@ -233,12 +316,15 @@ class ApiClient: files: Files to upload headers: Additional headers content_type: Content type of the request. Defaults to application/json. + retry_count: Internal parameter for tracking retries, do not set manually Returns: Parsed JSON response Raises: - requests.RequestException: If the request fails + LocalNetworkError: If local network connectivity issues are detected + ApiServerError: If the API server is unreachable but internet is working + Exception: For other request failures """ url = urljoin(self.base_url, path) self.check_auth(self.auth_token, self.comfy_api_key) @@ -265,6 +351,16 @@ class ApiClient: else: payload_args = self._create_json_payload_args(data, request_headers) + operation_id = self._generate_operation_id(path) + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, + request_url=url, + request_headers=request_headers, + request_params=params, + request_data=data if content_type == "application/json" else "[form-data or other]" + ) + try: response = requests.request( method=method, @@ -275,50 +371,228 @@ class ApiClient: **payload_args, ) + # Check if we should retry based on status code + if (response.status_code in self.retry_status_codes and + retry_count < self.max_retries): + + # Calculate delay with exponential backoff + delay = self.retry_delay * (self.retry_backoff_factor ** retry_count) + + logging.warning( + f"Request failed with status {response.status_code}. " + f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})" + ) + + time.sleep(delay) + return self.request( + method=method, + path=path, + params=params, + data=data, + files=files, + headers=headers, + content_type=content_type, + multipart_parser=multipart_parser, + retry_count=retry_count + 1, + ) + # Raise exception for error status codes response.raise_for_status() - except requests.ConnectionError: - raise Exception( - f"Unable to connect to the API server at {self.base_url}. Please check your internet connection or verify the service is available." + + # Log successful response + response_content_to_log = response.content + try: + # Attempt to parse JSON for prettier logging, fallback to raw content + response_content_to_log = response.json() + except json.JSONDecodeError: + pass # Keep as bytes/str if not JSON + + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, # Pass request details again for context in log + request_url=url, + response_status_code=response.status_code, + response_headers=dict(response.headers), + response_content=response_content_to_log ) - except requests.Timeout: - raise Exception( - f"Request timed out after {self.timeout} seconds. The server might be experiencing high load or the operation is taking longer than expected." + except requests.ConnectionError as e: + error_message = f"ConnectionError: {str(e)}" + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, + request_url=url, + error_message=error_message ) + # Only perform connectivity check if we've exhausted all retries + if retry_count >= self.max_retries: + # Check connectivity to determine if it's a local or API issue + connectivity = self._check_connectivity(self.base_url) + + if connectivity["is_local_issue"]: + raise LocalNetworkError( + "Unable to connect to the API server due to local network issues. " + "Please check your internet connection and try again." + ) from e + elif connectivity["is_api_issue"]: + raise ApiServerError( + f"The API server at {self.base_url} is currently unreachable. " + f"The service may be experiencing issues. Please try again later." + ) from e + + # If we haven't exhausted retries yet, retry the request + if retry_count < self.max_retries: + delay = self.retry_delay * (self.retry_backoff_factor ** retry_count) + logging.warning( + f"Connection error: {str(e)}. " + f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})" + ) + time.sleep(delay) + return self.request( + method=method, + path=path, + params=params, + data=data, + files=files, + headers=headers, + content_type=content_type, + multipart_parser=multipart_parser, + retry_count=retry_count + 1, + ) + + # If we've exhausted retries and didn't identify the specific issue, + # raise a generic exception + final_error_message = ( + f"Unable to connect to the API server after {self.max_retries} attempts. " + f"Please check your internet connection or try again later." + ) + request_logger.log_request_response( # Log final failure + operation_id=operation_id, + request_method=method, request_url=url, + error_message=final_error_message + ) + raise Exception(final_error_message) from e + + except requests.Timeout as e: + error_message = f"Timeout: {str(e)}" + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, request_url=url, + error_message=error_message + ) + # Retry timeouts if we haven't exhausted retries + if retry_count < self.max_retries: + delay = self.retry_delay * (self.retry_backoff_factor ** retry_count) + logging.warning( + f"Request timed out. " + f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})" + ) + time.sleep(delay) + return self.request( + method=method, + path=path, + params=params, + data=data, + files=files, + headers=headers, + content_type=content_type, + multipart_parser=multipart_parser, + retry_count=retry_count + 1, + ) + final_error_message = ( + f"Request timed out after {self.timeout} seconds and {self.max_retries} retry attempts. " + f"The server might be experiencing high load or the operation is taking longer than expected." + ) + request_logger.log_request_response( # Log final failure + operation_id=operation_id, + request_method=method, request_url=url, + error_message=final_error_message + ) + raise Exception(final_error_message) from e except requests.HTTPError as e: status_code = e.response.status_code if hasattr(e, "response") else None - error_message = f"HTTP Error: {str(e)}" + original_error_message = f"HTTP Error: {str(e)}" + error_content_for_log = None + if hasattr(e, "response") and e.response is not None: + error_content_for_log = e.response.content + try: + error_content_for_log = e.response.json() + except json.JSONDecodeError: + pass + + + # Try to extract detailed error message from JSON response for user display + # but log the full error content. + user_display_error_message = original_error_message - # Try to extract detailed error message from JSON response try: - if hasattr(e, "response") and e.response.content: + if hasattr(e, "response") and e.response is not None and e.response.content: error_json = e.response.json() if "error" in error_json and "message" in error_json["error"]: - error_message = f"API Error: {error_json['error']['message']}" + user_display_error_message = f"API Error: {error_json['error']['message']}" if "type" in error_json["error"]: - error_message += f" (Type: {error_json['error']['type']})" + user_display_error_message += f" (Type: {error_json['error']['type']})" + elif isinstance(error_json, dict): # Handle cases where error is just a JSON dict + user_display_error_message = f"API Error: {json.dumps(error_json)}" + else: # Non-dict JSON error + user_display_error_message = f"API Error: {str(error_json)}" + except json.JSONDecodeError: + # If not JSON, use the raw content if it's not too long, or a summary + if hasattr(e, "response") and e.response is not None and e.response.content: + raw_content = e.response.content.decode(errors='ignore') + if len(raw_content) < 200: # Arbitrary limit for display + user_display_error_message = f"API Error (raw): {raw_content}" else: - error_message = f"API Error: {error_json}" - except Exception as json_error: - # If we can't parse the JSON, fall back to the original error message - logging.debug( - f"[DEBUG] Failed to parse error response: {str(json_error)}" + user_display_error_message = f"API Error (raw, status {status_code})" + + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, request_url=url, + response_status_code=status_code, + response_headers=dict(e.response.headers) if hasattr(e, "response") and e.response is not None else None, + response_content=error_content_for_log, + error_message=original_error_message # Log the original exception string as error + ) + + logging.debug(f"[DEBUG] API Error: {user_display_error_message} (Status: {status_code})") + if hasattr(e, "response") and e.response is not None and e.response.content: + logging.debug(f"[DEBUG] Response content: {e.response.content}") + + # Retry if the status code is in our retry list and we haven't exhausted retries + if (status_code in self.retry_status_codes and + retry_count < self.max_retries): + + delay = self.retry_delay * (self.retry_backoff_factor ** retry_count) + logging.warning( + f"HTTP error {status_code}. " + f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})" + ) + time.sleep(delay) + return self.request( + method=method, + path=path, + params=params, + data=data, + files=files, + headers=headers, + content_type=content_type, + multipart_parser=multipart_parser, + retry_count=retry_count + 1, ) - logging.debug(f"[DEBUG] API Error: {error_message} (Status: {status_code})") - if hasattr(e, "response") and e.response.content: - logging.debug(f"[DEBUG] Response content: {e.response.content}") + # Specific error messages for common status codes for user display if status_code == 401: - error_message = "Unauthorized: Please login first to use this node." - if status_code == 402: - error_message = "Payment Required: Please add credits to your account to use this node." - if status_code == 409: - error_message = "There is a problem with your account. Please contact support@comfy.org. " - if status_code == 429: - error_message = "Rate Limit Exceeded: Please try again later." - raise Exception(error_message) + user_display_error_message = "Unauthorized: Please login first to use this node." + elif status_code == 402: + user_display_error_message = "Payment Required: Please add credits to your account to use this node." + elif status_code == 409: + user_display_error_message = "There is a problem with your account. Please contact support@comfy.org." + elif status_code == 429: + user_display_error_message = "Rate Limit Exceeded: Please try again later." + # else, user_display_error_message remains as parsed from response or original HTTPError string + + raise Exception(user_display_error_message) # Raise with the user-friendly message # Parse and return JSON response if response.content: @@ -336,26 +610,126 @@ class ApiClient: upload_url: str, file: io.BytesIO | str, content_type: str | None = None, + max_retries: int = 3, + retry_delay: float = 1.0, + retry_backoff_factor: float = 2.0, ): - """Upload a file to the API. Make sure the file has a filename equal to what the url expects. + """Upload a file to the API with retry logic. Args: upload_url: The URL to upload to file: Either a file path string, BytesIO object, or tuple of (file_path, filename) - mime_type: Optional mime type to set for the upload + content_type: Optional mime type to set for the upload + max_retries: Maximum number of retry attempts + retry_delay: Initial delay between retries in seconds + retry_backoff_factor: Multiplier for the delay after each retry """ headers = {} if content_type: headers["Content-Type"] = content_type + # Prepare the file data if isinstance(file, io.BytesIO): file.seek(0) # Ensure we're at the start of the file data = file.read() - return requests.put(upload_url, data=data, headers=headers) elif isinstance(file, str): with open(file, "rb") as f: data = f.read() - return requests.put(upload_url, data=data, headers=headers) + else: + raise ValueError("File must be either a BytesIO object or a file path string") + + # Try the upload with retries + last_exception = None + operation_id = f"upload_{upload_url.split('/')[-1]}_{uuid.uuid4().hex[:8]}" # Simplified ID for uploads + + # Log initial attempt (without full file data for brevity) + request_logger.log_request_response( + operation_id=operation_id, + request_method="PUT", + request_url=upload_url, + request_headers=headers, + request_data=f"[File data of type {content_type or 'unknown'}, size {len(data)} bytes]" + ) + + for retry_attempt in range(max_retries + 1): + try: + response = requests.put(upload_url, data=data, headers=headers) + response.raise_for_status() + request_logger.log_request_response( + operation_id=operation_id, + request_method="PUT", request_url=upload_url, # For context + response_status_code=response.status_code, + response_headers=dict(response.headers), + response_content="File uploaded successfully." # Or response.text if available + ) + return response + + except (requests.ConnectionError, requests.Timeout, requests.HTTPError) as e: + last_exception = e + error_message_for_log = f"{type(e).__name__}: {str(e)}" + response_content_for_log = None + status_code_for_log = None + headers_for_log = None + + if hasattr(e, 'response') and e.response is not None: + status_code_for_log = e.response.status_code + headers_for_log = dict(e.response.headers) + try: + response_content_for_log = e.response.json() + except json.JSONDecodeError: + response_content_for_log = e.response.content + + + request_logger.log_request_response( + operation_id=operation_id, + request_method="PUT", request_url=upload_url, + response_status_code=status_code_for_log, + response_headers=headers_for_log, + response_content=response_content_for_log, + error_message=error_message_for_log + ) + + if retry_attempt < max_retries: + delay = retry_delay * (retry_backoff_factor ** retry_attempt) + logging.warning( + f"File upload failed: {str(e)}. " + f"Retrying in {delay:.2f}s ({retry_attempt + 1}/{max_retries})" + ) + time.sleep(delay) + else: + break # Max retries reached + + # If we've exhausted all retries, determine the final error type and raise + final_error_message = f"Failed to upload file after {max_retries + 1} attempts. Error: {str(last_exception)}" + try: + # Check basic internet connectivity + check_response = requests.get("https://www.google.com", timeout=5.0, verify=True) # Assuming verify=True is desired + if check_response.status_code >= 500: # Google itself has an issue (rare) + final_error_message = (f"Failed to upload file. Internet connectivity check to Google failed " + f"(status {check_response.status_code}). Original error: {str(last_exception)}") + # Not raising LocalNetworkError here as Google itself might be down. + # If Google is reachable, the issue is likely with the upload server or a more specific local problem + # not caught by a simple Google ping (e.g., DNS for the specific upload URL, firewall). + # The original last_exception is probably most relevant. + + except (requests.RequestException, socket.error) as conn_check_exc: + # Could not reach Google, likely a local network issue + final_error_message = (f"Failed to upload file due to network connectivity issues " + f"(cannot reach Google: {str(conn_check_exc)}). " + f"Original upload error: {str(last_exception)}") + request_logger.log_request_response( # Log final failure reason + operation_id=operation_id, + request_method="PUT", request_url=upload_url, + error_message=final_error_message + ) + raise LocalNetworkError(final_error_message) from last_exception + + request_logger.log_request_response( # Log final failure reason if not LocalNetworkError + operation_id=operation_id, + request_method="PUT", request_url=upload_url, + error_message=final_error_message + ) + raise Exception(final_error_message) from last_exception class ApiEndpoint(Generic[T, R]): @@ -403,6 +777,9 @@ class SynchronousOperation(Generic[T, R]): verify_ssl: bool = True, content_type: str = "application/json", multipart_parser: Callable = None, + max_retries: int = 3, + retry_delay: float = 1.0, + retry_backoff_factor: float = 2.0, ): self.endpoint = endpoint self.request = request @@ -419,8 +796,12 @@ class SynchronousOperation(Generic[T, R]): self.files = files self.content_type = content_type self.multipart_parser = multipart_parser + self.max_retries = max_retries + self.retry_delay = retry_delay + self.retry_backoff_factor = retry_backoff_factor + def execute(self, client: Optional[ApiClient] = None) -> R: - """Execute the API operation using the provided client or create one""" + """Execute the API operation using the provided client or create one with retry support""" try: # Create client if not provided if client is None: @@ -430,6 +811,9 @@ class SynchronousOperation(Generic[T, R]): comfy_api_key=self.comfy_api_key, timeout=self.timeout, verify_ssl=self.verify_ssl, + max_retries=self.max_retries, + retry_delay=self.retry_delay, + retry_backoff_factor=self.retry_backoff_factor, ) # Convert request model to dict, but use None for EmptyRequest @@ -443,11 +827,6 @@ class SynchronousOperation(Generic[T, R]): if isinstance(value, Enum): request_dict[key] = value.value - if request_dict: - for key, value in request_dict.items(): - if isinstance(value, Enum): - request_dict[key] = value.value - # Debug log for request logging.debug( f"[DEBUG] API Request: {self.endpoint.method.value} {self.endpoint.path}" @@ -455,7 +834,7 @@ class SynchronousOperation(Generic[T, R]): logging.debug(f"[DEBUG] Request Data: {json.dumps(request_dict, indent=2)}") logging.debug(f"[DEBUG] Query Params: {self.endpoint.query_params}") - # Make the request + # Make the request with built-in retry resp = client.request( method=self.endpoint.method.value, path=self.endpoint.path, @@ -476,8 +855,18 @@ class SynchronousOperation(Generic[T, R]): # Parse and return the response return self._parse_response(resp) + except LocalNetworkError as e: + # Propagate specific network error types + logging.error(f"[ERROR] Local network error: {str(e)}") + raise + + except ApiServerError as e: + # Propagate API server errors + logging.error(f"[ERROR] API server error: {str(e)}") + raise + except Exception as e: - logging.error(f"[DEBUG] API Exception: {str(e)}") + logging.error(f"[ERROR] API Exception: {str(e)}") raise Exception(str(e)) def _parse_response(self, resp): @@ -517,6 +906,10 @@ class PollingOperation(Generic[T, R]): comfy_api_key: Optional[str] = None, auth_kwargs: Optional[Dict[str,str]] = None, poll_interval: float = 5.0, + max_poll_attempts: int = 120, # Default max polling attempts (10 minutes with 5s interval) + max_retries: int = 3, # Max retries per individual API call + retry_delay: float = 1.0, + retry_backoff_factor: float = 2.0, ): self.poll_endpoint = poll_endpoint self.request = request @@ -527,6 +920,10 @@ class PollingOperation(Generic[T, R]): self.auth_token = auth_kwargs.get("auth_token", self.auth_token) self.comfy_api_key = auth_kwargs.get("comfy_api_key", self.comfy_api_key) self.poll_interval = poll_interval + self.max_poll_attempts = max_poll_attempts + self.max_retries = max_retries + self.retry_delay = retry_delay + self.retry_backoff_factor = retry_backoff_factor # Polling configuration self.status_extractor = status_extractor or ( @@ -548,8 +945,23 @@ class PollingOperation(Generic[T, R]): base_url=self.api_base, auth_token=self.auth_token, comfy_api_key=self.comfy_api_key, + max_retries=self.max_retries, + retry_delay=self.retry_delay, + retry_backoff_factor=self.retry_backoff_factor, ) return self._poll_until_complete(client) + except LocalNetworkError as e: + # Provide clear message for local network issues + raise Exception( + f"Polling failed due to local network issues. Please check your internet connection. " + f"Details: {str(e)}" + ) from e + except ApiServerError as e: + # Provide clear message for API server issues + raise Exception( + f"Polling failed due to API server issues. The service may be experiencing problems. " + f"Please try again later. Details: {str(e)}" + ) from e except Exception as e: raise Exception(f"Error during polling: {str(e)}") @@ -569,10 +981,13 @@ class PollingOperation(Generic[T, R]): def _poll_until_complete(self, client: ApiClient) -> R: """Poll until the task is complete""" poll_count = 0 + consecutive_errors = 0 + max_consecutive_errors = min(5, self.max_retries * 2) # Limit consecutive errors + if self.progress_extractor: progress = utils.ProgressBar(PROGRESS_BAR_MAX) - while True: + while poll_count < self.max_poll_attempts: try: poll_count += 1 logging.debug(f"[DEBUG] Polling attempt #{poll_count}") @@ -599,8 +1014,12 @@ class PollingOperation(Generic[T, R]): data=request_dict, ) + # Successfully got a response, reset consecutive error count + consecutive_errors = 0 + # Parse response response_obj = self.poll_endpoint.response_model.model_validate(resp) + # Check if task is complete status = self._check_task_status(response_obj) logging.debug(f"[DEBUG] Task Status: {status}") @@ -630,6 +1049,38 @@ class PollingOperation(Generic[T, R]): ) time.sleep(self.poll_interval) + except (LocalNetworkError, ApiServerError) as e: + # For network-related errors, increment error count and potentially abort + consecutive_errors += 1 + if consecutive_errors >= max_consecutive_errors: + raise Exception( + f"Polling aborted after {consecutive_errors} consecutive network errors: {str(e)}" + ) from e + + # Log the error but continue polling + logging.warning( + f"Network error during polling (attempt {poll_count}/{self.max_poll_attempts}): {str(e)}. " + f"Will retry in {self.poll_interval} seconds." + ) + time.sleep(self.poll_interval) + except Exception as e: + # For other errors, increment count and potentially abort + consecutive_errors += 1 + if consecutive_errors >= max_consecutive_errors: + raise Exception( + f"Polling aborted after {consecutive_errors} consecutive errors: {str(e)}" + ) from e + logging.error(f"[DEBUG] Polling error: {str(e)}") - raise Exception(f"Error while polling: {str(e)}") + logging.warning( + f"Error during polling (attempt {poll_count}/{self.max_poll_attempts}): {str(e)}. " + f"Will retry in {self.poll_interval} seconds." + ) + time.sleep(self.poll_interval) + + # If we've exhausted all polling attempts + raise Exception( + f"Polling timed out after {poll_count} attempts ({poll_count * self.poll_interval} seconds). " + f"The operation may still be running on the server but is taking longer than expected." + ) diff --git a/comfy_api_nodes/apis/request_logger.py b/comfy_api_nodes/apis/request_logger.py new file mode 100644 index 000000000..93517ede9 --- /dev/null +++ b/comfy_api_nodes/apis/request_logger.py @@ -0,0 +1,125 @@ +import os +import datetime +import json +import logging +import folder_paths + +# Get the logger instance +logger = logging.getLogger(__name__) + +def get_log_directory(): + """ + Ensures the API log directory exists within ComfyUI's temp directory + and returns its path. + """ + base_temp_dir = folder_paths.get_temp_directory() + log_dir = os.path.join(base_temp_dir, "api_logs") + try: + os.makedirs(log_dir, exist_ok=True) + except Exception as e: + logger.error(f"Error creating API log directory {log_dir}: {e}") + # Fallback to base temp directory if sub-directory creation fails + return base_temp_dir + return log_dir + +def _format_data_for_logging(data): + """Helper to format data (dict, str, bytes) for logging.""" + if isinstance(data, bytes): + try: + return data.decode('utf-8') # Try to decode as text + except UnicodeDecodeError: + return f"[Binary data of length {len(data)} bytes]" + elif isinstance(data, (dict, list)): + try: + return json.dumps(data, indent=2, ensure_ascii=False) + except TypeError: + return str(data) # Fallback for non-serializable objects + return str(data) + +def log_request_response( + operation_id: str, + request_method: str, + request_url: str, + request_headers: dict | None = None, + request_params: dict | None = None, + request_data: any = None, + response_status_code: int | None = None, + response_headers: dict | None = None, + response_content: any = None, + error_message: str | None = None +): + """ + Logs API request and response details to a file in the temp/api_logs directory. + """ + log_dir = get_log_directory() + timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f") + filename = f"{timestamp}_{operation_id.replace('/', '_').replace(':', '_')}.log" + filepath = os.path.join(log_dir, filename) + + log_content = [] + + log_content.append(f"Timestamp: {datetime.datetime.now().isoformat()}") + log_content.append(f"Operation ID: {operation_id}") + log_content.append("-" * 30 + " REQUEST " + "-" * 30) + log_content.append(f"Method: {request_method}") + log_content.append(f"URL: {request_url}") + if request_headers: + log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}") + if request_params: + log_content.append(f"Params:\n{_format_data_for_logging(request_params)}") + if request_data: + log_content.append(f"Data/Body:\n{_format_data_for_logging(request_data)}") + + log_content.append("\n" + "-" * 30 + " RESPONSE " + "-" * 30) + if response_status_code is not None: + log_content.append(f"Status Code: {response_status_code}") + if response_headers: + log_content.append(f"Headers:\n{_format_data_for_logging(response_headers)}") + if response_content: + log_content.append(f"Content:\n{_format_data_for_logging(response_content)}") + if error_message: + log_content.append(f"Error:\n{error_message}") + + try: + with open(filepath, "w", encoding="utf-8") as f: + f.write("\n".join(log_content)) + logger.debug(f"API log saved to: {filepath}") + except Exception as e: + logger.error(f"Error writing API log to {filepath}: {e}") + +if __name__ == '__main__': + # Example usage (for testing the logger directly) + logger.setLevel(logging.DEBUG) + # Mock folder_paths for direct execution if not running within ComfyUI full context + if not hasattr(folder_paths, 'get_temp_directory'): + class MockFolderPaths: + def get_temp_directory(self): + # Create a local temp dir for testing if needed + p = os.path.join(os.path.dirname(__file__), 'temp_test_logs') + os.makedirs(p, exist_ok=True) + return p + folder_paths = MockFolderPaths() + + log_request_response( + operation_id="test_operation_get", + request_method="GET", + request_url="https://api.example.com/test", + request_headers={"Authorization": "Bearer testtoken"}, + request_params={"param1": "value1"}, + response_status_code=200, + response_content={"message": "Success!"} + ) + log_request_response( + operation_id="test_operation_post_error", + request_method="POST", + request_url="https://api.example.com/submit", + request_data={"key": "value", "nested": {"num": 123}}, + error_message="Connection timed out" + ) + log_request_response( + operation_id="test_binary_response", + request_method="GET", + request_url="https://api.example.com/image.png", + response_status_code=200, + response_content=b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR...' # Sample binary data + ) From 98ff01e1486353a3b0ddd8fa82fcbb25401f8364 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Tue, 13 May 2025 21:33:18 -0700 Subject: [PATCH 109/193] Display progress and result URL directly on API nodes (#8102) * [Luma] Print download URL of successful task result directly on nodes (#177) [Veo] Print download URL of successful task result directly on nodes (#184) [Recraft] Print download URL of successful task result directly on nodes (#183) [Pixverse] Print download URL of successful task result directly on nodes (#182) [Kling] Print download URL of successful task result directly on nodes (#181) [MiniMax] Print progress text and download URL of successful task result directly on nodes (#179) [Docs] Link to docs in `API_NODE` class property type annotation comment (#178) [Ideogram] Print download URL of successful task result directly on nodes (#176) [Kling] Print download URL of successful task result directly on nodes (#181) [Veo] Print download URL of successful task result directly on nodes (#184) [Recraft] Print download URL of successful task result directly on nodes (#183) [Pixverse] Print download URL of successful task result directly on nodes (#182) [MiniMax] Print progress text and download URL of successful task result directly on nodes (#179) [Docs] Link to docs in `API_NODE` class property type annotation comment (#178) [Luma] Print download URL of successful task result directly on nodes (#177) [Ideogram] Print download URL of successful task result directly on nodes (#176) Show output URL and progress text on Pika nodes (#168) [BFL] Print download URL of successful task result directly on nodes (#175) [OpenAI ] Print download URL of successful task result directly on nodes (#174) * fix ruff errors * fix 3.10 syntax error --- comfy/comfy_types/node_typing.py | 2 +- comfy_api_nodes/apinode_utils.py | 11 +- comfy_api_nodes/apis/client.py | 42 +++++++- comfy_api_nodes/nodes_bfl.py | 63 ++++++++---- comfy_api_nodes/nodes_ideogram.py | 24 ++++- comfy_api_nodes/nodes_kling.py | 164 ++++++++++++++++++++++++++---- comfy_api_nodes/nodes_luma.py | 33 ++++++ comfy_api_nodes/nodes_minimax.py | 27 ++++- comfy_api_nodes/nodes_openai.py | 12 ++- comfy_api_nodes/nodes_pika.py | 42 ++++++-- comfy_api_nodes/nodes_pixverse.py | 99 ++++++++++++------ comfy_api_nodes/nodes_recraft.py | 21 ++++ comfy_api_nodes/nodes_veo2.py | 26 ++++- 13 files changed, 474 insertions(+), 92 deletions(-) diff --git a/comfy/comfy_types/node_typing.py b/comfy/comfy_types/node_typing.py index 2ffc9c021..470eb9fdb 100644 --- a/comfy/comfy_types/node_typing.py +++ b/comfy/comfy_types/node_typing.py @@ -235,7 +235,7 @@ class ComfyNodeABC(ABC): DEPRECATED: bool """Flags a node as deprecated, indicating to users that they should find alternatives to this node.""" API_NODE: Optional[bool] - """Flags a node as an API node.""" + """Flags a node as an API node. See: https://docs.comfy.org/tutorials/api-nodes/overview.""" @classmethod @abstractmethod diff --git a/comfy_api_nodes/apinode_utils.py b/comfy_api_nodes/apinode_utils.py index e28d7d607..87d8c3e1d 100644 --- a/comfy_api_nodes/apinode_utils.py +++ b/comfy_api_nodes/apinode_utils.py @@ -1,7 +1,7 @@ from __future__ import annotations import io import logging -from typing import Optional +from typing import Optional, Union from comfy.utils import common_upscale from comfy_api.input_impl import VideoFromFile from comfy_api.util import VideoContainer, VideoCodec @@ -15,6 +15,7 @@ from comfy_api_nodes.apis.client import ( UploadRequest, UploadResponse, ) +from server import PromptServer import numpy as np @@ -60,7 +61,9 @@ def downscale_image_tensor(image, total_pixels=1536 * 1024) -> torch.Tensor: return s -def validate_and_cast_response(response, timeout: int = None) -> torch.Tensor: +def validate_and_cast_response( + response, timeout: int = None, node_id: Union[str, None] = None +) -> torch.Tensor: """Validates and casts a response to a torch.Tensor. Args: @@ -94,6 +97,10 @@ def validate_and_cast_response(response, timeout: int = None) -> torch.Tensor: img = Image.open(io.BytesIO(img_data)) elif image_url: + if node_id: + PromptServer.instance.send_progress_text( + f"Result URL: {image_url}", node_id + ) img_response = requests.get(image_url, timeout=timeout) if img_response.status_code != 200: raise ValueError("Failed to download the image") diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py index 158d20deb..838ff1e8d 100644 --- a/comfy_api_nodes/apis/client.py +++ b/comfy_api_nodes/apis/client.py @@ -103,6 +103,7 @@ from urllib.parse import urljoin, urlparse from pydantic import BaseModel, Field import uuid # For generating unique operation IDs +from server import PromptServer from comfy.cli_args import args from comfy import utils from . import request_logger @@ -900,6 +901,7 @@ class PollingOperation(Generic[T, R]): failed_statuses: list, status_extractor: Callable[[R], str], progress_extractor: Callable[[R], float] = None, + result_url_extractor: Callable[[R], str] = None, request: Optional[T] = None, api_base: str | None = None, auth_token: Optional[str] = None, @@ -910,6 +912,8 @@ class PollingOperation(Generic[T, R]): max_retries: int = 3, # Max retries per individual API call retry_delay: float = 1.0, retry_backoff_factor: float = 2.0, + estimated_duration: Optional[float] = None, + node_id: Optional[str] = None, ): self.poll_endpoint = poll_endpoint self.request = request @@ -924,12 +928,15 @@ class PollingOperation(Generic[T, R]): self.max_retries = max_retries self.retry_delay = retry_delay self.retry_backoff_factor = retry_backoff_factor + self.estimated_duration = estimated_duration # Polling configuration self.status_extractor = status_extractor or ( lambda x: getattr(x, "status", None) ) self.progress_extractor = progress_extractor + self.result_url_extractor = result_url_extractor + self.node_id = node_id self.completed_statuses = completed_statuses self.failed_statuses = failed_statuses @@ -965,6 +972,26 @@ class PollingOperation(Generic[T, R]): except Exception as e: raise Exception(f"Error during polling: {str(e)}") + def _display_text_on_node(self, text: str): + """Sends text to the client which will be displayed on the node in the UI""" + if not self.node_id: + return + + PromptServer.instance.send_progress_text(text, self.node_id) + + def _display_time_progress_on_node(self, time_completed: int): + if not self.node_id: + return + + if self.estimated_duration is not None: + estimated_time_remaining = max( + 0, int(self.estimated_duration) - int(time_completed) + ) + message = f"Task in progress: {time_completed:.0f}s (~{estimated_time_remaining:.0f}s remaining)" + else: + message = f"Task in progress: {time_completed:.0f}s" + self._display_text_on_node(message) + def _check_task_status(self, response: R) -> TaskStatus: """Check task status using the status extractor function""" try: @@ -1031,7 +1058,15 @@ class PollingOperation(Generic[T, R]): progress.update_absolute(new_progress, total=PROGRESS_BAR_MAX) if status == TaskStatus.COMPLETED: - logging.debug("[DEBUG] Task completed successfully") + message = "Task completed successfully" + if self.result_url_extractor: + result_url = self.result_url_extractor(response_obj) + if result_url: + message = f"Result URL: {result_url}" + else: + message = "Task completed successfully!" + logging.debug(f"[DEBUG] {message}") + self._display_text_on_node(message) self.final_response = response_obj if self.progress_extractor: progress.update(100) @@ -1047,7 +1082,10 @@ class PollingOperation(Generic[T, R]): logging.debug( f"[DEBUG] Waiting {self.poll_interval} seconds before next poll" ) - time.sleep(self.poll_interval) + for i in range(int(self.poll_interval)): + time_completed = (poll_count * self.poll_interval) + i + self._display_time_progress_on_node(time_completed) + time.sleep(1) except (LocalNetworkError, ApiServerError) as e: # For network-related errors, increment error count and potentially abort diff --git a/comfy_api_nodes/nodes_bfl.py b/comfy_api_nodes/nodes_bfl.py index 66ef1b391..509170b34 100644 --- a/comfy_api_nodes/nodes_bfl.py +++ b/comfy_api_nodes/nodes_bfl.py @@ -1,5 +1,6 @@ import io from inspect import cleandoc +from typing import Union from comfy.comfy_types.node_typing import IO, ComfyNodeABC from comfy_api_nodes.apis.bfl_api import ( BFLStatus, @@ -30,6 +31,7 @@ import requests import torch import base64 import time +from server import PromptServer def convert_mask_to_image(mask: torch.Tensor): @@ -42,14 +44,19 @@ def convert_mask_to_image(mask: torch.Tensor): def handle_bfl_synchronous_operation( - operation: SynchronousOperation, timeout_bfl_calls=360 + operation: SynchronousOperation, + timeout_bfl_calls=360, + node_id: Union[str, None] = None, ): response_api: BFLFluxProGenerateResponse = operation.execute() return _poll_until_generated( - response_api.polling_url, timeout=timeout_bfl_calls + response_api.polling_url, timeout=timeout_bfl_calls, node_id=node_id ) -def _poll_until_generated(polling_url: str, timeout=360): + +def _poll_until_generated( + polling_url: str, timeout=360, node_id: Union[str, None] = None +): # used bfl-comfy-nodes to verify code implementation: # https://github.com/black-forest-labs/bfl-comfy-nodes/tree/main start_time = time.time() @@ -61,11 +68,21 @@ def _poll_until_generated(polling_url: str, timeout=360): request = requests.Request(method=HttpMethod.GET, url=polling_url) # NOTE: should True loop be replaced with checking if workflow has been interrupted? while True: + if node_id: + time_elapsed = time.time() - start_time + PromptServer.instance.send_progress_text( + f"Generating ({time_elapsed:.0f}s)", node_id + ) + response = requests.Session().send(request.prepare()) if response.status_code == 200: result = response.json() if result["status"] == BFLStatus.ready: img_url = result["result"]["sample"] + if node_id: + PromptServer.instance.send_progress_text( + f"Result URL: {img_url}", node_id + ) img_response = requests.get(img_url) return process_image_response(img_response) elif result["status"] in [ @@ -180,6 +197,7 @@ class FluxProUltraImageNode(ComfyNodeABC): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -212,6 +230,7 @@ class FluxProUltraImageNode(ComfyNodeABC): seed=0, image_prompt=None, image_prompt_strength=0.1, + unique_id: Union[str, None] = None, **kwargs, ): if image_prompt is None: @@ -246,7 +265,7 @@ class FluxProUltraImageNode(ComfyNodeABC): ), auth_kwargs=kwargs, ) - output_image = handle_bfl_synchronous_operation(operation) + output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) return (output_image,) @@ -320,6 +339,7 @@ class FluxProImageNode(ComfyNodeABC): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -338,6 +358,7 @@ class FluxProImageNode(ComfyNodeABC): seed=0, image_prompt=None, # image_prompt_strength=0.1, + unique_id: Union[str, None] = None, **kwargs, ): image_prompt = ( @@ -363,7 +384,7 @@ class FluxProImageNode(ComfyNodeABC): ), auth_kwargs=kwargs, ) - output_image = handle_bfl_synchronous_operation(operation) + output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) return (output_image,) @@ -457,11 +478,11 @@ class FluxProExpandNode(ComfyNodeABC): }, ), }, - "optional": { - }, + "optional": {}, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -483,6 +504,7 @@ class FluxProExpandNode(ComfyNodeABC): steps: int, guidance: float, seed=0, + unique_id: Union[str, None] = None, **kwargs, ): image = convert_image_to_base64(image) @@ -508,7 +530,7 @@ class FluxProExpandNode(ComfyNodeABC): ), auth_kwargs=kwargs, ) - output_image = handle_bfl_synchronous_operation(operation) + output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) return (output_image,) @@ -568,11 +590,11 @@ class FluxProFillNode(ComfyNodeABC): }, ), }, - "optional": { - }, + "optional": {}, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -591,13 +613,14 @@ class FluxProFillNode(ComfyNodeABC): steps: int, guidance: float, seed=0, + unique_id: Union[str, None] = None, **kwargs, ): # prepare mask mask = resize_mask_to_image(mask, image) mask = convert_image_to_base64(convert_mask_to_image(mask)) # make sure image will have alpha channel removed - image = convert_image_to_base64(image[:,:,:,:3]) + image = convert_image_to_base64(image[:, :, :, :3]) operation = SynchronousOperation( endpoint=ApiEndpoint( @@ -617,7 +640,7 @@ class FluxProFillNode(ComfyNodeABC): ), auth_kwargs=kwargs, ) - output_image = handle_bfl_synchronous_operation(operation) + output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) return (output_image,) @@ -702,11 +725,11 @@ class FluxProCannyNode(ComfyNodeABC): }, ), }, - "optional": { - }, + "optional": {}, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -727,9 +750,10 @@ class FluxProCannyNode(ComfyNodeABC): steps: int, guidance: float, seed=0, + unique_id: Union[str, None] = None, **kwargs, ): - control_image = convert_image_to_base64(control_image[:,:,:,:3]) + control_image = convert_image_to_base64(control_image[:, :, :, :3]) preprocessed_image = None # scale canny threshold between 0-500, to match BFL's API @@ -765,7 +789,7 @@ class FluxProCannyNode(ComfyNodeABC): ), auth_kwargs=kwargs, ) - output_image = handle_bfl_synchronous_operation(operation) + output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) return (output_image,) @@ -830,11 +854,11 @@ class FluxProDepthNode(ComfyNodeABC): }, ), }, - "optional": { - }, + "optional": {}, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -853,6 +877,7 @@ class FluxProDepthNode(ComfyNodeABC): steps: int, guidance: float, seed=0, + unique_id: Union[str, None] = None, **kwargs, ): control_image = convert_image_to_base64(control_image[:,:,:,:3]) @@ -880,7 +905,7 @@ class FluxProDepthNode(ComfyNodeABC): ), auth_kwargs=kwargs, ) - output_image = handle_bfl_synchronous_operation(operation) + output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) return (output_image,) diff --git a/comfy_api_nodes/nodes_ideogram.py b/comfy_api_nodes/nodes_ideogram.py index d25468b17..b1cbf511d 100644 --- a/comfy_api_nodes/nodes_ideogram.py +++ b/comfy_api_nodes/nodes_ideogram.py @@ -23,6 +23,7 @@ from comfy_api_nodes.apinode_utils import ( bytesio_to_image_tensor, resize_mask_to_image, ) +from server import PromptServer V1_V1_RES_MAP = { "Auto":"AUTO", @@ -232,6 +233,19 @@ def download_and_process_images(image_urls): return stacked_tensors +def display_image_urls_on_node(image_urls, node_id): + if node_id and image_urls: + if len(image_urls) == 1: + PromptServer.instance.send_progress_text( + f"Generated Image URL:\n{image_urls[0]}", node_id + ) + else: + urls_text = "Generated Image URLs:\n" + "\n".join( + f"{i+1}. {url}" for i, url in enumerate(image_urls) + ) + PromptServer.instance.send_progress_text(urls_text, node_id) + + class IdeogramV1(ComfyNodeABC): """ Generates images using the Ideogram V1 model. @@ -304,6 +318,7 @@ class IdeogramV1(ComfyNodeABC): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -322,6 +337,7 @@ class IdeogramV1(ComfyNodeABC): seed=0, negative_prompt="", num_images=1, + unique_id=None, **kwargs, ): # Determine the model based on turbo setting @@ -361,6 +377,7 @@ class IdeogramV1(ComfyNodeABC): if not image_urls: raise Exception("No image URLs were generated in the response") + display_image_urls_on_node(image_urls, unique_id) return (download_and_process_images(image_urls),) @@ -460,6 +477,7 @@ class IdeogramV2(ComfyNodeABC): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -481,6 +499,7 @@ class IdeogramV2(ComfyNodeABC): negative_prompt="", num_images=1, color_palette="", + unique_id=None, **kwargs, ): aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None) @@ -534,6 +553,7 @@ class IdeogramV2(ComfyNodeABC): if not image_urls: raise Exception("No image URLs were generated in the response") + display_image_urls_on_node(image_urls, unique_id) return (download_and_process_images(image_urls),) class IdeogramV3(ComfyNodeABC): @@ -623,6 +643,7 @@ class IdeogramV3(ComfyNodeABC): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -643,6 +664,7 @@ class IdeogramV3(ComfyNodeABC): seed=0, num_images=1, rendering_speed="BALANCED", + unique_id=None, **kwargs, ): # Check if both image and mask are provided for editing mode @@ -762,6 +784,7 @@ class IdeogramV3(ComfyNodeABC): if not image_urls: raise Exception("No image URLs were generated in the response") + display_image_urls_on_node(image_urls, unique_id) return (download_and_process_images(image_urls),) @@ -776,4 +799,3 @@ NODE_DISPLAY_NAME_MAPPINGS = { "IdeogramV2": "Ideogram V2", "IdeogramV3": "Ideogram V3", } - diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py index 2d0fd8883..456a86905 100644 --- a/comfy_api_nodes/nodes_kling.py +++ b/comfy_api_nodes/nodes_kling.py @@ -6,6 +6,7 @@ For source of truth on the allowed permutations of request fields, please refere from __future__ import annotations from typing import Optional, TypeVar, Any +from collections.abc import Callable import math import logging @@ -86,6 +87,15 @@ MAX_PROMPT_LENGTH_IMAGE_GEN = 500 MAX_NEGATIVE_PROMPT_LENGTH_IMAGE_GEN = 200 MAX_PROMPT_LENGTH_LIP_SYNC = 120 +# TODO: adjust based on tests +AVERAGE_DURATION_T2V = 319 # 319, +AVERAGE_DURATION_I2V = 164 # 164, +AVERAGE_DURATION_LIP_SYNC = 120 +AVERAGE_DURATION_VIRTUAL_TRY_ON = 19 # 19, +AVERAGE_DURATION_IMAGE_GEN = 32 +AVERAGE_DURATION_VIDEO_EFFECTS = 320 +AVERAGE_DURATION_VIDEO_EXTEND = 320 + R = TypeVar("R") @@ -95,7 +105,13 @@ class KlingApiError(Exception): pass -def poll_until_finished(auth_kwargs: dict[str,str], api_endpoint: ApiEndpoint[Any, R]) -> R: +def poll_until_finished( + auth_kwargs: dict[str, str], + api_endpoint: ApiEndpoint[Any, R], + result_url_extractor: Optional[Callable[[R], str]] = None, + estimated_duration: Optional[int] = None, + node_id: Optional[str] = None, +) -> R: """Polls the Kling API endpoint until the task reaches a terminal state, then returns the response.""" return PollingOperation( poll_endpoint=api_endpoint, @@ -109,6 +125,9 @@ def poll_until_finished(auth_kwargs: dict[str,str], api_endpoint: ApiEndpoint[An else None ), auth_kwargs=auth_kwargs, + result_url_extractor=result_url_extractor, + estimated_duration=estimated_duration, + node_id=node_id, ).execute() @@ -227,7 +246,9 @@ def get_camera_control_input_config( def get_video_from_response(response) -> KlingVideoResult: - """Returns the first video object from the Kling video generation task result.""" + """Returns the first video object from the Kling video generation task result. + Will raise an error if the response is not valid. + """ video = response.data.task_result.videos[0] logging.info( "Kling task %s succeeded. Video URL: %s", response.data.task_id, video.url @@ -235,12 +256,37 @@ def get_video_from_response(response) -> KlingVideoResult: return video +def get_video_url_from_response(response) -> Optional[str]: + """Returns the first video url from the Kling video generation task result. + Will not raise an error if the response is not valid. + """ + if response and is_valid_video_response(response): + return str(get_video_from_response(response).url) + else: + return None + + def get_images_from_response(response) -> list[KlingImageResult]: + """Returns the list of image objects from the Kling image generation task result. + Will raise an error if the response is not valid. + """ images = response.data.task_result.images logging.info("Kling task %s succeeded. Images: %s", response.data.task_id, images) return images +def get_images_urls_from_response(response) -> Optional[str]: + """Returns the list of image urls from the Kling image generation task result. + Will not raise an error if the response is not valid. If there is only one image, returns the url as a string. If there are multiple images, returns a list of urls. + """ + if response and is_valid_image_response(response): + images = get_images_from_response(response) + image_urls = [str(image.url) for image in images] + return "\n".join(image_urls) + else: + return None + + def video_result_to_node_output( video: KlingVideoResult, ) -> tuple[VideoFromFile, str, str]: @@ -312,6 +358,7 @@ class KlingCameraControls(KlingNodeBase): RETURN_TYPES = ("CAMERA_CONTROL",) RETURN_NAMES = ("camera_control",) FUNCTION = "main" + API_NODE = False # This is just a helper node, it doesn't make an API call @classmethod def VALIDATE_INPUTS( @@ -421,6 +468,7 @@ class KlingTextToVideoNode(KlingNodeBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -428,7 +476,9 @@ class KlingTextToVideoNode(KlingNodeBase): RETURN_NAMES = ("VIDEO", "video_id", "duration") DESCRIPTION = "Kling Text to Video Node" - def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingText2VideoResponse: + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> KlingText2VideoResponse: return poll_until_finished( auth_kwargs, ApiEndpoint( @@ -437,6 +487,9 @@ class KlingTextToVideoNode(KlingNodeBase): request_model=EmptyRequest, response_model=KlingText2VideoResponse, ), + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_T2V, + node_id=node_id, ) def api_call( @@ -449,6 +502,7 @@ class KlingTextToVideoNode(KlingNodeBase): camera_control: Optional[KlingCameraControl] = None, model_name: Optional[str] = None, duration: Optional[str] = None, + unique_id: Optional[str] = None, **kwargs, ) -> tuple[VideoFromFile, str, str]: validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V) @@ -478,7 +532,9 @@ class KlingTextToVideoNode(KlingNodeBase): validate_task_creation_response(task_creation_response) task_id = task_creation_response.data.task_id - final_response = self.get_response(task_id, auth_kwargs=kwargs) + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) validate_video_result_response(final_response) video = get_video_from_response(final_response) @@ -528,6 +584,7 @@ class KlingCameraControlT2VNode(KlingTextToVideoNode): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -540,6 +597,7 @@ class KlingCameraControlT2VNode(KlingTextToVideoNode): cfg_scale: float, aspect_ratio: str, camera_control: Optional[KlingCameraControl] = None, + unique_id: Optional[str] = None, **kwargs, ): return super().api_call( @@ -613,6 +671,7 @@ class KlingImage2VideoNode(KlingNodeBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -620,7 +679,9 @@ class KlingImage2VideoNode(KlingNodeBase): RETURN_NAMES = ("VIDEO", "video_id", "duration") DESCRIPTION = "Kling Image to Video Node" - def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingImage2VideoResponse: + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> KlingImage2VideoResponse: return poll_until_finished( auth_kwargs, ApiEndpoint( @@ -629,6 +690,9 @@ class KlingImage2VideoNode(KlingNodeBase): request_model=KlingImage2VideoRequest, response_model=KlingImage2VideoResponse, ), + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_I2V, + node_id=node_id, ) def api_call( @@ -643,6 +707,7 @@ class KlingImage2VideoNode(KlingNodeBase): duration: str, camera_control: Optional[KlingCameraControl] = None, end_frame: Optional[torch.Tensor] = None, + unique_id: Optional[str] = None, **kwargs, ) -> tuple[VideoFromFile]: validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_I2V) @@ -681,7 +746,9 @@ class KlingImage2VideoNode(KlingNodeBase): validate_task_creation_response(task_creation_response) task_id = task_creation_response.data.task_id - final_response = self.get_response(task_id, auth_kwargs=kwargs) + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) validate_video_result_response(final_response) video = get_video_from_response(final_response) @@ -734,6 +801,7 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -747,6 +815,7 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode): cfg_scale: float, aspect_ratio: str, camera_control: KlingCameraControl, + unique_id: Optional[str] = None, **kwargs, ): return super().api_call( @@ -759,6 +828,7 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode): prompt=prompt, negative_prompt=negative_prompt, camera_control=camera_control, + unique_id=unique_id, **kwargs, ) @@ -830,6 +900,7 @@ class KlingStartEndFrameNode(KlingImage2VideoNode): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -844,6 +915,7 @@ class KlingStartEndFrameNode(KlingImage2VideoNode): cfg_scale: float, aspect_ratio: str, mode: str, + unique_id: Optional[str] = None, **kwargs, ): mode, duration, model_name = KlingStartEndFrameNode.get_mode_string_mapping()[ @@ -859,6 +931,7 @@ class KlingStartEndFrameNode(KlingImage2VideoNode): aspect_ratio=aspect_ratio, duration=duration, end_frame=end_frame, + unique_id=unique_id, **kwargs, ) @@ -892,6 +965,7 @@ class KlingVideoExtendNode(KlingNodeBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -899,7 +973,9 @@ class KlingVideoExtendNode(KlingNodeBase): RETURN_NAMES = ("VIDEO", "video_id", "duration") DESCRIPTION = "Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes." - def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingVideoExtendResponse: + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> KlingVideoExtendResponse: return poll_until_finished( auth_kwargs, ApiEndpoint( @@ -908,6 +984,9 @@ class KlingVideoExtendNode(KlingNodeBase): request_model=EmptyRequest, response_model=KlingVideoExtendResponse, ), + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_VIDEO_EXTEND, + node_id=node_id, ) def api_call( @@ -916,6 +995,7 @@ class KlingVideoExtendNode(KlingNodeBase): negative_prompt: str, cfg_scale: float, video_id: str, + unique_id: Optional[str] = None, **kwargs, ) -> tuple[VideoFromFile, str, str]: validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V) @@ -939,7 +1019,9 @@ class KlingVideoExtendNode(KlingNodeBase): validate_task_creation_response(task_creation_response) task_id = task_creation_response.data.task_id - final_response = self.get_response(task_id, auth_kwargs=kwargs) + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) validate_video_result_response(final_response) video = get_video_from_response(final_response) @@ -952,7 +1034,9 @@ class KlingVideoEffectsBase(KlingNodeBase): RETURN_TYPES = ("VIDEO", "STRING", "STRING") RETURN_NAMES = ("VIDEO", "video_id", "duration") - def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingVideoEffectsResponse: + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> KlingVideoEffectsResponse: return poll_until_finished( auth_kwargs, ApiEndpoint( @@ -961,6 +1045,9 @@ class KlingVideoEffectsBase(KlingNodeBase): request_model=EmptyRequest, response_model=KlingVideoEffectsResponse, ), + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_VIDEO_EFFECTS, + node_id=node_id, ) def api_call( @@ -972,6 +1059,7 @@ class KlingVideoEffectsBase(KlingNodeBase): image_1: torch.Tensor, image_2: Optional[torch.Tensor] = None, mode: Optional[KlingVideoGenMode] = None, + unique_id: Optional[str] = None, **kwargs, ): if dual_character: @@ -1009,7 +1097,9 @@ class KlingVideoEffectsBase(KlingNodeBase): validate_task_creation_response(task_creation_response) task_id = task_creation_response.data.task_id - final_response = self.get_response(task_id, auth_kwargs=kwargs) + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) validate_video_result_response(final_response) video = get_video_from_response(final_response) @@ -1053,6 +1143,7 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -1068,6 +1159,7 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase): model_name: KlingCharacterEffectModelName, mode: KlingVideoGenMode, duration: KlingVideoGenDuration, + unique_id: Optional[str] = None, **kwargs, ): video, _, duration = super().api_call( @@ -1078,10 +1170,12 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase): duration=duration, image_1=image_left, image_2=image_right, + unique_id=unique_id, **kwargs, ) return video, duration + class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase): """Kling Single Image Video Effect Node""" @@ -1117,6 +1211,7 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -1128,6 +1223,7 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase): effect_scene: KlingSingleImageEffectsScene, model_name: KlingSingleImageEffectModelName, duration: KlingVideoGenDuration, + unique_id: Optional[str] = None, **kwargs, ): return super().api_call( @@ -1136,6 +1232,7 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase): model_name=model_name, duration=duration, image_1=image, + unique_id=unique_id, **kwargs, ) @@ -1154,7 +1251,9 @@ class KlingLipSyncBase(KlingNodeBase): f"Text is too long. Maximum length is {MAX_PROMPT_LENGTH_LIP_SYNC} characters." ) - def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingLipSyncResponse: + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> KlingLipSyncResponse: """Polls the Kling API endpoint until the task reaches a terminal state.""" return poll_until_finished( auth_kwargs, @@ -1164,6 +1263,9 @@ class KlingLipSyncBase(KlingNodeBase): request_model=EmptyRequest, response_model=KlingLipSyncResponse, ), + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_LIP_SYNC, + node_id=node_id, ) def api_call( @@ -1175,7 +1277,8 @@ class KlingLipSyncBase(KlingNodeBase): text: Optional[str] = None, voice_speed: Optional[float] = None, voice_id: Optional[str] = None, - **kwargs + unique_id: Optional[str] = None, + **kwargs, ) -> tuple[VideoFromFile, str, str]: if text: self.validate_text(text) @@ -1217,7 +1320,9 @@ class KlingLipSyncBase(KlingNodeBase): validate_task_creation_response(task_creation_response) task_id = task_creation_response.data.task_id - final_response = self.get_response(task_id, auth_kwargs=kwargs) + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) validate_video_result_response(final_response) video = get_video_from_response(final_response) @@ -1243,6 +1348,7 @@ class KlingLipSyncAudioToVideoNode(KlingLipSyncBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -1253,6 +1359,7 @@ class KlingLipSyncAudioToVideoNode(KlingLipSyncBase): video: VideoInput, audio: AudioInput, voice_language: str, + unique_id: Optional[str] = None, **kwargs, ): return super().api_call( @@ -1260,6 +1367,7 @@ class KlingLipSyncAudioToVideoNode(KlingLipSyncBase): audio=audio, voice_language=voice_language, mode="audio2video", + unique_id=unique_id, **kwargs, ) @@ -1352,6 +1460,7 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -1363,6 +1472,7 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase): text: str, voice: str, voice_speed: float, + unique_id: Optional[str] = None, **kwargs, ): voice_id, voice_language = KlingLipSyncTextToVideoNode.get_voice_config()[voice] @@ -1373,6 +1483,7 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase): voice_id=voice_id, voice_speed=voice_speed, mode="text2video", + unique_id=unique_id, **kwargs, ) @@ -1413,13 +1524,14 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } - DESCRIPTION = "Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human." + DESCRIPTION = "Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human. You can merge multiple clothing item pictures into one image with a white background." def get_response( - self, task_id: str, auth_kwargs: dict[str,str] = None + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None ) -> KlingVirtualTryOnResponse: return poll_until_finished( auth_kwargs, @@ -1429,6 +1541,9 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase): request_model=EmptyRequest, response_model=KlingVirtualTryOnResponse, ), + result_url_extractor=get_images_urls_from_response, + estimated_duration=AVERAGE_DURATION_VIRTUAL_TRY_ON, + node_id=node_id, ) def api_call( @@ -1436,6 +1551,7 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase): human_image: torch.Tensor, cloth_image: torch.Tensor, model_name: KlingVirtualTryOnModelName, + unique_id: Optional[str] = None, **kwargs, ): initial_operation = SynchronousOperation( @@ -1457,7 +1573,9 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase): validate_task_creation_response(task_creation_response) task_id = task_creation_response.data.task_id - final_response = self.get_response(task_id, auth_kwargs=kwargs) + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) validate_image_result_response(final_response) images = get_images_from_response(final_response) @@ -1528,13 +1646,17 @@ class KlingImageGenerationNode(KlingImageGenerationBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } DESCRIPTION = "Kling Image Generation Node. Generate an image from a text prompt with an optional reference image." def get_response( - self, task_id: str, auth_kwargs: Optional[dict[str,str]] = None + self, + task_id: str, + auth_kwargs: Optional[dict[str, str]], + node_id: Optional[str] = None, ) -> KlingImageGenerationsResponse: return poll_until_finished( auth_kwargs, @@ -1544,6 +1666,9 @@ class KlingImageGenerationNode(KlingImageGenerationBase): request_model=EmptyRequest, response_model=KlingImageGenerationsResponse, ), + result_url_extractor=get_images_urls_from_response, + estimated_duration=AVERAGE_DURATION_IMAGE_GEN, + node_id=node_id, ) def api_call( @@ -1557,6 +1682,7 @@ class KlingImageGenerationNode(KlingImageGenerationBase): n: int, aspect_ratio: KlingImageGenAspectRatio, image: Optional[torch.Tensor] = None, + unique_id: Optional[str] = None, **kwargs, ): self.validate_prompt(prompt, negative_prompt) @@ -1589,7 +1715,9 @@ class KlingImageGenerationNode(KlingImageGenerationBase): validate_task_creation_response(task_creation_response) task_id = task_creation_response.data.task_id - final_response = self.get_response(task_id, auth_kwargs=kwargs) + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) validate_image_result_response(final_response) images = get_images_from_response(final_response) diff --git a/comfy_api_nodes/nodes_luma.py b/comfy_api_nodes/nodes_luma.py index bd33a53e0..525dc38e6 100644 --- a/comfy_api_nodes/nodes_luma.py +++ b/comfy_api_nodes/nodes_luma.py @@ -36,11 +36,20 @@ from comfy_api_nodes.apinode_utils import ( process_image_response, validate_string, ) +from server import PromptServer import requests import torch from io import BytesIO +LUMA_T2V_AVERAGE_DURATION = 105 +LUMA_I2V_AVERAGE_DURATION = 100 + +def image_result_url_extractor(response: LumaGeneration): + return response.assets.image if hasattr(response, "assets") and hasattr(response.assets, "image") else None + +def video_result_url_extractor(response: LumaGeneration): + return response.assets.video if hasattr(response, "assets") and hasattr(response.assets, "video") else None class LumaReferenceNode(ComfyNodeABC): """ @@ -204,6 +213,7 @@ class LumaImageGenerationNode(ComfyNodeABC): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -217,6 +227,7 @@ class LumaImageGenerationNode(ComfyNodeABC): image_luma_ref: LumaReferenceChain = None, style_image: torch.Tensor = None, character_image: torch.Tensor = None, + unique_id: str = None, **kwargs, ): validate_string(prompt, strip_whitespace=True, min_length=3) @@ -271,6 +282,8 @@ class LumaImageGenerationNode(ComfyNodeABC): completed_statuses=[LumaState.completed], failed_statuses=[LumaState.failed], status_extractor=lambda x: x.state, + result_url_extractor=image_result_url_extractor, + node_id=unique_id, auth_kwargs=kwargs, ) response_poll = operation.execute() @@ -353,6 +366,7 @@ class LumaImageModifyNode(ComfyNodeABC): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -363,6 +377,7 @@ class LumaImageModifyNode(ComfyNodeABC): image: torch.Tensor, image_weight: float, seed, + unique_id: str = None, **kwargs, ): # first, upload image @@ -399,6 +414,8 @@ class LumaImageModifyNode(ComfyNodeABC): completed_statuses=[LumaState.completed], failed_statuses=[LumaState.failed], status_extractor=lambda x: x.state, + result_url_extractor=image_result_url_extractor, + node_id=unique_id, auth_kwargs=kwargs, ) response_poll = operation.execute() @@ -473,6 +490,7 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -486,6 +504,7 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC): loop: bool, seed, luma_concepts: LumaConceptChain = None, + unique_id: str = None, **kwargs, ): validate_string(prompt, strip_whitespace=False, min_length=3) @@ -512,6 +531,9 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC): ) response_api: LumaGeneration = operation.execute() + if unique_id: + PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id) + operation = PollingOperation( poll_endpoint=ApiEndpoint( path=f"/proxy/luma/generations/{response_api.id}", @@ -522,6 +544,9 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC): completed_statuses=[LumaState.completed], failed_statuses=[LumaState.failed], status_extractor=lambda x: x.state, + result_url_extractor=video_result_url_extractor, + node_id=unique_id, + estimated_duration=LUMA_T2V_AVERAGE_DURATION, auth_kwargs=kwargs, ) response_poll = operation.execute() @@ -597,6 +622,7 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -611,6 +637,7 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC): first_image: torch.Tensor = None, last_image: torch.Tensor = None, luma_concepts: LumaConceptChain = None, + unique_id: str = None, **kwargs, ): if first_image is None and last_image is None: @@ -642,6 +669,9 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC): ) response_api: LumaGeneration = operation.execute() + if unique_id: + PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id) + operation = PollingOperation( poll_endpoint=ApiEndpoint( path=f"/proxy/luma/generations/{response_api.id}", @@ -652,6 +682,9 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC): completed_statuses=[LumaState.completed], failed_statuses=[LumaState.failed], status_extractor=lambda x: x.state, + result_url_extractor=video_result_url_extractor, + node_id=unique_id, + estimated_duration=LUMA_I2V_AVERAGE_DURATION, auth_kwargs=kwargs, ) response_poll = operation.execute() diff --git a/comfy_api_nodes/nodes_minimax.py b/comfy_api_nodes/nodes_minimax.py index fd64aeb0b..9b46636db 100644 --- a/comfy_api_nodes/nodes_minimax.py +++ b/comfy_api_nodes/nodes_minimax.py @@ -1,3 +1,7 @@ +from typing import Union +import logging +import torch + from comfy.comfy_types.node_typing import IO from comfy_api.input_impl.video_types import VideoFromFile from comfy_api_nodes.apis import ( @@ -20,16 +24,19 @@ from comfy_api_nodes.apinode_utils import ( upload_images_to_comfyapi, validate_string, ) +from server import PromptServer -import torch -import logging +I2V_AVERAGE_DURATION = 114 +T2V_AVERAGE_DURATION = 234 class MinimaxTextToVideoNode: """ Generates videos synchronously based on a prompt, and optional parameters using MiniMax's API. """ + AVERAGE_DURATION = T2V_AVERAGE_DURATION + @classmethod def INPUT_TYPES(s): return { @@ -68,6 +75,7 @@ class MinimaxTextToVideoNode: "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -85,6 +93,7 @@ class MinimaxTextToVideoNode: model="T2V-01", image: torch.Tensor=None, # used for ImageToVideo subject: torch.Tensor=None, # used for SubjectToVideo + unique_id: Union[str, None]=None, **kwargs, ): ''' @@ -138,6 +147,8 @@ class MinimaxTextToVideoNode: completed_statuses=["Success"], failed_statuses=["Fail"], status_extractor=lambda x: x.status.value, + estimated_duration=self.AVERAGE_DURATION, + node_id=unique_id, auth_kwargs=kwargs, ) task_result = video_generate_operation.execute() @@ -164,6 +175,12 @@ class MinimaxTextToVideoNode: f"No video was found in the response. Full response: {file_result.model_dump()}" ) logging.info(f"Generated video URL: {file_url}") + if unique_id: + if hasattr(file_result.file, "backup_download_url"): + message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}" + else: + message = f"Result URL: {file_url}" + PromptServer.instance.send_progress_text(message, unique_id) video_io = download_url_to_bytesio(file_url) if video_io is None: @@ -178,6 +195,8 @@ class MinimaxImageToVideoNode(MinimaxTextToVideoNode): Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API. """ + AVERAGE_DURATION = I2V_AVERAGE_DURATION + @classmethod def INPUT_TYPES(s): return { @@ -223,6 +242,7 @@ class MinimaxImageToVideoNode(MinimaxTextToVideoNode): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -239,6 +259,8 @@ class MinimaxSubjectToVideoNode(MinimaxTextToVideoNode): Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API. """ + AVERAGE_DURATION = T2V_AVERAGE_DURATION + @classmethod def INPUT_TYPES(s): return { @@ -282,6 +304,7 @@ class MinimaxSubjectToVideoNode(MinimaxTextToVideoNode): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py index c63908be2..ce8054afc 100644 --- a/comfy_api_nodes/nodes_openai.py +++ b/comfy_api_nodes/nodes_openai.py @@ -96,6 +96,7 @@ class OpenAIDalle2(ComfyNodeABC): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -113,6 +114,7 @@ class OpenAIDalle2(ComfyNodeABC): mask=None, n=1, size="1024x1024", + unique_id=None, **kwargs ): validate_string(prompt, strip_whitespace=False) @@ -176,7 +178,7 @@ class OpenAIDalle2(ComfyNodeABC): response = operation.execute() - img_tensor = validate_and_cast_response(response) + img_tensor = validate_and_cast_response(response, node_id=unique_id) return (img_tensor,) @@ -242,6 +244,7 @@ class OpenAIDalle3(ComfyNodeABC): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -258,6 +261,7 @@ class OpenAIDalle3(ComfyNodeABC): style="natural", quality="standard", size="1024x1024", + unique_id=None, **kwargs ): validate_string(prompt, strip_whitespace=False) @@ -284,7 +288,7 @@ class OpenAIDalle3(ComfyNodeABC): response = operation.execute() - img_tensor = validate_and_cast_response(response) + img_tensor = validate_and_cast_response(response, node_id=unique_id) return (img_tensor,) @@ -375,6 +379,7 @@ class OpenAIGPTImage1(ComfyNodeABC): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -394,6 +399,7 @@ class OpenAIGPTImage1(ComfyNodeABC): mask=None, n=1, size="1024x1024", + unique_id=None, **kwargs ): validate_string(prompt, strip_whitespace=False) @@ -476,7 +482,7 @@ class OpenAIGPTImage1(ComfyNodeABC): response = operation.execute() - img_tensor = validate_and_cast_response(response) + img_tensor = validate_and_cast_response(response, node_id=unique_id) return (img_tensor,) diff --git a/comfy_api_nodes/nodes_pika.py b/comfy_api_nodes/nodes_pika.py index 08ec9cf07..30562790a 100644 --- a/comfy_api_nodes/nodes_pika.py +++ b/comfy_api_nodes/nodes_pika.py @@ -121,7 +121,10 @@ class PikaNodeBase(ComfyNodeABC): RETURN_TYPES = ("VIDEO",) def poll_for_task_status( - self, task_id: str, auth_kwargs: Optional[dict[str,str]] = None + self, + task_id: str, + auth_kwargs: Optional[dict[str, str]] = None, + node_id: Optional[str] = None, ) -> PikaGenerateResponse: polling_operation = PollingOperation( poll_endpoint=ApiEndpoint( @@ -141,13 +144,19 @@ class PikaNodeBase(ComfyNodeABC): response.progress if hasattr(response, "progress") else None ), auth_kwargs=auth_kwargs, + result_url_extractor=lambda response: ( + response.url if hasattr(response, "url") else None + ), + node_id=node_id, + estimated_duration=60 ) return polling_operation.execute() def execute_task( self, initial_operation: SynchronousOperation[R, PikaGenerateResponse], - auth_kwargs: Optional[dict[str,str]] = None, + auth_kwargs: Optional[dict[str, str]] = None, + node_id: Optional[str] = None, ) -> tuple[VideoFromFile]: """Executes the initial operation then polls for the task status until it is completed. @@ -208,7 +217,8 @@ class PikaImageToVideoV2_2(PikaNodeBase): seed: int, resolution: str, duration: int, - **kwargs + unique_id: str, + **kwargs, ) -> tuple[VideoFromFile]: # Convert image to BytesIO image_bytes_io = tensor_to_bytesio(image) @@ -238,7 +248,7 @@ class PikaImageToVideoV2_2(PikaNodeBase): auth_kwargs=kwargs, ) - return self.execute_task(initial_operation, auth_kwargs=kwargs) + return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) class PikaTextToVideoNodeV2_2(PikaNodeBase): @@ -262,6 +272,7 @@ class PikaTextToVideoNodeV2_2(PikaNodeBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -275,6 +286,7 @@ class PikaTextToVideoNodeV2_2(PikaNodeBase): resolution: str, duration: int, aspect_ratio: float, + unique_id: str, **kwargs, ) -> tuple[VideoFromFile]: initial_operation = SynchronousOperation( @@ -296,7 +308,7 @@ class PikaTextToVideoNodeV2_2(PikaNodeBase): content_type="application/x-www-form-urlencoded", ) - return self.execute_task(initial_operation, auth_kwargs=kwargs) + return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) class PikaScenesV2_2(PikaNodeBase): @@ -340,6 +352,7 @@ class PikaScenesV2_2(PikaNodeBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -354,6 +367,7 @@ class PikaScenesV2_2(PikaNodeBase): duration: int, ingredients_mode: str, aspect_ratio: float, + unique_id: str, image_ingredient_1: Optional[torch.Tensor] = None, image_ingredient_2: Optional[torch.Tensor] = None, image_ingredient_3: Optional[torch.Tensor] = None, @@ -403,7 +417,7 @@ class PikaScenesV2_2(PikaNodeBase): auth_kwargs=kwargs, ) - return self.execute_task(initial_operation, auth_kwargs=kwargs) + return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) class PikAdditionsNode(PikaNodeBase): @@ -439,6 +453,7 @@ class PikAdditionsNode(PikaNodeBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -451,6 +466,7 @@ class PikAdditionsNode(PikaNodeBase): prompt_text: str, negative_prompt: str, seed: int, + unique_id: str, **kwargs, ) -> tuple[VideoFromFile]: # Convert video to BytesIO @@ -487,7 +503,7 @@ class PikAdditionsNode(PikaNodeBase): auth_kwargs=kwargs, ) - return self.execute_task(initial_operation, auth_kwargs=kwargs) + return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) class PikaSwapsNode(PikaNodeBase): @@ -532,6 +548,7 @@ class PikaSwapsNode(PikaNodeBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -546,6 +563,7 @@ class PikaSwapsNode(PikaNodeBase): prompt_text: str, negative_prompt: str, seed: int, + unique_id: str, **kwargs, ) -> tuple[VideoFromFile]: # Convert video to BytesIO @@ -592,7 +610,7 @@ class PikaSwapsNode(PikaNodeBase): auth_kwargs=kwargs, ) - return self.execute_task(initial_operation, auth_kwargs=kwargs) + return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) class PikaffectsNode(PikaNodeBase): @@ -637,6 +655,7 @@ class PikaffectsNode(PikaNodeBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -649,6 +668,7 @@ class PikaffectsNode(PikaNodeBase): prompt_text: str, negative_prompt: str, seed: int, + unique_id: str, **kwargs, ) -> tuple[VideoFromFile]: @@ -670,7 +690,7 @@ class PikaffectsNode(PikaNodeBase): auth_kwargs=kwargs, ) - return self.execute_task(initial_operation, auth_kwargs=kwargs) + return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) class PikaStartEndFrameNode2_2(PikaNodeBase): @@ -689,6 +709,7 @@ class PikaStartEndFrameNode2_2(PikaNodeBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -703,6 +724,7 @@ class PikaStartEndFrameNode2_2(PikaNodeBase): seed: int, resolution: str, duration: int, + unique_id: str, **kwargs, ) -> tuple[VideoFromFile]: @@ -733,7 +755,7 @@ class PikaStartEndFrameNode2_2(PikaNodeBase): auth_kwargs=kwargs, ) - return self.execute_task(initial_operation, auth_kwargs=kwargs) + return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) NODE_CLASS_MAPPINGS = { diff --git a/comfy_api_nodes/nodes_pixverse.py b/comfy_api_nodes/nodes_pixverse.py index 0c29e77c2..ef4a9a802 100644 --- a/comfy_api_nodes/nodes_pixverse.py +++ b/comfy_api_nodes/nodes_pixverse.py @@ -1,5 +1,5 @@ from inspect import cleandoc - +from typing import Optional from comfy_api_nodes.apis.pixverse_api import ( PixverseTextVideoRequest, PixverseImageVideoRequest, @@ -34,11 +34,22 @@ import requests from io import BytesIO +AVERAGE_DURATION_T2V = 32 +AVERAGE_DURATION_I2V = 30 +AVERAGE_DURATION_T2T = 52 + + +def get_video_url_from_response( + response: PixverseGenerationStatusResponse, +) -> Optional[str]: + if response.Resp is None or response.Resp.url is None: + return None + return str(response.Resp.url) + + def upload_image_to_pixverse(image: torch.Tensor, auth_kwargs=None): # first, upload image to Pixverse and get image id to use in actual generation call - files = { - "image": tensor_to_bytesio(image) - } + files = {"image": tensor_to_bytesio(image)} operation = SynchronousOperation( endpoint=ApiEndpoint( path="/proxy/pixverse/image/upload", @@ -54,7 +65,9 @@ def upload_image_to_pixverse(image: torch.Tensor, auth_kwargs=None): response_upload: PixverseImageUploadResponse = operation.execute() if response_upload.Resp is None: - raise Exception(f"PixVerse image upload request failed: '{response_upload.ErrMsg}'") + raise Exception( + f"PixVerse image upload request failed: '{response_upload.ErrMsg}'" + ) return response_upload.Resp.img_id @@ -73,7 +86,7 @@ class PixverseTemplateNode: def INPUT_TYPES(s): return { "required": { - "template": (list(pixverse_templates.keys()), ), + "template": (list(pixverse_templates.keys()),), } } @@ -87,7 +100,7 @@ class PixverseTemplateNode: class PixverseTextToVideoNode(ComfyNodeABC): """ - Generates videos synchronously based on prompt and output_size. + Generates videos based on prompt and output_size. """ RETURN_TYPES = (IO.VIDEO,) @@ -108,9 +121,7 @@ class PixverseTextToVideoNode(ComfyNodeABC): "tooltip": "Prompt for the video generation", }, ), - "aspect_ratio": ( - [ratio.value for ratio in PixverseAspectRatio], - ), + "aspect_ratio": ([ratio.value for ratio in PixverseAspectRatio],), "quality": ( [resolution.value for resolution in PixverseQuality], { @@ -143,12 +154,13 @@ class PixverseTextToVideoNode(ComfyNodeABC): PixverseIO.TEMPLATE, { "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node." - } - ) + }, + ), }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -160,8 +172,9 @@ class PixverseTextToVideoNode(ComfyNodeABC): duration_seconds: int, motion_mode: str, seed, - negative_prompt: str=None, - pixverse_template: int=None, + negative_prompt: str = None, + pixverse_template: int = None, + unique_id: Optional[str] = None, **kwargs, ): validate_string(prompt, strip_whitespace=False) @@ -205,19 +218,27 @@ class PixverseTextToVideoNode(ComfyNodeABC): response_model=PixverseGenerationStatusResponse, ), completed_statuses=[PixverseStatus.successful], - failed_statuses=[PixverseStatus.contents_moderation, PixverseStatus.failed, PixverseStatus.deleted], + failed_statuses=[ + PixverseStatus.contents_moderation, + PixverseStatus.failed, + PixverseStatus.deleted, + ], status_extractor=lambda x: x.Resp.status, auth_kwargs=kwargs, + node_id=unique_id, + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_T2V, ) response_poll = operation.execute() vid_response = requests.get(response_poll.Resp.url) + return (VideoFromFile(BytesIO(vid_response.content)),) class PixverseImageToVideoNode(ComfyNodeABC): """ - Generates videos synchronously based on prompt and output_size. + Generates videos based on prompt and output_size. """ RETURN_TYPES = (IO.VIDEO,) @@ -230,9 +251,7 @@ class PixverseImageToVideoNode(ComfyNodeABC): def INPUT_TYPES(s): return { "required": { - "image": ( - IO.IMAGE, - ), + "image": (IO.IMAGE,), "prompt": ( IO.STRING, { @@ -273,12 +292,13 @@ class PixverseImageToVideoNode(ComfyNodeABC): PixverseIO.TEMPLATE, { "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node." - } - ) + }, + ), }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -290,8 +310,9 @@ class PixverseImageToVideoNode(ComfyNodeABC): duration_seconds: int, motion_mode: str, seed, - negative_prompt: str=None, - pixverse_template: int=None, + negative_prompt: str = None, + pixverse_template: int = None, + unique_id: Optional[str] = None, **kwargs, ): validate_string(prompt, strip_whitespace=False) @@ -337,9 +358,16 @@ class PixverseImageToVideoNode(ComfyNodeABC): response_model=PixverseGenerationStatusResponse, ), completed_statuses=[PixverseStatus.successful], - failed_statuses=[PixverseStatus.contents_moderation, PixverseStatus.failed, PixverseStatus.deleted], + failed_statuses=[ + PixverseStatus.contents_moderation, + PixverseStatus.failed, + PixverseStatus.deleted, + ], status_extractor=lambda x: x.Resp.status, auth_kwargs=kwargs, + node_id=unique_id, + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_I2V, ) response_poll = operation.execute() @@ -349,7 +377,7 @@ class PixverseImageToVideoNode(ComfyNodeABC): class PixverseTransitionVideoNode(ComfyNodeABC): """ - Generates videos synchronously based on prompt and output_size. + Generates videos based on prompt and output_size. """ RETURN_TYPES = (IO.VIDEO,) @@ -362,12 +390,8 @@ class PixverseTransitionVideoNode(ComfyNodeABC): def INPUT_TYPES(s): return { "required": { - "first_frame": ( - IO.IMAGE, - ), - "last_frame": ( - IO.IMAGE, - ), + "first_frame": (IO.IMAGE,), + "last_frame": (IO.IMAGE,), "prompt": ( IO.STRING, { @@ -408,6 +432,7 @@ class PixverseTransitionVideoNode(ComfyNodeABC): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -420,7 +445,8 @@ class PixverseTransitionVideoNode(ComfyNodeABC): duration_seconds: int, motion_mode: str, seed, - negative_prompt: str=None, + negative_prompt: str = None, + unique_id: Optional[str] = None, **kwargs, ): validate_string(prompt, strip_whitespace=False) @@ -467,9 +493,16 @@ class PixverseTransitionVideoNode(ComfyNodeABC): response_model=PixverseGenerationStatusResponse, ), completed_statuses=[PixverseStatus.successful], - failed_statuses=[PixverseStatus.contents_moderation, PixverseStatus.failed, PixverseStatus.deleted], + failed_statuses=[ + PixverseStatus.contents_moderation, + PixverseStatus.failed, + PixverseStatus.deleted, + ], status_extractor=lambda x: x.Resp.status, auth_kwargs=kwargs, + node_id=unique_id, + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_T2V, ) response_poll = operation.execute() diff --git a/comfy_api_nodes/nodes_recraft.py b/comfy_api_nodes/nodes_recraft.py index 767d93e3c..e369c4b7e 100644 --- a/comfy_api_nodes/nodes_recraft.py +++ b/comfy_api_nodes/nodes_recraft.py @@ -1,5 +1,6 @@ from __future__ import annotations from inspect import cleandoc +from typing import Optional from comfy.utils import ProgressBar from comfy_extras.nodes_images import SVG # Added from comfy.comfy_types.node_typing import IO @@ -29,6 +30,8 @@ from comfy_api_nodes.apinode_utils import ( resize_mask_to_image, validate_string, ) +from server import PromptServer + import torch from io import BytesIO from PIL import UnidentifiedImageError @@ -388,6 +391,7 @@ class RecraftTextToImageNode: "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -400,6 +404,7 @@ class RecraftTextToImageNode: recraft_style: RecraftStyle = None, negative_prompt: str = None, recraft_controls: RecraftControls = None, + unique_id: Optional[str] = None, **kwargs, ): validate_string(prompt, strip_whitespace=False, max_length=1000) @@ -436,8 +441,15 @@ class RecraftTextToImageNode: ) response: RecraftImageGenerationResponse = operation.execute() images = [] + urls = [] for data in response.data: with handle_recraft_image_output(): + if unique_id and data.url: + urls.append(data.url) + urls_string = '\n'.join(urls) + PromptServer.instance.send_progress_text( + f"Result URL: {urls_string}", unique_id + ) image = bytesio_to_image_tensor( download_url_to_bytesio(data.url, timeout=1024) ) @@ -763,6 +775,7 @@ class RecraftTextToVectorNode: "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -775,6 +788,7 @@ class RecraftTextToVectorNode: seed, negative_prompt: str = None, recraft_controls: RecraftControls = None, + unique_id: Optional[str] = None, **kwargs, ): validate_string(prompt, strip_whitespace=False, max_length=1000) @@ -809,7 +823,14 @@ class RecraftTextToVectorNode: ) response: RecraftImageGenerationResponse = operation.execute() svg_data = [] + urls = [] for data in response.data: + if unique_id and data.url: + urls.append(data.url) + # Print result on each iteration in case of error + PromptServer.instance.send_progress_text( + f"Result URL: {' '.join(urls)}", unique_id + ) svg_data.append(download_url_to_bytesio(data.url, timeout=1024)) return (SVG(svg_data),) diff --git a/comfy_api_nodes/nodes_veo2.py b/comfy_api_nodes/nodes_veo2.py index 2740179c8..df846d5dd 100644 --- a/comfy_api_nodes/nodes_veo2.py +++ b/comfy_api_nodes/nodes_veo2.py @@ -3,6 +3,7 @@ import logging import base64 import requests import torch +from typing import Optional from comfy.comfy_types.node_typing import IO, ComfyNodeABC from comfy_api.input_impl.video_types import VideoFromFile @@ -24,6 +25,8 @@ from comfy_api_nodes.apinode_utils import ( tensor_to_base64_string ) +AVERAGE_DURATION_VIDEO_GEN = 32 + def convert_image_to_base64(image: torch.Tensor): if image is None: return None @@ -31,6 +34,22 @@ def convert_image_to_base64(image: torch.Tensor): scaled_image = downscale_image_tensor(image, total_pixels=2048*2048) return tensor_to_base64_string(scaled_image) + +def get_video_url_from_response(poll_response: Veo2GenVidPollResponse) -> Optional[str]: + if ( + poll_response.response + and hasattr(poll_response.response, "videos") + and poll_response.response.videos + and len(poll_response.response.videos) > 0 + ): + video = poll_response.response.videos[0] + else: + return None + if hasattr(video, "gcsUri") and video.gcsUri: + return str(video.gcsUri) + return None + + class VeoVideoGenerationNode(ComfyNodeABC): """ Generates videos from text prompts using Google's Veo API. @@ -115,6 +134,7 @@ class VeoVideoGenerationNode(ComfyNodeABC): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -134,6 +154,7 @@ class VeoVideoGenerationNode(ComfyNodeABC): person_generation="ALLOW", seed=0, image=None, + unique_id: Optional[str] = None, **kwargs, ): # Prepare the instances for the request @@ -215,7 +236,10 @@ class VeoVideoGenerationNode(ComfyNodeABC): operationName=operation_name ), auth_kwargs=kwargs, - poll_interval=5.0 + poll_interval=5.0, + result_url_extractor=get_video_url_from_response, + node_id=unique_id, + estimated_duration=AVERAGE_DURATION_VIDEO_GEN, ) # Execute the polling operation From f3ff5c40db3b68449b47112591eef31094cffe70 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Tue, 13 May 2025 22:28:30 -0700 Subject: [PATCH 110/193] don't retry if API returns task failure (#8111) --- comfy_api_nodes/apis/client.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py index 838ff1e8d..62866216f 100644 --- a/comfy_api_nodes/apis/client.py +++ b/comfy_api_nodes/apis/client.py @@ -1105,7 +1105,7 @@ class PollingOperation(Generic[T, R]): except Exception as e: # For other errors, increment count and potentially abort consecutive_errors += 1 - if consecutive_errors >= max_consecutive_errors: + if consecutive_errors >= max_consecutive_errors or status == TaskStatus.FAILED: raise Exception( f"Polling aborted after {consecutive_errors} consecutive errors: {str(e)}" ) from e From 08368f8e00c07d6888907557c5ef1da7a64b5e42 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 14 May 2025 14:54:50 -0700 Subject: [PATCH 111/193] Update comment on ROCm pytorch attention in README. (#8123) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index deee70c6b..dcdaa353c 100644 --- a/README.md +++ b/README.md @@ -302,7 +302,7 @@ For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 pyt ### AMD ROCm Tips -You can enable experimental memory efficient attention on pytorch 2.5 in ComfyUI on RDNA3 and potentially other AMD GPUs using this command: +You can enable experimental memory efficient attention on recent pytorch in ComfyUI on some AMD GPUs using this command, it should already be enabled by default on RDNA3. If this improves speed for you on latest pytorch on your GPU please report it so that I can enable it by default. ```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention``` From f1f9763b4c0e7c361ec2808b205ad32297c4777b Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Wed, 14 May 2025 21:11:41 -0700 Subject: [PATCH 112/193] Add `get_duration` method to Comfy VIDEO type (#8122) * get duration from VIDEO type * video get_duration unit test * fix Windows unit test: can't delete opened temp file --- comfy_api/input/video_types.py | 10 + comfy_api/input_impl/video_types.py | 32 +++ tests-unit/comfy_api_test/video_types_test.py | 239 ++++++++++++++++++ 3 files changed, 281 insertions(+) create mode 100644 tests-unit/comfy_api_test/video_types_test.py diff --git a/comfy_api/input/video_types.py b/comfy_api/input/video_types.py index 0676e0e66..dc22d34ff 100644 --- a/comfy_api/input/video_types.py +++ b/comfy_api/input/video_types.py @@ -43,3 +43,13 @@ class VideoInput(ABC): components = self.get_components() return components.images.shape[2], components.images.shape[1] + def get_duration(self) -> float: + """ + Returns the duration of the video in seconds. + + Returns: + Duration in seconds + """ + components = self.get_components() + frame_count = components.images.shape[0] + return float(frame_count / components.frame_rate) diff --git a/comfy_api/input_impl/video_types.py b/comfy_api/input_impl/video_types.py index ae48dbaa4..197f6558c 100644 --- a/comfy_api/input_impl/video_types.py +++ b/comfy_api/input_impl/video_types.py @@ -80,6 +80,38 @@ class VideoFromFile(VideoInput): return stream.width, stream.height raise ValueError(f"No video stream found in file '{self.__file}'") + def get_duration(self) -> float: + """ + Returns the duration of the video in seconds. + + Returns: + Duration in seconds + """ + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) + with av.open(self.__file, mode="r") as container: + if container.duration is not None: + return float(container.duration / av.time_base) + + # Fallback: calculate from frame count and frame rate + video_stream = next( + (s for s in container.streams if s.type == "video"), None + ) + if video_stream and video_stream.frames and video_stream.average_rate: + return float(video_stream.frames / video_stream.average_rate) + + # Last resort: decode frames to count them + if video_stream and video_stream.average_rate: + frame_count = 0 + container.seek(0) + for packet in container.demux(video_stream): + for _ in packet.decode(): + frame_count += 1 + if frame_count > 0: + return float(frame_count / video_stream.average_rate) + + raise ValueError(f"Could not determine duration for file '{self.__file}'") + def get_components_internal(self, container: InputContainer) -> VideoComponents: # Get video frames frames = [] diff --git a/tests-unit/comfy_api_test/video_types_test.py b/tests-unit/comfy_api_test/video_types_test.py new file mode 100644 index 000000000..b25fcb1ca --- /dev/null +++ b/tests-unit/comfy_api_test/video_types_test.py @@ -0,0 +1,239 @@ +import pytest +import torch +import tempfile +import os +import av +import io +from fractions import Fraction +from comfy_api.input_impl.video_types import VideoFromFile, VideoFromComponents +from comfy_api.util.video_types import VideoComponents +from comfy_api.input.basic_types import AudioInput +from av.error import InvalidDataError + +EPSILON = 0.0001 + + +@pytest.fixture +def sample_images(): + """3-frame 2x2 RGB video tensor""" + return torch.rand(3, 2, 2, 3) + + +@pytest.fixture +def sample_audio(): + """Stereo audio with 44.1kHz sample rate""" + return AudioInput( + { + "waveform": torch.rand(1, 2, 1000), + "sample_rate": 44100, + } + ) + + +@pytest.fixture +def video_components(sample_images, sample_audio): + """VideoComponents with images, audio, and metadata""" + return VideoComponents( + images=sample_images, + audio=sample_audio, + frame_rate=Fraction(30), + metadata={"test": "metadata"}, + ) + + +def create_test_video(width=4, height=4, frames=3, fps=30): + """Helper to create a temporary video file""" + tmp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) + with av.open(tmp.name, mode="w") as container: + stream = container.add_stream("h264", rate=fps) + stream.width = width + stream.height = height + stream.pix_fmt = "yuv420p" + + for i in range(frames): + frame = av.VideoFrame.from_ndarray( + torch.ones(height, width, 3, dtype=torch.uint8).numpy() * (i * 85), + format="rgb24", + ) + frame = frame.reformat(format="yuv420p") + packet = stream.encode(frame) + container.mux(packet) + + # Flush + packet = stream.encode(None) + container.mux(packet) + + return tmp.name + + +@pytest.fixture +def simple_video_file(): + """4x4 video with 3 frames at 30fps""" + file_path = create_test_video() + yield file_path + os.unlink(file_path) + + +def test_video_from_components_get_duration(video_components): + """Duration calculated correctly from frame count and frame rate""" + video = VideoFromComponents(video_components) + duration = video.get_duration() + + expected_duration = 3.0 / 30.0 + assert duration == pytest.approx(expected_duration) + + +def test_video_from_components_get_duration_different_frame_rates(sample_images): + """Duration correct for different frame rates including fractional""" + # Test with 60 fps + components_60fps = VideoComponents(images=sample_images, frame_rate=Fraction(60)) + video_60fps = VideoFromComponents(components_60fps) + assert video_60fps.get_duration() == pytest.approx(3.0 / 60.0) + + # Test with fractional frame rate (23.976fps) + components_frac = VideoComponents( + images=sample_images, frame_rate=Fraction(24000, 1001) + ) + video_frac = VideoFromComponents(components_frac) + expected_frac = 3.0 / (24000.0 / 1001.0) + assert video_frac.get_duration() == pytest.approx(expected_frac) + + +def test_video_from_components_get_duration_empty_video(): + """Duration is zero for empty video""" + empty_components = VideoComponents( + images=torch.zeros(0, 2, 2, 3), frame_rate=Fraction(30) + ) + video = VideoFromComponents(empty_components) + assert video.get_duration() == 0.0 + + +def test_video_from_components_get_dimensions(video_components): + """Dimensions returned correctly from image tensor shape""" + video = VideoFromComponents(video_components) + width, height = video.get_dimensions() + assert width == 2 + assert height == 2 + + +def test_video_from_file_get_duration(simple_video_file): + """Duration extracted from file metadata""" + video = VideoFromFile(simple_video_file) + duration = video.get_duration() + assert duration == pytest.approx(0.1, abs=0.01) + + +def test_video_from_file_get_dimensions(simple_video_file): + """Dimensions read from stream without decoding frames""" + video = VideoFromFile(simple_video_file) + width, height = video.get_dimensions() + assert width == 4 + assert height == 4 + + +def test_video_from_file_bytesio_input(): + """VideoFromFile works with BytesIO input""" + buffer = io.BytesIO() + with av.open(buffer, mode="w", format="mp4") as container: + stream = container.add_stream("h264", rate=30) + stream.width = 2 + stream.height = 2 + stream.pix_fmt = "yuv420p" + + frame = av.VideoFrame.from_ndarray( + torch.zeros(2, 2, 3, dtype=torch.uint8).numpy(), format="rgb24" + ) + frame = frame.reformat(format="yuv420p") + packet = stream.encode(frame) + container.mux(packet) + packet = stream.encode(None) + container.mux(packet) + + buffer.seek(0) + video = VideoFromFile(buffer) + + assert video.get_dimensions() == (2, 2) + assert video.get_duration() == pytest.approx(1 / 30, abs=0.01) + + +def test_video_from_file_invalid_file_error(): + """InvalidDataError raised for non-video files""" + with tempfile.NamedTemporaryFile(suffix=".txt", delete=False) as tmp: + tmp.write(b"not a video file") + tmp.flush() + tmp_name = tmp.name + + try: + with pytest.raises(InvalidDataError): + video = VideoFromFile(tmp_name) + video.get_dimensions() + finally: + os.unlink(tmp_name) + + +def test_video_from_file_audio_only_error(): + """ValueError raised for audio-only files""" + with tempfile.NamedTemporaryFile(suffix=".m4a", delete=False) as tmp: + tmp_name = tmp.name + + try: + with av.open(tmp_name, mode="w") as container: + stream = container.add_stream("aac", rate=44100) + stream.sample_rate = 44100 + stream.format = "fltp" + + audio_data = torch.zeros(1, 1024).numpy() + audio_frame = av.AudioFrame.from_ndarray( + audio_data, format="fltp", layout="mono" + ) + audio_frame.sample_rate = 44100 + audio_frame.pts = 0 + packet = stream.encode(audio_frame) + container.mux(packet) + + for packet in stream.encode(None): + container.mux(packet) + + with pytest.raises(ValueError, match="No video stream found"): + video = VideoFromFile(tmp_name) + video.get_dimensions() + finally: + os.unlink(tmp_name) + + +def test_single_frame_video(): + """Single frame video has correct duration""" + components = VideoComponents( + images=torch.rand(1, 10, 10, 3), frame_rate=Fraction(1) + ) + video = VideoFromComponents(components) + assert video.get_duration() == 1.0 + + +@pytest.mark.parametrize( + "frame_rate,expected_fps", + [ + (Fraction(24000, 1001), 24000 / 1001), + (Fraction(30000, 1001), 30000 / 1001), + (Fraction(25, 1), 25.0), + (Fraction(50, 2), 25.0), + ], +) +def test_fractional_frame_rates(frame_rate, expected_fps): + """Duration calculated correctly for various fractional frame rates""" + components = VideoComponents(images=torch.rand(100, 4, 4, 3), frame_rate=frame_rate) + video = VideoFromComponents(components) + duration = video.get_duration() + expected_duration = 100.0 / expected_fps + assert duration == pytest.approx(expected_duration) + + +def test_duration_consistency(video_components): + """get_duration() consistent with manual calculation from components""" + video = VideoFromComponents(video_components) + + duration = video.get_duration() + components = video.get_components() + manual_duration = float(components.images.shape[0] / components.frame_rate) + + assert duration == pytest.approx(manual_duration) From 6a2e4bb9e00c1ef467000d880abbbaf284c26d4a Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 15 May 2025 05:21:47 -0700 Subject: [PATCH 113/193] Remove old hack used to fix windows pytorch 2.4 on the portable. (#8139) Not necessary anymore. --- fix_torch.py | 28 ---------------------------- main.py | 7 ------- 2 files changed, 35 deletions(-) delete mode 100644 fix_torch.py diff --git a/fix_torch.py b/fix_torch.py deleted file mode 100644 index ce117b639..000000000 --- a/fix_torch.py +++ /dev/null @@ -1,28 +0,0 @@ -import importlib.util -import shutil -import os -import ctypes -import logging - - -def fix_pytorch_libomp(): - """ - Fix PyTorch libomp DLL issue on Windows by copying the correct DLL file if needed. - """ - torch_spec = importlib.util.find_spec("torch") - for folder in torch_spec.submodule_search_locations: - lib_folder = os.path.join(folder, "lib") - test_file = os.path.join(lib_folder, "fbgemm.dll") - dest = os.path.join(lib_folder, "libomp140.x86_64.dll") - if os.path.exists(dest): - break - - with open(test_file, "rb") as f: - contents = f.read() - if b"libomp140.x86_64.dll" not in contents: - break - try: - ctypes.cdll.LoadLibrary(test_file) - except FileNotFoundError: - logging.warning("Detected pytorch version with libomp issue, patching.") - shutil.copyfile(os.path.join(lib_folder, "libiomp5md.dll"), dest) diff --git a/main.py b/main.py index 221e48e41..0fde6d221 100644 --- a/main.py +++ b/main.py @@ -125,13 +125,6 @@ if __name__ == "__main__": import cuda_malloc -if args.windows_standalone_build: - try: - from fix_torch import fix_pytorch_libomp - fix_pytorch_libomp() - except: - pass - import comfy.utils import execution From c820ef950d10a6b4e4fa8ab28bc09274d563b13c Mon Sep 17 00:00:00 2001 From: George0726 <38740075+George0726@users.noreply.github.com> Date: Thu, 15 May 2025 19:00:43 -0400 Subject: [PATCH 114/193] Add Wan-FUN Camera Control models and Add WanCameraImageToVideo node (#8013) * support wan camera models * fix by ruff check * change camera_condition type; make camera_condition optional * support camera trajectory nodes * fix camera direction --------- Co-authored-by: Qirui Sun --- comfy/ldm/wan/model.py | 143 ++++++++++++++++ comfy/model_base.py | 11 ++ comfy/supported_models.py | 12 +- comfy_extras/nodes_camera_trajectory.py | 218 ++++++++++++++++++++++++ comfy_extras/nodes_wan.py | 47 +++++ nodes.py | 1 + 6 files changed, 431 insertions(+), 1 deletion(-) create mode 100644 comfy_extras/nodes_camera_trajectory.py diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py index fc5ff40c5..a996dedf4 100644 --- a/comfy/ldm/wan/model.py +++ b/comfy/ldm/wan/model.py @@ -247,6 +247,60 @@ class VaceWanAttentionBlock(WanAttentionBlock): return c_skip, c +class WanCamAdapter(nn.Module): + def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1, operation_settings={}): + super(WanCamAdapter, self).__init__() + + # Pixel Unshuffle: reduce spatial dimensions by a factor of 8 + self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8) + + # Convolution: reduce spatial dimensions by a factor + # of 2 (without overlap) + self.conv = operation_settings.get("operations").Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + + # Residual blocks for feature extraction + self.residual_blocks = nn.Sequential( + *[WanCamResidualBlock(out_dim, operation_settings = operation_settings) for _ in range(num_residual_blocks)] + ) + + def forward(self, x): + # Reshape to merge the frame dimension into batch + bs, c, f, h, w = x.size() + x = x.permute(0, 2, 1, 3, 4).contiguous().view(bs * f, c, h, w) + + # Pixel Unshuffle operation + x_unshuffled = self.pixel_unshuffle(x) + + # Convolution operation + x_conv = self.conv(x_unshuffled) + + # Feature extraction with residual blocks + out = self.residual_blocks(x_conv) + + # Reshape to restore original bf dimension + out = out.view(bs, f, out.size(1), out.size(2), out.size(3)) + + # Permute dimensions to reorder (if needed), e.g., swap channels and feature frames + out = out.permute(0, 2, 1, 3, 4) + + return out + + +class WanCamResidualBlock(nn.Module): + def __init__(self, dim, operation_settings={}): + super(WanCamResidualBlock, self).__init__() + self.conv1 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.relu = nn.ReLU(inplace=True) + self.conv2 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + + def forward(self, x): + residual = x + out = self.relu(self.conv1(x)) + out = self.conv2(out) + out += residual + return out + + class Head(nn.Module): def __init__(self, dim, out_dim, patch_size, eps=1e-6, operation_settings={}): @@ -637,3 +691,92 @@ class VaceWanModel(WanModel): # unpatchify x = self.unpatchify(x, grid_sizes) return x + +class CameraWanModel(WanModel): + r""" + Wan diffusion backbone supporting both text-to-video and image-to-video. + """ + + def __init__(self, + model_type='camera', + patch_size=(1, 2, 2), + text_len=512, + in_dim=16, + dim=2048, + ffn_dim=8192, + freq_dim=256, + text_dim=4096, + out_dim=16, + num_heads=16, + num_layers=32, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=True, + eps=1e-6, + flf_pos_embed_token_number=None, + image_model=None, + in_dim_control_adapter=24, + device=None, + dtype=None, + operations=None, + ): + + super().__init__(model_type='i2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations) + operation_settings = {"operations": operations, "device": device, "dtype": dtype} + + self.control_adapter = WanCamAdapter(in_dim_control_adapter, dim, kernel_size=patch_size[1:], stride=patch_size[1:], operation_settings=operation_settings) + + + def forward_orig( + self, + x, + t, + context, + clip_fea=None, + freqs=None, + camera_conditions = None, + transformer_options={}, + **kwargs, + ): + # embeddings + x = self.patch_embedding(x.float()).to(x.dtype) + if self.control_adapter is not None and camera_conditions is not None: + x_camera = self.control_adapter(camera_conditions).to(x.dtype) + x = x + x_camera + grid_sizes = x.shape[2:] + x = x.flatten(2).transpose(1, 2) + + # time embeddings + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype)) + e0 = self.time_projection(e).unflatten(1, (6, self.dim)) + + # context + context = self.text_embedding(context) + + context_img_len = None + if clip_fea is not None: + if self.img_emb is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + context_img_len = clip_fea.shape[-2] + + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len) + return out + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap}) + x = out["img"] + else: + x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) + + # head + x = self.head(x, e) + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return x diff --git a/comfy/model_base.py b/comfy/model_base.py index 047861593..f475e837e 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -1079,6 +1079,17 @@ class WAN21_Vace(WAN21): out['vace_strength'] = comfy.conds.CONDConstant(vace_strength) return out +class WAN21_Camera(WAN21): + def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): + super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.CameraWanModel) + self.image_to_video = image_to_video + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + camera_conditions = kwargs.get("camera_conditions", None) + if camera_conditions is not None: + out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions) + return out class Hunyuan3Dv2(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): diff --git a/comfy/supported_models.py b/comfy/supported_models.py index fef25eb24..667393ac0 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -992,6 +992,16 @@ class WAN21_FunControl2V(WAN21_T2V): out = model_base.WAN21(self, image_to_video=False, device=device) return out +class WAN21_Camera(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "i2v", + "in_dim": 32, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN21_Camera(self, image_to_video=False, device=device) + return out class WAN21_Vace(WAN21_T2V): unet_config = { "image_model": "wan2.1", @@ -1129,6 +1139,6 @@ class ACEStep(supported_models_base.BASE): def clip_target(self, state_dict={}): return supported_models_base.ClipTarget(comfy.text_encoders.ace.AceT5Tokenizer, comfy.text_encoders.ace.AceT5Model) -models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep] +models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep] models += [SVD_img2vid] diff --git a/comfy_extras/nodes_camera_trajectory.py b/comfy_extras/nodes_camera_trajectory.py new file mode 100644 index 000000000..b84b4785c --- /dev/null +++ b/comfy_extras/nodes_camera_trajectory.py @@ -0,0 +1,218 @@ +import nodes +import torch +import numpy as np +from einops import rearrange +import comfy.model_management + + + +MAX_RESOLUTION = nodes.MAX_RESOLUTION + +CAMERA_DICT = { + "base_T_norm": 1.5, + "base_angle": np.pi/3, + "Static": { "angle":[0., 0., 0.], "T":[0., 0., 0.]}, + "Pan Up": { "angle":[0., 0., 0.], "T":[0., -1., 0.]}, + "Pan Down": { "angle":[0., 0., 0.], "T":[0.,1.,0.]}, + "Pan Left": { "angle":[0., 0., 0.], "T":[-1.,0.,0.]}, + "Pan Right": { "angle":[0., 0., 0.], "T": [1.,0.,0.]}, + "Zoom In": { "angle":[0., 0., 0.], "T": [0.,0.,2.]}, + "Zoom Out": { "angle":[0., 0., 0.], "T": [0.,0.,-2.]}, + "Anti Clockwise (ACW)": { "angle": [0., 0., -1.], "T":[0., 0., 0.]}, + "ClockWise (CW)": { "angle": [0., 0., 1.], "T":[0., 0., 0.]}, +} + + +def process_pose_params(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu'): + + def get_relative_pose(cam_params): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params] + abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params] + cam_to_origin = 0 + target_cam_c2w = np.array([ + [1, 0, 0, 0], + [0, 1, 0, -cam_to_origin], + [0, 0, 1, 0], + [0, 0, 0, 1] + ]) + abs2rel = target_cam_c2w @ abs_w2cs[0] + ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]] + ret_poses = np.array(ret_poses, dtype=np.float32) + return ret_poses + + """Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + cam_params = [Camera(cam_param) for cam_param in cam_params] + + sample_wh_ratio = width / height + pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed + + if pose_wh_ratio > sample_wh_ratio: + resized_ori_w = height * pose_wh_ratio + for cam_param in cam_params: + cam_param.fx = resized_ori_w * cam_param.fx / width + else: + resized_ori_h = width / pose_wh_ratio + for cam_param in cam_params: + cam_param.fy = resized_ori_h * cam_param.fy / height + + intrinsic = np.asarray([[cam_param.fx * width, + cam_param.fy * height, + cam_param.cx * width, + cam_param.cy * height] + for cam_param in cam_params], dtype=np.float32) + + K = torch.as_tensor(intrinsic)[None] # [1, 1, 4] + c2ws = get_relative_pose(cam_params) # Assuming this function is defined elsewhere + c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4] + plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W + plucker_embedding = plucker_embedding[None] + plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0] + return plucker_embedding + +class Camera(object): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + def __init__(self, entry): + fx, fy, cx, cy = entry[1:5] + self.fx = fx + self.fy = fy + self.cx = cx + self.cy = cy + c2w_mat = np.array(entry[7:]).reshape(4, 4) + self.c2w_mat = c2w_mat + self.w2c_mat = np.linalg.inv(c2w_mat) + +def ray_condition(K, c2w, H, W, device): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + # c2w: B, V, 4, 4 + # K: B, V, 4 + + B = K.shape[0] + + j, i = torch.meshgrid( + torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype), + torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype), + indexing='ij' + ) + i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] + j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] + + fx, fy, cx, cy = K.chunk(4, dim=-1) # B,V, 1 + + zs = torch.ones_like(i) # [B, HxW] + xs = (i - cx) / fx * zs + ys = (j - cy) / fy * zs + zs = zs.expand_as(ys) + + directions = torch.stack((xs, ys, zs), dim=-1) # B, V, HW, 3 + directions = directions / directions.norm(dim=-1, keepdim=True) # B, V, HW, 3 + + rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2) # B, V, 3, HW + rays_o = c2w[..., :3, 3] # B, V, 3 + rays_o = rays_o[:, :, None].expand_as(rays_d) # B, V, 3, HW + # c2w @ dirctions + rays_dxo = torch.cross(rays_o, rays_d) + plucker = torch.cat([rays_dxo, rays_d], dim=-1) + plucker = plucker.reshape(B, c2w.shape[1], H, W, 6) # B, V, H, W, 6 + # plucker = plucker.permute(0, 1, 4, 2, 3) + return plucker + +def get_camera_motion(angle, T, speed, n=81): + def compute_R_form_rad_angle(angles): + theta_x, theta_y, theta_z = angles + Rx = np.array([[1, 0, 0], + [0, np.cos(theta_x), -np.sin(theta_x)], + [0, np.sin(theta_x), np.cos(theta_x)]]) + + Ry = np.array([[np.cos(theta_y), 0, np.sin(theta_y)], + [0, 1, 0], + [-np.sin(theta_y), 0, np.cos(theta_y)]]) + + Rz = np.array([[np.cos(theta_z), -np.sin(theta_z), 0], + [np.sin(theta_z), np.cos(theta_z), 0], + [0, 0, 1]]) + + R = np.dot(Rz, np.dot(Ry, Rx)) + return R + RT = [] + for i in range(n): + _angle = (i/n)*speed*(CAMERA_DICT["base_angle"])*angle + R = compute_R_form_rad_angle(_angle) + _T=(i/n)*speed*(CAMERA_DICT["base_T_norm"])*(T.reshape(3,1)) + _RT = np.concatenate([R,_T], axis=1) + RT.append(_RT) + RT = np.stack(RT) + return RT + +class WanCameraEmbeding: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "camera_pose":(["Static","Pan Up","Pan Down","Pan Left","Pan Right","Zoom In","Zoom Out","Anti Clockwise (ACW)", "ClockWise (CW)"],{"default":"Static"}), + "width": ("INT", {"default": 832, "min": 16, "max": MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 81, "min": 1, "max": MAX_RESOLUTION, "step": 4}), + }, + "optional":{ + "speed":("FLOAT",{"default":1.0, "min": 0, "max": 10.0, "step": 0.1}), + "fx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}), + "fy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}), + "cx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}), + "cy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}), + } + + } + + RETURN_TYPES = ("WAN_CAMERA_EMBEDDING","INT","INT","INT") + RETURN_NAMES = ("camera_embedding","width","height","length") + FUNCTION = "run" + CATEGORY = "camera" + + def run(self, camera_pose, width, height, length, speed=1.0, fx=0.5, fy=0.5, cx=0.5, cy=0.5): + """ + Use Camera trajectory as extrinsic parameters to calculate Plücker embeddings (Sitzmannet al., 2021) + Adapted from https://github.com/aigc-apps/VideoX-Fun/blob/main/comfyui/comfyui_nodes.py + """ + motion_list = [camera_pose] + speed = speed + angle = np.array(CAMERA_DICT[motion_list[0]]["angle"]) + T = np.array(CAMERA_DICT[motion_list[0]]["T"]) + RT = get_camera_motion(angle, T, speed, length) + + trajs=[] + for cp in RT.tolist(): + traj=[fx,fy,cx,cy,0,0] + traj.extend(cp[0]) + traj.extend(cp[1]) + traj.extend(cp[2]) + traj.extend([0,0,0,1]) + trajs.append(traj) + + cam_params = np.array([[float(x) for x in pose] for pose in trajs]) + cam_params = np.concatenate([np.zeros_like(cam_params[:, :1]), cam_params], 1) + control_camera_video = process_pose_params(cam_params, width=width, height=height) + control_camera_video = control_camera_video.permute([3, 0, 1, 2]).unsqueeze(0).to(device=comfy.model_management.intermediate_device()) + + control_camera_video = torch.concat( + [ + torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2), + control_camera_video[:, :, 1:] + ], dim=2 + ).transpose(1, 2) + + # Reshape, transpose, and view into desired shape + b, f, c, h, w = control_camera_video.shape + control_camera_video = control_camera_video.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3) + control_camera_video = control_camera_video.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2) + + return (control_camera_video, width, height, length) + + +NODE_CLASS_MAPPINGS = { + "WanCameraEmbeding": WanCameraEmbeding, +} diff --git a/comfy_extras/nodes_wan.py b/comfy_extras/nodes_wan.py index 9dda64597..a91b4aba9 100644 --- a/comfy_extras/nodes_wan.py +++ b/comfy_extras/nodes_wan.py @@ -297,6 +297,52 @@ class TrimVideoLatent: samples_out["samples"] = s1[:, :, trim_amount:] return (samples_out,) +class WanCameraImageToVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }, + "optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ), + "start_image": ("IMAGE", ), + "camera_conditions": ("WAN_CAMERA_EMBEDDING", ), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, camera_conditions=None): + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent) + + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + concat_latent_image = vae.encode(start_image[:, :, :, :3]) + concat_latent[:,:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]] + + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent}) + + if camera_conditions is not None: + positive = node_helpers.conditioning_set_values(positive, {'camera_conditions': camera_conditions}) + negative = node_helpers.conditioning_set_values(negative, {'camera_conditions': camera_conditions}) + + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) + + out_latent = {} + out_latent["samples"] = latent + return (positive, negative, out_latent) NODE_CLASS_MAPPINGS = { "WanImageToVideo": WanImageToVideo, @@ -305,4 +351,5 @@ NODE_CLASS_MAPPINGS = { "WanFirstLastFrameToVideo": WanFirstLastFrameToVideo, "WanVaceToVideo": WanVaceToVideo, "TrimVideoLatent": TrimVideoLatent, + "WanCameraImageToVideo": WanCameraImageToVideo, } diff --git a/nodes.py b/nodes.py index 54e3886a3..0a9db1393 100644 --- a/nodes.py +++ b/nodes.py @@ -2265,6 +2265,7 @@ def init_builtin_extra_nodes(): "nodes_preview_any.py", "nodes_ace.py", "nodes_string.py", + "nodes_camera_trajectory.py", ] import_failed = [] From 1c2d45d2b575c40efcb303a61318d4a415242e52 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 15 May 2025 16:02:19 -0700 Subject: [PATCH 115/193] Fix typo in last PR. (#8144) More robust model detection for future proofing. --- comfy/model_detection.py | 2 ++ comfy/supported_models.py | 2 +- comfy_extras/nodes_camera_trajectory.py | 4 ++-- 3 files changed, 5 insertions(+), 3 deletions(-) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 28c586389..20f287df9 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -361,6 +361,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config["model_type"] = "vace" dit_config["vace_in_dim"] = state_dict['{}vace_patch_embedding.weight'.format(key_prefix)].shape[1] dit_config["vace_layers"] = count_blocks(state_dict_keys, '{}vace_blocks.'.format(key_prefix) + '{}.') + elif '{}control_adapter.conv.weight'.format(key_prefix) in state_dict_keys: + dit_config["model_type"] = "camera" else: if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys: dit_config["model_type"] = "i2v" diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 667393ac0..efe2e6b8f 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -995,7 +995,7 @@ class WAN21_FunControl2V(WAN21_T2V): class WAN21_Camera(WAN21_T2V): unet_config = { "image_model": "wan2.1", - "model_type": "i2v", + "model_type": "camera", "in_dim": 32, } diff --git a/comfy_extras/nodes_camera_trajectory.py b/comfy_extras/nodes_camera_trajectory.py index b84b4785c..5e0e39f91 100644 --- a/comfy_extras/nodes_camera_trajectory.py +++ b/comfy_extras/nodes_camera_trajectory.py @@ -148,7 +148,7 @@ def get_camera_motion(angle, T, speed, n=81): RT = np.stack(RT) return RT -class WanCameraEmbeding: +class WanCameraEmbedding: @classmethod def INPUT_TYPES(cls): return { @@ -214,5 +214,5 @@ class WanCameraEmbeding: NODE_CLASS_MAPPINGS = { - "WanCameraEmbeding": WanCameraEmbeding, + "WanCameraEmbedding": WanCameraEmbedding, } From 7046983d9538d49d1dd286a513aa6db42b9a74fd Mon Sep 17 00:00:00 2001 From: filtered <176114999+webfiltered@users.noreply.github.com> Date: Sat, 17 May 2025 03:45:36 +1000 Subject: [PATCH 116/193] Remove Desktop versioning claim from README (#8155) --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index dcdaa353c..9b5f301c9 100644 --- a/README.md +++ b/README.md @@ -110,7 +110,6 @@ ComfyUI follows a weekly release cycle every Friday, with three interconnected r 2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)** - Builds a new release using the latest stable core version - - Version numbers match the core release (e.g., Desktop v1.7.0 uses Core v1.7.0) 3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)** - Weekly frontend updates are merged into the core repository From dc46db7aa48acd035dfc10730a064a9c994fb76b Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 16 May 2025 12:15:55 -0700 Subject: [PATCH 117/193] Make ImagePadForOutpaint return a 3 channel mask. (#8157) --- nodes.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nodes.py b/nodes.py index 0a9db1393..95e831b8b 100644 --- a/nodes.py +++ b/nodes.py @@ -1940,7 +1940,7 @@ class ImagePadForOutpaint: mask[top:top + d2, left:left + d3] = t - return (new_image, mask) + return (new_image, mask.unsqueeze(0)) NODE_CLASS_MAPPINGS = { From aee2908d0395577a6e2e13d1307aaf271424108b Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 17 May 2025 03:27:34 -0700 Subject: [PATCH 118/193] Remove useless log. (#8166) --- comfy/utils.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/comfy/utils.py b/comfy/utils.py index 561e1b858..1f8d71292 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -78,8 +78,6 @@ def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False): pl_sd = torch.load(ckpt, map_location=device, weights_only=True, **torch_args) else: pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle) - if "global_step" in pl_sd: - logging.debug(f"Global Step: {pl_sd['global_step']}") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: From f5e4e976f43c9ed79e75b27f73719b2708f9ded9 Mon Sep 17 00:00:00 2001 From: Silver <65376327+silveroxides@users.noreply.github.com> Date: Sun, 18 May 2025 08:59:06 +0200 Subject: [PATCH 119/193] Add missing category for T5TokenizerOption (#8177) Change it if you need to but it should at least have a category. --- comfy_extras/nodes_cond.py | 1 + 1 file changed, 1 insertion(+) diff --git a/comfy_extras/nodes_cond.py b/comfy_extras/nodes_cond.py index 574262178..58c16f621 100644 --- a/comfy_extras/nodes_cond.py +++ b/comfy_extras/nodes_cond.py @@ -31,6 +31,7 @@ class T5TokenizerOptions: } } + CATEGORY = "_for_testing/conditioning" RETURN_TYPES = ("CLIP",) FUNCTION = "set_options" From 05eb10b43a42929033f449def7cd5a8feeb84673 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Sun, 18 May 2025 01:08:47 -0700 Subject: [PATCH 120/193] Validate video inputs (#8133) * validate kling lip sync input video * add tooltips * update duration estimates * decrease epsilon * fix rebase error --- comfy_api_nodes/nodes_kling.py | 51 ++++++------ comfy_api_nodes/util/__init__.py | 0 comfy_api_nodes/util/validation_utils.py | 100 +++++++++++++++++++++++ 3 files changed, 126 insertions(+), 25 deletions(-) create mode 100644 comfy_api_nodes/util/__init__.py create mode 100644 comfy_api_nodes/util/validation_utils.py diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py index 456a86905..641cd6353 100644 --- a/comfy_api_nodes/nodes_kling.py +++ b/comfy_api_nodes/nodes_kling.py @@ -65,6 +65,12 @@ from comfy_api_nodes.apinode_utils import ( download_url_to_image_tensor, ) from comfy_api_nodes.mapper_utils import model_field_to_node_input +from comfy_api_nodes.util.validation_utils import ( + validate_image_dimensions, + validate_image_aspect_ratio, + validate_video_dimensions, + validate_video_duration, +) from comfy_api.input.basic_types import AudioInput from comfy_api.input.video_types import VideoInput from comfy_api.input_impl import VideoFromFile @@ -80,18 +86,16 @@ PATH_CHARACTER_IMAGE = f"/proxy/kling/{KLING_API_VERSION}/images/generations" PATH_VIRTUAL_TRY_ON = f"/proxy/kling/{KLING_API_VERSION}/images/kolors-virtual-try-on" PATH_IMAGE_GENERATIONS = f"/proxy/kling/{KLING_API_VERSION}/images/generations" - MAX_PROMPT_LENGTH_T2V = 2500 MAX_PROMPT_LENGTH_I2V = 500 MAX_PROMPT_LENGTH_IMAGE_GEN = 500 MAX_NEGATIVE_PROMPT_LENGTH_IMAGE_GEN = 200 MAX_PROMPT_LENGTH_LIP_SYNC = 120 -# TODO: adjust based on tests -AVERAGE_DURATION_T2V = 319 # 319, -AVERAGE_DURATION_I2V = 164 # 164, -AVERAGE_DURATION_LIP_SYNC = 120 -AVERAGE_DURATION_VIRTUAL_TRY_ON = 19 # 19, +AVERAGE_DURATION_T2V = 319 +AVERAGE_DURATION_I2V = 164 +AVERAGE_DURATION_LIP_SYNC = 455 +AVERAGE_DURATION_VIRTUAL_TRY_ON = 19 AVERAGE_DURATION_IMAGE_GEN = 32 AVERAGE_DURATION_VIDEO_EFFECTS = 320 AVERAGE_DURATION_VIDEO_EXTEND = 320 @@ -211,23 +215,8 @@ def validate_input_image(image: torch.Tensor) -> None: See: https://app.klingai.com/global/dev/document-api/apiReference/model/imageToVideo """ - if len(image.shape) == 4: - height, width = image.shape[1], image.shape[2] - elif len(image.shape) == 3: - height, width = image.shape[0], image.shape[1] - else: - raise ValueError("Invalid image tensor shape.") - - # Ensure minimum resolution is met - if height < 300: - raise ValueError("Image height must be at least 300px") - if width < 300: - raise ValueError("Image width must be at least 300px") - - # Ensure aspect ratio is within acceptable range - aspect_ratio = width / height - if aspect_ratio < 1 / 2.5 or aspect_ratio > 2.5: - raise ValueError("Image aspect ratio must be between 1:2.5 and 2.5:1") + validate_image_dimensions(image, min_width=300, min_height=300) + validate_image_aspect_ratio(image, min_aspect_ratio=1 / 2.5, max_aspect_ratio=2.5) def get_camera_control_input_config( @@ -1243,6 +1232,17 @@ class KlingLipSyncBase(KlingNodeBase): RETURN_TYPES = ("VIDEO", "STRING", "STRING") RETURN_NAMES = ("VIDEO", "video_id", "duration") + def validate_lip_sync_video(self, video: VideoInput): + """ + Validates the input video adheres to the expectations of the Kling Lip Sync API: + - Video length does not exceed 10s and is not shorter than 2s + - Length and width dimensions should both be between 720px and 1920px + + See: https://app.klingai.com/global/dev/document-api/apiReference/model/videoTolip + """ + validate_video_dimensions(video, 720, 1920) + validate_video_duration(video, 2, 10) + def validate_text(self, text: str): if not text: raise ValueError("Text is required") @@ -1282,6 +1282,7 @@ class KlingLipSyncBase(KlingNodeBase): ) -> tuple[VideoFromFile, str, str]: if text: self.validate_text(text) + self.validate_lip_sync_video(video) # Upload video to Comfy API and get download URL video_url = upload_video_to_comfyapi(video, auth_kwargs=kwargs) @@ -1352,7 +1353,7 @@ class KlingLipSyncAudioToVideoNode(KlingLipSyncBase): }, } - DESCRIPTION = "Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file." + DESCRIPTION = "Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file. When using, ensure that the audio contains clearly distinguishable vocals and that the video contains a distinct face. The audio file should not be larger than 5MB. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length." def api_call( self, @@ -1464,7 +1465,7 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase): }, } - DESCRIPTION = "Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt." + DESCRIPTION = "Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length." def api_call( self, diff --git a/comfy_api_nodes/util/__init__.py b/comfy_api_nodes/util/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/comfy_api_nodes/util/validation_utils.py b/comfy_api_nodes/util/validation_utils.py new file mode 100644 index 000000000..031b9fbd3 --- /dev/null +++ b/comfy_api_nodes/util/validation_utils.py @@ -0,0 +1,100 @@ +import logging +from typing import Optional + +import torch +from comfy_api.input.video_types import VideoInput + + +def get_image_dimensions(image: torch.Tensor) -> tuple[int, int]: + if len(image.shape) == 4: + return image.shape[1], image.shape[2] + elif len(image.shape) == 3: + return image.shape[0], image.shape[1] + else: + raise ValueError("Invalid image tensor shape.") + + +def validate_image_dimensions( + image: torch.Tensor, + min_width: Optional[int] = None, + max_width: Optional[int] = None, + min_height: Optional[int] = None, + max_height: Optional[int] = None, +): + height, width = get_image_dimensions(image) + + if min_width is not None and width < min_width: + raise ValueError(f"Image width must be at least {min_width}px, got {width}px") + if max_width is not None and width > max_width: + raise ValueError(f"Image width must be at most {max_width}px, got {width}px") + if min_height is not None and height < min_height: + raise ValueError( + f"Image height must be at least {min_height}px, got {height}px" + ) + if max_height is not None and height > max_height: + raise ValueError(f"Image height must be at most {max_height}px, got {height}px") + + +def validate_image_aspect_ratio( + image: torch.Tensor, + min_aspect_ratio: Optional[float] = None, + max_aspect_ratio: Optional[float] = None, +): + width, height = get_image_dimensions(image) + aspect_ratio = width / height + + if min_aspect_ratio is not None and aspect_ratio < min_aspect_ratio: + raise ValueError( + f"Image aspect ratio must be at least {min_aspect_ratio}, got {aspect_ratio}" + ) + if max_aspect_ratio is not None and aspect_ratio > max_aspect_ratio: + raise ValueError( + f"Image aspect ratio must be at most {max_aspect_ratio}, got {aspect_ratio}" + ) + + +def validate_video_dimensions( + video: VideoInput, + min_width: Optional[int] = None, + max_width: Optional[int] = None, + min_height: Optional[int] = None, + max_height: Optional[int] = None, +): + try: + width, height = video.get_dimensions() + except Exception as e: + logging.error("Error getting dimensions of video: %s", e) + return + + if min_width is not None and width < min_width: + raise ValueError(f"Video width must be at least {min_width}px, got {width}px") + if max_width is not None and width > max_width: + raise ValueError(f"Video width must be at most {max_width}px, got {width}px") + if min_height is not None and height < min_height: + raise ValueError( + f"Video height must be at least {min_height}px, got {height}px" + ) + if max_height is not None and height > max_height: + raise ValueError(f"Video height must be at most {max_height}px, got {height}px") + + +def validate_video_duration( + video: VideoInput, + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, +): + try: + duration = video.get_duration() + except Exception as e: + logging.error("Error getting duration of video: %s", e) + return + + epsilon = 0.0001 + if min_duration is not None and min_duration - epsilon > duration: + raise ValueError( + f"Video duration must be at least {min_duration}s, got {duration}s" + ) + if max_duration is not None and duration > max_duration + epsilon: + raise ValueError( + f"Video duration must be at most {max_duration}s, got {duration}s" + ) From 62690eddec9b7d715b4c37246f71abf5ca1c5844 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 18 May 2025 01:09:56 -0700 Subject: [PATCH 121/193] Node to add pixel space noise to an image. (#8182) --- comfy_extras/nodes_images.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py index 77c305619..29a5d5b61 100644 --- a/comfy_extras/nodes_images.py +++ b/comfy_extras/nodes_images.py @@ -13,6 +13,7 @@ import os import re from io import BytesIO from inspect import cleandoc +import torch from comfy.comfy_types import FileLocator @@ -74,6 +75,24 @@ class ImageFromBatch: s = s_in[batch_index:batch_index + length].clone() return (s,) + +class ImageAddNoise: + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}), + "strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "repeat" + + CATEGORY = "image" + + def repeat(self, image, seed, strength): + generator = torch.manual_seed(seed) + s = torch.clip((image + strength * torch.randn(image.size(), generator=generator, device="cpu").to(image)), min=0.0, max=1.0) + return (s,) + class SaveAnimatedWEBP: def __init__(self): self.output_dir = folder_paths.get_output_directory() @@ -295,6 +314,7 @@ NODE_CLASS_MAPPINGS = { "ImageCrop": ImageCrop, "RepeatImageBatch": RepeatImageBatch, "ImageFromBatch": ImageFromBatch, + "ImageAddNoise": ImageAddNoise, "SaveAnimatedWEBP": SaveAnimatedWEBP, "SaveAnimatedPNG": SaveAnimatedPNG, "SaveSVGNode": SaveSVGNode, From 3d44a09812c4f0880c30fcd1876125b7319300b4 Mon Sep 17 00:00:00 2001 From: LaVie024 <62406970+LaVie024@users.noreply.github.com> Date: Sun, 18 May 2025 08:11:11 +0000 Subject: [PATCH 122/193] Update nodes_string.py (#8173) --- comfy_extras/nodes_string.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/comfy_extras/nodes_string.py b/comfy_extras/nodes_string.py index a852326e5..9eaa71236 100644 --- a/comfy_extras/nodes_string.py +++ b/comfy_extras/nodes_string.py @@ -8,7 +8,8 @@ class StringConcatenate(): return { "required": { "string_a": (IO.STRING, {"multiline": True}), - "string_b": (IO.STRING, {"multiline": True}) + "string_b": (IO.STRING, {"multiline": True}), + "delimiter": (IO.STRING, {"multiline": False, "default": ", "}) } } @@ -16,8 +17,8 @@ class StringConcatenate(): FUNCTION = "execute" CATEGORY = "utils/string" - def execute(self, string_a, string_b, **kwargs): - return string_a + string_b, + def execute(self, string_a, string_b, delimiter, **kwargs): + return delimiter.join((string_a, string_b)), class StringSubstring(): @classmethod From d8e5662822168101afb5e08a8ba75b6eefff6e02 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 18 May 2025 01:12:12 -0700 Subject: [PATCH 123/193] Remove default delimiter. (#8183) --- comfy_extras/nodes_string.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_extras/nodes_string.py b/comfy_extras/nodes_string.py index 9eaa71236..b24222cee 100644 --- a/comfy_extras/nodes_string.py +++ b/comfy_extras/nodes_string.py @@ -9,7 +9,7 @@ class StringConcatenate(): "required": { "string_a": (IO.STRING, {"multiline": True}), "string_b": (IO.STRING, {"multiline": True}), - "delimiter": (IO.STRING, {"multiline": False, "default": ", "}) + "delimiter": (IO.STRING, {"multiline": False, "default": ""}) } } From e930a387d62cc819117502993b0b821b1e3f2687 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 19 May 2025 01:58:41 -0700 Subject: [PATCH 124/193] Update AMD instructions in README. (#8198) --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9b5f301c9..15157f527 100644 --- a/README.md +++ b/README.md @@ -197,11 +197,11 @@ Put your VAE in: models/vae ### AMD GPUs (Linux only) AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version: -```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2.4``` +```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.3``` This is the command to install the nightly with ROCm 6.3 which might have some performance improvements: -```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.3``` +```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.4``` ### Intel GPUs (Windows and Linux) From 4f3b50ba510e02fa3fdd8c755ef9ad319b36bd61 Mon Sep 17 00:00:00 2001 From: filtered <176114999+webfiltered@users.noreply.github.com> Date: Tue, 20 May 2025 06:40:55 +1000 Subject: [PATCH 125/193] Update README ROCm text to match link (#8199) - Follow-up on #8198 --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 15157f527..47514d1b4 100644 --- a/README.md +++ b/README.md @@ -199,7 +199,7 @@ AMD users can install rocm and pytorch with pip if you don't have it already ins ```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.3``` -This is the command to install the nightly with ROCm 6.3 which might have some performance improvements: +This is the command to install the nightly with ROCm 6.4 which might have some performance improvements: ```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.4``` From 7e84bf53737879ace37a68dc93e0df7704a53514 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 20 May 2025 02:29:23 -0700 Subject: [PATCH 126/193] This doesn't seem to be needed on chroma. (#8209) --- comfy/ldm/chroma/layers.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/comfy/ldm/chroma/layers.py b/comfy/ldm/chroma/layers.py index 35da91ee2..18a4a9cfc 100644 --- a/comfy/ldm/chroma/layers.py +++ b/comfy/ldm/chroma/layers.py @@ -109,9 +109,6 @@ class DoubleStreamBlock(nn.Module): txt += txt_mod1.gate * self.txt_attn.proj(txt_attn) txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) - if txt.dtype == torch.float16: - txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) - return img, txt @@ -163,8 +160,6 @@ class SingleStreamBlock(nn.Module): # compute activation in mlp stream, cat again and run second linear layer output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) x += mod.gate * output - if x.dtype == torch.float16: - x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) return x From 87f91307782ce0b401786d8edddd8f618b955141 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 20 May 2025 02:39:55 -0700 Subject: [PATCH 127/193] Revert "This doesn't seem to be needed on chroma. (#8209)" (#8210) This reverts commit 7e84bf53737879ace37a68dc93e0df7704a53514. --- comfy/ldm/chroma/layers.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/comfy/ldm/chroma/layers.py b/comfy/ldm/chroma/layers.py index 18a4a9cfc..35da91ee2 100644 --- a/comfy/ldm/chroma/layers.py +++ b/comfy/ldm/chroma/layers.py @@ -109,6 +109,9 @@ class DoubleStreamBlock(nn.Module): txt += txt_mod1.gate * self.txt_attn.proj(txt_attn) txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) + if txt.dtype == torch.float16: + txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) + return img, txt @@ -160,6 +163,8 @@ class SingleStreamBlock(nn.Module): # compute activation in mlp stream, cat again and run second linear layer output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) x += mod.gate * output + if x.dtype == torch.float16: + x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) return x From 10024a38ea8d7e8950b26500a540cd0323d0e611 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 21 May 2025 04:50:37 -0400 Subject: [PATCH 128/193] ComfyUI version v0.3.35 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index b740b378d..8db3bc803 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.34" +__version__ = "0.3.35" diff --git a/pyproject.toml b/pyproject.toml index 80061b39a..a33fc4370 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.34" +version = "0.3.35" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9" From 65da29aaa965afcb0811a9c8dac1cc0facb006d4 Mon Sep 17 00:00:00 2001 From: Jedrzej Kosinski Date: Wed, 21 May 2025 01:56:56 -0700 Subject: [PATCH 129/193] Make torch.compile LoRA/key-compatible (#8213) * Make torch compile node use wrapper instead of object_patch for the entire diffusion_models object, allowing key assotiations on diffusion_models to not break (loras, getting attributes, etc.) * Moved torch compile code into comfy_api so it can be used by custom nodes with a degree of confidence * Refactor set_torch_compile_wrapper to support a list of keys instead of just diffusion_model, as well as additional torch.compile args * remove unused import * Moved torch compile kwargs to be stored in model_options instead of attachments; attachments are more intended for things to be 'persisted', AKA not deepcopied * Add some comments * Remove random line of code, not sure how it got there --- comfy_api/torch_helpers/__init__.py | 5 ++ comfy_api/torch_helpers/torch_compile.py | 69 ++++++++++++++++++++++++ comfy_extras/nodes_torch_compile.py | 5 +- 3 files changed, 77 insertions(+), 2 deletions(-) create mode 100644 comfy_api/torch_helpers/__init__.py create mode 100644 comfy_api/torch_helpers/torch_compile.py diff --git a/comfy_api/torch_helpers/__init__.py b/comfy_api/torch_helpers/__init__.py new file mode 100644 index 000000000..be7ae7a61 --- /dev/null +++ b/comfy_api/torch_helpers/__init__.py @@ -0,0 +1,5 @@ +from .torch_compile import set_torch_compile_wrapper + +__all__ = [ + "set_torch_compile_wrapper", +] diff --git a/comfy_api/torch_helpers/torch_compile.py b/comfy_api/torch_helpers/torch_compile.py new file mode 100644 index 000000000..9223f58db --- /dev/null +++ b/comfy_api/torch_helpers/torch_compile.py @@ -0,0 +1,69 @@ +from __future__ import annotations +import torch + +import comfy.utils +from comfy.patcher_extension import WrappersMP +from typing import TYPE_CHECKING, Callable, Optional +if TYPE_CHECKING: + from comfy.model_patcher import ModelPatcher + from comfy.patcher_extension import WrapperExecutor + + +COMPILE_KEY = "torch.compile" +TORCH_COMPILE_KWARGS = "torch_compile_kwargs" + + +def apply_torch_compile_factory(compiled_module_dict: dict[str, Callable]) -> Callable: + ''' + Create a wrapper that will refer to the compiled_diffusion_model. + ''' + def apply_torch_compile_wrapper(executor: WrapperExecutor, *args, **kwargs): + try: + orig_modules = {} + for key, value in compiled_module_dict.items(): + orig_modules[key] = comfy.utils.get_attr(executor.class_obj, key) + comfy.utils.set_attr(executor.class_obj, key, value) + return executor(*args, **kwargs) + finally: + for key, value in orig_modules.items(): + comfy.utils.set_attr(executor.class_obj, key, value) + return apply_torch_compile_wrapper + + +def set_torch_compile_wrapper(model: ModelPatcher, backend: str, options: Optional[dict[str,str]]=None, + mode: Optional[str]=None, fullgraph=False, dynamic: Optional[bool]=None, + keys: list[str]=["diffusion_model"], *args, **kwargs): + ''' + Perform torch.compile that will be applied at sample time for either the whole model or specific params of the BaseModel instance. + + When keys is None, it will default to using ["diffusion_model"], compiling the whole diffusion_model. + When a list of keys is provided, it will perform torch.compile on only the selected modules. + ''' + # clear out any other torch.compile wrappers + model.remove_wrappers_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY) + # if no keys, default to 'diffusion_model' + if not keys: + keys = ["diffusion_model"] + # create kwargs dict that can be referenced later + compile_kwargs = { + "backend": backend, + "options": options, + "mode": mode, + "fullgraph": fullgraph, + "dynamic": dynamic, + } + # get a dict of compiled keys + compiled_modules = {} + for key in keys: + compiled_modules[key] = torch.compile( + model=model.get_model_object(key), + **compile_kwargs, + ) + # add torch.compile wrapper + wrapper_func = apply_torch_compile_factory( + compiled_module_dict=compiled_modules, + ) + # store wrapper to run on BaseModel's apply_model function + model.add_wrapper_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY, wrapper_func) + # keep compile kwargs for reference + model.model_options[TORCH_COMPILE_KWARGS] = compile_kwargs diff --git a/comfy_extras/nodes_torch_compile.py b/comfy_extras/nodes_torch_compile.py index 1fe6f42c7..605536678 100644 --- a/comfy_extras/nodes_torch_compile.py +++ b/comfy_extras/nodes_torch_compile.py @@ -1,4 +1,5 @@ -import torch +from comfy_api.torch_helpers import set_torch_compile_wrapper + class TorchCompileModel: @classmethod @@ -14,7 +15,7 @@ class TorchCompileModel: def patch(self, model, backend): m = model.clone() - m.add_object_patch("diffusion_model", torch.compile(model=m.get_model_object("diffusion_model"), backend=backend)) + set_torch_compile_wrapper(model=m, backend=backend) return (m, ) NODE_CLASS_MAPPINGS = { From 57893c843f44ea9e8a0be79292d19e5a5e16e9e2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=BC=96=E7=A8=8B=E7=95=8C=E7=9A=84=E5=B0=8F=E5=AD=A6?= =?UTF-8?q?=E7=94=9F?= <15620646321@163.com> Date: Wed, 21 May 2025 16:59:42 +0800 Subject: [PATCH 130/193] Code Optimization and Issues Fixes in ComfyUI server (#8196) * Update server.py * Update server.py --- server.