Merge branch 'seedvr2' of https://github.com/yousef-rafat/ComfyUI into seedvr2

app/model_manager.py

@@ -44,7 +44,7 @@ class ModelFileManager:
         @routes.get("/experiment/models/{folder}")
         async def get_all_models(request):
             folder = request.match_info.get("folder", None)
-            if not folder in folder_paths.folder_names_and_paths:
+            if folder not in folder_paths.folder_names_and_paths:
                 return web.Response(status=404)
             files = self.get_model_file_list(folder)
             return web.json_response(files)
@@ -55,7 +55,7 @@ class ModelFileManager:
             path_index = int(request.match_info.get("path_index", None))
             filename = request.match_info.get("filename", None)
 
-            if not folder_name in folder_paths.folder_names_and_paths:
+            if folder_name not in folder_paths.folder_names_and_paths:
                 return web.Response(status=404)
 
             folders = folder_paths.folder_names_and_paths[folder_name]

comfy/clip_model.py

@@ -2,6 +2,25 @@ import torch
 from comfy.ldm.modules.attention import optimized_attention_for_device
 import comfy.ops
 
+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)
+    if not (image.shape[2] == size and image.shape[3] == size):
+        if crop:
+            scale = (size / min(image.shape[2], image.shape[3]))
+            scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
+        else:
+            scale_size = (size, size)
+
+        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
+        h = (image.shape[2] - size)//2
+        w = (image.shape[3] - size)//2
+        image = image[:,:,h:h+size,w:w+size]
+    image = torch.clip((255. * image), 0, 255).round() / 255.0
+    return (image - mean.view([3,1,1])) / std.view([3,1,1])
+
 class CLIPAttention(torch.nn.Module):
     def __init__(self, embed_dim, heads, dtype, device, operations):
         super().__init__()

comfy/clip_vision.py

@@ -1,6 +1,5 @@
 from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace
 import os
-import torch
 import json
 import logging
 
@@ -17,24 +16,7 @@ class Output:
     def __setitem__(self, key, item):
         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)
-    if not (image.shape[2] == size and image.shape[3] == size):
-        if crop:
-            scale = (size / min(image.shape[2], image.shape[3]))
-            scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
-        else:
-            scale_size = (size, size)
-
-        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
-        h = (image.shape[2] - size)//2
-        w = (image.shape[3] - size)//2
-        image = image[:,:,h:h+size,w:w+size]
-    image = torch.clip((255. * image), 0, 255).round() / 255.0
-    return (image - mean.view([3,1,1])) / std.view([3,1,1])
+clip_preprocess = comfy.clip_model.clip_preprocess  # Prevent some stuff from breaking, TODO: remove eventually
 
 IMAGE_ENCODERS = {
     "clip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
@@ -73,7 +55,7 @@ class ClipVisionModel():
 
     def encode_image(self, image, crop=True):
         comfy.model_management.load_model_gpu(self.patcher)
-        pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
+        pixel_values = comfy.clip_model.clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
         out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2)
 
         outputs = Output()

comfy/context_windows.py

@@ -188,6 +188,12 @@ class IndexListContextHandler(ContextHandlerABC):
                                 audio_cond = cond_value.cond
                                 if audio_cond.ndim > 1 and audio_cond.size(1) == x_in.size(self.dim):
                                     new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(audio_cond, device, dim=1))
+                            # Handle vace_context (temporal dim is 3)
+                            elif cond_key == "vace_context" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
+                                vace_cond = cond_value.cond
+                                if vace_cond.ndim >= 4 and vace_cond.size(3) == x_in.size(self.dim):
+                                    sliced_vace = window.get_tensor(vace_cond, device, dim=3, retain_index_list=self.cond_retain_index_list)
+                                    new_cond_item[cond_key] = cond_value._copy_with(sliced_vace)
                             # if has cond that is a Tensor, check if needs to be subset
                             elif hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
                                 if  (self.dim < cond_value.cond.ndim and cond_value.cond.size(self.dim) == x_in.size(self.dim)) or \

comfy/hooks.py

@@ -527,7 +527,8 @@ class HookKeyframeGroup:
                         if self._current_keyframe.get_effective_guarantee_steps(max_sigma) > 0:
                             break
                     # if eval_c is outside the percent range, stop looking further
-                    else: break
+                    else:
+                        break
         # update steps current context is used
         self._current_used_steps += 1
         # update current timestep this was performed on

comfy/ldm/chroma_radiance/model.py

@@ -270,7 +270,7 @@ class ChromaRadiance(Chroma):
         bad_keys = tuple(
             k
             for k, v in overrides.items()
-            if type(v) != type(getattr(params, k)) and (v is not None or k not in nullable_keys)
+            if not isinstance(v, type(getattr(params, k))) and (v is not None or k not in nullable_keys)
         )
         if bad_keys:
             e = f"Invalid value(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}"

comfy/ldm/hunyuan_video/upsampler.py

@@ -3,7 +3,8 @@ import torch.nn as nn
 import torch.nn.functional as F
 from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, VideoConv3d
 from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm
-import model_management, model_patcher
+import model_management
+import model_patcher
 
 class SRResidualCausalBlock3D(nn.Module):
     def __init__(self, channels: int):

comfy/ldm/modules/attention.py

@@ -36,6 +36,13 @@ except ImportError as e:
             raise e
         exit(-1)
 
+SAGE_ATTENTION3_IS_AVAILABLE = False
+try:
+    from sageattn3 import sageattn3_blackwell
+    SAGE_ATTENTION3_IS_AVAILABLE = True
+except ImportError:
+    pass
+
 FLASH_ATTENTION_IS_AVAILABLE = False
 try:
     from flash_attn import flash_attn_func, flash_attn_varlen_func
@@ -639,6 +646,93 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
             out = out.reshape(b, -1, heads * dim_head)
     return out
 
+@wrap_attn
+def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    exception_fallback = False
+    if (q.device.type != "cuda" or
+        q.dtype not in (torch.float16, torch.bfloat16) or
+        mask is not None):
+        return attention_pytorch(
+            q, k, v, heads,
+            mask=mask,
+            attn_precision=attn_precision,
+            skip_reshape=skip_reshape,
+            skip_output_reshape=skip_output_reshape,
+            **kwargs
+        )
+
+    if skip_reshape:
+        B, H, L, D = q.shape
+        if H != heads:
+            return attention_pytorch(
+                q, k, v, heads,
+                mask=mask,
+                attn_precision=attn_precision,
+                skip_reshape=True,
+                skip_output_reshape=skip_output_reshape,
+                **kwargs
+            )
+        q_s, k_s, v_s = q, k, v
+        N = q.shape[2]
+        dim_head = D
+    else:
+        B, N, inner_dim = q.shape
+        if inner_dim % heads != 0:
+            return attention_pytorch(
+                q, k, v, heads,
+                mask=mask,
+                attn_precision=attn_precision,
+                skip_reshape=False,
+                skip_output_reshape=skip_output_reshape,
+                **kwargs
+            )
+        dim_head = inner_dim // heads
+
+    if dim_head >= 256 or N <= 1024:
+        return attention_pytorch(
+                q, k, v, heads,
+                mask=mask,
+                attn_precision=attn_precision,
+                skip_reshape=skip_reshape,
+                skip_output_reshape=skip_output_reshape,
+                **kwargs
+            )
+
+    if not skip_reshape:
+        q_s, k_s, v_s = map(
+            lambda t: t.view(B, -1, heads, dim_head).permute(0, 2, 1, 3).contiguous(),
+            (q, k, v),
+        )
+        B, H, L, D = q_s.shape
+
+    try:
+        out = sageattn3_blackwell(q_s, k_s, v_s, is_causal=False)
+    except Exception as e:
+        exception_fallback = True
+        logging.error("Error running SageAttention3: %s, falling back to pytorch attention.", e)
+
+    if exception_fallback:
+        if not skip_reshape:
+            del q_s, k_s, v_s
+        return attention_pytorch(
+                q, k, v, heads,
+                mask=mask,
+                attn_precision=attn_precision,
+                skip_reshape=False,
+                skip_output_reshape=skip_output_reshape,
+                **kwargs
+            )
+
+    if skip_reshape:
+        if not skip_output_reshape:
+            out = out.permute(0, 2, 1, 3).reshape(B, L, H * D)
+    else:
+        if skip_output_reshape:
+            pass
+        else:
+            out = out.permute(0, 2, 1, 3).reshape(B, L, H * D)
+
+    return out
 
 try:
     @torch.library.custom_op("flash_attention::flash_attn", mutates_args=())
@@ -735,6 +829,8 @@ optimized_attention_masked = optimized_attention
 # register core-supported attention functions
 if SAGE_ATTENTION_IS_AVAILABLE:
     register_attention_function("sage", attention_sage)
+if SAGE_ATTENTION3_IS_AVAILABLE:
+    register_attention_function("sage3", attention3_sage)
 if FLASH_ATTENTION_IS_AVAILABLE:
     register_attention_function("flash", attention_flash)
 if model_management.xformers_enabled():

comfy/ldm/modules/diffusionmodules/model.py

@@ -397,7 +397,8 @@ class Model(nn.Module):
                  attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
                  resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
         super().__init__()
-        if use_linear_attn: attn_type = "linear"
+        if use_linear_attn:
+            attn_type = "linear"
         self.ch = ch
         self.temb_ch = self.ch*4
         self.num_resolutions = len(ch_mult)
@@ -551,7 +552,8 @@ class Encoder(nn.Module):
                  conv3d=False, time_compress=None,
                  **ignore_kwargs):
         super().__init__()
-        if use_linear_attn: attn_type = "linear"
+        if use_linear_attn:
+            attn_type = "linear"
         self.ch = ch
         self.temb_ch = 0
         self.num_resolutions = len(ch_mult)

comfy/ldm/modules/ema.py

@@ -45,7 +45,7 @@ class LitEma(nn.Module):
                     shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
                     shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
                 else:
-                    assert not key in self.m_name2s_name
+                    assert key not in self.m_name2s_name
 
     def copy_to(self, model):
         m_param = dict(model.named_parameters())
@@ -54,7 +54,7 @@ class LitEma(nn.Module):
             if m_param[key].requires_grad:
                 m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
             else:
-                assert not key in self.m_name2s_name
+                assert key not in self.m_name2s_name
 
     def store(self, parameters):
         """

comfy/ldm/util.py

@@ -71,7 +71,7 @@ def count_params(model, verbose=False):
 
 
 def instantiate_from_config(config):
-    if not "target" in config:
+    if "target" not in config:
         if config == '__is_first_stage__':
             return None
         elif config == "__is_unconditional__":

comfy/model_management.py

@@ -1542,6 +1542,10 @@ def soft_empty_cache(force=False):
 def unload_all_models():
     free_memory(1e30, get_torch_device())
 
+def debug_memory_summary():
+    if is_amd() or is_nvidia():
+        return torch.cuda.memory.memory_summary()
+    return ""
 
