diff --git a/README.md b/README.md index 61036bd81..c75353d36 100644 --- a/README.md +++ b/README.md @@ -140,7 +140,7 @@ ComfyUI follows a weekly release cycle targeting Monday but this regularly chang - Commits outside of the stable release tags may be very unstable and break many custom nodes. - Serves as the foundation for the desktop release -2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)** +2. **[ComfyUI Desktop](https://github.com/Comfy-Org/Comfy-Desktop)** - Builds a new release using the latest stable core version 3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)** @@ -309,7 +309,7 @@ After this you should have everything installed and can proceed to running Comfy #### Apple Mac silicon -You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version. +You can install ComfyUI in Apple Mac silicon (M1, M2, M3 or M4) with any recent macOS version. 1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide (make sure to install the latest pytorch nightly). 1. 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generation and editing/Text to image", + "description": "This subgraph generates non-photorealistic illustrations from text prompts using a 2-billion-parameter model optimized for anime concepts, characters, and styles. It is ideal for creating artistic images, concept art, or stylized illustrations where photorealism is not required. The model excels with anime and artistic content but performs poorly on realistic subjects." + } + ] + }, + "extra": { + "BlueprintDescription": "This subgraph generates non-photorealistic illustrations from text prompts using a 2-billion-parameter model optimized for anime concepts, characters, and styles. It is ideal for creating artistic images, concept art, or stylized illustrations where photorealism is not required. The model excels with anime and artistic content but performs poorly on realistic subjects." + } +} \ No newline at end of file diff --git a/blueprints/Text to Image (Anima).json b/blueprints/Text to Image (Anima).json index 787908ca9..dcf6e5973 100644 --- a/blueprints/Text to Image (Anima).json +++ b/blueprints/Text to Image (Anima).json @@ -1077,9 +1077,12 @@ } ], "extra": {}, - "category": "Image generation and editing/Text to image" + "category": "Image generation and editing/Text to image", + "description": "This subgraph converts text prompts into non-photorealistic illustrations using a 2-billion-parameter model optimized for anime and artistic styles. It is ideal for generating concept art, character designs, or stylized illustrations where photorealism is not required. The model excels with anime and artistic content but performs poorly on realistic subjects." } ] }, - "extra": {} + "extra": { + "BlueprintDescription": "This subgraph converts text prompts into non-photorealistic illustrations using a 2-billion-parameter model optimized for anime and artistic styles. It is ideal for generating concept art, character designs, or stylized illustrations where photorealism is not required. 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It outputs depth maps, camera poses, and optionally 3D Gaussian parameters for novel view synthesis." + } +} \ No newline at end of file diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py index 282408891..1c9782a38 100644 --- a/comfy/ldm/wan/model.py +++ b/comfy/ldm/wan/model.py @@ -1665,7 +1665,7 @@ class SCAILWanModel(WanModel): # embeddings x = self.patch_embedding(x.float()).to(x.dtype) - if ref_mask_latents is not None: # SCAIL-2 additive mask stream + if ref_mask_latents is not None: # SCAIL-2 additive mask stream (one identity mask frame per reference, then video) x = x + self.patch_embedding_mask(ref_mask_latents.float()).to(x.dtype) grid_sizes = x.shape[2:] transformer_options["grid_sizes"] = grid_sizes @@ -1728,22 +1728,25 @@ class SCAILWanModel(WanModel): # ref_mask_flag is a scalar bool (CONDConstant, SCAIL-2 only). False => replacement mode, # which places ref/pose via H/W rope shifts instead of the animation-mode temporal offset. + # reference_latent may stack several frames: the last is the primary reference adjacent to the video, the earlier frames are additional references. def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, pose_latents=None, reference_latent=None, ref_mask_flag=None, transformer_options={}): + ref_t_patches = 0 + if reference_latent is not None: + ref_t_patches = (reference_latent.shape[2] + (self.patch_size[0] // 2)) // self.patch_size[0] + if ref_mask_flag is not None and not bool(ref_mask_flag): REF_ROPE_H = 120.0 POSE_ROPE_W = 120.0 - ref_t_patches = 0 - if reference_latent is not None: - ref_t_patches = (reference_latent.shape[2] + (self.patch_size[0] // 2)) // self.patch_size[0] main_t_patches = t - ref_t_patches + video_t_start = max(ref_t_patches - 1, 0) parts = [] if ref_t_patches > 0: ref_tf = {"rope_options": {"shift_y": REF_ROPE_H, "shift_x": 0.0, "scale_y": 1.0, "scale_x": 1.0}} parts.append(super().rope_encode(ref_t_patches, h, w, t_start=0, device=device, dtype=dtype, transformer_options=ref_tf)) if main_t_patches > 0: - parts.append(super().rope_encode(main_t_patches, h, w, t_start=0, device=device, dtype=dtype, transformer_options=transformer_options)) + parts.append(super().rope_encode(main_t_patches, h, w, t_start=video_t_start, device=device, dtype=dtype, transformer_options=transformer_options)) if pose_latents is not None: F_pose, H_pose, W_pose = pose_latents.shape[-3], pose_latents.shape[-2], pose_latents.shape[-1] @@ -1752,7 +1755,7 @@ class SCAILWanModel(WanModel): h_shift = (h_scale - 1) / 2 w_shift = (w_scale - 1) / 2 pose_tf = {"rope_options": {"shift_y": h_shift, "shift_x": POSE_ROPE_W + w_shift, "scale_y": h_scale, "scale_x": w_scale}} - parts.append(super().rope_encode(F_pose, H_pose, W_pose, t_start=0, device=device, dtype=dtype, transformer_options=pose_tf)) + parts.append(super().rope_encode(F_pose, H_pose, W_pose, t_start=video_t_start, device=device, dtype=dtype, transformer_options=pose_tf)) return torch.cat(parts, dim=1) @@ -1761,10 +1764,6 @@ class SCAILWanModel(WanModel): if pose_latents is None: return main_freqs - ref_t_patches = 0 - if reference_latent is not None: - ref_t_patches = (reference_latent.shape[2] + (self.patch_size[0] // 2)) // self.patch_size[0] - F_pose, H_pose, W_pose = pose_latents.shape[-3], pose_latents.shape[-2], pose_latents.shape[-1] # if pose is at half resolution, scale_y/scale_x=2 stretches the position range to cover the same RoPE extent as the main frames diff --git a/comfy/model_base.py b/comfy/model_base.py index ab4a11022..d143dc06f 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -1747,10 +1747,14 @@ class WAN21_SCAIL(WAN21): reference_latents = kwargs.get("reference_latents", None) if reference_latents is not None: - ref_latent = self.process_latent_in(reference_latents[-1]) - ref_mask = torch.ones_like(ref_latent[:, :4]) - ref_latent = torch.cat([ref_latent, ref_mask], dim=1) - out['reference_latent'] = comfy.conds.CONDRegular(ref_latent) + # SCAIL-2 multi-reference: reference_latents[0] is the primary ref, [1:] are additional + # references. Stack as [additional..., primary] so the primary stays adjacent to the video. + ordered = list(reference_latents[1:]) + list(reference_latents[:1]) + stacked = [] + for lat in ordered: + lat = self.process_latent_in(lat) + stacked.append(torch.cat([lat, torch.ones_like(lat[:, :4])], dim=1)) + out['reference_latent'] = comfy.conds.CONDRegular(torch.cat(stacked, dim=2)) pose_latents = kwargs.get("pose_video_latent", None) if pose_latents is not None: @@ -1792,6 +1796,7 @@ class WAN21_SCAIL2(WAN21_SCAIL): if driving_mask_28ch is not None: out['sam_latents'] = comfy.conds.CONDRegular(driving_mask_28ch.movedim(1, 2).contiguous()) + # ref_mask_28ch holds one identity mask per stacked reference frame (additional refs first, then the primary ref), followed by zeros over the video frames. ref_mask_28ch = kwargs.get("ref_mask_28ch", None) if ref_mask_28ch is not None: out['ref_mask_latents'] = comfy.conds.CONDRegular(ref_mask_28ch.movedim(1, 2).contiguous()) @@ -1819,10 +1824,11 @@ class WAN21_SCAIL2(WAN21_SCAIL): # Return sliced view omitting retain_index_list return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_offset=0) if cond_key == "ref_mask_latents" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor): - # The ref mask is just a single frame padded with frames of zeros, so just grab the first frames for all windows + # The ref mask is N leading ref frames padded with frames of zeros, so just grab the first frames for all windows full_ref_mask = cond_value.cond video_frame_count = x_in.shape[2] - if full_ref_mask.shape[2] != video_frame_count + 1: + ref_frame_count = full_ref_mask.shape[2] - video_frame_count + if ref_frame_count < 1: return None window_length = len(window.index_list) @@ -1831,7 +1837,7 @@ class WAN21_SCAIL2(WAN21_SCAIL): if anchor_index is not None and anchor_index >= 0: window_length += 1 - window_ref_mask = full_ref_mask[:, :, :window_length + 1].to(device) + window_ref_mask = full_ref_mask[:, :, :window_length + ref_frame_count].to(device) return cond_value._copy_with(window_ref_mask) return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list) diff --git a/comfy/sd.py b/comfy/sd.py index a66ba1bfb..348fe4958 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -67,6 +67,7 @@ import comfy.text_encoders.anima import comfy.text_encoders.ace15 import comfy.text_encoders.longcat_image import comfy.text_encoders.qwen35 +import comfy.text_encoders.qwen3vl import comfy.text_encoders.ernie import comfy.text_encoders.gemma4 import comfy.text_encoders.cogvideo @@ -1353,6 +1354,8 @@ class TEModel(Enum): GEMMA_4_31B = 31 T5_GEMMA = 32 GPT_OSS_20B = 33 + QWEN3VL_4B = 34 + QWEN3VL_8B = 35 def detect_te_model(sd): @@ -1414,6 +1417,8 @@ def detect_te_model(sd): if weight.shape[0] == 5120: return TEModel.QWEN35_27B return TEModel.QWEN35_2B + if "model.visual.deepstack_merger_list.0.norm.weight" in sd: # DeepStack is unique to Qwen3-VL + return TEModel.QWEN3VL_4B if sd["model.visual.merger.linear_fc2.weight"].shape[0] == 2560 else TEModel.QWEN3VL_8B if "model.layers.0.post_attention_layernorm.weight" in sd: weight = sd['model.layers.0.post_attention_layernorm.weight'] if 'model.layers.0.self_attn.q_norm.weight' in sd: @@ -1612,6 +1617,20 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip qwen35_type = {TEModel.QWEN35_08B: "qwen35_08b", TEModel.QWEN35_2B: "qwen35_2b", TEModel.QWEN35_4B: "qwen35_4b", TEModel.QWEN35_9B: "qwen35_9b", TEModel.QWEN35_27B: "qwen35_27b"}[te_model] clip_target.clip = comfy.text_encoders.qwen35.te(**llama_detect(clip_data), model_type=qwen35_type) clip_target.tokenizer = comfy.text_encoders.qwen35.tokenizer(model_type=qwen35_type) + elif te_model in (TEModel.QWEN3VL_4B, TEModel.QWEN3VL_8B): + if clip_type == CLIPType.IDEOGRAM4 and te_model == TEModel.QWEN3VL_8B: # Ideogram4 reuses the full Qwen3-VL-8B (13-layer tap for conditioning + multimodal generate). + clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."}) + clip_target.clip = comfy.text_encoders.ideogram4.te_qwen3vl(**llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.ideogram4.Ideogram4Qwen3VLTokenizer + elif clip_type in (CLIPType.FLUX, CLIPType.FLUX2): # Flux2 Klein reuses the Qwen3-VL LM (3-layer tap -> 12288); visual unused. + klein_model_type = "qwen3_8b" if te_model == TEModel.QWEN3VL_8B else "qwen3_4b" + clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type=klein_model_type) + clip_target.tokenizer = comfy.text_encoders.flux.KleinTokenizer8B if te_model == TEModel.QWEN3VL_8B else comfy.text_encoders.flux.KleinTokenizer + else: + clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."