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/server.py b/server.py index cb1c6a8fd..16cd88d91 100644 --- a/server.py +++ b/server.py @@ -226,7 +226,7 @@ class PromptServer(): return response @routes.get("/embeddings") - def get_embeddings(self): + def get_embeddings(request): embeddings = folder_paths.get_filename_list("embeddings") return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings))) @@ -282,7 +282,6 @@ class PromptServer(): a.update(f.read()) b.update(image.file.read()) image.file.seek(0) - f.close() return a.hexdigest() == b.hexdigest() return False From 8bb858e4d39f7f6a6969c584aeeaa1d606a812d6 Mon Sep 17 00:00:00 2001 From: Michael Abrahams Date: Wed, 21 May 2025 05:14:17 -0400 Subject: [PATCH 131/193] Improve performance with large number of queued prompts (#8176) * get_current_queue_volatile * restore get_current_queue method * remove extra import --- execution.py | 9 ++++++++- main.py | 3 +-- server.py | 5 +++-- 3 files changed, 12 insertions(+), 5 deletions(-) diff --git a/execution.py b/execution.py index e5d1c69d9..15ff7567c 100644 --- a/execution.py +++ b/execution.py @@ -909,7 +909,6 @@ class PromptQueue: self.currently_running = {} self.history = {} self.flags = {} - server.prompt_queue = self def put(self, item): with self.mutex: @@ -954,6 +953,7 @@ class PromptQueue: self.history[prompt[1]].update(history_result) self.server.queue_updated() + # Note: slow def get_current_queue(self): with self.mutex: out = [] @@ -961,6 +961,13 @@ class PromptQueue: out += [x] return (out, copy.deepcopy(self.queue)) + # read-safe as long as queue items are immutable + def get_current_queue_volatile(self): + with self.mutex: + running = [x for x in self.currently_running.values()] + queued = copy.copy(self.queue) + return (running, queued) + def get_tasks_remaining(self): with self.mutex: return len(self.queue) + len(self.currently_running) diff --git a/main.py b/main.py index 0fde6d221..fb1f8d20b 100644 --- a/main.py +++ b/main.py @@ -260,7 +260,6 @@ def start_comfyui(asyncio_loop=None): asyncio_loop = asyncio.new_event_loop() asyncio.set_event_loop(asyncio_loop) prompt_server = server.PromptServer(asyncio_loop) - q = execution.PromptQueue(prompt_server) hook_breaker_ac10a0.save_functions() nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes, init_api_nodes=not args.disable_api_nodes) @@ -271,7 +270,7 @@ def start_comfyui(asyncio_loop=None): prompt_server.add_routes() hijack_progress(prompt_server) - threading.Thread(target=prompt_worker, daemon=True, args=(q, prompt_server,)).start() + threading.Thread(target=prompt_worker, daemon=True, args=(prompt_server.prompt_queue, prompt_server,)).start() if args.quick_test_for_ci: exit(0) diff --git a/server.py b/server.py index 16cd88d91..1b0a73601 100644 --- a/server.py +++ b/server.py @@ -29,6 +29,7 @@ import comfy.model_management import node_helpers from comfyui_version import __version__ from app.frontend_management import FrontendManager + from app.user_manager import UserManager from app.model_manager import ModelFileManager from app.custom_node_manager import CustomNodeManager @@ -159,7 +160,7 @@ class PromptServer(): self.custom_node_manager = CustomNodeManager() self.internal_routes = InternalRoutes(self) self.supports = ["custom_nodes_from_web"] - self.prompt_queue = None + self.prompt_queue = execution.PromptQueue(self) self.loop = loop self.messages = asyncio.Queue() self.client_session:Optional[aiohttp.ClientSession] = None @@ -620,7 +621,7 @@ class PromptServer(): @routes.get("/queue") async def get_queue(request): queue_info = {} - current_queue = self.prompt_queue.get_current_queue() + current_queue = self.prompt_queue.get_current_queue_volatile() queue_info['queue_running'] = current_queue[0] queue_info['queue_pending'] = current_queue[1] return web.json_response(queue_info) From ded60c33a0c0231c7109f30072c25e64e360e636 Mon Sep 17 00:00:00 2001 From: ComfyUI Wiki Date: Thu, 22 May 2025 02:40:08 +0800 Subject: [PATCH 132/193] Update templates to 0.1.18 (#8224) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 8f7a78984..858e6343c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.19.9 -comfyui-workflow-templates==0.1.14 +comfyui-workflow-templates==0.1.18 torch torchsde torchvision From fc39184ea9a442b6e9a346fa23d1b3cad3a6f493 Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Thu, 22 May 2025 02:24:36 -0400 Subject: [PATCH 133/193] Update frontend to 1.20 (#8232) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 858e6343c..5a988ecd9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.19.9 +comfyui-frontend-package==1.20.4 comfyui-workflow-templates==0.1.18 torch torchsde From b838c367209a8530dc1c56c4150988a1d8af7ed6 Mon Sep 17 00:00:00 2001 From: Terry Jia Date: Thu, 22 May 2025 08:08:36 -0400 Subject: [PATCH 134/193] remove mtl from 3d model file list (#8192) --- comfy_extras/nodes_load_3d.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_extras/nodes_load_3d.py b/comfy_extras/nodes_load_3d.py index d5b4d9111..40d03e18a 100644 --- a/comfy_extras/nodes_load_3d.py +++ b/comfy_extras/nodes_load_3d.py @@ -16,7 +16,7 @@ class Load3D(): os.makedirs(input_dir, exist_ok=True) - files = [normalize_path(os.path.join("3d", f)) for f in os.listdir(input_dir) if f.endswith(('.gltf', '.glb', '.obj', '.mtl', '.fbx', '.stl'))] + files = [normalize_path(os.path.join("3d", f)) for f in os.listdir(input_dir) if f.endswith(('.gltf', '.glb', '.obj', '.fbx', '.stl'))] return {"required": { "model_file": (sorted(files), {"file_upload": True}), From 4202e956a0172178f5d4ce1971da8c07c93420a9 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 22 May 2025 05:11:13 -0700 Subject: [PATCH 135/193] Add append feature to conditioning_set_values (#8239) Refactor unclipconditioning node. --- node_helpers.py | 10 ++++++++-- nodes.py | 11 +---------- 2 files changed, 9 insertions(+), 12 deletions(-) diff --git a/node_helpers.py b/node_helpers.py index c3e1a14ca..4ff960ef8 100644 --- a/node_helpers.py +++ b/node_helpers.py @@ -5,12 +5,18 @@ from comfy.cli_args import args from PIL import ImageFile, UnidentifiedImageError -def conditioning_set_values(conditioning, values={}): +def conditioning_set_values(conditioning, values={}, append=False): c = [] for t in conditioning: n = [t[0], t[1].copy()] for k in values: - n[1][k] = values[k] + val = values[k] + if append: + old_val = n[1].get(k, None) + if old_val is not None: + val = old_val + val + + n[1][k] = val c.append(n) return c diff --git a/nodes.py b/nodes.py index 95e831b8b..1e328651b 100644 --- a/nodes.py +++ b/nodes.py @@ -1103,16 +1103,7 @@ class unCLIPConditioning: if strength == 0: return (conditioning, ) - c = [] - for t in conditioning: - o = t[1].copy() - x = {"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation} - if "unclip_conditioning" in o: - o["unclip_conditioning"] = o["unclip_conditioning"][:] + [x] - else: - o["unclip_conditioning"] = [x] - n = [t[0], o] - c.append(n) + c = node_helpers.conditioning_set_values(conditioning, {"unclip_conditioning": [{"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation}]}, append=True) return (c, ) class GLIGENLoader: From f85c08df0615a587e0974678b01199b88a1caae0 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 22 May 2025 16:22:26 -0700 Subject: [PATCH 136/193] Make VACE conditionings stackable. (#8240) --- comfy/ldm/wan/model.py | 10 +++++++--- comfy/model_base.py | 21 +++++++++++++-------- comfy_extras/nodes_wan.py | 5 +++-- 3 files changed, 23 insertions(+), 13 deletions(-) diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py index a996dedf4..1b51a4e4a 100644 --- a/comfy/ldm/wan/model.py +++ b/comfy/ldm/wan/model.py @@ -635,7 +635,7 @@ class VaceWanModel(WanModel): t, context, vace_context, - vace_strength=1.0, + vace_strength, clip_fea=None, freqs=None, transformer_options={}, @@ -661,8 +661,11 @@ class VaceWanModel(WanModel): context = torch.concat([context_clip, context], dim=1) context_img_len = clip_fea.shape[-2] + orig_shape = list(vace_context.shape) + vace_context = vace_context.movedim(0, 1).reshape([-1] + orig_shape[2:]) c = self.vace_patch_embedding(vace_context.float()).to(vace_context.dtype) c = c.flatten(2).transpose(1, 2) + c = list(c.split(orig_shape[0], dim=0)) # arguments x_orig = x @@ -682,8 +685,9 @@ class VaceWanModel(WanModel): ii = self.vace_layers_mapping.get(i, None) if ii is not None: - c_skip, c = self.vace_blocks[ii](c, x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) - x += c_skip * vace_strength + for iii in range(len(c)): + c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) + x += c_skip * vace_strength[iii] del c_skip # head x = self.head(x, e) diff --git a/comfy/model_base.py b/comfy/model_base.py index f475e837e..fb4724690 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -1062,20 +1062,25 @@ class WAN21_Vace(WAN21): vace_frames = kwargs.get("vace_frames", None) if vace_frames is None: noise_shape[1] = 32 - vace_frames = torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype) - - for i in range(0, vace_frames.shape[1], 16): - vace_frames = vace_frames.clone() - vace_frames[:, i:i + 16] = self.process_latent_in(vace_frames[:, i:i + 16]) + vace_frames = [torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)] mask = kwargs.get("vace_mask", None) if mask is None: noise_shape[1] = 64 - mask = torch.ones(noise_shape, device=noise.device, dtype=noise.dtype) + mask = [torch.ones(noise_shape, device=noise.device, dtype=noise.dtype)] * len(vace_frames) - out['vace_context'] = comfy.conds.CONDRegular(torch.cat([vace_frames.to(noise), mask.to(noise)], dim=1)) + vace_frames_out = [] + for j in range(len(vace_frames)): + vf = vace_frames[j].clone() + for i in range(0, vf.shape[1], 16): + vf[:, i:i + 16] = self.process_latent_in(vf[:, i:i + 16]) + vf = torch.cat([vf, mask[j]], dim=1) + vace_frames_out.append(vf) - vace_strength = kwargs.get("vace_strength", 1.0) + vace_frames = torch.stack(vace_frames_out, dim=1) + out['vace_context'] = comfy.conds.CONDRegular(vace_frames) + + vace_strength = kwargs.get("vace_strength", [1.0] * len(vace_frames_out)) out['vace_strength'] = comfy.conds.CONDConstant(vace_strength) return out diff --git a/comfy_extras/nodes_wan.py b/comfy_extras/nodes_wan.py index a91b4aba9..c35c4871c 100644 --- a/comfy_extras/nodes_wan.py +++ b/comfy_extras/nodes_wan.py @@ -268,8 +268,9 @@ class WanVaceToVideo: trim_latent = reference_image.shape[2] mask = mask.unsqueeze(0) - positive = node_helpers.conditioning_set_values(positive, {"vace_frames": control_video_latent, "vace_mask": mask, "vace_strength": strength}) - negative = node_helpers.conditioning_set_values(negative, {"vace_frames": control_video_latent, "vace_mask": mask, "vace_strength": strength}) + + positive = node_helpers.conditioning_set_values(positive, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True) + negative = node_helpers.conditioning_set_values(negative, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True) latent = torch.zeros([batch_size, 16, latent_length, height // 8, width // 8], device=comfy.model_management.intermediate_device()) out_latent = {} From 30b2eb8a93ce931f7b8e15f9f7dbc7bf751b1c17 Mon Sep 17 00:00:00 2001 From: drhead <1313496+drhead@users.noreply.github.com> Date: Fri, 23 May 2025 16:15:06 -0400 Subject: [PATCH 137/193] create arange on-device (#8255) --- comfy/ldm/chroma/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/ldm/chroma/model.py b/comfy/ldm/chroma/model.py index 636748fc5..c75023a31 100644 --- a/comfy/ldm/chroma/model.py +++ b/comfy/ldm/chroma/model.py @@ -163,7 +163,7 @@ class Chroma(nn.Module): distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype) # get all modulation index - modulation_index = timestep_embedding(torch.arange(mod_index_length), 32).to(img.device, img.dtype) + modulation_index = timestep_embedding(torch.arange(mod_index_length, device=img.device), 32).to(img.device, img.dtype) # we need to broadcast the modulation index here so each batch has all of the index modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype) # and we need to broadcast timestep and guidance along too From 0b50d4c0db025f4c1ede7d1094567bb22a8901bf Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 23 May 2025 14:43:50 -0700 Subject: [PATCH 138/193] Add argument to explicitly enable fp8 compute support. (#8257) This can be used to test if your current GPU/pytorch version supports fp8 matrix mult in combination with --fast or the fp8_e4m3fn_fast dtype. --- comfy/cli_args.py | 1 + comfy/model_management.py | 3 +++ 2 files changed, 4 insertions(+) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index de292d9b3..4fb675f99 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -88,6 +88,7 @@ parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE" parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.") parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.") +parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.") class LatentPreviewMethod(enum.Enum): NoPreviews = "none" diff --git a/comfy/model_management.py b/comfy/model_management.py index 44aff3762..a49ed83e6 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -1257,6 +1257,9 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma return False def supports_fp8_compute(device=None): + if args.supports_fp8_compute: + return True + if not is_nvidia(): return False From 464aece92b86c93694390a1b385b3c505190d0cd Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Fri, 23 May 2025 21:53:49 -0700 Subject: [PATCH 139/193] update frontend package to v1.20.5 (#8260) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 5a988ecd9..48631633d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.20.4 +comfyui-frontend-package==1.20.5 comfyui-workflow-templates==0.1.18 torch torchsde From 5a87757ef96f807cf1cf5b41c55a0a84c9551f20 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 24 May 2025 03:43:12 -0700 Subject: [PATCH 140/193] Better error if sageattention is installed but a dependency is missing. (#8264) --- comfy/ldm/modules/attention.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 45f9e311e..2cb77d85d 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -20,8 +20,11 @@ if model_management.xformers_enabled(): if model_management.sage_attention_enabled(): try: from sageattention import sageattn - except ModuleNotFoundError: - logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention") + except ModuleNotFoundError as e: + if e.name == "sageattention": + logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention") + else: + raise e exit(-1) if model_management.flash_attention_enabled(): From ad3bd8aa4904de8c3798e148fcf02b00ac14277c Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Sat, 24 May 2025 17:30:37 -0400 Subject: [PATCH 141/193] ComfyUI version 0.3.36 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 8db3bc803..817b7d83b 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.35" +__version__ = "0.3.36" diff --git a/pyproject.toml b/pyproject.toml index a33fc4370..accf6f864 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.35" +version = "0.3.36" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9" From a0651359d7a1ee968d5cf01c1b7302e41435e779 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 25 May 2025 02:28:11 -0700 Subject: [PATCH 142/193] Return proper error if diffusion model not detected properly. (#8272) --- comfy/model_detection.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 20f287df9..74f539598 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -620,6 +620,9 @@ def convert_config(unet_config): def unet_config_from_diffusers_unet(state_dict, dtype=None): + if "conv_in.weight" not in state_dict: + return None + match = {} transformer_depth = [] From e5799c4899f968d03a1a656c9c5df2926a0768b1 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 26 May 2025 01:29:25 -0700 Subject: [PATCH 143/193] Enable pytorch attention by default on AMD gfx1151 (#8282) --- comfy/model_management.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index a49ed83e6..1e6e8553f 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -301,7 +301,7 @@ try: logging.info("AMD arch: {}".format(arch)) if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: if torch_version_numeric[0] >= 2 and torch_version_numeric[1] >= 7: # works on 2.6 but doesn't actually seem to improve much - if any((a in arch) for a in ["gfx1100", "gfx1101"]): # TODO: more arches + if any((a in arch) for a in ["gfx1100", "gfx1101", "gfx1151"]): # TODO: more arches ENABLE_PYTORCH_ATTENTION = True except: pass From 89a84e32d2a771743ba10d6a6a0634f7e321fef5 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 26 May 2025 13:39:27 -0700 Subject: [PATCH 144/193] Disable initial GPU load when novram is used. (#8294) --- comfy/model_management.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 1e6e8553f..f5b37e68e 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -695,7 +695,7 @@ def unet_inital_load_device(parameters, dtype): return torch_dev cpu_dev = torch.device("cpu") - if DISABLE_SMART_MEMORY: + if DISABLE_SMART_MEMORY or vram_state == VRAMState.NO_VRAM: return cpu_dev model_size = dtype_size(dtype) * parameters From 3a10b9641ce05d7e80b7051fda4195978c8ba656 Mon Sep 17 00:00:00 2001 From: filtered <176114999+webfiltered@users.noreply.github.com> Date: Tue, 27 May 2025 16:47:06 +1000 Subject: [PATCH 145/193] [BugFix] Update frontend to 1.20.6 (#8296) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 48631633d..f56b3e096 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.20.5 +comfyui-frontend-package==1.20.6 comfyui-workflow-templates==0.1.18 torch torchsde From f58f0f56969ccfe8d57a18976cd296719d795730 Mon Sep 17 00:00:00 2001 From: Robin Huang Date: Tue, 27 May 2025 00:00:58 -0700 Subject: [PATCH 146/193] More API nodes: Gemini/Open AI Chat, Tripo, Rodin, Runway Image (#8295) * Add Ideogram generate node. * Add staging api. * Add API_NODE and common error for missing auth token (#5) * Add Minimax Video Generation + Async Task queue polling example (#6) * [Minimax] Show video preview and embed workflow in ouput (#7) * Remove uv.lock * Remove polling operations. * Revert "Remove polling operations." This reverts commit 8415404ce8fbc0262b7de54fc700c5c8854a34fc. * Update stubs. * Added Ideogram and Minimax back in. * Added initial BFL Flux 1.1 [pro] Ultra node (#11) * Manually add BFL polling status response schema (#15) * Add function for uploading files. (#18) * Add Luma nodes (#16) Co-authored-by: Robin Huang * Refactor util functions (#20) * Add rest of Luma node functionality (#19) Co-authored-by: Robin Huang * Fix image_luma_ref not working (#28) Co-authored-by: Robin Huang * [Bug] Remove duplicated option T2V-01 in MinimaxTextToVideoNode (#31) * add veo2, bump av req (#32) * Add Recraft nodes (#29) * Add Kling Nodes (#12) * Add Camera Concepts (luma_concepts) to Luma Video nodes (#33) Co-authored-by: Robin Huang * Add Runway nodes (#17) * Convert Minimax node to use VIDEO output type (#34) * Standard `CATEGORY` system for api nodes (#35) * Set `Content-Type` header when uploading files (#36) * add better error propagation to veo2 (#37) * Add Realistic Image and Logo Raster styles for Recraft v3 (#38) * Fix runway image upload and progress polling (#39) * Fix image upload for Luma: only include `Content-Type` header field if it's set explicitly (#40) * Moved Luma nodes to nodes_luma.py (#47) * Moved Recraft nodes to nodes_recraft.py (#48) * Move and fix BFL nodes to node_bfl.py (#49) * Move and edit Minimax node to nodes_minimax.py (#50) * Add Recraft Text to Vector node, add Save SVG node to handle its output (#53) * Added pixverse_template support to Pixverse Text to Video node (#54) * Added Recraft Controls + Recraft Color RGB nodes (#57) * split remaining nodes out of nodes_api, make utility lib, refactor ideogram (#61) * Set request type explicitly (#66) * Add `control_after_generate` to all seed inputs (#69) * Fix bug: deleting `Content-Type` when property does not exist (#73) * Add Pixverse and updated Kling types (#75) * Added Recraft Style - Infinite Style Library node (#82) * add ideogram v3 (#83) * [Kling] Split Camera Control config to its own node (#81) * Add Pika i2v and t2v nodes (#52) * Remove Runway nodes (#88) * Fix: Prompt text can't be validated in Kling nodes when using primitive nodes (#90) * Update Pika Duration and Resolution options (#94) * Removed Infinite Style Library until later (#99) * fix multi image return (#101) close #96 * Serve SVG files directly (#107) * Add a bunch of nodes, 3 ready to use, the rest waiting for endpoint support (#108) * Revert "Serve SVG files directly" (#111) * Expose 4 remaining Recraft nodes (#112) * [Kling] Add `Duration` and `Video ID` outputs (#105) * Add Kling nodes: camera control, start-end frame, lip-sync, video extend (#115) * Fix error for Recraft ImageToImage error for nonexistent random_seed param (#118) * Add remaining Pika nodes (#119) * Make controls input work for Recraft Image to Image node (#120) * Fix: Nested `AnyUrl` in request model cannot be serialized (Kling, Runway) (#129) * Show errors and API output URLs to the user (change log levels) (#131) * Apply small fixes and most prompt validation (if needed to avoid API error) (#135) * Node name/category modifications (#140) * Add back Recraft Style - Infinite Style Library node (#141) * [Kling] Fix: Correct/verify supported subset of input combos in Kling nodes (#149) * Remove pixverse_template from PixVerse Transition Video node (#155) * Use 3.9 compat syntax (#164) * Handle Comfy API key based authorizaton (#167) Co-authored-by: Jedrzej Kosinski * [BFL] Print download URL of successful task result directly on nodes (#175) * Show output URL and progress text on Pika nodes (#168) * [Ideogram] Print download URL of successful task result directly on nodes (#176) * [Kling] Print download URL of successful task result directly on nodes (#181) * Merge upstream may 14 25 (#186) Co-authored-by: comfyanonymous Co-authored-by: AustinMroz Co-authored-by: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Co-authored-by: Benjamin Lu Co-authored-by: Andrew Kvochko Co-authored-by: Pam <42671363+pamparamm@users.noreply.github.com> Co-authored-by: chaObserv <154517000+chaObserv@users.noreply.github.com> Co-authored-by: Yoland Yan <4950057+yoland68@users.noreply.github.com> Co-authored-by: guill Co-authored-by: Chenlei Hu Co-authored-by: Terry Jia Co-authored-by: Silver <65376327+silveroxides@users.noreply.github.com> Co-authored-by: catboxanon <122327233+catboxanon@users.noreply.github.com> Co-authored-by: liesen Co-authored-by: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Co-authored-by: Jedrzej Kosinski Co-authored-by: Robin Huang Co-authored-by: thot experiment <94414189+thot-experiment@users.noreply.github.com> Co-authored-by: blepping <157360029+blepping@users.noreply.github.com> * Update instructions on how to develop API Nodes. (#171) * Add Runway FLF and I2V nodes (#187) * Add OpenAI chat node (#188) * Update README. * Add Google Gemini API node (#191) * Add Runway Gen 4 Text to Image Node (#193) * [Runway, Gemini] Update node display names and attributes (#194) * Update path from "image-to-video" to "image_to_video" (#197) * [Runway] Split I2V nodes into separate gen3 and gen4 nodes (#198) * Update runway i2v ratio enum (#201) * Rodin3D: implement Rodin3D API Nodes (#190) Co-authored-by: WhiteGiven Co-authored-by: Robin Huang * Add Tripo Nodes. (#189) Co-authored-by: Robin Huang * Change casing of categories "3D" => "3d" (#208) * [tripo] fix negtive_prompt and mv2model (#212) * [tripo] set default param to None (#215) * Add description and tooltip to Tripo Refine model. (#218) * Update. * Fix rebase errors. * Fix rebase errors. * Update templates. * Bump frontend. * Add file type info for file inputs. --------- Co-authored-by: Christian Byrne Co-authored-by: Jedrzej Kosinski Co-authored-by: Chenlei Hu Co-authored-by: thot experiment <94414189+thot-experiment@users.noreply.github.com> Co-authored-by: comfyanonymous Co-authored-by: AustinMroz Co-authored-by: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Co-authored-by: Benjamin Lu Co-authored-by: Andrew Kvochko Co-authored-by: Pam <42671363+pamparamm@users.noreply.github.com> Co-authored-by: chaObserv <154517000+chaObserv@users.noreply.github.com> Co-authored-by: Yoland Yan <4950057+yoland68@users.noreply.github.com> Co-authored-by: guill Co-authored-by: Terry Jia Co-authored-by: Silver <65376327+silveroxides@users.noreply.github.com> Co-authored-by: catboxanon <122327233+catboxanon@users.noreply.github.com> Co-authored-by: liesen Co-authored-by: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Co-authored-by: blepping <157360029+blepping@users.noreply.github.com> Co-authored-by: Changrz <51637999+WhiteGiven@users.noreply.github.com> Co-authored-by: WhiteGiven Co-authored-by: seed93 --- comfy_api_nodes/README.md | 26 +- comfy_api_nodes/apinode_utils.py | 125 +- comfy_api_nodes/apis/__init__.py | 5906 ++++++++++++++--------------- comfy_api_nodes/apis/client.py | 2 +- comfy_api_nodes/apis/rodin_api.py | 57 + comfy_api_nodes/apis/tripo_api.py | 275 ++ comfy_api_nodes/nodes_gemini.py | 446 +++ comfy_api_nodes/nodes_openai.py | 524 ++- comfy_api_nodes/nodes_rodin.py | 462 +++ comfy_api_nodes/nodes_runway.py | 635 ++++ comfy_api_nodes/nodes_tripo.py | 574 +++ nodes.py | 4 + requirements.txt | 2 +- 13 files changed, 5870 insertions(+), 3168 deletions(-) create mode 100644 comfy_api_nodes/apis/rodin_api.py create mode 100644 comfy_api_nodes/apis/tripo_api.py create mode 100644 comfy_api_nodes/nodes_gemini.py create mode 100644 comfy_api_nodes/nodes_rodin.py create mode 100644 comfy_api_nodes/nodes_runway.py create mode 100644 comfy_api_nodes/nodes_tripo.py diff --git a/comfy_api_nodes/README.md b/comfy_api_nodes/README.md index e2633a769..64a389cc1 100644 --- a/comfy_api_nodes/README.md +++ b/comfy_api_nodes/README.md @@ -18,6 +18,8 @@ Follow the instructions [here](https://github.com/Comfy-Org/ComfyUI_frontend) to python run main.py --comfy-api-base https://stagingapi.comfy.org ``` +To authenticate to staging, please login and then ask one of Comfy Org team to whitelist you for access to staging. + API stubs are generated through automatic codegen tools from OpenAPI definitions. Since the Comfy Org OpenAPI definition contains many things from the Comfy Registry as well, we use redocly/cli to filter out only the paths relevant for API nodes. ### Redocly Instructions @@ -28,7 +30,7 @@ When developing locally, use the `redocly-dev.yaml` file to generate pydantic mo Before your API node PR merges, make sure to add the `Released` tag to the `openapi.yaml` file and test in staging. ```bash -# Download the OpenAPI file from prod server. +# Download the OpenAPI file from staging server. curl -o openapi.yaml https://stagingapi.comfy.org/openapi # Filter out unneeded API definitions. @@ -39,3 +41,25 @@ redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_no datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel ``` + + +# Merging to Master + +Before merging to comfyanonymous/ComfyUI master, follow these steps: + +1. Add the "Released" tag to the ComfyUI OpenAPI yaml file for each endpoint you are using in the nodes. +1. Make sure the ComfyUI API is deployed to prod with your changes. +1. Run the code generation again with `redocly.yaml` and the production OpenAPI yaml file. + +```bash +# Download the OpenAPI file from prod server. +curl -o openapi.yaml https://api.comfy.org/openapi + +# Filter out unneeded API definitions. +npm install -g @redocly/cli +redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly.yaml --remove-unused-components + +# Generate the pydantic datamodels for validation. +datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel + +``` diff --git a/comfy_api_nodes/apinode_utils.py b/comfy_api_nodes/apinode_utils.py index 87d8c3e1d..788e2803f 100644 --- a/comfy_api_nodes/apinode_utils.py +++ b/comfy_api_nodes/apinode_utils.py @@ -1,6 +1,7 @@ from __future__ import annotations import io import logging +import mimetypes from typing import Optional, Union from comfy.utils import common_upscale from comfy_api.input_impl import VideoFromFile @@ -214,6 +215,7 @@ def download_url_to_image_tensor(url: str, timeout: int = None) -> torch.Tensor: image_bytesio = download_url_to_bytesio(url, timeout) return bytesio_to_image_tensor(image_bytesio) + def process_image_response(response: requests.Response) -> torch.Tensor: """Uses content from a Response object and converts it to a torch.Tensor""" return bytesio_to_image_tensor(BytesIO(response.content)) @@ -318,11 +320,27 @@ def tensor_to_data_uri( return f"data:{mime_type};base64,{base64_string}" +def text_filepath_to_base64_string(filepath: str) -> str: + """Converts a text file to a base64 string.""" + with open(filepath, "rb") as f: + file_content = f.read() + return base64.b64encode(file_content).decode("utf-8") + + +def text_filepath_to_data_uri(filepath: str) -> str: + """Converts a text file to a data URI.""" + base64_string = text_filepath_to_base64_string(filepath) + mime_type, _ = mimetypes.guess_type(filepath) + if mime_type is None: + mime_type = "application/octet-stream" + return f"data:{mime_type};base64,{base64_string}" + + def upload_file_to_comfyapi( file_bytes_io: BytesIO, filename: str, upload_mime_type: str, - auth_kwargs: Optional[dict[str,str]] = None, + auth_kwargs: Optional[dict[str, str]] = None, ) -> str: """ Uploads a single file to ComfyUI API and returns its download URL. @@ -357,9 +375,33 @@ def upload_file_to_comfyapi( return response.download_url +def video_to_base64_string( + video: VideoInput, + container_format: VideoContainer = None, + codec: VideoCodec = None +) -> str: + """ + Converts a video input to a base64 string. + + Args: + video: The video input to convert + container_format: Optional container format to use (defaults to video.container if available) + codec: Optional codec to use (defaults to video.codec if available) + """ + video_bytes_io = io.BytesIO() + + # Use provided format/codec if specified, otherwise use video's own if available + format_to_use = container_format if container_format is not None else getattr(video, 'container', VideoContainer.MP4) + codec_to_use = codec if codec is not None else getattr(video, 'codec', VideoCodec.H264) + + video.save_to(video_bytes_io, format=format_to_use, codec=codec_to_use) + video_bytes_io.seek(0) + return base64.b64encode(video_bytes_io.getvalue()).decode("utf-8") + + def upload_video_to_comfyapi( video: VideoInput, - auth_kwargs: Optional[dict[str,str]] = None, + auth_kwargs: Optional[dict[str, str]] = None, container: VideoContainer = VideoContainer.MP4, codec: VideoCodec = VideoCodec.H264, max_duration: Optional[int] = None, @@ -461,7 +503,7 @@ def audio_ndarray_to_bytesio( def upload_audio_to_comfyapi( audio: AudioInput, - auth_kwargs: Optional[dict[str,str]] = None, + auth_kwargs: Optional[dict[str, str]] = None, container_format: str = "mp4", codec_name: str = "aac", mime_type: str = "audio/mp4", @@ -488,8 +530,25 @@ def upload_audio_to_comfyapi( return upload_file_to_comfyapi(audio_bytes_io, filename, mime_type, auth_kwargs) +def audio_to_base64_string( + audio: AudioInput, container_format: str = "mp4", codec_name: str = "aac" +) -> str: + """Converts an audio input to a base64 string.""" + sample_rate: int = audio["sample_rate"] + waveform: torch.Tensor = audio["waveform"] + audio_data_np = audio_tensor_to_contiguous_ndarray(waveform) + audio_bytes_io = audio_ndarray_to_bytesio( + audio_data_np, sample_rate, container_format, codec_name + ) + audio_bytes = audio_bytes_io.getvalue() + return base64.b64encode(audio_bytes).decode("utf-8") + + def upload_images_to_comfyapi( - image: torch.Tensor, max_images=8, auth_kwargs: Optional[dict[str,str]] = None, mime_type: Optional[str] = None + image: torch.Tensor, + max_images=8, + auth_kwargs: Optional[dict[str, str]] = None, + mime_type: Optional[str] = None, ) -> list[str]: """ Uploads images to ComfyUI API and returns download URLs. @@ -554,17 +613,24 @@ def upload_images_to_comfyapi( return download_urls -def resize_mask_to_image(mask: torch.Tensor, image: torch.Tensor, - upscale_method="nearest-exact", crop="disabled", - allow_gradient=True, add_channel_dim=False): +def resize_mask_to_image( + mask: torch.Tensor, + image: torch.Tensor, + upscale_method="nearest-exact", + crop="disabled", + allow_gradient=True, + add_channel_dim=False, +): """ Resize mask to be the same dimensions as an image, while maintaining proper format for API calls. """ _, H, W, _ = image.shape mask = mask.unsqueeze(-1) - mask = mask.movedim(-1,1) - mask = common_upscale(mask, width=W, height=H, upscale_method=upscale_method, crop=crop) - mask = mask.movedim(1,-1) + mask = mask.movedim(-1, 1) + mask = common_upscale( + mask, width=W, height=H, upscale_method=upscale_method, crop=crop + ) + mask = mask.movedim(1, -1) if not add_channel_dim: mask = mask.squeeze(-1) if not allow_gradient: @@ -572,12 +638,41 @@ def resize_mask_to_image(mask: torch.Tensor, image: torch.Tensor, return mask -def validate_string(string: str, strip_whitespace=True, field_name="prompt", min_length=None, max_length=None): +def validate_string( + string: str, + strip_whitespace=True, + field_name="prompt", + min_length=None, + max_length=None, +): + if string is None: + raise Exception(f"Field '{field_name}' cannot be empty.") if strip_whitespace: string = string.strip() if min_length and len(string) < min_length: - raise Exception(f"Field '{field_name}' cannot be shorter than {min_length} characters; was {len(string)} characters long.") + raise Exception( + f"Field '{field_name}' cannot be shorter than {min_length} characters; was {len(string)} characters long." + ) if max_length and len(string) > max_length: - raise Exception(f" Field '{field_name} cannot be longer than {max_length} characters; was {len(string)} characters long.") - if not string: - raise Exception(f"Field '{field_name}' cannot be empty.") + raise Exception( + f" Field '{field_name} cannot be longer than {max_length} characters; was {len(string)} characters long." + ) + + +def image_tensor_pair_to_batch( + image1: torch.Tensor, image2: torch.Tensor +) -> torch.Tensor: + """ + Converts a pair of image tensors to a batch tensor. + If the images are not the same size, the smaller image is resized to + match the larger image. + """ + if image1.shape[1:] != image2.shape[1:]: + image2 = common_upscale( + image2.movedim(-1, 1), + image1.shape[2], + image1.shape[1], + "bilinear", + "center", + ).movedim(1, -1) + return torch.cat((image1, image2), dim=0) diff --git a/comfy_api_nodes/apis/__init__.py b/comfy_api_nodes/apis/__init__.py index aa1c4ce0b..e38d38cc9 100644 --- a/comfy_api_nodes/apis/__init__.py +++ b/comfy_api_nodes/apis/__init__.py @@ -1,67 +1,197 @@ # generated by datamodel-codegen: # filename: filtered-openapi.yaml -# timestamp: 2025-05-04T04:12:39+00:00 +# timestamp: 2025-05-19T21:38:55+00:00 from __future__ import annotations -from datetime import datetime +from datetime import date, datetime from enum import Enum from typing import Any, Dict, List, Literal, Optional, Union from uuid import UUID -from pydantic import AnyUrl, BaseModel, Field, RootModel, StrictBytes +from pydantic import AnyUrl, BaseModel, ConfigDict, Field, RootModel, StrictBytes -class PersonalAccessToken(BaseModel): - id: Optional[UUID] = Field(None, description='Unique identifier for the GitCommit') - name: Optional[str] = Field( - None, - description='Required. The name of the token. Can be a simple description.', - ) - description: Optional[str] = Field( - None, - description="Optional. A more detailed description of the token's intended use.", +class APIKey(BaseModel): + created_at: Optional[datetime] = None + description: Optional[str] = None + id: Optional[str] = None + key_prefix: Optional[str] = None + name: Optional[str] = None + + +class APIKeyWithPlaintext(APIKey): + plaintext_key: Optional[str] = Field( + None, description='The full API key (only returned at creation)' ) + + +class AuditLog(BaseModel): createdAt: Optional[datetime] = Field( - None, description='[Output Only]The date and time the token was created.' + None, description='The date and time the event was created' ) - token: Optional[str] = Field( + event_id: Optional[str] = Field(None, description='the id of the event') + event_type: Optional[str] = Field(None, description='the type of the event') + params: Optional[Dict[str, Any]] = Field( + None, description='data related to the event' + ) + + +class OutputFormat(str, Enum): + jpeg = 'jpeg' + png = 'png' + + +class BFLFluxPro11GenerateRequest(BaseModel): + height: int = Field(..., description='Height of the generated image') + image_prompt: Optional[str] = Field(None, description='Optional image prompt') + output_format: Optional[OutputFormat] = Field( + None, description='Output image format' + ) + prompt: str = Field(..., description='The main text prompt for image generation') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to use prompt upsampling' + ) + safety_tolerance: Optional[int] = Field(None, description='Safety tolerance level') + seed: Optional[int] = Field(None, description='Random seed for reproducibility') + webhook_secret: Optional[str] = Field( + None, description='Optional webhook secret for async processing' + ) + webhook_url: Optional[str] = Field( + None, description='Optional webhook URL for async processing' + ) + width: int = Field(..., description='Width of the generated image') + + +class BFLFluxPro11GenerateResponse(BaseModel): + id: str = Field(..., description='Job ID for tracking') + polling_url: str = Field(..., description='URL to poll for results') + + +class BFLFluxProGenerateRequest(BaseModel): + guidance_scale: Optional[float] = Field( + None, description='The guidance scale for generation.', ge=1.0, le=20.0 + ) + height: int = Field( + ..., description='The height of the image to generate.', ge=64, le=2048 + ) + negative_prompt: Optional[str] = Field( + None, description='The negative prompt for image generation.' + ) + num_images: Optional[int] = Field( + None, description='The number of images to generate.', ge=1, le=4 + ) + num_inference_steps: Optional[int] = Field( + None, description='The number of inference steps.', ge=1, le=100 + ) + prompt: str = Field(..., description='The text prompt for image generation.') + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + width: int = Field( + ..., description='The width of the image to generate.', ge=64, le=2048 + ) + + +class BFLFluxProGenerateResponse(BaseModel): + id: str = Field(..., description='The unique identifier for the generation task.') + polling_url: str = Field(..., description='URL to poll for the generation result.') + + +class Status(str, Enum): + in_progress = 'in_progress' + completed = 'completed' + incomplete = 'incomplete' + + +class Type(str, Enum): + computer_call = 'computer_call' + + +class ComputerToolCall(BaseModel): + action: Dict[str, Any] + call_id: str = Field( + ..., + description='An identifier used when responding to the tool call with output.\n', + ) + id: str = Field(..., description='The unique ID of the computer call.') + status: Status = Field( + ..., + description='The status of the item. One of `in_progress`, `completed`, or\n`incomplete`. Populated when items are returned via API.\n', + ) + type: Type = Field( + ..., description='The type of the computer call. Always `computer_call`.' + ) + + +class Environment(str, Enum): + windows = 'windows' + mac = 'mac' + linux = 'linux' + ubuntu = 'ubuntu' + browser = 'browser' + + +class Type1(str, Enum): + computer_use_preview = 'computer_use_preview' + + +class ComputerUsePreviewTool(BaseModel): + display_height: int = Field(..., description='The height of the computer display.') + display_width: int = Field(..., description='The width of the computer display.') + environment: Environment = Field( + ..., description='The type of computer environment to control.' + ) + type: Literal['ComputerUsePreviewTool'] = Field( + ..., + description='The type of the computer use tool. Always `computer_use_preview`.', + ) + + +class CreateAPIKeyRequest(BaseModel): + description: Optional[str] = None + name: str + + +class Customer(BaseModel): + createdAt: Optional[datetime] = Field( + None, description='The date and time the user was created' + ) + email: Optional[str] = Field(None, description='The email address for this user') + id: str = Field(..., description='The firebase UID of the user') + is_admin: Optional[bool] = Field(None, description='Whether the user is an admin') + metronome_id: Optional[str] = Field(None, description='The Metronome customer ID') + name: Optional[str] = Field(None, description='The name for this user') + stripe_id: Optional[str] = Field(None, description='The Stripe customer ID') + updatedAt: Optional[datetime] = Field( + None, description='The date and time the user was last updated' + ) + + +class CustomerStorageResourceResponse(BaseModel): + download_url: Optional[str] = Field( None, - description='[Output Only]. The personal access token. Only returned during creation.', + description='The signed URL to use for downloading the file from the specified path', + ) + existing_file: Optional[bool] = Field( + None, description='Whether an existing file with the same hash was found' + ) + expires_at: Optional[datetime] = Field( + None, description='When the signed URL will expire' + ) + upload_url: Optional[str] = Field( + None, + description='The signed URL to use for uploading the file to the specified path', ) -class GitCommitSummary(BaseModel): - commit_hash: Optional[str] = Field(None, description='The hash of the commit') - commit_name: Optional[str] = Field(None, description='The name of the commit') - branch_name: Optional[str] = Field( - None, description='The branch where the commit was made' - ) - author: Optional[str] = Field(None, description='The author of the commit') - timestamp: Optional[datetime] = Field( - None, description='The timestamp when the commit was made' - ) - status_summary: Optional[Dict[str, str]] = Field( - None, description='A map of operating system to status pairs' - ) +class Role(str, Enum): + user = 'user' + assistant = 'assistant' + system = 'system' + developer = 'developer' -class User(BaseModel): - id: Optional[str] = Field(None, description='The unique id for this user.') - email: Optional[str] = Field(None, description='The email address for this user.') - name: Optional[str] = Field(None, description='The name for this user.') - isApproved: Optional[bool] = Field( - None, description='Indicates if the user is approved.' - ) - isAdmin: Optional[bool] = Field( - None, description='Indicates if the user has admin privileges.' - ) - - -class PublisherUser(BaseModel): - id: Optional[str] = Field(None, description='The unique id for this user.') - email: Optional[str] = Field(None, description='The email address for this user.') - name: Optional[str] = Field(None, description='The name for this user.') +class Type2(str, Enum): + message = 'message' class ErrorResponse(BaseModel): @@ -69,168 +199,247 @@ class ErrorResponse(BaseModel): message: str -class StorageFile(BaseModel): - id: Optional[UUID] = Field( - None, description='Unique identifier for the storage file' - ) - file_path: Optional[str] = Field(None, description='Path to the file in storage') - public_url: Optional[str] = Field(None, description='Public URL') +class Type3(str, Enum): + file_search = 'file_search' -class PublisherMember(BaseModel): - id: Optional[str] = Field( - None, description='The unique identifier for the publisher member.' - ) - user: Optional[PublisherUser] = Field( - None, description='The user associated with this publisher member.' - ) - role: Optional[str] = Field( - None, description='The role of the user in the publisher.' +class FileSearchTool(BaseModel): + type: Literal['FileSearchTool'] = Field(..., description='The type of tool') + vector_store_ids: List[str] = Field( + ..., description='IDs of vector stores to search in' ) -class ComfyNode(BaseModel): - comfy_node_name: Optional[str] = Field( - None, description='Unique identifier for the node' +class Result(BaseModel): + file_id: Optional[str] = Field(None, description='The unique ID of the file.\n') + filename: Optional[str] = Field(None, description='The name of the file.\n') + score: Optional[float] = Field( + None, description='The relevance score of the file - a value between 0 and 1.\n' ) - category: Optional[str] = Field( - None, - description='UI category where the node is listed, used for grouping nodes.', + text: Optional[str] = Field( + None, description='The text that was retrieved from the file.\n' ) + + +class Status1(str, Enum): + in_progress = 'in_progress' + searching = 'searching' + completed = 'completed' + incomplete = 'incomplete' + failed = 'failed' + + +class Type4(str, Enum): + file_search_call = 'file_search_call' + + +class FileSearchToolCall(BaseModel): + id: str = Field(..., description='The unique ID of the file search tool call.\n') + queries: List[str] = Field( + ..., description='The queries used to search for files.\n' + ) + results: Optional[List[Result]] = Field( + None, description='The results of the file search tool call.\n' + ) + status: Status1 = Field( + ..., + description='The status of the file search tool call. One of `in_progress`, \n`searching`, `incomplete` or `failed`,\n', + ) + type: Type4 = Field( + ..., + description='The type of the file search tool call. Always `file_search_call`.\n', + ) + + +class Type5(str, Enum): + function = 'function' + + +class FunctionTool(BaseModel): description: Optional[str] = Field( - None, description="Brief description of the node's functionality or purpose." + None, description='Description of what the function does' ) - input_types: Optional[str] = Field(None, description='Defines input parameters') - deprecated: Optional[bool] = Field( + name: str = Field(..., description='Name of the function') + parameters: Dict[str, Any] = Field( + ..., description='JSON Schema object describing the function parameters' + ) + type: Literal['FunctionTool'] = Field(..., description='The type of tool') + + +class Status2(str, Enum): + in_progress = 'in_progress' + completed = 'completed' + incomplete = 'incomplete' + + +class Type6(str, Enum): + function_call = 'function_call' + + +class FunctionToolCall(BaseModel): + arguments: str = Field( + ..., description='A JSON string of the arguments to pass to the function.\n' + ) + call_id: str = Field( + ..., + description='The unique ID of the function tool call generated by the model.\n', + ) + id: Optional[str] = Field( + None, description='The unique ID of the function tool call.\n' + ) + name: str = Field(..., description='The name of the function to run.\n') + status: Optional[Status2] = Field( None, - description='Indicates if the node is deprecated. Deprecated nodes are hidden in the UI.', + description='The status of the item. One of `in_progress`, `completed`, or\n`incomplete`. Populated when items are returned via API.\n', ) - experimental: Optional[bool] = Field( + type: Type6 = Field( + ..., description='The type of the function tool call. Always `function_call`.\n' + ) + + +class GeminiCitation(BaseModel): + authors: Optional[List[str]] = None + endIndex: Optional[int] = None + license: Optional[str] = None + publicationDate: Optional[date] = None + startIndex: Optional[int] = None + title: Optional[str] = None + uri: Optional[str] = None + + +class GeminiCitationMetadata(BaseModel): + citations: Optional[List[GeminiCitation]] = None + + +class Role1(str, Enum): + user = 'user' + model = 'model' + + +class GeminiFunctionDeclaration(BaseModel): + description: Optional[str] = None + name: str + parameters: Dict[str, Any] = Field( + ..., description='JSON schema for the function parameters' + ) + + +class GeminiGenerationConfig(BaseModel): + maxOutputTokens: Optional[int] = Field( None, - description='Indicates if the node is experimental, subject to changes or removal.', + description='Maximum number of tokens that can be generated in the response. A token is approximately 4 characters. 100 tokens correspond to roughly 60-80 words.\n', + examples=[2048], + ge=16, + le=8192, ) - output_is_list: Optional[List[bool]] = Field( - None, description='Boolean values indicating if each output is a list.' - ) - return_names: Optional[str] = Field( - None, description='Names of the outputs for clarity in workflows.' - ) - return_types: Optional[str] = Field( - None, description='Specifies the types of outputs produced by the node.' - ) - function: Optional[str] = Field( - None, description='Name of the entry-point function to execute the node.' - ) - - -class ComfyNodeCloudBuildInfo(BaseModel): - project_id: Optional[str] = None - project_number: Optional[str] = None - location: Optional[str] = None - build_id: Optional[str] = None - - -class Error(BaseModel): - message: Optional[str] = Field( - None, description='A clear and concise description of the error.' - ) - details: Optional[List[str]] = Field( + seed: Optional[int] = Field( None, - description='Optional detailed information about the error or hints for resolving it.', + description="When seed is fixed to a specific value, the model makes a best effort to provide the same response for repeated requests. Deterministic output isn't guaranteed. Also, changing the model or parameter settings, such as the temperature, can cause variations in the response even when you use the same seed value. By default, a random seed value is used. Available for the following models:, gemini-2.5-flash-preview-04-1, gemini-2.5-pro-preview-05-0, gemini-2.0-flash-lite-00, gemini-2.0-flash-001\n", + examples=[343940597], + ) + stopSequences: Optional[List[str]] = None + temperature: Optional[float] = Field( + 1, + description="The temperature is used for sampling during response generation, which occurs when topP and topK are applied. Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 means that the highest probability tokens are always selected. In this case, responses for a given prompt are mostly deterministic, but a small amount of variation is still possible. If the model returns a response that's too generic, too short, or the model gives a fallback response, try increasing the temperature\n", + ge=0.0, + le=2.0, + ) + topK: Optional[int] = Field( + 40, + description="Top-K changes how the model selects tokens for output. A top-K of 1 means the next selected token is the most probable among all tokens in the model's vocabulary. A top-K of 3 means that the next token is selected from among the 3 most probable tokens by using temperature.\n", + examples=[40], + ge=1, + ) + topP: Optional[float] = Field( + 0.95, + description='If specified, nucleus sampling is used.\nTop-P changes how the model selects tokens for output. Tokens are selected from the most (see top-K) to least probable until the sum of their probabilities equals the top-P value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-P value is 0.5, then the model will select either A or B as the next token by using temperature and excludes C as a candidate.\nSpecify a lower value for less random responses and a higher value for more random responses.\n', + ge=0.0, + le=1.0, ) -class NodeVersionUpdateRequest(BaseModel): - changelog: Optional[str] = Field( - None, description='The changelog describing the version changes.' +class GeminiMimeType(str, Enum): + application_pdf = 'application/pdf' + audio_mpeg = 'audio/mpeg' + audio_mp3 = 'audio/mp3' + audio_wav = 'audio/wav' + image_png = 'image/png' + image_jpeg = 'image/jpeg' + image_webp = 'image/webp' + text_plain = 'text/plain' + video_mov = 'video/mov' + video_mpeg = 'video/mpeg' + video_mp4 = 'video/mp4' + video_mpg = 'video/mpg' + video_avi = 'video/avi' + video_wmv = 'video/wmv' + video_mpegps = 'video/mpegps' + video_flv = 'video/flv' + + +class GeminiOffset(BaseModel): + nanos: Optional[int] = Field( + None, + description='Signed fractions of a second at nanosecond resolution. Negative second values with fractions must still have non-negative nanos values.\n', + examples=[0], + ge=0, + le=999999999, ) - deprecated: Optional[bool] = Field( - None, description='Whether the version is deprecated.' + seconds: Optional[int] = Field( + None, + description='Signed seconds of the span of time. Must be from -315,576,000,000 to +315,576,000,000 inclusive.\n', + examples=[60], + ge=-315576000000, + le=315576000000, ) -class NodeStatus(str, Enum): - NodeStatusActive = 'NodeStatusActive' - NodeStatusDeleted = 'NodeStatusDeleted' - NodeStatusBanned = 'NodeStatusBanned' +class GeminiSafetyCategory(str, Enum): + HARM_CATEGORY_SEXUALLY_EXPLICIT = 'HARM_CATEGORY_SEXUALLY_EXPLICIT' + HARM_CATEGORY_HATE_SPEECH = 'HARM_CATEGORY_HATE_SPEECH' + HARM_CATEGORY_HARASSMENT = 'HARM_CATEGORY_HARASSMENT' + HARM_CATEGORY_DANGEROUS_CONTENT = 'HARM_CATEGORY_DANGEROUS_CONTENT' -class NodeVersionStatus(str, Enum): - NodeVersionStatusActive = 'NodeVersionStatusActive' - NodeVersionStatusDeleted = 'NodeVersionStatusDeleted' - NodeVersionStatusBanned = 'NodeVersionStatusBanned' - NodeVersionStatusPending = 'NodeVersionStatusPending' - NodeVersionStatusFlagged = 'NodeVersionStatusFlagged' +class Probability(str, Enum): + NEGLIGIBLE = 'NEGLIGIBLE' + LOW = 'LOW' + MEDIUM = 'MEDIUM' + HIGH = 'HIGH' + UNKNOWN = 'UNKNOWN' -class PublisherStatus(str, Enum): - PublisherStatusActive = 'PublisherStatusActive' - PublisherStatusBanned = 'PublisherStatusBanned' - - -class WorkflowRunStatus(str, Enum): - WorkflowRunStatusStarted = 'WorkflowRunStatusStarted' - WorkflowRunStatusFailed = 'WorkflowRunStatusFailed' - WorkflowRunStatusCompleted = 'WorkflowRunStatusCompleted' - - -class MachineStats(BaseModel): - machine_name: Optional[str] = Field(None, description='Name of the machine.') - os_version: Optional[str] = Field( - None, description='The operating system version. eg. Ubuntu Linux 20.04' - ) - gpu_type: Optional[str] = Field( - None, description='The GPU type. eg. NVIDIA Tesla K80' - ) - cpu_capacity: Optional[str] = Field(None, description='Total CPU on the machine.') - initial_cpu: Optional[str] = Field( - None, description='Initial CPU available before the job starts.' - ) - memory_capacity: Optional[str] = Field( - None, description='Total memory on the machine.' - ) - initial_ram: Optional[str] = Field( - None, description='Initial RAM available before the job starts.' - ) - vram_time_series: Optional[Dict[str, Any]] = Field( - None, description='Time series of VRAM usage.' - ) - disk_capacity: Optional[str] = Field( - None, description='Total disk capacity on the machine.' - ) - initial_disk: Optional[str] = Field( - None, description='Initial disk available before the job starts.' - ) - pip_freeze: Optional[str] = Field(None, description='The pip freeze output') - - -class Customer(BaseModel): - id: str = Field(..., description='The firebase UID of the user') - email: Optional[str] = Field(None, description='The email address for this user') - name: Optional[str] = Field(None, description='The name for this user') - createdAt: Optional[datetime] = Field( - None, description='The date and time the user was created' - ) - updatedAt: Optional[datetime] = Field( - None, description='The date and time the user was last updated' +class GeminiSafetyRating(BaseModel): + category: Optional[GeminiSafetyCategory] = None + probability: Optional[Probability] = Field( + None, + description='The probability that the content violates the specified safety category', ) -class MagicPrompt(str, Enum): - ON = 'ON' +class GeminiSafetyThreshold(str, Enum): OFF = 'OFF' + BLOCK_NONE = 'BLOCK_NONE' + BLOCK_LOW_AND_ABOVE = 'BLOCK_LOW_AND_ABOVE' + BLOCK_MEDIUM_AND_ABOVE = 'BLOCK_MEDIUM_AND_ABOVE' + BLOCK_ONLY_HIGH = 'BLOCK_ONLY_HIGH' -class ColorPalette(BaseModel): - name: str = Field(..., description='Name of the color palette', examples=['PASTEL']) +class GeminiTextPart(BaseModel): + text: Optional[str] = Field( + None, + description='A text prompt or code snippet.', + examples=['Answer as concisely as possible'], + ) -class StyleCode(RootModel[str]): - root: str = Field(..., pattern='^[0-9A-Fa-f]{8}$') +class GeminiTool(BaseModel): + functionDeclarations: Optional[List[GeminiFunctionDeclaration]] = None -class StyleType(str, Enum): - GENERAL = 'GENERAL' +class GeminiVideoMetadata(BaseModel): + endOffset: Optional[GeminiOffset] = None + startOffset: Optional[GeminiOffset] = None class IdeogramColorPalette1(BaseModel): @@ -262,17 +471,34 @@ class IdeogramColorPalette( class ImageRequest(BaseModel): - prompt: str = Field( - ..., description='Required. The prompt to use to generate the image.' - ) aspect_ratio: Optional[str] = Field( None, description="Optional. The aspect ratio (e.g., 'ASPECT_16_9', 'ASPECT_1_1'). Cannot be used with resolution. Defaults to 'ASPECT_1_1' if unspecified.", ) - model: str = Field(..., description="The model used (e.g., 'V_2', 'V_2A_TURBO')") + color_palette: Optional[Dict[str, Any]] = Field( + None, description='Optional. Color palette object. Only for V_2, V_2_TURBO.' + ) magic_prompt_option: Optional[str] = Field( None, description="Optional. MagicPrompt usage ('AUTO', 'ON', 'OFF')." ) + model: str = Field(..., description="The model used (e.g., 'V_2', 'V_2A_TURBO')") + negative_prompt: Optional[str] = Field( + None, + description='Optional. Description of what to exclude. Only for V_1, V_1_TURBO, V_2, V_2_TURBO.', + ) + num_images: Optional[int] = Field( + 1, + description='Optional. Number of images to generate (1-8). Defaults to 1.', + ge=1, + le=8, + ) + prompt: str = Field( + ..., description='Required. The prompt to use to generate the image.' + ) + resolution: Optional[str] = Field( + None, + description="Optional. Resolution (e.g., 'RESOLUTION_1024_1024'). Only for model V_2. Cannot be used with aspect_ratio.", + ) seed: Optional[int] = Field( None, description='Optional. A number between 0 and 2147483647.', @@ -283,23 +509,6 @@ class ImageRequest(BaseModel): None, description="Optional. Style type ('AUTO', 'GENERAL', 'REALISTIC', 'DESIGN', 'RENDER_3D', 'ANIME'). Only for models V_2 and above.", ) - negative_prompt: Optional[str] = Field( - None, - description='Optional. Description of what to exclude. Only for V_1, V_1_TURBO, V_2, V_2_TURBO.', - ) - num_images: Optional[int] = Field( - 1, - description='Optional. Number of images to generate (1-8). Defaults to 1.', - ge=1, - le=8, - ) - resolution: Optional[str] = Field( - None, - description="Optional. Resolution (e.g., 'RESOLUTION_1024_1024'). Only for model V_2. Cannot be used with aspect_ratio.", - ) - color_palette: Optional[Dict[str, Any]] = Field( - None, description='Optional. Color palette object. Only for V_2, V_2_TURBO.' - ) class IdeogramGenerateRequest(BaseModel): @@ -309,23 +518,23 @@ class IdeogramGenerateRequest(BaseModel): class Datum(BaseModel): + is_image_safe: Optional[bool] = Field( + None, description='Indicates whether the image is considered safe.' + ) prompt: Optional[str] = Field( None, description='The prompt used to generate this image.' ) resolution: Optional[str] = Field( None, description="The resolution of the generated image (e.g., '1024x1024')." ) - is_image_safe: Optional[bool] = Field( - None, description='Indicates whether the image is considered safe.' - ) seed: Optional[int] = Field( None, description='The seed value used for this generation.' ) - url: Optional[str] = Field(None, description='URL to the generated image.') style_type: Optional[str] = Field( None, description="The style type used for generation (e.g., 'REALISTIC', 'ANIME').", ) + url: Optional[str] = Field(None, description='URL to the generated image.') class IdeogramGenerateResponse(BaseModel): @@ -337,49 +546,17 @@ class IdeogramGenerateResponse(BaseModel): ) -class RenderingSpeed1(str, Enum): - TURBO = 'TURBO' - DEFAULT = 'DEFAULT' - QUALITY = 'QUALITY' - - -class MagicPrompt1(str, Enum): - AUTO = 'AUTO' - ON = 'ON' - OFF = 'OFF' - - -class StyleType1(str, Enum): - AUTO = 'AUTO' - GENERAL = 'GENERAL' - REALISTIC = 'REALISTIC' - DESIGN = 'DESIGN' - - -class IdeogramV3RemixRequest(BaseModel): - image: Optional[StrictBytes] = None - prompt: str - image_weight: Optional[int] = Field(50, ge=1, le=100) - seed: Optional[int] = Field(None, ge=0, le=2147483647) - resolution: Optional[str] = None - aspect_ratio: Optional[str] = None - rendering_speed: Optional[RenderingSpeed1] = None - magic_prompt: Optional[MagicPrompt1] = None - negative_prompt: Optional[str] = None - num_images: Optional[int] = Field(None, ge=1, le=8) - color_palette: Optional[Dict[str, Any]] = None - style_codes: Optional[List[str]] = None - style_type: Optional[StyleType1] = None - style_reference_images: Optional[List[StrictBytes]] = None +class StyleCode(RootModel[str]): + root: str = Field(..., pattern='^[0-9A-Fa-f]{8}$') class Datum1(BaseModel): + is_image_safe: Optional[bool] = None prompt: Optional[str] = None resolution: Optional[str] = None - is_image_safe: Optional[bool] = None seed: Optional[int] = None - url: Optional[str] = None style_type: Optional[str] = None + url: Optional[str] = None class IdeogramV3IdeogramResponse(BaseModel): @@ -387,74 +564,201 @@ class IdeogramV3IdeogramResponse(BaseModel): data: Optional[List[Datum1]] = None +class RenderingSpeed1(str, Enum): + TURBO = 'TURBO' + DEFAULT = 'DEFAULT' + QUALITY = 'QUALITY' + + class IdeogramV3ReframeRequest(BaseModel): - image: Optional[StrictBytes] = None - resolution: str - num_images: Optional[int] = Field(None, ge=1, le=8) - seed: Optional[int] = Field(None, ge=0, le=2147483647) - rendering_speed: Optional[RenderingSpeed1] = None color_palette: Optional[Dict[str, Any]] = None + image: Optional[StrictBytes] = None + num_images: Optional[int] = Field(None, ge=1, le=8) + rendering_speed: Optional[RenderingSpeed1] = None + resolution: str + seed: Optional[int] = Field(None, ge=0, le=2147483647) style_codes: Optional[List[str]] = None style_reference_images: Optional[List[StrictBytes]] = None +class MagicPrompt(str, Enum): + AUTO = 'AUTO' + ON = 'ON' + OFF = 'OFF' + + +class StyleType(str, Enum): + AUTO = 'AUTO' + GENERAL = 'GENERAL' + REALISTIC = 'REALISTIC' + DESIGN = 'DESIGN' + + +class IdeogramV3RemixRequest(BaseModel): + aspect_ratio: Optional[str] = None + color_palette: Optional[Dict[str, Any]] = None + image: Optional[StrictBytes] = None + image_weight: Optional[int] = Field(50, ge=1, le=100) + magic_prompt: Optional[MagicPrompt] = None + negative_prompt: Optional[str] = None + num_images: Optional[int] = Field(None, ge=1, le=8) + prompt: str + rendering_speed: Optional[RenderingSpeed1] = None + resolution: Optional[str] = None + seed: Optional[int] = Field(None, ge=0, le=2147483647) + style_codes: Optional[List[str]] = None + style_reference_images: Optional[List[StrictBytes]] = None + style_type: Optional[StyleType] = None + + class IdeogramV3ReplaceBackgroundRequest(BaseModel): - image: Optional[StrictBytes] = None - prompt: str - magic_prompt: Optional[MagicPrompt1] = None - num_images: Optional[int] = Field(None, ge=1, le=8) - seed: Optional[int] = Field(None, ge=0, le=2147483647) - rendering_speed: Optional[RenderingSpeed1] = None color_palette: Optional[Dict[str, Any]] = None + image: Optional[StrictBytes] = None + magic_prompt: Optional[MagicPrompt] = None + num_images: Optional[int] = Field(None, ge=1, le=8) + prompt: str + rendering_speed: Optional[RenderingSpeed1] = None + seed: Optional[int] = Field(None, ge=0, le=2147483647) style_codes: Optional[List[str]] = None style_reference_images: Optional[List[StrictBytes]] = None -class KlingTaskStatus(str, Enum): - submitted = 'submitted' - processing = 'processing' - succeed = 'succeed' - failed = 'failed' +class ColorPalette(BaseModel): + name: str = Field(..., description='Name of the color palette', examples=['PASTEL']) -class KlingVideoGenModelName(str, Enum): - kling_v1 = 'kling-v1' - kling_v1_5 = 'kling-v1-5' - kling_v1_6 = 'kling-v1-6' - kling_v2_master = 'kling-v2-master' +class MagicPrompt2(str, Enum): + ON = 'ON' + OFF = 'OFF' -class KlingVideoGenMode(str, Enum): - std = 'std' - pro = 'pro' +class StyleType1(str, Enum): + GENERAL = 'GENERAL' -class KlingVideoGenAspectRatio(str, Enum): - field_16_9 = '16:9' - field_9_16 = '9:16' +class ImagenImageGenerationInstance(BaseModel): + prompt: str = Field(..., description='Text prompt for image generation') + + +class AspectRatio(str, Enum): field_1_1 = '1:1' + field_9_16 = '9:16' + field_16_9 = '16:9' + field_3_4 = '3:4' + field_4_3 = '4:3' -class KlingVideoGenDuration(str, Enum): - field_5 = '5' - field_10 = '10' +class PersonGeneration(str, Enum): + dont_allow = 'dont_allow' + allow_adult = 'allow_adult' + allow_all = 'allow_all' -class KlingVideoGenCfgScale(RootModel[float]): - root: float = Field( - ..., - description="Flexibility in video generation. The higher the value, the lower the model's degree of flexibility, and the stronger the relevance to the user's prompt.", - ge=0.0, - le=1.0, +class SafetySetting(str, Enum): + block_most = 'block_most' + block_some = 'block_some' + block_few = 'block_few' + block_fewest = 'block_fewest' + + +class ImagenImagePrediction(BaseModel): + bytesBase64Encoded: Optional[str] = Field( + None, description='Base64-encoded image content' + ) + mimeType: Optional[str] = Field( + None, description='MIME type of the generated image' + ) + prompt: Optional[str] = Field( + None, description='Enhanced or rewritten prompt used to generate this image' ) -class KlingCameraControlType(str, Enum): - simple = 'simple' - down_back = 'down_back' - forward_up = 'forward_up' - right_turn_forward = 'right_turn_forward' - left_turn_forward = 'left_turn_forward' +class MimeType(str, Enum): + image_png = 'image/png' + image_jpeg = 'image/jpeg' + + +class ImagenOutputOptions(BaseModel): + compressionQuality: Optional[int] = Field(None, ge=0, le=100) + mimeType: Optional[MimeType] = None + + +class Includable(str, Enum): + file_search_call_results = 'file_search_call.results' + message_input_image_image_url = 'message.input_image.image_url' + computer_call_output_output_image_url = 'computer_call_output.output.image_url' + + +class Type7(str, Enum): + input_file = 'input_file' + + +class InputFileContent(BaseModel): + file_data: Optional[str] = Field( + None, description='The content of the file to be sent to the model.