 #TODO: might be cleaner to put this somewhere else
 import threading

comfy/taesd/taehv.py

@@ -154,7 +154,8 @@ class TAEHV(nn.Module):
             self._show_progress_bar = value
 
     def encode(self, x, **kwargs):
-        if self.patch_size > 1: x = F.pixel_unshuffle(x, self.patch_size)
+        if self.patch_size > 1:
+            x = F.pixel_unshuffle(x, self.patch_size)
         x = x.movedim(2, 1)  # [B, C, T, H, W] -> [B, T, C, H, W]
         if x.shape[1] % 4 != 0:
             # pad at end to multiple of 4
@@ -167,5 +168,6 @@ class TAEHV(nn.Module):
     def decode(self, x, **kwargs):
         x = self.process_in(x).movedim(2, 1)  # [B, C, T, H, W] -> [B, T, C, H, W]
         x = apply_model_with_memblocks(self.decoder, x, self.parallel, self.show_progress_bar)
-        if self.patch_size > 1: x = F.pixel_shuffle(x, self.patch_size)
+        if self.patch_size > 1:
+            x = F.pixel_shuffle(x, self.patch_size)
         return x[:, self.frames_to_trim:].movedim(2, 1)

comfy/text_encoders/llama.py

@@ -8,7 +8,6 @@ from comfy.ldm.modules.attention import optimized_attention_for_device
 import comfy.model_management
 import comfy.ldm.common_dit
 
-import comfy.model_management
 from . import qwen_vl
 
 @dataclass

comfy/utils.py

@@ -1230,6 +1230,8 @@ def convert_old_quants(state_dict, model_prefix="", metadata={}):
             out_sd = {}
             layers = {}
             for k in list(state_dict.keys()):
+                if k == scaled_fp8_key:
+                    continue
                 if not k.startswith(model_prefix):
                     out_sd[k] = state_dict[k]
                     continue

comfy_api/latest/__init__.py

@@ -10,7 +10,6 @@ from ._input_impl import VideoFromFile, VideoFromComponents
 from ._util import VideoCodec, VideoContainer, VideoComponents, MESH, VOXEL
 from . import _io_public as io
 from . import _ui_public as ui
-# from comfy_api.latest._resources import _RESOURCES as resources  #noqa: F401
 from comfy_execution.utils import get_executing_context
 from comfy_execution.progress import get_progress_state, PreviewImageTuple
 from PIL import Image

comfy_api/latest/_io.py

@@ -26,7 +26,6 @@ if TYPE_CHECKING:
     from comfy_api.input import VideoInput
 from comfy_api.internal import (_ComfyNodeInternal, _NodeOutputInternal, classproperty, copy_class, first_real_override, is_class,
     prune_dict, shallow_clone_class)
-from ._resources import Resources, ResourcesLocal
 from comfy_execution.graph_utils import ExecutionBlocker
 from ._util import MESH, VOXEL, SVG as _SVG
 
@@ -76,16 +75,6 @@ class NumberDisplay(str, Enum):
     slider = "slider"
 
 
-class _StringIOType(str):
-    def __ne__(self, value: object) -> bool:
-        if self == "*" or value == "*":
-            return False
-        if not isinstance(value, str):
-            return True
-        a = frozenset(self.split(","))
-        b = frozenset(value.split(","))
-        return not (b.issubset(a) or a.issubset(b))
-
 class _ComfyType(ABC):
     Type = Any
     io_type: str = None
@@ -125,8 +114,7 @@ def comfytype(io_type: str, **kwargs):
             new_cls.__module__ = cls.__module__
             new_cls.__doc__ = cls.__doc__
             # assign ComfyType attributes, if needed
-        # NOTE: use __ne__ trick for io_type (see node_typing.IO.__ne__ for details)
-        new_cls.io_type = _StringIOType(io_type)
+        new_cls.io_type = io_type
         if hasattr(new_cls, "Input") and new_cls.Input is not None:
             new_cls.Input.Parent = new_cls
         if hasattr(new_cls, "Output") and new_cls.Output is not None:
@@ -165,7 +153,7 @@ class Input(_IO_V3):
     '''
     Base class for a V3 Input.
     '''
-    def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
+    def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None, raw_link: bool=None):
         super().__init__()
         self.id = id
         self.display_name = display_name
@@ -173,6 +161,7 @@ class Input(_IO_V3):
         self.tooltip = tooltip
         self.lazy = lazy
         self.extra_dict = extra_dict if extra_dict is not None else {}
+        self.rawLink = raw_link
 
     def as_dict(self):
         return prune_dict({
@@ -180,10 +169,11 @@ class Input(_IO_V3):
             "optional": self.optional,
             "tooltip": self.tooltip,
             "lazy": self.lazy,
+            "rawLink": self.rawLink,
         }) | prune_dict(self.extra_dict)
 
     def get_io_type(self):
-        return _StringIOType(self.io_type)
+        return self.io_type
 
     def get_all(self) -> list[Input]:
         return [self]
@@ -194,8 +184,8 @@ class WidgetInput(Input):
     '''
     def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                  default: Any=None,
-                 socketless: bool=None, widget_type: str=None, force_input: bool=None, extra_dict=None):
-        super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+                 socketless: bool=None, widget_type: str=None, force_input: bool=None, extra_dict=None, raw_link: bool=None):
+        super().__init__(id, display_name, optional, tooltip, lazy, extra_dict, raw_link)
         self.default = default
         self.socketless = socketless
         self.widget_type = widget_type
@@ -217,13 +207,14 @@ class Output(_IO_V3):
     def __init__(self, id: str=None, display_name: str=None, tooltip: str=None,
                  is_output_list=False):
         self.id = id
-        self.display_name = display_name
+        self.display_name = display_name if display_name else id
         self.tooltip = tooltip
         self.is_output_list = is_output_list
 
     def as_dict(self):
+        display_name = self.display_name if self.display_name else self.id
         return prune_dict({
-            "display_name": self.display_name,
+            "display_name": display_name,
             "tooltip": self.tooltip,
             "is_output_list": self.is_output_list,
         })
@@ -251,8 +242,8 @@ class Boolean(ComfyTypeIO):
         '''Boolean input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: bool=None, label_on: str=None, label_off: str=None,
-                    socketless: bool=None, force_input: bool=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input)
+                    socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link)
             self.label_on = label_on
             self.label_off = label_off
             self.default: bool
@@ -271,8 +262,8 @@ class Int(ComfyTypeIO):
         '''Integer input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: int=None, min: int=None, max: int=None, step: int=None, control_after_generate: bool=None,
-                    display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input)
+                    display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link)
             self.min = min
             self.max = max
             self.step = step
@@ -297,8 +288,8 @@ class Float(ComfyTypeIO):
         '''Float input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: float=None, min: float=None, max: float=None, step: float=None, round: float=None,
-                    display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input)
+                    display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link)
             self.min = min
             self.max = max
             self.step = step
@@ -323,8 +314,8 @@ class String(ComfyTypeIO):
         '''String input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     multiline=False, placeholder: str=None, default: str=None, dynamic_prompts: bool=None,
-                    socketless: bool=None, force_input: bool=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input)
+                    socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link)
             self.multiline = multiline
             self.placeholder = placeholder
             self.dynamic_prompts = dynamic_prompts
@@ -357,12 +348,14 @@ class Combo(ComfyTypeIO):
             image_folder: FolderType=None,
             remote: RemoteOptions=None,
             socketless: bool=None,
+            extra_dict=None,
+            raw_link: bool=None,
         ):
             if isinstance(options, type) and issubclass(options, Enum):
                 options = [v.value for v in options]
             if isinstance(default, Enum):
                 default = default.value
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless)
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, None, extra_dict, raw_link)
             self.multiselect = False
             self.options = options
             self.control_after_generate = control_after_generate
@@ -386,10 +379,6 @@ class Combo(ComfyTypeIO):
             super().__init__(id, display_name, tooltip, is_output_list)
             self.options = options if options is not None else []
 
-        @property
-        def io_type(self):
-            return self.options
-
 @comfytype(io_type="COMBO")
 class MultiCombo(ComfyTypeI):
     '''Multiselect Combo input (dropdown for selecting potentially more than one value).'''
@@ -398,8 +387,8 @@ class MultiCombo(ComfyTypeI):
     class Input(Combo.Input):
         def __init__(self, id: str, options: list[str], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: list[str]=None, placeholder: str=None, chip: bool=None, control_after_generate: bool=None,
-                    socketless: bool=None):
-            super().__init__(id, options, display_name, optional, tooltip, lazy, default, control_after_generate, socketless=socketless)
+                    socketless: bool=None, extra_dict=None, raw_link: bool=None):
+            super().__init__(id, options, display_name, optional, tooltip, lazy, default, control_after_generate, socketless=socketless, extra_dict=extra_dict, raw_link=raw_link)
             self.multiselect = True
             self.placeholder = placeholder
             self.chip = chip
@@ -432,9 +421,9 @@ class Webcam(ComfyTypeIO):
         Type = str
         def __init__(
                 self, id: str, display_name: str=None, optional=False,
-                tooltip: str=None, lazy: bool=None, default: str=None, socketless: bool=None
+                tooltip: str=None, lazy: bool=None, default: str=None, socketless: bool=None, extra_dict=None, raw_link: bool=None
         ):
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless)
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, None, extra_dict, raw_link)
 
 
 @comfytype(io_type="MASK")
@@ -787,7 +776,7 @@ class MultiType:
         '''
         Input that permits more than one input type; if `id` is an instance of `ComfyType.Input`, then that input will be used to create a widget (if applicable) with overridden values.
         '''
-        def __init__(self, id: str | Input, types: list[type[_ComfyType] | _ComfyType], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
+        def __init__(self, id: str | Input, types: list[type[_ComfyType] | _ComfyType], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None, raw_link: bool=None):
             # if id is an Input, then use that Input with overridden values
             self.input_override = None
             if isinstance(id, Input):
@@ -800,7 +789,7 @@ class MultiType:
                 # if is a widget input, make sure widget_type is set appropriately
                 if isinstance(self.input_override, WidgetInput):
                     self.input_override.widget_type = self.input_override.get_io_type()
-            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict, raw_link)
             self._io_types = types
 
         @property
@@ -854,8 +843,8 @@ class MatchType(ComfyTypeIO):
 
     class Input(Input):
         def __init__(self, id: str, template: MatchType.Template,
-                    display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+                    display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None, raw_link: bool=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict, raw_link)
             self.template = template
 
         def as_dict(self):
@@ -866,6 +855,8 @@ class MatchType(ComfyTypeIO):
     class Output(Output):
         def __init__(self, template: MatchType.Template, id: str=None, display_name: str=None, tooltip: str=None,
                      is_output_list=False):
+            if not id and not display_name:
+                display_name = "MATCHTYPE"
             super().__init__(id, display_name, tooltip, is_output_list)
             self.template = template
 
@@ -878,24 +869,30 @@ class DynamicInput(Input, ABC):
     '''
     Abstract class for dynamic input registration.
     '''
-    def get_dynamic(self) -> list[Input]:
-        return []
-
-    def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
-        pass
+    pass
 
 
 class DynamicOutput(Output, ABC):
     '''
     Abstract class for dynamic output registration.
     '''
-    def __init__(self, id: str=None, display_name: str=None, tooltip: str=None,
-                 is_output_list=False):
-        super().__init__(id, display_name, tooltip, is_output_list)
+    pass
 
-    def get_dynamic(self) -> list[Output]:
-        return []
 