}) + qwen3vl_type = {TEModel.QWEN3VL_4B: "qwen3vl_4b", TEModel.QWEN3VL_8B: "qwen3vl_8b"}[te_model] + clip_target.clip = comfy.text_encoders.qwen3vl.te(**llama_detect(clip_data), model_type=qwen3vl_type) + clip_target.tokenizer = comfy.text_encoders.qwen3vl.tokenizer(model_type=qwen3vl_type) elif te_model == TEModel.QWEN3_06B: clip_target.clip = comfy.text_encoders.anima.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.anima.AnimaTokenizer diff --git a/comfy/text_encoders/ideogram4.py b/comfy/text_encoders/ideogram4.py index 84243772d..151b43c53 100644 --- a/comfy/text_encoders/ideogram4.py +++ b/comfy/text_encoders/ideogram4.py @@ -9,6 +9,7 @@ import os from transformers import Qwen2Tokenizer import comfy.text_encoders.llama +import comfy.text_encoders.qwen3vl from comfy import sd1_clip # Reference taps outputs of layers (0,3,...,35); comfy captures layer inputs, offset by +1. @@ -77,3 +78,43 @@ def te(dtype_llama=None, llama_quantization_metadata=None): model_options["quantization_metadata"] = llama_quantization_metadata super().__init__(device=device, dtype=dtype, model_options=model_options) return Ideogram4TEModel_ + + +# Full Qwen3-VL-8B variant with vision + +class Ideogram4Qwen3VLClipModel(comfy.text_encoders.qwen3vl.Qwen3VLClipModel): + def __init__(self, device="cpu", dtype=None, attention_mask=True, model_options={}): + super().__init__(device=device, layer=IDEOGRAM4_TAP_LAYERS, layer_idx=None, dtype=dtype, + attention_mask=attention_mask, model_options=model_options, model_type="qwen3vl_8b") + + +class Ideogram4Qwen3VLTEModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="qwen3vl_8b", clip_model=Ideogram4Qwen3VLClipModel, model_options=model_options) + + def encode_token_weights(self, token_weight_pairs): + out, pooled, extra = super().encode_token_weights(token_weight_pairs) + b, n, seq, h = out.shape # (B, n_taps=13, seq, 4096), ascending layer order. + out = out.permute(0, 2, 3, 1).reshape(b, seq, h * n) # (B, seq, 4096*13 = 53248). + return out, pooled, extra + + +class Ideogram4Qwen3VLTokenizer(comfy.text_encoders.qwen3vl.Qwen3VLTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type="qwen3vl_8b") + + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=True, **kwargs): + # Ideogram 4 conditions on the no-think template; default thinking=True drops the empty think block qwen3vl adds. + return super().tokenize_with_weights(text, return_word_ids=return_word_ids, llama_template=llama_template, images=images, prevent_empty_text=prevent_empty_text, thinking=thinking, **kwargs) + + +def te_qwen3vl(dtype_llama=None, llama_quantization_metadata=None): + class Ideogram4Qwen3VLTEModel_(Ideogram4Qwen3VLTEModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if dtype_llama is not None: + dtype = dtype_llama + if llama_quantization_metadata is not None: + model_options = model_options.copy() + model_options["quantization_metadata"] = llama_quantization_metadata + super().__init__(device=device, dtype=dtype, model_options=model_options) + return Ideogram4Qwen3VLTEModel_ diff --git a/comfy/text_encoders/llama.py b/comfy/text_encoders/llama.py index 5087228ca..e9f38a9a2 100644 --- a/comfy/text_encoders/llama.py +++ b/comfy/text_encoders/llama.py @@ -251,6 +251,19 @@ class Qwen3_8BConfig: lm_head: bool = True stop_tokens = [151643, 151645] +@dataclass +class Qwen3VL_8BConfig(Qwen3_8BConfig): + max_position_embeddings: int = 262144 + rope_theta: float = 5000000.0 + rope_dims = [24, 20, 20] + interleaved_mrope = True + +@dataclass +class Qwen3VL_4BConfig(Qwen3VL_8BConfig): + hidden_size: int = 2560 + intermediate_size: int = 9728 + lm_head: bool = False # 4B ties word embeddings + @dataclass class Ovis25_2BConfig: vocab_size: int = 151936 @@ -703,7 +716,8 @@ class Llama2_(nn.Module): interleaved_mrope=getattr(self.config, "interleaved_mrope", False), device=device) - def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[], past_key_values=None, input_ids=None): + def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, + dtype=None, position_ids=None, embeds_info=[], past_key_values=None, input_ids=None,deepstack_embeds=None, visual_pos_masks=None): if embeds is not None: x = embeds else: @@ -767,6 +781,10 @@ class Llama2_(nn.Module): if current_kv is not None: next_key_values.append(current_kv) + # DeepStack: add per-layer visual features into the first len() decoder layers at image positions (Qwen3-VL) + if deepstack_embeds is not None and i < len(deepstack_embeds): + x[visual_pos_masks] = x[visual_pos_masks] + deepstack_embeds[i].to(x) + if i == intermediate_output: intermediate = x.clone() @@ -860,7 +878,7 @@ class BaseGenerate: torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0)) return past_key_values - def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0, presence_penalty=0.0, initial_input_ids=None): + def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0, presence_penalty=0.0, initial_input_ids=None, position_ids=None, deepstack_embeds=None, visual_pos_masks=None): device = embeds.device if stop_tokens is None: @@ -884,10 +902,18 @@ class BaseGenerate: generated_token_ids = [] pbar = comfy.utils.ProgressBar(max_length) + # MRoPE: prefill uses explicit 3D position_ids, decode continues from the last position + next_pos = int(position_ids[:, -1].max()) + 1 if position_ids is not None else None + # Generation loop current_input_ids = initial_input_ids for step in tqdm(range(max_length), desc="Generating tokens"): - x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values, input_ids=current_input_ids) + # DeepStack visual features are injected on the prefill only; gemma4's forward lacks these kwargs. + extra = {} + if step == 0 and deepstack_embeds is not None: + extra["deepstack_embeds"] = deepstack_embeds + extra["visual_pos_masks"] = visual_pos_masks + x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values, input_ids=current_input_ids, position_ids=position_ids, **extra) logits = self.logits(x)[:, -1] next_token = self.sample_token(logits, temperature, top_k, top_p, min_p, repetition_penalty, initial_tokens + generated_token_ids, generator, do_sample=do_sample, presence_penalty=presence_penalty) token_id = next_token[0].item() @@ -895,6 +921,9 @@ class BaseGenerate: embeds = self.model.embed_tokens(next_token).to(execution_dtype) current_input_ids = next_token if initial_input_ids is not None else None + if next_pos is not None: # advance MRoPE position for the next (decode) step + position_ids = torch.tensor([[next_pos]], device=device) + next_pos += 1 pbar.update(1) if token_id in stop_tokens: diff --git a/comfy/text_encoders/qwen35.py b/comfy/text_encoders/qwen35.py index 416ce9d18..71a17990f 100644 --- a/comfy/text_encoders/qwen35.py +++ b/comfy/text_encoders/qwen35.py @@ -3,7 +3,6 @@ import torch.nn as nn import torch.nn.functional as F from dataclasses import dataclass, field import os -import math import comfy.model_management from comfy.ldm.modules.attention import optimized_attention_for_device @@ -563,6 +562,8 @@ class Qwen35VisionModel(nn.Module): for _ in range(config["depth"]) ]) self.merger = Qwen35VisionPatchMerger(self.hidden_size, self.spatial_merge_size, config["out_hidden_size"], device=device, dtype=dtype, ops=ops) + self.deepstack_visual_indexes = [] # DeepStack, per-layer visual features (Qwen3-VL) + self.deepstack_merger_list = None def rot_pos_emb(self, grid_thw): merge_size = self.spatial_merge_size @@ -664,9 +665,14 @@ class Qwen35VisionModel(nn.Module): ).cumsum(dim=0, dtype=torch.int32) cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0) optimized_attention = optimized_attention_for_device(x.device, mask=False, small_input=True) - for blk in self.blocks: + deepstack_features = [] + for layer_num, blk in enumerate(self.blocks): x = blk(x, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings, optimized_attention=optimized_attention) + if self.deepstack_merger_list is not None and layer_num in self.deepstack_visual_indexes: + deepstack_features.append(self.deepstack_merger_list[self.deepstack_visual_indexes.index(layer_num)](x)) merged = self.merger(x) + if self.deepstack_merger_list is not None: + return merged, deepstack_features return merged # Model Wrapper @@ -690,30 +696,7 @@ class Qwen35(BaseLlama, BaseGenerate, torch.nn.Module): return None, None def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[], past_key_values=None): - grid = None - position_ids = None - offset = 0 - for e in embeds_info: - if e.get("type") == "image": - grid = e.get("extra", None) - start = e.get("index") - if position_ids is None: - position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device) - position_ids[:, :start] = torch.arange(0, start, device=embeds.device) - end = e.get("size") + start - len_max = int(grid.max()) // 2 - start_next = len_max + start - position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device) - position_ids[0, start:end] = start + offset - max_d = int(grid[0][1]) // 2 - position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start] - max_d = int(grid[0][2]) // 2 - position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start] - offset += len_max - (end - start) - - if grid is None: - position_ids = None - + position_ids = comfy.text_encoders.qwen_vl.qwen2vl_mrope_position_ids(embeds_info, embeds.shape[1], embeds.device) return super().forward(x, attention_mask=attention_mask, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=final_layer_norm_intermediate, dtype=dtype, position_ids=position_ids, past_key_values=past_key_values) def init_kv_cache(self, batch, max_cache_len, device, execution_dtype): diff --git a/comfy/text_encoders/qwen3vl.py b/comfy/text_encoders/qwen3vl.py new file mode 100644 index 000000000..59c9aae6d --- /dev/null +++ b/comfy/text_encoders/qwen3vl.py @@ -0,0 +1,193 @@ +import os + +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers import Qwen2Tokenizer + +from comfy import sd1_clip +import comfy.text_encoders.qwen_vl +from .qwen35 import Qwen35VisionModel +from .llama import BaseLlama, BaseQwen3, BaseGenerate, Llama2_, Qwen3VL_4BConfig, Qwen3VL_8BConfig + + +QWEN3VL_VISION = { + "qwen3vl_4b": dict(hidden_size=1024, intermediate_size=4096, depth=24, deepstack_visual_indexes=[5, 11, 17]), + "qwen3vl_8b": dict(hidden_size=1152, intermediate_size=4304, depth=27, deepstack_visual_indexes=[8, 16, 24]), +} +QWEN3VL_VISION_COMMON = dict(num_heads=16, patch_size=16, temporal_patch_size=2, in_channels=3, + spatial_merge_size=2, num_position_embeddings=2304) + +QWEN3VL_CONFIGS = {"qwen3vl_4b": Qwen3VL_4BConfig, "qwen3vl_8b": Qwen3VL_8BConfig} + + +class Qwen3VLDeepstackMerger(nn.Module): + # DeepStack merger: postshuffle LayerNorm (applied after spatial merge), unlike the main merger. + def __init__(self, hidden_size, spatial_merge_size, out_hidden_size, device=None, dtype=None, ops=None): + super().__init__() + self.merge_dim = hidden_size * (spatial_merge_size ** 2) + self.norm = ops.LayerNorm(self.merge_dim, eps=1e-6, device=device, dtype=dtype) + self.linear_fc1 = ops.Linear(self.merge_dim, self.merge_dim, device=device, dtype=dtype) + self.linear_fc2 = ops.Linear(self.merge_dim, out_hidden_size, device=device, dtype=dtype) + + def forward(self, x): + x = self.norm(x.view(-1, self.merge_dim)) + return self.linear_fc2(F.gelu(self.linear_fc1(x))) + + +class Qwen3VLVisionModel(Qwen35VisionModel): + # Qwen3.5 vision + DeepStack + def __init__(self, config, device=None, dtype=None, ops=None): + super().