\n' + ) + file_id: Optional[str] = Field( + None, description='The ID of the file to be sent to the model.' + ) + filename: Optional[str] = Field( + None, description='The name of the file to be sent to the model.' + ) + type: Type7 = Field( + ..., description='The type of the input item. Always `input_file`.' + ) + + +class Detail(str, Enum): + low = 'low' + high = 'high' + auto = 'auto' + + +class Type8(str, Enum): + input_image = 'input_image' + + +class InputImageContent(BaseModel): + detail: Detail = Field( + ..., + description='The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`.', + ) + file_id: Optional[str] = Field( + None, description='The ID of the file to be sent to the model.' + ) + image_url: Optional[str] = Field( + None, + description='The URL of the image to be sent to the model. A fully qualified URL or base64 encoded image in a data URL.', + ) + type: Type8 = Field( + ..., description='The type of the input item. Always `input_image`.' + ) + + +class Role3(str, Enum): + user = 'user' + system = 'system' + developer = 'developer' + + +class Type9(str, Enum): + message = 'message' + + +class Type10(str, Enum): + input_text = 'input_text' + + +class InputTextContent(BaseModel): + text: str = Field(..., description='The text input to the model.') + type: Type10 = Field( + ..., description='The type of the input item. Always `input_text`.' + ) + + +class KlingAudioUploadType(str, Enum): + file = 'file' + url = 'url' class KlingCameraConfig(BaseModel): @@ -464,27 +768,27 @@ class KlingCameraConfig(BaseModel): ge=-10.0, le=10.0, ) - vertical: Optional[float] = Field( - None, - description="Controls camera's movement along vertical axis (y-axis). Negative indicates downward, positive indicates upward.", - ge=-10.0, - le=10.0, - ) pan: Optional[float] = Field( None, description="Controls camera's rotation in vertical plane (x-axis). Negative indicates downward rotation, positive indicates upward rotation.", ge=-10.0, le=10.0, ) + roll: Optional[float] = Field( + None, + description="Controls camera's rolling amount (z-axis). Negative indicates counterclockwise, positive indicates clockwise.", + ge=-10.0, + le=10.0, + ) tilt: Optional[float] = Field( None, description="Controls camera's rotation in horizontal plane (y-axis). Negative indicates left rotation, positive indicates right rotation.", ge=-10.0, le=10.0, ) - roll: Optional[float] = Field( + vertical: Optional[float] = Field( None, - description="Controls camera's rolling amount (z-axis). Negative indicates counterclockwise, positive indicates clockwise.", + description="Controls camera's movement along vertical axis (y-axis). Negative indicates downward, positive indicates upward.", ge=-10.0, le=10.0, ) @@ -496,39 +800,12 @@ class KlingCameraConfig(BaseModel): ) -class KlingVideoResult(BaseModel): - id: Optional[str] = Field(None, description='Generated video ID') - url: Optional[AnyUrl] = Field(None, description='URL for generated video') - duration: Optional[str] = Field(None, description='Total video duration') - - -class KlingAudioUploadType(str, Enum): - file = 'file' - url = 'url' - - -class KlingLipSyncMode(str, Enum): - text2video = 'text2video' - audio2video = 'audio2video' - - -class KlingLipSyncVoiceLanguage(str, Enum): - zh = 'zh' - en = 'en' - - -class KlingDualCharacterEffectsScene(str, Enum): - hug = 'hug' - kiss = 'kiss' - heart_gesture = 'heart_gesture' - - -class KlingSingleImageEffectsScene(str, Enum): - bloombloom = 'bloombloom' - dizzydizzy = 'dizzydizzy' - fuzzyfuzzy = 'fuzzyfuzzy' - squish = 'squish' - expansion = 'expansion' +class KlingCameraControlType(str, Enum): + simple = 'simple' + down_back = 'down_back' + forward_up = 'forward_up' + right_turn_forward = 'right_turn_forward' + left_turn_forward = 'left_turn_forward' class KlingCharacterEffectModelName(str, Enum): @@ -537,18 +814,50 @@ class KlingCharacterEffectModelName(str, Enum): kling_v1_6 = 'kling-v1-6' -class KlingSingleImageEffectModelName(str, Enum): - kling_v1_6 = 'kling-v1-6' - - -class KlingSingleImageEffectDuration(str, Enum): - field_5 = '5' +class KlingDualCharacterEffectsScene(str, Enum): + hug = 'hug' + kiss = 'kiss' + heart_gesture = 'heart_gesture' class KlingDualCharacterImages(RootModel[List[str]]): root: List[str] = Field(..., max_length=2, min_length=2) +class KlingErrorResponse(BaseModel): + code: int = Field( + ..., + description='- 1000: Authentication failed\n- 1001: Authorization is empty\n- 1002: Authorization is invalid\n- 1003: Authorization is not yet valid\n- 1004: Authorization has expired\n- 1100: Account exception\n- 1101: Account in arrears (postpaid scenario)\n- 1102: Resource pack depleted or expired (prepaid scenario)\n- 1103: Unauthorized access to requested resource\n- 1200: Invalid request parameters\n- 1201: Invalid parameters\n- 1202: Invalid request method\n- 1203: Requested resource does not exist\n- 1300: Trigger platform strategy\n- 1301: Trigger content security policy\n- 1302: API request too frequent\n- 1303: Concurrency/QPS exceeds limit\n- 1304: Trigger IP whitelist policy\n- 5000: Internal server error\n- 5001: Service temporarily unavailable\n- 5002: Server internal timeout\n', + ) + message: str = Field(..., description='Human-readable error message') + request_id: str = Field( + ..., description='Request ID for tracking and troubleshooting' + ) + + +class Trajectory(BaseModel): + x: Optional[int] = Field( + None, + description='The horizontal coordinate of trajectory point. Based on bottom-left corner of image as origin (0,0).', + ) + y: Optional[int] = Field( + None, + description='The vertical coordinate of trajectory point. Based on bottom-left corner of image as origin (0,0).', + ) + + +class DynamicMask(BaseModel): + mask: Optional[AnyUrl] = Field( + None, + description='Dynamic Brush Application Area (Mask image created by users using the motion brush). The aspect ratio must match the input image.', + ) + trajectories: Optional[List[Trajectory]] = None + + +class TaskInfo(BaseModel): + external_task_id: Optional[str] = None + + class KlingImageGenAspectRatio(str, Enum): field_16_9 = '16:9' field_9_16 = '9:16' @@ -571,278 +880,42 @@ class KlingImageGenModelName(str, Enum): kling_v2 = 'kling-v2' +class KlingImageGenerationsRequest(BaseModel): + aspect_ratio: Optional[KlingImageGenAspectRatio] = '16:9' + callback_url: Optional[AnyUrl] = Field( + None, description='The callback notification address' + ) + human_fidelity: Optional[float] = Field( + 0.45, description='Subject reference similarity', ge=0.0, le=1.0 + ) + image: Optional[str] = Field( + None, description='Reference Image - Base64 encoded string or image URL' + ) + image_fidelity: Optional[float] = Field( + 0.5, description='Reference intensity for user-uploaded images', ge=0.0, le=1.0 + ) + image_reference: Optional[KlingImageGenImageReferenceType] = None + model_name: Optional[KlingImageGenModelName] = 'kling-v1' + n: Optional[int] = Field(1, description='Number of generated images', ge=1, le=9) + negative_prompt: Optional[str] = Field( + None, description='Negative text prompt', max_length=200 + ) + prompt: str = Field(..., description='Positive text prompt', max_length=500) + + class KlingImageResult(BaseModel): index: Optional[int] = Field(None, description='Image Number (0-9)') url: Optional[AnyUrl] = Field(None, description='URL for generated image') -class KlingVirtualTryOnModelName(str, Enum): - kolors_virtual_try_on_v1 = 'kolors-virtual-try-on-v1' - kolors_virtual_try_on_v1_5 = 'kolors-virtual-try-on-v1-5' +class KlingLipSyncMode(str, Enum): + text2video = 'text2video' + audio2video = 'audio2video' -class TaskInfo(BaseModel): - external_task_id: Optional[str] = None - - -class TaskResult(BaseModel): - videos: Optional[List[KlingVideoResult]] = None - - -class Data(BaseModel): - task_id: Optional[str] = Field(None, description='Task ID') - task_status: Optional[KlingTaskStatus] = None - task_info: Optional[TaskInfo] = None - created_at: Optional[int] = Field(None, description='Task creation time') - updated_at: Optional[int] = Field(None, description='Task update time') - task_result: Optional[TaskResult] = None - - -class KlingText2VideoResponse(BaseModel): - code: Optional[int] = Field(None, description='Error code') - message: Optional[str] = Field(None, description='Error message') - request_id: Optional[str] = Field(None, description='Request ID') - data: Optional[Data] = None - - -class Trajectory(BaseModel): - x: Optional[int] = Field( - None, - description='The horizontal coordinate of trajectory point. Based on bottom-left corner of image as origin (0,0).', - ) - y: Optional[int] = Field( - None, - description='The vertical coordinate of trajectory point. Based on bottom-left corner of image as origin (0,0).', - ) - - -class DynamicMask(BaseModel): - mask: Optional[AnyUrl] = Field( - None, - description='Dynamic Brush Application Area (Mask image created by users using the motion brush). The aspect ratio must match the input image.', - ) - trajectories: Optional[List[Trajectory]] = None - - -class Data1(BaseModel): - task_id: Optional[str] = Field(None, description='Task ID') - task_status: Optional[KlingTaskStatus] = None - task_info: Optional[TaskInfo] = None - created_at: Optional[int] = Field(None, description='Task creation time') - updated_at: Optional[int] = Field(None, description='Task update time') - task_result: Optional[TaskResult] = None - - -class KlingImage2VideoResponse(BaseModel): - code: Optional[int] = Field(None, description='Error code') - message: Optional[str] = Field(None, description='Error message') - request_id: Optional[str] = Field(None, description='Request ID') - data: Optional[Data1] = None - - -class KlingVideoExtendRequest(BaseModel): - video_id: Optional[str] = Field( - None, - description='The ID of the video to be extended. Supports videos generated by text-to-video, image-to-video, and previous video extension operations. Cannot exceed 3 minutes total duration after extension.', - ) - prompt: Optional[str] = Field( - None, - description='Positive text prompt for guiding the video extension', - max_length=2500, - ) - negative_prompt: Optional[str] = Field( - None, - description='Negative text prompt for elements to avoid in the extended video', - max_length=2500, - ) - cfg_scale: Optional[KlingVideoGenCfgScale] = Field( - default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) - ) - callback_url: Optional[AnyUrl] = Field( - None, - description='The callback notification address. Server will notify when the task status changes.', - ) - - -class Data2(BaseModel): - task_id: Optional[str] = Field(None, description='Task ID') - task_status: Optional[KlingTaskStatus] = None - task_info: Optional[TaskInfo] = None - created_at: Optional[int] = Field(None, description='Task creation time') - updated_at: Optional[int] = Field(None, description='Task update time') - task_result: Optional[TaskResult] = None - - -class KlingVideoExtendResponse(BaseModel): - code: Optional[int] = Field(None, description='Error code') - message: Optional[str] = Field(None, description='Error message') - request_id: Optional[str] = Field(None, description='Request ID') - data: Optional[Data2] = None - - -class KlingLipSyncInputObject(BaseModel): - video_id: Optional[str] = Field( - None, - description='The ID of the video generated by Kling AI. Only supports 5-second and 10-second videos generated within the last 30 days.', - ) - video_url: Optional[str] = Field( - None, - description='Get link for uploaded video. Video files support .mp4/.mov, file size does not exceed 100MB, video length between 2-10s.', - ) - mode: KlingLipSyncMode - text: Optional[str] = Field( - None, - description='Text Content for Lip-Sync Video Generation. Required when mode is text2video. Maximum length is 120 characters.', - ) - voice_id: Optional[str] = Field( - None, - description='Voice ID. Required when mode is text2video. The system offers a variety of voice options to choose from.', - ) - voice_language: Optional[KlingLipSyncVoiceLanguage] = 'en' - voice_speed: Optional[float] = Field( - 1, - description='Speech Rate. Valid range: 0.8~2.0, accurate to one decimal place.', - ge=0.8, - le=2.0, - ) - audio_type: Optional[KlingAudioUploadType] = None - audio_file: Optional[str] = Field( - None, - description='Local Path of Audio File. Supported formats: .mp3/.wav/.m4a/.aac, maximum file size of 5MB. Base64 code.', - ) - audio_url: Optional[str] = Field( - None, - description='Audio File Download URL. Supported formats: .mp3/.wav/.m4a/.aac, maximum file size of 5MB.', - ) - - -class KlingLipSyncRequest(BaseModel): - input: KlingLipSyncInputObject - callback_url: Optional[AnyUrl] = Field( - None, - description='The callback notification address. Server will notify when the task status changes.', - ) - - -class Data3(BaseModel): - task_id: Optional[str] = Field(None, description='Task ID') - task_status: Optional[KlingTaskStatus] = None - task_info: Optional[TaskInfo] = None - created_at: Optional[int] = Field(None, description='Task creation time') - updated_at: Optional[int] = Field(None, description='Task update time') - task_result: Optional[TaskResult] = None - - -class KlingLipSyncResponse(BaseModel): - code: Optional[int] = Field(None, description='Error code') - message: Optional[str] = Field(None, description='Error message') - request_id: Optional[str] = Field(None, description='Request ID') - data: Optional[Data3] = None - - -class KlingSingleImageEffectInput(BaseModel): - model_name: KlingSingleImageEffectModelName - image: str = Field( - ..., - description='Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1.', - ) - duration: KlingSingleImageEffectDuration - - -class KlingDualCharacterEffectInput(BaseModel): - model_name: Optional[KlingCharacterEffectModelName] = 'kling-v1' - mode: Optional[KlingVideoGenMode] = 'std' - images: KlingDualCharacterImages - duration: KlingVideoGenDuration - - -class Data4(BaseModel): - task_id: Optional[str] = Field(None, description='Task ID') - task_status: Optional[KlingTaskStatus] = None - task_info: Optional[TaskInfo] = None - created_at: Optional[int] = Field(None, description='Task creation time') - updated_at: Optional[int] = Field(None, description='Task update time') - task_result: Optional[TaskResult] = None - - -class KlingVideoEffectsResponse(BaseModel): - code: Optional[int] = Field(None, description='Error code') - message: Optional[str] = Field(None, description='Error message') - request_id: Optional[str] = Field(None, description='Request ID') - data: Optional[Data4] = None - - -class KlingImageGenerationsRequest(BaseModel): - model_name: Optional[KlingImageGenModelName] = 'kling-v1' - prompt: str = Field(..., description='Positive text prompt', max_length=500) - negative_prompt: Optional[str] = Field( - None, description='Negative text prompt', max_length=200 - ) - image: Optional[str] = Field( - None, description='Reference Image - Base64 encoded string or image URL' - ) - image_reference: Optional[KlingImageGenImageReferenceType] = None - image_fidelity: Optional[float] = Field( - 0.5, description='Reference intensity for user-uploaded images', ge=0.0, le=1.0 - ) - human_fidelity: Optional[float] = Field( - 0.45, description='Subject reference similarity', ge=0.0, le=1.0 - ) - n: Optional[int] = Field(1, description='Number of generated images', ge=1, le=9) - aspect_ratio: Optional[KlingImageGenAspectRatio] = '16:9' - callback_url: Optional[AnyUrl] = Field( - None, description='The callback notification address' - ) - - -class TaskResult5(BaseModel): - images: Optional[List[KlingImageResult]] = None - - -class Data5(BaseModel): - task_id: Optional[str] = Field(None, description='Task ID') - task_status: Optional[KlingTaskStatus] = None - task_status_msg: Optional[str] = Field(None, description='Task status information') - created_at: Optional[int] = Field(None, description='Task creation time') - updated_at: Optional[int] = Field(None, description='Task update time') - task_result: Optional[TaskResult5] = None - - -class KlingImageGenerationsResponse(BaseModel): - code: Optional[int] = Field(None, description='Error code') - message: Optional[str] = Field(None, description='Error message') - request_id: Optional[str] = Field(None, description='Request ID') - data: Optional[Data5] = None - - -class KlingVirtualTryOnRequest(BaseModel): - model_name: Optional[KlingVirtualTryOnModelName] = 'kolors-virtual-try-on-v1' - human_image: str = Field( - ..., description='Reference human image - Base64 encoded string or image URL' - ) - cloth_image: Optional[str] = Field( - None, - description='Reference clothing image - Base64 encoded string or image URL', - ) - callback_url: Optional[AnyUrl] = Field( - None, description='The callback notification address' - ) - - -class Data6(BaseModel): - task_id: Optional[str] = Field(None, description='Task ID') - task_status: Optional[KlingTaskStatus] = None - task_status_msg: Optional[str] = Field(None, description='Task status information') - created_at: Optional[int] = Field(None, description='Task creation time') - updated_at: Optional[int] = Field(None, description='Task update time') - task_result: Optional[TaskResult5] = None - - -class KlingVirtualTryOnResponse(BaseModel): - code: Optional[int] = Field(None, description='Error code') - message: Optional[str] = Field(None, description='Error message') - request_id: Optional[str] = Field(None, description='Request ID') - data: Optional[Data6] = None +class KlingLipSyncVoiceLanguage(str, Enum): + zh = 'zh' + en = 'en' class ResourcePackType(str, Enum): @@ -850,7 +923,7 @@ class ResourcePackType(str, Enum): constant_period = 'constant_period' -class Status(str, Enum): +class Status4(str, Enum): toBeOnline = 'toBeOnline' online = 'online' expired = 'expired' @@ -858,29 +931,29 @@ class Status(str, Enum): class ResourcePackSubscribeInfo(BaseModel): - resource_pack_name: Optional[str] = Field(None, description='Resource package name') - resource_pack_id: Optional[str] = Field(None, description='Resource package ID') - resource_pack_type: Optional[ResourcePackType] = Field( - None, - description='Resource package type (decreasing_total=decreasing total, constant_period=constant periodicity)', - ) - total_quantity: Optional[float] = Field(None, description='Total quantity') - remaining_quantity: Optional[float] = Field( - None, description='Remaining quantity (updated with a 12-hour delay)' - ) - purchase_time: Optional[int] = Field( - None, description='Purchase time, Unix timestamp in ms' - ) effective_time: Optional[int] = Field( None, description='Effective time, Unix timestamp in ms' ) invalid_time: Optional[int] = Field( None, description='Expiration time, Unix timestamp in ms' ) - status: Optional[Status] = Field(None, description='Resource Package Status') + purchase_time: Optional[int] = Field( + None, description='Purchase time, Unix timestamp in ms' + ) + remaining_quantity: Optional[float] = Field( + None, description='Remaining quantity (updated with a 12-hour delay)' + ) + resource_pack_id: Optional[str] = Field(None, description='Resource package ID') + resource_pack_name: Optional[str] = Field(None, description='Resource package name') + resource_pack_type: Optional[ResourcePackType] = Field( + None, + description='Resource package type (decreasing_total=decreasing total, constant_period=constant periodicity)', + ) + status: Optional[Status4] = Field(None, description='Resource Package Status') + total_quantity: Optional[float] = Field(None, description='Total quantity') -class Data7(BaseModel): +class Data3(BaseModel): code: Optional[int] = Field(None, description='Error code; 0 indicates success') msg: Optional[str] = Field(None, description='Error information') resource_pack_subscribe_infos: Optional[List[ResourcePackSubscribeInfo]] = Field( @@ -890,137 +963,313 @@ class Data7(BaseModel): class KlingResourcePackageResponse(BaseModel): code: Optional[int] = Field(None, description='Error code; 0 indicates success') + data: Optional[Data3] = None message: Optional[str] = Field(None, description='Error information') request_id: Optional[str] = Field( None, description='Request ID, generated by the system, used to track requests and troubleshoot problems', ) + + +class KlingSingleImageEffectDuration(str, Enum): + field_5 = '5' + + +class KlingSingleImageEffectModelName(str, Enum): + kling_v1_6 = 'kling-v1-6' + + +class KlingSingleImageEffectsScene(str, Enum): + bloombloom = 'bloombloom' + dizzydizzy = 'dizzydizzy' + fuzzyfuzzy = 'fuzzyfuzzy' + squish = 'squish' + expansion = 'expansion' + + +class KlingTaskStatus(str, Enum): + submitted = 'submitted' + processing = 'processing' + succeed = 'succeed' + failed = 'failed' + + +class KlingTextToVideoModelName(str, Enum): + kling_v1 = 'kling-v1' + kling_v1_6 = 'kling-v1-6' + + +class KlingVideoGenAspectRatio(str, Enum): + field_16_9 = '16:9' + field_9_16 = '9:16' + field_1_1 = '1:1' + + +class KlingVideoGenCfgScale(RootModel[float]): + root: float = Field( + ..., + description="Flexibility in video generation. The higher the value, the lower the model's degree of flexibility, and the stronger the relevance to the user's prompt.", + ge=0.0, + le=1.0, + ) + + +class KlingVideoGenDuration(str, Enum): + field_5 = '5' + field_10 = '10' + + +class KlingVideoGenMode(str, Enum): + std = 'std' + pro = 'pro' + + +class KlingVideoGenModelName(str, Enum): + kling_v1 = 'kling-v1' + kling_v1_5 = 'kling-v1-5' + kling_v1_6 = 'kling-v1-6' + kling_v2_master = 'kling-v2-master' + + +class KlingVideoResult(BaseModel): + duration: Optional[str] = Field(None, description='Total video duration') + id: Optional[str] = Field(None, description='Generated video ID') + url: Optional[AnyUrl] = Field(None, description='URL for generated video') + + +class KlingVirtualTryOnModelName(str, Enum): + kolors_virtual_try_on_v1 = 'kolors-virtual-try-on-v1' + kolors_virtual_try_on_v1_5 = 'kolors-virtual-try-on-v1-5' + + +class KlingVirtualTryOnRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, description='The callback notification address' + ) + cloth_image: Optional[str] = Field( + None, + description='Reference clothing image - Base64 encoded string or image URL', + ) + human_image: str = Field( + ..., description='Reference human image - Base64 encoded string or image URL' + ) + model_name: Optional[KlingVirtualTryOnModelName] = 'kolors-virtual-try-on-v1' + + +class TaskResult6(BaseModel): + images: Optional[List[KlingImageResult]] = None + + +class Data7(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_result: Optional[TaskResult6] = None + task_status: Optional[KlingTaskStatus] = None + task_status_msg: Optional[str] = Field(None, description='Task status information') + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingVirtualTryOnResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') data: Optional[Data7] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') -class Object(str, Enum): - event = 'event' +class LumaAspectRatio(str, Enum): + field_1_1 = '1:1' + field_16_9 = '16:9' + field_9_16 = '9:16' + field_4_3 = '4:3' + field_3_4 = '3:4' + field_21_9 = '21:9' + field_9_21 = '9:21' -class Type(str, Enum): - payment_intent_succeeded = 'payment_intent.succeeded' +class LumaAssets(BaseModel): + image: Optional[AnyUrl] = Field(None, description='The URL of the image') + progress_video: Optional[AnyUrl] = Field( + None, description='The URL of the progress video' + ) + video: Optional[AnyUrl] = Field(None, description='The URL of the video') -class StripeRequestInfo(BaseModel): - id: Optional[str] = None - idempotency_key: Optional[str] = None +class GenerationType(str, Enum): + add_audio = 'add_audio' -class Object1(str, Enum): - payment_intent = 'payment_intent' +class LumaAudioGenerationRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, description='The callback URL for the audio' + ) + generation_type: Optional[GenerationType] = 'add_audio' + negative_prompt: Optional[str] = Field( + None, description='The negative prompt of the audio' + ) + prompt: Optional[str] = Field(None, description='The prompt of the audio') -class StripeAmountDetails(BaseModel): - tip: Optional[Dict[str, Any]] = None +class LumaError(BaseModel): + detail: Optional[str] = Field(None, description='The error message') -class Object2(str, Enum): - charge = 'charge' +class Type11(str, Enum): + generation = 'generation' -class StripeAddress(BaseModel): - city: Optional[str] = None - country: Optional[str] = None - line1: Optional[str] = None - line2: Optional[str] = None - postal_code: Optional[str] = None - state: Optional[str] = None +class LumaGenerationReference(BaseModel): + id: UUID = Field(..., description='The ID of the generation') + type: Literal['generation'] -class StripeOutcome(BaseModel): - advice_code: Optional[Any] = None - network_advice_code: Optional[Any] = None - network_decline_code: Optional[Any] = None - network_status: Optional[str] = None - reason: Optional[Any] = None - risk_level: Optional[str] = None - risk_score: Optional[int] = None - seller_message: Optional[str] = None - type: Optional[str] = None +class GenerationType1(str, Enum): + video = 'video' -class Checks(BaseModel): - address_line1_check: Optional[Any] = None - address_postal_code_check: Optional[Any] = None - cvc_check: Optional[str] = None +class LumaGenerationType(str, Enum): + video = 'video' + image = 'image' -class ExtendedAuthorization(BaseModel): - status: Optional[str] = None +class GenerationType2(str, Enum): + image = 'image' -class IncrementalAuthorization(BaseModel): - status: Optional[str] = None +class LumaImageIdentity(BaseModel): + images: Optional[List[AnyUrl]] = Field( + None, description='The URLs of the image identity' + ) -class Multicapture(BaseModel): - status: Optional[str] = None +class LumaImageModel(str, Enum): + photon_1 = 'photon-1' + photon_flash_1 = 'photon-flash-1' -class NetworkToken(BaseModel): - used: Optional[bool] = None +class LumaImageRef(BaseModel): + url: Optional[AnyUrl] = Field(None, description='The URL of the image reference') + weight: Optional[float] = Field( + None, description='The weight of the image reference' + ) -class Overcapture(BaseModel): - maximum_amount_capturable: Optional[int] = None - status: Optional[str] = None +class Type12(str, Enum): + image = 'image' -class StripeCardDetails(BaseModel): - amount_authorized: Optional[int] = None - authorization_code: Optional[Any] = None - brand: Optional[str] = None - checks: Optional[Checks] = None - country: Optional[str] = None - exp_month: Optional[int] = None - exp_year: Optional[int] = None - extended_authorization: Optional[ExtendedAuthorization] = None - fingerprint: Optional[str] = None - funding: Optional[str] = None - incremental_authorization: Optional[IncrementalAuthorization] = None - installments: Optional[Any] = None - last4: Optional[str] = None - mandate: Optional[Any] = None - multicapture: Optional[Multicapture] = None - network: Optional[str] = None - network_token: Optional[NetworkToken] = None - network_transaction_id: Optional[str] = None - overcapture: Optional[Overcapture] = None - regulated_status: Optional[str] = None - three_d_secure: Optional[Any] = None - wallet: Optional[Any] = None +class LumaImageReference(BaseModel): + type: Literal['image'] + url: AnyUrl = Field(..., description='The URL of the image') -class StripeRefundList(BaseModel): - object: Optional[str] = None - data: Optional[List[Dict[str, Any]]] = None - has_more: Optional[bool] = None - total_count: Optional[int] = None - url: Optional[str] = None +class LumaKeyframe(RootModel[Union[LumaGenerationReference, LumaImageReference]]): + root: Union[LumaGenerationReference, LumaImageReference] = Field( + ..., + description='A keyframe can be either a Generation reference, an Image, or a Video', + discriminator='type', + ) -class Card(BaseModel): - installments: Optional[Any] = None - mandate_options: Optional[Any] = None - network: Optional[Any] = None - request_three_d_secure: Optional[str] = None +class LumaKeyframes(BaseModel): + frame0: Optional[LumaKeyframe] = None + frame1: Optional[LumaKeyframe] = None -class StripePaymentMethodOptions(BaseModel): - card: Optional[Card] = None +class LumaModifyImageRef(BaseModel): + url: Optional[AnyUrl] = Field(None, description='The URL of the image reference') + weight: Optional[float] = Field( + None, description='The weight of the modify image reference' + ) -class StripeShipping(BaseModel): - address: Optional[StripeAddress] = None - carrier: Optional[str] = None - name: Optional[str] = None - phone: Optional[str] = None - tracking_number: Optional[str] = None +class LumaState(str, Enum): + queued = 'queued' + dreaming = 'dreaming' + completed = 'completed' + failed = 'failed' + + +class GenerationType3(str, Enum): + upscale_video = 'upscale_video' + + +class LumaVideoModel(str, Enum): + ray_2 = 'ray-2' + ray_flash_2 = 'ray-flash-2' + ray_1_6 = 'ray-1-6' + + +class LumaVideoModelOutputDuration1(str, Enum): + field_5s = '5s' + field_9s = '9s' + + +class LumaVideoModelOutputDuration( + RootModel[Union[LumaVideoModelOutputDuration1, str]] +): + root: Union[LumaVideoModelOutputDuration1, str] + + +class LumaVideoModelOutputResolution1(str, Enum): + field_540p = '540p' + field_720p = '720p' + field_1080p = '1080p' + field_4k = '4k' + + +class LumaVideoModelOutputResolution( + RootModel[Union[LumaVideoModelOutputResolution1, str]] +): + root: Union[LumaVideoModelOutputResolution1, str] + + +class MinimaxBaseResponse(BaseModel): + status_code: int = Field( + ..., + description='Status code. 0 indicates success, other values indicate errors.', + ) + status_msg: str = Field( + ..., description='Specific error details or success message.' + ) + + +class File(BaseModel): + bytes: Optional[int] = Field(None, description='File size in bytes') + created_at: Optional[int] = Field( + None, description='Unix timestamp when the file was created, in seconds' + ) + download_url: Optional[str] = Field( + None, description='The URL to download the video' + ) + file_id: Optional[int] = Field(None, description='Unique identifier for the file') + filename: Optional[str] = Field(None, description='The name of the file') + purpose: Optional[str] = Field(None, description='The purpose of using the file') + + +class MinimaxFileRetrieveResponse(BaseModel): + base_resp: MinimaxBaseResponse + file: File + + +class Status5(str, Enum): + Queueing = 'Queueing' + Preparing = 'Preparing' + Processing = 'Processing' + Success = 'Success' + Fail = 'Fail' + + +class MinimaxTaskResultResponse(BaseModel): + base_resp: MinimaxBaseResponse + file_id: Optional[str] = Field( + None, + description='After the task status changes to Success, this field returns the file ID corresponding to the generated video.', + ) + status: Status5 = Field( + ..., + description="Task status: 'Queueing' (in queue), 'Preparing' (task is preparing), 'Processing' (generating), 'Success' (task completed successfully), or 'Fail' (task failed).", + ) + task_id: str = Field(..., description='The task ID being queried.') class Model(str, Enum): @@ -1043,6 +1292,14 @@ class SubjectReferenceItem(BaseModel): class MinimaxVideoGenerationRequest(BaseModel): + callback_url: Optional[str] = Field( + None, + description='Optional. URL to receive real-time status updates about the video generation task.', + ) + first_frame_image: Optional[str] = Field( + None, + description='URL or base64 encoding of the first frame image. Required when model is I2V-01, I2V-01-Director, or I2V-01-live.', + ) model: Model = Field( ..., description='Required. ID of model. Options: T2V-01-Director, I2V-01-Director, S2V-01, I2V-01, I2V-01-live, T2V-01', @@ -1056,927 +1313,175 @@ class MinimaxVideoGenerationRequest(BaseModel): True, description='If true (default), the model will automatically optimize the prompt. Set to false for more precise control.', ) - first_frame_image: Optional[str] = Field( - None, - description='URL or base64 encoding of the first frame image. Required when model is I2V-01, I2V-01-Director, or I2V-01-live.', - ) subject_reference: Optional[List[SubjectReferenceItem]] = Field( None, description='Only available when model is S2V-01. The model will generate a video based on the subject uploaded through this parameter.', ) - callback_url: Optional[str] = Field( - None, - description='Optional. URL to receive real-time status updates about the video generation task.', - ) - - -class MinimaxBaseResponse(BaseModel): - status_code: int = Field( - ..., - description='Status code. 0 indicates success, other values indicate errors.', - ) - status_msg: str = Field( - ..., description='Specific error details or success message.' - ) class MinimaxVideoGenerationResponse(BaseModel): + base_resp: MinimaxBaseResponse task_id: str = Field( ..., description='The task ID for the asynchronous video generation task.' ) - base_resp: MinimaxBaseResponse -class File(BaseModel): - file_id: Optional[int] = Field(None, description='Unique identifier for the file') - bytes: Optional[int] = Field(None, description='File size in bytes') - created_at: Optional[int] = Field( - None, description='Unix timestamp when the file was created, in seconds' +class Truncation(str, Enum): + disabled = 'disabled' + auto = 'auto' + + +class ModelResponseProperties(BaseModel): + instructions: Optional[str] = Field( + None, description='Instructions for the model on how to generate the response' ) - filename: Optional[str] = Field(None, description='The name of the file') - purpose: Optional[str] = Field(None, description='The purpose of using the file') - download_url: Optional[str] = Field( - None, description='The URL to download the video' + max_output_tokens: Optional[int] = Field( + None, description='Maximum number of tokens to generate' + ) + model: Optional[str] = Field( + None, description='The model used to generate the response' + ) + temperature: Optional[float] = Field( + 1, description='Controls randomness in the response', ge=0.0, le=2.0 + ) + top_p: Optional[float] = Field( + 1, + description='Controls diversity of the response via nucleus sampling', + ge=0.0, + le=1.0, + ) + truncation: Optional[Truncation] = Field( + 'disabled', description='How to handle truncation of the response' ) -class MinimaxFileRetrieveResponse(BaseModel): - file: File - base_resp: MinimaxBaseResponse +class Moderation(str, Enum): + low = 'low' + auto = 'auto' -class Status1(str, Enum): - Queueing = 'Queueing' - Preparing = 'Preparing' - Processing = 'Processing' - Success = 'Success' - Fail = 'Fail' - - -class MinimaxTaskResultResponse(BaseModel): - task_id: str = Field(..., description='The task ID being queried.') - status: Status1 = Field( - ..., - description="Task status: 'Queueing' (in queue), 'Preparing' (task is preparing), 'Processing' (generating), 'Success' (task completed successfully), or 'Fail' (task failed).", - ) - file_id: Optional[str] = Field( - None, - description='After the task status changes to Success, this field returns the file ID corresponding to the generated video.', - ) - base_resp: MinimaxBaseResponse - - -class OutputFormat(str, Enum): - jpeg = 'jpeg' +class OutputFormat1(str, Enum): png = 'png' - - -class BFLFluxPro11GenerateRequest(BaseModel): - prompt: str = Field(..., description='The main text prompt for image generation') - image_prompt: Optional[str] = Field(None, description='Optional image prompt') - width: int = Field(..., description='Width of the generated image') - height: int = Field(..., description='Height of the generated image') - prompt_upsampling: Optional[bool] = Field( - None, description='Whether to use prompt upsampling' - ) - seed: Optional[int] = Field(None, description='Random seed for reproducibility') - safety_tolerance: Optional[int] = Field(None, description='Safety tolerance level') - output_format: Optional[OutputFormat] = Field( - None, description='Output image format' - ) - webhook_url: Optional[str] = Field( - None, description='Optional webhook URL for async processing' - ) - webhook_secret: Optional[str] = Field( - None, description='Optional webhook secret for async processing' - ) - - -class BFLFluxPro11GenerateResponse(BaseModel): - id: str = Field(..., description='Job ID for tracking') - polling_url: str = Field(..., description='URL to poll for results') - - -class BFLFluxProGenerateRequest(BaseModel): - prompt: str = Field(..., description='The text prompt for image generation.') - negative_prompt: Optional[str] = Field( - None, description='The negative prompt for image generation.' - ) - width: int = Field( - ..., description='The width of the image to generate.', ge=64, le=2048 - ) - height: int = Field( - ..., description='The height of the image to generate.', ge=64, le=2048 - ) - num_inference_steps: Optional[int] = Field( - None, description='The number of inference steps.', ge=1, le=100 - ) - guidance_scale: Optional[float] = Field( - None, description='The guidance scale for generation.', ge=1.0, le=20.0 - ) - seed: Optional[int] = Field(None, description='The seed value for reproducibility.') - num_images: Optional[int] = Field( - None, description='The number of images to generate.', ge=1, le=4 - ) - - -class BFLFluxProGenerateResponse(BaseModel): - id: str = Field(..., description='The unique identifier for the generation task.') - polling_url: str = Field(..., description='URL to poll for the generation result.') - - -class Steps(RootModel[int]): - root: int = Field( - ..., - description='Number of steps for the image generation process', - examples=[50], - ge=15, - le=50, - title='Steps', - ) - - -class Guidance(RootModel[float]): - root: float = Field( - ..., - description='Guidance strength for the image generation process', - ge=1.5, - le=100.0, - title='Guidance', - ) - - -class WebhookUrl(RootModel[AnyUrl]): - root: AnyUrl = Field( - ..., description='URL to receive webhook notifications', title='Webhook Url' - ) - - -class BFLAsyncResponse(BaseModel): - id: str = Field(..., title='Id') - polling_url: str = Field(..., title='Polling Url') - - -class BFLAsyncWebhookResponse(BaseModel): - id: str = Field(..., title='Id') - status: str = Field(..., title='Status') - webhook_url: str = Field(..., title='Webhook Url') - - -class Top(RootModel[int]): - root: int = Field( - ..., - description='Number of pixels to expand at the top of the image', - ge=0, - le=2048, - title='Top', - ) - - -class Bottom(RootModel[int]): - root: int = Field( - ..., - description='Number of pixels to expand at the bottom of the image', - ge=0, - le=2048, - title='Bottom', - ) - - -class Left(RootModel[int]): - root: int = Field( - ..., - description='Number of pixels to expand on the left side of the image', - ge=0, - le=2048, - title='Left', - ) - - -class Right(RootModel[int]): - root: int = Field( - ..., - description='Number of pixels to expand on the right side of the image', - ge=0, - le=2048, - title='Right', - ) - - -class CannyLowThreshold(RootModel[int]): - root: int = Field( - ..., - description='Low threshold for Canny edge detection', - ge=0, - le=500, - title='Canny Low Threshold', - ) - - -class CannyHighThreshold(RootModel[int]): - root: int = Field( - ..., - description='High threshold for Canny edge detection', - ge=0, - le=500, - title='Canny High Threshold', - ) - - -class Steps2(RootModel[int]): - root: int = Field( - ..., - description='Number of steps for the image generation process', - ge=15, - le=50, - title='Steps', - ) - - -class Guidance2(RootModel[float]): - root: float = Field( - ..., - description='Guidance strength for the image generation process', - ge=1.0, - le=100.0, - title='Guidance', - ) - - -class BFLOutputFormat(str, Enum): - jpeg = 'jpeg' - png = 'png' - - -class BFLValidationError(BaseModel): - loc: List[Union[str, int]] = Field(..., title='Location') - msg: str = Field(..., title='Message') - type: str = Field(..., title='Error Type') - - -class Datum2(BaseModel): - image_id: Optional[str] = Field( - None, description='Unique identifier for the generated image' - ) - url: Optional[str] = Field(None, description='URL to access the generated image') - - -class RecraftImageGenerationResponse(BaseModel): - created: int = Field( - ..., description='Unix timestamp when the generation was created' - ) - credits: int = Field(..., description='Number of credits used for the generation') - data: List[Datum2] = Field(..., description='Array of generated image information') - - -class RecraftImageFeatures(BaseModel): - nsfw_score: Optional[float] = None - - -class RecraftTextLayoutItem(BaseModel): - bbox: List[List[float]] - text: str - - -class RecraftImageColor(BaseModel): - rgb: Optional[List[int]] = None - std: Optional[List[float]] = None - weight: Optional[float] = None - - -class RecraftImageStyle(str, Enum): - digital_illustration = 'digital_illustration' - icon = 'icon' - realistic_image = 'realistic_image' - vector_illustration = 'vector_illustration' - - -class RecraftImageSubStyle(str, Enum): - field_2d_art_poster = '2d_art_poster' - field_3d = '3d' - field_80s = '80s' - glow = 'glow' - grain = 'grain' - hand_drawn = 'hand_drawn' - infantile_sketch = 'infantile_sketch' - kawaii = 'kawaii' - pixel_art = 'pixel_art' - psychedelic = 'psychedelic' - seamless = 'seamless' - voxel = 'voxel' - watercolor = 'watercolor' - broken_line = 'broken_line' - colored_outline = 'colored_outline' - colored_shapes = 'colored_shapes' - colored_shapes_gradient = 'colored_shapes_gradient' - doodle_fill = 'doodle_fill' - doodle_offset_fill = 'doodle_offset_fill' - offset_fill = 'offset_fill' - outline = 'outline' - outline_gradient = 'outline_gradient' - uneven_fill = 'uneven_fill' - field_70s = '70s' - cartoon = 'cartoon' - doodle_line_art = 'doodle_line_art' - engraving = 'engraving' - flat_2 = 'flat_2' - kawaii_1 = 'kawaii' - line_art = 'line_art' - linocut = 'linocut' - seamless_1 = 'seamless' - b_and_w = 'b_and_w' - enterprise = 'enterprise' - hard_flash = 'hard_flash' - hdr = 'hdr' - motion_blur = 'motion_blur' - natural_light = 'natural_light' - studio_portrait = 'studio_portrait' - line_circuit = 'line_circuit' - field_2d_art_poster_2 = '2d_art_poster_2' - engraving_color = 'engraving_color' - flat_air_art = 'flat_air_art' - hand_drawn_outline = 'hand_drawn_outline' - handmade_3d = 'handmade_3d' - stickers_drawings = 'stickers_drawings' - plastic = 'plastic' - pictogram = 'pictogram' - - -class RecraftTransformModel(str, Enum): - refm1 = 'refm1' - recraft20b = 'recraft20b' - recraftv2 = 'recraftv2' - recraftv3 = 'recraftv3' - flux1_1pro = 'flux1_1pro' - flux1dev = 'flux1dev' - imagen3 = 'imagen3' - hidream_i1_dev = 'hidream_i1_dev' - - -class RecraftImageFormat(str, Enum): webp = 'webp' - png = 'png' + jpeg = 'jpeg' -class RecraftResponseFormat(str, Enum): +class OpenAIImageEditRequest(BaseModel): + background: Optional[str] = Field( + None, description='Background transparency', examples=['opaque'] + ) + model: str = Field( + ..., description='The model to use for image editing', examples=['gpt-image-1'] + ) + moderation: Optional[Moderation] = Field( + None, description='Content moderation setting', examples=['auto'] + ) + n: Optional[int] = Field( + None, description='The number of images to generate', examples=[1] + ) + output_compression: Optional[int] = Field( + None, description='Compression level for JPEG or WebP (0-100)', examples=[100] + ) + output_format: Optional[OutputFormat1] = Field( + None, description='Format of the output image', examples=['png'] + ) + prompt: str = Field( + ..., + description='A text description of the desired edit', + examples=['Give the rocketship rainbow coloring'], + ) + quality: Optional[str] = Field( + None, description='The quality of the edited image', examples=['low'] + ) + size: Optional[str] = Field( + None, description='Size of the output image', examples=['1024x1024'] + ) + user: Optional[str] = Field( + None, + description='A unique identifier for end-user monitoring', + examples=['user-1234'], + ) + + +class Background(str, Enum): + transparent = 'transparent' + opaque = 'opaque' + + +class Quality(str, Enum): + low = 'low' + medium = 'medium' + high = 'high' + standard = 'standard' + hd = 'hd' + + +class ResponseFormat(str, Enum): url = 'url' b64_json = 'b64_json' -class RecraftImage(BaseModel): - b64_json: Optional[str] = None - features: Optional[RecraftImageFeatures] = None - image_id: UUID - revised_prompt: Optional[str] = None - url: Optional[str] = None - - -class RecraftUserControls(BaseModel): - artistic_level: Optional[int] = None - background_color: Optional[RecraftImageColor] = None - colors: Optional[List[RecraftImageColor]] = None - no_text: Optional[bool] = None - - -class RecraftTextLayout(RootModel[List[RecraftTextLayoutItem]]): - root: List[RecraftTextLayoutItem] - - -class RecraftProcessImageRequest(BaseModel): - image: StrictBytes - image_format: Optional[RecraftImageFormat] = None - response_format: Optional[RecraftResponseFormat] = None - - -class RecraftProcessImageResponse(BaseModel): - created: int - credits: int - image: RecraftImage - - -class RecraftImageToImageRequest(BaseModel): - block_nsfw: Optional[bool] = None - calculate_features: Optional[bool] = None - controls: Optional[RecraftUserControls] = None - image: StrictBytes - image_format: Optional[RecraftImageFormat] = None - model: Optional[RecraftTransformModel] = None - n: Optional[int] = None - negative_prompt: Optional[str] = None - prompt: str - random_seed: Optional[int] = None - response_format: Optional[RecraftResponseFormat] = None - strength: float - style: Optional[RecraftImageStyle] = None - style_id: Optional[UUID] = None - substyle: Optional[RecraftImageSubStyle] = None - text_layout: Optional[RecraftTextLayout] = None - - -class RecraftGenerateImageResponse(BaseModel): - created: int - credits: int - data: List[RecraftImage] - - -class RecraftTransformImageWithMaskRequest(BaseModel): - block_nsfw: Optional[bool] = None - calculate_features: Optional[bool] = None - image: StrictBytes - image_format: Optional[RecraftImageFormat] = None - mask: StrictBytes - model: Optional[RecraftTransformModel] = None - n: Optional[int] = None - negative_prompt: Optional[str] = None - prompt: str - random_seed: Optional[int] = None - response_format: Optional[RecraftResponseFormat] = None - style: Optional[RecraftImageStyle] = None - style_id: Optional[UUID] = None - substyle: Optional[RecraftImageSubStyle] = None - text_layout: Optional[RecraftTextLayout] = None - - -class KlingErrorResponse(BaseModel): - code: int = Field( - ..., - description='- 1000: Authentication failed\n- 1001: Authorization is empty\n- 1002: Authorization is invalid\n- 1003: Authorization is not yet valid\n- 1004: Authorization has expired\n- 1100: Account exception\n- 1101: Account in arrears (postpaid scenario)\n- 1102: Resource pack depleted or expired (prepaid scenario)\n- 1103: Unauthorized access to requested resource\n- 1200: Invalid request parameters\n- 1201: Invalid parameters\n- 1202: Invalid request method\n- 1203: Requested resource does not exist\n- 1300: Trigger platform strategy\n- 1301: Trigger content security policy\n- 1302: API request too frequent\n- 1303: Concurrency/QPS exceeds limit\n- 1304: Trigger IP whitelist policy\n- 5000: Internal server error\n- 5001: Service temporarily unavailable\n- 5002: Server internal timeout\n', - ) - message: str = Field(..., description='Human-readable error message') - request_id: str = Field( - ..., description='Request ID for tracking and troubleshooting' - ) - - -class LumaAspectRatio(str, Enum): - field_1_1 = '1:1' - field_16_9 = '16:9' - field_9_16 = '9:16' - field_4_3 = '4:3' - field_3_4 = '3:4' - field_21_9 = '21:9' - field_9_21 = '9:21' - - -class LumaVideoModel(str, Enum): - ray_2 = 'ray-2' - ray_flash_2 = 'ray-flash-2' - ray_1_6 = 'ray-1-6' - - -class LumaVideoModelOutputResolution1(str, Enum): - field_540p = '540p' - field_720p = '720p' - field_1080p = '1080p' - field_4k = '4k' - - -class LumaVideoModelOutputResolution( - RootModel[Union[LumaVideoModelOutputResolution1, str]] -): - root: Union[LumaVideoModelOutputResolution1, str] - - -class LumaVideoModelOutputDuration1(str, Enum): - field_5s = '5s' - field_9s = '9s' - - -class LumaVideoModelOutputDuration( - RootModel[Union[LumaVideoModelOutputDuration1, str]] -): - root: Union[LumaVideoModelOutputDuration1, str] - - -class LumaImageModel(str, Enum): - photon_1 = 'photon-1' - photon_flash_1 = 'photon-flash-1' - - -class LumaImageRef(BaseModel): - url: Optional[AnyUrl] = Field(None, description='The URL of the image reference') - weight: Optional[float] = Field( - None, description='The weight of the image reference' - ) - - -class LumaImageIdentity(BaseModel): - images: Optional[List[AnyUrl]] = Field( - None, description='The URLs of the image identity' - ) - - -class LumaModifyImageRef(BaseModel): - url: Optional[AnyUrl] = Field(None, description='The URL of the image reference') - weight: Optional[float] = Field( - None, description='The weight of the modify image reference' - ) - - -class Type1(str, Enum): - generation = 'generation' - - -class LumaGenerationReference(BaseModel): - type: Literal['generation'] - id: UUID = Field(..., description='The ID of the generation') - - -class Type2(str, Enum): - image = 'image' - - -class LumaImageReference(BaseModel): - type: Literal['image'] - url: AnyUrl = Field(..., description='The URL of the image') - - -class LumaKeyframe(RootModel[Union[LumaGenerationReference, LumaImageReference]]): - root: Union[LumaGenerationReference, LumaImageReference] = Field( - ..., - description='A keyframe can be either a Generation reference, an Image, or a Video', - discriminator='type', - ) - - -class LumaGenerationType(str, Enum): - video = 'video' - image = 'image' - - -class LumaState(str, Enum): - queued = 'queued' - dreaming = 'dreaming' - completed = 'completed' - failed = 'failed' - - -class LumaAssets(BaseModel): - video: Optional[AnyUrl] = Field(None, description='The URL of the video') - image: Optional[AnyUrl] = Field(None, description='The URL of the image') - progress_video: Optional[AnyUrl] = Field( - None, description='The URL of the progress video' - ) - - -class GenerationType(str, Enum): - video = 'video' - - -class GenerationType1(str, Enum): - image = 'image' - - -class CharacterRef(BaseModel): - identity0: Optional[LumaImageIdentity] = None - - -class LumaImageGenerationRequest(BaseModel): - generation_type: Optional[GenerationType1] = 'image' - model: Optional[LumaImageModel] = 'photon-1' - prompt: Optional[str] = Field(None, description='The prompt of the generation') - aspect_ratio: Optional[LumaAspectRatio] = '16:9' - callback_url: Optional[AnyUrl] = Field( - None, description='The callback URL for the generation' - ) - image_ref: Optional[List[LumaImageRef]] = None - style_ref: Optional[List[LumaImageRef]] = None - character_ref: Optional[CharacterRef] = None - modify_image_ref: Optional[LumaModifyImageRef] = None - - -class GenerationType2(str, Enum): - upscale_video = 'upscale_video' - - -class LumaUpscaleVideoGenerationRequest(BaseModel): - generation_type: Optional[GenerationType2] = 'upscale_video' - resolution: Optional[LumaVideoModelOutputResolution] = None - callback_url: Optional[AnyUrl] = Field( - None, description='The callback URL for the upscale' - ) - - -class GenerationType3(str, Enum): - add_audio = 'add_audio' - - -class LumaAudioGenerationRequest(BaseModel): - generation_type: Optional[GenerationType3] = 'add_audio' - prompt: Optional[str] = Field(None, description='The prompt of the audio') - negative_prompt: Optional[str] = Field( - None, description='The negative prompt of the audio' - ) - callback_url: Optional[AnyUrl] = Field( - None, description='The callback URL for the audio' - ) - - -class LumaError(BaseModel): - detail: Optional[str] = Field(None, description='The error message') - - -class AspectRatio(str, Enum): - field_16_9 = '16:9' - field_4_3 = '4:3' - field_1_1 = '1:1' - field_3_4 = '3:4' - field_9_16 = '9:16' - - -class Duration(int, Enum): - integer_5 = 5 - integer_8 = 8 - - -class Model1(str, Enum): - v3_5 = 'v3.5' - - -class MotionMode(str, Enum): - normal = 'normal' - fast = 'fast' - - -class Quality(str, Enum): - field_360p = '360p' - field_540p = '540p' - field_720p = '720p' - field_1080p = '1080p' - - class Style(str, Enum): - anime = 'anime' - field_3d_animation = '3d_animation' - clay = 'clay' - comic = 'comic' - cyberpunk = 'cyberpunk' + vivid = 'vivid' + natural = 'natural' -class PixverseTextVideoRequest(BaseModel): - aspect_ratio: AspectRatio - duration: Duration - model: Model1 - motion_mode: Optional[MotionMode] = None - negative_prompt: Optional[str] = None - prompt: str - quality: Quality - seed: Optional[int] = None - style: Optional[Style] = None - template_id: Optional[int] = None - water_mark: Optional[bool] = None - - -class Resp(BaseModel): - video_id: Optional[int] = None - - -class PixverseVideoResponse(BaseModel): - ErrCode: Optional[int] = None - ErrMsg: Optional[str] = None - Resp_1: Optional[Resp] = Field(None, alias='Resp') - - -class Resp1(BaseModel): - img_id: Optional[int] = None - - -class PixverseImageUploadResponse(BaseModel): - ErrCode: Optional[int] = None - ErrMsg: Optional[str] = None - Resp: Optional[Resp1] = None - - -class PixverseImageVideoRequest(BaseModel): - img_id: int - model: Model1 - prompt: str - duration: Duration - quality: Quality - motion_mode: Optional[MotionMode] = None - seed: Optional[int] = None - style: Optional[Style] = None - template_id: Optional[int] = None - water_mark: Optional[bool] = None - - -class PixverseTransitionVideoRequest(BaseModel): - first_frame_img: int - last_frame_img: int - model: Model1 - duration: Duration - quality: Quality - motion_mode: MotionMode - seed: int - prompt: str - style: Optional[Style] = None - template_id: Optional[int] = None - water_mark: Optional[bool] = None - - -class Status2(int, Enum): - integer_1 = 1 - integer_5 = 5 - integer_6 = 6 - integer_7 = 7 - integer_8 = 8 - - -class Resp2(BaseModel): - create_time: Optional[str] = None - id: Optional[int] = None - modify_time: Optional[str] = None - negative_prompt: Optional[str] = None - outputHeight: Optional[int] = None - outputWidth: Optional[int] = None - prompt: Optional[str] = None - resolution_ratio: Optional[int] = None - seed: Optional[int] = None - size: Optional[int] = None - status: Optional[Status2] = Field( +class OpenAIImageGenerationRequest(BaseModel): + background: Optional[Background] = Field( + None, description='Background transparency', examples=['opaque'] + ) + model: Optional[str] = Field( + None, description='The model to use for image generation', examples=['dall-e-3'] + ) + moderation: Optional[Moderation] = Field( + None, description='Content moderation setting', examples=['auto'] + ) + n: Optional[int] = Field( None, - description='Video generation status codes:\n* 1 - Generation successful\n* 5 - Generating\n* 6 - Deleted\n* 7 - Contents moderation failed\n* 8 - Generation failed\n', + description='The number of images to generate (1-10). Only 1 supported for dall-e-3.', + examples=[1], ) - style: Optional[str] = None - url: Optional[str] = None - - -class PixverseVideoResultResponse(BaseModel): - ErrCode: Optional[int] = None - ErrMsg: Optional[str] = None - Resp: Optional[Resp2] = None - - -class Image(BaseModel): - bytesBase64Encoded: str - gcsUri: Optional[str] = None - mimeType: Optional[str] = None - - -class Image1(BaseModel): - bytesBase64Encoded: Optional[str] = None - gcsUri: str - mimeType: Optional[str] = None - - -class Instance(BaseModel): - prompt: str = Field(..., description='Text description of the video') - image: Optional[Union[Image, Image1]] = Field( - None, description='Optional image to guide video generation' + output_compression: Optional[int] = Field( + None, description='Compression level for JPEG or WebP (0-100)', examples=[100] ) - - -class PersonGeneration(str, Enum): - ALLOW = 'ALLOW' - BLOCK = 'BLOCK' - - -class Parameters(BaseModel): - aspectRatio: Optional[str] = Field(None, examples=['16:9']) - negativePrompt: Optional[str] = None - personGeneration: Optional[PersonGeneration] = None - sampleCount: Optional[int] = None - seed: Optional[int] = None - storageUri: Optional[str] = Field( - None, description='Optional Cloud Storage URI to upload the video' + output_format: Optional[OutputFormat1] = Field( + None, description='Format of the output image', examples=['png'] ) - durationSeconds: Optional[int] = None - enhancePrompt: Optional[bool] = None - - -class Veo2GenVidRequest(BaseModel): - instances: Optional[List[Instance]] = None - parameters: Optional[Parameters] = None - - -class Veo2GenVidResponse(BaseModel): - name: str = Field( + prompt: str = Field( ..., - description='Operation resource name', - examples=[ - 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8' - ], + description='A text description of the desired image', + examples=['Draw a rocket in front of a blackhole in deep space'], ) - - -class Veo2GenVidPollRequest(BaseModel): - operationName: str = Field( - ..., - description='Full operation name (from predict response)', - examples=[ - 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID' - ], + quality: Optional[Quality] = Field( + None, description='The quality of the generated image', examples=['high'] ) - - -class Video(BaseModel): - gcsUri: Optional[str] = Field(None, description='Cloud Storage URI of the video') - bytesBase64Encoded: Optional[str] = Field( - None, description='Base64-encoded video content' + response_format: Optional[ResponseFormat] = Field( + None, description='Response format of image data', examples=['b64_json'] ) - mimeType: Optional[str] = Field(None, description='Video MIME type') - - -class Response(BaseModel): - field_type: Optional[str] = Field( + size: Optional[str] = Field( None, - alias='@type', - examples=[ - 'type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse' - ], + description='Size of the image (e.g., 1024x1024, 1536x1024, auto)', + examples=['1024x1536'], ) - raiMediaFilteredCount: Optional[int] = Field( - None, description='Count of media filtered by responsible AI policies' + style: Optional[Style] = Field( + None, description='Style of the image (only for dall-e-3)', examples=['vivid'] ) - raiMediaFilteredReasons: Optional[List[str]] = Field( - None, description='Reasons why media was filtered by responsible AI policies' - ) - videos: Optional[List[Video]] = None - - -class Error1(BaseModel): - code: Optional[int] = Field(None, description='Error code') - message: Optional[str] = Field(None, description='Error message') - - -class Veo2GenVidPollResponse(BaseModel): - name: Optional[str] = None - done: Optional[bool] = None - response: Optional[Response] = Field( - None, description='The actual prediction response if done is true' - ) - error: Optional[Error1] = Field( - None, description='Error details if operation failed' + user: Optional[str] = Field( + None, + description='A unique identifier for end-user monitoring', + examples=['user-1234'], ) -class RunwayImageToVideoResponse(BaseModel): - id: Optional[str] = Field(None, description='Task ID') - - -class RunwayTaskStatusEnum(str, Enum): - SUCCEEDED = 'SUCCEEDED' - RUNNING = 'RUNNING' - FAILED = 'FAILED' - PENDING = 'PENDING' - CANCELLED = 'CANCELLED' - THROTTLED = 'THROTTLED' - - -class RunwayModelEnum(str, Enum): - gen4_turbo = 'gen4_turbo' - gen3a_turbo = 'gen3a_turbo' - - -class Position(str, Enum): - first = 'first' - last = 'last' - - -class RunwayPromptImageDetailedObject(BaseModel): - uri: str = Field( - ..., description='A HTTPS URL or data URI containing an encoded image.' - ) - position: Position = Field( - ..., - description="The position of the image in the output video. 'last' is currently supported for gen3a_turbo only.", - ) - - -class RunwayDurationEnum(int, Enum): - integer_5 = 5 - integer_10 = 10 - - -class RunwayAspectRatioEnum(str, Enum): - field_1280_720 = '1280:720' - field_720_1280 = '720:1280' - field_1104_832 = '1104:832' - field_832_1104 = '832:1104' - field_960_960 = '960:960' - field_1584_672 = '1584:672' - field_1280_768 = '1280:768' - field_768_1280 = '768:1280' - - -class RunwayPromptImageObject( - RootModel[Union[str, List[RunwayPromptImageDetailedObject]]] -): - root: Union[str, List[RunwayPromptImageDetailedObject]] = Field( - ..., - description='Image(s) to use for the video generation. Can be a single URI or an array of image objects with positions.', - ) - - -class Datum3(BaseModel): +class Datum2(BaseModel): b64_json: Optional[str] = Field(None, description='Base64 encoded image data') - url: Optional[str] = Field(None, description='URL of the image') revised_prompt: Optional[str] = Field(None, description='Revised prompt') + url: Optional[str] = Field(None, description='URL of the image') class InputTokensDetails(BaseModel): - text_tokens: Optional[int] = None image_tokens: Optional[int] = None + text_tokens: Optional[int] = None class Usage(BaseModel): @@ -1987,143 +1492,204 @@ class Usage(BaseModel): class OpenAIImageGenerationResponse(BaseModel): - data: Optional[List[Datum3]] = None + data: Optional[List[Datum2]] = None usage: Optional[Usage] = None -class Quality3(str, Enum): - low = 'low' - medium = 'medium' - high = 'high' - standard = 'standard' - hd = 'hd' +class OpenAIModels(str, Enum): + gpt_4 = 'gpt-4' + gpt_4_0314 = 'gpt-4-0314' + gpt_4_0613 = 'gpt-4-0613' + gpt_4_32k = 'gpt-4-32k' + gpt_4_32k_0314 = 'gpt-4-32k-0314' + gpt_4_32k_0613 = 'gpt-4-32k-0613' + gpt_4_0125_preview = 'gpt-4-0125-preview' + gpt_4_turbo = 'gpt-4-turbo' + gpt_4_turbo_2024_04_09 = 'gpt-4-turbo-2024-04-09' + gpt_4_turbo_preview = 'gpt-4-turbo-preview' + gpt_4_1106_preview = 'gpt-4-1106-preview' + gpt_4_vision_preview = 'gpt-4-vision-preview' + gpt_3_5_turbo = 'gpt-3.5-turbo' + gpt_3_5_turbo_16k = 'gpt-3.5-turbo-16k' + gpt_3_5_turbo_0301 = 'gpt-3.5-turbo-0301' + gpt_3_5_turbo_0613 = 'gpt-3.5-turbo-0613' + gpt_3_5_turbo_1106 = 'gpt-3.5-turbo-1106' + gpt_3_5_turbo_0125 = 'gpt-3.5-turbo-0125' + gpt_3_5_turbo_16k_0613 = 'gpt-3.5-turbo-16k-0613' + gpt_4_1 = 'gpt-4.1' + gpt_4_1_mini = 'gpt-4.1-mini' + gpt_4_1_nano = 'gpt-4.1-nano' + gpt_4_1_2025_04_14 = 'gpt-4.1-2025-04-14' + gpt_4_1_mini_2025_04_14 = 'gpt-4.1-mini-2025-04-14' + gpt_4_1_nano_2025_04_14 = 'gpt-4.1-nano-2025-04-14' + o1 = 'o1' + o1_mini = 'o1-mini' + o1_preview = 'o1-preview' + o1_pro = 'o1-pro' + o1_2024_12_17 = 'o1-2024-12-17' + o1_preview_2024_09_12 = 'o1-preview-2024-09-12' + o1_mini_2024_09_12 = 'o1-mini-2024-09-12' + o1_pro_2025_03_19 = 'o1-pro-2025-03-19' + o3 = 'o3' + o3_mini = 'o3-mini' + o3_2025_04_16 = 'o3-2025-04-16' + o3_mini_2025_01_31 = 'o3-mini-2025-01-31' + o4_mini = 'o4-mini' + o4_mini_2025_04_16 = 'o4-mini-2025-04-16' + gpt_4o = 'gpt-4o' + gpt_4o_mini = 'gpt-4o-mini' + gpt_4o_2024_11_20 = 'gpt-4o-2024-11-20' + gpt_4o_2024_08_06 = 'gpt-4o-2024-08-06' + gpt_4o_2024_05_13 = 'gpt-4o-2024-05-13' + gpt_4o_mini_2024_07_18 = 'gpt-4o-mini-2024-07-18' + gpt_4o_audio_preview = 'gpt-4o-audio-preview' + gpt_4o_audio_preview_2024_10_01 = 'gpt-4o-audio-preview-2024-10-01' + gpt_4o_audio_preview_2024_12_17 = 'gpt-4o-audio-preview-2024-12-17' + gpt_4o_mini_audio_preview = 'gpt-4o-mini-audio-preview' + gpt_4o_mini_audio_preview_2024_12_17 = 'gpt-4o-mini-audio-preview-2024-12-17' + gpt_4o_search_preview = 'gpt-4o-search-preview' + gpt_4o_mini_search_preview = 'gpt-4o-mini-search-preview' + gpt_4o_search_preview_2025_03_11 = 'gpt-4o-search-preview-2025-03-11' + gpt_4o_mini_search_preview_2025_03_11 = 'gpt-4o-mini-search-preview-2025-03-11' + computer_use_preview = 'computer-use-preview' + computer_use_preview_2025_03_11 = 'computer-use-preview-2025-03-11' + chatgpt_4o_latest = 'chatgpt-4o-latest' -class OutputFormat1(str, Enum): - png = 'png' - webp = 'webp' - jpeg = 'jpeg' +class Reason(str, Enum): + max_output_tokens = 'max_output_tokens' + content_filter = 'content_filter' -class Moderation(str, Enum): - low = 'low' - auto = 'auto' - - -class Background(str, Enum): - transparent = 'transparent' - opaque = 'opaque' - - -class ResponseFormat(str, Enum): - url = 'url' - b64_json = 'b64_json' - - -class Style3(str, Enum): - vivid = 'vivid' - natural = 'natural' - - -class OpenAIImageGenerationRequest(BaseModel): - model: Optional[str] = Field( - None, description='The model to use for image generation', examples=['dall-e-3'] +class IncompleteDetails(BaseModel): + reason: Optional[Reason] = Field( + None, description='The reason why the response is incomplete.' ) - prompt: str = Field( + + +class Object(str, Enum): + response = 'response' + + +class Status6(str, Enum): + completed = 'completed' + failed = 'failed' + in_progress = 'in_progress' + incomplete = 'incomplete' + + +class Type13(str, Enum): + output_audio = 'output_audio' + + +class OutputAudioContent(BaseModel): + data: str = Field(..., description='Base64-encoded audio data') + transcript: str = Field(..., description='Transcript of the audio') + type: Type13 = Field(..., description='The type of output content') + + +class Role4(str, Enum): + assistant = 'assistant' + + +class Type14(str, Enum): + message = 'message' + + +class Type15(str, Enum): + output_text = 'output_text' + + +class OutputTextContent(BaseModel): + text: str = Field(..., description='The text content') + type: Type15 = Field(..., description='The type of output content') + + +class AspectRatio1(RootModel[float]): + root: float = Field( ..., - description='A text description of the desired image', - examples=['Draw a rocket in front of a blackhole in deep space'], - ) - n: Optional[int] = Field( - None, - description='The number of images to generate (1-10). Only 1 supported for dall-e-3.', - examples=[1], - ) - quality: Optional[Quality3] = Field( - None, description='The quality of the generated image', examples=['high'] - ) - size: Optional[str] = Field( - None, - description='Size of the image (e.g., 1024x1024, 1536x1024, auto)', - examples=['1024x1536'], - ) - output_format: Optional[OutputFormat1] = Field( - None, description='Format of the output image', examples=['png'] - ) - output_compression: Optional[int] = Field( - None, description='Compression level for JPEG or WebP (0-100)', examples=[100] - ) - moderation: Optional[Moderation] = Field( - None, description='Content moderation setting', examples=['auto'] - ) - background: Optional[Background] = Field( - None, description='Background transparency', examples=['opaque'] - ) - response_format: Optional[ResponseFormat] = Field( - None, description='Response format of image data', examples=['b64_json'] - ) - style: Optional[Style3] = Field( - None, description='Style of the image (only for dall-e-3)', examples=['vivid'] - ) - user: Optional[str] = Field( - None, - description='A unique identifier for end-user monitoring', - examples=['user-1234'], + description='Aspect ratio (width / height)', + ge=0.4, + le=2.5, + title='Aspectratio', ) -class OpenAIImageEditRequest(BaseModel): - model: str = Field( - ..., description='The model to use for image editing', examples=['gpt-image-1'] - ) - prompt: str = Field( - ..., - description='A text description of the desired edit', - examples=['Give the rocketship rainbow coloring'], - ) - n: Optional[int] = Field( - None, description='The number of images to generate', examples=[1] - ) - quality: Optional[str] = Field( - None, description='The quality of the edited image', examples=['low'] - ) - size: Optional[str] = Field( - None, description='Size of the output image', examples=['1024x1024'] - ) - output_format: Optional[OutputFormat1] = Field( - None, description='Format of the output image', examples=['png'] - ) - output_compression: Optional[int] = Field( - None, description='Compression level for JPEG or WebP (0-100)', examples=[100] - ) - moderation: Optional[Moderation] = Field( - None, description='Content moderation setting', examples=['auto'] - ) - background: Optional[str] = Field( - None, description='Background transparency', examples=['opaque'] - ) - user: Optional[str] = Field( - None, - description='A unique identifier for end-user monitoring', - examples=['user-1234'], - ) +class IngredientsMode(str, Enum): + creative = 'creative' + precise = 'precise' -class CustomerStorageResourceResponse(BaseModel): - download_url: Optional[str] = Field( +class PikaBodyGenerate22C2vGenerate22PikascenesPost(BaseModel): + aspectRatio: Optional[AspectRatio1] = Field( + None, description='Aspect ratio (width / height)', title='Aspectratio' + ) + duration: Optional[int] = Field(5, title='Duration') + images: Optional[List[StrictBytes]] = Field(None, title='Images') + ingredientsMode: IngredientsMode = Field(..., title='Ingredientsmode') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: Optional[str] = Field(None, title='Prompttext') + resolution: Optional[str] = Field('1080p', title='Resolution') + seed: Optional[int] = Field(None, title='Seed') + + +class PikaBodyGeneratePikadditionsGeneratePikadditionsPost(BaseModel): + image: Optional[StrictBytes] = Field(None, title='Image') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: Optional[str] = Field(None, title='Prompttext') + seed: Optional[int] = Field(None, title='Seed') + video: Optional[StrictBytes] = Field(None, title='Video') + + +class PikaBodyGeneratePikaswapsGeneratePikaswapsPost(BaseModel): + image: Optional[StrictBytes] = Field(None, title='Image') + modifyRegionMask: Optional[StrictBytes] = Field( None, - description='The signed URL to use for downloading the file from the specified path', + description='A mask image that specifies the region to modify, where the mask is white and the background is black', + title='Modifyregionmask', ) - upload_url: Optional[str] = Field( + modifyRegionRoi: Optional[str] = Field( None, - description='The signed URL to use for uploading the file to the specified path', - ) - expires_at: Optional[datetime] = Field( - None, description='When the signed URL will expire' - ) - existing_file: Optional[bool] = Field( - None, description='Whether an existing file with the same hash was found' + description='Plaintext description of the object / region to modify', + title='Modifyregionroi', ) + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: Optional[str] = Field(None, title='Prompttext') + seed: Optional[int] = Field(None, title='Seed') + video: Optional[StrictBytes] = Field(None, title='Video') + + +class PikaDurationEnum(int, Enum): + integer_5 = 5 + integer_10 = 10 + + +class PikaGenerateResponse(BaseModel): + video_id: str = Field(..., title='Video Id') + + +class PikaResolutionEnum(str, Enum): + field_1080p = '1080p' + field_720p = '720p' + + +class PikaStatusEnum(str, Enum): + queued = 'queued' + started = 'started' + finished = 'finished' + + +class PikaValidationError(BaseModel): + loc: List[Union[str, int]] = Field(..., title='Location') + msg: str = Field(..., title='Message') + type: str = Field(..., title='Error Type') + + +class PikaVideoResponse(BaseModel): + id: str = Field(..., title='Id') + progress: Optional[int] = Field(None, title='Progress') + status: PikaStatusEnum + url: Optional[str] = Field(None, title='Url') class Pikaffect(str, Enum): @@ -2145,92 +1711,135 @@ class Pikaffect(str, Enum): Tear = 'Tear' -class PikaBodyGeneratePikaffectsGeneratePikaffectsPost(BaseModel): - image: Optional[StrictBytes] = Field(None, title='Image') - pikaffect: Optional[Pikaffect] = Field(None, title='Pikaffect') - promptText: Optional[str] = Field(None, title='Prompttext') - negativePrompt: Optional[str] = Field(None, title='Negativeprompt') - seed: Optional[int] = Field(None, title='Seed') +class Resp(BaseModel): + img_id: Optional[int] = None -class PikaGenerateResponse(BaseModel): - video_id: str = Field(..., title='Video Id') +class PixverseImageUploadResponse(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp_1: Optional[Resp] = Field(None, alias='Resp') -class PikaBodyGeneratePikadditionsGeneratePikadditionsPost(BaseModel): - video: Optional[StrictBytes] = Field(None, title='Video') - image: Optional[StrictBytes] = Field(None, title='Image') - promptText: Optional[str] = Field(None, title='Prompttext') - negativePrompt: Optional[str] = Field(None, title='Negativeprompt') - seed: Optional[int] = Field(None, title='Seed') - - -class PikaBodyGeneratePikaswapsGeneratePikaswapsPost(BaseModel): - video: Optional[StrictBytes] = Field(None, title='Video') - image: Optional[StrictBytes] = Field(None, title='Image') - promptText: Optional[str] = Field(None, title='Prompttext') - modifyRegionMask: Optional[StrictBytes] = Field( - None, - description='A mask image that specifies the region to modify, where the mask is white and the background is black', - title='Modifyregionmask', - ) - modifyRegionRoi: Optional[str] = Field( - None, - description='Plaintext description of the object / region to modify', - title='Modifyregionroi', - ) - negativePrompt: Optional[str] = Field(None, title='Negativeprompt') - seed: Optional[int] = Field(None, title='Seed') - - -class IngredientsMode(str, Enum): - creative = 'creative' - precise = 'precise' - - -class AspectRatio1(RootModel[float]): - root: float = Field( - ..., - description='Aspect ratio (width / height)', - ge=0.4, - le=2.5, - title='Aspectratio', - ) - - -class PikaBodyGenerate22C2vGenerate22PikascenesPost(BaseModel): - images: Optional[List[StrictBytes]] = Field(None, title='Images') - ingredientsMode: IngredientsMode = Field(..., title='Ingredientsmode') - promptText: Optional[str] = Field(None, title='Prompttext') - negativePrompt: Optional[str] = Field(None, title='Negativeprompt') - seed: Optional[int] = Field(None, title='Seed') - resolution: Optional[str] = Field('1080p', title='Resolution') - duration: Optional[int] = Field(5, title='Duration') - aspectRatio: Optional[AspectRatio1] = Field( - None, description='Aspect ratio (width / height)', title='Aspectratio' - ) - - -class PikaStatusEnum(str, Enum): - queued = 'queued' - started = 'started' - finished = 'finished' - - -class PikaValidationError(BaseModel): - loc: List[Union[str, int]] = Field(..., title='Location') - msg: str = Field(..., title='Message') - type: str = Field(..., title='Error Type') - - -class PikaResolutionEnum(str, Enum): - field_1080p = '1080p' - field_720p = '720p' - - -class PikaDurationEnum(int, Enum): +class Duration(int, Enum): integer_5 = 5 - integer_10 = 10 + integer_8 = 8 + + +class Model1(str, Enum): + v3_5 = 'v3.5' + + +class MotionMode(str, Enum): + normal = 'normal' + fast = 'fast' + + +class Quality1(str, Enum): + field_360p = '360p' + field_540p = '540p' + field_720p = '720p' + field_1080p = '1080p' + + +class Style1(str, Enum): + anime = 'anime' + field_3d_animation = '3d_animation' + clay = 'clay' + comic = 'comic' + cyberpunk = 'cyberpunk' + + +class PixverseImageVideoRequest(BaseModel): + duration: Duration + img_id: int + model: Model1 + motion_mode: Optional[MotionMode] = None + prompt: str + quality: Quality1 + seed: Optional[int] = None + style: Optional[Style1] = None + template_id: Optional[int] = None + water_mark: Optional[bool] = None + + +class AspectRatio2(str, Enum): + field_16_9 = '16:9' + field_4_3 = '4:3' + field_1_1 = '1:1' + field_3_4 = '3:4' + field_9_16 = '9:16' + + +class PixverseTextVideoRequest(BaseModel): + aspect_ratio: AspectRatio2 + duration: Duration + model: Model1 + motion_mode: Optional[MotionMode] = None + negative_prompt: Optional[str] = None + prompt: str + quality: Quality1 + seed: Optional[int] = None + style: Optional[Style1] = None + template_id: Optional[int] = None + water_mark: Optional[bool] = None + + +class PixverseTransitionVideoRequest(BaseModel): + duration: Duration + first_frame_img: int + last_frame_img: int + model: Model1 + motion_mode: MotionMode + prompt: str + quality: Quality1 + seed: int + style: Optional[Style1] = None + template_id: Optional[int] = None + water_mark: Optional[bool] = None + + +class Resp1(BaseModel): + video_id: Optional[int] = None + + +class PixverseVideoResponse(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp: Optional[Resp1] = None + + +class Status7(int, Enum): + integer_1 = 1 + integer_5 = 5 + integer_6 = 6 + integer_7 = 7 + integer_8 = 8 + + +class Resp2(BaseModel): + create_time: Optional[str] = None + id: Optional[int] = None + modify_time: Optional[str] = None + negative_prompt: Optional[str] = None + outputHeight: Optional[int] = None + outputWidth: Optional[int] = None + prompt: Optional[str] = None + resolution_ratio: Optional[int] = None + seed: Optional[int] = None + size: Optional[int] = None + status: Optional[Status7] = Field( + None, + description='Video generation status codes:\n* 1 - Generation successful\n* 5 - Generating\n* 6 - Deleted\n* 7 - Contents moderation failed\n* 8 - Generation failed\n', + ) + style: Optional[str] = None + url: Optional[str] = None + + +class PixverseVideoResultResponse(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp: Optional[Resp2] = None class RgbItem(RootModel[int]): @@ -2241,213 +1850,364 @@ class RGBColor(BaseModel): rgb: List[RgbItem] = Field(..., max_length=3, min_length=3) -class StabilityStabilityClientID(RootModel[str]): - root: str = Field( +class GenerateSummary(str, Enum): + auto = 'auto' + concise = 'concise' + detailed = 'detailed' + + +class Summary(str, Enum): + auto = 'auto' + concise = 'concise' + detailed = 'detailed' + + +class ReasoningEffort(str, Enum): + low = 'low' + medium = 'medium' + high = 'high' + + +class Status8(str, Enum): + in_progress = 'in_progress' + completed = 'completed' + incomplete = 'incomplete' + + +class Type16(str, Enum): + summary_text = 'summary_text' + + +class SummaryItem(BaseModel): + text: str = Field( ..., - description='The name of your application, used to help us communicate app-specific debugging or moderation issues to you.', - examples=['my-awesome-app'], - max_length=256, + description='A short summary of the reasoning used by the model when generating\nthe response.