+def handle_prefix(prefix_list: list[str] | None, id: str | None = None) -> list[str]:
+    if prefix_list is None:
+        prefix_list = []
+    if id is not None:
+        prefix_list = prefix_list + [id]
+    return prefix_list
+
+def finalize_prefix(prefix_list: list[str] | None, id: str | None = None) -> str:
+    assert not (prefix_list is None and id is None)
+    if prefix_list is None:
+        return id
+    elif id is not None:
+        prefix_list = prefix_list + [id]
+    return ".".join(prefix_list)
 
 @comfytype(io_type="COMFY_AUTOGROW_V3")
 class Autogrow(ComfyTypeI):
@@ -932,14 +929,6 @@ class Autogrow(ComfyTypeI):
         def validate(self):
             self.input.validate()
 
-        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
-            real_inputs = []
-            for name, input in self.cached_inputs.items():
-                if name in live_inputs:
-                    real_inputs.append(input)
-            add_to_input_dict_v1(d, real_inputs, live_inputs, curr_prefix)
-            add_dynamic_id_mapping(d, real_inputs, curr_prefix)
-
     class TemplatePrefix(_AutogrowTemplate):
         def __init__(self, input: Input, prefix: str, min: int=1, max: int=10):
             super().__init__(input)
@@ -984,22 +973,45 @@ class Autogrow(ComfyTypeI):
                 "template": self.template.as_dict(),
             })
 
-        def get_dynamic(self) -> list[Input]:
-            return self.template.get_all()
-
         def get_all(self) -> list[Input]:
             return [self] + self.template.get_all()
 
         def validate(self):
             self.template.validate()
 
-        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
-            curr_prefix = f"{curr_prefix}{self.id}."
-            # need to remove self from expected inputs dictionary; replaced by template inputs in frontend
-            for inner_dict in d.values():
-                if self.id in inner_dict:
-                    del inner_dict[self.id]
-            self.template.expand_schema_for_dynamic(d, live_inputs, curr_prefix)
+    @staticmethod
+    def _expand_schema_for_dynamic(out_dict: dict[str, Any], live_inputs: dict[str, Any], value: tuple[str, dict[str, Any]], input_type: str, curr_prefix: list[str] | None):
+        # NOTE: purposely do not include self in out_dict; instead use only the template inputs
+        # need to figure out names based on template type
+        is_names = ("names" in value[1]["template"])
+        is_prefix = ("prefix" in value[1]["template"])
+        input = value[1]["template"]["input"]
+        if is_names:
+            min = value[1]["template"]["min"]
+            names = value[1]["template"]["names"]
+            max = len(names)
+        elif is_prefix:
+            prefix = value[1]["template"]["prefix"]
+            min = value[1]["template"]["min"]
+            max = value[1]["template"]["max"]
+            names = [f"{prefix}{i}" for i in range(max)]
+        # need to create a new input based on the contents of input
+        template_input = None
+        for _, dict_input in input.items():
+            # for now, get just the first value from dict_input
+            template_input = list(dict_input.values())[0]
+        new_dict = {}
+        for i, name in enumerate(names):
+            expected_id = finalize_prefix(curr_prefix, name)
+            if expected_id in live_inputs:
+                # required
+                if i < min:
+                    type_dict = new_dict.setdefault("required", {})
+                # optional
+                else:
+                    type_dict = new_dict.setdefault("optional", {})
+                type_dict[name] = template_input
+        parse_class_inputs(out_dict, live_inputs, new_dict, curr_prefix)
 
 @comfytype(io_type="COMFY_DYNAMICCOMBO_V3")
 class DynamicCombo(ComfyTypeI):
@@ -1022,23 +1034,6 @@ class DynamicCombo(ComfyTypeI):
             super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
             self.options = options
 
-        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
-            # check if dynamic input's id is in live_inputs
-            if self.id in live_inputs:
-                curr_prefix = f"{curr_prefix}{self.id}."
-                key = live_inputs[self.id]
-                selected_option = None
-                for option in self.options:
-                    if option.key == key:
-                        selected_option = option
-                        break
-                if selected_option is not None:
-                    add_to_input_dict_v1(d, selected_option.inputs, live_inputs, curr_prefix)
-                    add_dynamic_id_mapping(d, selected_option.inputs, curr_prefix, self)
-
-        def get_dynamic(self) -> list[Input]:
-            return [input for option in self.options for input in option.inputs]
-
         def get_all(self) -> list[Input]:
             return [self] + [input for option in self.options for input in option.inputs]
 
@@ -1053,6 +1048,24 @@ class DynamicCombo(ComfyTypeI):
                 for input in option.inputs:
                     input.validate()
 
+    @staticmethod
+    def _expand_schema_for_dynamic(out_dict: dict[str, Any], live_inputs: dict[str, Any], value: tuple[str, dict[str, Any]], input_type: str, curr_prefix: list[str] | None):
+        finalized_id = finalize_prefix(curr_prefix)
+        if finalized_id in live_inputs:
+            key = live_inputs[finalized_id]
+            selected_option = None
+            # get options from dict
+            options: list[dict[str, str | dict[str, Any]]] = value[1]["options"]
+            for option in options:
+                if option["key"] == key:
+                    selected_option = option
+                    break
+            if selected_option is not None:
+                parse_class_inputs(out_dict, live_inputs, selected_option["inputs"], curr_prefix)
+                # add self to inputs
+                out_dict[input_type][finalized_id] = value
+                out_dict["dynamic_paths"][finalized_id] = finalize_prefix(curr_prefix, curr_prefix[-1])
+
 @comfytype(io_type="COMFY_DYNAMICSLOT_V3")
 class DynamicSlot(ComfyTypeI):
     Type = dict[str, Any]
@@ -1075,17 +1088,8 @@ class DynamicSlot(ComfyTypeI):
                 self.force_input = True
                 self.slot.force_input = True
 
-        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
-            if self.id in live_inputs:
-                curr_prefix = f"{curr_prefix}{self.id}."
-                add_to_input_dict_v1(d, self.inputs, live_inputs, curr_prefix)
-                add_dynamic_id_mapping(d, [self.slot] + self.inputs, curr_prefix)
-
-        def get_dynamic(self) -> list[Input]:
-            return [self.slot] + self.inputs
-
         def get_all(self) -> list[Input]:
-            return [self] + [self.slot] + self.inputs
+            return [self.slot] + self.inputs
 
         def as_dict(self):
             return super().as_dict() | prune_dict({
@@ -1099,17 +1103,41 @@ class DynamicSlot(ComfyTypeI):
             for input in self.inputs:
                 input.validate()
 
-def add_dynamic_id_mapping(d: dict[str, Any], inputs: list[Input], curr_prefix: str, self: DynamicInput=None):
-    dynamic = d.setdefault("dynamic_paths", {})
-    if self is not None:
-        dynamic[self.id] = f"{curr_prefix}{self.id}"
-    for i in inputs:
-        if not isinstance(i, DynamicInput):
-            dynamic[f"{i.id}"] = f"{curr_prefix}{i.id}"
+    @staticmethod
+    def _expand_schema_for_dynamic(out_dict: dict[str, Any], live_inputs: dict[str, Any], value: tuple[str, dict[str, Any]], input_type: str, curr_prefix: list[str] | None):
+        finalized_id = finalize_prefix(curr_prefix)
+        if finalized_id in live_inputs:
+            inputs = value[1]["inputs"]
+            parse_class_inputs(out_dict, live_inputs, inputs, curr_prefix)
+            # add self to inputs
+            out_dict[input_type][finalized_id] = value
+            out_dict["dynamic_paths"][finalized_id] = finalize_prefix(curr_prefix, curr_prefix[-1])
+
+DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {}
+def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]):
+    DYNAMIC_INPUT_LOOKUP[io_type] = func
+
+def get_dynamic_input_func(io_type: str) -> Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]:
+    return DYNAMIC_INPUT_LOOKUP[io_type]
+
+def setup_dynamic_input_funcs():
+    # Autogrow.Input
+    register_dynamic_input_func(Autogrow.io_type, Autogrow._expand_schema_for_dynamic)
+    # DynamicCombo.Input
+    register_dynamic_input_func(DynamicCombo.io_type, DynamicCombo._expand_schema_for_dynamic)
+    # DynamicSlot.Input
+    register_dynamic_input_func(DynamicSlot.io_type, DynamicSlot._expand_schema_for_dynamic)
+
+if len(DYNAMIC_INPUT_LOOKUP) == 0:
+    setup_dynamic_input_funcs()
 
 class V3Data(TypedDict):
     hidden_inputs: dict[str, Any]
+    'Dictionary where the keys are the hidden input ids and the values are the values of the hidden inputs.'
     dynamic_paths: dict[str, Any]
+    'Dictionary where the keys are the input ids and the values dictate how to turn the inputs into a nested dictionary.'
+    create_dynamic_tuple: bool
+    'When True, the value of the dynamic input will be in the format (value, path_key).'
 
 class HiddenHolder:
     def __init__(self, unique_id: str, prompt: Any,
@@ -1145,6 +1173,10 @@ class HiddenHolder:
             api_key_comfy_org=d.get(Hidden.api_key_comfy_org, None),
         )
 
+    @classmethod
+    def from_v3_data(cls, v3_data: V3Data | None) -> HiddenHolder:
+        return cls.from_dict(v3_data["hidden_inputs"] if v3_data else None)
+
 class Hidden(str, Enum):
     '''
     Enumerator for requesting hidden variables in nodes.
@@ -1250,61 +1282,56 @@ class Schema:
         - verify ids on inputs and outputs are unique - both internally and in relation to each other
         '''
         nested_inputs: list[Input] = []
-        if self.inputs is not None:
-            for input in self.inputs:
+        for input in self.inputs:
+            if not isinstance(input, DynamicInput):
                 nested_inputs.extend(input.get_all())
-        input_ids = [i.id for i in nested_inputs] if nested_inputs is not None else []
-        output_ids = [o.id for o in self.outputs] if self.outputs is not None else []
+        input_ids = [i.id for i in nested_inputs]
+        output_ids = [o.id for o in self.outputs]
         input_set = set(input_ids)
         output_set = set(output_ids)
-        issues = []
+        issues: list[str] = []
         # verify ids are unique per list
         if len(input_set) != len(input_ids):
             issues.append(f"Input ids must be unique, but {[item for item, count in Counter(input_ids).items() if count > 1]} are not.")
         if len(output_set) != len(output_ids):
             issues.append(f"Output ids must be unique, but {[item for item, count in Counter(output_ids).items() if count > 1]} are not.")
-        # verify ids are unique between lists
-        intersection = input_set & output_set
-        if len(intersection) > 0:
-            issues.append(f"Ids must be unique between inputs and outputs, but {intersection} are not.")
         if len(issues) > 0:
             raise ValueError("\n".join(issues))
         # validate inputs and outputs
-        if self.inputs is not None:
-            for input in self.inputs:
-                input.validate()
-        if self.outputs is not None:
-            for output in self.outputs:
-                output.validate()
+        for input in self.inputs:
+            input.validate()
+        for output in self.outputs:
+            output.validate()
 
     def finalize(self):
         """Add hidden based on selected schema options, and give outputs without ids default ids."""
+        # ensure inputs, outputs, and hidden are lists
+        if self.inputs is None:
+            self.inputs = []
+        if self.outputs is None:
+            self.outputs = []
+        if self.hidden is None:
+            self.hidden = []
         # if is an api_node, will need key-related hidden
         if self.is_api_node:
-            if self.hidden is None:
-                self.hidden = []
             if Hidden.auth_token_comfy_org not in self.hidden:
                 self.hidden.append(Hidden.auth_token_comfy_org)
             if Hidden.api_key_comfy_org not in self.hidden:
                 self.hidden.append(Hidden.api_key_comfy_org)
         # if is an output_node, will need prompt and extra_pnginfo
         if self.is_output_node:
-            if self.hidden is None:
-                self.hidden = []
             if Hidden.prompt not in self.hidden:
                 self.hidden.append(Hidden.prompt)
             if Hidden.extra_pnginfo not in self.hidden:
                 self.hidden.append(Hidden.extra_pnginfo)
         # give outputs without ids default ids
-        if self.outputs is not None:
-            for i, output in enumerate(self.outputs):
-                if output.id is None:
-                    output.id = f"_{i}_{output.io_type}_"
+        for i, output in enumerate(self.outputs):
+            if output.id is None:
+                output.id = f"_{i}_{output.io_type}_"
 