__init__(config, device=device, dtype=dtype, ops=ops) + self.deepstack_visual_indexes = config["deepstack_visual_indexes"] + self.deepstack_merger_list = nn.ModuleList([ + Qwen3VLDeepstackMerger(self.hidden_size, self.spatial_merge_size, config["out_hidden_size"], device=device, dtype=dtype, ops=ops) + for _ in self.deepstack_visual_indexes + ]) + + +class Qwen3VL(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module): + model_type = "qwen3vl_8b" + + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + config = QWEN3VL_CONFIGS[self.model_type](**config_dict) + self.num_layers = config.num_hidden_layers + self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) + vision_config = {**QWEN3VL_VISION_COMMON, **QWEN3VL_VISION[self.model_type], "out_hidden_size": config.hidden_size} + self.visual = Qwen3VLVisionModel(vision_config, device=device, dtype=dtype, ops=operations) + self.dtype = dtype + + def preprocess_embed(self, embed, device): + if embed["type"] == "image": + # Qwen3-VL normalizes to [-1, 1] (mean/std 0.5), unlike Qwen2.5-VL's CLIP normalization. + image, grid = comfy.text_encoders.qwen_vl.process_qwen2vl_images(embed["data"], patch_size=16, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5]) + merged, deepstack = self.visual(image.to(device, dtype=torch.float32), grid) + return merged, {"grid": grid, "deepstack": deepstack} + return None, None + + def build_image_inputs(self, embeds, embeds_info): + # Returns (position_ids, visual_pos_masks, deepstack) for the prompt + images = sorted([e for e in embeds_info if e.get("type") == "image"], key=lambda e: e["index"]) + if len(images) == 0: + return None, None, None + + device = embeds.device + seq = embeds.shape[1] + position_ids = comfy.text_encoders.qwen_vl.qwen2vl_mrope_position_ids(embeds_info, seq, device) + + # DeepStack: mask of image positions + per-vision-layer features to inject there. + visual_pos_masks = torch.zeros((1, seq), dtype=torch.bool, device=device) + deepstack = None + for e in images: + start = e["index"] + end = e["size"] + start + visual_pos_masks[0, start:end] = True + ds = e["extra"]["deepstack"] + if deepstack is None: + deepstack = [d for d in ds] + else: + deepstack = [torch.cat([deepstack[i], ds[i]], dim=0) for i in range(len(ds))] + return position_ids, visual_pos_masks, deepstack + + +def _make_qwen3vl_model(model_type): + class Qwen3VL_(Qwen3VL): + pass + Qwen3VL_.model_type = model_type + return Qwen3VL_ + + +class Qwen3VLClipModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="hidden", layer_idx=-1, dtype=None, attention_mask=True, model_options={}, model_type="qwen3vl_8b"): + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, + dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, + model_class=_make_qwen3vl_model(model_type), enable_attention_masks=attention_mask, + return_attention_masks=attention_mask, model_options=model_options) + + def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty=0.0): + if isinstance(tokens, dict): + tokens = next(iter(tokens.values())) + tokens_only = [[t[0] for t in b] for b in tokens] + embeds, _, _, embeds_info = self.process_tokens(tokens_only, self.execution_device) + position_ids, visual_pos_masks, deepstack = self.transformer.build_image_inputs(embeds, embeds_info) + return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, + presence_penalty=presence_penalty, position_ids=position_ids, + visual_pos_masks=visual_pos_masks, deepstack_embeds=deepstack) + + +class Qwen3VLTEModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, model_type="qwen3vl_8b"): + clip_model = lambda **kw: Qwen3VLClipModel(**kw, model_type=model_type) + super().__init__(device=device, dtype=dtype, name=model_type, clip_model=clip_model, model_options=model_options) + + +class Qwen3VLSDTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}, embedding_size=4096, embedding_key="qwen3vl_8b"): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") + super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=embedding_size, embedding_key=embedding_key, tokenizer_class=Qwen2Tokenizer, + has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data) + + +class Qwen3VLTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}, model_type="qwen3vl_8b"): + embedding_size = 2560 if model_type == "qwen3vl_4b" else 4096 + tokenizer = lambda *a, **kw: Qwen3VLSDTokenizer(*a, **kw, embedding_size=embedding_size, embedding_key=model_type) + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=model_type, tokenizer=tokenizer) + self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" + self.llama_template_images = "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n" + + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=False, **kwargs): + image = kwargs.get("image", None) + if image is not None and len(images) == 0: + images = [image[i:i + 1] for i in range(image.shape[0])] + + skip_template = text.startswith('<|im_start|>') + if prevent_empty_text and text == '': + text = ' ' + + if skip_template: + llama_text = text + else: + if llama_template is not None: + template = llama_template + elif len(images) == 0: + template = self.llama_template + else: + template = self.llama_template_images + if len(images) > 1: + vision_block = "<|vision_start|><|image_pad|><|vision_end|>" + template = template.replace(vision_block, vision_block * len(images), 1) + llama_text = template.format(text) + if not thinking: # Qwen3 convention: empty think block suppresses reasoning + llama_text += "\n\n\n\n" + + tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs) + key_name = next(iter(tokens)) + embed_count = 0 + for r in tokens[key_name]: + for i in range(len(r)): + if r[i][0] == 151655: # <|image_pad|> + if len(images) > embed_count: + r[i] = ({"type": "image", "data": images[embed_count], "original_type": "image"},) + r[i][1:] + embed_count += 1 + return tokens + + +def tokenizer(model_type="qwen3vl_8b"): + class Qwen3VLTokenizer_(Qwen3VLTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type=model_type) + return Qwen3VLTokenizer_ + + +def te(dtype_llama=None, llama_quantization_metadata=None, model_type="qwen3vl_8b"): + class Qwen3VLTEModel_(Qwen3VLTEModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if dtype_llama is not None: + dtype = dtype_llama + if llama_quantization_metadata is not None: + model_options = model_options.copy() + model_options["quantization_metadata"] = llama_quantization_metadata + super().__init__(device=device, dtype=dtype, model_options=model_options, model_type=model_type) + return Qwen3VLTEModel_ diff --git a/comfy/text_encoders/qwen_vl.py b/comfy/text_encoders/qwen_vl.py index 98c350a12..924eb6ad8 100644 --- a/comfy/text_encoders/qwen_vl.py +++ b/comfy/text_encoders/qwen_vl.py @@ -88,6 +88,32 @@ def process_qwen2vl_images( return flatten_patches, image_grid_thw +def qwen2vl_mrope_position_ids(embeds_info, seq_len, device): + # (3, seq_len) T/H/W MRoPE position ids: text runs sequentially, each image span gets its grid positions. + # Returns None when there are no image embeds. `extra` is the image grid_thw, or a dict carrying it under "grid". + position_ids = None + offset = 0 + for e in embeds_info: + if e.get("type") == "image": + extra = e.get("extra", None) + grid = extra["grid"] if isinstance(extra, dict) else extra + start = e.get("index") + if position_ids is None: + position_ids = torch.zeros((3, seq_len), device=device) + position_ids[:, :start] = torch.arange(0, start, device=device) + end = e.get("size") + start + len_max = int(grid.max()) // 2 + start_next = len_max + start + position_ids[:, end:] = torch.arange(start_next + offset, start_next + (seq_len - end) + offset, device=device) + position_ids[0, start:end] = start + offset + max_d = int(grid[0][1]) // 2 + position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start] + max_d = int(grid[0][2]) // 2 + position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start] + offset += len_max - (end - start) + return position_ids + + class VisionPatchEmbed(nn.Module): def __init__( self, diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py index 92a1298c0..6c69256ab 100644 --- a/comfy_api/latest/_input_impl/video_types.py +++ b/comfy_api/latest/_input_impl/video_types.py @@ -325,21 +325,25 @@ class VideoFromFile(VideoInput): checked_alpha = True # Fix non-deterministic video decode when the video width is not a multiple of 32 - # For non-yuvj pixel formats (all H.264/H.265 video) + # For non-yuvj pixel formats: most H.264/H.265 video and static images (e.g. lossy WebP via LoadImage) + # Pad both axes to a multiple of 32 and smear the border so the alignment padding never bleeds into the cropped edges if image_format in ('gbrpf32le', 'gbrapf32le') and frame.width % 32 != 0: if align_graph is None: pad_w = ((frame.width + 31) // 32) * 32 + pad_h = ((frame.height + 31) // 32) * 32 g = av.filter.Graph() g_src = g.add_buffer(width=frame.width, height=frame.height, format=frame.format.name, time_base=video_stream.time_base) - g_pad = g.add('pad', f'{pad_w}:{frame.height}:0:0') + g_pad = g.add('pad', f'{pad_w}:{pad_h}:0:0') + g_fill = g.add('fillborders', f'left=0:right={pad_w - frame.width}:top=0:bottom={pad_h - frame.height}:mode=smear') g_sink = g.add('buffersink') g_src.link_to(g_pad) - g_pad.link_to(g_sink) + g_pad.link_to(g_fill) + g_fill.link_to(g_sink) g.configure() align_graph = (g, g_src, g_sink) align_graph[1].push(frame) - img = np.ascontiguousarray(align_graph[2].pull().to_ndarray(format=image_format)[:, :frame.width]) + img = np.ascontiguousarray(align_graph[2].pull().to_ndarray(format=image_format)[:frame.height, :frame.width]) else: img = frame.to_ndarray(format=image_format) if frame.rotation != 0: diff --git a/comfy_api_nodes/nodes_sonilo.py b/comfy_api_nodes/nodes_sonilo.py index d146f63ea..2ad35531a 100644 --- a/comfy_api_nodes/nodes_sonilo.py +++ b/comfy_api_nodes/nodes_sonilo.py @@ -100,8 +100,7 @@ class SoniloTextToMusic(IO.ComfyNode): node_id="SoniloTextToMusic", display_name="Sonilo Text to Music", category="partner/audio/Sonilo", - description="Generate music from a text prompt using Sonilo's AI model. " - "Leave duration at 0 to let the model infer it from the prompt.", + description="Generate music from a text prompt using Sonilo's AI model.", inputs=[ IO.String.Input( "prompt", @@ -135,13 +134,7 @@ class SoniloTextToMusic(IO.ComfyNode): is_api_node=True, price_badge=IO.PriceBadge( depends_on=IO.PriceBadgeDepends(widgets=["duration"]), - expr=""" - ( - widgets.duration > 0 - ? {"type":"usd","usd": 0.005 * widgets.duration} - : {"type":"usd","usd": 0.005, "format":{"suffix":"/second"}} - ) - """, + expr='{"type":"usd","usd": 0.0025 * widgets.duration}', ), ) diff --git a/comfy_extras/nodes_ace.py b/comfy_extras/nodes_ace.py index 044077b18..eaf234d5b 100644 --- a/comfy_extras/nodes_ace.py +++ b/comfy_extras/nodes_ace.py @@ -11,7 +11,7 @@ class TextEncodeAceStepAudio(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="TextEncodeAceStepAudio", - category="model/conditioning", + category="model/conditioning/ace", inputs=[ IO.Clip.Input("clip"), IO.String.Input("tags", multiline=True, dynamic_prompts=True), @@ -33,7 +33,7 @@ class