\n', + ) + type: Type16 = Field( + ..., description='The type of the object. Always `summary_text`.\n' ) -class StabilityStabilityClientUserID(RootModel[str]): - root: str = Field( - ..., - description='A unique identifier for your end user. Used to help us communicate user-specific debugging or moderation issues to you. Feel free to obfuscate this value to protect user privacy.', - examples=['DiscordUser#9999'], - max_length=256, - ) +class Type17(str, Enum): + reasoning = 'reasoning' -class StabilityStabilityClientVersion(RootModel[str]): - root: str = Field( - ..., - description='The version of your application, used to help us communicate version-specific debugging or moderation issues to you.', - examples=['1.2.1'], - max_length=256, - ) - - -class Name(str, Enum): - content_moderation = 'content_moderation' - - -class StabilityContentModerationResponse(BaseModel): +class ReasoningItem(BaseModel): id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new) you file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, + ..., description='The unique identifier of the reasoning content.\n' ) - name: Name = Field( - ..., - description='Our content moderation system has flagged some part of your request and subsequently denied it. You were not charged for this request. While this may at times be frustrating, it is necessary to maintain the integrity of our platform and ensure a safe experience for all users. If you would like to provide feedback, please use the [Support Form](https://kb.stability.ai/knowledge-base/kb-tickets/new).', + status: Optional[Status8] = Field( + None, + description='The status of the item. One of `in_progress`, `completed`, or\n`incomplete`. Populated when items are returned via API.\n', ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, + summary: List[SummaryItem] = Field(..., description='Reasoning text contents.\n') + type: Type17 = Field( + ..., description='The type of the object. Always `reasoning`.\n' ) +class Controls(BaseModel): + artistic_level: Optional[int] = Field( + None, + description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity.', + ge=0, + le=5, + ) + background_color: Optional[RGBColor] = None + colors: Optional[List[RGBColor]] = Field( + None, description='An array of preferable colors' + ) + no_text: Optional[bool] = Field(None, description='Do not embed text layouts') + + +class RecraftImageGenerationRequest(BaseModel): + controls: Optional[Controls] = Field( + None, description='The controls for the generated image' + ) + model: str = Field( + ..., description='The model to use for generation (e.g., "recraftv3")' + ) + n: int = Field(..., description='The number of images to generate', ge=1, le=4) + prompt: str = Field( + ..., description='The text prompt describing the image to generate' + ) + size: str = Field( + ..., description='The size of the generated image (e.g., "1024x1024")' + ) + style: Optional[str] = Field( + None, + description='The style to apply to the generated image (e.g., "digital_illustration")', + ) + style_id: Optional[str] = Field( + None, + description='The style ID to apply to the generated image (e.g., "123e4567-e89b-12d3-a456-426614174000"). If style_id is provided, style should not be provided.', + ) + + +class Datum3(BaseModel): + image_id: Optional[str] = Field( + None, description='Unique identifier for the generated image' + ) + url: Optional[str] = Field(None, description='URL to access the generated image') + + +class RecraftImageGenerationResponse(BaseModel): + created: int = Field( + ..., description='Unix timestamp when the generation was created' + ) + credits: int = Field(..., description='Number of credits used for the generation') + data: List[Datum3] = Field(..., description='Array of generated image information') + + class RenderingSpeed(str, Enum): BALANCED = 'BALANCED' TURBO = 'TURBO' QUALITY = 'QUALITY' -class StabilityCreativity(RootModel[float]): - root: float = Field( +class ResponseErrorCode(str, Enum): + server_error = 'server_error' + rate_limit_exceeded = 'rate_limit_exceeded' + invalid_prompt = 'invalid_prompt' + vector_store_timeout = 'vector_store_timeout' + invalid_image = 'invalid_image' + invalid_image_format = 'invalid_image_format' + invalid_base64_image = 'invalid_base64_image' + invalid_image_url = 'invalid_image_url' + image_too_large = 'image_too_large' + image_too_small = 'image_too_small' + image_parse_error = 'image_parse_error' + image_content_policy_violation = 'image_content_policy_violation' + invalid_image_mode = 'invalid_image_mode' + image_file_too_large = 'image_file_too_large' + unsupported_image_media_type = 'unsupported_image_media_type' + empty_image_file = 'empty_image_file' + failed_to_download_image = 'failed_to_download_image' + image_file_not_found = 'image_file_not_found' + + +class Type18(str, Enum): + json_object = 'json_object' + + +class ResponseFormatJsonObject(BaseModel): + type: Type18 = Field( ..., - description='Controls the likelihood of creating additional details not heavily conditioned by the init image.', - ge=0.2, - le=0.5, + description='The type of response format being defined. Always `json_object`.', ) -class StabilityGenerationID(RootModel[str]): - root: str = Field( +class ResponseFormatJsonSchemaSchema(BaseModel): + pass + model_config = ConfigDict( + extra='allow', + ) + + +class Type19(str, Enum): + text = 'text' + + +class ResponseFormatText(BaseModel): + type: Type19 = Field( + ..., description='The type of response format being defined. Always `text`.' + ) + + +class Truncation1(str, Enum): + auto = 'auto' + disabled = 'disabled' + + +class InputTokensDetails1(BaseModel): + cached_tokens: int = Field( ..., - description='The `id` of a generation, typically used for async generations, that can be used to check the status of the generation or retrieve the result.', - examples=['a6dc6c6e20acda010fe14d71f180658f2896ed9b4ec25aa99a6ff06c796987c4'], - max_length=64, - min_length=64, + description='The number of tokens that were retrieved from the cache. \n[More on prompt caching](/docs/guides/prompt-caching).\n', ) -class Mode(str, Enum): - text_to_image = 'text-to-image' - image_to_image = 'image-to-image' +class OutputTokensDetails(BaseModel): + reasoning_tokens: int = Field(..., description='The number of reasoning tokens.') -class AspectRatio2(str, Enum): - field_21_9 = '21:9' - field_16_9 = '16:9' - field_3_2 = '3:2' - field_5_4 = '5:4' - field_1_1 = '1:1' - field_4_5 = '4:5' - field_2_3 = '2:3' - field_9_16 = '9:16' - field_9_21 = '9:21' +class ResponseUsage(BaseModel): + input_tokens: int = Field(..., description='The number of input tokens.') + input_tokens_details: InputTokensDetails1 = Field( + ..., description='A detailed breakdown of the input tokens.' + ) + output_tokens: int = Field(..., description='The number of output tokens.') + output_tokens_details: OutputTokensDetails = Field( + ..., description='A detailed breakdown of the output tokens.' + ) + total_tokens: int = Field(..., description='The total number of tokens used.') -class Model4(str, Enum): - sd3_5_large = 'sd3.5-large' - sd3_5_large_turbo = 'sd3.5-large-turbo' - sd3_5_medium = 'sd3.5-medium' +class Rodin3DCheckStatusRequest(BaseModel): + subscription_key: str = Field( + ..., description='subscription from generate endpoint' + ) -class OutputFormat3(str, Enum): - png = 'png' - jpeg = 'jpeg' +class Rodin3DCheckStatusResponse(BaseModel): + pass -class StylePreset(str, Enum): - enhance = 'enhance' - anime = 'anime' - photographic = 'photographic' - digital_art = 'digital-art' - comic_book = 'comic-book' - fantasy_art = 'fantasy-art' - line_art = 'line-art' - analog_film = 'analog-film' - neon_punk = 'neon-punk' - isometric = 'isometric' - low_poly = 'low-poly' - origami = 'origami' - modeling_compound = 'modeling-compound' - cinematic = 'cinematic' - field_3d_model = '3d-model' - pixel_art = 'pixel-art' - tile_texture = 'tile-texture' +class Rodin3DDownloadRequest(BaseModel): + task_uuid: str = Field(..., description='Task UUID') -class StabilityImageGenrationSD3Request(BaseModel): - prompt: str = Field( +class RodinGenerateJobsData(BaseModel): + subscription_key: Optional[str] = Field(None, description='Subscription Key.') + uuids: Optional[List[str]] = Field(None, description='subjobs uuid.') + + +class RodinMaterialType(str, Enum): + PBR = 'PBR' + Shaded = 'Shaded' + + +class RodinMeshModeType(str, Enum): + Quad = 'Quad' + Raw = 'Raw' + + +class RodinQualityType(str, Enum): + extra_low = 'extra-low' + low = 'low' + medium = 'medium' + high = 'high' + + +class RodinResourceItem(BaseModel): + name: Optional[str] = Field(None, description='File name') + url: Optional[str] = Field(None, description='Download url') + + +class RodinTierType(str, Enum): + Regular = 'Regular' + Sketch = 'Sketch' + Detail = 'Detail' + Smooth = 'Smooth' + + +class RunwayAspectRatioEnum(str, Enum): + field_1280_720 = '1280:720' + field_720_1280 = '720:1280' + field_1104_832 = '1104:832' + field_832_1104 = '832:1104' + field_960_960 = '960:960' + field_1584_672 = '1584:672' + field_1280_768 = '1280:768' + field_768_1280 = '768:1280' + + +class RunwayDurationEnum(int, Enum): + integer_5 = 5 + integer_10 = 10 + + +class RunwayImageToVideoResponse(BaseModel): + id: Optional[str] = Field(None, description='Task ID') + + +class RunwayModelEnum(str, Enum): + gen4_turbo = 'gen4_turbo' + gen3a_turbo = 'gen3a_turbo' + + +class Position(str, Enum): + first = 'first' + last = 'last' + + +class RunwayPromptImageDetailedObject(BaseModel): + position: Position = Field( ..., - description='What you wish to see in the output image. A strong, descriptive prompt that clearly defines\nelements, colors, and subjects will lead to better results.', - max_length=10000, - min_length=1, + description="The position of the image in the output video. 'last' is currently supported for gen3a_turbo only.", ) - mode: Optional[Mode] = Field( - 'text-to-image', - description='Controls whether this is a text-to-image or image-to-image generation, which affects which parameters are required:\n- **text-to-image** requires only the `prompt` parameter\n- **image-to-image** requires the `prompt`, `image`, and `strength` parameters', - title='GenerationMode', + uri: str = Field( + ..., description='A HTTPS URL or data URI containing an encoded image.' ) - image: Optional[StrictBytes] = Field( + + +class RunwayPromptImageObject( + RootModel[Union[str, List[RunwayPromptImageDetailedObject]]] +): + root: Union[str, List[RunwayPromptImageDetailedObject]] = Field( + ..., + description='Image(s) to use for the video generation. Can be a single URI or an array of image objects with positions.', + ) + + +class RunwayTaskStatusEnum(str, Enum): + SUCCEEDED = 'SUCCEEDED' + RUNNING = 'RUNNING' + FAILED = 'FAILED' + PENDING = 'PENDING' + CANCELLED = 'CANCELLED' + THROTTLED = 'THROTTLED' + + +class RunwayTaskStatusResponse(BaseModel): + createdAt: datetime = Field(..., description='Task creation timestamp') + id: str = Field(..., description='Task ID') + output: Optional[List[str]] = Field(None, description='Array of output video URLs') + progress: Optional[float] = Field( None, - description='The image to use as the starting point for the generation.\n\nSupported formats:\n\n\n\n - jpeg\n - png\n - webp\n\nSupported dimensions:\n\n\n\n - Every side must be at least 64 pixels\n\n> **Important:** This parameter is only valid for **image-to-image** requests.', - ) - strength: Optional[float] = Field( - None, - description='Sometimes referred to as _denoising_, this parameter controls how much influence the\n`image` parameter has on the generated image. A value of 0 would yield an image that\nis identical to the input. A value of 1 would be as if you passed in no image at all.\n\n> **Important:** This parameter is only valid for **image-to-image** requests.', + description='Float value between 0 and 1 representing the progress of the task. Only available if status is RUNNING.', ge=0.0, le=1.0, ) - aspect_ratio: Optional[AspectRatio2] = Field( - '1:1', - description='Controls the aspect ratio of the generated image. Defaults to 1:1.\n\n> **Important:** This parameter is only valid for **text-to-image** requests.', - ) - model: Optional[Model4] = Field( - 'sd3.5-large', - description='The model to use for generation.\n\n- `sd3.5-large` requires 6.5 credits per generation\n- `sd3.5-large-turbo` requires 4 credits per generation\n- `sd3.5-medium` requires 3.5 credits per generation\n- As of the April 17, 2025, `sd3-large`, `sd3-large-turbo` and `sd3-medium`\n\n\n\n are re-routed to their `sd3.5-[model version]` equivalent, at the same price.', - ) - seed: Optional[float] = Field( - 0, - description="A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass `0` to use a random seed.)", - ge=0.0, - le=4294967294.0, - ) - output_format: Optional[OutputFormat3] = Field( - 'png', description='Dictates the `content-type` of the generated image.' - ) - style_preset: Optional[StylePreset] = Field( - None, description='Guides the image model towards a particular style.' - ) - negative_prompt: Optional[str] = Field( - None, - description='Keywords of what you **do not** wish to see in the output image.\nThis is an advanced feature.', - max_length=10000, - ) - cfg_scale: Optional[float] = Field( - None, - description='How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt). The _Large_ and _Medium_ models use a default of `4`. The _Turbo_ model uses a default of `1`.', - ge=1.0, - le=10.0, + status: RunwayTaskStatusEnum + + +class RunwayTextToImageAspectRatioEnum(str, Enum): + field_1920_1080 = '1920:1080' + field_1080_1920 = '1080:1920' + field_1024_1024 = '1024:1024' + field_1360_768 = '1360:768' + field_1080_1080 = '1080:1080' + field_1168_880 = '1168:880' + field_1440_1080 = '1440:1080' + field_1080_1440 = '1080:1440' + field_1808_768 = '1808:768' + field_2112_912 = '2112:912' + +class Model4(str, Enum): + gen4_image = 'gen4_image' + + +class ReferenceImage(BaseModel): + uri: Optional[str] = Field( + None, description='A HTTPS URL or data URI containing an encoded image' ) -class FinishReason(str, Enum): - SUCCESS = 'SUCCESS' - CONTENT_FILTERED = 'CONTENT_FILTERED' +class RunwayTextToImageRequest(BaseModel): + model: Model4 = Field(..., description='Model to use for generation') + promptText: str = Field( + ..., description='Text prompt for the image generation', max_length=1000 + ) + ratio: RunwayTextToImageAspectRatioEnum + referenceImages: Optional[List[ReferenceImage]] = Field( + None, description='Array of reference images to guide the generation' + ) -class StabilityImageGenrationSD3Response200(BaseModel): - image: str = Field( +class RunwayTextToImageResponse(BaseModel): + id: Optional[str] = Field(None, description='Task ID') + + +class StabilityError(BaseModel): + errors: List[str] = Field( ..., - description='The generated image, encoded to base64.', - examples=['AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1...'], + description='One or more error messages indicating what went wrong.', + examples=[[{'some-field': 'is required'}]], + min_length=1, ) - seed: Optional[float] = Field( - 0, - description='The seed used as random noise for this generation.', - examples=[343940597], - ge=0.0, - le=4294967294.0, - ) - finish_reason: FinishReason = Field( - ..., - description='The reason the generation finished.\n\n- `SUCCESS` = successful generation.\n- `CONTENT_FILTERED` = successful generation, however the output violated our content moderation\npolicy and has been blurred as a result.', - examples=['SUCCESS'], - ) - - -class StabilityImageGenrationSD3Response400(BaseModel): id: str = Field( ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new) you file, as it will greatly assist us in diagnosing the root cause of the problem.\n', examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], min_length=1, ) @@ -2457,704 +2217,501 @@ class StabilityImageGenrationSD3Response400(BaseModel): examples=['bad_request'], min_length=1, ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) -class StabilityImageGenrationSD3Response413(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) +class Status9(str, Enum): + in_progress = 'in-progress' -class StabilityImageGenrationSD3Response422(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationSD3Response429(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationSD3Response500(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class OutputFormat4(str, Enum): - jpeg = 'jpeg' - png = 'png' - webp = 'webp' - - -class StabilityImageGenrationUpscaleConservativeRequest(BaseModel): - image: StrictBytes = Field( - ..., - description='The image you wish to upscale.\n\nSupported Formats:\n- jpeg\n- png\n- webp\n\nValidation Rules:\n- Every side must be at least 64 pixels\n- Total pixel count must be between 4,096 and 9,437,184 pixels\n- The aspect ratio must be between 1:2.5 and 2.5:1', - examples=['./some/image.png'], - ) - prompt: str = Field( - ..., - description="What you wish to see in the output image. A strong, descriptive prompt that clearly defines\nelements, colors, and subjects will lead to better results.\n\nTo control the weight of a given word use the format `(word:weight)`,\nwhere `word` is the word you'd like to control the weight of and `weight`\nis a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`\nwould convey a sky that was blue and green, but more green than blue.", - max_length=10000, - min_length=1, - ) - negative_prompt: Optional[str] = Field( - None, - description='A blurb of text describing what you **do not** wish to see in the output image.\nThis is an advanced feature.', - max_length=10000, - ) - seed: Optional[float] = Field( - 0, - description="A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass `0` to use a random seed.)", - ge=0.0, - le=4294967294.0, - ) - output_format: Optional[OutputFormat4] = Field( - 'png', description='Dictates the `content-type` of the generated image.' - ) - creativity: Optional[StabilityCreativity] = Field( - default_factory=lambda: StabilityCreativity.model_validate(0.35) - ) - - -class StabilityImageGenrationUpscaleConservativeResponse200(BaseModel): - image: str = Field( - ..., - description='The generated image, encoded to base64.', - examples=['AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1...'], - ) - seed: Optional[float] = Field( - 0, - description='The seed used as random noise for this generation.', - examples=[343940597], - ge=0.0, - le=4294967294.0, - ) - finish_reason: FinishReason = Field( - ..., - description='The reason the generation finished.\n\n- `SUCCESS` = successful generation.\n- `CONTENT_FILTERED` = successful generation, however the output violated our content moderation\npolicy and has been blurred as a result.', - examples=['SUCCESS'], - ) - - -class StabilityImageGenrationUpscaleConservativeResponse400(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleConservativeResponse413(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleConservativeResponse422(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleConservativeResponse429(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleConservativeResponse500(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleCreativeRequest(BaseModel): - image: StrictBytes = Field( - ..., - description='The image you wish to upscale.\n\nSupported Formats:\n- jpeg\n- png\n- webp\n\nValidation Rules:\n- Every side must be at least 64 pixels\n- Total pixel count must be between 4,096 and 1,048,576 pixels', - examples=['./some/image.png'], - ) - prompt: str = Field( - ..., - description="What you wish to see in the output image. A strong, descriptive prompt that clearly defines\nelements, colors, and subjects will lead to better results.\n\nTo control the weight of a given word use the format `(word:weight)`,\nwhere `word` is the word you'd like to control the weight of and `weight`\nis a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`\nwould convey a sky that was blue and green, but more green than blue.", - max_length=10000, - min_length=1, - ) - negative_prompt: Optional[str] = Field( - None, - description='A blurb of text describing what you **do not** wish to see in the output image.\nThis is an advanced feature.', - max_length=10000, - ) - output_format: Optional[OutputFormat4] = Field( - 'png', description='Dictates the `content-type` of the generated image.' - ) - seed: Optional[float] = Field( - 0, - description="A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass `0` to use a random seed.)", - ge=0.0, - le=4294967294.0, - ) - creativity: Optional[float] = Field( - 0.3, - description='Indicates how creative the model should be when upscaling an image.\nHigher values will result in more details being added to the image during upscaling.', - ge=0.1, - le=0.5, - ) - style_preset: Optional[StylePreset] = Field( - None, description='Guides the image model towards a particular style.' - ) - - -class StabilityImageGenrationUpscaleCreativeResponse200(BaseModel): - id: StabilityGenerationID - - -class StabilityImageGenrationUpscaleCreativeResponse400(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleCreativeResponse413(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleCreativeResponse422(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleCreativeResponse429(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleCreativeResponse500(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleFastRequest(BaseModel): - image: StrictBytes = Field( - ..., - description='The image you wish to upscale.\n\nSupported Formats:\n- jpeg\n- png\n- webp\n\nValidation Rules:\n- Width must be between 32 and 1,536 pixels\n- Height must be between 32 and 1,536 pixels\n- Total pixel count must be between 1,024 and 1,048,576 pixels', - examples=['./some/image.png'], - ) - output_format: Optional[OutputFormat4] = Field( - 'png', description='Dictates the `content-type` of the generated image.' - ) - - -class StabilityImageGenrationUpscaleFastResponse200(BaseModel): - image: str = Field( - ..., - description='The generated image, encoded to base64.', - examples=['AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1...'], - ) - seed: Optional[float] = Field( - 0, - description='The seed used as random noise for this generation.', - examples=[343940597], - ge=0.0, - le=4294967294.0, - ) - finish_reason: FinishReason = Field( - ..., - description='The reason the generation finished.\n\n- `SUCCESS` = successful generation.\n- `CONTENT_FILTERED` = successful generation, however the output violated our content moderation\npolicy and has been blurred as a result.', - examples=['SUCCESS'], - ) - - -class StabilityImageGenrationUpscaleFastResponse400(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleFastResponse413(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleFastResponse422(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleFastResponse429(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleFastResponse500(BaseModel): - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - - -class ActionJobResult(BaseModel): - id: Optional[UUID] = Field(None, description='Unique identifier for the job result') - workflow_name: Optional[str] = Field(None, description='Name of the workflow') - operating_system: Optional[str] = Field(None, description='Operating system used') - python_version: Optional[str] = Field(None, description='PyTorch version used') - pytorch_version: Optional[str] = Field(None, description='PyTorch version used') - action_run_id: Optional[str] = Field( - None, description='Identifier of the run this result belongs to' - ) - action_job_id: Optional[str] = Field( - None, description='Identifier of the job this result belongs to' - ) - cuda_version: Optional[str] = Field(None, description='CUDA version used') - branch_name: Optional[str] = Field( - None, description='Name of the relevant git branch' - ) - commit_hash: Optional[str] = Field(None, description='The hash of the commit') - commit_id: Optional[str] = Field(None, description='The ID of the commit') - commit_time: Optional[int] = Field( - None, description='The Unix timestamp when the commit was made' - ) - commit_message: Optional[str] = Field(None, description='The message of the commit') - comfy_run_flags: Optional[str] = Field( - None, description='The comfy run flags. E.g. `--low-vram`' - ) - git_repo: Optional[str] = Field(None, description='The repository name') - pr_number: Optional[str] = Field(None, description='The pull request number') - start_time: Optional[int] = Field( - None, description='The start time of the job as a Unix timestamp.' - ) - end_time: Optional[int] = Field( - None, description='The end time of the job as a Unix timestamp.' - ) - avg_vram: Optional[int] = Field( - None, description='The average VRAM used by the job' - ) - peak_vram: Optional[int] = Field(None, description='The peak VRAM used by the job') - job_trigger_user: Optional[str] = Field( - None, description='The user who triggered the job.' - ) - author: Optional[str] = Field(None, description='The author of the commit') - machine_stats: Optional[MachineStats] = None - status: Optional[WorkflowRunStatus] = None - storage_file: Optional[StorageFile] = None - - -class Publisher(BaseModel): - name: Optional[str] = None +class StabilityGetResultResponse202(BaseModel): id: Optional[str] = Field( + None, description='The ID of the generation result.', examples=[1234567890] + ) + status: Optional[Status9] = None + + +class Type20(str, Enum): + json_schema = 'json_schema' + + +class TextResponseFormatJsonSchema(BaseModel): + description: Optional[str] = Field( None, - description="The unique identifier for the publisher. It's akin to a username. Should be lowercase.", + description='A description of what the response format is for, used by the model to\ndetermine how to respond in the format.\n', ) - description: Optional[str] = None - website: Optional[str] = None - support: Optional[str] = None - source_code_repo: Optional[str] = None - logo: Optional[str] = Field(None, description="URL to the publisher's logo.") - createdAt: Optional[datetime] = Field( - None, description='The date and time the publisher was created.' + name: str = Field( + ..., + description='The name of the response format. Must be a-z, A-Z, 0-9, or contain\nunderscores and dashes, with a maximum length of 64.\n', ) - members: Optional[List[PublisherMember]] = Field( - None, description='A list of members in the publisher.' + schema_: ResponseFormatJsonSchemaSchema = Field(..., alias='schema') + strict: Optional[bool] = Field( + False, + description='Whether to enable strict schema adherence when generating the output.\nIf set to true, the model will always follow the exact schema defined\nin the `schema` field. Only a subset of JSON Schema is supported when\n`strict` is `true`. To learn more, read the [Structured Outputs\nguide](/docs/guides/structured-outputs).\n', ) - status: Optional[PublisherStatus] = Field( - None, description='The status of the publisher.' + type: Type20 = Field( + ..., + description='The type of response format being defined. Always `json_schema`.', ) -class NodeVersion(BaseModel): - id: Optional[str] = None - version: Optional[str] = Field( +class Type21(str, Enum): + function = 'function' + + +class ToolChoiceFunction(BaseModel): + name: str = Field(..., description='The name of the function to call.') + type: Type21 = Field( + ..., description='For function calling, the type is always `function`.' + ) + + +class ToolChoiceOptions(str, Enum): + none = 'none' + auto = 'auto' + required = 'required' + + +class Type22(str, Enum): + file_search = 'file_search' + web_search_preview = 'web_search_preview' + computer_use_preview = 'computer_use_preview' + web_search_preview_2025_03_11 = 'web_search_preview_2025_03_11' + + +class ToolChoiceTypes(BaseModel): + type: Type22 = Field( + ..., + description='The type of hosted tool the model should to use. Learn more about\n[built-in tools](/docs/guides/tools).\n\nAllowed values are:\n- `file_search`\n- `web_search_preview`\n- `computer_use_preview`\n', + ) + + +class TripoAnimation(str, Enum): + preset_idle = 'preset:idle' + preset_walk = 'preset:walk' + preset_climb = 'preset:climb' + preset_jump = 'preset:jump' + preset_run = 'preset:run' + preset_slash = 'preset:slash' + preset_shoot = 'preset:shoot' + preset_hurt = 'preset:hurt' + preset_fall = 'preset:fall' + preset_turn = 'preset:turn' + + +class TripoBalance(BaseModel): + balance: float + frozen: float + + +class TripoConvertFormat(str, Enum): + GLTF = 'GLTF' + USDZ = 'USDZ' + FBX = 'FBX' + OBJ = 'OBJ' + STL = 'STL' + field_3MF = '3MF' + + +class Code(int, Enum): + integer_1001 = 1001 + integer_2000 = 2000 + integer_2001 = 2001 + integer_2002 = 2002 + integer_2003 = 2003 + integer_2004 = 2004 + integer_2006 = 2006 + integer_2007 = 2007 + integer_2008 = 2008 + integer_2010 = 2010 + + +class TripoErrorResponse(BaseModel): + code: Code + message: str + suggestion: str + + +class TripoImageToModel(str, Enum): + image_to_model = 'image_to_model' + + +class TripoModelStyle(str, Enum): + person_person2cartoon = 'person:person2cartoon' + animal_venom = 'animal:venom' + object_clay = 'object:clay' + object_steampunk = 'object:steampunk' + object_christmas = 'object:christmas' + object_barbie = 'object:barbie' + gold = 'gold' + ancient_bronze = 'ancient_bronze' + + +class TripoModelVersion(str, Enum): + V2_5 = 'v2.5-20250123' + V2_0 = 'v2.0-20240919' + V1_4 = 'v1.4-20240625' + + +class TripoMultiviewMode(str, Enum): + LEFT = 'LEFT' + RIGHT = 'RIGHT' + + +class TripoMultiviewToModel(str, Enum): + multiview_to_model = 'multiview_to_model' + + +class TripoOrientation(str, Enum): + align_image = 'align_image' + default = 'default' + + +class TripoResponseSuccessCode(RootModel[int]): + root: int = Field( + ..., + description='Standard success code for Tripo API responses. Typically 0 for success.', + examples=[0], + ) + + +class TripoSpec(str, Enum): + mixamo = 'mixamo' + tripo = 'tripo' + + +class TripoStandardFormat(str, Enum): + glb = 'glb' + fbx = 'fbx' + + +class TripoStylizeOptions(str, Enum): + lego = 'lego' + voxel = 'voxel' + voronoi = 'voronoi' + minecraft = 'minecraft' + + +class Code1(int, Enum): + integer_0 = 0 + + +class Data8(BaseModel): + task_id: str = Field(..., description='used for getTask') + + +class TripoSuccessTask(BaseModel): + code: Code1 + data: Data8 + + +class Topology(str, Enum): + bip = 'bip' + quad = 'quad' + + +class Output(BaseModel): + base_model: Optional[str] = None + model: Optional[str] = None + pbr_model: Optional[str] = None + rendered_image: Optional[str] = None + riggable: Optional[bool] = None + topology: Optional[Topology] = None + + +class Status10(str, Enum): + queued = 'queued' + running = 'running' + success = 'success' + failed = 'failed' + cancelled = 'cancelled' + unknown = 'unknown' + banned = 'banned' + expired = 'expired' + + +class TripoTask(BaseModel): + create_time: int + input: Dict[str, Any] + output: Output + progress: int = Field(..., ge=0, le=100) + status: Status10 + task_id: str + type: str + + +class TripoTextToModel(str, Enum): + text_to_model = 'text_to_model' + + +class TripoTextureAlignment(str, Enum): + original_image = 'original_image' + geometry = 'geometry' + + +class TripoTextureFormat(str, Enum): + BMP = 'BMP' + DPX = 'DPX' + HDR = 'HDR' + JPEG = 'JPEG' + OPEN_EXR = 'OPEN_EXR' + PNG = 'PNG' + TARGA = 'TARGA' + TIFF = 'TIFF' + WEBP = 'WEBP' + + +class TripoTextureQuality(str, Enum): + standard = 'standard' + detailed = 'detailed' + + +class TripoTopology(str, Enum): + bip = 'bip' + quad = 'quad' + + +class TripoTypeAnimatePrerigcheck(str, Enum): + animate_prerigcheck = 'animate_prerigcheck' + + +class TripoTypeAnimateRetarget(str, Enum): + animate_retarget = 'animate_retarget' + + +class TripoTypeAnimateRig(str, Enum): + animate_rig = 'animate_rig' + + +class TripoTypeConvertModel(str, Enum): + convert_model = 'convert_model' + + +class TripoTypeRefineModel(str, Enum): + refine_model = 'refine_model' + + +class TripoTypeStylizeModel(str, Enum): + stylize_model = 'stylize_model' + + +class TripoTypeTextureModel(str, Enum): + texture_model = 'texture_model' + + +class Veo2GenVidPollRequest(BaseModel): + operationName: str = Field( + ..., + description='Full operation name (from predict response)', + examples=[ + 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID' + ], + ) + + +class Error(BaseModel): + code: Optional[int] = Field(None, description='Error code') + message: Optional[str] = Field(None, description='Error message') + + +class Video(BaseModel): + bytesBase64Encoded: Optional[str] = Field( + None, description='Base64-encoded video content' + ) + gcsUri: Optional[str] = Field(None, description='Cloud Storage URI of the video') + mimeType: Optional[str] = Field(None, description='Video MIME type') + + +class Response(BaseModel): + field_type: Optional[str] = Field( None, - description='The version identifier, following semantic versioning. Must be unique for the node.', + alias='@type', + examples=[ + 'type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse' + ], ) - createdAt: Optional[datetime] = Field( - None, description='The date and time the version was created.' + raiMediaFilteredCount: Optional[int] = Field( + None, description='Count of media filtered by responsible AI policies' ) - changelog: Optional[str] = Field( - None, description='Summary of changes made in this version' + raiMediaFilteredReasons: Optional[List[str]] = Field( + None, description='Reasons why media was filtered by responsible AI policies' ) - dependencies: Optional[List[str]] = Field( - None, description='A list of pip dependencies required by the node.' + videos: Optional[List[Video]] = None + + +class Veo2GenVidPollResponse(BaseModel): + done: Optional[bool] = None + error: Optional[Error] = Field( + None, description='Error details if operation failed' ) - downloadUrl: Optional[str] = Field( - None, description='[Output Only] URL to download this version of the node' - ) - deprecated: Optional[bool] = Field( - None, description='Indicates if this version is deprecated.' - ) - status: Optional[NodeVersionStatus] = Field( - None, description='The status of the node version.' - ) - status_reason: Optional[str] = Field( - None, description='The reason for the status change.' - ) - node_id: Optional[str] = Field( - None, description='The unique identifier of the node.' - ) - comfy_node_extract_status: Optional[str] = Field( - None, description='The status of comfy node extraction process.' + name: Optional[str] = None + response: Optional[Response] = Field( + None, description='The actual prediction response if done is true' ) -class IdeogramV3Request(BaseModel): - prompt: str = Field(..., description='The text prompt for image generation') - seed: Optional[int] = Field( - None, description='Seed value for reproducible generation' +class Image(BaseModel): + bytesBase64Encoded: str + gcsUri: Optional[str] = None + mimeType: Optional[str] = None + + +class Image1(BaseModel): + bytesBase64Encoded: Optional[str] = None + gcsUri: str + mimeType: Optional[str] = None + + +class Instance(BaseModel): + image: Optional[Union[Image, Image1]] = Field( + None, description='Optional image to guide video generation' ) - resolution: Optional[str] = Field( - None, description='Image resolution in format WxH', examples=['1280x800'] + prompt: str = Field(..., description='Text description of the video') + + +class PersonGeneration1(str, Enum): + ALLOW = 'ALLOW' + BLOCK = 'BLOCK' + + +class Parameters(BaseModel): + aspectRatio: Optional[str] = Field(None, examples=['16:9']) + durationSeconds: Optional[int] = None + enhancePrompt: Optional[bool] = None + negativePrompt: Optional[str] = None + personGeneration: Optional[PersonGeneration1] = None + sampleCount: Optional[int] = None + seed: Optional[int] = None + storageUri: Optional[str] = Field( + None, description='Optional Cloud Storage URI to upload the video' ) - aspect_ratio: Optional[str] = Field( - None, description='Aspect ratio in format WxH', examples=['1x3'] + + +class Veo2GenVidRequest(BaseModel): + instances: Optional[List[Instance]] = None + parameters: Optional[Parameters] = None + + +class Veo2GenVidResponse(BaseModel): + name: str = Field( + ..., + description='Operation resource name', + examples=[ + 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8' + ], ) - rendering_speed: RenderingSpeed - magic_prompt: Optional[MagicPrompt] = Field( - None, description='Whether to enable magic prompt enhancement' + + +class SearchContextSize(str, Enum): + low = 'low' + medium = 'medium' + high = 'high' + + +class Type23(str, Enum): + web_search_preview = 'web_search_preview' + web_search_preview_2025_03_11 = 'web_search_preview_2025_03_11' + + +class WebSearchPreviewTool(BaseModel): + search_context_size: Optional[SearchContextSize] = Field( + None, + description='High level guidance for the amount of context window space to use for the search. One of `low`, `medium`, or `high`. `medium` is the default.', ) - negative_prompt: Optional[str] = Field( - None, description='Text prompt specifying what to avoid in the generation' + type: Literal['WebSearchPreviewTool'] = Field( + ..., + description='The type of the web search tool. One of `web_search_preview` or `web_search_preview_2025_03_11`.', ) - num_images: Optional[int] = Field( - None, description='Number of images to generate', ge=1 + + +class Status11(str, Enum): + in_progress = 'in_progress' + searching = 'searching' + completed = 'completed' + failed = 'failed' + + +class Type24(str, Enum): + web_search_call = 'web_search_call' + + +class WebSearchToolCall(BaseModel): + id: str = Field(..., description='The unique ID of the web search tool call.\n') + status: Status11 = Field( + ..., description='The status of the web search tool call.\n' ) - color_palette: Optional[ColorPalette] = None - style_codes: Optional[List[StyleCode]] = Field( - None, description='Array of style codes in hexadecimal format' + type: Type24 = Field( + ..., + description='The type of the web search tool call. Always `web_search_call`.\n', ) - style_type: Optional[StyleType] = Field( - None, description='The type of style to apply' + + +class CreateModelResponseProperties(ModelResponseProperties): + pass + + +class GeminiInlineData(BaseModel): + data: Optional[str] = Field( + None, + description='The base64 encoding of the image, PDF, or video to include inline in the prompt. When including media inline, you must also specify the media type (mimeType) of the data. Size limit: 20MB\n', ) - style_reference_images: Optional[List[str]] = Field( - None, description='Array of reference image URLs or identifiers' + mimeType: Optional[GeminiMimeType] = None + + +class GeminiPart(BaseModel): + inlineData: Optional[GeminiInlineData] = None + text: Optional[str] = Field( + None, + description='A text prompt or code snippet.', + examples=['Write a story about a robot learning to paint'], + ) + + +class GeminiPromptFeedback(BaseModel): + blockReason: Optional[str] = None + blockReasonMessage: Optional[str] = None + safetyRatings: Optional[List[GeminiSafetyRating]] = None + + +class GeminiSafetySetting(BaseModel): + category: GeminiSafetyCategory + threshold: GeminiSafetyThreshold + + +class GeminiSystemInstructionContent(BaseModel): + parts: List[GeminiTextPart] = Field( + ..., + description='A list of ordered parts that make up a single message. Different parts may have different IANA MIME types. For limits on the inputs, such as the maximum number of tokens or the number of images, see the model specifications on the Google models page.\n', + ) + role: Role1 = Field( + ..., + description='The identity of the entity that creates the message. The following values are supported: user: This indicates that the message is sent by a real person, typically a user-generated message. model: This indicates that the message is generated by the model. The model value is used to insert messages from the model into the conversation during multi-turn conversations. For non-multi-turn conversations, this field can be left blank or unset.\n', + examples=['user'], ) class IdeogramV3EditRequest(BaseModel): + color_palette: Optional[IdeogramColorPalette] = None image: Optional[StrictBytes] = Field( None, description='The image being edited (max size 10MB); only JPEG, WebP and PNG formats are supported at this time.', ) - mask: Optional[StrictBytes] = Field( - None, - description='A black and white image of the same size as the image being edited (max size 10MB). Black regions in the mask should match up with the regions of the image that you would like to edit; only JPEG, WebP and PNG formats are supported at this time.', - ) - prompt: str = Field( - ..., description='The prompt used to describe the edited result.' - ) magic_prompt: Optional[str] = Field( None, description='Determine if MagicPrompt should be used in generating the request or not.', ) + mask: Optional[StrictBytes] = Field( + None, + description='A black and white image of the same size as the image being edited (max size 10MB). Black regions in the mask should match up with the regions of the image that you would like to edit; only JPEG, WebP and PNG formats are supported at this time.', + ) num_images: Optional[int] = Field( None, description='The number of images to generate.' ) - seed: Optional[int] = Field( - None, description='Random seed. Set for reproducible generation.' + prompt: str = Field( + ..., description='The prompt used to describe the edited result.' ) rendering_speed: RenderingSpeed - color_palette: Optional[IdeogramColorPalette] = Field( - None, - description='A color palette for generation, must EITHER be specified via one of the presets (name) or explicitly via hexadecimal representations of the color with optional weights (members). Not supported by V_1, V_1_TURBO, V_2A and V_2A_TURBO models.', + seed: Optional[int] = Field( + None, description='Random seed. Set for reproducible generation.' ) style_codes: Optional[List[StyleCode]] = Field( None, @@ -3166,34 +2723,102 @@ class IdeogramV3EditRequest(BaseModel): ) -class KlingCameraControl(BaseModel): - type: Optional[KlingCameraControlType] = None - config: Optional[KlingCameraConfig] = None - - -class KlingText2VideoRequest(BaseModel): - model_name: Optional[KlingVideoGenModelName] = 'kling-v2-master' - prompt: Optional[str] = Field( - None, description='Positive text prompt', max_length=2500 +class IdeogramV3Request(BaseModel): + aspect_ratio: Optional[str] = Field( + None, description='Aspect ratio in format WxH', examples=['1x3'] + ) + color_palette: Optional[ColorPalette] = None + magic_prompt: Optional[MagicPrompt2] = Field( + None, description='Whether to enable magic prompt enhancement' ) negative_prompt: Optional[str] = Field( - None, description='Negative text prompt', max_length=2500 + None, description='Text prompt specifying what to avoid in the generation' ) - cfg_scale: Optional[KlingVideoGenCfgScale] = Field( - default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) + num_images: Optional[int] = Field( + None, description='Number of images to generate', ge=1 ) + prompt: str = Field(..., description='The text prompt for image generation') + rendering_speed: RenderingSpeed + resolution: Optional[str] = Field( + None, description='Image resolution in format WxH', examples=['1280x800'] + ) + seed: Optional[int] = Field( + None, description='Seed value for reproducible generation' + ) + style_codes: Optional[List[StyleCode]] = Field( + None, description='Array of style codes in hexadecimal format' + ) + style_reference_images: Optional[List[str]] = Field( + None, description='Array of reference image URLs or identifiers' + ) + style_type: Optional[StyleType1] = Field( + None, description='The type of style to apply' + ) + + +class ImagenGenerateImageResponse(BaseModel): + predictions: Optional[List[ImagenImagePrediction]] = None + + +class ImagenImageGenerationParameters(BaseModel): + addWatermark: Optional[bool] = None + aspectRatio: Optional[AspectRatio] = None + enhancePrompt: Optional[bool] = None + includeRaiReason: Optional[bool] = None + includeSafetyAttributes: Optional[bool] = None + outputOptions: Optional[ImagenOutputOptions] = None + personGeneration: Optional[PersonGeneration] = None + safetySetting: Optional[SafetySetting] = None + sampleCount: Optional[int] = Field(None, ge=1, le=4) + seed: Optional[int] = None + storageUri: Optional[AnyUrl] = None + + +class InputContent( + RootModel[Union[InputTextContent, InputImageContent, InputFileContent]] +): + root: Union[InputTextContent, InputImageContent, InputFileContent] + + +class InputMessageContentList(RootModel[List[InputContent]]): + root: List[InputContent] = Field( + ..., + description='A list of one or many input items to the model, containing different content \ntypes.\n', + title='Input item content list', + ) + + +class KlingCameraControl(BaseModel): + config: Optional[KlingCameraConfig] = None + type: Optional[KlingCameraControlType] = None + + +class KlingDualCharacterEffectInput(BaseModel): + duration: KlingVideoGenDuration + images: KlingDualCharacterImages mode: Optional[KlingVideoGenMode] = 'std' - camera_control: Optional[KlingCameraControl] = None - aspect_ratio: Optional[KlingVideoGenAspectRatio] = '16:9' - duration: Optional[KlingVideoGenDuration] = '5' - callback_url: Optional[AnyUrl] = Field( - None, description='The callback notification address' - ) - external_task_id: Optional[str] = Field(None, description='Customized Task ID') + model_name: Optional[KlingCharacterEffectModelName] = 'kling-v1' class KlingImage2VideoRequest(BaseModel): - model_name: Optional[KlingVideoGenModelName] = 'kling-v2-master' + aspect_ratio: Optional[KlingVideoGenAspectRatio] = '16:9' + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback notification address. Server will notify when the task status changes.', + ) + camera_control: Optional[KlingCameraControl] = None + cfg_scale: Optional[KlingVideoGenCfgScale] = Field( + default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) + ) + duration: Optional[KlingVideoGenDuration] = '5' + dynamic_masks: Optional[List[DynamicMask]] = Field( + None, + description='Dynamic Brush Configuration List (up to 6 groups). For 5-second videos, trajectory length must not exceed 77 coordinates.', + ) + external_task_id: Optional[str] = Field( + None, + description='Customized Task ID. Must be unique within a single user account.', + ) image: Optional[str] = Field( None, description='Reference Image - URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1. Base64 should not include data:image prefix.', @@ -3202,35 +2827,168 @@ class KlingImage2VideoRequest(BaseModel): None, description='Reference Image - End frame control. URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px. Base64 should not include data:image prefix.', ) - prompt: Optional[str] = Field( - None, description='Positive text prompt', max_length=2500 - ) + mode: Optional[KlingVideoGenMode] = 'std' + model_name: Optional[KlingVideoGenModelName] = 'kling-v2-master' negative_prompt: Optional[str] = Field( None, description='Negative text prompt', max_length=2500 ) - cfg_scale: Optional[KlingVideoGenCfgScale] = Field( - default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) + prompt: Optional[str] = Field( + None, description='Positive text prompt', max_length=2500 ) - mode: Optional[KlingVideoGenMode] = 'std' static_mask: Optional[str] = Field( None, description='Static Brush Application Area (Mask image created by users using the motion brush). The aspect ratio must match the input image.', ) - dynamic_masks: Optional[List[DynamicMask]] = Field( + + +class TaskResult(BaseModel): + videos: Optional[List[KlingVideoResult]] = None + + +class Data(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult] = None + task_status: Optional[KlingTaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingImage2VideoResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class TaskResult1(BaseModel): + images: Optional[List[KlingImageResult]] = None + + +class Data1(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_result: Optional[TaskResult1] = None + task_status: Optional[KlingTaskStatus] = None + task_status_msg: Optional[str] = Field(None, description='Task status information') + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingImageGenerationsResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data1] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class KlingLipSyncInputObject(BaseModel): + audio_file: Optional[str] = Field( None, - description='Dynamic Brush Configuration List (up to 6 groups). For 5-second videos, trajectory length must not exceed 77 coordinates.', + description='Local Path of Audio File. Supported formats: .mp3/.wav/.m4a/.aac, maximum file size of 5MB. Base64 code.', ) - camera_control: Optional[KlingCameraControl] = None - aspect_ratio: Optional[KlingVideoGenAspectRatio] = '16:9' - duration: Optional[KlingVideoGenDuration] = '5' + audio_type: Optional[KlingAudioUploadType] = None + audio_url: Optional[str] = Field( + None, + description='Audio File Download URL. Supported formats: .mp3/.wav/.m4a/.aac, maximum file size of 5MB.', + ) + mode: KlingLipSyncMode + text: Optional[str] = Field( + None, + description='Text Content for Lip-Sync Video Generation. Required when mode is text2video. Maximum length is 120 characters.', + ) + video_id: Optional[str] = Field( + None, + description='The ID of the video generated by Kling AI. Only supports 5-second and 10-second videos generated within the last 30 days.', + ) + video_url: Optional[str] = Field( + None, + description='Get link for uploaded video. Video files support .mp4/.mov, file size does not exceed 100MB, video length between 2-10s.', + ) + voice_id: Optional[str] = Field( + None, + description='Voice ID. Required when mode is text2video. The system offers a variety of voice options to choose from.', + ) + voice_language: Optional[KlingLipSyncVoiceLanguage] = 'en' + voice_speed: Optional[float] = Field( + 1, + description='Speech Rate. Valid range: 0.8~2.0, accurate to one decimal place.', + ge=0.8, + le=2.0, + ) + + +class KlingLipSyncRequest(BaseModel): callback_url: Optional[AnyUrl] = Field( None, description='The callback notification address. Server will notify when the task status changes.', ) - external_task_id: Optional[str] = Field( - None, - description='Customized Task ID. Must be unique within a single user account.', + input: KlingLipSyncInputObject + + +class TaskResult2(BaseModel): + videos: Optional[List[KlingVideoResult]] = None + + +class Data2(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult2] = None + task_status: Optional[KlingTaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingLipSyncResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data2] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class KlingSingleImageEffectInput(BaseModel): + duration: KlingSingleImageEffectDuration + image: str = Field( + ..., + description='Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1.', ) + model_name: KlingSingleImageEffectModelName + + +class KlingText2VideoRequest(BaseModel): + aspect_ratio: Optional[KlingVideoGenAspectRatio] = '16:9' + callback_url: Optional[AnyUrl] = Field( + None, description='The callback notification address' + ) + camera_control: Optional[KlingCameraControl] = None + cfg_scale: Optional[KlingVideoGenCfgScale] = Field( + default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) + ) + duration: Optional[KlingVideoGenDuration] = '5' + external_task_id: Optional[str] = Field(None, description='Customized Task ID') + mode: Optional[KlingVideoGenMode] = 'std' + model_name: Optional[KlingTextToVideoModelName] = 'kling-v1' + negative_prompt: Optional[str] = Field( + None, description='Negative text prompt', max_length=2500 + ) + prompt: Optional[str] = Field( + None, description='Positive text prompt', max_length=2500 + ) + + +class Data4(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult2] = None + task_status: Optional[KlingTaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingText2VideoResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data4] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') class KlingVideoEffectsInput( @@ -3239,351 +2997,325 @@ class KlingVideoEffectsInput( root: Union[KlingSingleImageEffectInput, KlingDualCharacterEffectInput] -class StripeBillingDetails(BaseModel): - address: Optional[StripeAddress] = None - email: Optional[str] = None - name: Optional[str] = None - phone: Optional[str] = None - tax_id: Optional[Any] = None - - -class StripePaymentMethodDetails(BaseModel): - card: Optional[StripeCardDetails] = None - type: Optional[str] = None - - -class BFLFluxProFillInputs(BaseModel): - image: str = Field( - ..., - description='A Base64-encoded string representing the image you wish to modify. Can contain alpha mask if desired.', - title='Image', - ) - mask: Optional[str] = Field( +class KlingVideoEffectsRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( None, - description='A Base64-encoded string representing a mask for the areas you want to modify in the image. The mask should be the same dimensions as the image and in black and white. Black areas (0%) indicate no modification, while white areas (100%) specify areas for inpainting. Optional if you provide an alpha mask in the original image. Validation: The endpoint verifies that the dimensions of the mask match the original image.', - title='Mask', + description='The callback notification address for the result of this task.', + ) + effect_scene: Union[KlingDualCharacterEffectsScene, KlingSingleImageEffectsScene] + external_task_id: Optional[str] = Field( + None, + description='Customized Task ID. Must be unique within a single user account.', + ) + input: KlingVideoEffectsInput + + +class Data5(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult2] = None + task_status: Optional[KlingTaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingVideoEffectsResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data5] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class KlingVideoExtendRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback notification address. Server will notify when the task status changes.', + ) + cfg_scale: Optional[KlingVideoGenCfgScale] = Field( + default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) + ) + negative_prompt: Optional[str] = Field( + None, + description='Negative text prompt for elements to avoid in the extended video', + max_length=2500, ) prompt: Optional[str] = Field( - '', - description='The description of the changes you want to make. This text guides the inpainting process, allowing you to specify features, styles, or modifications for the masked area.', - examples=['ein fantastisches bild'], - title='Prompt', - ) - steps: Optional[Steps] = Field( - default_factory=lambda: Steps.model_validate(50), - description='Number of steps for the image generation process', - examples=[50], - title='Steps', - ) - prompt_upsampling: Optional[bool] = Field( - False, - description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation', - title='Prompt Upsampling', - ) - seed: Optional[int] = Field( - None, description='Optional seed for reproducibility', title='Seed' - ) - guidance: Optional[Guidance] = Field( - default_factory=lambda: Guidance.model_validate(60), - description='Guidance strength for the image generation process', - title='Guidance', - ) - output_format: Optional[BFLOutputFormat] = Field( - 'jpeg', - description="Output format for the generated image. Can be 'jpeg' or 'png'.", - ) - safety_tolerance: Optional[int] = Field( - 2, - description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', - examples=[2], - ge=0, - le=6, - title='Safety Tolerance', - ) - webhook_url: Optional[WebhookUrl] = Field( - None, description='URL to receive webhook notifications', title='Webhook Url' - ) - webhook_secret: Optional[str] = Field( None, - description='Optional secret for webhook signature verification', - title='Webhook Secret', + description='Positive text prompt for guiding the video extension', + max_length=2500, ) - - -class BFLHTTPValidationError(BaseModel): - detail: Optional[List[BFLValidationError]] = Field(None, title='Detail') - - -class BFLFluxProExpandInputs(BaseModel): - image: str = Field( - ..., - description='A Base64-encoded string representing the image you wish to expand.', - title='Image', - ) - top: Optional[Top] = Field( - 0, description='Number of pixels to expand at the top of the image', title='Top' - ) - bottom: Optional[Bottom] = Field( - 0, - description='Number of pixels to expand at the bottom of the image', - title='Bottom', - ) - left: Optional[Left] = Field( - 0, - description='Number of pixels to expand on the left side of the image', - title='Left', - ) - right: Optional[Right] = Field( - 0, - description='Number of pixels to expand on the right side of the image', - title='Right', - ) - prompt: Optional[str] = Field( - '', - description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.', - examples=['ein fantastisches bild'], - title='Prompt', - ) - steps: Optional[Steps] = Field( - default_factory=lambda: Steps.model_validate(50), - description='Number of steps for the image generation process', - examples=[50], - title='Steps', - ) - prompt_upsampling: Optional[bool] = Field( - False, - description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation', - title='Prompt Upsampling', - ) - seed: Optional[int] = Field( - None, description='Optional seed for reproducibility', title='Seed' - ) - guidance: Optional[Guidance] = Field( - default_factory=lambda: Guidance.model_validate(60), - description='Guidance strength for the image generation process', - title='Guidance', - ) - output_format: Optional[BFLOutputFormat] = Field( - 'jpeg', - description="Output format for the generated image. Can be 'jpeg' or 'png'.", - ) - safety_tolerance: Optional[int] = Field( - 2, - description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', - examples=[2], - ge=0, - le=6, - title='Safety Tolerance', - ) - webhook_url: Optional[WebhookUrl] = Field( - None, description='URL to receive webhook notifications', title='Webhook Url' - ) - webhook_secret: Optional[str] = Field( + video_id: Optional[str] = Field( None, - description='Optional secret for webhook signature verification', - title='Webhook Secret', + description='The ID of the video to be extended. Supports videos generated by text-to-video, image-to-video, and previous video extension operations. Cannot exceed 3 minutes total duration after extension.', ) -class BFLCannyInputs(BaseModel): - prompt: str = Field( - ..., - description='Text prompt for image generation', - examples=['ein fantastisches bild'], - title='Prompt', - ) - control_image: Optional[str] = Field( - None, - description='Base64 encoded image to use as control input if no preprocessed image is provided', - title='Control Image', - ) - preprocessed_image: Optional[str] = Field( - None, - description='Optional pre-processed image that will bypass the control preprocessing step', - title='Preprocessed Image', - ) - canny_low_threshold: Optional[CannyLowThreshold] = Field( - default_factory=lambda: CannyLowThreshold.model_validate(50), - description='Low threshold for Canny edge detection', - title='Canny Low Threshold', - ) - canny_high_threshold: Optional[CannyHighThreshold] = Field( - default_factory=lambda: CannyHighThreshold.model_validate(200), - description='High threshold for Canny edge detection', - title='Canny High Threshold', - ) - prompt_upsampling: Optional[bool] = Field( - False, - description='Whether to perform upsampling on the prompt', - title='Prompt Upsampling', - ) - seed: Optional[int] = Field( - None, - description='Optional seed for reproducibility', - examples=[42], - title='Seed', - ) - steps: Optional[Steps2] = Field( - default_factory=lambda: Steps2.model_validate(50), - description='Number of steps for the image generation process', - title='Steps', - ) - output_format: Optional[BFLOutputFormat] = Field( - 'jpeg', - description="Output format for the generated image. Can be 'jpeg' or 'png'.", - ) - guidance: Optional[Guidance2] = Field( - default_factory=lambda: Guidance2.model_validate(30), - description='Guidance strength for the image generation process', - title='Guidance', - ) - safety_tolerance: Optional[int] = Field( - 2, - description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', - ge=0, - le=6, - title='Safety Tolerance', - ) - webhook_url: Optional[WebhookUrl] = Field( - None, description='URL to receive webhook notifications', title='Webhook Url' - ) - webhook_secret: Optional[str] = Field( - None, - description='Optional secret for webhook signature verification', - title='Webhook Secret', - ) +class Data6(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult2] = None + task_status: Optional[KlingTaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') -class BFLDepthInputs(BaseModel): - prompt: str = Field( - ..., - description='Text prompt for image generation', - examples=['ein fantastisches bild'], - title='Prompt', - ) - control_image: Optional[str] = Field( - None, - description='Base64 encoded image to use as control input', - title='Control Image', - ) - preprocessed_image: Optional[str] = Field( - None, - description='Optional pre-processed image that will bypass the control preprocessing step', - title='Preprocessed Image', - ) - prompt_upsampling: Optional[bool] = Field( - False, - description='Whether to perform upsampling on the prompt', - title='Prompt Upsampling', - ) - seed: Optional[int] = Field( - None, - description='Optional seed for reproducibility', - examples=[42], - title='Seed', - ) - steps: Optional[Steps2] = Field( - default_factory=lambda: Steps2.model_validate(50), - description='Number of steps for the image generation process', - title='Steps', - ) - output_format: Optional[BFLOutputFormat] = Field( - 'jpeg', - description="Output format for the generated image. Can be 'jpeg' or 'png'.", - ) - guidance: Optional[Guidance2] = Field( - default_factory=lambda: Guidance2.model_validate(15), - description='Guidance strength for the image generation process', - title='Guidance', - ) - safety_tolerance: Optional[int] = Field( - 2, - description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', - ge=0, - le=6, - title='Safety Tolerance', - ) - webhook_url: Optional[WebhookUrl] = Field( - None, description='URL to receive webhook notifications', title='Webhook Url' - ) - webhook_secret: Optional[str] = Field( - None, - description='Optional secret for webhook signature verification', - title='Webhook Secret', - ) - - -class Controls(BaseModel): - artistic_level: Optional[int] = Field( - None, - description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity.', - ge=0, - le=5, - ) - colors: Optional[List[RGBColor]] = Field( - None, description='An array of preferable colors' - ) - background_color: Optional[RGBColor] = Field( - None, description='Use given color as a desired background color' - ) - no_text: Optional[bool] = Field(None, description='Do not embed text layouts') - - -class RecraftImageGenerationRequest(BaseModel): - prompt: str = Field( - ..., description='The text prompt describing the image to generate' - ) - model: str = Field( - ..., description='The model to use for generation (e.g., "recraftv3")' - ) - style: Optional[str] = Field( - None, - description='The style to apply to the generated image (e.g., "digital_illustration")', - ) - style_id: Optional[str] = Field( - None, - description='The style ID to apply to the generated image (e.g., "123e4567-e89b-12d3-a456-426614174000"). If style_id is provided, style should not be provided.', - ) - size: str = Field( - ..., description='The size of the generated image (e.g., "1024x1024")' - ) - controls: Optional[Controls] = Field( - None, description='The controls for the generated image' - ) - n: int = Field(..., description='The number of images to generate', ge=1, le=4) - - -class LumaKeyframes(BaseModel): - frame0: Optional[LumaKeyframe] = None - frame1: Optional[LumaKeyframe] = None +class KlingVideoExtendResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data6] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') class LumaGenerationRequest(BaseModel): - generation_type: Optional[GenerationType] = 'video' - prompt: str = Field(..., description='The prompt of the generation') aspect_ratio: LumaAspectRatio - loop: Optional[bool] = Field(None, description='Whether to loop the video') - keyframes: Optional[LumaKeyframes] = None callback_url: Optional[AnyUrl] = Field( None, description='The callback URL of the generation, a POST request with Generation object will be sent to the callback URL when the generation is dreaming, completed, or failed', ) - model: LumaVideoModel - resolution: LumaVideoModelOutputResolution duration: LumaVideoModelOutputDuration + generation_type: Optional[GenerationType1] = 'video' + keyframes: Optional[LumaKeyframes] = None + loop: Optional[bool] = Field(None, description='Whether to loop the video') + model: LumaVideoModel + prompt: str = Field(..., description='The prompt of the generation') + resolution: LumaVideoModelOutputResolution + + +class CharacterRef(BaseModel): + identity0: Optional[LumaImageIdentity] = None + + +class LumaImageGenerationRequest(BaseModel): + aspect_ratio: Optional[LumaAspectRatio] = '16:9' + callback_url: Optional[AnyUrl] = Field( + None, description='The callback URL for the generation' + ) + character_ref: Optional[CharacterRef] = None + generation_type: Optional[GenerationType2] = 'image' + image_ref: Optional[List[LumaImageRef]] = None + model: Optional[LumaImageModel] = 'photon-1' + modify_image_ref: Optional[LumaModifyImageRef] = None + prompt: Optional[str] = Field(None, description='The prompt of the generation') + style_ref: Optional[List[LumaImageRef]] = None + + +class LumaUpscaleVideoGenerationRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, description='The callback URL for the upscale' + ) + generation_type: Optional[GenerationType3] = 'upscale_video' + resolution: Optional[LumaVideoModelOutputResolution] = None + + +class OutputContent(RootModel[Union[OutputTextContent, OutputAudioContent]]): + root: Union[OutputTextContent, OutputAudioContent] + + +class OutputMessage(BaseModel): + content: List[OutputContent] = Field(..., description='The content of the message') + role: Role4 = Field(..., description='The role of the message') + type: Type14 = Field(..., description='The type of output item') + + +class PikaBodyGenerate22I2vGenerate22I2vPost(BaseModel): + duration: Optional[PikaDurationEnum] = 5 + image: Optional[StrictBytes] = Field(None, title='Image') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: Optional[str] = Field(None, title='Prompttext') + resolution: Optional[PikaResolutionEnum] = '1080p' + seed: Optional[int] = Field(None, title='Seed') + + +class PikaBodyGenerate22KeyframeGenerate22PikaframesPost(BaseModel): + duration: Optional[int] = Field(None, ge=5, le=10, title='Duration') + keyFrames: Optional[List[StrictBytes]] = Field( + None, description='Array of keyframe images', title='Keyframes' + ) + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: str = Field(..., title='Prompttext') + resolution: Optional[PikaResolutionEnum] = '1080p' + seed: Optional[int] = Field(None, title='Seed') + + +class PikaBodyGenerate22T2vGenerate22T2vPost(BaseModel): + aspectRatio: Optional[float] = Field( + 1.7777777777777777, + description='Aspect ratio (width / height)', + ge=0.4, + le=2.5, + title='Aspectratio', + ) + duration: Optional[PikaDurationEnum] = 5 + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: str = Field(..., title='Prompttext') + resolution: Optional[PikaResolutionEnum] = '1080p' + seed: Optional[int] = Field(None, title='Seed') + + +class PikaBodyGeneratePikaffectsGeneratePikaffectsPost(BaseModel): + image: Optional[StrictBytes] = Field(None, title='Image') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + pikaffect: Optional[Pikaffect] = None + promptText: Optional[str] = Field(None, title='Prompttext') + seed: Optional[int] = Field(None, title='Seed') + + +class PikaHTTPValidationError(BaseModel): + detail: Optional[List[PikaValidationError]] = Field(None, title='Detail') + + +class Reasoning(BaseModel): + effort: Optional[ReasoningEffort] = 'medium' + generate_summary: Optional[GenerateSummary] = Field( + None, + description="**Deprecated:** use `summary` instead.