-    def get_v1_info(self, cls, live_inputs: dict[str, Any]=None) -> NodeInfoV1:
-        # NOTE: live_inputs will not be used anymore very soon and this will be done another way
+    def get_v1_info(self, cls) -> NodeInfoV1:
         # get V1 inputs
-        input = create_input_dict_v1(self.inputs, live_inputs)
+        input = create_input_dict_v1(self.inputs)
         if self.hidden:
             for hidden in self.hidden:
                 input.setdefault("hidden", {})[hidden.name] = (hidden.value,)
@@ -1384,33 +1411,54 @@ class Schema:
         )
         return info
 
+def get_finalized_class_inputs(d: dict[str, Any], live_inputs: dict[str, Any], include_hidden=False) -> tuple[dict[str, Any], V3Data]:
+    out_dict = {
+        "required": {},
+        "optional": {},
+        "dynamic_paths": {},
+    }
+    d = d.copy()
+    # ignore hidden for parsing
+    hidden = d.pop("hidden", None)
+    parse_class_inputs(out_dict, live_inputs, d)
+    if hidden is not None and include_hidden:
+        out_dict["hidden"] = hidden
+    v3_data = {}
+    dynamic_paths = out_dict.pop("dynamic_paths", None)
+    if dynamic_paths is not None:
+        v3_data["dynamic_paths"] = dynamic_paths
+    return out_dict, hidden, v3_data
 
-def create_input_dict_v1(inputs: list[Input], live_inputs: dict[str, Any]=None) -> dict:
+def parse_class_inputs(out_dict: dict[str, Any], live_inputs: dict[str, Any], curr_dict: dict[str, Any], curr_prefix: list[str] | None=None) -> None:
+    for input_type, inner_d in curr_dict.items():
+        for id, value in inner_d.items():
+            io_type = value[0]
+            if io_type in DYNAMIC_INPUT_LOOKUP:
+                # dynamic inputs need to be handled with lookup functions
+                dynamic_input_func = get_dynamic_input_func(io_type)
+                new_prefix = handle_prefix(curr_prefix, id)
+                dynamic_input_func(out_dict, live_inputs, value, input_type, new_prefix)
+            else:
+                # non-dynamic inputs get directly transferred
+                finalized_id = finalize_prefix(curr_prefix, id)
+                out_dict[input_type][finalized_id] = value
+                if curr_prefix:
+                    out_dict["dynamic_paths"][finalized_id] = finalized_id
+
+def create_input_dict_v1(inputs: list[Input]) -> dict:
     input = {
         "required": {}
     }
-    add_to_input_dict_v1(input, inputs, live_inputs)
+    for i in inputs:
+        add_to_dict_v1(i, input)
     return input
 
-def add_to_input_dict_v1(d: dict[str, Any], inputs: list[Input], live_inputs: dict[str, Any]=None, curr_prefix=''):
-    for i in inputs:
-        if isinstance(i, DynamicInput):
-            add_to_dict_v1(i, d)
-            if live_inputs is not None:
-                i.expand_schema_for_dynamic(d, live_inputs, curr_prefix)
-        else:
-            add_to_dict_v1(i, d)
-
-def add_to_dict_v1(i: Input, d: dict, dynamic_dict: dict=None):
+def add_to_dict_v1(i: Input, d: dict):
     key = "optional" if i.optional else "required"
     as_dict = i.as_dict()
     # for v1, we don't want to include the optional key
     as_dict.pop("optional", None)
-    if dynamic_dict is None:
-        value = (i.get_io_type(), as_dict)
-    else:
-        value = (i.get_io_type(), as_dict, dynamic_dict)
-    d.setdefault(key, {})[i.id] = value
+    d.setdefault(key, {})[i.id] = (i.get_io_type(), as_dict)
 
 def add_to_dict_v3(io: Input | Output, d: dict):
     d[io.id] = (io.get_io_type(), io.as_dict())
@@ -1422,6 +1470,8 @@ def build_nested_inputs(values: dict[str, Any], v3_data: V3Data):
     values = values.copy()
     result = {}
 
+    create_tuple = v3_data.get("create_dynamic_tuple", False)
+
     for key, path in paths.items():
         parts = path.split(".")
         current = result
@@ -1430,7 +1480,10 @@ def build_nested_inputs(values: dict[str, Any], v3_data: V3Data):
             is_last = (i == len(parts) - 1)
 
             if is_last:
-                current[p] = values.pop(key, None)
+                value = values.pop(key, None)
+                if create_tuple:
+                    value = (value, key)
+                current[p] = value
             else:
                 current = current.setdefault(p, {})
 
@@ -1445,7 +1498,6 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
     SCHEMA = None
 
     # filled in during execution
-    resources: Resources = None
     hidden: HiddenHolder = None
 
     @classmethod
@@ -1492,7 +1544,6 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
         return [name for name in kwargs if kwargs[name] is None]
 
     def __init__(self):
-        self.local_resources: ResourcesLocal = None
         self.__class__.VALIDATE_CLASS()
 
     @classmethod
@@ -1560,7 +1611,7 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
         c_type: type[ComfyNode] = cls if is_class(cls) else type(cls)
         type_clone: type[ComfyNode] = shallow_clone_class(c_type)
         # set hidden
-        type_clone.hidden = HiddenHolder.from_dict(v3_data["hidden_inputs"] if v3_data else None)
+        type_clone.hidden = HiddenHolder.from_v3_data(v3_data)
         return type_clone
 
     @final
@@ -1677,19 +1728,10 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
 
     @final
     @classmethod
-    def INPUT_TYPES(cls, include_hidden=True, return_schema=False, live_inputs=None) -> dict[str, dict] | tuple[dict[str, dict], Schema, V3Data]:
+    def INPUT_TYPES(cls) -> dict[str, dict]:
         schema = cls.FINALIZE_SCHEMA()
-        info = schema.get_v1_info(cls, live_inputs)
-        input = info.input
-        if not include_hidden:
-            input.pop("hidden", None)
-        if return_schema:
-            v3_data: V3Data = {}
-            dynamic = input.pop("dynamic_paths", None)
-            if dynamic is not None:
-                v3_data["dynamic_paths"] = dynamic
-            return input, schema, v3_data
-        return input
+        info = schema.get_v1_info(cls)
+        return info.input
 
     @final
     @classmethod
@@ -1808,7 +1850,7 @@ class NodeOutput(_NodeOutputInternal):
         return self.args if len(self.args) > 0 else None
 
     @classmethod
-    def from_dict(cls, data: dict[str, Any]) -> "NodeOutput":
+    def from_dict(cls, data: dict[str, Any]) -> NodeOutput:
         args = ()
         ui = None
         expand = None
@@ -1903,8 +1945,8 @@ __all__ = [
     "Tracks",
     # Dynamic Types
     "MatchType",
-    # "DynamicCombo",
-    # "Autogrow",
+    "DynamicCombo",
+    "Autogrow",
     # Other classes
     "HiddenHolder",
     "Hidden",

comfy_api/latest/_resources.py

@@ -1,72 +0,0 @@
-from __future__ import annotations
-import comfy.utils
-import folder_paths
-import logging
-from abc import ABC, abstractmethod
-from typing import Any
-import torch
-
-class ResourceKey(ABC):
-    Type = Any
-    def __init__(self):
-        ...
-
-class TorchDictFolderFilename(ResourceKey):
-    '''Key for requesting a torch file via file_name from a folder category.'''
-    Type = dict[str, torch.Tensor]
-    def __init__(self, folder_name: str, file_name: str):
-        self.folder_name = folder_name
-        self.file_name = file_name
-
-    def __hash__(self):
-        return hash((self.folder_name, self.file_name))
-
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, TorchDictFolderFilename):
-            return False
-        return self.folder_name == other.folder_name and self.file_name == other.file_name
-
-    def __str__(self):
-        return f"{self.folder_name} -> {self.file_name}"
-
-class Resources(ABC):
-    def __init__(self):
-        ...
-
-    @abstractmethod
-    def get(self, key: ResourceKey, default: Any=...) -> Any:
-        pass
-
-class ResourcesLocal(Resources):
-    def __init__(self):
-        super().__init__()
-        self.local_resources: dict[ResourceKey, Any] = {}
-
-    def get(self, key: ResourceKey, default: Any=...) -> Any:
-        cached = self.local_resources.get(key, None)
-        if cached is not None:
-            logging.info(f"Using cached resource '{key}'")
-            return cached
-        logging.info(f"Loading resource '{key}'")
-        to_return = None
-        if isinstance(key, TorchDictFolderFilename):
-            if default is ...:
-                to_return = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise(key.folder_name, key.file_name), safe_load=True)
-            else:
-                full_path = folder_paths.get_full_path(key.folder_name, key.file_name)
-                if full_path is not None:
-                    to_return = comfy.utils.load_torch_file(full_path, safe_load=True)
-
-        if to_return is not None:
-            self.local_resources[key] = to_return
-            return to_return
-        if default is not ...:
-            return default
-        raise Exception(f"Unsupported resource key type: {type(key)}")
-
-
-class _RESOURCES:
-    ResourceKey = ResourceKey
-    TorchDictFolderFilename = TorchDictFolderFilename
-    Resources = Resources
-    ResourcesLocal = ResourcesLocal

comfy_api_nodes/nodes_bytedance.py

@@ -229,6 +229,7 @@ class ByteDanceImageEditNode(IO.ComfyNode):
                 IO.Hidden.unique_id,
             ],
             is_api_node=True,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -269,7 +270,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ByteDanceSeedreamNode",
-            display_name="ByteDance Seedream 4",
+            display_name="ByteDance Seedream 4.5",
             category="api node/image/ByteDance",
             description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
             inputs=[