TextEncodeAceStepAudio15(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="TextEncodeAceStepAudio1.5", - category="model/conditioning", + category="model/conditioning/ace", inputs=[ IO.Clip.Input("clip"), IO.String.Input("tags", multiline=True, dynamic_prompts=True), @@ -67,7 +67,7 @@ class EmptyAceStepLatentAudio(IO.ComfyNode): return IO.Schema( node_id="EmptyAceStepLatentAudio", display_name="Empty Ace Step 1.0 Latent Audio", - category="model/latent/audio", + category="model/latent/ace", inputs=[ IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.1), IO.Int.Input( @@ -90,7 +90,7 @@ class EmptyAceStep15LatentAudio(IO.ComfyNode): return IO.Schema( node_id="EmptyAceStep1.5LatentAudio", display_name="Empty Ace Step 1.5 Latent Audio", - category="model/latent/audio", + category="model/latent/ace", inputs=[ IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.01), IO.Int.Input( @@ -111,8 +111,8 @@ class ReferenceAudio(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="ReferenceTimbreAudio", - display_name="Reference Audio", - category="advanced/conditioning/audio", + display_name="Set Reference Audio", + category="model/conditioning", is_experimental=True, description="This node sets the reference audio for ace step 1.5", inputs=[ diff --git a/comfy_extras/nodes_apg.py b/comfy_extras/nodes_apg.py index 4a352038a..6e69b73f7 100644 --- a/comfy_extras/nodes_apg.py +++ b/comfy_extras/nodes_apg.py @@ -16,7 +16,7 @@ class APG(io.ComfyNode): return io.Schema( node_id="APG", display_name="Adaptive Projected Guidance", - category="model/sampling/custom_sampling", + category="model/sampling/custom", inputs=[ io.Model.Input("model"), io.Float.Input( diff --git a/comfy_extras/nodes_ar_video.py b/comfy_extras/nodes_ar_video.py index c22359eb2..9d8f64b20 100644 --- a/comfy_extras/nodes_ar_video.py +++ b/comfy_extras/nodes_ar_video.py @@ -19,7 +19,7 @@ class EmptyARVideoLatent(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="EmptyARVideoLatent", - category="model/latent/video", + category="model/latent/autoregressive", inputs=[ io.Int.Input("width", default=832, min=16, max=8192, step=16), io.Int.Input("height", default=480, min=16, max=8192, step=16), @@ -85,7 +85,7 @@ class ARVideoI2V(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="ARVideoI2V", - category="model/conditioning/video_models", + category="model/conditioning/autoregressive", inputs=[ io.Model.Input("model"), io.Vae.Input("vae"), diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py index 1dc97ecd7..77f124e28 100644 --- a/comfy_extras/nodes_audio.py +++ b/comfy_extras/nodes_audio.py @@ -16,7 +16,7 @@ class EmptyLatentAudio(IO.ComfyNode): return IO.Schema( node_id="EmptyLatentAudio", display_name="Empty Latent Audio", - category="model/latent/audio", + category="model/latent", essentials_category="Audio", inputs=[ IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1), @@ -41,7 +41,7 @@ class ConditioningStableAudio(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="ConditioningStableAudio", - category="model/conditioning", + category="model/conditioning/stable audio", inputs=[ IO.Conditioning.Input("positive"), IO.Conditioning.Input("negative"), @@ -70,7 +70,7 @@ class VAEEncodeAudio(IO.ComfyNode): node_id="VAEEncodeAudio", search_aliases=["audio to latent"], display_name="VAE Encode Audio", - category="model/latent/audio", + category="model/latent", inputs=[ IO.Audio.Input("audio"), IO.Vae.Input("vae"), @@ -115,7 +115,7 @@ class VAEDecodeAudio(IO.ComfyNode): node_id="VAEDecodeAudio", search_aliases=["latent to audio"], display_name="VAE Decode Audio", - category="model/latent/audio", + category="model/latent", inputs=[ IO.Latent.Input("samples"), IO.Vae.Input("vae"), @@ -137,7 +137,7 @@ class VAEDecodeAudioTiled(IO.ComfyNode): node_id="VAEDecodeAudioTiled", search_aliases=["latent to audio"], display_name="VAE Decode Audio (Tiled)", - category="model/latent/audio", + category="model/latent", inputs=[ IO.Latent.Input("samples"), IO.Vae.Input("vae"), diff --git a/comfy_extras/nodes_bernini.py b/comfy_extras/nodes_bernini.py index 227fa5753..0537e0806 100644 --- a/comfy_extras/nodes_bernini.py +++ b/comfy_extras/nodes_bernini.py @@ -39,9 +39,9 @@ class BerniniConditioning(io.ComfyNode): return io.Schema( node_id="BerniniConditioning", display_name="Bernini Conditioning", - category="conditioning/video_models", + category="model/conditioning/bernini", description="Conditioning node for Bernini in-context video/image conditioning. It can be used for the following tasks: t2v (text-to-video), v2v (video-to-video), rv2v (reference-guided video editing), r2v (reference-to-video), ads2v (insert image/video into video)." - "Reference images injected as in-context tokens (r2v, rv2v) are encoded independently at their own native aspect ratio (long edge capped at ref_max_size)", + "Reference images injected as in-context tokens (r2v, rv2v) are encoded independently at their own native aspect ratio (long edge capped at ref_max_size)", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -50,14 +50,11 @@ class BerniniConditioning(io.ComfyNode): io.Int.Input("height", default=480, min=16, max=8192, step=16), io.Int.Input("length", default=81, min=1, max=8192, step=4), io.Int.Input("batch_size", default=1, min=1, max=4096), - io.Image.Input("source_video", optional=True, tooltip=( - "Source video to edit or restyle (v2v, rv2v). Resized to width/height and trimmed to length.")), - io.Image.Input("reference_video", optional=True, tooltip=( - "Video to insert into the source video (ads2v).")), + io.Image.Input("source_video", optional=True, tooltip=("Source video to edit or restyle (v2v, rv2v). Resized to width/height and trimmed to length.")), + io.Image.Input("reference_video", optional=True, tooltip=("Video to insert into the source video (ads2v).")), io.Autogrow.Input("reference_images", optional=True, template=io.Autogrow.TemplatePrefix( - input=io.Image.Input("reference_image", tooltip=( - "Reference image injected as an in-context token (r2v, rv2v).")), + input=io.Image.Input("reference_image", tooltip=("Reference image injected as an in-context token (r2v, rv2v).")), prefix="reference_image_", min=0, max=8)), io.Int.Input("ref_max_size", default=848, min=16, max=8192, step=16, optional=True, tooltip=( "Max size for the long edge of reference_video and reference_images. Resized with preserved aspect ratio and snapped to 16px.")), @@ -70,10 +67,8 @@ class BerniniConditioning(io.ComfyNode): ) @classmethod - def execute(cls, positive, negative, vae, width, height, length, batch_size, - source_video=None, reference_video=None, reference_images=None, ref_max_size=848) -> io.NodeOutput: - latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], - device=comfy.model_management.intermediate_device()) + def execute(cls, positive, negative, vae, width, height, length, batch_size, source_video=None, reference_video=None, reference_images=None, ref_max_size=848) -> io.NodeOutput: + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) # source_video (1), reference_video (2), reference_images (3, 4, ...). context = [] @@ -106,9 +101,7 @@ class BerniniConditioning(io.ComfyNode): class BerniniExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[io.ComfyNode]]: - return [ - BerniniConditioning, - ] + return [BerniniConditioning,] async def comfy_entrypoint() -> BerniniExtension: diff --git a/comfy_extras/nodes_camera_trajectory.py b/comfy_extras/nodes_camera_trajectory.py index 13a1448f4..280d136af 100644 --- a/comfy_extras/nodes_camera_trajectory.py +++ b/comfy_extras/nodes_camera_trajectory.py @@ -153,7 +153,7 @@ class WanCameraEmbedding(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanCameraEmbedding", - category="model/conditioning/video_models", + category="model/conditioning/wan/camera", inputs=[ io.Combo.Input( "camera_pose", diff --git a/comfy_extras/nodes_chroma_radiance.py b/comfy_extras/nodes_chroma_radiance.py index a4f673001..059344f3c 100644 --- a/comfy_extras/nodes_chroma_radiance.py +++ b/comfy_extras/nodes_chroma_radiance.py @@ -13,7 +13,7 @@ class EmptyChromaRadianceLatentImage(io.ComfyNode): def define_schema(cls) -> io.Schema: return io.Schema( node_id="EmptyChromaRadianceLatentImage", - category="model/latent/chroma_radiance", + category="model/latent/chroma radiance", inputs=[ io.Int.Input(id="width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), io.Int.Input(id="height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), @@ -33,7 +33,7 @@ class ChromaRadianceOptions(io.ComfyNode): def define_schema(cls) -> io.Schema: return io.Schema( node_id="ChromaRadianceOptions", - category="model/patch/chroma_radiance", + category="model/patch/chroma radiance", description="Allows setting advanced options for the Chroma Radiance model.", inputs=[ io.Model.Input(id="model"), diff --git a/comfy_extras/nodes_clip_sdxl.py b/comfy_extras/nodes_clip_sdxl.py index 7a001af6f..08fbbd827 100644 --- a/comfy_extras/nodes_clip_sdxl.py +++ b/comfy_extras/nodes_clip_sdxl.py @@ -9,7 +9,8 @@ class CLIPTextEncodeSDXLRefiner(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="CLIPTextEncodeSDXLRefiner", - category="advanced/conditioning", + display_name="CLIP Text Encode (SDXL Refiner)", + category="model/conditioning/stable diffusion", inputs=[ io.Float.Input("ascore", default=6.0, min=0.0, max=1000.0, step=0.01), io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION), @@ -30,7 +31,8 @@ class CLIPTextEncodeSDXL(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="CLIPTextEncodeSDXL", - category="advanced/conditioning", + display_name="CLIP Text Encode (SDXL)", + category="model/conditioning/stable diffusion", inputs=[ io.Clip.Input("clip"), io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION), diff --git a/comfy_extras/nodes_context_windows.py b/comfy_extras/nodes_context_windows.py index d9e32b9d9..098c26f23 100644 --- a/comfy_extras/nodes_context_windows.py +++ b/comfy_extras/nodes_context_windows.py @@ -66,6 +66,7 @@ class WanContextWindowsManualNode(ContextWindowsManualNode): schema.node_id = "WanContextWindowsManual" schema.display_name = "WAN Context Windows (Manual)" schema.description = "Manually set context windows for WAN-like models (dim=2)." + schema.category="model/patch/wan" schema.inputs = [ io.Model.Input("model", tooltip="The model to apply context windows to during sampling."), io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window.", advanced=True), diff --git a/comfy_extras/nodes_controlnet.py b/comfy_extras/nodes_controlnet.py index 17d965405..eb476f497 100644 --- a/comfy_extras/nodes_controlnet.py +++ b/comfy_extras/nodes_controlnet.py @@ -9,6 +9,8 @@ class SetUnionControlNetType(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="SetUnionControlNetType", + search_aliases=["set controlnet type", "union controlnet type"], + display_name="Set Union ControlNet Type", category="model/conditioning/controlnet", inputs=[ io.ControlNet.Input("control_net"), @@ -39,6 +41,7 @@ class ControlNetInpaintingAliMamaApply(io.ComfyNode): return io.Schema( node_id="ControlNetInpaintingAliMamaApply", search_aliases=["masked controlnet"], + display_name="Apply ControlNet Inpainting (AliMama)", category="model/conditioning/controlnet", inputs=[ io.Conditioning.Input("positive"), diff --git a/comfy_extras/nodes_cosmos.py b/comfy_extras/nodes_cosmos.py index d754ab442..93cc67a6c 100644 --- a/comfy_extras/nodes_cosmos.py +++ b/comfy_extras/nodes_cosmos.py @@ -13,7 +13,7 @@ class EmptyCosmosLatentVideo(io.ComfyNode): def