\n\nA summary of the reasoning performed by the model. This can be\nuseful for debugging and understanding the model's reasoning process.\nOne of `auto`, `concise`, or `detailed`.\n", + ) + summary: Optional[Summary] = Field( + None, + description="A summary of the reasoning performed by the model. This can be\nuseful for debugging and understanding the model's reasoning process.\nOne of `auto`, `concise`, or `detailed`.\n", + ) + + +class ResponseError(BaseModel): + code: ResponseErrorCode + message: str = Field(..., description='A human-readable description of the error.') + + +class Rodin3DDownloadResponse(BaseModel): + list: Optional[RodinResourceItem] = None + + +class Rodin3DGenerateRequest(BaseModel): + images: str = Field(..., description='The reference images to generate 3D Assets.') + material: Optional[RodinMaterialType] = None + mesh_mode: Optional[RodinMeshModeType] = None + quality: Optional[RodinQualityType] = None + seed: Optional[int] = Field(None, description='Seed.') + tier: Optional[RodinTierType] = None + + +class Rodin3DGenerateResponse(BaseModel): + jobs: Optional[RodinGenerateJobsData] = None + message: Optional[str] = Field(None, description='message') + prompt: Optional[str] = Field(None, description='prompt') + submit_time: Optional[str] = Field(None, description='Time') + uuid: Optional[str] = Field(None, description='Task UUID') + + +class RunwayImageToVideoRequest(BaseModel): + duration: RunwayDurationEnum + model: RunwayModelEnum + promptImage: RunwayPromptImageObject + promptText: Optional[str] = Field( + None, description='Text prompt for the generation', max_length=1000 + ) + ratio: RunwayAspectRatioEnum + seed: int = Field( + ..., description='Random seed for generation', ge=0, le=4294967295 + ) + + +class TextResponseFormatConfiguration( + RootModel[ + Union[ + ResponseFormatText, TextResponseFormatJsonSchema, ResponseFormatJsonObject + ] + ] +): + root: Union[ + ResponseFormatText, TextResponseFormatJsonSchema, ResponseFormatJsonObject + ] = Field( + ..., + description='An object specifying the format that the model must output.\n\nConfiguring `{ "type": "json_schema" }` enables Structured Outputs, \nwhich ensures the model will match your supplied JSON schema. Learn more in the \n[Structured Outputs guide](/docs/guides/structured-outputs).\n\nThe default format is `{ "type": "text" }` with no additional options.\n\n**Not recommended for gpt-4o and newer models:**\n\nSetting to `{ "type": "json_object" }` enables the older JSON mode, which\nensures the message the model generates is valid JSON. Using `json_schema`\nis preferred for models that support it.\n', + ) + + +class Tool( + RootModel[ + Union[ + FileSearchTool, FunctionTool, WebSearchPreviewTool, ComputerUsePreviewTool + ] + ] +): + root: Union[ + FileSearchTool, FunctionTool, WebSearchPreviewTool, ComputerUsePreviewTool + ] = Field(..., discriminator='type') + + +class EasyInputMessage(BaseModel): + content: Union[str, InputMessageContentList] = Field( + ..., + description='Text, image, or audio input to the model, used to generate a response.\nCan also contain previous assistant responses.\n', + ) + role: Role = Field( + ..., + description='The role of the message input. One of `user`, `assistant`, `system`, or\n`developer`.\n', + ) + type: Optional[Type2] = Field( + None, description='The type of the message input. Always `message`.\n' + ) + + +class GeminiContent(BaseModel): + parts: List[GeminiPart] + role: Role1 = Field(..., examples=['user']) + + +class GeminiGenerateContentRequest(BaseModel): + contents: List[GeminiContent] + generationConfig: Optional[GeminiGenerationConfig] = None + safetySettings: Optional[List[GeminiSafetySetting]] = None + systemInstruction: Optional[GeminiSystemInstructionContent] = None + tools: Optional[List[GeminiTool]] = None + videoMetadata: Optional[GeminiVideoMetadata] = None + + +class ImagenGenerateImageRequest(BaseModel): + instances: List[ImagenImageGenerationInstance] + parameters: ImagenImageGenerationParameters + + +class InputMessage(BaseModel): + content: Optional[InputMessageContentList] = None + role: Optional[Role3] = None + status: Optional[Status2] = None + type: Optional[Type9] = None + + +class Item( + RootModel[ + Union[ + InputMessage, + OutputMessage, + FileSearchToolCall, + ComputerToolCall, + WebSearchToolCall, + FunctionToolCall, + ReasoningItem, + ] + ] +): + root: Union[ + InputMessage, + OutputMessage, + FileSearchToolCall, + ComputerToolCall, + WebSearchToolCall, + FunctionToolCall, + ReasoningItem, + ] = Field(..., description='Content item used to generate a response.\n') class LumaGeneration(BaseModel): - id: Optional[UUID] = Field(None, description='The ID of the generation') - generation_type: Optional[LumaGenerationType] = None - state: Optional[LumaState] = None - failure_reason: Optional[str] = Field( - None, description='The reason for the state of the generation' - ) + assets: Optional[LumaAssets] = None created_at: Optional[datetime] = Field( None, description='The date and time when the generation was created' ) - assets: Optional[LumaAssets] = None + failure_reason: Optional[str] = Field( + None, description='The reason for the state of the generation' + ) + generation_type: Optional[LumaGenerationType] = None + id: Optional[UUID] = Field(None, description='The ID of the generation') model: Optional[str] = Field(None, description='The model used for the generation') request: Optional[ Union[ @@ -3593,237 +3325,129 @@ class LumaGeneration(BaseModel): LumaAudioGenerationRequest, ] ] = Field(None, description='The request of the generation') + state: Optional[LumaState] = None -class RunwayImageToVideoRequest(BaseModel): - promptImage: RunwayPromptImageObject - seed: int = Field( - ..., description='Random seed for generation', ge=0, le=4294967295 +class OutputItem( + RootModel[ + Union[ + OutputMessage, + FileSearchToolCall, + FunctionToolCall, + WebSearchToolCall, + ComputerToolCall, + ReasoningItem, + ] + ] +): + root: Union[ + OutputMessage, + FileSearchToolCall, + FunctionToolCall, + WebSearchToolCall, + ComputerToolCall, + ReasoningItem, + ] + + +class Text(BaseModel): + format: Optional[TextResponseFormatConfiguration] = None + + +class ResponseProperties(BaseModel): + instructions: Optional[str] = Field( + None, + description="Inserts a system (or developer) message as the first item in the model's context.\n\nWhen using along with `previous_response_id`, the instructions from a previous\nresponse will not be carried over to the next response. This makes it simple\nto swap out system (or developer) messages in new responses.\n", ) - model: RunwayModelEnum = Field(..., description='Model to use for generation') - promptText: Optional[str] = Field( - None, description='Text prompt for the generation', max_length=1000 + max_output_tokens: Optional[int] = Field( + None, + description='An upper bound for the number of tokens that can be generated for a response, including visible output tokens and [reasoning tokens](/docs/guides/reasoning).\n', ) - duration: RunwayDurationEnum = Field( - ..., description='The number of seconds of duration for the output video.' + model: Optional[OpenAIModels] = None + previous_response_id: Optional[str] = Field( + None, + description='The unique ID of the previous response to the model. Use this to\ncreate multi-turn conversations. Learn more about \n[conversation state](/docs/guides/conversation-state).\n', ) - ratio: RunwayAspectRatioEnum = Field( + reasoning: Optional[Reasoning] = None + text: Optional[Text] = None + tool_choice: Optional[ + Union[ToolChoiceOptions, ToolChoiceTypes, ToolChoiceFunction] + ] = Field( + None, + description='How the model should select which tool (or tools) to use when generating\na response. See the `tools` parameter to see how to specify which tools\nthe model can call.\n', + ) + tools: Optional[List[Tool]] = None + truncation: Optional[Truncation1] = Field( + 'disabled', + description="The truncation strategy to use for the model response.\n- `auto`: If the context of this response and previous ones exceeds\n the model's context window size, the model will truncate the \n response to fit the context window by dropping input items in the\n middle of the conversation. \n- `disabled` (default): If a model response will exceed the context window \n size for a model, the request will fail with a 400 error.\n", + ) + + +class GeminiCandidate(BaseModel): + citationMetadata: Optional[GeminiCitationMetadata] = None + content: Optional[GeminiContent] = None + finishReason: Optional[str] = None + safetyRatings: Optional[List[GeminiSafetyRating]] = None + + +class GeminiGenerateContentResponse(BaseModel): + candidates: Optional[List[GeminiCandidate]] = None + promptFeedback: Optional[GeminiPromptFeedback] = None + + +class InputItem(RootModel[Union[EasyInputMessage, Item]]): + root: Union[EasyInputMessage, Item] + + +class OpenAICreateResponse(CreateModelResponseProperties, ResponseProperties): + include: Optional[List[Includable]] = Field( + None, + description='Specify additional output data to include in the model response. Currently\nsupported values are:\n- `file_search_call.results`: Include the search results of\n the file search tool call.\n- `message.input_image.image_url`: Include image urls from the input message.\n- `computer_call_output.output.image_url`: Include image urls from the computer call output.\n', + ) + input: Union[str, List[InputItem]] = Field( ..., - description='The resolution (aspect ratio) of the output video. Allowable values depend on the selected model. 1280:768 and 768:1280 are only supported for gen3a_turbo.', + description='Text, image, or file inputs to the model, used to generate a response.\n\nLearn more:\n- [Text inputs and outputs](/docs/guides/text)\n- [Image inputs](/docs/guides/images)\n- [File inputs](/docs/guides/pdf-files)\n- [Conversation state](/docs/guides/conversation-state)\n- [Function calling](/docs/guides/function-calling)\n', ) - - -class RunwayTaskStatusResponse(BaseModel): - id: Optional[str] = Field(None, description='Task ID') - status: Optional[RunwayTaskStatusEnum] = Field(None, description='Task status') - createdAt: Optional[datetime] = Field(None, description='Task creation timestamp') - output: Optional[List[str]] = Field(None, description='Array of output video URLs') - - -class PikaHTTPValidationError(BaseModel): - detail: Optional[List[PikaValidationError]] = Field(None, title='Detail') - - -class PikaBodyGenerate22T2vGenerate22T2vPost(BaseModel): - promptText: str = Field(..., title='Prompttext') - negativePrompt: Optional[str] = Field(None, title='Negativeprompt') - seed: Optional[int] = Field(None, title='Seed') - resolution: Optional[PikaResolutionEnum] = Field('1080p', title='Resolution') - duration: Optional[PikaDurationEnum] = Field(5, title='Duration') - aspectRatio: Optional[float] = Field( - 1.7777777777777777, - description='Aspect ratio (width / height)', - ge=0.4, - le=2.5, - title='Aspectratio', + parallel_tool_calls: Optional[bool] = Field( + True, description='Whether to allow the model to run tool calls in parallel.\n' ) - - -class PikaBodyGenerate22I2vGenerate22I2vPost(BaseModel): - image: Optional[StrictBytes] = Field(None, title='Image') - promptText: Optional[str] = Field(None, title='Prompttext') - negativePrompt: Optional[str] = Field(None, title='Negativeprompt') - seed: Optional[int] = Field(None, title='Seed') - resolution: Optional[PikaResolutionEnum] = Field('1080p', title='Resolution') - duration: Optional[PikaDurationEnum] = Field(5, title='Duration') - - -class PikaBodyGenerate22KeyframeGenerate22PikaframesPost(BaseModel): - keyFrames: Optional[List[StrictBytes]] = Field( - None, description='Array of keyframe images', title='Keyframes' + store: Optional[bool] = Field( + True, + description='Whether to store the generated model response for later retrieval via\nAPI.\n', ) - promptText: str = Field(..., title='Prompttext') - negativePrompt: Optional[str] = Field(None, title='Negativeprompt') - seed: Optional[int] = Field(None, title='Seed') - resolution: Optional[PikaResolutionEnum] = Field('1080p', title='Resolution') - duration: Optional[int] = Field(None, ge=5, le=10, title='Duration') + stream: Optional[bool] = Field( + False, + description='If set to true, the model response data will be streamed to the client\nas it is generated using [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format).\nSee the [Streaming section below](/docs/api-reference/responses-streaming)\nfor more information.\n', + ) + usage: Optional[ResponseUsage] = None -class PikaVideoResponse(BaseModel): - id: str = Field(..., title='Id') - status: PikaStatusEnum = Field( - ..., description='The status of the video', title='Status' - ) - url: Optional[str] = Field(None, title='Url') - progress: Optional[int] = Field(None, title='Progress') - - -class Node(BaseModel): - id: Optional[str] = Field(None, description='The unique identifier of the node.') - name: Optional[str] = Field(None, description='The display name of the node.') - category: Optional[str] = Field(None, description='The category of the node.') - description: Optional[str] = None - author: Optional[str] = None - license: Optional[str] = Field( - None, description="The path to the LICENSE file in the node's repository." - ) - icon: Optional[str] = Field(None, description="URL to the node's icon.") - repository: Optional[str] = Field(None, description="URL to the node's repository.") - tags: Optional[List[str]] = None - latest_version: Optional[NodeVersion] = Field( - None, description='The latest version of the node.' - ) - rating: Optional[float] = Field(None, description='The average rating of the node.') - downloads: Optional[int] = Field( - None, description='The number of downloads of the node.' - ) - publisher: Optional[Publisher] = Field( - None, description='The publisher of the node.' - ) - status: Optional[NodeStatus] = Field(None, description='The status of the node.') - status_detail: Optional[str] = Field( - None, description='The status detail of the node.' - ) - translations: Optional[Dict[str, Dict[str, Any]]] = None - - -class KlingVideoEffectsRequest(BaseModel): - effect_scene: Union[KlingDualCharacterEffectsScene, KlingSingleImageEffectsScene] - input: KlingVideoEffectsInput - callback_url: Optional[AnyUrl] = Field( +class OpenAIResponse(ModelResponseProperties, ResponseProperties): + created_at: Optional[float] = Field( None, - description='The callback notification address for the result of this task.', + description='Unix timestamp (in seconds) of when this Response was created.', ) - external_task_id: Optional[str] = Field( + error: Optional[ResponseError] = None + id: Optional[str] = Field(None, description='Unique identifier for this Response.') + incomplete_details: Optional[IncompleteDetails] = Field( + None, description='Details about why the response is incomplete.\n' + ) + object: Optional[Object] = Field( + None, description='The object type of this resource - always set to `response`.' + ) + output: Optional[List[OutputItem]] = Field( None, - description='Customized Task ID. Must be unique within a single user account.', + description="An array of content items generated by the model.\n\n- The length and order of items in the `output` array is dependent\n on the model's response.\n- Rather than accessing the first item in the `output` array and \n assuming it's an `assistant` message with the content generated by\n the model, you might consider using the `output_text` property where\n supported in SDKs.\n", ) - - -class StripeCharge(BaseModel): - id: Optional[str] = None - object: Optional[Object2] = None - amount: Optional[int] = None - amount_captured: Optional[int] = None - amount_refunded: Optional[int] = None - application: Optional[str] = None - application_fee: Optional[str] = None - application_fee_amount: Optional[int] = None - balance_transaction: Optional[str] = None - billing_details: Optional[StripeBillingDetails] = None - calculated_statement_descriptor: Optional[str] = None - captured: Optional[bool] = None - created: Optional[int] = None - currency: Optional[str] = None - customer: Optional[str] = None - description: Optional[str] = None - destination: Optional[Any] = None - dispute: Optional[Any] = None - disputed: Optional[bool] = None - failure_balance_transaction: Optional[Any] = None - failure_code: Optional[Any] = None - failure_message: Optional[Any] = None - fraud_details: Optional[Dict[str, Any]] = None - invoice: Optional[Any] = None - livemode: Optional[bool] = None - metadata: Optional[Dict[str, Any]] = None - on_behalf_of: Optional[Any] = None - order: Optional[Any] = None - outcome: Optional[StripeOutcome] = None - paid: Optional[bool] = None - payment_intent: Optional[str] = None - payment_method: Optional[str] = None - payment_method_details: Optional[StripePaymentMethodDetails] = None - radar_options: Optional[Dict[str, Any]] = None - receipt_email: Optional[str] = None - receipt_number: Optional[str] = None - receipt_url: Optional[str] = None - refunded: Optional[bool] = None - refunds: Optional[StripeRefundList] = None - review: Optional[Any] = None - shipping: Optional[StripeShipping] = None - source: Optional[Any] = None - source_transfer: Optional[Any] = None - statement_descriptor: Optional[Any] = None - statement_descriptor_suffix: Optional[Any] = None - status: Optional[str] = None - transfer_data: Optional[Any] = None - transfer_group: Optional[Any] = None - - -class StripeChargeList(BaseModel): - object: Optional[str] = None - data: Optional[List[StripeCharge]] = None - has_more: Optional[bool] = None - total_count: Optional[int] = None - url: Optional[str] = None - - -class StripePaymentIntent(BaseModel): - id: Optional[str] = None - object: Optional[Object1] = None - amount: Optional[int] = None - amount_capturable: Optional[int] = None - amount_details: Optional[StripeAmountDetails] = None - amount_received: Optional[int] = None - application: Optional[str] = None - application_fee_amount: Optional[int] = None - automatic_payment_methods: Optional[Any] = None - canceled_at: Optional[int] = None - cancellation_reason: Optional[str] = None - capture_method: Optional[str] = None - charges: Optional[StripeChargeList] = None - client_secret: Optional[str] = None - confirmation_method: Optional[str] = None - created: Optional[int] = None - currency: Optional[str] = None - customer: Optional[str] = None - description: Optional[str] = None - invoice: Optional[str] = None - last_payment_error: Optional[Any] = None - latest_charge: Optional[str] = None - livemode: Optional[bool] = None - metadata: Optional[Dict[str, Any]] = None - next_action: Optional[Any] = None - on_behalf_of: Optional[Any] = None - payment_method: Optional[str] = None - payment_method_configuration_details: Optional[Any] = None - payment_method_options: Optional[StripePaymentMethodOptions] = None - payment_method_types: Optional[List[str]] = None - processing: Optional[Any] = None - receipt_email: Optional[str] = None - review: Optional[Any] = None - setup_future_usage: Optional[Any] = None - shipping: Optional[StripeShipping] = None - source: Optional[Any] = None - statement_descriptor: Optional[Any] = None - statement_descriptor_suffix: Optional[Any] = None - status: Optional[str] = None - transfer_data: Optional[Any] = None - transfer_group: Optional[Any] = None - - -class Data8(BaseModel): - object: Optional[StripePaymentIntent] = None - - -class StripeEvent(BaseModel): - id: str - object: Object - api_version: Optional[str] = None - created: Optional[int] = None - data: Data8 - livemode: Optional[bool] = None - pending_webhooks: Optional[int] = None - request: Optional[StripeRequestInfo] = None - type: Type + output_text: Optional[str] = Field( + None, + description='SDK-only convenience property that contains the aggregated text output \nfrom all `output_text` items in the `output` array, if any are present. \nSupported in the Python and JavaScript SDKs.\n', + ) + parallel_tool_calls: Optional[bool] = Field( + True, description='Whether to allow the model to run tool calls in parallel.\n' + ) + status: Optional[Status6] = Field( + None, + description='The status of the response generation. One of `completed`, `failed`, `in_progress`, or `incomplete`.', + ) + usage: Optional[ResponseUsage] = None diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py index 62866216f..0897d5d78 100644 --- a/comfy_api_nodes/apis/client.py +++ b/comfy_api_nodes/apis/client.py @@ -139,7 +139,7 @@ class EmptyRequest(BaseModel): class UploadRequest(BaseModel): file_name: str = Field(..., description="Filename to upload") - content_type: str | None = Field( + content_type: Optional[str] = Field( None, description="Mime type of the file. For example: image/png, image/jpeg, video/mp4, etc.", ) diff --git a/comfy_api_nodes/apis/rodin_api.py b/comfy_api_nodes/apis/rodin_api.py new file mode 100644 index 000000000..b0cf171fa --- /dev/null +++ b/comfy_api_nodes/apis/rodin_api.py @@ -0,0 +1,57 @@ +from __future__ import annotations + +from enum import Enum +from typing import Optional, List +from pydantic import BaseModel, Field + + +class Rodin3DGenerateRequest(BaseModel): + seed: int = Field(..., description="seed_") + tier: str = Field(..., description="Tier of generation.") + material: str = Field(..., description="The material type.") + quality: str = Field(..., description="The generation quality of the mesh.") + mesh_mode: str = Field(..., description="It controls the type of faces of generated models.") + +class GenerateJobsData(BaseModel): + uuids: List[str] = Field(..., description="str LIST") + subscription_key: str = Field(..., description="subscription key") + +class Rodin3DGenerateResponse(BaseModel): + message: Optional[str] = Field(None, description="Return message.") + prompt: Optional[str] = Field(None, description="Generated Prompt from image.") + submit_time: Optional[str] = Field(None, description="Submit Time") + uuid: Optional[str] = Field(None, description="Task str") + jobs: Optional[GenerateJobsData] = Field(None, description="Details of jobs") + +class JobStatus(str, Enum): + """ + Status for jobs + """ + Done = "Done" + Failed = "Failed" + Generating = "Generating" + Waiting = "Waiting" + +class Rodin3DCheckStatusRequest(BaseModel): + subscription_key: str = Field(..., description="subscription from generate endpoint") + +class JobItem(BaseModel): + uuid: str = Field(..., description="uuid") + status: JobStatus = Field(...,description="Status Currently") + +class Rodin3DCheckStatusResponse(BaseModel): + jobs: List[JobItem] = Field(..., description="Job status List") + +class Rodin3DDownloadRequest(BaseModel): + task_uuid: str = Field(..., description="Task str") + +class RodinResourceItem(BaseModel): + url: str = Field(..., description="Download Url") + name: str = Field(..., description="File name with ext") + +class Rodin3DDownloadResponse(BaseModel): + list: List[RodinResourceItem] = Field(..., description="Source List") + + + + diff --git a/comfy_api_nodes/apis/tripo_api.py b/comfy_api_nodes/apis/tripo_api.py new file mode 100644 index 000000000..626e8d277 --- /dev/null +++ b/comfy_api_nodes/apis/tripo_api.py @@ -0,0 +1,275 @@ +from __future__ import annotations +from comfy_api_nodes.apis import ( + TripoModelVersion, + TripoTextureQuality, +) +from enum import Enum +from typing import Optional, List, Dict, Any, Union + +from pydantic import BaseModel, Field, RootModel + +class TripoStyle(str, Enum): + PERSON_TO_CARTOON = "person:person2cartoon" + ANIMAL_VENOM = "animal:venom" + OBJECT_CLAY = "object:clay" + OBJECT_STEAMPUNK = "object:steampunk" + OBJECT_CHRISTMAS = "object:christmas" + OBJECT_BARBIE = "object:barbie" + GOLD = "gold" + ANCIENT_BRONZE = "ancient_bronze" + NONE = "None" + +class TripoTaskType(str, Enum): + TEXT_TO_MODEL = "text_to_model" + IMAGE_TO_MODEL = "image_to_model" + MULTIVIEW_TO_MODEL = "multiview_to_model" + TEXTURE_MODEL = "texture_model" + REFINE_MODEL = "refine_model" + ANIMATE_PRERIGCHECK = "animate_prerigcheck" + ANIMATE_RIG = "animate_rig" + ANIMATE_RETARGET = "animate_retarget" + STYLIZE_MODEL = "stylize_model" + CONVERT_MODEL = "convert_model" + +class TripoTextureAlignment(str, Enum): + ORIGINAL_IMAGE = "original_image" + GEOMETRY = "geometry" + +class TripoOrientation(str, Enum): + ALIGN_IMAGE = "align_image" + DEFAULT = "default" + +class TripoOutFormat(str, Enum): + GLB = "glb" + FBX = "fbx" + +class TripoTopology(str, Enum): + BIP = "bip" + QUAD = "quad" + +class TripoSpec(str, Enum): + MIXAMO = "mixamo" + TRIPO = "tripo" + +class TripoAnimation(str, Enum): + IDLE = "preset:idle" + WALK = "preset:walk" + CLIMB = "preset:climb" + JUMP = "preset:jump" + RUN = "preset:run" + SLASH = "preset:slash" + SHOOT = "preset:shoot" + HURT = "preset:hurt" + FALL = "preset:fall" + TURN = "preset:turn" + +class TripoStylizeStyle(str, Enum): + LEGO = "lego" + VOXEL = "voxel" + VORONOI = "voronoi" + MINECRAFT = "minecraft" + +class TripoConvertFormat(str, Enum): + GLTF = "GLTF" + USDZ = "USDZ" + FBX = "FBX" + OBJ = "OBJ" + STL = "STL" + _3MF = "3MF" + +class TripoTextureFormat(str, Enum): + BMP = "BMP" + DPX = "DPX" + HDR = "HDR" + JPEG = "JPEG" + OPEN_EXR = "OPEN_EXR" + PNG = "PNG" + TARGA = "TARGA" + TIFF = "TIFF" + WEBP = "WEBP" + +class TripoTaskStatus(str, Enum): + QUEUED = "queued" + RUNNING = "running" + SUCCESS = "success" + FAILED = "failed" + CANCELLED = "cancelled" + UNKNOWN = "unknown" + BANNED = "banned" + EXPIRED = "expired" + +class TripoFileTokenReference(BaseModel): + type: Optional[str] = Field(None, description='The type of the reference') + file_token: str + +class TripoUrlReference(BaseModel): + type: Optional[str] = Field(None, description='The type of the reference') + url: str + +class TripoObjectStorage(BaseModel): + bucket: str + key: str + +class TripoObjectReference(BaseModel): + type: str + object: TripoObjectStorage + +class TripoFileEmptyReference(BaseModel): + pass + +class TripoFileReference(RootModel): + root: Union[TripoFileTokenReference, TripoUrlReference, TripoObjectReference, TripoFileEmptyReference] + +class TripoGetStsTokenRequest(BaseModel): + format: str = Field(..., description='The format of the image') + +class TripoTextToModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.TEXT_TO_MODEL, description='Type of task') + prompt: str = Field(..., description='The text prompt describing the model to generate', max_length=1024) + negative_prompt: Optional[str] = Field(None, description='The negative text prompt', max_length=1024) + model_version: Optional[TripoModelVersion] = TripoModelVersion.V2_5 + face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to') + texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model') + pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model') + image_seed: Optional[int] = Field(None, description='The seed for the text') + model_seed: Optional[int] = Field(None, description='The seed for the model') + texture_seed: Optional[int] = Field(None, description='The seed for the texture') + texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard + style: Optional[TripoStyle] = None + auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model') + quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model') + +class TripoImageToModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.IMAGE_TO_MODEL, description='Type of task') + file: TripoFileReference = Field(..., description='The file reference to convert to a model') + model_version: Optional[TripoModelVersion] = Field(None, description='The model version to use for generation') + face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to') + texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model') + pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model') + model_seed: Optional[int] = Field(None, description='The seed for the model') + texture_seed: Optional[int] = Field(None, description='The seed for the texture') + texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard + texture_alignment: Optional[TripoTextureAlignment] = Field(TripoTextureAlignment.ORIGINAL_IMAGE, description='The texture alignment method') + style: Optional[TripoStyle] = Field(None, description='The style to apply to the generated model') + auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model') + orientation: Optional[TripoOrientation] = TripoOrientation.DEFAULT + quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model') + +class TripoMultiviewToModelRequest(BaseModel): + type: TripoTaskType = TripoTaskType.MULTIVIEW_TO_MODEL + files: List[TripoFileReference] = Field(..., description='The file references to convert to a model') + model_version: Optional[TripoModelVersion] = Field(None, description='The model version to use for generation') + orthographic_projection: Optional[bool] = Field(False, description='Whether to use orthographic projection') + face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to') + texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model') + pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model') + model_seed: Optional[int] = Field(None, description='The seed for the model') + texture_seed: Optional[int] = Field(None, description='The seed for the texture') + texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard + texture_alignment: Optional[TripoTextureAlignment] = TripoTextureAlignment.ORIGINAL_IMAGE + auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model') + orientation: Optional[TripoOrientation] = Field(TripoOrientation.DEFAULT, description='The orientation for the model') + quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model') + +class TripoTextureModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.TEXTURE_MODEL, description='Type of task') + original_model_task_id: str = Field(..., description='The task ID of the original model') + texture: Optional[bool] = Field(True, description='Whether to apply texture to the model') + pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the model') + model_seed: Optional[int] = Field(None, description='The seed for the model') + texture_seed: Optional[int] = Field(None, description='The seed for the texture') + texture_quality: Optional[TripoTextureQuality] = Field(None, description='The quality of the texture') + texture_alignment: Optional[TripoTextureAlignment] = Field(TripoTextureAlignment.ORIGINAL_IMAGE, description='The texture alignment method') + +class TripoRefineModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.REFINE_MODEL, description='Type of task') + draft_model_task_id: str = Field(..., description='The task ID of the draft model') + +class TripoAnimatePrerigcheckRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.ANIMATE_PRERIGCHECK, description='Type of task') + original_model_task_id: str = Field(..., description='The task ID of the original model') + +class TripoAnimateRigRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.ANIMATE_RIG, description='Type of task') + original_model_task_id: str = Field(..., description='The task ID of the original model') + out_format: Optional[TripoOutFormat] = Field(TripoOutFormat.GLB, description='The output format') + spec: Optional[TripoSpec] = Field(TripoSpec.TRIPO, description='The specification for rigging') + +class TripoAnimateRetargetRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.ANIMATE_RETARGET, description='Type of task') + original_model_task_id: str = Field(..., description='The task ID of the original model') + animation: TripoAnimation = Field(..., description='The animation to apply') + out_format: Optional[TripoOutFormat] = Field(TripoOutFormat.GLB, description='The output format') + bake_animation: Optional[bool] = Field(True, description='Whether to bake the animation') + +class TripoStylizeModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.STYLIZE_MODEL, description='Type of task') + style: TripoStylizeStyle = Field(..., description='The style to apply to the model') + original_model_task_id: str = Field(..., description='The task ID of the original model') + block_size: Optional[int] = Field(80, description='The block size for stylization') + +class TripoConvertModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.CONVERT_MODEL, description='Type of task') + format: TripoConvertFormat = Field(..., description='The format to convert to') + original_model_task_id: str = Field(..., description='The task ID of the original model') + quad: Optional[bool] = Field(False, description='Whether to apply quad to the model') + force_symmetry: Optional[bool] = Field(False, description='Whether to force symmetry') + face_limit: Optional[int] = Field(10000, description='The number of faces to limit the conversion to') + flatten_bottom: Optional[bool] = Field(False, description='Whether to flatten the bottom of the model') + flatten_bottom_threshold: Optional[float] = Field(0.01, description='The threshold for flattening the bottom') + texture_size: Optional[int] = Field(4096, description='The size of the texture') + texture_format: Optional[TripoTextureFormat] = Field(TripoTextureFormat.JPEG, description='The format of the texture') + pivot_to_center_bottom: Optional[bool] = Field(False, description='Whether to pivot to the center bottom') + +class TripoTaskRequest(RootModel): + root: Union[ + TripoTextToModelRequest, + TripoImageToModelRequest, + TripoMultiviewToModelRequest, + TripoTextureModelRequest, + TripoRefineModelRequest, + TripoAnimatePrerigcheckRequest, + TripoAnimateRigRequest, + TripoAnimateRetargetRequest, + TripoStylizeModelRequest, + TripoConvertModelRequest + ] + +class TripoTaskOutput(BaseModel): + model: Optional[str] = Field(None, description='URL to the model') + base_model: Optional[str] = Field(None, description='URL to the base model') + pbr_model: Optional[str] = Field(None, description='URL to the PBR model') + rendered_image: Optional[str] = Field(None, description='URL to the rendered image') + riggable: Optional[bool] = Field(None, description='Whether the model is riggable') + +class TripoTask(BaseModel): + task_id: str = Field(..., description='The task ID') + type: Optional[str] = Field(None, description='The type of task') + status: Optional[TripoTaskStatus] = Field(None, description='The status of the task') + input: Optional[Dict[str, Any]] = Field(None, description='The input parameters for the task') + output: Optional[TripoTaskOutput] = Field(None, description='The output of the task') + progress: Optional[int] = Field(None, description='The progress of the task', ge=0, le=100) + create_time: Optional[int] = Field(None, description='The creation time of the task') + running_left_time: Optional[int] = Field(None, description='The estimated time left for the task') + queue_position: Optional[int] = Field(None, description='The position in the queue') + +class TripoTaskResponse(BaseModel): + code: int = Field(0, description='The response code') + data: TripoTask = Field(..., description='The task data') + +class TripoGeneralResponse(BaseModel): + code: int = Field(0, description='The response code') + data: Dict[str, str] = Field(..., description='The task ID data') + +class TripoBalanceData(BaseModel): + balance: float = Field(..., description='The account balance') + frozen: float = Field(..., description='The frozen balance') + +class TripoBalanceResponse(BaseModel): + code: int = Field(0, description='The response code') + data: TripoBalanceData = Field(..., description='The balance data') + +class TripoErrorResponse(BaseModel): + code: int = Field(..., description='The error code') + message: str = Field(..., description='The error message') + suggestion: str = Field(..., description='The suggestion for fixing the error') diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py new file mode 100644 index 000000000..ae7b04846 --- /dev/null +++ b/comfy_api_nodes/nodes_gemini.py @@ -0,0 +1,446 @@ +""" +API Nodes for Gemini Multimodal LLM Usage via Remote API +See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference +""" + +import os +from enum import Enum +from typing import Optional, Literal + +import torch + +import folder_paths +from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict +from server import PromptServer +from comfy_api_nodes.apis import ( + GeminiContent, + GeminiGenerateContentRequest, + GeminiGenerateContentResponse, + GeminiInlineData, + GeminiPart, + GeminiMimeType, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, +) +from comfy_api_nodes.apinode_utils import ( + validate_string, + audio_to_base64_string, + video_to_base64_string, + tensor_to_base64_string, +) + + +GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini" +GEMINI_MAX_INPUT_FILE_SIZE = 20 * 1024 * 1024 # 20 MB + + +class GeminiModel(str, Enum): + """ + Gemini Model Names allowed by comfy-api + """ + + gemini_2_5_pro_preview_05_06 = "gemini-2.5-pro-preview-05-06" + gemini_2_5_flash_preview_04_17 = "gemini-2.5-flash-preview-04-17" + + +def get_gemini_endpoint( + model: GeminiModel, +) -> ApiEndpoint[GeminiGenerateContentRequest, GeminiGenerateContentResponse]: + """ + Get the API endpoint for a given Gemini model. + + Args: + model: The Gemini model to use, either as enum or string value. + + Returns: + ApiEndpoint configured for the specific Gemini model. + """ + if isinstance(model, str): + model = GeminiModel(model) + return ApiEndpoint( + path=f"{GEMINI_BASE_ENDPOINT}/{model.value}", + method=HttpMethod.POST, + request_model=GeminiGenerateContentRequest, + response_model=GeminiGenerateContentResponse, + ) + + +class GeminiNode(ComfyNodeABC): + """ + Node to generate text responses from a Gemini model. + + This node allows users to interact with Google's Gemini AI models, providing + multimodal inputs (text, images, audio, video, files) to generate coherent + text responses. The node works with the latest Gemini models, handling the + API communication and response parsing. + """ + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text inputs to the model, used to generate a response. You can include detailed instructions, questions, or context for the model.", + }, + ), + "model": ( + IO.COMBO, + { + "tooltip": "The Gemini model to use for generating responses.", + "options": [model.value for model in GeminiModel], + "default": GeminiModel.gemini_2_5_pro_preview_05_06.value, + }, + ), + "seed": ( + IO.INT, + { + "default": 42, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "When seed is fixed to a specific value, the model makes a best effort to provide the same response for repeated requests. Deterministic output isn't guaranteed. Also, changing the model or parameter settings, such as the temperature, can cause variations in the response even when you use the same seed value. By default, a random seed value is used.", + }, + ), + }, + "optional": { + "images": ( + IO.IMAGE, + { + "default": None, + "tooltip": "Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.", + }, + ), + "audio": ( + IO.AUDIO, + { + "tooltip": "Optional audio to use as context for the model.", + "default": None, + }, + ), + "video": ( + IO.VIDEO, + { + "tooltip": "Optional video to use as context for the model.", + "default": None, + }, + ), + "files": ( + "GEMINI_INPUT_FILES", + { + "default": None, + "tooltip": "Optional file(s) to use as context for the model. Accepts inputs from the Gemini Generate Content Input Files node.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Generate text responses with Google's Gemini AI model. You can provide multiple types of inputs (text, images, audio, video) as context for generating more relevant and meaningful responses." + RETURN_TYPES = ("STRING",) + FUNCTION = "api_call" + CATEGORY = "api node/text/Gemini" + API_NODE = True + + def get_parts_from_response( + self, response: GeminiGenerateContentResponse + ) -> list[GeminiPart]: + """ + Extract all parts from the Gemini API response. + + Args: + response: The API response from Gemini. + + Returns: + List of response parts from the first candidate. + """ + return response.candidates[0].content.parts + + def get_parts_by_type( + self, response: GeminiGenerateContentResponse, part_type: Literal["text"] | str + ) -> list[GeminiPart]: + """ + Filter response parts by their type. + + Args: + response: The API response from Gemini. + part_type: Type of parts to extract ("text" or a MIME type). + + Returns: + List of response parts matching the requested type. + """ + parts = [] + for part in self.get_parts_from_response(response): + if part_type == "text" and hasattr(part, "text") and part.text: + parts.append(part) + elif ( + hasattr(part, "inlineData") + and part.inlineData + and part.inlineData.mimeType == part_type + ): + parts.append(part) + # Skip parts that don't match the requested type + return parts + + def get_text_from_response(self, response: GeminiGenerateContentResponse) -> str: + """ + Extract and concatenate all text parts from the response. + + Args: + response: The API response from Gemini. + + Returns: + Combined text from all text parts in the response. + """ + parts = self.get_parts_by_type(response, "text") + return "\n".join([part.text for part in parts]) + + def create_video_parts(self, video_input: IO.VIDEO, **kwargs) -> list[GeminiPart]: + """ + Convert video input to Gemini API compatible parts. + + Args: + video_input: Video tensor from ComfyUI. + **kwargs: Additional arguments to pass to the conversion function. + + Returns: + List of GeminiPart objects containing the encoded video. + """ + from comfy_api.util import VideoContainer, VideoCodec + base_64_string = video_to_base64_string( + video_input, + container_format=VideoContainer.MP4, + codec=VideoCodec.H264 + ) + return [ + GeminiPart( + inlineData=GeminiInlineData( + mimeType=GeminiMimeType.video_mp4, + data=base_64_string, + ) + ) + ] + + def create_audio_parts(self, audio_input: IO.AUDIO) -> list[GeminiPart]: + """ + Convert audio input to Gemini API compatible parts. + + Args: + audio_input: Audio input from ComfyUI, containing waveform tensor and sample rate. + + Returns: + List of GeminiPart objects containing the encoded audio. + """ + audio_parts: list[GeminiPart] = [] + for batch_index in range(audio_input["waveform"].shape[0]): + # Recreate an IO.AUDIO object for the given batch dimension index + audio_at_index = { + "waveform": audio_input["waveform"][batch_index].unsqueeze(0), + "sample_rate": audio_input["sample_rate"], + } + # Convert to MP3 format for compatibility with Gemini API + audio_bytes = audio_to_base64_string( + audio_at_index, + container_format="mp3", + codec_name="libmp3lame", + ) + audio_parts.append( + GeminiPart( + inlineData=GeminiInlineData( + mimeType=GeminiMimeType.audio_mp3, + data=audio_bytes, + ) + ) + ) + return audio_parts + + def create_image_parts(self, image_input: torch.Tensor) -> list[GeminiPart]: + """ + Convert image tensor input to Gemini API compatible parts. + + Args: + image_input: Batch of image tensors from ComfyUI. + + Returns: + List of GeminiPart objects containing the encoded images. + """ + image_parts: list[GeminiPart] = [] + for image_index in range(image_input.shape[0]): + image_as_b64 = tensor_to_base64_string( + image_input[image_index].unsqueeze(0) + ) + image_parts.append( + GeminiPart( + inlineData=GeminiInlineData( + mimeType=GeminiMimeType.image_png, + data=image_as_b64, + ) + ) + ) + return image_parts + + def create_text_part(self, text: str) -> GeminiPart: + """ + Create a text part for the Gemini API request. + + Args: + text: The text content to include in the request. + + Returns: + A GeminiPart object with the text content. + """ + return GeminiPart(text=text) + + def api_call( + self, + prompt: str, + model: GeminiModel, + images: Optional[IO.IMAGE] = None, + audio: Optional[IO.AUDIO] = None, + video: Optional[IO.VIDEO] = None, + files: Optional[list[GeminiPart]] = None, + unique_id: Optional[str] = None, + **kwargs, + ) -> tuple[str]: + # Validate inputs + validate_string(prompt, strip_whitespace=False) + + # Create parts list with text prompt as the first part + parts: list[GeminiPart] = [self.create_text_part(prompt)] + + # Add other modal parts + if images is not None: + image_parts = self.create_image_parts(images) + parts.extend(image_parts) + if audio is not None: + parts.extend(self.create_audio_parts(audio)) + if video is not None: + parts.extend(self.create_video_parts(video)) + if files is not None: + parts.extend(files) + + # Create response + response = SynchronousOperation( + endpoint=get_gemini_endpoint(model), + request=GeminiGenerateContentRequest( + contents=[ + GeminiContent( + role="user", + parts=parts, + ) + ] + ), + auth_kwargs=kwargs, + ).execute() + + # Get result output + output_text = self.get_text_from_response(response) + if unique_id and output_text: + PromptServer.instance.send_progress_text(output_text, node_id=unique_id) + + return (output_text or "Empty response from Gemini model...",) + + +class GeminiInputFiles(ComfyNodeABC): + """ + Loads and formats input files for use with the Gemini API. + + This node allows users to include text (.txt) and PDF (.pdf) files as input + context for the Gemini model. Files are converted to the appropriate format + required by the API and can be chained together to include multiple files + in a single request. + """ + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + """ + For details about the supported file input types, see: + https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference + """ + input_dir = folder_paths.get_input_directory() + input_files = [ + f + for f in os.scandir(input_dir) + if f.is_file() + and (f.name.endswith(".txt") or f.name.endswith(".pdf")) + and f.stat().st_size < GEMINI_MAX_INPUT_FILE_SIZE + ] + input_files = sorted(input_files, key=lambda x: x.name) + input_files = [f.name for f in input_files] + return { + "required": { + "file": ( + IO.COMBO, + { + "tooltip": "Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.", + "options": input_files, + "default": input_files[0] if input_files else None, + }, + ), + }, + "optional": { + "GEMINI_INPUT_FILES": ( + "GEMINI_INPUT_FILES", + { + "tooltip": "An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.", + "default": None, + }, + ), + }, + } + + DESCRIPTION = "Loads and prepares input files to include as inputs for Gemini LLM nodes. The files will be read by the Gemini model when generating a response. The contents of the text file count toward the token limit. 🛈 TIP: Can be chained together with other Gemini Input File nodes." + RETURN_TYPES = ("GEMINI_INPUT_FILES",) + FUNCTION = "prepare_files" + CATEGORY = "api node/text/Gemini" + + def create_file_part(self, file_path: str) -> GeminiPart: + mime_type = ( + GeminiMimeType.pdf + if file_path.endswith(".pdf") + else GeminiMimeType.text_plain + ) + # Use base64 string directly, not the data URI + with open(file_path, "rb") as f: + file_content = f.read() + import base64 + base64_str = base64.b64encode(file_content).decode("utf-8") + + return GeminiPart( + inlineData=GeminiInlineData( + mimeType=mime_type, + data=base64_str, + ) + ) + + def prepare_files( + self, file: str, GEMINI_INPUT_FILES: list[GeminiPart] = [] + ) -> tuple[list[GeminiPart]]: + """ + Loads and formats input files for Gemini API. + """ + file_path = folder_paths.get_annotated_filepath(file) + input_file_content = self.create_file_part(file_path) + files = [input_file_content] + GEMINI_INPUT_FILES + return (files,) + + +NODE_CLASS_MAPPINGS = { + "GeminiNode": GeminiNode, + "GeminiInputFiles": GeminiInputFiles, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "GeminiNode": "Google Gemini", + "GeminiInputFiles": "Gemini Input Files", +} diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py index ce8054afc..be1d2de4a 100644 --- a/comfy_api_nodes/nodes_openai.py +++ b/comfy_api_nodes/nodes_openai.py @@ -1,29 +1,86 @@ import io +from typing import TypedDict, Optional +import json +import os +import time +import re +import uuid +from enum import Enum from inspect import cleandoc import numpy as np import torch from PIL import Image - from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict +from server import PromptServer +import folder_paths from comfy_api_nodes.apis import ( OpenAIImageGenerationRequest, OpenAIImageEditRequest, OpenAIImageGenerationResponse, + OpenAICreateResponse, + OpenAIResponse, + CreateModelResponseProperties, + Item, + Includable, + OutputContent, + InputImageContent, + Detail, + InputTextContent, + InputMessage, + InputMessageContentList, + InputContent, + InputFileContent, ) from comfy_api_nodes.apis.client import ( ApiEndpoint, HttpMethod, SynchronousOperation, + PollingOperation, + EmptyRequest, ) from comfy_api_nodes.apinode_utils import ( downscale_image_tensor, validate_and_cast_response, validate_string, + tensor_to_base64_string, + text_filepath_to_data_uri, ) +from comfy_api_nodes.mapper_utils import model_field_to_node_input + + +RESPONSES_ENDPOINT = "/proxy/openai/v1/responses" +STARTING_POINT_ID_PATTERN = r"" + + +class HistoryEntry(TypedDict): + """Type definition for a single history entry in the chat.""" + + prompt: str + response: str + response_id: str + timestamp: float + + +class ChatHistory(TypedDict): + """Type definition for the chat history dictionary.""" + + __annotations__: dict[str, list[HistoryEntry]] + + +class SupportedOpenAIModel(str, Enum): + o4_mini = "o4-mini" + o1 = "o1" + o3 = "o3" + o1_pro = "o1-pro" + gpt_4o = "gpt-4o" + gpt_4_1 = "gpt-4.1" + gpt_4_1_mini = "gpt-4.1-mini" + gpt_4_1_nano = "gpt-4.1-nano" + class OpenAIDalle2(ComfyNodeABC): """ @@ -115,7 +172,7 @@ class OpenAIDalle2(ComfyNodeABC): n=1, size="1024x1024", unique_id=None, - **kwargs + **kwargs, ): validate_string(prompt, strip_whitespace=False) model = "dall-e-2" @@ -262,7 +319,7 @@ class OpenAIDalle3(ComfyNodeABC): quality="standard", size="1024x1024", unique_id=None, - **kwargs + **kwargs, ): validate_string(prompt, strip_whitespace=False) model = "dall-e-3" @@ -400,12 +457,12 @@ class OpenAIGPTImage1(ComfyNodeABC): n=1, size="1024x1024", unique_id=None, - **kwargs + **kwargs, ): validate_string(prompt, strip_whitespace=False) model = "gpt-image-1" path = "/proxy/openai/images/generations" - content_type="application/json" + content_type = "application/json" request_class = OpenAIImageGenerationRequest img_binaries = [] mask_binary = None @@ -414,7 +471,7 @@ class OpenAIGPTImage1(ComfyNodeABC): if image is not None: path = "/proxy/openai/images/edits" request_class = OpenAIImageEditRequest - content_type ="multipart/form-data" + content_type = "multipart/form-data" batch_size = image.shape[0] @@ -486,17 +543,466 @@ class OpenAIGPTImage1(ComfyNodeABC): return (img_tensor,) -# A dictionary that contains all nodes you want to export with their names -# NOTE: names should be globally unique +class OpenAITextNode(ComfyNodeABC): + """ + Base class for OpenAI text generation nodes. + """ + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "api_call" + CATEGORY = "api node/text/OpenAI" + API_NODE = True + + +class OpenAIChatNode(OpenAITextNode): + """ + Node to generate text responses from an OpenAI model. + """ + + def __init__(self) -> None: + """Initialize the chat node with a new session ID and empty history.""" + self.current_session_id: str = str(uuid.uuid4()) + self.history: dict[str, list[HistoryEntry]] = {} + self.previous_response_id: Optional[str] = None + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text inputs to the model, used to generate a response.", + }, + ), + "persist_context": ( + IO.BOOLEAN, + { + "default": True, + "tooltip": "Persist chat context between calls (multi-turn conversation)", + }, + ), + "model": model_field_to_node_input( + IO.COMBO, + OpenAICreateResponse, + "model", + enum_type=SupportedOpenAIModel, + ), + }, + "optional": { + "images": ( + IO.IMAGE, + { + "default": None, + "tooltip": "Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.", + }, + ), + "files": ( + "OPENAI_INPUT_FILES", + { + "default": None, + "tooltip": "Optional file(s) to use as context for the model. Accepts inputs from the OpenAI Chat Input Files node.", + }, + ), + "advanced_options": ( + "OPENAI_CHAT_CONFIG", + { + "default": None, + "tooltip": "Optional configuration for the model. Accepts inputs from the OpenAI Chat Advanced Options node.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Generate text responses from an OpenAI model." + + def get_result_response( + self, + response_id: str, + include: Optional[list[Includable]] = None, + auth_kwargs: Optional[dict[str, str]] = None, + ) -> OpenAIResponse: + """ + Retrieve a model response with the given ID from the OpenAI API. + + Args: + response_id (str): The ID of the response to retrieve. + include (Optional[List[Includable]]): Additional fields to include + in the response. See the `include` parameter for Response + creation above for more information. + + """ + return PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"{RESPONSES_ENDPOINT}/{response_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=OpenAIResponse, + query_params={"include": include}, + ), + completed_statuses=["completed"], + failed_statuses=["failed"], + status_extractor=lambda response: response.status, + auth_kwargs=auth_kwargs, + ).execute() + + def get_message_content_from_response( + self, response: OpenAIResponse + ) -> list[OutputContent]: + """Extract message content from the API response.""" + for output in response.output: + if output.root.type == "message": + return output.root.content + raise TypeError("No output message found in response") + + def get_text_from_message_content( + self, message_content: list[OutputContent] + ) -> str: + """Extract text content from message content.""" + for content_item in message_content: + if content_item.root.type == "output_text": + return str(content_item.root.text) + return "No text output found in response" + + def get_history_text(self, session_id: str) -> str: + """Convert the entire history for a given session to JSON string.""" + return json.dumps(self.history[session_id]) + + def display_history_on_node(self, session_id: str, node_id: str) -> None: + """Display formatted chat history on the node UI.""" + render_spec = { + "node_id": node_id, + "component": "ChatHistoryWidget", + "props": { + "history": self.get_history_text(session_id), + }, + } + PromptServer.instance.send_sync( + "display_component", + render_spec, + ) + + def add_to_history( + self, session_id: str, prompt: str, output_text: str, response_id: str + ) -> None: + """Add a new entry to the chat history.""" + if session_id not in self.history: + self.history[session_id] = [] + self.history[session_id].append( + { + "prompt": prompt, + "response": output_text, + "response_id": response_id, + "timestamp": time.time(), + } + ) + + def parse_output_text_from_response(self, response: OpenAIResponse) -> str: + """Extract text output from the API response.""" + message_contents = self.get_message_content_from_response(response) + return self.get_text_from_message_content(message_contents) + + def generate_new_session_id(self) -> str: + """Generate a new unique session ID.""" + return str(uuid.uuid4()) + + def get_session_id(self, persist_context: bool) -> str: + """Get the current or generate a new session ID based on context persistence.""" + return ( + self.current_session_id + if persist_context + else self.generate_new_session_id() + ) + + def tensor_to_input_image_content( + self, image: torch.Tensor, detail_level: Detail = "auto" + ) -> InputImageContent: + """Convert a tensor to an input image content object.""" + return InputImageContent( + detail=detail_level, + image_url=f"data:image/png;base64,{tensor_to_base64_string(image)}", + type="input_image", + ) + + def create_input_message_contents( + self, + prompt: str, + image: Optional[torch.Tensor] = None, + files: Optional[list[InputFileContent]] = None, + ) -> InputMessageContentList: + """Create a list of input message contents from prompt and optional image.""" + content_list: list[InputContent] = [ + InputTextContent(text=prompt, type="input_text"), + ] + if image is not None: + for i in range(image.shape[0]): + content_list.append( + self.tensor_to_input_image_content(image[i].unsqueeze(0)) + ) + if files is not None: + content_list.extend(files) + + return InputMessageContentList( + root=content_list, + ) + + def parse_response_id_from_prompt(self, prompt: str) -> Optional[str]: + """Extract response ID from prompt if it exists.""" + parsed_id = re.search(STARTING_POINT_ID_PATTERN, prompt) + return parsed_id.group(1) if parsed_id else None + + def strip_response_tag_from_prompt(self, prompt: str) -> str: + """Remove the response ID tag from the prompt.""" + return re.sub(STARTING_POINT_ID_PATTERN, "", prompt.strip()) + + def delete_history_after_response_id( + self, new_start_id: str, session_id: str + ) -> None: + """Delete history entries after a specific response ID.""" + if session_id not in self.history: + return + + new_history = [] + i = 0 + while ( + i < len(self.history[session_id]) + and self.history[session_id][i]["response_id"] != new_start_id + ): + new_history.append(self.history[session_id][i]) + i += 1 + + # Since it's the new starting point (not the response being edited), we include it as well + if i < len(self.history[session_id]): + new_history.append(self.history[session_id][i]) + + self.history[session_id] = new_history + + def api_call( + self, + prompt: str, + persist_context: bool, + model: SupportedOpenAIModel, + unique_id: Optional[str] = None, + images: Optional[torch.Tensor] = None, + files: Optional[list[InputFileContent]] = None, + advanced_options: Optional[CreateModelResponseProperties] = None, + **kwargs, + ) -> tuple[str]: + # Validate inputs + validate_string(prompt, strip_whitespace=False) + + session_id = self.get_session_id(persist_context) + response_id_override = self.parse_response_id_from_prompt(prompt) + if response_id_override: + is_starting_from_beginning = response_id_override == "start" + if is_starting_from_beginning: + self.history[session_id] = [] + previous_response_id = None + else: + previous_response_id = response_id_override + self.delete_history_after_response_id(response_id_override, session_id) + prompt = self.strip_response_tag_from_prompt(prompt) + elif persist_context: + previous_response_id = self.previous_response_id + else: + previous_response_id = None + + # Create response + create_response = SynchronousOperation( + endpoint=ApiEndpoint( + path=RESPONSES_ENDPOINT, + method=HttpMethod.POST, + request_model=OpenAICreateResponse, + response_model=OpenAIResponse, + ), + request=OpenAICreateResponse( + input=[ + Item( + root=InputMessage( + content=self.create_input_message_contents( + prompt, images, files + ), + role="user", + ) + ), + ], + store=True, + stream=False, + model=model, + previous_response_id=previous_response_id, + **( + advanced_options.model_dump(exclude_none=True) + if advanced_options + else {} + ), + ), + auth_kwargs=kwargs, + ).execute() + response_id = create_response.id + + # Get result output + result_response = self.get_result_response(response_id, auth_kwargs=kwargs) + output_text = self.parse_output_text_from_response(result_response) + + # Update history + self.add_to_history(session_id, prompt, output_text, response_id) + self.display_history_on_node(session_id, unique_id) + self.previous_response_id = response_id + + return (output_text,) + + +class OpenAIInputFiles(ComfyNodeABC): + """ + Loads and formats input files for OpenAI API. + """ + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + """ + For details about the supported file input types, see: + https://platform.openai.com/docs/guides/pdf-files?api-mode=responses + """ + input_dir = folder_paths.get_input_directory() + input_files = [ + f + for f in os.scandir(input_dir) + if f.is_file() + and (f.name.endswith(".txt") or f.name.endswith(".pdf")) + and f.stat().st_size < 32 * 1024 * 1024 + ] + input_files = sorted(input_files, key=lambda x: x.name) + input_files = [f.name for f in input_files] + return { + "required": { + "file": ( + IO.COMBO, + { + "tooltip": "Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.", + "options": input_files, + "default": input_files[0] if input_files else None, + }, + ), + }, + "optional": { + "OPENAI_INPUT_FILES": ( + "OPENAI_INPUT_FILES", + { + "tooltip": "An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.", + "default": None, + }, + ), + }, + } + + DESCRIPTION = "Loads and prepares input files (text, pdf, etc.) to include as inputs for the OpenAI Chat Node. The files will be read by the OpenAI model when generating a response. 