comfy_api_nodes/nodes_kling.py

@@ -807,6 +807,7 @@ class OmniProTextToVideoNode(IO.ComfyNode):
                 ),
                 IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
                 IO.Combo.Input("duration", options=[5, 10]),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -826,6 +827,7 @@ class OmniProTextToVideoNode(IO.ComfyNode):
         prompt: str,
         aspect_ratio: str,
         duration: int,
+        resolution: str = "1080p",
     ) -> IO.NodeOutput:
         validate_string(prompt, min_length=1, max_length=2500)
         response = await sync_op(
@@ -837,6 +839,7 @@ class OmniProTextToVideoNode(IO.ComfyNode):
                 prompt=prompt,
                 aspect_ratio=aspect_ratio,
                 duration=str(duration),
+                mode="pro" if resolution == "1080p" else "std",
             ),
         )
         return await finish_omni_video_task(cls, response)
@@ -872,6 +875,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
                     optional=True,
                     tooltip="Up to 6 additional reference images.",
                 ),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -893,6 +897,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
         first_frame: Input.Image,
         end_frame: Input.Image | None = None,
         reference_images: Input.Image | None = None,
+        resolution: str = "1080p",
     ) -> IO.NodeOutput:
         prompt = normalize_omni_prompt_references(prompt)
         validate_string(prompt, min_length=1, max_length=2500)
@@ -936,6 +941,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
                 prompt=prompt,
                 duration=str(duration),
                 image_list=image_list,
+                mode="pro" if resolution == "1080p" else "std",
             ),
         )
         return await finish_omni_video_task(cls, response)
@@ -964,6 +970,7 @@ class OmniProImageToVideoNode(IO.ComfyNode):
                     "reference_images",
                     tooltip="Up to 7 reference images.",
                 ),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -984,6 +991,7 @@ class OmniProImageToVideoNode(IO.ComfyNode):
         aspect_ratio: str,
         duration: int,
         reference_images: Input.Image,
+        resolution: str = "1080p",
     ) -> IO.NodeOutput:
         prompt = normalize_omni_prompt_references(prompt)
         validate_string(prompt, min_length=1, max_length=2500)
@@ -1005,6 +1013,7 @@ class OmniProImageToVideoNode(IO.ComfyNode):
                 aspect_ratio=aspect_ratio,
                 duration=str(duration),
                 image_list=image_list,
+                mode="pro" if resolution == "1080p" else "std",
             ),
         )
         return await finish_omni_video_task(cls, response)
@@ -1036,6 +1045,7 @@ class OmniProVideoToVideoNode(IO.ComfyNode):
                     tooltip="Up to 4 additional reference images.",
                     optional=True,
                 ),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -1058,6 +1068,7 @@ class OmniProVideoToVideoNode(IO.ComfyNode):
         reference_video: Input.Video,
         keep_original_sound: bool,
         reference_images: Input.Image | None = None,
+        resolution: str = "1080p",
     ) -> IO.NodeOutput:
         prompt = normalize_omni_prompt_references(prompt)
         validate_string(prompt, min_length=1, max_length=2500)
@@ -1090,6 +1101,7 @@ class OmniProVideoToVideoNode(IO.ComfyNode):
                 duration=str(duration),
                 image_list=image_list if image_list else None,
                 video_list=video_list,
+                mode="pro" if resolution == "1080p" else "std",
             ),
         )
         return await finish_omni_video_task(cls, response)
@@ -1119,6 +1131,7 @@ class OmniProEditVideoNode(IO.ComfyNode):
                     tooltip="Up to 4 additional reference images.",
                     optional=True,
                 ),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -1139,6 +1152,7 @@ class OmniProEditVideoNode(IO.ComfyNode):
         video: Input.Video,
         keep_original_sound: bool,
         reference_images: Input.Image | None = None,
+        resolution: str = "1080p",
     ) -> IO.NodeOutput:
         prompt = normalize_omni_prompt_references(prompt)
         validate_string(prompt, min_length=1, max_length=2500)
@@ -1171,6 +1185,7 @@ class OmniProEditVideoNode(IO.ComfyNode):
                 duration=None,
                 image_list=image_list if image_list else None,
                 video_list=video_list,
+                mode="pro" if resolution == "1080p" else "std",
             ),
         )
         return await finish_omni_video_task(cls, response)

comfy_api_nodes/nodes_tripo.py

@@ -155,7 +155,7 @@ class TripoTextToModelNode(IO.ComfyNode):
                 model_seed=model_seed,
                 texture_seed=texture_seed,
                 texture_quality=texture_quality,
-                face_limit=face_limit,
+                face_limit=face_limit if face_limit != -1 else None,
                 geometry_quality=geometry_quality,
                 auto_size=True,
                 quad=quad,
@@ -255,7 +255,7 @@ class TripoImageToModelNode(IO.ComfyNode):
                 texture_alignment=texture_alignment,
                 texture_seed=texture_seed,
                 texture_quality=texture_quality,
-                face_limit=face_limit,
+                face_limit=face_limit if face_limit != -1 else None,
                 auto_size=True,
                 quad=quad,
             ),
@@ -369,7 +369,7 @@ class TripoMultiviewToModelNode(IO.ComfyNode):
                 texture_quality=texture_quality,
                 geometry_quality=geometry_quality,
                 texture_alignment=texture_alignment,
-                face_limit=face_limit,
+                face_limit=face_limit if face_limit != -1 else None,
                 quad=quad,
             ),
         )

comfy_api_nodes/util/_helpers.py

@@ -1,16 +1,22 @@
 import asyncio
 import contextlib
 import os
+import re
 import time
 from collections.abc import Callable
 from io import BytesIO
 
+from yarl import URL
+
 from comfy.cli_args import args
 from comfy.model_management import processing_interrupted
 from comfy_api.latest import IO
 
 from .common_exceptions import ProcessingInterrupted
 
+_HAS_PCT_ESC = re.compile(r"%[0-9A-Fa-f]{2}")  # any % followed by 2 hex digits
+_HAS_BAD_PCT = re.compile(r"%(?![0-9A-Fa-f]{2})")  # any % not followed by 2 hex digits
+
 
 def is_processing_interrupted() -> bool:
     """Return True if user/runtime requested interruption."""
@@ -69,3 +75,17 @@ def get_fs_object_size(path_or_object: str | BytesIO) -> int:
     if isinstance(path_or_object, str):
         return os.path.getsize(path_or_object)
     return len(path_or_object.getvalue())
+
+
+def to_aiohttp_url(url: str) -> URL:
+    """If `url` appears to be already percent-encoded (contains at least one valid %HH
+    escape and no malformed '%' sequences) and contains no raw whitespace/control
+    characters preserve the original encoding byte-for-byte (important for signed/presigned URLs).
+    Otherwise, return `URL(url)` and allow yarl to normalize/quote as needed."""
+    if any(c.isspace() for c in url) or any(ord(c) < 0x20 for c in url):
+        # Avoid encoded=True if URL contains raw whitespace/control chars
+        return URL(url)
+    if _HAS_PCT_ESC.search(url) and not _HAS_BAD_PCT.search(url):
+        # Preserve encoding only if it appears pre-encoded AND has no invalid % sequences
+        return URL(url, encoded=True)
+    return URL(url)

comfy_api_nodes/util/download_helpers.py

@@ -19,6 +19,7 @@ from ._helpers import (
     get_auth_header,
     is_processing_interrupted,
     sleep_with_interrupt,
+    to_aiohttp_url,
 )
 from .client import _diagnose_connectivity
 from .common_exceptions import ApiServerError, LocalNetworkError, ProcessingInterrupted
@@ -94,7 +95,7 @@ async def download_url_to_bytesio(
 
             monitor_task = asyncio.create_task(_monitor())
 
-            req_task = asyncio.create_task(session.get(url, headers=headers))
+            req_task = asyncio.create_task(session.get(to_aiohttp_url(url), headers=headers))
             done, pending = await asyncio.wait({req_task, monitor_task}, return_when=asyncio.FIRST_COMPLETED)
 
             if monitor_task in done and req_task in pending:

comfy_execution/graph.py

@@ -97,6 +97,11 @@ def get_input_info(
         extra_info = input_info[1]
     else:
         extra_info = {}
+    # if input_type is a list, it is a Combo defined in outdated format; convert it.
+    # NOTE: uncomment this when we are confident old format going away won't cause too much trouble.
+    # if isinstance(input_type, list):
+    #     extra_info["options"] = input_type
+    #     input_type = IO.Combo.io_type
     return input_type, input_category, extra_info
 
 class TopologicalSort:
@@ -202,15 +207,15 @@ class ExecutionList(TopologicalSort):
         return self.output_cache.get(node_id) is not None
 
     def cache_link(self, from_node_id, to_node_id):
-        if not to_node_id in self.execution_cache:
+        if to_node_id not in self.execution_cache:
             self.execution_cache[to_node_id] = {}
         self.execution_cache[to_node_id][from_node_id] = self.output_cache.get(from_node_id)
-        if not from_node_id in self.execution_cache_listeners:
+        if from_node_id not in self.execution_cache_listeners:
             self.execution_cache_listeners[from_node_id] = set()
         self.execution_cache_listeners[from_node_id].add(to_node_id)
 
     def get_cache(self, from_node_id, to_node_id):
-        if not to_node_id in self.execution_cache:
+        if to_node_id not in self.execution_cache:
             return None
         value = self.execution_cache[to_node_id].get(from_node_id)
         if value is None:

comfy_execution/validation.py

@@ -21,14 +21,24 @@ def validate_node_input(
     """
     # If the types are exactly the same, we can return immediately
     # Use pre-union behaviour: inverse of `__ne__`
+    # NOTE: this lets legacy '*' Any types work that override the __ne__ method of the str class.
     if not received_type != input_type:
         return True
 
+    # If one of the types is '*', we can return True immediately; this is the 'Any' type.
+    if received_type == IO.AnyType.io_type or input_type == IO.AnyType.io_type:
+        return True
+
     # If the received type or input_type is a MatchType, we can return True immediately;
     # validation for this is handled by the frontend
     if received_type == IO.MatchType.io_type or input_type == IO.MatchType.io_type:
         return True
 
+    # This accounts for some custom nodes that output lists of options as the type;
+    # if we ever want to break them on purpose, this can be removed
+    if isinstance(received_type, list) and input_type == IO.Combo.io_type:
+        return True
+
     # Not equal, and not strings
     if not isinstance(received_type, str) or not isinstance(input_type, str):
         return False
@@ -37,6 +47,10 @@ def validate_node_input(
     received_types = set(t.strip() for t in received_type.split(","))
     input_types = set(t.strip() for t in input_type.split(","))
 
+    # If any of the types is '*', we can return True immediately; this is the 'Any' type.
+    if IO.AnyType.io_type in received_types or IO.AnyType.io_type in input_types:
+        return True
+
     if strict:
         # In strict mode, all received types must be in the input types
         return received_types.issubset(input_types)

comfy_extras/nodes_apg.py

@@ -55,7 +55,8 @@ class APG(io.ComfyNode):
         def pre_cfg_function(args):
             nonlocal running_avg, prev_sigma
 
-            if len(args["conds_out"]) == 1: return args["conds_out"]
+            if len(args["conds_out"]) == 1:
+                return args["conds_out"]
 
             cond = args["conds_out"][0]
             uncond = args["conds_out"][1]

comfy_extras/nodes_latent.py

@@ -255,6 +255,7 @@ class LatentBatch(io.ComfyNode):
         return io.Schema(
             node_id="LatentBatch",
             category="latent/batch",
+            is_deprecated=True,
             inputs=[
                 io.Latent.Input("samples1"),
                 io.Latent.Input("samples2"),

comfy_extras/nodes_logic.py

@@ -1,8 +1,11 @@
+from __future__ import annotations
 from typing import TypedDict
 from typing_extensions import override
 from comfy_api.latest import ComfyExtension, io
 from comfy_api.latest import _io
 
+# sentinel for missing inputs
+MISSING = object()
 
 
 class SwitchNode(io.ComfyNode):
@@ -14,6 +17,37 @@ class SwitchNode(io.ComfyNode):
             display_name="Switch",
             category="logic",
             is_experimental=True,
+            inputs=[
+                io.Boolean.Input("switch"),
+                io.MatchType.Input("on_false", template=template, lazy=True),
+                io.MatchType.Input("on_true", template=template, lazy=True),
+            ],
+            outputs=[
+                io.MatchType.Output(template=template, display_name="output"),
+            ],
+        )
+
+    @classmethod
+    def check_lazy_status(cls, switch, on_false=None, on_true=None):
+        if switch and on_true is None:
+            return ["on_true"]
+        if not switch and on_false is None:
+            return ["on_false"]
+
+    @classmethod
+    def execute(cls, switch, on_true, on_false) -> io.NodeOutput:
+        return io.NodeOutput(on_true if switch else on_false)
+
+
+class SoftSwitchNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = io.MatchType.Template("switch")
+        return io.Schema(
+            node_id="ComfySoftSwitchNode",
+            display_name="Soft Switch",
+            category="logic",
+            is_experimental=True,
             inputs=[
                 io.Boolean.Input("switch"),
                 io.MatchType.Input("on_false", template=template, lazy=True, optional=True),
@@ -25,14 +59,14 @@ class SwitchNode(io.ComfyNode):
         )
 
     @classmethod
-    def check_lazy_status(cls, switch, on_false=..., on_true=...):
-        # We use ... instead of None, as None is passed for connected-but-unevaluated inputs.
+    def check_lazy_status(cls, switch, on_false=MISSING, on_true=MISSING):
+        # We use MISSING instead of None, as None is passed for connected-but-unevaluated inputs.
         # This trick allows us to ignore the value of the switch and still be able to run execute().
 