define_schema(cls) -> io.Schema: return io.Schema( node_id="EmptyCosmosLatentVideo", - category="model/latent/video", + category="model/latent/cosmos", inputs=[ io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16), io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16), @@ -45,7 +45,7 @@ class CosmosImageToVideoLatent(io.ComfyNode): def define_schema(cls) -> io.Schema: return io.Schema( node_id="CosmosImageToVideoLatent", - category="model/conditioning/inpaint", + category="model/conditioning/cosmos", inputs=[ io.Vae.Input("vae"), io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16), @@ -88,7 +88,7 @@ class CosmosPredict2ImageToVideoLatent(io.ComfyNode): def define_schema(cls) -> io.Schema: return io.Schema( node_id="CosmosPredict2ImageToVideoLatent", - category="model/conditioning/inpaint", + category="model/conditioning/cosmos", inputs=[ io.Vae.Input("vae"), io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16), diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index 3e97084a4..c9d7e06fc 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -729,7 +729,7 @@ class SamplerCustom(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="SamplerCustom", - category="model/sampling/custom_sampling", + category="model/sampling/custom", inputs=[ io.Model.Input("model"), io.Boolean.Input("add_noise", default=True, advanced=True), @@ -1015,7 +1015,7 @@ class SamplerCustomAdvanced(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="SamplerCustomAdvanced", - category="model/sampling/custom_sampling", + category="model/sampling/custom", inputs=[ io.Noise.Input("noise"), io.Guider.Input("guider"), @@ -1143,7 +1143,7 @@ class CFGOverride(io.ComfyNode): display_name="CFG Override", description="Override cfg to a fixed value over a [start, end] percent (sigma) range. " "With multiple overrides, the one nearest the sampler wins on overlap.", - category="sampling/custom_sampling", + category="model/sampling/guiders", inputs=[ io.Model.Input("model"), io.Float.Input("cfg", default=1.0, min=0.0, max=100.0, step=0.1, round=0.01), diff --git a/comfy_extras/nodes_easycache.py b/comfy_extras/nodes_easycache.py index 923c2bb05..9e907d371 100644 --- a/comfy_extras/nodes_easycache.py +++ b/comfy_extras/nodes_easycache.py @@ -363,7 +363,7 @@ class EasyCacheNode(io.ComfyNode): node_id="EasyCache", display_name="EasyCache", description="Native EasyCache implementation.", - category="advanced/debug/model", + category="advanced/debug", is_experimental=True, inputs=[ io.Model.Input("model", tooltip="The model to add EasyCache to."), @@ -496,7 +496,7 @@ class LazyCacheNode(io.ComfyNode): node_id="LazyCache", display_name="LazyCache", description="A homebrew version of EasyCache - even 'easier' version of EasyCache to implement. Overall works worse than EasyCache, but better in some rare cases AND universal compatibility with everything in ComfyUI.", - category="advanced/debug/model", + category="advanced/debug", is_experimental=True, inputs=[ io.Model.Input("model", tooltip="The model to add LazyCache to."), diff --git a/comfy_extras/nodes_edit_model.py b/comfy_extras/nodes_edit_model.py index 36da66f34..d0d20ae7a 100644 --- a/comfy_extras/nodes_edit_model.py +++ b/comfy_extras/nodes_edit_model.py @@ -8,7 +8,8 @@ class ReferenceLatent(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="ReferenceLatent", - category="advanced/conditioning/edit_models", + display_name="Set Reference Latent", + category="model/conditioning", description="This node sets the guiding latent for an edit model. If the model supports it you can chain multiple to set multiple reference images.", inputs=[ io.Conditioning.Input("conditioning"), diff --git a/comfy_extras/nodes_flux.py b/comfy_extras/nodes_flux.py index ef1757ae5..e9986c9e7 100644 --- a/comfy_extras/nodes_flux.py +++ b/comfy_extras/nodes_flux.py @@ -13,7 +13,7 @@ class CLIPTextEncodeFlux(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="CLIPTextEncodeFlux", - category="advanced/conditioning/flux", + category="model/conditioning/flux", inputs=[ io.Clip.Input("clip"), io.String.Input("clip_l", multiline=True, dynamic_prompts=True), @@ -40,7 +40,7 @@ class EmptyFlux2LatentImage(io.ComfyNode): return io.Schema( node_id="EmptyFlux2LatentImage", display_name="Empty Flux 2 Latent", - category="model/latent", + category="model/latent/flux", inputs=[ io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), @@ -61,7 +61,7 @@ class FluxGuidance(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="FluxGuidance", - category="advanced/conditioning/flux", + category="model/conditioning/flux", inputs=[ io.Conditioning.Input("conditioning"), io.Float.Input("guidance", default=3.5, min=0.0, max=100.0, step=0.1), @@ -84,7 +84,7 @@ class FluxDisableGuidance(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="FluxDisableGuidance", - category="advanced/conditioning/flux", + category="model/conditioning/flux", description="This node completely disables the guidance embed on Flux and Flux like models", inputs=[ io.Conditioning.Input("conditioning"), @@ -128,7 +128,7 @@ class FluxKontextImageScale(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="FluxKontextImageScale", - category="advanced/conditioning/flux", + category="model/conditioning/flux", description="This node resizes the image to one that is more optimal for flux kontext.", inputs=[ io.Image.Input("image"), @@ -156,7 +156,7 @@ class FluxKontextMultiReferenceLatentMethod(io.ComfyNode): return io.Schema( node_id="FluxKontextMultiReferenceLatentMethod", display_name="Edit Model Reference Method", - category="advanced/conditioning/flux", + category="model/conditioning/flux", inputs=[ io.Conditioning.Input("conditioning"), io.Combo.Input( diff --git a/comfy_extras/nodes_hidream.py b/comfy_extras/nodes_hidream.py index e345fe51d..65248561b 100644 --- a/comfy_extras/nodes_hidream.py +++ b/comfy_extras/nodes_hidream.py @@ -11,8 +11,9 @@ class QuadrupleCLIPLoader(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="QuadrupleCLIPLoader", - category="advanced/loaders", - description="[Recipes]\n\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct", + display_name="Load CLIP (Quadruple)", + category="model/loaders", + description="Recipes:\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct", inputs=[ io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")), io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")), @@ -38,8 +39,9 @@ class CLIPTextEncodeHiDream(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="CLIPTextEncodeHiDream", + display_name="CLIP Text Encode (HiDream)", search_aliases=["hidream prompt"], - category="advanced/conditioning", + category="model/conditioning/hidream", inputs=[ io.Clip.Input("clip"), io.String.Input("clip_l", multiline=True, dynamic_prompts=True), diff --git a/comfy_extras/nodes_hidream_o1.py b/comfy_extras/nodes_hidream_o1.py index 8648d2e26..85693fce6 100644 --- a/comfy_extras/nodes_hidream_o1.py +++ b/comfy_extras/nodes_hidream_o1.py @@ -14,7 +14,7 @@ class EmptyHiDreamO1LatentImage(io.ComfyNode): return io.Schema( node_id="EmptyHiDreamO1LatentImage", display_name="Empty HiDream-O1 Latent Image", - category="model/latent/image", + category="model/latent/hidream", description=( "Empty pixel-space latent for HiDream-O1-Image. The model was " "trained at ~4 megapixels; lower resolutions go off-distribution " @@ -47,7 +47,7 @@ class HiDreamO1ReferenceImages(io.ComfyNode): return io.Schema( node_id="HiDreamO1ReferenceImages", display_name="HiDream-O1 Reference Images", - category="model/conditioning/image", + category="model/conditioning/hidream", description=( "Attach 1-10 reference images to conditioning, one for edit instruction" "or multiple for subject-driven personalization." @@ -117,7 +117,7 @@ class HiDreamO1PatchSeamSmoothing(io.ComfyNode): return io.Schema( node_id="HiDreamO1PatchSeamSmoothing", display_name="HiDream-O1 Patch Seam Smoothing", - category="advanced/model", + category="model/patch/hidream", is_experimental=True, description=( "Average the model output across multiple shifted patch-grid " diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py index 16fff12af..8df2c8908 100644 --- a/comfy_extras/nodes_hunyuan.py +++ b/comfy_extras/nodes_hunyuan.py @@ -14,7 +14,8 @@ class CLIPTextEncodeHunyuanDiT(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="CLIPTextEncodeHunyuanDiT", - category="advanced/conditioning", + display_name="CLIP Text Encode (Hunyuan Image)", + category="model/conditioning/hunyuan image", inputs=[ io.Clip.Input("clip"), io.String.Input("bert", multiline=True, dynamic_prompts=True), @@ -41,7 +42,7 @@ class EmptyHunyuanLatentVideo(io.ComfyNode): return io.Schema( node_id="EmptyHunyuanLatentVideo", display_name="Empty HunyuanVideo 1.0 Latent", - category="model/latent/video", + category="model/latent/hunyuan video", inputs=[ io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16), io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), @@ -67,6 +68,7 @@ class EmptyHunyuanVideo15Latent(EmptyHunyuanLatentVideo): schema = super().define_schema() schema.node_id = "EmptyHunyuanVideo15Latent" schema.display_name = "Empty HunyuanVideo 1.5 Latent" + schema.category = "model/latent/hunyuan video" return schema @classmethod @@ -81,7 +83,7 @@ class HunyuanVideo15ImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="HunyuanVideo15ImageToVideo", - category="model/conditioning/video_models", + category="model/conditioning/hunyuan video", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -132,7 +134,7 @@ class HunyuanVideo15SuperResolution(io.ComfyNode): return io.Schema( node_id="HunyuanVideo15SuperResolution", display_name="Hunyuan Video 1.5 Super Resolution", - category="model/conditioning/video_models", + category="model/conditioning/hunyuan video", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -227,7 +229,7 @@ class HunyuanVideo15LatentUpscaleWithModel(io.ComfyNode): return io.Schema( node_id="HunyuanVideo15LatentUpscaleWithModel", display_name="Hunyuan Video 15 Latent Upscale With Model", - category="model/latent", + category="model/latent/hunyhuan video", inputs=[ io.LatentUpscaleModel.Input("model"), io.Latent.Input("samples"), @@ -276,7 +278,7 @@ class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="TextEncodeHunyuanVideo_ImageToVideo", - category="advanced/conditioning", + category="model/conditioning/hunyuan video", inputs=[ io.Clip.Input("clip"), io.ClipVisionOutput.Input("clip_vision_output"), @@ -308,7 +310,7 @@ class HunyuanImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="HunyuanImageToVideo", - category="model/conditioning/video_models", + category="model/conditioning/hunyuan video", inputs=[ io.Conditioning.Input("positive"), io.Vae.Input("vae"), @@ -359,7 +361,7 @@ class EmptyHunyuanImageLatent(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="EmptyHunyuanImageLatent", - category="model/latent", + category="model/latent/hunyuan image", inputs=[ io.Int.Input("width", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32), io.Int.Input("height", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32), @@ -384,7 +386,7 @@ class HunyuanRefinerLatent(io.ComfyNode): return io.Schema( node_id="HunyuanRefinerLatent", display_name="Hunyuan Latent Refiner", - category="model/conditioning/video_models", + category="model/conditioning/hunyuan video", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), diff --git a/comfy_extras/nodes_hunyuan3d.py