🛈 TIP: Can be chained together with other OpenAI Input File nodes." + RETURN_TYPES = ("OPENAI_INPUT_FILES",) + FUNCTION = "prepare_files" + CATEGORY = "api node/text/OpenAI" + + def create_input_file_content(self, file_path: str) -> InputFileContent: + return InputFileContent( + file_data=text_filepath_to_data_uri(file_path), + filename=os.path.basename(file_path), + type="input_file", + ) + + def prepare_files( + self, file: str, OPENAI_INPUT_FILES: list[InputFileContent] = [] + ) -> tuple[list[InputFileContent]]: + """ + Loads and formats input files for OpenAI API. + """ + file_path = folder_paths.get_annotated_filepath(file) + input_file_content = self.create_input_file_content(file_path) + files = [input_file_content] + OPENAI_INPUT_FILES + return (files,) + + +class OpenAIChatConfig(ComfyNodeABC): + """Allows setting additional configuration for the OpenAI Chat Node.""" + + RETURN_TYPES = ("OPENAI_CHAT_CONFIG",) + FUNCTION = "configure" + DESCRIPTION = ( + "Allows specifying advanced configuration options for the OpenAI Chat Nodes." + ) + CATEGORY = "api node/text/OpenAI" + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "truncation": ( + IO.COMBO, + { + "options": ["auto", "disabled"], + "default": "auto", + "tooltip": "The truncation strategy to use for the model response. auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.disabled: If a model response will exceed the context window size for a model, the request will fail with a 400 error", + }, + ), + }, + "optional": { + "max_output_tokens": model_field_to_node_input( + IO.INT, + OpenAICreateResponse, + "max_output_tokens", + min=16, + default=4096, + max=16384, + tooltip="An upper bound for the number of tokens that can be generated for a response, including visible output tokens", + ), + "instructions": model_field_to_node_input( + IO.STRING, OpenAICreateResponse, "instructions", multiline=True + ), + }, + } + + def configure( + self, + truncation: bool, + instructions: Optional[str] = None, + max_output_tokens: Optional[int] = None, + ) -> tuple[CreateModelResponseProperties]: + """ + Configure advanced options for the OpenAI Chat Node. + + Note: + While `top_p` and `temperature` are listed as properties in the + spec, they are not supported for all models (e.g., o4-mini). + They are not exposed as inputs at all to avoid having to manually + remove depending on model choice. + """ + return ( + CreateModelResponseProperties( + instructions=instructions, + truncation=truncation, + max_output_tokens=max_output_tokens, + ), + ) + + NODE_CLASS_MAPPINGS = { "OpenAIDalle2": OpenAIDalle2, "OpenAIDalle3": OpenAIDalle3, "OpenAIGPTImage1": OpenAIGPTImage1, + "OpenAIChatNode": OpenAIChatNode, + "OpenAIInputFiles": OpenAIInputFiles, + "OpenAIChatConfig": OpenAIChatConfig, } -# A dictionary that contains the friendly/humanly readable titles for the nodes NODE_DISPLAY_NAME_MAPPINGS = { "OpenAIDalle2": "OpenAI DALL·E 2", "OpenAIDalle3": "OpenAI DALL·E 3", "OpenAIGPTImage1": "OpenAI GPT Image 1", + "OpenAIChatNode": "OpenAI Chat", + "OpenAIInputFiles": "OpenAI Chat Input Files", + "OpenAIChatConfig": "OpenAI Chat Advanced Options", } diff --git a/comfy_api_nodes/nodes_rodin.py b/comfy_api_nodes/nodes_rodin.py new file mode 100644 index 000000000..67f90478c --- /dev/null +++ b/comfy_api_nodes/nodes_rodin.py @@ -0,0 +1,462 @@ +""" +ComfyUI X Rodin3D(Deemos) API Nodes + +Rodin API docs: https://developer.hyper3d.ai/ + +""" + +from __future__ import annotations +from inspect import cleandoc +from comfy.comfy_types.node_typing import IO +import folder_paths as comfy_paths +import requests +import os +import datetime +import shutil +import time +import io +import logging +import math +from PIL import Image +from comfy_api_nodes.apis.rodin_api import ( + Rodin3DGenerateRequest, + Rodin3DGenerateResponse, + Rodin3DCheckStatusRequest, + Rodin3DCheckStatusResponse, + Rodin3DDownloadRequest, + Rodin3DDownloadResponse, + JobStatus, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, +) + + +COMMON_PARAMETERS = { + "Seed": ( + IO.INT, + { + "default":0, + "min":0, + "max":65535, + "display":"number" + } + ), + "Material_Type": ( + IO.COMBO, + { + "options": ["PBR", "Shaded"], + "default": "PBR" + } + ), + "Polygon_count": ( + IO.COMBO, + { + "options": ["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "200K-Triangle"], + "default": "18K-Quad" + } + ) +} + +def create_task_error(response: Rodin3DGenerateResponse): + """Check if the response has error""" + return hasattr(response, "error") + + + +class Rodin3DAPI: + """ + Generate 3D Assets using Rodin API + """ + RETURN_TYPES = (IO.STRING,) + RETURN_NAMES = ("3D Model Path",) + CATEGORY = "api node/3d/Rodin" + DESCRIPTION = cleandoc(__doc__ or "") + FUNCTION = "api_call" + API_NODE = True + + def tensor_to_filelike(self, tensor, max_pixels: int = 2048*2048): + """ + Converts a PyTorch tensor to a file-like object. + + Args: + - tensor (torch.Tensor): A tensor representing an image of shape (H, W, C) + where C is the number of channels (3 for RGB), H is height, and W is width. + + Returns: + - io.BytesIO: A file-like object containing the image data. + """ + array = tensor.cpu().numpy() + array = (array * 255).astype('uint8') + image = Image.fromarray(array, 'RGB') + + original_width, original_height = image.size + original_pixels = original_width * original_height + if original_pixels > max_pixels: + scale = math.sqrt(max_pixels / original_pixels) + new_width = int(original_width * scale) + new_height = int(original_height * scale) + else: + new_width, new_height = original_width, original_height + + if new_width != original_width or new_height != original_height: + image = image.resize((new_width, new_height), Image.Resampling.LANCZOS) + + img_byte_arr = io.BytesIO() + image.save(img_byte_arr, format='PNG') # PNG is used for lossless compression + img_byte_arr.seek(0) + return img_byte_arr + + def check_rodin_status(self, response: Rodin3DCheckStatusResponse) -> str: + has_failed = any(job.status == JobStatus.Failed for job in response.jobs) + all_done = all(job.status == JobStatus.Done for job in response.jobs) + status_list = [str(job.status) for job in response.jobs] + logging.info(f"[ Rodin3D API - CheckStatus ] Generate Status: {status_list}") + if has_failed: + logging.error(f"[ Rodin3D API - CheckStatus ] Generate Failed: {status_list}, Please try again.") + raise Exception("[ Rodin3D API ] Generate Failed, Please Try again.") + elif all_done: + return "DONE" + else: + return "Generating" + + def CreateGenerateTask(self, images=None, seed=1, material="PBR", quality="medium", tier="Regular", mesh_mode="Quad", **kwargs): + if images == None: + raise Exception("Rodin 3D generate requires at least 1 image.") + if len(images) >= 5: + raise Exception("Rodin 3D generate requires up to 5 image.") + + path = "/proxy/rodin/api/v2/rodin" + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=Rodin3DGenerateRequest, + response_model=Rodin3DGenerateResponse, + ), + request=Rodin3DGenerateRequest( + seed=seed, + tier=tier, + material=material, + quality=quality, + mesh_mode=mesh_mode + ), + files=[ + ( + "images", + open(image, "rb") if isinstance(image, str) else self.tensor_to_filelike(image) + ) + for image in images if image is not None + ], + content_type = "multipart/form-data", + auth_kwargs=kwargs, + ) + + response = operation.execute() + + if create_task_error(response): + error_message = f"Rodin3D Create 3D generate Task Failed. Message: {response.message}, error: {response.error}" + logging.error(error_message) + raise Exception(error_message) + + logging.info("[ Rodin3D API - Submit Jobs ] Submit Generate Task Success!") + subscription_key = response.jobs.subscription_key + task_uuid = response.uuid + logging.info(f"[ Rodin3D API - Submit Jobs ] UUID: {task_uuid}") + return task_uuid, subscription_key + + def poll_for_task_status(self, subscription_key, **kwargs) -> Rodin3DCheckStatusResponse: + + path = "/proxy/rodin/api/v2/status" + + poll_operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path = path, + method=HttpMethod.POST, + request_model=Rodin3DCheckStatusRequest, + response_model=Rodin3DCheckStatusResponse, + ), + request=Rodin3DCheckStatusRequest( + subscription_key = subscription_key + ), + completed_statuses=["DONE"], + failed_statuses=["FAILED"], + status_extractor=self.check_rodin_status, + poll_interval=3.0, + auth_kwargs=kwargs, + ) + + logging.info("[ Rodin3D API - CheckStatus ] Generate Start!") + + return poll_operation.execute() + + + + def GetRodinDownloadList(self, uuid, **kwargs) -> Rodin3DDownloadResponse: + logging.info("[ Rodin3D API - Downloading ] Generate Successfully!") + + path = "/proxy/rodin/api/v2/download" + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=Rodin3DDownloadRequest, + response_model=Rodin3DDownloadResponse, + ), + request=Rodin3DDownloadRequest( + task_uuid=uuid + ), + auth_kwargs=kwargs + ) + + return operation.execute() + + def GetQualityAndMode(self, PolyCount): + if PolyCount == "200K-Triangle": + mesh_mode = "Raw" + quality = "medium" + else: + mesh_mode = "Quad" + if PolyCount == "4K-Quad": + quality = "extra-low" + elif PolyCount == "8K-Quad": + quality = "low" + elif PolyCount == "18K-Quad": + quality = "medium" + elif PolyCount == "50K-Quad": + quality = "high" + else: + quality = "medium" + + return mesh_mode, quality + + def DownLoadFiles(self, Url_List): + Save_path = os.path.join(comfy_paths.get_output_directory(), "Rodin3D", datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")) + os.makedirs(Save_path, exist_ok=True) + model_file_path = None + for Item in Url_List.list: + url = Item.url + file_name = Item.name + file_path = os.path.join(Save_path, file_name) + if file_path.endswith(".glb"): + model_file_path = file_path + logging.info(f"[ Rodin3D API - download_files ] Downloading file: {file_path}") + max_retries = 5 + for attempt in range(max_retries): + try: + with requests.get(url, stream=True) as r: + r.raise_for_status() + with open(file_path, "wb") as f: + shutil.copyfileobj(r.raw, f) + break + except Exception as e: + logging.info(f"[ Rodin3D API - download_files ] Error downloading {file_path}:{e}") + if attempt < max_retries - 1: + logging.info("Retrying...") + time.sleep(2) + else: + logging.info(f"[ Rodin3D API - download_files ] Failed to download {file_path} after {max_retries} attempts.") + + return model_file_path + + +class Rodin3D_Regular(Rodin3DAPI): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "Images": + ( + IO.IMAGE, + { + "forceInput":True, + } + ) + }, + "optional": { + **COMMON_PARAMETERS + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + Images, + Seed, + Material_Type, + Polygon_count, + **kwargs + ): + tier = "Regular" + num_images = Images.shape[0] + m_images = [] + for i in range(num_images): + m_images.append(Images[i]) + mesh_mode, quality = self.GetQualityAndMode(Polygon_count) + task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=Material_Type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs) + self.poll_for_task_status(subscription_key, **kwargs) + Download_List = self.GetRodinDownloadList(task_uuid, **kwargs) + model = self.DownLoadFiles(Download_List) + + return (model,) + +class Rodin3D_Detail(Rodin3DAPI): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "Images": + ( + IO.IMAGE, + { + "forceInput":True, + } + ) + }, + "optional": { + **COMMON_PARAMETERS + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + Images, + Seed, + Material_Type, + Polygon_count, + **kwargs + ): + tier = "Detail" + num_images = Images.shape[0] + m_images = [] + for i in range(num_images): + m_images.append(Images[i]) + mesh_mode, quality = self.GetQualityAndMode(Polygon_count) + task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=Material_Type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs) + self.poll_for_task_status(subscription_key, **kwargs) + Download_List = self.GetRodinDownloadList(task_uuid, **kwargs) + model = self.DownLoadFiles(Download_List) + + return (model,) + +class Rodin3D_Smooth(Rodin3DAPI): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "Images": + ( + IO.IMAGE, + { + "forceInput":True, + } + ) + }, + "optional": { + **COMMON_PARAMETERS + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + Images, + Seed, + Material_Type, + Polygon_count, + **kwargs + ): + tier = "Smooth" + num_images = Images.shape[0] + m_images = [] + for i in range(num_images): + m_images.append(Images[i]) + mesh_mode, quality = self.GetQualityAndMode(Polygon_count) + task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=Material_Type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs) + self.poll_for_task_status(subscription_key, **kwargs) + Download_List = self.GetRodinDownloadList(task_uuid, **kwargs) + model = self.DownLoadFiles(Download_List) + + return (model,) + +class Rodin3D_Sketch(Rodin3DAPI): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "Images": + ( + IO.IMAGE, + { + "forceInput":True, + } + ) + }, + "optional": { + "Seed": + ( + IO.INT, + { + "default":0, + "min":0, + "max":65535, + "display":"number" + } + ) + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + Images, + Seed, + **kwargs + ): + tier = "Sketch" + num_images = Images.shape[0] + m_images = [] + for i in range(num_images): + m_images.append(Images[i]) + material_type = "PBR" + quality = "medium" + mesh_mode = "Quad" + task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=material_type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs) + self.poll_for_task_status(subscription_key, **kwargs) + Download_List = self.GetRodinDownloadList(task_uuid, **kwargs) + model = self.DownLoadFiles(Download_List) + + return (model,) + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "Rodin3D_Regular": Rodin3D_Regular, + "Rodin3D_Detail": Rodin3D_Detail, + "Rodin3D_Smooth": Rodin3D_Smooth, + "Rodin3D_Sketch": Rodin3D_Sketch, +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "Rodin3D_Regular": "Rodin 3D Generate - Regular Generate", + "Rodin3D_Detail": "Rodin 3D Generate - Detail Generate", + "Rodin3D_Smooth": "Rodin 3D Generate - Smooth Generate", + "Rodin3D_Sketch": "Rodin 3D Generate - Sketch Generate", +} diff --git a/comfy_api_nodes/nodes_runway.py b/comfy_api_nodes/nodes_runway.py new file mode 100644 index 000000000..af4b321f9 --- /dev/null +++ b/comfy_api_nodes/nodes_runway.py @@ -0,0 +1,635 @@ +"""Runway API Nodes + +API Docs: + - https://docs.dev.runwayml.com/api/#tag/Task-management/paths/~1v1~1tasks~1%7Bid%7D/delete + +User Guides: + - https://help.runwayml.com/hc/en-us/sections/30265301423635-Gen-3-Alpha + - https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video + - https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo + - https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3 + +""" + +from typing import Union, Optional, Any +from enum import Enum + +import torch + +from comfy_api_nodes.apis import ( + RunwayImageToVideoRequest, + RunwayImageToVideoResponse, + RunwayTaskStatusResponse as TaskStatusResponse, + RunwayTaskStatusEnum as TaskStatus, + RunwayModelEnum as Model, + RunwayDurationEnum as Duration, + RunwayAspectRatioEnum as AspectRatio, + RunwayPromptImageObject, + RunwayPromptImageDetailedObject, + RunwayTextToImageRequest, + RunwayTextToImageResponse, + Model4, + ReferenceImage, + RunwayTextToImageAspectRatioEnum, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + upload_images_to_comfyapi, + download_url_to_video_output, + image_tensor_pair_to_batch, + validate_string, + download_url_to_image_tensor, +) +from comfy_api_nodes.mapper_utils import model_field_to_node_input +from comfy_api.input_impl import VideoFromFile +from comfy.comfy_types.node_typing import IO, ComfyNodeABC + +PATH_IMAGE_TO_VIDEO = "/proxy/runway/image_to_video" +PATH_TEXT_TO_IMAGE = "/proxy/runway/text_to_image" +PATH_GET_TASK_STATUS = "/proxy/runway/tasks" + +AVERAGE_DURATION_I2V_SECONDS = 64 +AVERAGE_DURATION_FLF_SECONDS = 256 +AVERAGE_DURATION_T2I_SECONDS = 41 + + +class RunwayApiError(Exception): + """Base exception for Runway API errors.""" + + pass + + +class RunwayGen4TurboAspectRatio(str, Enum): + """Aspect ratios supported for Image to Video API when using gen4_turbo model.""" + + field_1280_720 = "1280:720" + field_720_1280 = "720:1280" + field_1104_832 = "1104:832" + field_832_1104 = "832:1104" + field_960_960 = "960:960" + field_1584_672 = "1584:672" + + +class RunwayGen3aAspectRatio(str, Enum): + """Aspect ratios supported for Image to Video API when using gen3a_turbo model.""" + + field_768_1280 = "768:1280" + field_1280_768 = "1280:768" + + +def get_video_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]: + """Returns the video URL from the task status response if it exists.""" + if response.output and len(response.output) > 0: + return response.output[0] + return None + + +# TODO: replace with updated image validation utils (upstream) +def validate_input_image(image: torch.Tensor) -> bool: + """ + Validate the input image is within the size limits for the Runway API. + See: https://docs.dev.runwayml.com/assets/inputs/#common-error-reasons + """ + return image.shape[2] < 8000 and image.shape[1] < 8000 + + +def poll_until_finished( + auth_kwargs: dict[str, str], + api_endpoint: ApiEndpoint[Any, TaskStatusResponse], + estimated_duration: Optional[int] = None, + node_id: Optional[str] = None, +) -> TaskStatusResponse: + """Polls the Runway API endpoint until the task reaches a terminal state, then returns the response.""" + return PollingOperation( + poll_endpoint=api_endpoint, + completed_statuses=[ + TaskStatus.SUCCEEDED.value, + ], + failed_statuses=[ + TaskStatus.FAILED.value, + TaskStatus.CANCELLED.value, + ], + status_extractor=lambda response: (response.status.value), + auth_kwargs=auth_kwargs, + result_url_extractor=get_video_url_from_task_status, + estimated_duration=estimated_duration, + node_id=node_id, + progress_extractor=extract_progress_from_task_status, + ).execute() + + +def extract_progress_from_task_status( + response: TaskStatusResponse, +) -> Union[float, None]: + if hasattr(response, "progress") and response.progress is not None: + return response.progress * 100 + return None + + +def get_image_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]: + """Returns the image URL from the task status response if it exists.""" + if response.output and len(response.output) > 0: + return response.output[0] + return None + + +class RunwayVideoGenNode(ComfyNodeABC): + """Runway Video Node Base.""" + + RETURN_TYPES = ("VIDEO",) + FUNCTION = "api_call" + CATEGORY = "api node/video/Runway" + API_NODE = True + + def validate_task_created(self, response: RunwayImageToVideoResponse) -> bool: + """ + Validate the task creation response from the Runway API matches + expected format. + """ + if not bool(response.id): + raise RunwayApiError("Invalid initial response from Runway API.") + return True + + def validate_response(self, response: RunwayImageToVideoResponse) -> bool: + """ + Validate the successful task status response from the Runway API + matches expected format. + """ + if not response.output or len(response.output) == 0: + raise RunwayApiError( + "Runway task succeeded but no video data found in response." + ) + return True + + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> RunwayImageToVideoResponse: + """Poll the task status until it is finished then get the response.""" + return poll_until_finished( + auth_kwargs, + ApiEndpoint( + path=f"{PATH_GET_TASK_STATUS}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=TaskStatusResponse, + ), + estimated_duration=AVERAGE_DURATION_FLF_SECONDS, + node_id=node_id, + ) + + def generate_video( + self, + request: RunwayImageToVideoRequest, + auth_kwargs: dict[str, str], + node_id: Optional[str] = None, + ) -> tuple[VideoFromFile]: + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_IMAGE_TO_VIDEO, + method=HttpMethod.POST, + request_model=RunwayImageToVideoRequest, + response_model=RunwayImageToVideoResponse, + ), + request=request, + auth_kwargs=auth_kwargs, + ) + + initial_response = initial_operation.execute() + self.validate_task_created(initial_response) + task_id = initial_response.id + + final_response = self.get_response(task_id, auth_kwargs, node_id) + self.validate_response(final_response) + + video_url = get_video_url_from_task_status(final_response) + return (download_url_to_video_output(video_url),) + + +class RunwayImageToVideoNodeGen3a(RunwayVideoGenNode): + """Runway Image to Video Node using Gen3a Turbo model.""" + + DESCRIPTION = "Generate a video from a single starting frame using Gen3a Turbo model. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo." + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True + ), + "start_frame": ( + IO.IMAGE, + {"tooltip": "Start frame to be used for the video"}, + ), + "duration": model_field_to_node_input( + IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration + ), + "ratio": model_field_to_node_input( + IO.COMBO, + RunwayImageToVideoRequest, + "ratio", + enum_type=RunwayGen3aAspectRatio, + ), + "seed": model_field_to_node_input( + IO.INT, + RunwayImageToVideoRequest, + "seed", + control_after_generate=True, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + def api_call( + self, + prompt: str, + start_frame: torch.Tensor, + duration: str, + ratio: str, + seed: int, + unique_id: Optional[str] = None, + **kwargs, + ) -> tuple[VideoFromFile]: + # Validate inputs + validate_string(prompt, min_length=1) + validate_input_image(start_frame) + + # Upload image + download_urls = upload_images_to_comfyapi( + start_frame, + max_images=1, + mime_type="image/png", + auth_kwargs=kwargs, + ) + if len(download_urls) != 1: + raise RunwayApiError("Failed to upload one or more images to comfy api.") + + return self.generate_video( + RunwayImageToVideoRequest( + promptText=prompt, + seed=seed, + model=Model("gen3a_turbo"), + duration=Duration(duration), + ratio=AspectRatio(ratio), + promptImage=RunwayPromptImageObject( + root=[ + RunwayPromptImageDetailedObject( + uri=str(download_urls[0]), position="first" + ) + ] + ), + ), + auth_kwargs=kwargs, + node_id=unique_id, + ) + + +class RunwayImageToVideoNodeGen4(RunwayVideoGenNode): + """Runway Image to Video Node using Gen4 Turbo model.""" + + DESCRIPTION = "Generate a video from a single starting frame using Gen4 Turbo model. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video." + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True + ), + "start_frame": ( + IO.IMAGE, + {"tooltip": "Start frame to be used for the video"}, + ), + "duration": model_field_to_node_input( + IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration + ), + "ratio": model_field_to_node_input( + IO.COMBO, + RunwayImageToVideoRequest, + "ratio", + enum_type=RunwayGen4TurboAspectRatio, + ), + "seed": model_field_to_node_input( + IO.INT, + RunwayImageToVideoRequest, + "seed", + control_after_generate=True, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + def api_call( + self, + prompt: str, + start_frame: torch.Tensor, + duration: str, + ratio: str, + seed: int, + unique_id: Optional[str] = None, + **kwargs, + ) -> tuple[VideoFromFile]: + # Validate inputs + validate_string(prompt, min_length=1) + validate_input_image(start_frame) + + # Upload image + download_urls = upload_images_to_comfyapi( + start_frame, + max_images=1, + mime_type="image/png", + auth_kwargs=kwargs, + ) + if len(download_urls) != 1: + raise RunwayApiError("Failed to upload one or more images to comfy api.") + + return self.generate_video( + RunwayImageToVideoRequest( + promptText=prompt, + seed=seed, + model=Model("gen4_turbo"), + duration=Duration(duration), + ratio=AspectRatio(ratio), + promptImage=RunwayPromptImageObject( + root=[ + RunwayPromptImageDetailedObject( + uri=str(download_urls[0]), position="first" + ) + ] + ), + ), + auth_kwargs=kwargs, + node_id=unique_id, + ) + + +class RunwayFirstLastFrameNode(RunwayVideoGenNode): + """Runway First-Last Frame Node.""" + + DESCRIPTION = "Upload first and last keyframes, draft a prompt, and generate a video. More complex transitions, such as cases where the Last frame is completely different from the First frame, may benefit from the longer 10s duration. This would give the generation more time to smoothly transition between the two inputs. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3." + + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> RunwayImageToVideoResponse: + return poll_until_finished( + auth_kwargs, + ApiEndpoint( + path=f"{PATH_GET_TASK_STATUS}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=TaskStatusResponse, + ), + estimated_duration=AVERAGE_DURATION_FLF_SECONDS, + node_id=node_id, + ) + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True + ), + "start_frame": ( + IO.IMAGE, + {"tooltip": "Start frame to be used for the video"}, + ), + "end_frame": ( + IO.IMAGE, + { + "tooltip": "End frame to be used for the video. Supported for gen3a_turbo only." + }, + ), + "duration": model_field_to_node_input( + IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration + ), + "ratio": model_field_to_node_input( + IO.COMBO, + RunwayImageToVideoRequest, + "ratio", + enum_type=RunwayGen3aAspectRatio, + ), + "seed": model_field_to_node_input( + IO.INT, + RunwayImageToVideoRequest, + "seed", + control_after_generate=True, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "unique_id": "UNIQUE_ID", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + prompt: str, + start_frame: torch.Tensor, + end_frame: torch.Tensor, + duration: str, + ratio: str, + seed: int, + unique_id: Optional[str] = None, + **kwargs, + ) -> tuple[VideoFromFile]: + # Validate inputs + validate_string(prompt, min_length=1) + validate_input_image(start_frame) + validate_input_image(end_frame) + + # Upload images + stacked_input_images = image_tensor_pair_to_batch(start_frame, end_frame) + download_urls = upload_images_to_comfyapi( + stacked_input_images, + max_images=2, + mime_type="image/png", + auth_kwargs=kwargs, + ) + if len(download_urls) != 2: + raise RunwayApiError("Failed to upload one or more images to comfy api.") + + return self.generate_video( + RunwayImageToVideoRequest( + promptText=prompt, + seed=seed, + model=Model("gen3a_turbo"), + duration=Duration(duration), + ratio=AspectRatio(ratio), + promptImage=RunwayPromptImageObject( + root=[ + RunwayPromptImageDetailedObject( + uri=str(download_urls[0]), position="first" + ), + RunwayPromptImageDetailedObject( + uri=str(download_urls[1]), position="last" + ), + ] + ), + ), + auth_kwargs=kwargs, + node_id=unique_id, + ) + + +class RunwayTextToImageNode(ComfyNodeABC): + """Runway Text to Image Node.""" + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "api_call" + CATEGORY = "api node/image/Runway" + API_NODE = True + DESCRIPTION = "Generate an image from a text prompt using Runway's Gen 4 model. You can also include reference images to guide the generation." + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, RunwayTextToImageRequest, "promptText", multiline=True + ), + "ratio": model_field_to_node_input( + IO.COMBO, + RunwayTextToImageRequest, + "ratio", + enum_type=RunwayTextToImageAspectRatioEnum, + ), + }, + "optional": { + "reference_image": ( + IO.IMAGE, + {"tooltip": "Optional reference image to guide the generation"}, + ) + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + def validate_task_created(self, response: RunwayTextToImageResponse) -> bool: + """ + Validate the task creation response from the Runway API matches + expected format. + """ + if not bool(response.id): + raise RunwayApiError("Invalid initial response from Runway API.") + return True + + def validate_response(self, response: TaskStatusResponse) -> bool: + """ + Validate the successful task status response from the Runway API + matches expected format. + """ + if not response.output or len(response.output) == 0: + raise RunwayApiError( + "Runway task succeeded but no image data found in response." + ) + return True + + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> TaskStatusResponse: + """Poll the task status until it is finished then get the response.""" + return poll_until_finished( + auth_kwargs, + ApiEndpoint( + path=f"{PATH_GET_TASK_STATUS}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=TaskStatusResponse, + ), + estimated_duration=AVERAGE_DURATION_T2I_SECONDS, + node_id=node_id, + ) + + def api_call( + self, + prompt: str, + ratio: str, + reference_image: Optional[torch.Tensor] = None, + unique_id: Optional[str] = None, + **kwargs, + ) -> tuple[torch.Tensor]: + # Validate inputs + validate_string(prompt, min_length=1) + + # Prepare reference images if provided + reference_images = None + if reference_image is not None: + validate_input_image(reference_image) + download_urls = upload_images_to_comfyapi( + reference_image, + max_images=1, + mime_type="image/png", + auth_kwargs=kwargs, + ) + if len(download_urls) != 1: + raise RunwayApiError("Failed to upload reference image to comfy api.") + + reference_images = [ReferenceImage(uri=str(download_urls[0]))] + + # Create request + request = RunwayTextToImageRequest( + promptText=prompt, + model=Model4.gen4_image, + ratio=ratio, + referenceImages=reference_images, + ) + + # Execute initial request + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_TEXT_TO_IMAGE, + method=HttpMethod.POST, + request_model=RunwayTextToImageRequest, + response_model=RunwayTextToImageResponse, + ), + request=request, + auth_kwargs=kwargs, + ) + + initial_response = initial_operation.execute() + self.validate_task_created(initial_response) + task_id = initial_response.id + + # Poll for completion + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) + self.validate_response(final_response) + + # Download and return image + image_url = get_image_url_from_task_status(final_response) + return (download_url_to_image_tensor(image_url),) + + +NODE_CLASS_MAPPINGS = { + "RunwayFirstLastFrameNode": RunwayFirstLastFrameNode, + "RunwayImageToVideoNodeGen3a": RunwayImageToVideoNodeGen3a, + "RunwayImageToVideoNodeGen4": RunwayImageToVideoNodeGen4, + "RunwayTextToImageNode": RunwayTextToImageNode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "RunwayFirstLastFrameNode": "Runway First-Last-Frame to Video", + "RunwayImageToVideoNodeGen3a": "Runway Image to Video (Gen3a Turbo)", + "RunwayImageToVideoNodeGen4": "Runway Image to Video (Gen4 Turbo)", + "RunwayTextToImageNode": "Runway Text to Image", +} diff --git a/comfy_api_nodes/nodes_tripo.py b/comfy_api_nodes/nodes_tripo.py new file mode 100644 index 000000000..65f3b21f5 --- /dev/null +++ b/comfy_api_nodes/nodes_tripo.py @@ -0,0 +1,574 @@ +import os +from folder_paths import get_output_directory +from comfy_api_nodes.mapper_utils import model_field_to_node_input +from comfy.comfy_types.node_typing import IO +from comfy_api_nodes.apis import ( + TripoOrientation, + TripoModelVersion, +) +from comfy_api_nodes.apis.tripo_api import ( + TripoTaskType, + TripoStyle, + TripoFileReference, + TripoFileEmptyReference, + TripoUrlReference, + TripoTaskResponse, + TripoTaskStatus, + TripoTextToModelRequest, + TripoImageToModelRequest, + TripoMultiviewToModelRequest, + TripoTextureModelRequest, + TripoRefineModelRequest, + TripoAnimateRigRequest, + TripoAnimateRetargetRequest, + TripoConvertModelRequest, +) + +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + upload_images_to_comfyapi, + download_url_to_bytesio, +) + + +def upload_image_to_tripo(image, **kwargs): + urls = upload_images_to_comfyapi(image, max_images=1, auth_kwargs=kwargs) + return TripoFileReference(TripoUrlReference(url=urls[0], type="jpeg")) + +def get_model_url_from_response(response: TripoTaskResponse) -> str: + if response.data is not None: + for key in ["pbr_model", "model", "base_model"]: + if getattr(response.data.output, key, None) is not None: + return getattr(response.data.output, key) + raise RuntimeError(f"Failed to get model url from response: {response}") + + +def poll_until_finished( + kwargs: dict[str, str], + response: TripoTaskResponse, +) -> tuple[str, str]: + """Polls the Tripo API endpoint until the task reaches a terminal state, then returns the response.""" + if response.code != 0: + raise RuntimeError(f"Failed to generate mesh: {response.error}") + task_id = response.data.task_id + response_poll = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/tripo/v2/openapi/task/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=TripoTaskResponse, + ), + completed_statuses=[TripoTaskStatus.SUCCESS], + failed_statuses=[ + TripoTaskStatus.FAILED, + TripoTaskStatus.CANCELLED, + TripoTaskStatus.UNKNOWN, + TripoTaskStatus.BANNED, + TripoTaskStatus.EXPIRED, + ], + status_extractor=lambda x: x.data.status, + auth_kwargs=kwargs, + node_id=kwargs["unique_id"], + result_url_extractor=get_model_url_from_response, + progress_extractor=lambda x: x.data.progress, + ).execute() + if response_poll.data.status == TripoTaskStatus.SUCCESS: + url = get_model_url_from_response(response_poll) + bytesio = download_url_to_bytesio(url) + # Save the downloaded model file + model_file = f"tripo_model_{task_id}.glb" + with open(os.path.join(get_output_directory(), model_file), "wb") as f: + f.write(bytesio.getvalue()) + return model_file, task_id + raise RuntimeError(f"Failed to generate mesh: {response_poll}") + +class TripoTextToModelNode: + """ + Generates 3D models synchronously based on a text prompt using Tripo's API. + """ + AVERAGE_DURATION = 80 + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ("STRING", {"multiline": True}), + }, + "optional": { + "negative_prompt": ("STRING", {"multiline": True}), + "model_version": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "model_version", enum_type=TripoModelVersion), + "style": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "style", enum_type=TripoStyle, default="None"), + "texture": ("BOOLEAN", {"default": True}), + "pbr": ("BOOLEAN", {"default": True}), + "image_seed": ("INT", {"default": 42}), + "model_seed": ("INT", {"default": 42}), + "texture_seed": ("INT", {"default": 42}), + "texture_quality": (["standard", "detailed"], {"default": "standard"}), + "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}), + "quad": ("BOOLEAN", {"default": False}) + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("STRING", "MODEL_TASK_ID",) + RETURN_NAMES = ("model_file", "model task_id") + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + + def generate_mesh(self, prompt, negative_prompt=None, model_version=None, style=None, texture=None, pbr=None, image_seed=None, model_seed=None, texture_seed=None, texture_quality=None, face_limit=None, quad=None, **kwargs): + style_enum = None if style == "None" else style + if not prompt: + raise RuntimeError("Prompt is required") + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoTextToModelRequest, + response_model=TripoTaskResponse, + ), + request=TripoTextToModelRequest( + type=TripoTaskType.TEXT_TO_MODEL, + prompt=prompt, + negative_prompt=negative_prompt if negative_prompt else None, + model_version=model_version, + style=style_enum, + texture=texture, + pbr=pbr, + image_seed=image_seed, + model_seed=model_seed, + texture_seed=texture_seed, + texture_quality=texture_quality, + face_limit=face_limit, + auto_size=True, + quad=quad + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + +class TripoImageToModelNode: + """ + Generates 3D models synchronously based on a single image using Tripo's API. + """ + AVERAGE_DURATION = 80 + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + }, + "optional": { + "model_version": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "model_version", enum_type=TripoModelVersion), + "style": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "style", enum_type=TripoStyle, default="None"), + "texture": ("BOOLEAN", {"default": True}), + "pbr": ("BOOLEAN", {"default": True}), + "model_seed": ("INT", {"default": 42}), + "orientation": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "orientation", enum_type=TripoOrientation), + "texture_seed": ("INT", {"default": 42}), + "texture_quality": (["standard", "detailed"], {"default": "standard"}), + "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}), + "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}), + "quad": ("BOOLEAN", {"default": False}) + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("STRING", "MODEL_TASK_ID",) + RETURN_NAMES = ("model_file", "model task_id") + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + + def generate_mesh(self, image, model_version=None, style=None, texture=None, pbr=None, model_seed=None, orientation=None, texture_alignment=None, texture_seed=None, texture_quality=None, face_limit=None, quad=None, **kwargs): + style_enum = None if style == "None" else style + if image is None: + raise RuntimeError("Image is required") + tripo_file = upload_image_to_tripo(image, **kwargs) + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoImageToModelRequest, + response_model=TripoTaskResponse, + ), + request=TripoImageToModelRequest( + type=TripoTaskType.IMAGE_TO_MODEL, + file=tripo_file, + model_version=model_version, + style=style_enum, + texture=texture, + pbr=pbr, + model_seed=model_seed, + orientation=orientation, + texture_alignment=texture_alignment, + texture_seed=texture_seed, + texture_quality=texture_quality, + face_limit=face_limit, + auto_size=True, + quad=quad + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + +class TripoMultiviewToModelNode: + """ + Generates 3D models synchronously based on up to four images (front, left, back, right) using Tripo's API. + """ + AVERAGE_DURATION = 80 + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + }, + "optional": { + "image_left": ("IMAGE",), + "image_back": ("IMAGE",), + "image_right": ("IMAGE",), + "model_version": model_field_to_node_input(IO.COMBO, TripoMultiviewToModelRequest, "model_version", enum_type=TripoModelVersion), + "orientation": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "orientation", enum_type=TripoOrientation), + "texture": ("BOOLEAN", {"default": True}), + "pbr": ("BOOLEAN", {"default": True}), + "model_seed": ("INT", {"default": 42}), + "texture_seed": ("INT", {"default": 42}), + "texture_quality": (["standard", "detailed"], {"default": "standard"}), + "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}), + "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}), + "quad": ("BOOLEAN", {"default": False}) + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("STRING", "MODEL_TASK_ID",) + RETURN_NAMES = ("model_file", "model task_id") + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + + def generate_mesh(self, image, image_left=None, image_back=None, image_right=None, model_version=None, orientation=None, texture=None, pbr=None, model_seed=None, texture_seed=None, texture_quality=None, texture_alignment=None, face_limit=None, quad=None, **kwargs): + if image is None: + raise RuntimeError("front image for multiview is required") + images = [] + image_dict = { + "image": image, + "image_left": image_left, + "image_back": image_back, + "image_right": image_right + } + if image_left is None and image_back is None and image_right is None: + raise RuntimeError("At least one of left, back, or right image must be provided for multiview") + for image_name in ["image", "image_left", "image_back", "image_right"]: + image_ = image_dict[image_name] + if image_ is not None: + tripo_file = upload_image_to_tripo(image_, **kwargs) + images.append(tripo_file) + else: + images.append(TripoFileEmptyReference()) + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoMultiviewToModelRequest, + response_model=TripoTaskResponse, + ), + request=TripoMultiviewToModelRequest( + type=TripoTaskType.MULTIVIEW_TO_MODEL, + files=images, + model_version=model_version, + orientation=orientation, + texture=texture, + pbr=pbr, + model_seed=model_seed, + texture_seed=texture_seed, + texture_quality=texture_quality, + texture_alignment=texture_alignment, + face_limit=face_limit, + quad=quad, + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + +class TripoTextureNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model_task_id": ("MODEL_TASK_ID",), + }, + "optional": { + "texture": ("BOOLEAN", {"default": True}), + "pbr": ("BOOLEAN", {"default": True}), + "texture_seed": ("INT", {"default": 42}), + "texture_quality": (["standard", "detailed"], {"default": "standard"}), + "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("STRING", "MODEL_TASK_ID",) + RETURN_NAMES = ("model_file", "model task_id") + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + AVERAGE_DURATION = 80 + + def generate_mesh(self, model_task_id, texture=None, pbr=None, texture_seed=None, texture_quality=None, texture_alignment=None, **kwargs): + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoTextureModelRequest, + response_model=TripoTaskResponse, + ), + request=TripoTextureModelRequest( + original_model_task_id=model_task_id, + texture=texture, + pbr=pbr, + texture_seed=texture_seed, + texture_quality=texture_quality, + texture_alignment=texture_alignment + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + + +class TripoRefineNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model_task_id": ("MODEL_TASK_ID", { + "tooltip": "Must be a v1.4 Tripo model" + }), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Refine a draft model created by v1.4 Tripo models only." + + RETURN_TYPES = ("STRING", "MODEL_TASK_ID",) + RETURN_NAMES = ("model_file", "model task_id") + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + AVERAGE_DURATION = 240 + + def generate_mesh(self, model_task_id, **kwargs): + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoRefineModelRequest, + response_model=TripoTaskResponse, + ), + request=TripoRefineModelRequest( + draft_model_task_id=model_task_id + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + + +class TripoRigNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "original_model_task_id": ("MODEL_TASK_ID",), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("STRING", "RIG_TASK_ID") + RETURN_NAMES = ("model_file", "rig task_id") + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + AVERAGE_DURATION = 180 + + def generate_mesh(self, original_model_task_id, **kwargs): + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoAnimateRigRequest, + response_model=TripoTaskResponse, + ), + request=TripoAnimateRigRequest( + original_model_task_id=original_model_task_id, + out_format="glb", + spec="tripo" + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + +class TripoRetargetNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "original_model_task_id": ("RIG_TASK_ID",), + "animation": ([ + "preset:idle", + "preset:walk", + "preset:climb", + "preset:jump", + "preset:slash", + "preset:shoot", + "preset:hurt", + "preset:fall", + "preset:turn", + ],), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("STRING", "RETARGET_TASK_ID") + RETURN_NAMES = ("model_file", "retarget task_id") + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + AVERAGE_DURATION = 30 + + def generate_mesh(self, animation, original_model_task_id, **kwargs): + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoAnimateRetargetRequest, + response_model=TripoTaskResponse, + ), + request=TripoAnimateRetargetRequest( + original_model_task_id=original_model_task_id, + animation=animation, + out_format="glb", + bake_animation=True + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + +class TripoConversionNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "original_model_task_id": ("MODEL_TASK_ID,RIG_TASK_ID,RETARGET_TASK_ID",), + "format": (["GLTF", "USDZ", "FBX", "OBJ", "STL", "3MF"],), + }, + "optional": { + "quad": ("BOOLEAN", {"default": False}), + "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}), + "texture_size": ("INT", {"min": 128, "max": 4096, "default": 4096}), + "texture_format": (["BMP", "DPX", "HDR", "JPEG", "OPEN_EXR", "PNG", "TARGA", "TIFF", "WEBP"], {"default": "JPEG"}) + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + @classmethod + def VALIDATE_INPUTS(cls, input_types): + # The min and max of input1 and input2 are still validated because + # we didn't take `input1` or `input2` as arguments + if input_types["original_model_task_id"] not in ("MODEL_TASK_ID", "RIG_TASK_ID", "RETARGET_TASK_ID"): + return "original_model_task_id must be MODEL_TASK_ID, RIG_TASK_ID or RETARGET_TASK_ID type" + return True + + RETURN_TYPES = () + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + AVERAGE_DURATION = 30 + + def generate_mesh(self, original_model_task_id, format, quad, face_limit, texture_size, texture_format, **kwargs): + if not original_model_task_id: + raise RuntimeError("original_model_task_id is required") + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoConvertModelRequest, + response_model=TripoTaskResponse, + ), + request=TripoConvertModelRequest( + original_model_task_id=original_model_task_id, + format=format, + quad=quad if quad else None, + face_limit=face_limit if face_limit != -1 else None, + texture_size=texture_size if texture_size != 4096 else None, + texture_format=texture_format if texture_format != "JPEG" else None + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + +NODE_CLASS_MAPPINGS = { + "TripoTextToModelNode": TripoTextToModelNode, + "TripoImageToModelNode": TripoImageToModelNode, + "TripoMultiviewToModelNode": TripoMultiviewToModelNode, + "TripoTextureNode": TripoTextureNode, + "TripoRefineNode": TripoRefineNode, + "TripoRigNode": TripoRigNode, + "TripoRetargetNode": TripoRetargetNode, + "TripoConversionNode": TripoConversionNode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "TripoTextToModelNode": "Tripo: Text to Model", + "TripoImageToModelNode": "Tripo: Image to Model", + "TripoMultiviewToModelNode": "Tripo: Multiview to Model", + "TripoTextureNode": "Tripo: Texture model", + "TripoRefineNode": "Tripo: Refine Draft model", + "TripoRigNode": "Tripo: Rig model", + "TripoRetargetNode": "Tripo: Retarget rigged model", + "TripoConversionNode": "Tripo: Convert model", +} diff --git a/nodes.py b/nodes.py index 1e328651b..2d499051e 100644 --- a/nodes.py +++ b/nodes.py @@ -2281,6 +2281,10 @@ def init_builtin_api_nodes(): "nodes_pixverse.py", "nodes_stability.py", "nodes_pika.py", + "nodes_runway.py", + "nodes_tripo.py", + "nodes_rodin.py", + "nodes_gemini.py", ] if not load_custom_node(os.path.join(api_nodes_dir, "canary.py"), module_parent="comfy_api_nodes"): diff --git a/requirements.txt b/requirements.txt index f56b3e096..38991dbf9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.20.6 -comfyui-workflow-templates==0.1.18 +comfyui-workflow-templates==0.1.20 torch torchsde torchvision From c9e1821a7b49bb58f18f114336bae911160ac69d Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 27 May 2025 07:07:44 -0400 Subject: [PATCH 147/193] ComfyUI version 0.3.37 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 817b7d83b..c13b6501f 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.36" +__version__ = "0.3.37" diff --git a/pyproject.toml b/pyproject.toml index accf6f864..c21b9b5c6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.36" +version = "0.3.37" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9" From 06c661004ed67ef33a59df8de7136ad6f4542945 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 27 May 2025 12:09:05 -0700 Subject: [PATCH 148/193] Memory estimation code can now take into account conds. (#8307) --- comfy/conds.py | 8 ++++++++ comfy/model_base.py | 16 +++++++++++++--- comfy/sampler_helpers.py | 22 +++++++++++++++++++--- comfy/samplers.py | 8 +++++++- 4 files changed, 47 insertions(+), 7 deletions(-) diff --git a/comfy/conds.py b/comfy/conds.py index 211fb8d57..920e25488 100644 --- a/comfy/conds.py +++ b/comfy/conds.py @@ -24,6 +24,10 @@ class CONDRegular: conds.append(x.cond) return torch.cat(conds) + def size(self): + return list(self.cond.size()) + + class CONDNoiseShape(CONDRegular): def process_cond(self, batch_size, device, area, **kwargs): data = self.cond @@ -64,6 +68,7 @@ class CONDCrossAttn(CONDRegular): out.append(c) return torch.cat(out) + class CONDConstant(CONDRegular): def __init__(self, cond): self.cond = cond @@ -78,3 +83,6 @@ class CONDConstant(CONDRegular): def concat(self, others): return self.cond + + def size(self): + return [1] diff --git a/comfy/model_base.py b/comfy/model_base.py index fb4724690..cfd10d726 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -135,6 +135,7 @@ class BaseModel(torch.nn.Module): logging.info("model_type {}".format(model_type.name)) logging.debug("adm {}".format(self.adm_channels)) self.memory_usage_factor = model_config.memory_usage_factor + self.memory_usage_factor_conds = () def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( @@ -325,19 +326,28 @@ class BaseModel(torch.nn.Module): def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): return self.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image) - def memory_required(self, input_shape): + def memory_required(self, input_shape, cond_shapes={}): + input_shapes = [input_shape] + for c in self.memory_usage_factor_conds: + shape = cond_shapes.get(c, None) + if shape is not None and len(shape) > 0: + input_shapes += shape + if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention(): dtype = self.get_dtype() if self.manual_cast_dtype is not None: dtype = self.manual_cast_dtype #TODO: this needs to be tweaked - area = input_shape[0] * math.prod(input_shape[2:]) + area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes)) return (area * comfy.model_management.dtype_size(dtype) * 0.01 * self.memory_usage_factor) * (1024 * 1024) else: #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory. - area = input_shape[0] * math.prod(input_shape[2:]) + area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes)) return (area * 0.15 * self.memory_usage_factor) * (1024 * 1024) + def extra_conds_shapes(self, **kwargs): + return {} + def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None): adm_inputs = [] diff --git a/comfy/sampler_helpers.py b/comfy/sampler_helpers.py index 96a3040a1..8dbc41455 100644 --- a/comfy/sampler_helpers.py +++ b/comfy/sampler_helpers.py @@ -1,5 +1,7 @@ from __future__ import annotations import uuid +import math +import collections import comfy.model_management import comfy.conds import comfy.utils @@ -104,6 +106,21 @@ def cleanup_additional_models(models): if hasattr(m, 'cleanup'): m.cleanup() +def estimate_memory(model, noise_shape, conds): + cond_shapes = collections.defaultdict(list) + cond_shapes_min = {} + for _, cs in conds.items(): + for cond in cs: + for k, v in model.model.extra_conds_shapes(**cond).items(): + cond_shapes[k].append(v) + if cond_shapes_min.get(k, None) is None: + cond_shapes_min[k] = [v] + elif math.prod(v) > math.prod(cond_shapes_min[k][0]): + cond_shapes_min[k] = [v] + + memory_required = model.model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:]), cond_shapes=cond_shapes) + minimum_memory_required = model.model.memory_required([noise_shape[0]] + list(noise_shape[1:]), cond_shapes=cond_shapes_min) + return memory_required, minimum_memory_required def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None): executor = comfy.patcher_extension.WrapperExecutor.new_executor( @@ -117,9 +134,8 @@ def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=Non models, inference_memory = get_additional_models(conds, model.model_dtype()) models += get_additional_models_from_model_options(model_options) models += model.get_nested_additional_models() # TODO: does this require inference_memory update? - memory_required = model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory - minimum_memory_required = model.memory_required([noise_shape[0]] + list(noise_shape[1:])) + inference_memory - comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required) + memory_required, minimum_memory_required = estimate_memory(model, noise_shape, conds) + comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required + inference_memory, minimum_memory_required=minimum_memory_required + inference_memory) real_model = model.model return real_model, conds, models diff --git a/comfy/samplers.py b/comfy/samplers.py index 67ae09a25..efe9bf867 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -256,7 +256,13 @@ def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Te for i in range(1, len(to_batch_temp) + 1): batch_amount = to_batch_temp[:len(to_batch_temp)//i] input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:] - if model.memory_required(input_shape) * 1.5 < free_memory: + cond_shapes = collections.defaultdict(list) + for tt in batch_amount: + cond = {k: v.size() for k, v in to_run[tt][0].conditioning.items()} + for k, v in to_run[tt][0].conditioning.items(): + cond_shapes[k].append(v.size()) + + if model.memory_required(input_shape, cond_shapes=cond_shapes) * 1.5 < free_memory: to_batch = batch_amount break From ba37e67964b4a25be788042e01e72c6806038be0 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Tue, 27 May 2025 22:42:18 -0700 Subject: [PATCH 149/193] update frontend patch 1.20.7 (#8312) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 38991dbf9..5e3c60659 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.20.6 +comfyui-frontend-package==1.20.7 comfyui-workflow-templates==0.1.20 torch torchsde From e6609dacdeeafa371fe4e9f303016a605a333a76 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 28 May 2025 02:15:11 -0400 Subject: [PATCH 150/193] ComfyUI version 0.3.38 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index c13b6501f..64b326db8 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.37" +__version__ = "0.3.38" diff --git a/pyproject.toml b/pyproject.toml index c21b9b5c6..0a841c8ce 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.37" +version = "0.3.38" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9" From 1c1687ab1c3ba5b7d952d92359cfb0acd636da5f Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 28 May 2025 15:47:15 -0700 Subject: [PATCH 151/193] Support HiDream SimpleTuner loras. (#8318) --- comfy/lora.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/comfy/lora.py b/comfy/lora.py index ef110c164..387d5c52a 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -283,8 +283,9 @@ def model_lora_keys_unet(model, key_map={}): for k in sdk: if k.startswith("diffusion_model."): if k.endswith(".weight"): - key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") - key_map["lycoris_{}".format(key_lora)] = k #SimpleTuner lycoris format + key_lora = k[len("diffusion_model."):-len(".weight")] + key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k #SimpleTuner lycoris format + key_map["transformer.{}".format(key_lora)] = k #SimpleTuner regular format if isinstance(model, comfy.model_base.ACEStep): for k in sdk: From 592d05610072777d170cf44604366bc489ada81b Mon Sep 17 00:00:00 2001 From: Robin Huang Date: Wed, 28 May 2025 20:42:02 -0700 Subject: [PATCH 152/193] Add support for Veo3 API node. (#8320) --- comfy_api_nodes/nodes_veo2.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/comfy_api_nodes/nodes_veo2.py b/comfy_api_nodes/nodes_veo2.py index df846d5dd..e93f82a9a 100644 --- a/comfy_api_nodes/nodes_veo2.py +++ b/comfy_api_nodes/nodes_veo2.py @@ -54,6 +54,10 @@ class VeoVideoGenerationNode(ComfyNodeABC): """ Generates videos from text prompts using Google's Veo API. + Supported models: + - veo-2.0-generate-001 + - veo-3.0-generate-preview + This node can create videos from text descriptions and optional image inputs, with control over parameters like aspect ratio, duration, and more. """ @@ -130,6 +134,14 @@ class VeoVideoGenerationNode(ComfyNodeABC): "default": None, "tooltip": "Optional reference image to guide video generation", }), + "model": ( + IO.COMBO, + { + "options": ["veo-2.0-generate-001", "veo-3.0-generate-preview"], + "default": "veo-2.0-generate-001", + "tooltip": "Model to use for video generation. Defaults to veo 2.0", + }, + ), }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", @@ -154,6 +166,7 @@ class VeoVideoGenerationNode(ComfyNodeABC): person_generation="ALLOW", seed=0, image=None, + model="veo-2.0-generate-001", unique_id: Optional[str] = None, **kwargs, ): @@ -192,7 +205,7 @@ class VeoVideoGenerationNode(ComfyNodeABC): # Initial request to start video generation initial_operation = SynchronousOperation( endpoint=ApiEndpoint( - path="/proxy/veo/generate", + path=f"/proxy/veo/{model}/generate", method=HttpMethod.POST, request_model=Veo2GenVidRequest, response_model=Veo2GenVidResponse @@ -223,7 +236,7 @@ class VeoVideoGenerationNode(ComfyNodeABC): # Define the polling operation poll_operation = PollingOperation( poll_endpoint=ApiEndpoint( - path="/proxy/veo/poll", + path=f"/proxy/veo/{model}/poll", method=HttpMethod.POST, request_model=Veo2GenVidPollRequest, response_model=Veo2GenVidPollResponse @@ -304,5 +317,5 @@ NODE_CLASS_MAPPINGS = { } NODE_DISPLAY_NAME_MAPPINGS = { - "VeoVideoGenerationNode": "Google Veo2 Video Generation", + "VeoVideoGenerationNode": "Google Veo Video Generation", } From 4eba3161cf5481b2b275ab3d0efad581ef028f7e Mon Sep 17 00:00:00 2001 From: Yoland Yan <4950057+yoland68@users.noreply.github.com> Date: Wed, 28 May 2025 20:42:25 -0700 Subject: [PATCH 153/193] Refactor Pika API node imports and fix unique_id issue. (#8319) Added unique_id to hidden parameters and corrected description formatting in PikAdditionsNode. --- comfy_api_nodes/nodes_pika.py | 49 +++++++++++++++++++---------------- 1 file changed, 26 insertions(+), 23 deletions(-) diff --git a/comfy_api_nodes/nodes_pika.py b/comfy_api_nodes/nodes_pika.py index 30562790a..1cc708564 100644 --- a/comfy_api_nodes/nodes_pika.py +++ b/comfy_api_nodes/nodes_pika.py @@ -6,40 +6,42 @@ Pika API docs: https://pika-827374fb.mintlify.app/api-reference from __future__ import annotations import io -from typing import Optional, TypeVar import logging -import torch +from typing import Optional, TypeVar + import numpy as np +import torch + +from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeOptions +from comfy_api.input_impl import VideoFromFile +from comfy_api.input_impl.video_types import VideoCodec, VideoContainer, VideoInput +from comfy_api_nodes.apinode_utils import ( + download_url_to_video_output, + tensor_to_bytesio, +) from comfy_api_nodes.apis import ( - PikaBodyGenerate22T2vGenerate22T2vPost, - PikaGenerateResponse, - PikaBodyGenerate22I2vGenerate22I2vPost, - PikaVideoResponse, - PikaBodyGenerate22C2vGenerate22PikascenesPost, IngredientsMode, - PikaDurationEnum, - PikaResolutionEnum, - PikaBodyGeneratePikaffectsGeneratePikaffectsPost, - PikaBodyGeneratePikadditionsGeneratePikadditionsPost, - PikaBodyGeneratePikaswapsGeneratePikaswapsPost, + PikaBodyGenerate22C2vGenerate22PikascenesPost, + PikaBodyGenerate22I2vGenerate22I2vPost, PikaBodyGenerate22KeyframeGenerate22PikaframesPost, + PikaBodyGenerate22T2vGenerate22T2vPost, + PikaBodyGeneratePikadditionsGeneratePikadditionsPost, + PikaBodyGeneratePikaffectsGeneratePikaffectsPost, + PikaBodyGeneratePikaswapsGeneratePikaswapsPost, + PikaDurationEnum, Pikaffect, + PikaGenerateResponse, + PikaResolutionEnum, + PikaVideoResponse, ) from comfy_api_nodes.apis.client import ( ApiEndpoint, - HttpMethod, - SynchronousOperation, - PollingOperation, EmptyRequest, -) -from comfy_api_nodes.apinode_utils import ( - tensor_to_bytesio, - download_url_to_video_output, + HttpMethod, + PollingOperation, + SynchronousOperation, ) from comfy_api_nodes.mapper_utils import model_field_to_node_input -from comfy_api.input_impl.video_types import VideoInput, VideoContainer, VideoCodec -from comfy_api.input_impl import VideoFromFile -from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeOptions R = TypeVar("R") @@ -204,6 +206,7 @@ class PikaImageToVideoV2_2(PikaNodeBase): "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", }, } @@ -457,7 +460,7 @@ class PikAdditionsNode(PikaNodeBase): }, } - DESCRIPTION = "Add any object or image into your video. Upload a video and specify what you’d like to add to create a seamlessly integrated result." + DESCRIPTION = "Add any object or image into your video. Upload a video and specify what you'd like to add to create a seamlessly integrated result." def api_call( self, From 5e5e46d40c94a4efb7e0921d88493c798c021d82 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 28 May 2025 20:46:15 -0700 Subject: [PATCH 154/193] Not really tested WAN Phantom Support. (#8321) --- comfy/ldm/wan/model.py | 9 ++++++++- comfy/model_base.py | 5 +++++ comfy_extras/nodes_wan.py | 39 +++++++++++++++++++++++++++++++++++++++ 3 files changed, 52 insertions(+), 1 deletion(-) diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py index 1b51a4e4a..1d6edb354 100644 --- a/comfy/ldm/wan/model.py +++ b/comfy/ldm/wan/model.py @@ -539,13 +539,20 @@ class WanModel(torch.nn.Module): x = self.unpatchify(x, grid_sizes) return x - def forward(self, x, timestep, context, clip_fea=None, transformer_options={}, **kwargs): + def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs): bs, c, t, h, w = x.shape x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size) + patch_size = self.patch_size t_len = ((t + (patch_size[0] // 2)) // patch_size[0]) h_len = ((h + (patch_size[1] // 2)) // patch_size[1]) w_len = ((w + (patch_size[2] // 2)) // patch_size[2]) + + if time_dim_concat is not None: + time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size) + x = torch.cat([x, time_dim_concat], dim=2) + t_len = ((x.shape[2] + (patch_size[0] // 2)) // patch_size[0]) + img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype) img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1) img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1) diff --git a/comfy/model_base.py b/comfy/model_base.py index cfd10d726..8ed124277 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -1057,6 +1057,11 @@ class WAN21(BaseModel): clip_vision_output = kwargs.get("clip_vision_output", None) if clip_vision_output is not None: out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.penultimate_hidden_states) + + time_dim_concat = kwargs.get("time_dim_concat", None) + if time_dim_concat is not None: + out['time_dim_concat'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_concat)) + return out diff --git a/comfy_extras/nodes_wan.py b/comfy_extras/nodes_wan.py index c35c4871c..d6097a104 100644 --- a/comfy_extras/nodes_wan.py +++ b/comfy_extras/nodes_wan.py @@ -345,6 +345,44 @@ class WanCameraImageToVideo: out_latent["samples"] = latent return (positive, negative, out_latent) +class WanPhantomSubjectToVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }, + "optional": {"images": ("IMAGE", ), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative_text", "negative_img_text", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, positive, negative, vae, width, height, length, batch_size, images): + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + cond2 = negative + if images is not None: + images = comfy.utils.common_upscale(images[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + latent_images = [] + for i in images: + latent_images += [vae.encode(i.unsqueeze(0)[:, :, :, :3])] + concat_latent_image = torch.cat(latent_images, dim=2) + + positive = node_helpers.conditioning_set_values(positive, {"time_dim_concat": concat_latent_image}) + cond2 = node_helpers.conditioning_set_values(negative, {"time_dim_concat": concat_latent_image}) + negative = node_helpers.conditioning_set_values(negative, {"time_dim_concat": comfy.latent_formats.Wan21().process_out(torch.zeros_like(concat_latent_image))}) + + out_latent = {} + out_latent["samples"] = latent + return (positive, cond2, negative, out_latent) + NODE_CLASS_MAPPINGS = { "WanImageToVideo": WanImageToVideo, "WanFunControlToVideo": WanFunControlToVideo, @@ -353,4 +391,5 @@ NODE_CLASS_MAPPINGS = { "WanVaceToVideo": WanVaceToVideo, "TrimVideoLatent": TrimVideoLatent, "WanCameraImageToVideo": WanCameraImageToVideo, + "WanPhantomSubjectToVideo": WanPhantomSubjectToVideo, } From fb83eda2879f0125323c4b91a2b235501d8f061e Mon Sep 17 00:00:00 2001 From: Robin Huang Date: Thu, 29 May 2025 00:03:11 -0700 Subject: [PATCH 155/193] Revert "Add support for Veo3 API node." (#8322) This reverts commit 592d05610072777d170cf44604366bc489ada81b. --- comfy_api_nodes/nodes_veo2.py | 19 +++---------------- 1 file changed, 3 insertions(+), 16 deletions(-) diff --git a/comfy_api_nodes/nodes_veo2.py b/comfy_api_nodes/nodes_veo2.py index e93f82a9a..df846d5dd 100644 --- a/comfy_api_nodes/nodes_veo2.py +++ b/comfy_api_nodes/nodes_veo2.py @@ -54,10 +54,6 @@ class VeoVideoGenerationNode(ComfyNodeABC): """ Generates videos from text prompts using Google's Veo API. - Supported models: - - veo-2.0-generate-001 - - veo-3.0-generate-preview - This node can create videos from text descriptions and optional image inputs, with control over parameters like aspect ratio, duration, and more. """ @@ -134,14 +130,6 @@ class VeoVideoGenerationNode(ComfyNodeABC): "default": None, "tooltip": "Optional reference image to guide video generation", }), - "model": ( - IO.COMBO, - { - "options": ["veo-2.0-generate-001", "veo-3.0-generate-preview"], - "default": "veo-2.0-generate-001", - "tooltip": "Model to use for video generation. Defaults to veo 2.0", - }, - ), }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", @@ -166,7 +154,6 @@ class VeoVideoGenerationNode(ComfyNodeABC): person_generation="ALLOW", seed=0, image=None, - model="veo-2.0-generate-001", unique_id: Optional[str] = None, **kwargs, ): @@ -205,7 +192,7 @@ class VeoVideoGenerationNode(ComfyNodeABC): # Initial request to start video generation initial_operation = SynchronousOperation( endpoint=ApiEndpoint( - path=f"/proxy/veo/{model}/generate", + path="/proxy/veo/generate", method=HttpMethod.POST, request_model=Veo2GenVidRequest, response_model=Veo2GenVidResponse @@ -236,7 +223,7 @@ class VeoVideoGenerationNode(ComfyNodeABC): # Define the polling operation poll_operation = PollingOperation( poll_endpoint=ApiEndpoint( - path=f"/proxy/veo/{model}/poll", + path="/proxy/veo/poll", method=HttpMethod.POST, request_model=Veo2GenVidPollRequest, response_model=Veo2GenVidPollResponse @@ -317,5 +304,5 @@ NODE_CLASS_MAPPINGS = { } NODE_DISPLAY_NAME_MAPPINGS = { - "VeoVideoGenerationNode": "Google Veo Video Generation", + "VeoVideoGenerationNode": "Google Veo2 Video Generation", } From f2289a1f597082ef8dc5895d7dd4b57edfa96ac8 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 29 May 2025 05:29:37 -0700 Subject: [PATCH 156/193] Delete useless file. (#8327) --- comfy/text_encoders/long_clipl.json | 25 ------------------------- 1 file changed, 25 deletions(-) delete mode 100644 comfy/text_encoders/long_clipl.json diff --git a/comfy/text_encoders/long_clipl.json b/comfy/text_encoders/long_clipl.json deleted file mode 100644 index 5e2056ff3..000000000 --- a/comfy/text_encoders/long_clipl.