         # One of the inputs may be missing, in which case we need to evaluate the other input
-        if on_false is ...:
+        if on_false is MISSING:
             return ["on_true"]
-        if on_true is ...:
+        if on_true is MISSING:
             return ["on_false"]
         # Normal lazy switch operation
         if switch and on_true is None:
@@ -41,22 +75,50 @@ class SwitchNode(io.ComfyNode):
             return ["on_false"]
 
     @classmethod
-    def validate_inputs(cls, switch, on_false=..., on_true=...):
+    def validate_inputs(cls, switch, on_false=MISSING, on_true=MISSING):
         # This check happens before check_lazy_status(), so we can eliminate the case where
         # both inputs are missing.
-        if on_false is ... and on_true is ...:
+        if on_false is MISSING and on_true is MISSING:
             return "At least one of on_false or on_true must be connected to Switch node"
         return True
 
     @classmethod
-    def execute(cls, switch, on_true=..., on_false=...) -> io.NodeOutput:
-        if on_true is ...:
+    def execute(cls, switch, on_true=MISSING, on_false=MISSING) -> io.NodeOutput:
+        if on_true is MISSING:
             return io.NodeOutput(on_false)
-        if on_false is ...:
+        if on_false is MISSING:
             return io.NodeOutput(on_true)
         return io.NodeOutput(on_true if switch else on_false)
 
 
+class CustomComboNode(io.ComfyNode):
+    """
+    Frontend node that allows user to write their own options for a combo.
+    This is here to make sure the node has a backend-representation to avoid some annoyances.
+    """
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CustomCombo",
+            display_name="Custom Combo",
+            category="utils",
+            is_experimental=True,
+            inputs=[io.Combo.Input("choice", options=[])],
+            outputs=[io.String.Output()]
+        )
+
+    @classmethod
+    def validate_inputs(cls, choice: io.Combo.Type) -> bool:
+        # NOTE: DO NOT DO THIS unless you want to skip validation entirely on the node's inputs.
+        # I am doing that here because the widgets (besides the combo dropdown) on this node are fully frontend defined.
+        # I need to skip checking that the chosen combo option is in the options list, since those are defined by the user.
+        return True
+
+    @classmethod
+    def execute(cls, choice: io.Combo.Type) -> io.NodeOutput:
+        return io.NodeOutput(choice)
+
+
 class DCTestNode(io.ComfyNode):
     class DCValues(TypedDict):
         combo: str
@@ -72,14 +134,14 @@ class DCTestNode(io.ComfyNode):
             display_name="DCTest",
             category="logic",
             is_output_node=True,
-            inputs=[_io.DynamicCombo.Input("combo", options=[
-                _io.DynamicCombo.Option("option1", [io.String.Input("string")]),
-                _io.DynamicCombo.Option("option2", [io.Int.Input("integer")]),
-                _io.DynamicCombo.Option("option3", [io.Image.Input("image")]),
-                _io.DynamicCombo.Option("option4", [
-                    _io.DynamicCombo.Input("subcombo", options=[
-                        _io.DynamicCombo.Option("opt1", [io.Float.Input("float_x"), io.Float.Input("float_y")]),
-                        _io.DynamicCombo.Option("opt2", [io.Mask.Input("mask1", optional=True)]),
+            inputs=[io.DynamicCombo.Input("combo", options=[
+                io.DynamicCombo.Option("option1", [io.String.Input("string")]),
+                io.DynamicCombo.Option("option2", [io.Int.Input("integer")]),
+                io.DynamicCombo.Option("option3", [io.Image.Input("image")]),
+                io.DynamicCombo.Option("option4", [
+                    io.DynamicCombo.Input("subcombo", options=[
+                        io.DynamicCombo.Option("opt1", [io.Float.Input("float_x"), io.Float.Input("float_y")]),
+                        io.DynamicCombo.Option("opt2", [io.Mask.Input("mask1", optional=True)]),
                     ])
                 ])]
             )],
@@ -141,14 +203,65 @@ class AutogrowPrefixTestNode(io.ComfyNode):
         combined = ",".join([str(x) for x in vals])
         return io.NodeOutput(combined)
 
+class ComboOutputTestNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ComboOptionTestNode",
+            display_name="ComboOptionTest",
+            category="logic",
+            inputs=[io.Combo.Input("combo", options=["option1", "option2", "option3"]),
+                    io.Combo.Input("combo2", options=["option4", "option5", "option6"])],
+            outputs=[io.Combo.Output(), io.Combo.Output()],
+        )
+
+    @classmethod
+    def execute(cls, combo: io.Combo.Type, combo2: io.Combo.Type) -> io.NodeOutput:
+        return io.NodeOutput(combo, combo2)
+
+class ConvertStringToComboNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ConvertStringToComboNode",
+            display_name="Convert String to Combo",
+            category="logic",
+            inputs=[io.String.Input("string")],
+            outputs=[io.Combo.Output()],
+        )
+
+    @classmethod
+    def execute(cls, string: str) -> io.NodeOutput:
+        return io.NodeOutput(string)
+
+class InvertBooleanNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="InvertBooleanNode",
+            display_name="Invert Boolean",
+            category="logic",
+            inputs=[io.Boolean.Input("boolean")],
+            outputs=[io.Boolean.Output()],
+        )
+
+    @classmethod
+    def execute(cls, boolean: bool) -> io.NodeOutput:
+        return io.NodeOutput(not boolean)
+
 class LogicExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
         return [
-            # SwitchNode,
+            SwitchNode,
+            CustomComboNode,
+            # SoftSwitchNode,
+            # ConvertStringToComboNode,
             # DCTestNode,
             # AutogrowNamesTestNode,
             # AutogrowPrefixTestNode,
+            # ComboOutputTestNode,
+            # InvertBooleanNode,
         ]
 
 async def comfy_entrypoint() -> LogicExtension:

comfy_extras/nodes_mahiro.py

@@ -10,7 +10,7 @@ class Mahiro(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Mahiro",
-            display_name="Mahiro is so cute that she deserves a better guidance function!! (。・ω・。)",
+            display_name="Mahiro CFG",
             category="_for_testing",
             description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.",
             inputs=[

comfy_extras/nodes_post_processing.py

@@ -4,11 +4,15 @@ import torch
 import torch.nn.functional as F
 from PIL import Image
 import math
+from enum import Enum
+from typing import TypedDict, Literal
 
 import comfy.utils
 import comfy.model_management
+from comfy_extras.nodes_latent import reshape_latent_to
 import node_helpers
 from comfy_api.latest import ComfyExtension, io
+from nodes import MAX_RESOLUTION
 
 class Blend(io.ComfyNode):
     @classmethod
@@ -241,6 +245,353 @@ class ImageScaleToTotalPixels(io.ComfyNode):
         s = s.movedim(1,-1)
         return io.NodeOutput(s)
 