b/comfy_extras/nodes_hunyuan3d.py index 60e530626..c5fa946cc 100644 --- a/comfy_extras/nodes_hunyuan3d.py +++ b/comfy_extras/nodes_hunyuan3d.py @@ -12,7 +12,7 @@ class EmptyLatentHunyuan3Dv2(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="EmptyLatentHunyuan3Dv2", - category="model/latent/3d", + category="model/latent/hunyuan 3d", inputs=[ IO.Int.Input("resolution", default=3072, min=1, max=8192), IO.Int.Input("batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."), @@ -35,7 +35,7 @@ class Hunyuan3Dv2Conditioning(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="Hunyuan3Dv2Conditioning", - category="model/conditioning/3d_models", + category="model/conditioning/hunyuan 3d", inputs=[ IO.ClipVisionOutput.Input("clip_vision_output"), ], @@ -60,7 +60,7 @@ class Hunyuan3Dv2ConditioningMultiView(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="Hunyuan3Dv2ConditioningMultiView", - category="model/conditioning/3d_models", + category="model/conditioning/hunyuan 3d", inputs=[ IO.ClipVisionOutput.Input("front", optional=True), IO.ClipVisionOutput.Input("left", optional=True), @@ -97,7 +97,7 @@ class VAEDecodeHunyuan3D(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="VAEDecodeHunyuan3D", - category="model/latent/3d", + category="model/latent/hunyuan 3d", inputs=[ IO.Latent.Input("samples"), IO.Vae.Input("vae"), diff --git a/comfy_extras/nodes_ideogram4.py b/comfy_extras/nodes_ideogram4.py index d5827db4f..4070db17c 100644 --- a/comfy_extras/nodes_ideogram4.py +++ b/comfy_extras/nodes_ideogram4.py @@ -38,7 +38,7 @@ class Ideogram4Scheduler(io.ComfyNode): return io.Schema( node_id="Ideogram4Scheduler", display_name="Ideogram 4 Scheduler", - category="sampling/custom_sampling/schedulers", + category="model/sampling/schedulers", inputs=[ io.Int.Input("steps", default=20, min=1, max=200), io.Int.Input("width", default=1024, min=256, max=8192, step=16), diff --git a/comfy_extras/nodes_kandinsky5.py b/comfy_extras/nodes_kandinsky5.py index 015965498..96cca0386 100644 --- a/comfy_extras/nodes_kandinsky5.py +++ b/comfy_extras/nodes_kandinsky5.py @@ -13,7 +13,7 @@ class Kandinsky5ImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="Kandinsky5ImageToVideo", - category="model/conditioning/video_models", + category="model/conditioning/kandinsky", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -71,7 +71,7 @@ class NormalizeVideoLatentStart(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="NormalizeVideoLatentStart", - category="model/conditioning/video_models", + category="model/conditioning", description="Normalizes the initial frames of a video latent to match the mean and standard deviation of subsequent reference frames. Helps reduce differences between the starting frames and the rest of the video.", inputs=[ io.Latent.Input("latent"), @@ -104,8 +104,9 @@ class CLIPTextEncodeKandinsky5(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="CLIPTextEncodeKandinsky5", + display_name="CLIP Text Encode (Kandinsky 5)", search_aliases=["kandinsky prompt"], - category="advanced/conditioning/kandinsky5", + category="model/conditioning/kandinsky", inputs=[ io.Clip.Input("clip"), io.String.Input("clip_l", multiline=True, dynamic_prompts=True), diff --git a/comfy_extras/nodes_latent.py b/comfy_extras/nodes_latent.py index 32da9e8ac..1f93e34d6 100644 --- a/comfy_extras/nodes_latent.py +++ b/comfy_extras/nodes_latent.py @@ -262,6 +262,7 @@ class LatentBatch(io.ComfyNode): return io.Schema( node_id="LatentBatch", search_aliases=["combine latents", "merge latents", "join latents"], + display_name="Batch Latents (DEPRECATED)", category="model/latent/batch", is_deprecated=True, inputs=[ @@ -447,6 +448,7 @@ class ReplaceVideoLatentFrames(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="ReplaceVideoLatentFrames", + display_name="Replace Video Latent Frames", category="model/latent/batch", inputs=[ io.Latent.Input("destination", tooltip="The destination latent where frames will be replaced."), diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py index 6d6078abe..85d76ecef 100644 --- a/comfy_extras/nodes_lt.py +++ b/comfy_extras/nodes_lt.py @@ -25,7 +25,7 @@ class GetICLoRAParameters(io.ComfyNode): display_name="Get IC-LoRA Parameters", description="Extracts IC-LoRA parameters from the safetensors metadata of a LoRA-loaded " "model and outputs them for LTXVAddGuide (eg. reference_downscale_factor).", - category="model/conditioning/video_models", + category="model/conditioning/ltxv", search_aliases=["ic-lora", "ic lora", "iclora", "downscale factor", "reference downscale"], inputs=[ io.Model.Input( @@ -62,7 +62,7 @@ class EmptyLTXVLatentVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="EmptyLTXVLatentVideo", - category="model/latent/video/ltxv", + category="model/latent/ltxv", inputs=[ io.Int.Input("width", default=768, min=64, max=nodes.MAX_RESOLUTION, step=32), io.Int.Input("height", default=512, min=64, max=nodes.MAX_RESOLUTION, step=32), @@ -86,7 +86,7 @@ class LTXVImgToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="LTXVImgToVideo", - category="model/conditioning/video_models", + category="model/conditioning/ltxv", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -131,7 +131,7 @@ class LTXVImgToVideoInplace(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="LTXVImgToVideoInplace", - category="model/conditioning/video_models", + category="model/conditioning/ltxv", inputs=[ io.Vae.Input("vae"), io.Image.Input("image"), @@ -251,7 +251,7 @@ class LTXVAddGuide(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="LTXVAddGuide", - category="model/conditioning/video_models", + category="model/conditioning/ltxv", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -498,7 +498,7 @@ class LTXVCropGuides(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="LTXVCropGuides", - category="model/conditioning/video_models", + category="model/conditioning/ltxv", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -542,7 +542,7 @@ class LTXVConditioning(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="LTXVConditioning", - category="model/conditioning/video_models", + category="model/conditioning/ltxv", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -566,7 +566,7 @@ class ModelSamplingLTXV(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="ModelSamplingLTXV", - category="advanced/model", + category="model/patch/ltxv", inputs=[ io.Model.Input("model"), io.Float.Input("max_shift", default=2.05, min=0.0, max=100.0, step=0.01), @@ -746,7 +746,7 @@ class LTXVConcatAVLatent(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="LTXVConcatAVLatent", - category="model/latent/video/ltxv", + category="model/latent/ltxv", inputs=[ io.Latent.Input("video_latent"), io.Latent.Input("audio_latent"), @@ -781,7 +781,7 @@ class LTXVSeparateAVLatent(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="LTXVSeparateAVLatent", - category="model/latent/video/ltxv", + category="model/latent/ltxv", description="LTXV Separate AV Latent", inputs=[ io.Latent.Input("av_latent"), @@ -814,7 +814,7 @@ class LTXVReferenceAudio(io.ComfyNode): return io.Schema( node_id="LTXVReferenceAudio", display_name="LTXV Reference Audio (ID-LoRA)", - category="model/conditioning/audio", + category="model/conditioning/ltxv", description="Set reference audio for ID-LoRA speaker identity transfer. Encodes a reference audio clip into the conditioning and optionally patches the model with identity guidance (extra forward pass without reference, amplifying the speaker identity effect).", inputs=[ io.Model.Input("model"), diff --git a/comfy_extras/nodes_lt_audio.py b/comfy_extras/nodes_lt_audio.py index 052186083..2d774a0a3 100644 --- a/comfy_extras/nodes_lt_audio.py +++ b/comfy_extras/nodes_lt_audio.py @@ -40,7 +40,7 @@ class LTXVAudioVAEEncode(VAEEncodeAudio): return io.Schema( node_id="LTXVAudioVAEEncode", display_name="LTXV Audio VAE Encode", - category="model/latent/audio", + category="model/latent/ltxv", inputs=[ io.Audio.Input("audio", tooltip="The audio to be encoded."), io.Vae.Input( @@ -63,7 +63,7 @@ class LTXVAudioVAEDecode(io.ComfyNode): return io.Schema( node_id="LTXVAudioVAEDecode", display_name="LTXV Audio VAE Decode", - category="model/latent/audio", + category="model/latent/ltxv", inputs=[ io.Latent.Input("samples", tooltip="The latent to be decoded."), io.Vae.Input( @@ -96,7 +96,7 @@ class LTXVEmptyLatentAudio(io.ComfyNode): return io.Schema( node_id="LTXVEmptyLatentAudio", display_name="LTXV Empty Latent Audio", - category="model/latent/audio", + category="model/latent/ltxv", inputs=[ io.Int.Input( "frames_number", @@ -168,9 +168,9 @@ class LTXAVTextEncoderLoader(io.ComfyNode): def define_schema(cls) -> io.Schema: return io.Schema( node_id="LTXAVTextEncoderLoader", - display_name="LTXV Audio Text Encoder Loader", - category="advanced/loaders", - description="[Recipes]\n\nltxav: gemma 3 12B", + display_name="Load LTXV Audio Text Encoder", + category="model/loaders", + description="Recipes:\nltxav: gemma 3 12B", inputs=[ io.Combo.Input( "text_encoder", diff --git a/comfy_extras/nodes_lt_upsampler.py b/comfy_extras/nodes_lt_upsampler.py index be9a36e69..ef36109d1 100644 --- a/comfy_extras/nodes_lt_upsampler.py +++ b/comfy_extras/nodes_lt_upsampler.py @@ -13,7 +13,7 @@ class LTXVLatentUpsampler(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="LTXVLatentUpsampler", - category="model/latent/video", + category="model/latent/ltxv", is_experimental=True, inputs=[ IO.Latent.Input("samples"), diff --git a/comfy_extras/nodes_lumina2.py b/comfy_extras/nodes_lumina2.py index c060a86a0..bc543c242 100644 --- a/comfy_extras/nodes_lumina2.py +++ b/comfy_extras/nodes_lumina2.py @@ -9,7 +9,7 @@ class RenormCFG(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="RenormCFG", - category="advanced/model", + category="model/patch", inputs=[ io.Model.Input("model"), io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01, advanced=True), @@ -80,8 +80,8 @@ class CLIPTextEncodeLumina2(io.ComfyNode): return io.Schema( node_id="CLIPTextEncodeLumina2", search_aliases=["lumina prompt"], - display_name="CLIP Text Encode for Lumina2", - category="model/conditioning", + display_name="CLIP Text Encode (Lumina 2)", + category="model/conditioning/lumina", description="Encodes a system prompt and a user prompt using a CLIP model into an embedding " "that can be used to guide the diffusion model towards generating specific images.", inputs=[ diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py index 52484697a..76af338de 100644 --- a/comfy_extras/nodes_mask.py +++ b/comfy_extras/nodes_mask.py @@ -53,6 +53,7 @@ class LatentCompositeMasked(IO.ComfyNode): return IO.Schema( node_id="LatentCompositeMasked", search_aliases=["overlay latent", "layer latent", "paste latent", "inpaint latent"], + display_name="Latent Composite Masked", category="model/latent", inputs=[ IO.Latent.Input("destination"), diff --git a/comfy_extras/nodes_mochi.py b/comfy_extras/nodes_mochi.py index 3dcea6ab3..3aaf23e69 100644 --- a/comfy_extras/nodes_mochi.py +++ b/comfy_extras/nodes_mochi.py @@ -10,7 +10,7 @@ class EmptyMochiLatentVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="EmptyMochiLatentVideo", - category="model/latent/video", + category="model/latent/mochi", inputs=[ io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16), io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py index b27ac1296..a336ba079 100644 --- a/comfy_extras/nodes_model_advanced.py +++ b/comfy_extras/nodes_model_advanced.py @@ -59,7 +59,7 @@ class ModelSamplingDiscrete: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch" def patch(self, model, sampling, zsnr): m = model.clone() @@ -97,7 +97,7 @@ class ModelSamplingStableCascade: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch/stable cascade" def patch(self, model, shift): m = model.clone() @@ -123,7 +123,7 @@ class ModelSamplingSD3: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch/stable diffusion" def patch(self, model, shift, multiplier=1000): m = model.clone() @@ -150,6 +150,7 @@ class ModelSamplingAuraFlow(ModelSamplingSD3): }} FUNCTION = "patch_aura" + CATEGORY = "model/patch" def patch_aura(self, model, shift): return self.patch(model, shift, multiplier=1.0) @@ -167,7 +168,7 @@ class ModelSamplingFlux: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch/flux" def patch(self, model, max_shift, base_shift, width, height): m = model.clone() @@ -202,7 +203,7 @@ class ModelSamplingContinuousEDM: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch" def patch(self, model, sampling, sigma_max, sigma_min): m = model.clone() @@ -247,7 +248,7 @@ class ModelSamplingContinuousV: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch" def patch(self, model, sampling, sigma_max, sigma_min): m = model.clone() @@ -273,7 +274,7 @@ class RescaleCFG: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch" def patch(self, model, multiplier): def rescale_cfg(args): @@ -314,7 +315,7 @@ class ModelNoiseScale: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch" def patch(self, model, noise_scale): m = model.clone() @@ -337,7 +338,7 @@ class ModelComputeDtype: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/debug/model" + CATEGORY = "advanced/debug" def patch(self, model, dtype): m = model.clone() diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py index b6b29e34a..962d2a0bb 100644 --- a/comfy_extras/nodes_model_merging.py +++ b/comfy_extras/nodes_model_merging.py @@ -21,7 +21,7 @@ class ModelMergeSimple: RETURN_TYPES = ("MODEL",) FUNCTION = "merge" - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def merge(self, model1, model2, ratio): m = model1.clone() @@ -40,7 +40,7 @@ class ModelSubtract: RETURN_TYPES = ("MODEL",) FUNCTION = "merge" - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def merge(self, model1, model2, multiplier): m = model1.clone() @@ -58,7 +58,7 @@ class ModelAdd: RETURN_TYPES = ("MODEL",) FUNCTION = "merge" - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def merge(self, model1, model2): m = model1.clone() @@ -78,7 +78,7 @@ class CLIPMergeSimple: RETURN_TYPES = ("CLIP",) FUNCTION = "merge" - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def merge(self, clip1, clip2, ratio): m = clip1.clone() @@ -101,7 +101,7 @@ class CLIPSubtract: RETURN_TYPES = ("CLIP",) FUNCTION = "merge" - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def merge(self, clip1, clip2, multiplier): m = clip1.clone() @@ -123,7 +123,7 @@ class CLIPAdd: RETURN_TYPES = ("CLIP",) FUNCTION = "merge" - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def merge(self, clip1, clip2): m = clip1.clone() @@ -147,7 +147,7 @@ class ModelMergeBlocks: RETURN_TYPES = ("MODEL",) FUNCTION = "merge" - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def merge(self, model1, model2, **kwargs): m = model1.clone() @@ -242,7 +242,7 @@ class CheckpointSave: FUNCTION = "save" OUTPUT_NODE = True - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None): save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) @@ -261,7 +261,7 @@ class CLIPSave: FUNCTION = "save" OUTPUT_NODE = True - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None): prompt_info = "" @@ -318,7 +318,7 @@ class VAESave: FUNCTION = "save" OUTPUT_NODE = True - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None): full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) @@ -353,7 +353,7 @@ class ModelSave: FUNCTION = "save" OUTPUT_NODE = True - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def save(self, model, filename_prefix, prompt=None, extra_pnginfo=None): save_checkpoint(model, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) diff --git a/comfy_extras/nodes_model_merging_model_specific.py b/comfy_extras/nodes_model_merging_model_specific.py index 55eb3ccfe..2fa684b3a 100644 --- a/comfy_extras/nodes_model_merging_model_specific.py +++ b/comfy_extras/nodes_model_merging_model_specific.py @@ -1,7 +1,7 @@ import comfy_extras.nodes_model_merging class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): arg_dict = { "model1": ("MODEL",), @@ -27,7 +27,7 @@ class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks): class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -53,7 +53,7 @@ class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -77,7 +77,7 @@ class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks): class ModelMergeAuraflow(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -104,7 +104,7 @@ class ModelMergeAuraflow(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -130,7 +130,7 @@ class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -153,7 +153,7 @@ class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeMochiPreview(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -175,7 +175,7 @@ class ModelMergeMochiPreview(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -197,7 +197,7 @@ class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -221,7 +221,7 @@ class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -245,7 +245,7 @@ class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeWAN2_1(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" DESCRIPTION = "1.3B model has 30 blocks, 14B model has 40 blocks. Image to video model has the extra img_emb." @classmethod @@ -269,7 +269,7 @@ class ModelMergeWAN2_1(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeCosmosPredict2_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -292,7 +292,7 @@ class ModelMergeCosmosPredict2_2B(comfy_extras.nodes_model_merging.ModelMergeBlo return {"required": arg_dict} class ModelMergeCosmosPredict2_14B(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -315,7 +315,7 @@ class ModelMergeCosmosPredict2_14B(comfy_extras.nodes_model_merging.ModelMergeBl return {"required": arg_dict} class ModelMergeQwenImage(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): diff --git a/comfy_extras/nodes_model_patch.py b/comfy_extras/nodes_model_patch.py index bdccbf8c4..3f785c8b5 100644 --- a/comfy_extras/nodes_model_patch.py +++ b/comfy_extras/nodes_model_patch.py @@ -232,7 +232,7 @@ class ModelPatchLoader: FUNCTION = "load_model_patch" EXPERIMENTAL = True - CATEGORY = "advanced/loaders" + CATEGORY = "model/loaders" def load_model_patch(self, name): model_patch_path = folder_paths.get_full_path_or_raise("model_patches", name) @@ -479,7 +479,7 @@ class QwenImageDiffsynthControlnet: FUNCTION = "diffsynth_controlnet" EXPERIMENTAL = True - CATEGORY = "advanced/loaders/qwen" + CATEGORY = "model/patch/qwen" def diffsynth_controlnet(self, model, model_patch, vae, image=None, strength=1.0, inpaint_image=None, mask=None): model_patched = model.clone() @@ -512,7 +512,7 @@ class ZImageFunControlnet(QwenImageDiffsynthControlnet): }, "optional": {"image": ("IMAGE",), "inpaint_image": ("IMAGE",), "mask": ("MASK",)}} - CATEGORY = "advanced/loaders/zimage" + CATEGORY = "model/patch/z-image" class UsoStyleProjectorPatch: def __init__(self, model_patch, encoded_image): @@ -675,3 +675,11 @@ NODE_CLASS_MAPPINGS = { "USOStyleReference": USOStyleReference, "SUPIRApply": SUPIRApply, } + +NODE_DISPLAY_NAME_MAPPINGS = { + "ModelPatchLoader": "Load Model Patch", + "QwenImageDiffsynthControlnet": "Apply Qwen Image DiffSynth ControlNet", + "ZImageFunControlnet": "Apply Z-Image Fun ControlNet", + "USOStyleReference": "Apply USO Style Reference", + "SUPIRApply": "Apply SUPIR Patch", +} diff --git a/comfy_extras/nodes_pid.py b/comfy_extras/nodes_pid.py index 71855254e..a3ffd9671 100644 --- a/comfy_extras/nodes_pid.py +++ b/comfy_extras/nodes_pid.py @@ -14,10 +14,8 @@ class PiDConditioning(io.ComfyNode): return io.Schema( node_id="PiDConditioning", display_name="PiD Conditioning", - category="advanced/conditioning", - description=( - "Attaches a latent and a degrade_sigma scalar to a CONDITIONING for PiD decoding/upscaling" - ), + category="model/conditioning", + description=("Attaches a latent and a degrade_sigma scalar to a CONDITIONING for PiD decoding/upscaling"), inputs=[ io.Conditioning.Input("positive"), io.Latent.Input("latent", tooltip="latent (from VAEEncode or a KSampler)."), diff --git a/comfy_extras/nodes_pixart.py b/comfy_extras/nodes_pixart.py index 2f1b73e60..f878a33b5 100644 --- a/comfy_extras/nodes_pixart.py +++ b/comfy_extras/nodes_pixart.py @@ -7,8 +7,9 @@ class CLIPTextEncodePixArtAlpha(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="CLIPTextEncodePixArtAlpha", + display_name="CLIP Text Encode (PixArt Alpha)", search_aliases=["pixart prompt"], - category="advanced/conditioning", + category="model/conditioning/pixart", description="Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma.", inputs=[ io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION), diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py index 3e440433e..763b8a52f 100644 --- a/comfy_extras/nodes_post_processing.py +++ b/comfy_extras/nodes_post_processing.py @@ -616,7 +616,7 @@ class BatchLatentsNode(io.ComfyNode): node_id="BatchLatentsNode", search_aliases=["combine latents", "stack latents", "merge latents"], display_name="Batch Latents", - category="model/latent", + category="model/latent/batch", inputs=[ io.Autogrow.Input("latents", template=autogrow_template) ], diff --git a/comfy_extras/nodes_qwen.py b/comfy_extras/nodes_qwen.py index 5b92814a4..4960774db 100644 --- a/comfy_extras/nodes_qwen.py +++ b/comfy_extras/nodes_qwen.py @@ -12,7 +12,7 @@ class TextEncodeQwenImageEdit(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="TextEncodeQwenImageEdit", - category="advanced/conditioning", + category="model/conditioning/qwen image", inputs=[ io.Clip.Input("clip"), io.String.Input("prompt", multiline=True, dynamic_prompts=True), @@ -55,7 +55,7 @@ class TextEncodeQwenImageEditPlus(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="TextEncodeQwenImageEditPlus", - category="advanced/conditioning", + category="model/conditioning/qwen image", inputs=[ io.Clip.Input("clip"), io.String.Input("prompt", multiline=True, dynamic_prompts=True), diff --git a/comfy_extras/nodes_rtdetr.py b/comfy_extras/nodes_rtdetr.py index e5a9b3902..653f3af2f 100644 --- a/comfy_extras/nodes_rtdetr.py +++ b/comfy_extras/nodes_rtdetr.py @@ -14,7 +14,7 @@ class RTDETR_detect(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="RTDETR_detect", - display_name="RT-DETR Detect", + display_name="Run Real-Time Detection (RT-DETR)", category="image/detection", search_aliases=["bbox", "bounding box", "object detection", "coco"], inputs=[ diff --git a/comfy_extras/nodes_sam3.py b/comfy_extras/nodes_sam3.py index daac52f9b..f88aec925 100644 --- a/comfy_extras/nodes_sam3.py +++ b/comfy_extras/nodes_sam3.py @@ -264,7 +264,7 @@ class SAM3_VideoTrack(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="SAM3_VideoTrack", - display_name="SAM3 Video Track", + display_name="Run SAM3 Video Track", category="image/detection", search_aliases=["sam3", "video", "track", "propagate"], inputs=[ diff --git a/comfy_extras/nodes_scail.py b/comfy_extras/nodes_scail.py index