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "_name_or_path": "openai/clip-vit-large-patch14", - "architectures": [ - "CLIPTextModel" - ], - "attention_dropout": 0.0, - "bos_token_id": 0, - "dropout": 0.0, - "eos_token_id": 49407, - "hidden_act": "quick_gelu", - "hidden_size": 768, - "initializer_factor": 1.0, - "initializer_range": 0.02, - "intermediate_size": 3072, - "layer_norm_eps": 1e-05, - "max_position_embeddings": 248, - "model_type": "clip_text_model", - "num_attention_heads": 12, - "num_hidden_layers": 12, - "pad_token_id": 1, - "projection_dim": 768, - "torch_dtype": "float32", - "transformers_version": "4.24.0", - "vocab_size": 49408 -} From f1c9ca816ac1806982fe97b33eb8d7c76154bec1 Mon Sep 17 00:00:00 2001 From: Robin Huang Date: Thu, 29 May 2025 10:27:40 -0700 Subject: [PATCH 157/193] Add BFL Kontext API Nodes. (#8333) * Added initial Flux.1 Kontext Pro Image node - recreated branch to save myself sanity from rebase crap after master got rebased * Add safety filter to Kontext. * Make safety = 2 and input image is optional. * Add BFL kontext API nodes. --------- Co-authored-by: Jedrzej Kosinski --- comfy_api_nodes/apis/bfl_api.py | 34 ++++ comfy_api_nodes/nodes_bfl.py | 288 +++++++++++++++++++++++++++++++- 2 files changed, 321 insertions(+), 1 deletion(-) diff --git a/comfy_api_nodes/apis/bfl_api.py b/comfy_api_nodes/apis/bfl_api.py index c189038fb..504e507e1 100644 --- a/comfy_api_nodes/apis/bfl_api.py +++ b/comfy_api_nodes/apis/bfl_api.py @@ -108,6 +108,40 @@ class BFLFluxProGenerateRequest(BaseModel): # ) +class BFLFluxKontextProGenerateRequest(BaseModel): + prompt: str = Field(..., description='The text prompt for what you wannt to edit.') + input_image: Optional[str] = Field(None, description='Image to edit in base64 format') + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + guidance: confloat(ge=0.1, le=99.0) = Field(..., description='Guidance strength for the image generation process') + steps: conint(ge=1, le=150) = Field(..., description='Number of steps for the image generation process') + safety_tolerance: Optional[conint(ge=0, le=2)] = Field( + 2, description='Tolerance level for input and output moderation. Between 0 and 2, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + +class BFLFluxKontextMaxGenerateRequest(BaseModel): + prompt: str = Field(..., description='The text prompt for what you wannt to edit.') + input_image: Optional[str] = Field(None, description='Image to edit in base64 format') + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + guidance: confloat(ge=0.1, le=99.0) = Field(..., description='Guidance strength for the image generation process') + steps: conint(ge=1, le=150) = Field(..., description='Number of steps for the image generation process') + safety_tolerance: Optional[conint(ge=0, le=2)] = Field( + 2, description='Tolerance level for input and output moderation. Between 0 and 2, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + class BFLFluxProUltraGenerateRequest(BaseModel): prompt: str = Field(..., description='The text prompt for image generation.') prompt_upsampling: Optional[bool] = Field( diff --git a/comfy_api_nodes/nodes_bfl.py b/comfy_api_nodes/nodes_bfl.py index 509170b34..a762472e6 100644 --- a/comfy_api_nodes/nodes_bfl.py +++ b/comfy_api_nodes/nodes_bfl.py @@ -1,6 +1,6 @@ import io from inspect import cleandoc -from typing import Union +from typing import Union, Optional from comfy.comfy_types.node_typing import IO, ComfyNodeABC from comfy_api_nodes.apis.bfl_api import ( BFLStatus, @@ -9,6 +9,7 @@ from comfy_api_nodes.apis.bfl_api import ( BFLFluxCannyImageRequest, BFLFluxDepthImageRequest, BFLFluxProGenerateRequest, + BFLFluxKontextProGenerateRequest, BFLFluxProUltraGenerateRequest, BFLFluxProGenerateResponse, ) @@ -269,6 +270,287 @@ class FluxProUltraImageNode(ComfyNodeABC): return (output_image,) +class FluxKontextProImageNode(ComfyNodeABC): + """ + Edits images using Flux.1 Kontext Pro via api based on prompt and resolution. + """ + + MINIMUM_RATIO = 1 / 4 + MAXIMUM_RATIO = 4 / 1 + MINIMUM_RATIO_STR = "1:4" + MAXIMUM_RATIO_STR = "4:1" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation - specify what and how to edit.", + }, + ), + "aspect_ratio": ( + IO.STRING, + { + "default": "16:9", + "tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.", + }, + ), + "guidance": ( + IO.FLOAT, + { + "default": 3.0, + "min": 0.1, + "max": 99.0, + "step": 0.1, + "tooltip": "Guidance strength for the image generation process" + }, + ), + "steps": ( + IO.INT, + { + "default": 50, + "min": 1, + "max": 150, + "tooltip": "Number of steps for the image generation process" + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + "prompt_upsampling": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + }, + ), + }, + "optional": { + "input_image": (IO.IMAGE,), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + @classmethod + def VALIDATE_INPUTS(cls, aspect_ratio: str): + try: + validate_aspect_ratio( + aspect_ratio, + minimum_ratio=cls.MINIMUM_RATIO, + maximum_ratio=cls.MAXIMUM_RATIO, + minimum_ratio_str=cls.MINIMUM_RATIO_STR, + maximum_ratio_str=cls.MAXIMUM_RATIO_STR, + ) + except Exception as e: + return str(e) + return True + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/BFL" + + def api_call( + self, + prompt: str, + aspect_ratio: str, + guidance: float, + steps: int, + input_image: Optional[torch.Tensor]=None, + seed=0, + prompt_upsampling=False, + unique_id: Union[str, None] = None, + **kwargs, + ): + if input_image is None: + validate_string(prompt, strip_whitespace=False) + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/bfl/flux-kontext-pro/generate", + method=HttpMethod.POST, + request_model=BFLFluxKontextProGenerateRequest, + response_model=BFLFluxProGenerateResponse, + ), + request=BFLFluxKontextProGenerateRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + guidance=round(guidance, 1), + steps=steps, + seed=seed, + aspect_ratio=validate_aspect_ratio( + aspect_ratio, + minimum_ratio=self.MINIMUM_RATIO, + maximum_ratio=self.MAXIMUM_RATIO, + minimum_ratio_str=self.MINIMUM_RATIO_STR, + maximum_ratio_str=self.MAXIMUM_RATIO_STR, + ), + input_image=( + input_image + if input_image is None + else convert_image_to_base64(input_image) + ) + ), + auth_kwargs=kwargs, + ) + output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) + return (output_image,) + +class FluxKontextMaxImageNode(ComfyNodeABC): + """ + Edits images using Flux.1 Kontext Max via api based on prompt and resolution. + """ + + MINIMUM_RATIO = 1 / 4 + MAXIMUM_RATIO = 4 / 1 + MINIMUM_RATIO_STR = "1:4" + MAXIMUM_RATIO_STR = "4:1" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation - specify what and how to edit.", + }, + ), + "aspect_ratio": ( + IO.STRING, + { + "default": "16:9", + "tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.", + }, + ), + "guidance": ( + IO.FLOAT, + { + "default": 3.0, + "min": 0.1, + "max": 99.0, + "step": 0.1, + "tooltip": "Guidance strength for the image generation process" + }, + ), + "steps": ( + IO.INT, + { + "default": 50, + "min": 1, + "max": 150, + "tooltip": "Number of steps for the image generation process" + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + "prompt_upsampling": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + }, + ), + }, + "optional": { + "input_image": (IO.IMAGE,), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + @classmethod + def VALIDATE_INPUTS(cls, aspect_ratio: str): + try: + validate_aspect_ratio( + aspect_ratio, + minimum_ratio=cls.MINIMUM_RATIO, + maximum_ratio=cls.MAXIMUM_RATIO, + minimum_ratio_str=cls.MINIMUM_RATIO_STR, + maximum_ratio_str=cls.MAXIMUM_RATIO_STR, + ) + except Exception as e: + return str(e) + return True + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/BFL" + + def api_call( + self, + prompt: str, + aspect_ratio: str, + guidance: float, + steps: int, + input_image: Optional[torch.Tensor]=None, + seed=0, + prompt_upsampling=False, + unique_id: Union[str, None] = None, + **kwargs, + ): + if input_image is None: + validate_string(prompt, strip_whitespace=False) + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/bfl/flux-kontext-max/generate", + method=HttpMethod.POST, + request_model=BFLFluxKontextProGenerateRequest, + response_model=BFLFluxProGenerateResponse, + ), + request=BFLFluxKontextProGenerateRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + guidance=round(guidance, 1), + steps=steps, + seed=seed, + aspect_ratio=validate_aspect_ratio( + aspect_ratio, + minimum_ratio=self.MINIMUM_RATIO, + maximum_ratio=self.MAXIMUM_RATIO, + minimum_ratio_str=self.MINIMUM_RATIO_STR, + maximum_ratio_str=self.MAXIMUM_RATIO_STR, + ), + input_image=( + input_image + if input_image is None + else convert_image_to_base64(input_image) + ) + ), + auth_kwargs=kwargs, + ) + output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) + return (output_image,) class FluxProImageNode(ComfyNodeABC): """ @@ -914,6 +1196,8 @@ class FluxProDepthNode(ComfyNodeABC): NODE_CLASS_MAPPINGS = { "FluxProUltraImageNode": FluxProUltraImageNode, # "FluxProImageNode": FluxProImageNode, + "FluxKontextProImageNode": FluxKontextProImageNode, + "FluxKontextMaxImageNode": FluxKontextMaxImageNode, "FluxProExpandNode": FluxProExpandNode, "FluxProFillNode": FluxProFillNode, "FluxProCannyNode": FluxProCannyNode, @@ -924,6 +1208,8 @@ NODE_CLASS_MAPPINGS = { NODE_DISPLAY_NAME_MAPPINGS = { "FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image", # "FluxProImageNode": "Flux 1.1 [pro] Image", + "FluxKontextProImageNode": "Flux.1 Kontext Pro Image", + "FluxKontextMaxImageNode": "Flux.1 Kontext Max Image", "FluxProExpandNode": "Flux.1 Expand Image", "FluxProFillNode": "Flux.1 Fill Image", "FluxProCannyNode": "Flux.1 Canny Control Image", From 31260f02753d0809204225cb5f61bace900120fa Mon Sep 17 00:00:00 2001 From: filtered <176114999+webfiltered@users.noreply.github.com> Date: Fri, 30 May 2025 03:52:27 +1000 Subject: [PATCH 158/193] Update templates 0.1.22 (#8334) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 5e3c60659..d2baedf0d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.20.7 -comfyui-workflow-templates==0.1.20 +comfyui-workflow-templates==0.1.22 torch torchsde torchvision From 094306b626e9cf505690c5d8b445032b3b8a36fa Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Thu, 29 May 2025 14:26:39 -0400 Subject: [PATCH 159/193] ComfyUI version 0.3.39 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 64b326db8..f742410b1 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.38" +__version__ = "0.3.39" diff --git a/pyproject.toml b/pyproject.toml index 0a841c8ce..28a6158e0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.38" +version = "0.3.39" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9" From aeba0b3a268eeb66c3a12cae6fd97f7c2d28f36f Mon Sep 17 00:00:00 2001 From: Jedrzej Kosinski Date: Thu, 29 May 2025 14:14:27 -0700 Subject: [PATCH 160/193] Reduce code duplication for [pro] and [max], rename Pro and Max to [pro] and [max] to be consistent with other BFL nodes, make default seed for Kontext nodes be 1234. since 0 is interpreted by API as 'choose random seed' (#8337) --- comfy_api_nodes/apis/bfl_api.py | 16 ---- comfy_api_nodes/nodes_bfl.py | 153 +++----------------------------- 2 files changed, 12 insertions(+), 157 deletions(-) diff --git a/comfy_api_nodes/apis/bfl_api.py b/comfy_api_nodes/apis/bfl_api.py index 504e507e1..0e90aef7c 100644 --- a/comfy_api_nodes/apis/bfl_api.py +++ b/comfy_api_nodes/apis/bfl_api.py @@ -125,22 +125,6 @@ class BFLFluxKontextProGenerateRequest(BaseModel): None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' ) -class BFLFluxKontextMaxGenerateRequest(BaseModel): - prompt: str = Field(..., description='The text prompt for what you wannt to edit.') - input_image: Optional[str] = Field(None, description='Image to edit in base64 format') - seed: Optional[int] = Field(None, description='The seed value for reproducibility.') - guidance: confloat(ge=0.1, le=99.0) = Field(..., description='Guidance strength for the image generation process') - steps: conint(ge=1, le=150) = Field(..., description='Number of steps for the image generation process') - safety_tolerance: Optional[conint(ge=0, le=2)] = Field( - 2, description='Tolerance level for input and output moderation. Between 0 and 2, 0 being most strict, 6 being least strict. Defaults to 2.' - ) - output_format: Optional[BFLOutputFormat] = Field( - BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] - ) - aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.') - prompt_upsampling: Optional[bool] = Field( - None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' - ) class BFLFluxProUltraGenerateRequest(BaseModel): prompt: str = Field(..., description='The text prompt for image generation.') diff --git a/comfy_api_nodes/nodes_bfl.py b/comfy_api_nodes/nodes_bfl.py index a762472e6..010564704 100644 --- a/comfy_api_nodes/nodes_bfl.py +++ b/comfy_api_nodes/nodes_bfl.py @@ -272,7 +272,7 @@ class FluxProUltraImageNode(ComfyNodeABC): class FluxKontextProImageNode(ComfyNodeABC): """ - Edits images using Flux.1 Kontext Pro via api based on prompt and resolution. + Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio. """ MINIMUM_RATIO = 1 / 4 @@ -321,7 +321,7 @@ class FluxKontextProImageNode(ComfyNodeABC): "seed": ( IO.INT, { - "default": 0, + "default": 1234, "min": 0, "max": 0xFFFFFFFFFFFFFFFF, "control_after_generate": True, @@ -366,6 +366,8 @@ class FluxKontextProImageNode(ComfyNodeABC): API_NODE = True CATEGORY = "api node/image/BFL" + BFL_PATH = "/proxy/bfl/flux-kontext-pro/generate" + def api_call( self, prompt: str, @@ -382,7 +384,7 @@ class FluxKontextProImageNode(ComfyNodeABC): validate_string(prompt, strip_whitespace=False) operation = SynchronousOperation( endpoint=ApiEndpoint( - path="/proxy/bfl/flux-kontext-pro/generate", + path=self.BFL_PATH, method=HttpMethod.POST, request_model=BFLFluxKontextProGenerateRequest, response_model=BFLFluxProGenerateResponse, @@ -411,146 +413,15 @@ class FluxKontextProImageNode(ComfyNodeABC): output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) return (output_image,) -class FluxKontextMaxImageNode(ComfyNodeABC): + +class FluxKontextMaxImageNode(FluxKontextProImageNode): """ - Edits images using Flux.1 Kontext Max via api based on prompt and resolution. + Edits images using Flux.1 Kontext [max] via api based on prompt and aspect ratio. """ - MINIMUM_RATIO = 1 / 4 - MAXIMUM_RATIO = 4 / 1 - MINIMUM_RATIO_STR = "1:4" - MAXIMUM_RATIO_STR = "4:1" + DESCRIPTION = cleandoc(__doc__ or "") + BFL_PATH = "/proxy/bfl/flux-kontext-max/generate" - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation - specify what and how to edit.", - }, - ), - "aspect_ratio": ( - IO.STRING, - { - "default": "16:9", - "tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.", - }, - ), - "guidance": ( - IO.FLOAT, - { - "default": 3.0, - "min": 0.1, - "max": 99.0, - "step": 0.1, - "tooltip": "Guidance strength for the image generation process" - }, - ), - "steps": ( - IO.INT, - { - "default": 50, - "min": 1, - "max": 150, - "tooltip": "Number of steps for the image generation process" - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - "prompt_upsampling": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", - }, - ), - }, - "optional": { - "input_image": (IO.IMAGE,), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - @classmethod - def VALIDATE_INPUTS(cls, aspect_ratio: str): - try: - validate_aspect_ratio( - aspect_ratio, - minimum_ratio=cls.MINIMUM_RATIO, - maximum_ratio=cls.MAXIMUM_RATIO, - minimum_ratio_str=cls.MINIMUM_RATIO_STR, - maximum_ratio_str=cls.MAXIMUM_RATIO_STR, - ) - except Exception as e: - return str(e) - return True - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/BFL" - - def api_call( - self, - prompt: str, - aspect_ratio: str, - guidance: float, - steps: int, - input_image: Optional[torch.Tensor]=None, - seed=0, - prompt_upsampling=False, - unique_id: Union[str, None] = None, - **kwargs, - ): - if input_image is None: - validate_string(prompt, strip_whitespace=False) - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/bfl/flux-kontext-max/generate", - method=HttpMethod.POST, - request_model=BFLFluxKontextProGenerateRequest, - response_model=BFLFluxProGenerateResponse, - ), - request=BFLFluxKontextProGenerateRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - guidance=round(guidance, 1), - steps=steps, - seed=seed, - aspect_ratio=validate_aspect_ratio( - aspect_ratio, - minimum_ratio=self.MINIMUM_RATIO, - maximum_ratio=self.MAXIMUM_RATIO, - minimum_ratio_str=self.MINIMUM_RATIO_STR, - maximum_ratio_str=self.MAXIMUM_RATIO_STR, - ), - input_image=( - input_image - if input_image is None - else convert_image_to_base64(input_image) - ) - ), - auth_kwargs=kwargs, - ) - output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) - return (output_image,) class FluxProImageNode(ComfyNodeABC): """ @@ -1208,8 +1079,8 @@ NODE_CLASS_MAPPINGS = { NODE_DISPLAY_NAME_MAPPINGS = { "FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image", # "FluxProImageNode": "Flux 1.1 [pro] Image", - "FluxKontextProImageNode": "Flux.1 Kontext Pro Image", - "FluxKontextMaxImageNode": "Flux.1 Kontext Max Image", + "FluxKontextProImageNode": "Flux.1 Kontext [pro] Image", + "FluxKontextMaxImageNode": "Flux.1 Kontext [max] Image", "FluxProExpandNode": "Flux.1 Expand Image", "FluxProFillNode": "Flux.1 Fill Image", "FluxProCannyNode": "Flux.1 Canny Control Image", From 1d9fee79fd93505ee577f9881c8d9a0977affcd3 Mon Sep 17 00:00:00 2001 From: JettHu <35261585+JettHu@users.noreply.github.com> Date: Sat, 31 May 2025 03:08:59 +0800 Subject: [PATCH 161/193] Add node for regex replace(sub) operation (#8340) * Add node for regex replace(sub) operation * Apply suggestions from code review add tooltips Co-authored-by: Christian Byrne * Fix indentation --------- Co-authored-by: Christian Byrne --- comfy_extras/nodes_string.py | 41 ++++++++++++++++++++++++++++++++++-- 1 file changed, 39 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_string.py b/comfy_extras/nodes_string.py index b24222cee..b1a8ceef0 100644 --- a/comfy_extras/nodes_string.py +++ b/comfy_extras/nodes_string.py @@ -296,6 +296,41 @@ class RegexExtract(): return result, + +class RegexReplace(): + DESCRIPTION = "Find and replace text using regex patterns." + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "regex_pattern": (IO.STRING, {"multiline": True}), + "replace": (IO.STRING, {"multiline": True}), + }, + "optional": { + "case_insensitive": (IO.BOOLEAN, {"default": True}), + "multiline": (IO.BOOLEAN, {"default": False}), + "dotall": (IO.BOOLEAN, {"default": False, "tooltip": "When enabled, the dot (.) character will match any character including newline characters. When disabled, dots won't match newlines."}), + "count": (IO.INT, {"default": 0, "min": 0, "max": 100, "tooltip": "Maximum number of replacements to make. Set to 0 to replace all occurrences (default). Set to 1 to replace only the first match, 2 for the first two matches, etc."}), + } + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, regex_pattern, replace, case_insensitive=True, multiline=False, dotall=False, count=0, **kwargs): + flags = 0 + + if case_insensitive: + flags |= re.IGNORECASE + if multiline: + flags |= re.MULTILINE + if dotall: + flags |= re.DOTALL + result = re.sub(regex_pattern, replace, string, count=count, flags=flags) + return result, + NODE_CLASS_MAPPINGS = { "StringConcatenate": StringConcatenate, "StringSubstring": StringSubstring, @@ -306,7 +341,8 @@ NODE_CLASS_MAPPINGS = { "StringContains": StringContains, "StringCompare": StringCompare, "RegexMatch": RegexMatch, - "RegexExtract": RegexExtract + "RegexExtract": RegexExtract, + "RegexReplace": RegexReplace, } NODE_DISPLAY_NAME_MAPPINGS = { @@ -319,5 +355,6 @@ NODE_DISPLAY_NAME_MAPPINGS = { "StringContains": "Contains", "StringCompare": "Compare", "RegexMatch": "Regex Match", - "RegexExtract": "Regex Extract" + "RegexExtract": "Regex Extract", + "RegexReplace": "Regex Replace", } From 704fc788549112877a0739eebd32f37d6c85982e Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 30 May 2025 12:41:02 -0700 Subject: [PATCH 162/193] Put ROCm version in tuple to make it easier to enable stuff based on it. (#8348) --- comfy/model_management.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/comfy/model_management.py b/comfy/model_management.py index f5b37e68e..8ae5a5abb 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -297,8 +297,13 @@ except: try: if is_amd(): + try: + rocm_version = tuple(map(int, str(torch.version.hip).split(".")[:2])) + except: + rocm_version = (6, -1) arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName logging.info("AMD arch: {}".format(arch)) + logging.info("ROCm version: {}".format(rocm_version)) if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: if torch_version_numeric[0] >= 2 and torch_version_numeric[1] >= 7: # works on 2.6 but doesn't actually seem to improve much if any((a in arch) for a in ["gfx1100", "gfx1101", "gfx1151"]): # TODO: more arches From df1aebe52eb888f8087131456775a517934cb245 Mon Sep 17 00:00:00 2001 From: Chenlei Hu Date: Fri, 30 May 2025 17:27:52 -0400 Subject: [PATCH 163/193] Remove huchenlei from CODEOWNERS (#8350) --- CODEOWNERS | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/CODEOWNERS b/CODEOWNERS index 013ea8622..c4acbf06e 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -5,20 +5,20 @@ # Inlined the team members for now. # Maintainers -*.md @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne -/tests/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne -/tests-unit/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne -/notebooks/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne -/script_examples/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne -/.github/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne -/requirements.txt @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne -/pyproject.toml @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne +*.md @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne +/tests/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne +/tests-unit/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne +/notebooks/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne +/script_examples/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne +/.github/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne +/requirements.txt @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne +/pyproject.toml @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne # Python web server -/api_server/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne -/app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne -/utils/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne +/api_server/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne +/app/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne +/utils/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne # Node developers -/comfy_extras/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne -/comfy/comfy_types/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne +/comfy_extras/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne +/comfy/comfy_types/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne From 6c319cbb4e25f38d03d1045077638cc11819e636 Mon Sep 17 00:00:00 2001 From: BennyKok Date: Sat, 31 May 2025 05:51:28 +0800 Subject: [PATCH 164/193] fix: custom comfy-api-base works with subpath (#8332) --- comfy_api_nodes/apis/client.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py index 0897d5d78..2a4bac88b 100644 --- a/comfy_api_nodes/apis/client.py +++ b/comfy_api_nodes/apis/client.py @@ -327,7 +327,9 @@ class ApiClient: ApiServerError: If the API server is unreachable but internet is working Exception: For other request failures """ - url = urljoin(self.base_url, path) + # Use urljoin but ensure path is relative to avoid absolute path behavior + relative_path = path.lstrip('/') + url = urljoin(self.base_url, relative_path) self.check_auth(self.auth_token, self.comfy_api_key) # Combine default headers with any provided headers request_headers = self.get_headers() From 08b7cc750681be5abaf31cfa0f7003eb6fc8cf56 Mon Sep 17 00:00:00 2001 From: drhead <1313496+drhead@users.noreply.github.com> Date: Fri, 30 May 2025 18:09:54 -0400 Subject: [PATCH 165/193] use fused multiply-add pointwise ops in chroma (#8279) --- comfy/ldm/chroma/layers.py | 20 +++++++++----------- 1 file changed, 9 insertions(+), 11 deletions(-) diff --git a/comfy/ldm/chroma/layers.py b/comfy/ldm/chroma/layers.py index 35da91ee2..2a0dec606 100644 --- a/comfy/ldm/chroma/layers.py +++ b/comfy/ldm/chroma/layers.py @@ -80,15 +80,13 @@ class DoubleStreamBlock(nn.Module): (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec # prepare image for attention - img_modulated = self.img_norm1(img) - img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_modulated = torch.addcmul(img_mod1.shift, 1 + img_mod1.scale, self.img_norm1(img)) img_qkv = self.img_attn.qkv(img_modulated) img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) # prepare txt for attention - txt_modulated = self.txt_norm1(txt) - txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_modulated = torch.addcmul(txt_mod1.shift, 1 + txt_mod1.scale, self.txt_norm1(txt)) txt_qkv = self.txt_attn.qkv(txt_modulated) txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) @@ -102,12 +100,12 @@ class DoubleStreamBlock(nn.Module): txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] # calculate the img bloks - img = img + img_mod1.gate * self.img_attn.proj(img_attn) - img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) + img.addcmul_(img_mod1.gate, self.img_attn.proj(img_attn)) + img.addcmul_(img_mod2.gate, self.img_mlp(torch.addcmul(img_mod2.shift, 1 + img_mod2.scale, self.img_norm2(img)))) # calculate the txt bloks - txt += txt_mod1.gate * self.txt_attn.proj(txt_attn) - txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) + txt.addcmul_(txt_mod1.gate, self.txt_attn.proj(txt_attn)) + txt.addcmul_(txt_mod2.gate, self.txt_mlp(torch.addcmul(txt_mod2.shift, 1 + txt_mod2.scale, self.txt_norm2(txt)))) if txt.dtype == torch.float16: txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) @@ -152,7 +150,7 @@ class SingleStreamBlock(nn.Module): def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor: mod = vec - x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift + x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x)) qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) @@ -162,7 +160,7 @@ class SingleStreamBlock(nn.Module): attn = attention(q, k, v, pe=pe, mask=attn_mask) # compute activation in mlp stream, cat again and run second linear layer output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) - x += mod.gate * output + x.addcmul_(mod.gate, output) if x.dtype == torch.float16: x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) return x @@ -178,6 +176,6 @@ class LastLayer(nn.Module): shift, scale = vec shift = shift.squeeze(1) scale = scale.squeeze(1) - x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] + x = torch.addcmul(shift[:, None, :], 1 + scale[:, None, :], self.norm_final(x)) x = self.linear(x) return x From 97f23b81f3421255ec4b425d2d8f4841207e0cd8 Mon Sep 17 00:00:00 2001 From: ComfyUI Wiki Date: Sat, 31 May 2025 14:05:42 +0800 Subject: [PATCH 166/193] Bump template to 0.1.23 (#8353) Correct some error settings in VACE --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index d2baedf0d..3e2991563 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.20.7 -comfyui-workflow-templates==0.1.22 +comfyui-workflow-templates==0.1.23 torch torchsde torchvision From 19e45e9b0e235acafc120a7532ce3825b8a325b9 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 31 May 2025 17:00:20 -0700 Subject: [PATCH 167/193] Make it easier to pass lists of tensors to models. (#8358) --- comfy/conds.py | 42 ++++++++++++++++++++++++++++++++++++++++++ comfy/model_base.py | 5 +++++ 2 files changed, 47 insertions(+) diff --git a/comfy/conds.py b/comfy/conds.py index 920e25488..2af2a43a3 100644 --- a/comfy/conds.py +++ b/comfy/conds.py @@ -86,3 +86,45 @@ class CONDConstant(CONDRegular): def size(self): return [1] + + +class CONDList(CONDRegular): + def __init__(self, cond): + self.cond = cond + + def process_cond(self, batch_size, device, **kwargs): + out = [] + for c in self.cond: + out.append(comfy.utils.repeat_to_batch_size(c, batch_size).to(device)) + + return self._copy_with(out) + + def can_concat(self, other): + if len(self.cond) != len(other.cond): + return False + for i in range(len(self.cond)): + if self.cond[i].shape != other.cond[i].shape: + return False + + return True + + def concat(self, others): + out = [] + for i in range(len(self.cond)): + o = [self.cond[i]] + for x in others: + o.append(x.cond[i]) + out.append(torch.cat(o)) + + return out + + def size(self): # hackish implementation to make the mem estimation work + o = 0 + c = 1 + for c in self.cond: + size = c.size() + o += math.prod(size) + if len(size) > 1: + c = size[1] + + return [1, c, o // c] diff --git a/comfy/model_base.py b/comfy/model_base.py index 8ed124277..638b04092 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -168,6 +168,11 @@ class BaseModel(torch.nn.Module): if hasattr(extra, "dtype"): if extra.dtype != torch.int and extra.dtype != torch.long: extra = extra.to(dtype) + if isinstance(extra, list): + ex = [] + for ext in extra: + ex.append(ext.to(dtype)) + extra = ex extra_conds[o] = extra t = self.process_timestep(t, x=x, **extra_conds) From 456abad83486416baff00f25f97b7c5e32ab8b74 Mon Sep 17 00:00:00 2001 From: filtered <176114999+webfiltered@users.noreply.github.com> Date: Sun, 1 Jun 2025 15:10:04 +1000 Subject: [PATCH 168/193] Update frontend to 1.21 (#8366) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 3e2991563..c5219bd9f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.20.7 +comfyui-frontend-package==1.21.3 comfyui-workflow-templates==0.1.23 torch torchsde From d062fcc5c0bf32cf0dc55ff469470789e775ae27 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Sun, 1 Jun 2025 01:28:52 -0700 Subject: [PATCH 169/193] [feat] Add ImageStitch node for concatenating images (#8369) * [feat] Add ImageStitch node for concatenating images with borders Add ImageStitch node that concatenates images in four directions with optional borders and intelligent size handling. Features include optional second image input, configurable borders with color selection, automatic batch size matching, and dimension alignment via padding or resizing. Upstreamed from https://github.com/kijai/ComfyUI-KJNodes with enhancements for better error handling and comprehensive test coverage. * [fix] Fix CI issues with CUDA dependencies and linting - Mock CUDA-dependent modules in tests to avoid CI failures on CPU-only runners - Fix ruff linting issues for code style compliance * [fix] Improve CI compatibility by mocking nodes module import Prevent CUDA initialization chain by mocking the nodes module at import time, which is cleaner than deep mocking of CUDA-specific functions. * [refactor] Clean up ImageStitch tests - Remove unnecessary sys.path manipulation (pythonpath set in pytest.ini) - Remove metadata tests that test framework internals rather than functionality - Rename complex scenario test to be more descriptive of what it tests * [refactor] Rename 'border' to 'spacing' for semantic accuracy - Change border_width/border_color to spacing_width/spacing_color in API - Update all tests to use spacing terminology - Update comments and variable names throughout - More accurately describes the gap/separator between images --- comfy_extras/nodes_images.py | 182 +++++++++++++ nodes.py | 1 + tests-unit/comfy_extras_test/__init__.py | 0 .../comfy_extras_test/image_stitch_test.py | 240 ++++++++++++++++++ 4 files changed, 423 insertions(+) create mode 100644 tests-unit/comfy_extras_test/__init__.py create mode 100644 tests-unit/comfy_extras_test/image_stitch_test.py diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py index 29a5d5b61..6ebf1dbd8 100644 --- a/comfy_extras/nodes_images.py +++ b/comfy_extras/nodes_images.py @@ -14,6 +14,7 @@ import re from io import BytesIO from inspect import cleandoc import torch +import comfy.utils from comfy.comfy_types import FileLocator @@ -229,6 +230,186 @@ class SVG: all_svgs_list.extend(svg_item.data) return SVG(all_svgs_list) + +class ImageStitch: + """Upstreamed from https://github.com/kijai/ComfyUI-KJNodes""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image1": ("IMAGE",), + "direction": (["right", "down", "left", "up"], {"default": "right"}), + "match_image_size": ("BOOLEAN", {"default": True}), + "spacing_width": ( + "INT", + {"default": 0, "min": 0, "max": 1024, "step": 2}, + ), + "spacing_color": ( + ["white", "black", "red", "green", "blue"], + {"default": "white"}, + ), + }, + "optional": { + "image2": ("IMAGE",), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "stitch" + CATEGORY = "image/transform" + DESCRIPTION = """ +Stitches image2 to image1 in the specified direction. +If image2 is not provided, returns image1 unchanged. +Optional spacing can be added between images. +""" + + def stitch( + self, + image1, + direction, + match_image_size, + spacing_width, + spacing_color, + image2=None, + ): + if image2 is None: + return (image1,) + + # Handle batch size differences + if image1.shape[0] != image2.shape[0]: + max_batch = max(image1.shape[0], image2.shape[0]) + if image1.shape[0] < max_batch: + image1 = torch.cat( + [image1, image1[-1:].repeat(max_batch - image1.shape[0], 1, 1, 1)] + ) + if image2.shape[0] < max_batch: + image2 = torch.cat( + [image2, image2[-1:].repeat(max_batch - image2.shape[0], 1, 1, 1)] + ) + + # Match image sizes if requested + if match_image_size: + h1, w1 = image1.shape[1:3] + h2, w2 = image2.shape[1:3] + aspect_ratio = w2 / h2 + + if direction in ["left", "right"]: + target_h, target_w = h1, int(h1 * aspect_ratio) + else: # up, down + target_w, target_h = w1, int(w1 / aspect_ratio) + + image2 = comfy.utils.common_upscale( + image2.movedim(-1, 1), target_w, target_h, "lanczos", "disabled" + ).movedim(1, -1) + + # When not matching sizes, pad to align non-concat dimensions + if not match_image_size: + h1, w1 = image1.shape[1:3] + h2, w2 = image2.shape[1:3] + + if direction in ["left", "right"]: + # For horizontal concat, pad heights to match + if h1 != h2: + target_h = max(h1, h2) + if h1 < target_h: + pad_h = target_h - h1 + pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2 + image1 = torch.nn.functional.pad(image1, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=0.0) + if h2 < target_h: + pad_h = target_h - h2 + pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2 + image2 = torch.nn.functional.pad(image2, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=0.0) + else: # up, down + # For vertical concat, pad widths to match + if w1 != w2: + target_w = max(w1, w2) + if w1 < target_w: + pad_w = target_w - w1 + pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2 + image1 = torch.nn.functional.pad(image1, (0, 0, pad_left, pad_right), mode='constant', value=0.0) + if w2 < target_w: + pad_w = target_w - w2 + pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2 + image2 = torch.nn.functional.pad(image2, (0, 0, pad_left, pad_right), mode='constant', value=0.0) + + # Ensure same number of channels + if image1.shape[-1] != image2.shape[-1]: + max_channels = max(image1.shape[-1], image2.shape[-1]) + if image1.shape[-1] < max_channels: + image1 = torch.cat( + [ + image1, + torch.ones( + *image1.shape[:-1], + max_channels - image1.shape[-1], + device=image1.device, + ), + ], + dim=-1, + ) + if image2.shape[-1] < max_channels: + image2 = torch.cat( + [ + image2, + torch.ones( + *image2.shape[:-1], + max_channels - image2.shape[-1], + device=image2.device, + ), + ], + dim=-1, + ) + + # Add spacing if specified + if spacing_width > 0: + spacing_width = spacing_width + (spacing_width % 2) # Ensure even + + color_map = { + "white": 1.0, + "black": 0.0, + "red": (1.0, 0.0, 0.0), + "green": (0.0, 1.0, 0.0), + "blue": (0.0, 0.0, 1.0), + } + color_val = color_map[spacing_color] + + if direction in ["left", "right"]: + spacing_shape = ( + image1.shape[0], + max(image1.shape[1], image2.shape[1]), + spacing_width, + image1.shape[-1], + ) + else: + spacing_shape = ( + image1.shape[0], + spacing_width, + max(image1.shape[2], image2.shape[2]), + image1.shape[-1], + ) + + spacing = torch.full(spacing_shape, 0.0, device=image1.device) + if isinstance(color_val, tuple): + for i, c in enumerate(color_val): + if i < spacing.shape[-1]: + spacing[..., i] = c + if spacing.shape[-1] == 4: # Add alpha + spacing[..., 3] = 1.0 + else: + spacing[..., : min(3, spacing.shape[-1])] = color_val + if spacing.shape[-1] == 4: + spacing[..., 3] = 1.0 + + # Concatenate images + images = [image2, image1] if direction in ["left", "up"] else [image1, image2] + if spacing_width > 0: + images.insert(1, spacing) + + concat_dim = 2 if direction in ["left", "right"] else 1 + return (torch.cat(images, dim=concat_dim),) + + class SaveSVGNode: """ Save SVG files on disk. @@ -318,4 +499,5 @@ NODE_CLASS_MAPPINGS = { "SaveAnimatedWEBP": SaveAnimatedWEBP, "SaveAnimatedPNG": SaveAnimatedPNG, "SaveSVGNode": SaveSVGNode, + "ImageStitch": ImageStitch, } diff --git a/nodes.py b/nodes.py index 2d499051e..67360e7da 100644 --- a/nodes.py +++ b/nodes.py @@ -2061,6 +2061,7 @@ NODE_DISPLAY_NAME_MAPPINGS = { "ImagePadForOutpaint": "Pad Image for Outpainting", "ImageBatch": "Batch Images", "ImageCrop": "Image Crop", + "ImageStitch": "Image Stitch", "ImageBlend": "Image Blend", "ImageBlur": "Image Blur", "ImageQuantize": "Image Quantize", diff --git a/tests-unit/comfy_extras_test/__init__.py b/tests-unit/comfy_extras_test/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests-unit/comfy_extras_test/image_stitch_test.py b/tests-unit/comfy_extras_test/image_stitch_test.py new file mode 100644 index 000000000..fbaef756c --- /dev/null +++ b/tests-unit/comfy_extras_test/image_stitch_test.py @@ -0,0 +1,240 @@ +import torch +from unittest.mock import patch, MagicMock + +# Mock nodes module to prevent CUDA initialization during import +mock_nodes = MagicMock() +mock_nodes.MAX_RESOLUTION = 16384 + +with patch.dict('sys.modules', {'nodes': mock_nodes}): + from comfy_extras.nodes_images import ImageStitch + + +class TestImageStitch: + + def create_test_image(self, batch_size=1, height=64, width=64, channels=3): + """Helper to create test images with specific dimensions""" + return torch.rand(batch_size, height, width, channels) + + def test_no_image2_passthrough(self): + """Test that when image2 is None, image1 is returned unchanged""" + node = ImageStitch() + image1 = self.create_test_image() + + result = node.stitch(image1, "right", True, 0, "white", image2=None) + + assert len(result) == 1 + assert torch.equal(result[0], image1) + + def test_basic_horizontal_stitch_right(self): + """Test basic horizontal stitching to the right""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=32, width=24) + + result = node.stitch(image1, "right", False, 0, "white", image2) + + assert result[0].shape == (1, 32, 56, 3) # 32 + 24 width + + def test_basic_horizontal_stitch_left(self): + """Test basic horizontal stitching to the left""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=32, width=24) + + result = node.stitch(image1, "left", False, 0, "white", image2) + + assert result[0].shape == (1, 32, 56, 3) # 24 + 32 width + + def test_basic_vertical_stitch_down(self): + """Test basic vertical stitching downward""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=24, width=32) + + result = node.stitch(image1, "down", False, 0, "white", image2) + + assert result[0].shape == (1, 56, 32, 3) # 32 + 24 height + + def test_basic_vertical_stitch_up(self): + """Test basic vertical stitching upward""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=24, width=32) + + result = node.stitch(image1, "up", False, 0, "white", image2) + + assert result[0].shape == (1, 56, 32, 3) # 24 + 32 height + + def test_size_matching_horizontal(self): + """Test size matching for horizontal concatenation""" + node = ImageStitch() + image1 = self.create_test_image(height=64, width=64) + image2 = self.create_test_image(height=32, width=32) # Different aspect ratio + + result = node.stitch(image1, "right", True, 0, "white", image2) + + # image2 should be resized to match image1's height (64) with preserved aspect ratio + expected_width = 64 + 64 # original + resized (32*64/32 = 64) + assert result[0].shape == (1, 64, expected_width, 3) + + def test_size_matching_vertical(self): + """Test size matching for vertical concatenation""" + node = ImageStitch() + image1 = self.create_test_image(height=64, width=64) + image2 = self.create_test_image(height=32, width=32) + + result = node.stitch(image1, "down", True, 0, "white", image2) + + # image2 should be resized to match image1's width (64) with preserved aspect ratio + expected_height = 64 + 64 # original + resized (32*64/32 = 64) + assert result[0].shape == (1, expected_height, 64, 3) + + def test_padding_for_mismatched_heights_horizontal(self): + """Test padding when heights don't match in horizontal concatenation""" + node = ImageStitch() + image1 = self.create_test_image(height=64, width=32) + image2 = self.create_test_image(height=48, width=24) # Shorter height + + result = node.stitch(image1, "right", False, 0, "white", image2) + + # Both images should be padded to height 64 + assert result[0].shape == (1, 64, 56, 3) # 32 + 24 width, max(64,48) height + + def test_padding_for_mismatched_widths_vertical(self): + """Test padding when widths don't match in vertical concatenation""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=64) + image2 = self.create_test_image(height=24, width=48) # Narrower width + + result = node.stitch(image1, "down", False, 0, "white", image2) + + # Both images should be padded to width 64 + assert result[0].shape == (1, 56, 64, 3) # 32 + 24 height, max(64,48) width + + def test_spacing_horizontal(self): + """Test spacing addition in horizontal concatenation""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=32, width=24) + spacing_width = 16 + + result = node.stitch(image1, "right", False, spacing_width, "white", image2) + + # Expected width: 32 + 16 (spacing) + 24 = 72 + assert result[0].shape == (1, 32, 72, 3) + + def test_spacing_vertical(self): + """Test spacing addition in vertical concatenation""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=24, width=32) + spacing_width = 16 + + result = node.stitch(image1, "down", False, spacing_width, "white", image2) + + # Expected height: 32 + 16 (spacing) + 24 = 72 + assert result[0].shape == (1, 72, 32, 3) + + def test_spacing_color_values(self): + """Test that spacing colors are applied correctly""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=32, width=32) + + # Test white spacing + result_white = node.stitch(image1, "right", False, 16, "white", image2) + # Check that spacing region contains white values (close to 1.0) + spacing_region = result_white[0][:, :, 32:48, :] # Middle 16 pixels + assert torch.all(spacing_region >= 0.9) # Should be close to white + + # Test black spacing + result_black = node.stitch(image1, "right", False, 16, "black", image2) + spacing_region = result_black[0][:, :, 32:48, :] + assert torch.all(spacing_region <= 0.1) # Should be close to black + + def test_odd_spacing_width_made_even(self): + """Test that odd spacing widths are made even""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=32, width=32) + + # Use odd spacing width + result = node.stitch(image1, "right", False, 15, "white", image2) + + # Should be made even (16), so total width = 32 + 16 + 32 = 80 + assert result[0].shape == (1, 32, 80, 3) + + def test_batch_size_matching(self): + """Test that different batch sizes are handled correctly""" + node = ImageStitch() + image1 = self.create_test_image(batch_size=2, height=32, width=32) + image2 = self.create_test_image(batch_size=1, height=32, width=32) + + result = node.stitch(image1, "right", False, 0, "white", image2) + + # Should match larger batch size + assert result[0].shape == (2, 32, 64, 3) + + def test_channel_matching_rgb_to_rgba(self): + """Test that channel differences are handled (RGB + alpha)""" + node = ImageStitch() + image1 = self.create_test_image(channels=3) # RGB + image2 = self.create_test_image(channels=4) # RGBA + + result = node.stitch(image1, "right", False, 0, "white", image2) + + # Should have 4 channels (RGBA) + assert result[0].shape[-1] == 4 + + def test_channel_matching_rgba_to_rgb(self): + """Test that channel differences are handled (RGBA + RGB)""" + node = ImageStitch() + image1 = self.create_test_image(channels=4) # RGBA + image2 = self.create_test_image(channels=3) # RGB + + result = node.stitch(image1, "right", False, 0, "white", image2) + + # Should have 4 channels (RGBA) + assert result[0].shape[-1] == 4 + + def test_all_color_options(self): + """Test all available color options""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=32, width=32) + + colors = ["white", "black", "red", "green", "blue"] + + for color in colors: + result = node.stitch(image1, "right", False, 16, color, image2) + assert result[0].shape == (1, 32, 80, 3) # Basic shape check + + def test_all_directions(self): + """Test all direction options""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=32, width=32) + + directions = ["right", "left", "up", "down"] + + for direction in directions: + result = node.stitch(image1, direction, False, 0, "white", image2) + assert result[0].shape == (1, 32, 64, 3) if direction in ["right", "left"] else (1, 64, 32, 3) + + def test_batch_size_channel_spacing_integration(self): + """Test integration of batch matching, channel matching, size matching, and spacings""" + node = ImageStitch() + image1 = self.create_test_image(batch_size=2, height=64, width=48, channels=3) + image2 = self.create_test_image(batch_size=1, height=32, width=32, channels=4) + + result = node.stitch(image1, "right", True, 8, "red", image2) + + # Should handle: batch matching, size matching, channel matching, spacing + assert result[0].shape[0] == 2 # Batch size matched + assert result[0].shape[-1] == 4 # Channels matched to max + assert result[0].shape[1] == 64 # Height from image1 (size matching) + # Width should be: 48 + 8 (spacing) + resized_image2_width + expected_image2_width = int(64 * (32/32)) # Resized to height 64 + expected_total_width = 48 + 8 + expected_image2_width + assert result[0].shape[2] == expected_total_width + From 180db6753f019a1936b3774de24392821b53cd8c Mon Sep 17 00:00:00 2001 From: Benjamin Lu Date: Sun, 1 Jun 2025 04:32:32 -0400 Subject: [PATCH 170/193] Add Help Menu in NodeLibrarySidebarTab (#8179) --- app/frontend_management.py | 13 +++++++++++++ requirements.txt | 1 + server.py | 7 +++++++ 3 files changed, 21 insertions(+) diff --git a/app/frontend_management.py b/app/frontend_management.py index 7b7923b79..d9ef8c921 100644 --- a/app/frontend_management.py +++ b/app/frontend_management.py @@ -205,6 +205,19 @@ comfyui-workflow-templates is not installed. """.strip() ) + @classmethod + def embedded_docs_path(cls) -> str: + """Get the path to embedded documentation""" + try: + import comfyui_embedded_docs + + return str( + importlib.resources.files(comfyui_embedded_docs) / "docs" + ) + except ImportError: + logging.info("comfyui-embedded-docs package not found") + return None + @classmethod def parse_version_string(cls, value: str) -> tuple[str, str, str]: """ diff --git a/requirements.txt b/requirements.txt index c5219bd9f..60174ff57 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,6 @@ comfyui-frontend-package==1.21.3 comfyui-workflow-templates==0.1.23 +comfyui-embedded-docs==0.2.0 torch torchsde torchvision diff --git a/server.py b/server.py index 1b0a73601..6e283fe31 100644 --- a/server.py +++ b/server.py @@ -746,6 +746,13 @@ class PromptServer(): web.static('/templates', workflow_templates_path) ]) + # Serve embedded documentation from the package + embedded_docs_path = FrontendManager.embedded_docs_path() + if embedded_docs_path: + self.app.add_routes([ + web.static('/docs', embedded_docs_path) + ]) + self.app.add_routes([ web.static('/', self.web_root), ]) From fb4754624d0fd4d2b6f46ef15f2c9f3942a1bad5 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 1 Jun 2025 02:39:54 -0700 Subject: [PATCH 171/193] Make the casting in lists the same as regular inputs. (#8373) --- comfy/model_base.py | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index 638b04092..e0c2bcaa8 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -102,6 +102,13 @@ def model_sampling(model_config, model_type): return ModelSampling(model_config) +def convert_tensor(extra, dtype): + if hasattr(extra, "dtype"): + if extra.dtype != torch.int and extra.dtype != torch.long: + extra = extra.to(dtype) + return extra + + class BaseModel(torch.nn.Module): def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel): super().__init__() @@ -165,13 +172,13 @@ class BaseModel(torch.nn.Module): extra_conds = {} for o in kwargs: extra = kwargs[o] + if hasattr(extra, "dtype"): - if extra.dtype != torch.int and extra.dtype != torch.long: - extra = extra.to(dtype) - if isinstance(extra, list): + extra = convert_tensor(extra, dtype) + elif isinstance(extra, list): ex = [] for ext in extra: - ex.append(ext.to(dtype)) + ex.append(convert_tensor(ext, dtype)) extra = ex extra_conds[o] = extra From d3bd983b91d981b60da8cacd5489b18307303d2e Mon Sep 17 00:00:00 2001 From: ComfyUI Wiki Date: Sun, 1 Jun 2025 17:41:17 +0800 Subject: [PATCH 172/193] Bump template to 0.1.25 (#8372) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 60174ff57..b98dc1268 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.21.3 -comfyui-workflow-templates==0.1.23 +comfyui-workflow-templates==0.1.25 comfyui-embedded-docs==0.2.0 torch torchsde From fd943c928f549e96507e6e31ba34a7b541684560 Mon Sep 17 00:00:00 2001 From: filtered <176114999+webfiltered@users.noreply.github.com> Date: Mon, 2 Jun 2025 03:57:53 +1000 Subject: [PATCH 173/193] [BugFix] Update frontend to 1.21.4 (#8377) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index b98dc1268..181c23918 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.21.3 +comfyui-frontend-package==1.21.4 comfyui-workflow-templates==0.1.25 comfyui-embedded-docs==0.2.0 torch From 67f57c5bccb22124368cc462ba075ebaf54399ce Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Sun, 1 Jun 2025 12:47:07 -0700 Subject: [PATCH 174/193] [feat] add custom node testing requirement to issue templates (#8374) Adds mandatory checkbox to bug report and user support templates requiring users to confirm they've tested with custom nodes disabled before submitting issues. --- .github/ISSUE_TEMPLATE/bug-report.yml | 8 ++++++++ .github/ISSUE_TEMPLATE/user-support.yml | 8 ++++++++ 2 files changed, 16 insertions(+) diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml index 39d1992d7..69ce998eb 100644 --- a/.github/ISSUE_TEMPLATE/bug-report.yml +++ b/.github/ISSUE_TEMPLATE/bug-report.yml @@ -15,6 +15,14 @@ body: steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen. If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first. + - type: checkboxes + id: custom-nodes-test + attributes: + label: Custom Node Testing + description: Please confirm you have tried to reproduce the issue with all custom nodes disabled. + options: + - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help) + required: true - type: textarea attributes: label: Expected Behavior diff --git a/.github/ISSUE_TEMPLATE/user-support.yml b/.github/ISSUE_TEMPLATE/user-support.yml index df28804c6..50657d493 100644 --- a/.github/ISSUE_TEMPLATE/user-support.yml +++ b/.github/ISSUE_TEMPLATE/user-support.yml @@ -11,6 +11,14 @@ body: **2:** You have made an effort to find public answers to your question before asking here. In other words, you googled it first, and scrolled through recent help topics. If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first. + - type: checkboxes + id: custom-nodes-test + attributes: + label: Custom Node Testing + description: Please confirm you have tried to reproduce the issue with all custom nodes disabled. + options: + - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help) + required: true - type: textarea attributes: label: Your question From 6d46bb4b4c9db3bce46b2838c50252551330eba7 Mon Sep 17 00:00:00 2001 From: filtered <176114999+webfiltered@users.noreply.github.com> Date: Mon, 2 Jun 2025 06:47:14 +1000 Subject: [PATCH 175/193] [BugFix] Update frontend to 1.21.5 (#8382) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 181c23918..6be14767f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.21.4 +comfyui-frontend-package==1.21.5 comfyui-workflow-templates==0.1.25 comfyui-embedded-docs==0.2.0 torch From 010954d277d460b22635fee67513b5f610c8409d Mon Sep 17 00:00:00 2001 From: filtered <176114999+webfiltered@users.noreply.github.com> Date: Mon, 2 Jun 2025 14:57:44 +1000 Subject: [PATCH 176/193] [BugFix] Update frontend to 1.21.6 (#8383) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 6be14767f..1c1ff54ac 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.21.5 +comfyui-frontend-package==1.21.6 comfyui-workflow-templates==0.1.25 comfyui-embedded-docs==0.2.0 torch From 4f4f1c642ad77886a9b5716ad886cde556858a60 Mon Sep 17 00:00:00 2001 From: Jesse Gonyou <168374102+jessegonyou@users.noreply.github.com> Date: Mon, 2 Jun 2025 06:52:44 -0400 Subject: [PATCH 177/193] Update fix for potential XSS on /view (#8384) * Update fix for potential XSS on /view This commit uses mimetypes to add more restricted filetypes to prevent from being served, since mimetypes are what browsers use to determine how to serve files. * Fix typo Fixed a typo that prevented the program from running --- server.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/server.py b/server.py index 6e283fe31..aca67a2e7 100644 --- a/server.py +++ b/server.py @@ -476,9 +476,8 @@ class PromptServer(): # Get content type from mimetype, defaulting to 'application/octet-stream' content_type = mimetypes.guess_type(filename)[0] or 'application/octet-stream' - # For security, force certain extensions to download instead of display - file_extension = os.path.splitext(filename)[1].lower() - if file_extension in {'.html', '.htm', '.js', '.css'}: + # For security, force certain mimetypes to download instead of display + if content_type in {'text/html', 'text/html-sandboxed', 'application/xhtml+xml', 'text/javascript', 'text/css'}: content_type = 'application/octet-stream' # Forces download return web.FileResponse( From 312d511630db4907c3bed04dee297b28f61941a8 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 2 Jun 2025 04:22:02 -0700 Subject: [PATCH 178/193] Style fix. (#8390) --- server.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/server.py b/server.py index aca67a2e7..f772545dc 100644 --- a/server.py +++ b/server.py @@ -390,7 +390,7 @@ class PromptServer(): async def view_image(request): if "filename" in request.rel_url.query: filename = request.rel_url.query["filename"] - filename,output_dir = folder_paths.annotated_filepath(filename) + filename, output_dir = folder_paths.annotated_filepath(filename) if not filename: return web.Response(status=400) From 856448060ce42674eea66c835bd754644c322723 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Mon, 2 Jun 2025 18:57:50 -0700 Subject: [PATCH 179/193] [feat] Add GetImageSize node (#8386) * [feat] Add GetImageSize node to return image dimensions Added a simple GetImageSize node in comfy_extras/nodes_images.py that returns width and height of input images. The node displays dimensions on the UI via PromptServer and provides width/height as outputs for further processing. * add display name mapping * [fix] Add server module mock to unit tests for PromptServer import Updated test to mock server module preventing import errors from the new PromptServer usage in GetImageSize node. Uses direct import pattern consistent with rest of codebase. --- comfy_extras/nodes_images.py | 34 ++++++++++++++++++- nodes.py | 1 + .../comfy_extras_test/image_stitch_test.py | 5 ++- 3 files changed, 38 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py index 6ebf1dbd8..58b29f9a9 100644 --- a/comfy_extras/nodes_images.py +++ b/comfy_extras/nodes_images.py @@ -16,7 +16,8 @@ from inspect import cleandoc import torch import comfy.utils -from comfy.comfy_types import FileLocator +from comfy.comfy_types import FileLocator, IO +from server import PromptServer MAX_RESOLUTION = nodes.MAX_RESOLUTION @@ -491,6 +492,36 @@ class SaveSVGNode: counter += 1 return { "ui": { "images": results } } +class GetImageSize: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + } + } + + RETURN_TYPES = (IO.INT, IO.INT) + RETURN_NAMES = ("width", "height") + FUNCTION = "get_size" + + CATEGORY = "image" + DESCRIPTION = """Returns width and height of the image, and passes it through unchanged.""" + + def get_size(self, image, unique_id=None) -> tuple[int, int]: + height = image.shape[1] + width = image.shape[2] + + # Send progress text to display size on the node + if unique_id: + PromptServer.instance.send_progress_text(f"width: {width}, height: {height}", unique_id) + + return width, height + NODE_CLASS_MAPPINGS = { "ImageCrop": ImageCrop, "RepeatImageBatch": RepeatImageBatch, @@ -500,4 +531,5 @@ NODE_CLASS_MAPPINGS = { "SaveAnimatedPNG": SaveAnimatedPNG, "SaveSVGNode": SaveSVGNode, "ImageStitch": ImageStitch, + "GetImageSize": GetImageSize, } diff --git a/nodes.py b/nodes.py index 67360e7da..637279ffb 100644 --- a/nodes.py +++ b/nodes.py @@ -2067,6 +2067,7 @@ NODE_DISPLAY_NAME_MAPPINGS = { "ImageQuantize": "Image Quantize", "ImageSharpen": "Image Sharpen", "ImageScaleToTotalPixels": "Scale Image to Total Pixels", + "GetImageSize": "Get Image Size", # _for_testing "VAEDecodeTiled": "VAE Decode (Tiled)", "VAEEncodeTiled": "VAE Encode (Tiled)", diff --git a/tests-unit/comfy_extras_test/image_stitch_test.py b/tests-unit/comfy_extras_test/image_stitch_test.py index fbaef756c..b5a0f022c 100644 --- a/tests-unit/comfy_extras_test/image_stitch_test.py +++ b/tests-unit/comfy_extras_test/image_stitch_test.py @@ -5,7 +5,10 @@ from unittest.mock import patch, MagicMock mock_nodes = MagicMock() mock_nodes.MAX_RESOLUTION = 16384 -with patch.dict('sys.modules', {'nodes': mock_nodes}): +# Mock server module for PromptServer +mock_server = MagicMock() + +with patch.dict('sys.modules', {'nodes': mock_nodes, 'server': mock_server}): from comfy_extras.nodes_images import ImageStitch From 310f4b6ef842eb513eff1b68ce689eb0b990b6ec Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 3 Jun 2025 01:26:44 -0700 Subject: [PATCH 180/193] Add api nodes to readme. (#8402) --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 47514d1b4..1ceaccb3c 100644 --- a/README.md +++ b/README.md @@ -95,7 +95,8 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith - [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/) - Latent previews with [TAESD](#how-to-show-high-quality-previews) - Starts up very fast. -- Works fully offline: will never download anything. +- Works fully offline: core will never download anything unless you want to. +- Optional API nodes to use paid models from external providers through the online [Comfy API](https://docs.comfy.org/tutorials/api-nodes/overview). - [Config file](extra_model_paths.yaml.example) to set the search paths for models. Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/) From 47d55b8b452f409bea39d2cd1c2229f0bb460c75 Mon Sep 17 00:00:00 2001 From: Terry Jia Date: Tue, 3 Jun 2025 19:59:13 -0400 Subject: [PATCH 181/193] add support to read pyproject.toml from custom node (#8357) * add support to read pyproject.toml from custom node * sf * use pydantic instead * sf * use pydantic_settings * remove unnecessary try/catch and handle single-file python node * sf --- comfy_config/config_parser.py | 97 +++++++++++++++++++++++++++++++++++ comfy_config/types.py | 80 +++++++++++++++++++++++++++++ 2 files changed, 177 insertions(+) create mode 100644 comfy_config/config_parser.py create mode 100644 comfy_config/types.py diff --git a/comfy_config/config_parser.py b/comfy_config/config_parser.py new file mode 100644 index 000000000..a9cbd94dd --- /dev/null +++ b/comfy_config/config_parser.py @@ -0,0 +1,97 @@ +import os +from pathlib import Path +from typing import Optional + +from pydantic_settings import PydanticBaseSettingsSource, TomlConfigSettingsSource + +from comfy_config.types import ( + ComfyConfig, + ProjectConfig, + PyProjectConfig, + PyProjectSettings +) + +""" +Extract configuration from a custom node directory's pyproject.toml file or a Python file. + +This function reads and parses the pyproject.toml file in the specified directory +to extract project and ComfyUI-specific configuration information. If no +pyproject.toml file is found, it creates a minimal configuration using the +folder name as the project name. If a Python file is provided, it uses the +file name (without extension) as the project name. + +Args: + path (str): Path to the directory containing the pyproject.toml file, or + path to a .py file. If pyproject.toml doesn't exist in a directory, + the folder name will be used as the default project name. If a .py + file is provided, the filename (without .py extension) will be used + as the project name. + +Returns: + Optional[PyProjectConfig]: A PyProjectConfig object containing: + - project: Basic project information (name, version, dependencies, etc.) + - tool_comfy: ComfyUI-specific configuration (publisher_id, models, etc.) + Returns None if configuration extraction fails or if the provided file + is not a Python file. + +Notes: + - If pyproject.toml is missing in a directory, creates a default config with folder name + - If a .py file is provided, creates a default config with filename (without extension) + - Returns None for non-Python files + +Example: + >>> from comfy_config import config_parser + >>> # For directory + >>> custom_node_dir = os.path.dirname(os.path.realpath(__file__)) + >>> project_config = config_parser.extract_node_configuration(custom_node_dir) + >>> print(project_config.project.name) # "my_custom_node" or name from pyproject.toml + >>> + >>> # For single-file Python node file + >>> py_file_path = os.path.realpath(__file__) # "/path/to/my_node.py" + >>> project_config = config_parser.extract_node_configuration(py_file_path) + >>> print(project_config.project.name) # "my_node" +""" +def extract_node_configuration(path) -> Optional[PyProjectConfig]: + if os.path.isfile(path): + file_path = Path(path) + + if file_path.suffix.lower() != '.py': + return None + + project_name = file_path.stem + project = ProjectConfig(name=project_name) + comfy = ComfyConfig() + return PyProjectConfig(project=project, tool_comfy=comfy) + + folder_name = os.path.basename(path) + toml_path = Path(path) / "pyproject.toml" + + if not toml_path.exists(): + project = ProjectConfig(name=folder_name) + comfy = ComfyConfig() + return PyProjectConfig(project=project, tool_comfy=comfy) + + raw_settings = load_pyproject_settings(toml_path) + + project_data = raw_settings.project + + tool_data = raw_settings.tool + comfy_data = tool_data.get("comfy", {}) if tool_data else {} + + return PyProjectConfig(project=project_data, tool_comfy=comfy_data) + + +def load_pyproject_settings(toml_path: Path) -> PyProjectSettings: + class PyProjectLoader(PyProjectSettings): + @classmethod + def settings_customise_sources( + cls, + settings_cls, + init_settings: PydanticBaseSettingsSource, + env_settings: PydanticBaseSettingsSource, + dotenv_settings: PydanticBaseSettingsSource, + file_secret_settings: PydanticBaseSettingsSource, + ): + return (TomlConfigSettingsSource(settings_cls, toml_path),) + + return PyProjectLoader() diff --git a/comfy_config/types.py b/comfy_config/types.py new file mode 100644 index 000000000..611982083 --- /dev/null +++ b/comfy_config/types.py @@ -0,0 +1,80 @@ +from pydantic import BaseModel, Field +from pydantic_settings import BaseSettings, SettingsConfigDict +from typing import List, Optional + +# IMPORTANT: The type definitions specified in pyproject.toml for custom nodes +# must remain synchronized with the corresponding files in the https://github.com/Comfy-Org/comfy-cli/blob/main/comfy_cli/registry/types.py. +# Any changes to one must be reflected in the other to maintain consistency. + +class NodeVersion(BaseModel): + changelog: str + dependencies: List[str] + deprecated: bool + id: str + version: str + download_url: str + + +class Node(BaseModel): + id: str + name: str + description: str + author: Optional[str] = None + license: Optional[str] = None + icon: Optional[str] = None + repository: Optional[str] = None + tags: List[str] = Field(default_factory=list) + latest_version: Optional[NodeVersion] = None + + +class PublishNodeVersionResponse(BaseModel): + node_version: NodeVersion + signedUrl: str + + +class URLs(BaseModel): + homepage: str = Field(default="", alias="Homepage") + documentation: str = Field(default="", alias="Documentation") + repository: str = Field(default="", alias="Repository") + issues: str = Field(default="", alias="Issues") + + +class Model(BaseModel): + location: str + model_url: str + + +class ComfyConfig(BaseModel): + publisher_id: str = Field(default="", alias="PublisherId") + display_name: str = Field(default="", alias="DisplayName") + icon: str = Field(default="", alias="Icon") + models: List[Model] = Field(default_factory=list, alias="Models") + includes: List[str] = Field(default_factory=list) + + +class License(BaseModel): + file: str = "" + text: str = "" + + +class ProjectConfig(BaseModel): + name: str = "" + description: str = "" + version: str = "1.0.0" + requires_python: str = Field(default=">= 3.9", alias="requires-python") + dependencies: List[str] = Field(default_factory=list) + license: License = Field(default_factory=License) + urls: URLs = Field(default_factory=URLs) + + +class PyProjectConfig(BaseModel): + project: ProjectConfig = Field(default_factory=ProjectConfig) + tool_comfy: ComfyConfig = Field(default_factory=ComfyConfig) + + +class PyProjectSettings(BaseSettings): + project: dict = Field(default_factory=dict) + + tool: dict = Field(default_factory=dict) + + model_config = SettingsConfigDict() From 20687293fe48b5e47610294286cea54d6c5343a4 Mon Sep 17 00:00:00 2001 From: filtered <176114999+webfiltered@users.noreply.github.com> Date: Wed, 4 Jun 2025 05:57:13 -0700 Subject: [PATCH 182/193] Update frontend to 1.21.7 (#8410) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 1c1ff54ac..c470b9ead 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.21.6 +comfyui-frontend-package==1.21.7 comfyui-workflow-templates==0.1.25 comfyui-embedded-docs==0.2.0 torch From fcc1643c527bfe5d2f5472e66cb3ed3dcd95d08c Mon Sep 17 00:00:00 2001 From: SD Date: Wed, 4 Jun 2025 18:33:42 +0530 Subject: [PATCH 183/193] Sub call to deprecated pillow API `Image.ANTIALIAS` (#8415) ANTIALIAS was removed in Pillow 10.0.0 --- server.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/server.py b/server.py index f772545dc..878b5eeb1 100644 --- a/server.py +++ b/server.py @@ -788,7 +788,7 @@ class PromptServer(): if hasattr(Image, 'Resampling'): resampling = Image.Resampling.BILINEAR else: - resampling = Image.ANTIALIAS + resampling = Image.Resampling.LANCZOS image = ImageOps.contain(image, (max_size, max_size), resampling) type_num = 1 From 871749c20842dbdd7696ba77c78ee7d4c246ef6a Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 4 Jun 2025 06:40:21 -0700 Subject: [PATCH 184/193] Add batch to GetImageSize node. (#8419) --- comfy_extras/nodes_images.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py index 58b29f9a9..b1e0d4666 100644 --- a/comfy_extras/nodes_images.py +++ b/comfy_extras/nodes_images.py @@ -505,8 +505,8 @@ class GetImageSize: } } - RETURN_TYPES = (IO.INT, IO.INT) - RETURN_NAMES = ("width", "height") + RETURN_TYPES = (IO.INT, IO.INT, IO.INT) + RETURN_NAMES = ("width", "height", "batch_size") FUNCTION = "get_size" CATEGORY = "image" @@ -515,12 +515,13 @@ class GetImageSize: def get_size(self, image, unique_id=None) -> tuple[int, int]: height = image.shape[1] width = image.shape[2] + batch_size = image.shape[0] # Send progress text to display size on the node if unique_id: - PromptServer.instance.send_progress_text(f"width: {width}, height: {height}", unique_id) + PromptServer.instance.send_progress_text(f"width: {width}, height: {height}\n batch size: {batch_size}", unique_id) - return width, height + return width, height, batch_size NODE_CLASS_MAPPINGS = { "ImageCrop": ImageCrop, From 3aa83feeec3771d98eb780550d4b6cb9d98e88ae Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Wed, 4 Jun 2025 18:56:38 -0700 Subject: [PATCH 185/193] [refactor] remove version prefixes from Ideogram node categories (#8418) Simplifies node organization by consolidating all Ideogram nodes under a single category instead of version-specific subcategories. --- comfy_api_nodes/nodes_ideogram.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/comfy_api_nodes/nodes_ideogram.py b/comfy_api_nodes/nodes_ideogram.py index b1cbf511d..b8487355f 100644 --- a/comfy_api_nodes/nodes_ideogram.py +++ b/comfy_api_nodes/nodes_ideogram.py @@ -324,7 +324,7 @@ class IdeogramV1(ComfyNodeABC): RETURN_TYPES = (IO.IMAGE,) FUNCTION = "api_call" - CATEGORY = "api node/image/Ideogram/v1" + CATEGORY = "api node/image/Ideogram" DESCRIPTION = cleandoc(__doc__ or "") API_NODE = True @@ -483,7 +483,7 @@ class IdeogramV2(ComfyNodeABC): RETURN_TYPES = (IO.IMAGE,) FUNCTION = "api_call" - CATEGORY = "api node/image/Ideogram/v2" + CATEGORY = "api node/image/Ideogram" DESCRIPTION = cleandoc(__doc__ or "") API_NODE = True @@ -649,7 +649,7 @@ class IdeogramV3(ComfyNodeABC): RETURN_TYPES = (IO.IMAGE,) FUNCTION = "api_call" - CATEGORY = "api node/image/Ideogram/v3" + CATEGORY = "api node/image/Ideogram" DESCRIPTION = cleandoc(__doc__ or "") API_NODE = True From 866f6cdab4bd5de95ee6296d1b418c455f67f929 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Wed, 4 Jun 2025 22:18:54 -0400 Subject: [PATCH 186/193] ComfyUI version 0.3.40 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index f742410b1..6962c3661 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.3.39" +__version__ = "0.3.40" diff --git a/pyproject.toml b/pyproject.toml index 28a6158e0..03841bc94 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.3.39" +version = "0.3.40" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.9" From 4248b1618ffd0878ae2502fd4633e30bcbd7554b Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 5 Jun 2025 07:07:17 -0700 Subject: [PATCH 187/193] Let chroma TE work on regular flux. (#8429) --- comfy/ldm/flux/controlnet.py | 5 ++++- comfy/ldm/flux/model.py | 6 +++++- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/comfy/ldm/flux/controlnet.py b/comfy/ldm/flux/controlnet.py index 5322c4891..dbd2a47c0 100644 --- a/comfy/ldm/flux/controlnet.py +++ b/comfy/ldm/flux/controlnet.py @@ -121,6 +121,9 @@ class ControlNetFlux(Flux): if img.ndim != 3 or txt.ndim != 3: raise ValueError("Input img and txt tensors must have 3 dimensions.") + if y is None: + y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype) + # running on sequences img img = self.img_in(img) @@ -174,7 +177,7 @@ class ControlNetFlux(Flux): out["output"] = out_output[:self.main_model_single] return out - def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs): + def forward(self, x, timesteps, context, y=None, guidance=None, hint=None, **kwargs): patch_size = 2 if self.latent_input: hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size)) diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py index ef4ba4106..53f27e3a7 100644 --- a/comfy/ldm/flux/model.py +++ b/comfy/ldm/flux/model.py @@ -101,6 +101,10 @@ class Flux(nn.Module): transformer_options={}, attn_mask: Tensor = None, ) -> Tensor: + + if y is None: + y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype) + patches_replace = transformer_options.get("patches_replace", {}) if img.ndim != 3 or txt.ndim != 3: raise ValueError("Input img and txt tensors must have 3 dimensions.") @@ -188,7 +192,7 @@ class Flux(nn.Module): img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) return img - def forward(self, x, timestep, context, y, guidance=None, control=None, transformer_options={}, **kwargs): + def forward(self, x, timestep, context, y=None, guidance=None, control=None, transformer_options={}, **kwargs): bs, c, h, w = x.shape patch_size = self.patch_size x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size)) From d8759c772bd8ded66e08dac56bb8da636c837c9a Mon Sep 17 00:00:00 2001 From: Olexandr88 Date: Thu, 5 Jun 2025 20:44:29 +0300 Subject: [PATCH 188/193] Update README.md (#8427) --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 1ceaccb3c..9a35ab7ea 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,7 @@ [![Website][website-shield]][website-url] [![Dynamic JSON Badge][discord-shield]][discord-url] +[![Twitter][twitter-shield]][twitter-url] [![Matrix][matrix-shield]][matrix-url]
[![][github-release-shield]][github-release-link] @@ -20,6 +21,8 @@ [discord-shield]: https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2Fcomfyorg%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&logo=discord&logoColor=white&label=Discord&color=green&suffix=%20total [discord-url]: https://www.comfy.org/discord +[twitter-shield]: https://img.shields.io/twitter/follow/ComfyUI +[twitter-url]: https://x.com/ComfyUI [github-release-shield]: https://img.shields.io/github/v/release/comfyanonymous/ComfyUI?style=flat&sort=semver [github-release-link]: https://github.com/comfyanonymous/ComfyUI/releases From 3b4b171e18c9026eaf8560b9a766aab87f7639e5 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 6 Jun 2025 06:43:27 -0700 Subject: [PATCH 189/193] Alternate fix for #8435 (#8442) --- comfy/controlnet.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/comfy/controlnet.py b/comfy/controlnet.py index 11483e21d..9a47b86f2 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -390,8 +390,9 @@ class ControlLora(ControlNet): pass for k in self.control_weights: - if k not in {"lora_controlnet"}: - comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device())) + if (k not in {"lora_controlnet"}): + if (k.endswith(".up") or k.endswith(".down") or k.endswith(".weight") or k.endswith(".bias")) and ("__" not in k): + comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device())) def copy(self): c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling) From daf9d25ee282051055e773185aa161cd2a1ce3a6 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 7 Jun 2025 07:01:15 -0700 Subject: [PATCH 190/193] Cleaner torch version comparisons. (#8453) --- comfy/model_management.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 8ae5a5abb..a107f0d49 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -305,7 +305,7 @@ try: logging.info("AMD arch: {}".format(arch)) logging.info("ROCm version: {}".format(rocm_version)) if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: - if torch_version_numeric[0] >= 2 and torch_version_numeric[1] >= 7: # works on 2.6 but doesn't actually seem to improve much + if torch_version_numeric >= (2, 7): # works on 2.6 but doesn't actually seem to improve much if any((a in arch) for a in ["gfx1100", "gfx1101", "gfx1151"]): # TODO: more arches ENABLE_PYTORCH_ATTENTION = True except: @@ -328,7 +328,7 @@ except: pass try: - if torch_version_numeric[0] == 2 and torch_version_numeric[1] >= 5: + if torch_version_numeric >= (2, 5): torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True) except: logging.warning("Warning, could not set allow_fp16_bf16_reduction_math_sdp") @@ -1276,11 +1276,11 @@ def supports_fp8_compute(device=None): if props.minor < 9: return False - if torch_version_numeric[0] < 2 or (torch_version_numeric[0] == 2 and torch_version_numeric[1] < 3): + if torch_version_numeric < (2, 3): return False if WINDOWS: - if (torch_version_numeric[0] == 2 and torch_version_numeric[1] < 4): + if torch_version_numeric < (2, 4): return False return True From 97755eed46ccb797cb14a692a4c2931ebf3ad60c Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 8 Jun 2025 11:15:34 -0700 Subject: [PATCH 191/193] Enable fp8 ops by default on gfx1201 (#8464) --- comfy/model_management.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index a107f0d49..187402748 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -295,6 +295,7 @@ except: pass +SUPPORT_FP8_OPS = args.supports_fp8_compute try: if is_amd(): try: @@ -308,6 +309,10 @@ try: if torch_version_numeric >= (2, 7): # works on 2.6 but doesn't actually seem to improve much if any((a in arch) for a in ["gfx1100", "gfx1101", "gfx1151"]): # TODO: more arches ENABLE_PYTORCH_ATTENTION = True + if torch_version_numeric >= (2, 7) and rocm_version >= (6, 4): + if any((a in arch) for a in ["gfx1201"]): # TODO: more arches + SUPPORT_FP8_OPS = True + except: pass @@ -1262,7 +1267,7 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma return False def supports_fp8_compute(device=None): - if args.supports_fp8_compute: + if SUPPORT_FP8_OPS: return True if not is_nvidia(): From 7f800d04fae7a20f9d110d250d493ba94b5310f3 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 9 Jun 2025 09:50:39 -0700 Subject: [PATCH 192/193] Enable AMD fp8 and pytorch attention on some GPUs. (#8474) Information is from the pytorch source code. --- comfy/model_management.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 187402748..283815611 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -307,10 +307,10 @@ try: logging.info("ROCm version: {}".format(rocm_version)) if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: if torch_version_numeric >= (2, 7): # works on 2.6 but doesn't actually seem to improve much - if any((a in arch) for a in ["gfx1100", "gfx1101", "gfx1151"]): # TODO: more arches + if any((a in arch) for a in ["gfx90a", "gfx942", "gfx1100", "gfx1101", "gfx1151"]): # TODO: more arches, TODO: gfx1201 and gfx950 ENABLE_PYTORCH_ATTENTION = True if torch_version_numeric >= (2, 7) and rocm_version >= (6, 4): - if any((a in arch) for a in ["gfx1201"]): # TODO: more arches + if any((a in arch) for a in ["gfx1201", "gfx942", "gfx950"]): # TODO: more arches SUPPORT_FP8_OPS = True except: From c7b25784b141a298d8ee5f0d830a29da6e8a3d54 Mon Sep 17 00:00:00 2001 From: Kent Mewhort Date: Mon, 9 Jun 2025 13:05:54 -0400 Subject: [PATCH 193/193] Fix WebcamCapture IS_CHANGED signature (#8413) --- comfy_extras/nodes_webcam.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/comfy_extras/nodes_webcam.py b/comfy_extras/nodes_webcam.py index 062b15cf8..5bf80b4c6 100644 --- a/comfy_extras/nodes_webcam.py +++ b/comfy_extras/nodes_webcam.py @@ -23,6 +23,10 @@ class WebcamCapture(nodes.LoadImage): def load_capture(self, image, **kwargs): return super().load_image(folder_paths.get_annotated_filepath(image)) + @classmethod + def IS_CHANGED(cls, image, width, height, capture_on_queue): + return super().IS_CHANGED(image) + NODE_CLASS_MAPPINGS = { "WebcamCapture": WebcamCapture,