+class ResizeType(str, Enum):
+    SCALE_BY = "scale by multiplier"
+    SCALE_DIMENSIONS = "scale dimensions"
+    SCALE_LONGER_DIMENSION = "scale longer dimension"
+    SCALE_SHORTER_DIMENSION = "scale shorter dimension"
+    SCALE_WIDTH = "scale width"
+    SCALE_HEIGHT = "scale height"
+    SCALE_TOTAL_PIXELS = "scale total pixels"
+    MATCH_SIZE = "match size"
+
+def is_image(input: torch.Tensor) -> bool:
+    # images have 4 dimensions: [batch, height, width, channels]
+    # masks have 3 dimensions: [batch, height, width]
+    return len(input.shape) == 4
+
+def init_image_mask_input(input: torch.Tensor, is_type_image: bool) -> torch.Tensor:
+    if is_type_image:
+        input = input.movedim(-1, 1)
+    else:
+        input = input.unsqueeze(1)
+    return input
+
+def finalize_image_mask_input(input: torch.Tensor, is_type_image: bool) -> torch.Tensor:
+    if is_type_image:
+        input = input.movedim(1, -1)
+    else:
+        input = input.squeeze(1)
+    return input
+
+def scale_by(input: torch.Tensor, multiplier: float, scale_method: str) -> torch.Tensor:
+    is_type_image = is_image(input)
+    input = init_image_mask_input(input, is_type_image)
+    width = round(input.shape[-1] * multiplier)
+    height = round(input.shape[-2] * multiplier)
+
+    input = comfy.utils.common_upscale(input, width, height, scale_method, "disabled")
+    input = finalize_image_mask_input(input, is_type_image)
+    return input
+
+def scale_dimensions(input: torch.Tensor, width: int, height: int, scale_method: str, crop: str="disabled") -> torch.Tensor:
+    if width == 0 and height == 0:
+        return input
+    is_type_image = is_image(input)
+    input = init_image_mask_input(input, is_type_image)
+
+    if width == 0:
+        width = max(1, round(input.shape[-1] * height / input.shape[-2]))
+    elif height == 0:
+        height = max(1, round(input.shape[-2] * width / input.shape[-1]))
+
+    input = comfy.utils.common_upscale(input, width, height, scale_method, crop)
+    input = finalize_image_mask_input(input, is_type_image)
+    return input
+
+def scale_longer_dimension(input: torch.Tensor, longer_size: int, scale_method: str) -> torch.Tensor:
+    is_type_image = is_image(input)
+    input = init_image_mask_input(input, is_type_image)
+    width = input.shape[-1]
+    height = input.shape[-2]
+
+    if height > width:
+        width = round((width / height) * longer_size)
+        height = longer_size
+    elif width > height:
+        height = round((height / width) * longer_size)
+        width = longer_size
+    else:
+        height = longer_size
+        width = longer_size
+
+    input = comfy.utils.common_upscale(input, width, height, scale_method, "disabled")
+    input = finalize_image_mask_input(input, is_type_image)
+    return input
+
+def scale_shorter_dimension(input: torch.Tensor, shorter_size: int, scale_method: str) -> torch.Tensor:
+    is_type_image = is_image(input)
+    input = init_image_mask_input(input, is_type_image)
+    width = input.shape[-1]
+    height = input.shape[-2]
+
+    if height < width:
+        width = round((width / height) * shorter_size)
+        height = shorter_size
+    elif width > height:
+        height = round((height / width) * shorter_size)
+        width = shorter_size
+    else:
+        height = shorter_size
+        width = shorter_size
+
+    input = comfy.utils.common_upscale(input, width, height, scale_method, "disabled")
+    input = finalize_image_mask_input(input, is_type_image)
+    return input
+
+def scale_total_pixels(input: torch.Tensor, megapixels: float, scale_method: str) -> torch.Tensor:
+    is_type_image = is_image(input)
+    input = init_image_mask_input(input, is_type_image)
+    total = int(megapixels * 1024 * 1024)
+
+    scale_by = math.sqrt(total / (input.shape[-1] * input.shape[-2]))
+    width = round(input.shape[-1] * scale_by)
+    height = round(input.shape[-2] * scale_by)
+
+    input = comfy.utils.common_upscale(input, width, height, scale_method, "disabled")
+    input = finalize_image_mask_input(input, is_type_image)
+    return input
+
+def scale_match_size(input: torch.Tensor, match: torch.Tensor, scale_method: str, crop: str) -> torch.Tensor:
+    is_type_image = is_image(input)
+    input = init_image_mask_input(input, is_type_image)
+    match = init_image_mask_input(match, is_image(match))
+
+    width = match.shape[-1]
+    height = match.shape[-2]
+    input = comfy.utils.common_upscale(input, width, height, scale_method, crop)
+    input = finalize_image_mask_input(input, is_type_image)
+    return input
+
+class ResizeImageMaskNode(io.ComfyNode):
+
+    scale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
+    crop_methods = ["disabled", "center"]
+
+    class ResizeTypedDict(TypedDict):
+        resize_type: ResizeType
+        scale_method: Literal["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
+        crop: Literal["disabled", "center"]
+        multiplier: float
+        width: int
+        height: int
+        longer_size: int
+        shorter_size: int
+        megapixels: float
+
+    @classmethod
+    def define_schema(cls):
+        template = io.MatchType.Template("input_type", [io.Image, io.Mask])
+        crop_combo = io.Combo.Input("crop", options=cls.crop_methods, default="center")
+        return io.Schema(
+            node_id="ResizeImageMaskNode",
+            display_name="Resize Image/Mask",
+            category="transform",
+            inputs=[
+                io.MatchType.Input("input", template=template),
+                io.DynamicCombo.Input("resize_type", options=[
+                    io.DynamicCombo.Option(ResizeType.SCALE_BY, [
+                        io.Float.Input("multiplier", default=1.00, min=0.01, max=8.0, step=0.01),
+                        ]),
+                    io.DynamicCombo.Option(ResizeType.SCALE_DIMENSIONS, [
+                        io.Int.Input("width", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        io.Int.Input("height", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        crop_combo,
+                        ]),
+                    io.DynamicCombo.Option(ResizeType.SCALE_LONGER_DIMENSION, [
+                        io.Int.Input("longer_size", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        ]),
+                    io.DynamicCombo.Option(ResizeType.SCALE_SHORTER_DIMENSION, [
+                        io.Int.Input("shorter_size", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        ]),
+                    io.DynamicCombo.Option(ResizeType.SCALE_WIDTH, [
+                        io.Int.Input("width", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        ]),
+                    io.DynamicCombo.Option(ResizeType.SCALE_HEIGHT, [
+                        io.Int.Input("height", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        ]),
+                    io.DynamicCombo.Option(ResizeType.SCALE_TOTAL_PIXELS, [
+                        io.Float.Input("megapixels", default=1.0, min=0.01, max=16.0, step=0.01),
+                        ]),
+                    io.DynamicCombo.Option(ResizeType.MATCH_SIZE, [
+                        io.MultiType.Input("match", [io.Image, io.Mask]),
+                        crop_combo,
+                        ]),
+                ]),
+                io.Combo.Input("scale_method", options=cls.scale_methods, default="area"),
+            ],
+            outputs=[io.MatchType.Output(template=template, display_name="resized")]
+        )
+
+    @classmethod
+    def execute(cls, input: io.Image.Type | io.Mask.Type, scale_method: io.Combo.Type, resize_type: ResizeTypedDict) -> io.NodeOutput:
+        selected_type = resize_type["resize_type"]
+        if selected_type == ResizeType.SCALE_BY:
+            return io.NodeOutput(scale_by(input, resize_type["multiplier"], scale_method))
+        elif selected_type == ResizeType.SCALE_DIMENSIONS:
+            return io.NodeOutput(scale_dimensions(input, resize_type["width"], resize_type["height"], scale_method, resize_type["crop"]))
+        elif selected_type == ResizeType.SCALE_LONGER_DIMENSION:
+            return io.NodeOutput(scale_longer_dimension(input, resize_type["longer_size"], scale_method))
+        elif selected_type == ResizeType.SCALE_SHORTER_DIMENSION:
+            return io.NodeOutput(scale_shorter_dimension(input, resize_type["shorter_size"], scale_method))
+        elif selected_type == ResizeType.SCALE_WIDTH:
+            return io.NodeOutput(scale_dimensions(input, resize_type["width"], 0, scale_method))
+        elif selected_type == ResizeType.SCALE_HEIGHT:
+            return io.NodeOutput(scale_dimensions(input, 0, resize_type["height"], scale_method))
+        elif selected_type == ResizeType.SCALE_TOTAL_PIXELS:
+            return io.NodeOutput(scale_total_pixels(input, resize_type["megapixels"], scale_method))
+        elif selected_type == ResizeType.MATCH_SIZE:
+            return io.NodeOutput(scale_match_size(input, resize_type["match"], scale_method, resize_type["crop"]))
+        raise ValueError(f"Unsupported resize type: {selected_type}")
+
+def batch_images(images: list[torch.Tensor]) -> torch.Tensor | None:
+    if len(images) == 0:
+        return None
+    # first, get the max channels count
+    max_channels = max(image.shape[-1] for image in images)
+    # then, pad all images to have the same channels count
+    padded_images: list[torch.Tensor] = []
+    for image in images:
+        if image.shape[-1] < max_channels:
+            padded_images.append(torch.nn.functional.pad(image, (0,1), mode='constant', value=1.0))
+        else:
+            padded_images.append(image)
+    # resize all images to be the same size as the first image
+    resized_images: list[torch.Tensor] = []
+    first_image_shape = padded_images[0].shape
+    for image in padded_images:
+        if image.shape[1:] != first_image_shape[1:]:
+            resized_images.append(comfy.utils.common_upscale(image.movedim(-1,1), first_image_shape[2], first_image_shape[1], "bilinear", "center").movedim(1,-1))
+        else:
+            resized_images.append(image)
+    # batch the images in the format [b, h, w, c]
+    return torch.cat(resized_images, dim=0)
+
+def batch_masks(masks: list[torch.Tensor]) -> torch.Tensor | None:
+    if len(masks) == 0:
+        return None
+    # resize all masks to be the same size as the first mask
+    resized_masks: list[torch.Tensor] = []
+    first_mask_shape = masks[0].shape
+    for mask in masks:
+        if mask.shape[1:] != first_mask_shape[1:]:
+            mask = init_image_mask_input(mask, is_type_image=False)
+            mask = comfy.utils.common_upscale(mask, first_mask_shape[2], first_mask_shape[1], "bilinear", "center")
+            resized_masks.append(finalize_image_mask_input(mask, is_type_image=False))
+        else:
+            resized_masks.append(mask)
+    # batch the masks in the format [b, h, w]
+    return torch.cat(resized_masks, dim=0)
+
+def batch_latents(latents: list[dict[str, torch.Tensor]]) -> dict[str, torch.Tensor] | None:
+    if len(latents) == 0:
+        return None
+    samples_out = latents[0].copy()
+    samples_out["batch_index"] = []
+    first_samples = latents[0]["samples"]
+    tensors: list[torch.Tensor] = []
+    for latent in latents:
+        # first, deal with latent tensors
+        tensors.append(reshape_latent_to(first_samples.shape, latent["samples"], repeat_batch=False))
+        # next, deal with batch_index
+        samples_out["batch_index"].extend(latent.get("batch_index", [x for x in range(0, latent["samples"].shape[0])]))
+    samples_out["samples"] = torch.cat(tensors, dim=0)
+    return samples_out
+
+class BatchImagesNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        autogrow_template = io.Autogrow.TemplatePrefix(io.Image.Input("image"), prefix="image", min=2, max=50)
+        return io.Schema(
+            node_id="BatchImagesNode",
+            display_name="Batch Images",
+            category="image",
+            inputs=[
+                io.Autogrow.Input("images", template=autogrow_template)
+            ],
+            outputs=[
+                io.Image.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, images: io.Autogrow.Type) -> io.NodeOutput:
+        return io.NodeOutput(batch_images(list(images.values())))
+
+class BatchMasksNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        autogrow_template = io.Autogrow.TemplatePrefix(io.Mask.Input("mask"), prefix="mask", min=2, max=50)
+        return io.Schema(
+            node_id="BatchMasksNode",
+            display_name="Batch Masks",
+            category="mask",
+            inputs=[
+                io.Autogrow.Input("masks", template=autogrow_template)
+            ],
+            outputs=[
+                io.Mask.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, masks: io.Autogrow.Type) -> io.NodeOutput:
+        return io.NodeOutput(batch_masks(list(masks.values())))
+
+class BatchLatentsNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        autogrow_template = io.Autogrow.TemplatePrefix(io.Latent.Input("latent"), prefix="latent", min=2, max=50)
+        return io.Schema(
+            node_id="BatchLatentsNode",
+            display_name="Batch Latents",
+            category="latent",
+            inputs=[
+                io.Autogrow.Input("latents", template=autogrow_template)
+            ],
+            outputs=[
+                io.Latent.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, latents: io.Autogrow.Type) -> io.NodeOutput:
+        return io.NodeOutput(batch_latents(list(latents.values())))
+
+class BatchImagesMasksLatentsNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        matchtype_template = io.MatchType.Template("input", allowed_types=[io.Image, io.Mask, io.Latent])
+        autogrow_template = io.Autogrow.TemplatePrefix(
+                io.MatchType.Input("input", matchtype_template),
+                prefix="input", min=1, max=50)
+        return io.Schema(
+            node_id="BatchImagesMasksLatentsNode",
+            display_name="Batch Images/Masks/Latents",
+            category="util",
+            inputs=[
+                io.Autogrow.Input("inputs", template=autogrow_template)
+            ],
+            outputs=[
+                io.MatchType.Output(id=None, template=matchtype_template)
+            ]
+        )
+
+    @classmethod
+    def execute(cls, inputs: io.Autogrow.Type) -> io.NodeOutput:
+        batched = None
+        values = list(inputs.values())
+        # latents
+        if isinstance(values[0], dict):
+            batched = batch_latents(values)
+        # images
+        elif is_image(values[0]):
+            batched = batch_images(values)
+        # masks
+        else:
+            batched = batch_masks(values)
+        return io.NodeOutput(batched)
+
 class PostProcessingExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
@@ -250,6 +601,11 @@ class PostProcessingExtension(ComfyExtension):
             Quantize,
             Sharpen,
             ImageScaleToTotalPixels,
+            ResizeImageMaskNode,
+            BatchImagesNode,
+            BatchMasksNode,
+            BatchLatentsNode,
+            # BatchImagesMasksLatentsNode,
         ]
 
 async def comfy_entrypoint() -> PostProcessingExtension:

comfy_extras/nodes_primitive.py

@@ -66,7 +66,7 @@ class Float(io.ComfyNode):
             display_name="Float",
             category="utils/primitive",
             inputs=[
-                io.Float.Input("value", min=-sys.maxsize, max=sys.maxsize),
+                io.Float.Input("value", min=-sys.maxsize, max=sys.maxsize, step=0.1),
             ],
             outputs=[io.Float.Output()],
         )

comfy_extras/nodes_wan.py

@@ -817,7 +817,7 @@ def get_sample_indices(original_fps,
     if required_duration > total_frames / original_fps:
         raise ValueError("required_duration must be less than video length")
 
-    if not fixed_start is None and fixed_start >= 0:
+    if fixed_start is not None and fixed_start >= 0:
         start_frame = fixed_start
     else:
         max_start = total_frames - required_origin_frames

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.6.0"
+__version__ = "0.7.0"

execution.py

@@ -79,7 +79,7 @@ class IsChangedCache:
         # Intentionally do not use cached outputs here. We only want constants in IS_CHANGED
         input_data_all, _, v3_data = get_input_data(node["inputs"], class_def, node_id, None)
         try:
-            is_changed = await _async_map_node_over_list(self.prompt_id, node_id, class_def, input_data_all, is_changed_name)
+            is_changed = await _async_map_node_over_list(self.prompt_id, node_id, class_def, input_data_all, is_changed_name, v3_data=v3_data)
             is_changed = await resolve_map_node_over_list_results(is_changed)
             node["is_changed"] = [None if isinstance(x, ExecutionBlocker) else x for x in is_changed]
         except Exception as e:
@@ -148,13 +148,12 @@ SENSITIVE_EXTRA_DATA_KEYS = ("auth_token_comfy_org", "api_key_comfy_org")
 def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=None, extra_data={}):
     is_v3 = issubclass(class_def, _ComfyNodeInternal)
     v3_data: io.V3Data = {}
+    hidden_inputs_v3 = {}
+    valid_inputs = class_def.INPUT_TYPES()
     if is_v3:
-        valid_inputs, schema, v3_data = class_def.INPUT_TYPES(include_hidden=False, return_schema=True, live_inputs=inputs)
-    else:
-        valid_inputs = class_def.INPUT_TYPES()
+        valid_inputs, hidden, v3_data = _io.get_finalized_class_inputs(valid_inputs, inputs)
     input_data_all = {}
     missing_keys = {}
-    hidden_inputs_v3 = {}
     for x in inputs:
         input_data = inputs[x]
         _, input_category, input_info = get_input_info(class_def, x, valid_inputs)
@@ -180,18 +179,18 @@ def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=
             input_data_all[x] = [input_data]
 
     if is_v3:
-        if schema.hidden:
-            if io.Hidden.prompt in schema.hidden:
+        if hidden is not None:
+            if io.Hidden.prompt.name in hidden:
                 hidden_inputs_v3[io.Hidden.prompt] = dynprompt.get_original_prompt() if dynprompt is not None else {}
-            if io.Hidden.dynprompt in schema.hidden:
+            if io.Hidden.dynprompt.name in hidden:
                 hidden_inputs_v3[io.Hidden.dynprompt] = dynprompt
-            if io.Hidden.extra_pnginfo in schema.hidden:
+            if io.Hidden.extra_pnginfo.name in hidden:
                 hidden_inputs_v3[io.Hidden.extra_pnginfo] = extra_data.get('extra_pnginfo', None)
-            if io.Hidden.unique_id in schema.hidden:
+            if io.Hidden.unique_id.name in hidden:
                 hidden_inputs_v3[io.Hidden.unique_id] = unique_id
-            if io.Hidden.auth_token_comfy_org in schema.hidden:
+            if io.Hidden.auth_token_comfy_org.name in hidden:
                 hidden_inputs_v3[io.Hidden.auth_token_comfy_org] = extra_data.get("auth_token_comfy_org", None)
-            if io.Hidden.api_key_comfy_org in schema.hidden:
+            if io.Hidden.api_key_comfy_org.name in hidden:
                 hidden_inputs_v3[io.Hidden.api_key_comfy_org] = extra_data.get("api_key_comfy_org", None)
     else:
         if "hidden" in valid_inputs:
@@ -258,7 +257,7 @@ async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, f
                 pre_execute_cb(index)
             # V3
             if isinstance(obj, _ComfyNodeInternal) or (is_class(obj) and issubclass(obj, _ComfyNodeInternal)):
-                # if is just a class, then assign no resources or state, just create clone
+                # if is just a class, then assign no state, just create clone
                 if is_class(obj):
                     type_obj = obj
                     obj.VALIDATE_CLASS()
@@ -481,7 +480,10 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
             else:
                 lazy_status_present = getattr(obj, "check_lazy_status", None) is not None
             if lazy_status_present:
-                required_inputs = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, "check_lazy_status", allow_interrupt=True, v3_data=v3_data)
+                # for check_lazy_status, the returned data should include the original key of the input
+                v3_data_lazy = v3_data.copy()
+                v3_data_lazy["create_dynamic_tuple"] = True
+                required_inputs = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, "check_lazy_status", allow_interrupt=True, v3_data=v3_data_lazy)
                 required_inputs = await resolve_map_node_over_list_results(required_inputs)
                 required_inputs = set(sum([r for r in required_inputs if isinstance(r,list)], []))
                 required_inputs = [x for x in required_inputs if isinstance(x,str) and (
@@ -599,6 +601,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
 
         if isinstance(ex, comfy.model_management.OOM_EXCEPTION):
             tips = "This error means you ran out of memory on your GPU.\n\nTIPS: If the workflow worked before you might have accidentally set the batch_size to a large number."
+            logging.info("Memory summary: {}".format(comfy.model_management.debug_memory_summary()))
             logging.error("Got an OOM, unloading all loaded models.")
             comfy.model_management.unload_all_models()
 
@@ -756,10 +759,13 @@ async def validate_inputs(prompt_id, prompt, item, validated):
     errors = []
     valid = True
 
+    v3_data = None
     validate_function_inputs = []
     validate_has_kwargs = False
     if issubclass(obj_class, _ComfyNodeInternal):
-        class_inputs, _, _ = obj_class.INPUT_TYPES(include_hidden=False, return_schema=True, live_inputs=inputs)
+        obj_class: _io._ComfyNodeBaseInternal
+        class_inputs = obj_class.INPUT_TYPES()
+        class_inputs, _, v3_data = _io.get_finalized_class_inputs(class_inputs, inputs)
         validate_function_name = "validate_inputs"
         validate_function = first_real_override(obj_class, validate_function_name)
     else:
@@ -779,10 +785,11 @@ async def validate_inputs(prompt_id, prompt, item, validated):
         assert extra_info is not None
         if x not in inputs:
             if input_category == "required":
+                details = f"{x}" if not v3_data else x.split(".")[-1]
                 error = {
                     "type": "required_input_missing",
                     "message": "Required input is missing",
-                    "details": f"{x}",
+                    "details": details,
                     "extra_info": {
                         "input_name": x
                     }
@@ -916,8 +923,11 @@ async def validate_inputs(prompt_id, prompt, item, validated):
                     errors.append(error)
                     continue
 
-                if isinstance(input_type, list):
-                    combo_options = input_type
+                if isinstance(input_type, list) or input_type == io.Combo.io_type:
+                    if input_type == io.Combo.io_type:
+                        combo_options = extra_info.get("options", [])
+                    else:
+                        combo_options = input_type
                     if val not in combo_options:
                         input_config = info
                         list_info = ""

nodes.py

@@ -1663,8 +1663,6 @@ class LoadImage:
         output_masks = []
         w, h = None, None
 
-        excluded_formats = ['MPO']
-
         for i in ImageSequence.Iterator(img):
             i = node_helpers.pillow(ImageOps.exif_transpose, i)
 
@@ -1692,7 +1690,10 @@ class LoadImage:
             output_images.append(image)
             output_masks.append(mask.unsqueeze(0))
 
-        if len(output_images) > 1 and img.format not in excluded_formats:
+            if img.format == "MPO":
+                break  # ignore all frames except the first one for MPO format
+
+        if len(output_images) > 1:
             output_image = torch.cat(output_images, dim=0)
             output_mask = torch.cat(output_masks, dim=0)
         else:
@@ -1863,6 +1864,7 @@ class ImageBatch:
     FUNCTION = "batch"
 
     CATEGORY = "image"
+    DEPRECATED = True
 
     def batch(self, image1, image2):
         if image1.shape[-1] != image2.shape[-1]:
@@ -2241,8 +2243,10 @@ async def init_external_custom_nodes():
 
         for possible_module in possible_modules:
             module_path = os.path.join(custom_node_path, possible_module)
-            if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue
-            if module_path.endswith(".disabled"): continue
+            if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py":
+                continue
+            if module_path.endswith(".disabled"):
+                continue
             if args.disable_all_custom_nodes and possible_module not in args.whitelist_custom_nodes:
                 logging.info(f"Skipping {possible_module} due to disable_all_custom_nodes and whitelist_custom_nodes")
                 continue

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.6.0"
+version = "0.7.0"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"
@@ -15,12 +15,16 @@ lint.select = [
   "N805", # invalid-first-argument-name-for-method
   "S307", # suspicious-eval-usage
   "S102", # exec
+  "E",
   "T",    # print-usage
   "W",
   # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names.
   # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f
   "F",
 ]
+
+lint.ignore = ["E501", "E722", "E731", "E712", "E402", "E741"]
+
 exclude = ["*.ipynb", "**/generated/*.pyi"]
 
 [tool.pylint]

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.35.9
-comfyui-workflow-templates==0.7.64
+comfyui-workflow-templates==0.7.65
 comfyui-embedded-docs==0.3.1
 torch
 torchsde

server.py

@@ -324,7 +324,7 @@ class PromptServer():
         @routes.get("/models/{folder}")
         async def get_models(request):
             folder = request.match_info.get("folder", None)
-            if not folder in folder_paths.folder_names_and_paths:
+            if folder not in folder_paths.folder_names_and_paths:
                 return web.Response(status=404)
             files = folder_paths.get_filename_list(folder)
             return web.json_response(files)
@@ -579,7 +579,7 @@ class PromptServer():
             folder_name = request.match_info.get("folder_name", None)
             if folder_name is None:
                 return web.Response(status=404)
-            if not "filename" in request.rel_url.query:
+            if "filename" not in request.rel_url.query:
                 return web.Response(status=404)
 
             filename = request.rel_url.query["filename"]
@@ -593,7 +593,7 @@ class PromptServer():
             if out is None:
                 return web.Response(status=404)
             dt = json.loads(out)
-            if not "__metadata__" in dt:
+            if "__metadata__" not in dt:
                 return web.Response(status=404)
             return web.json_response(dt["__metadata__"])