bba0942d7..55c9897e3 100644 --- a/comfy_extras/nodes_scail.py +++ b/comfy_extras/nodes_scail.py @@ -34,14 +34,20 @@ def _unpack(track_data): return unpack_masks(packed) -def _first_frame_cx_area(masks_bool): - first = masks_bool[0].float() - H, W = first.shape[-2], first.shape[-1] - n_pixels = H * W - grid_x = torch.arange(W, device=first.device, dtype=first.dtype).view(1, W) - area = first.sum(dim=(-1, -2)).clamp_(min=1) - cx = (first * grid_x).sum(dim=(-1, -2)) / area - return (cx / W).tolist(), (area / n_pixels).tolist() +def _first_appearance_cx_area(masks_bool): + """Per object: first frame it appears in, plus centroid-x and area in that frame.""" + m = masks_bool.float() + T, H, W = m.shape[0], m.shape[-2], m.shape[-1] + grid_x = torch.arange(W, device=m.device, dtype=m.dtype).view(1, 1, 1, W) + area_t = m.sum(dim=(-1, -2)) + cx_t = (m * grid_x).sum(dim=(-1, -2)) / area_t.clamp(min=1) + present = area_t > 0 + frame_idx = torch.arange(T, device=m.device).unsqueeze(1) + first_t = torch.where(present, frame_idx, T).amin(dim=0) + sel = first_t.clamp(max=T - 1).unsqueeze(0) + cx = cx_t.gather(0, sel).squeeze(0) + area = area_t.gather(0, sel).squeeze(0) + return first_t.tolist(), (cx / W).tolist(), (area / (H * W)).tolist() def _subset_track_data(track_data, obj_indices): @@ -81,12 +87,26 @@ def _render_colored_masks(track_data, background="black"): masks_full.view(T * N_obj, 1, Hm, Wm), size=(H, W), mode="nearest" ).view(T, N_obj, H, W) > 0.5 any_mask = masks_full.any(dim=1) - obj_idx_map = masks_full.to(torch.uint8).argmax(dim=1) - color_overlay = colors[obj_idx_map] + color_overlay = colors[masks_full.to(torch.uint8).argmax(dim=1)] bg_tensor = torch.tensor(bg_rgb, device=device, dtype=color_overlay.dtype).view(1, 1, 1, 3) return torch.where(any_mask.unsqueeze(-1), color_overlay, bg_tensor.expand_as(color_overlay)) +def _render_mask_as_identity(mask, background="black"): + """Plain comfy MASK (B,H,W) or (H,W) -> (B,H,W,3) rendered as a single identity (palette[0]) + on the given background. A batch is treated as multiple views of that one subject.""" + device = comfy.model_management.intermediate_device() + dtype = comfy.model_management.intermediate_dtype() + if mask.ndim == 2: + mask = mask.unsqueeze(0) + mask = mask.to(device=device, dtype=dtype) + B, H, W = mask.shape + bg_rgb = (1.0, 1.0, 1.0) if background.startswith("white") else (0.0, 0.0, 0.0) + color = torch.tensor(DEFAULT_PALETTE[0], device=device, dtype=dtype).view(1, 1, 1, 3) + bg = torch.tensor(bg_rgb, device=device, dtype=dtype).view(1, 1, 1, 3) + return torch.where((mask > 0.5).unsqueeze(-1), color.expand(B, H, W, 3), bg.expand(B, H, W, 3)) + + def _extract_mask_to_28ch(rgb_video): """Colored RGB mask (T, H, W, 3) in [0, 1] -> SCAIL-2 28-channel binary latent (1, T_lat, 28, H_lat, W_lat). 7 per-color binary channels (white/r/g/b/y/m/c) @@ -123,7 +143,7 @@ class WanSCAILToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanSCAILToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/scail", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -138,8 +158,8 @@ class WanSCAILToVideo(io.ComfyNode): io.Float.Input("pose_strength", default=1.0, min=0.0, max=10.0, step=0.01, tooltip="Strength of the pose latent."), io.Float.Input("pose_start", default=0.0, min=0.0, max=1.0, step=0.01, tooltip="Start step of the pose conditioning."), io.Float.Input("pose_end", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="End step of the pose conditioning."), - io.Image.Input("reference_image", optional=True, tooltip="Reference image, for multiple references composite all on single image."), - io.Image.Input("reference_image_mask", optional=True, tooltip="SCAIL-2 only. Colored reference mask at the same resolution as reference_image."), + io.Image.Input("reference_image", optional=True, tooltip="Reference image. The first image is the primary reference (composite all identities onto it). SCAIL-2: extra batch images are used as additional views (back view, close-up, occluded background), each needing a matching reference_image_mask in that identity's color."), + io.Image.Input("reference_image_mask", optional=True, tooltip="SCAIL-2 only. Colored reference mask, batch matching reference_image (first = primary reference mask, rest = identity masks for the additional reference_image)."), io.ClipVisionOutput.Input("clip_vision_output", optional=True, tooltip="CLIP vision features for conditioning. Model is trained with stretch resize to aspect ratio."), io.Int.Input("video_frame_offset", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1, tooltip="Cumulative output frame this chunk begins at. Wire from the previous chunk's video_frame_offset output."), io.Int.Input("previous_frame_count", default=5, min=1, max=nodes.MAX_RESOLUTION, step=4, tooltip="Tail frames of previous_frames to anchor. SCAIL-2 trained at 5 (81-frame chunks, 76-frame step)."), @@ -171,19 +191,21 @@ class WanSCAILToVideo(io.ComfyNode): video_frame_offset -= prev_trimmed.shape[0] video_frame_offset = max(0, video_frame_offset) - ref_latent = None if reference_image is not None: - reference_image = comfy.utils.common_upscale(reference_image[:1].movedim(-1, 1), width, height, "bicubic", "center").movedim(1, -1) - # Replacement Mode: composite ref on black bg using reference_image_mask as alpha matte - if replacement_mode and reference_image_mask is not None: - rm = comfy.utils.common_upscale(reference_image_mask[:1].movedim(-1, 1), width, height, "nearest-exact", "center").movedim(1, -1) - is_char = (rm[..., :3].max(dim=-1, keepdim=True).values > 0.1).to(reference_image.dtype) - reference_image = reference_image * is_char - ref_latent = vae.encode(reference_image[:, :, :, :3]) + ref_imgs = comfy.utils.common_upscale(reference_image.movedim(-1, 1), width, height, "bicubic", "center").movedim(1, -1) + n_ref = ref_imgs.shape[0] + # SCAIL-2 multi-reference: the first image is the primary ref, the rest are additional references. - if ref_latent is not None: - positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True) - negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [ref_latent]}, append=True) + # Replacement Mode: composite each ref on black bg using its mask as alpha matte + if replacement_mode and reference_image_mask is not None: + rm = comfy.utils.common_upscale(reference_image_mask.movedim(-1, 1), width, height, "nearest-exact", "center").movedim(1, -1) + rm = rm[[min(i, rm.shape[0] - 1) for i in range(n_ref)]] + is_char = (rm[..., :3].max(dim=-1, keepdim=True).values > 0.1).to(ref_imgs.dtype) + ref_imgs = ref_imgs * is_char + # encode each ref individually so each stays a single latent frame (a batched encode would be treated as a video) + ref_latents = [vae.encode(ref_imgs[i:i + 1, :, :, :3]) for i in range(n_ref)] + positive = node_helpers.conditioning_set_values(positive, {"reference_latents": ref_latents}, append=True) + negative = node_helpers.conditioning_set_values(negative, {"reference_latents": ref_latents}, append=True) if clip_vision_output is not None: positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) @@ -221,11 +243,16 @@ class WanSCAILToVideo(io.ComfyNode): positive = node_helpers.conditioning_set_values(positive, {"driving_mask_28ch": driving_mask_28ch}) negative = node_helpers.conditioning_set_values(negative, {"driving_mask_28ch": driving_mask_28ch}) - if reference_image_mask is not None: - ref_mask_hw = comfy.utils.common_upscale(reference_image_mask[:1].movedim(-1, 1), width, height, "bicubic", "center").movedim(1, -1) - ref_mask_1f = _extract_mask_to_28ch(ref_mask_hw) + # The ref mask binds reference frames to identities, so it only applies when there's a reference image. + if reference_image_mask is not None and reference_image is not None: + ref_mask_hw = comfy.utils.common_upscale(reference_image_mask.movedim(-1, 1), width, height, "nearest-exact", "center").movedim(1, -1) + n_masks = ref_mask_hw.shape[0] + n_ref = reference_image.shape[0] + + add_masks = [_extract_mask_to_28ch(ref_mask_hw[min(i, n_masks - 1)][None]) for i in range(1, n_ref)] + ref_mask_1f = _extract_mask_to_28ch(ref_mask_hw[:1]) zeros = torch.zeros((1, latent.shape[2], 28, ref_mask_1f.shape[-2], ref_mask_1f.shape[-1]), device=ref_mask_1f.device, dtype=ref_mask_1f.dtype) - ref_mask_28ch = torch.cat([ref_mask_1f, zeros], dim=1) + ref_mask_28ch = torch.cat(add_masks + [ref_mask_1f, zeros], dim=1) positive = node_helpers.conditioning_set_values(positive, {"ref_mask_28ch": ref_mask_28ch}) negative = node_helpers.conditioning_set_values(negative, {"ref_mask_28ch": ref_mask_28ch}) @@ -244,12 +271,9 @@ class WanSCAILToVideo(io.ComfyNode): class SCAIL2ColoredMask(io.ComfyNode): - """Render SAM3 tracks for the driving pose video and (optionally) the reference - image into the two colored masks WanSCAILToVideo consumes. Shared `sort_by` - across both outputs guarantees identity K maps to the same color on both - sides, for multi-person workflow consistency. - reference_image_mask is always rendered black-bg (model convention) - pose_video_mask bg follows replacement_mode: black = Animation Mode, white = Replacement Mode + """Render SAM3 tracks for the driving pose video and reference image(s) into the + colored masks WanSCAILToVideo consumes. Shared `sort_by` keeps each identity on the + same color across both outputs. """ @classmethod @@ -257,18 +281,18 @@ class SCAIL2ColoredMask(io.ComfyNode): return io.Schema( node_id="SCAIL2ColoredMask", display_name="Create SCAIL-2 Colored Mask", - category="conditioning/video_models/scail", + category="model/conditioning/wan/scail", inputs=[ SAM3TrackData.Input("driving_track_data", tooltip="SAM3 track of the driving pose video. Will be rendered into the pose_video_mask output."), - SAM3TrackData.Input("ref_track_data", optional=True, - tooltip="SAM3 track of the reference image."), + io.MultiType.Input("ref_track_data", [SAM3TrackData, io.Mask], optional=True, display_name="reference_masks", + tooltip="SAM3 track of the reference image(s) (one identity per object, colored in batch order), or a plain MASK of the reference subject (rendered as a single identity)."), io.String.Input("object_indices", default="", tooltip="Comma-separated list of person indices to include (e.g. '0,2,3'). Applied to both reference and pose video masks. Empty = all."), io.Combo.Input("sort_by", options=["none", "left_to_right", "area"], default="left_to_right", - tooltip="Order in which palette colors are assigned to the tracked objects (applied to both reference and pose video so each identity keeps the same color). left_to_right = leftmost object (by first-frame centroid) gets the first color; area = biggest object (by first-frame mask area) gets the first color; none = keep SAM3's order."), + tooltip="Order in which palette colors are assigned to the tracked objects (applied to both reference and pose video so each identity keeps the same color). Objects that appear in earlier frames always come first; within a frame, left_to_right = leftmost object (by centroid at first appearance) gets the first color, area = biggest object (by mask area at first appearance) gets the first color; none = keep SAM3's order."), io.Boolean.Input("replacement_mode", default=False, - tooltip="False = Animation Mode (pose_video_mask has black background, reference_image_mask has white background). " - "True = Replacement Mode (pose_video_mask has white background, reference_image_mask has black background)."), + tooltip="False = Animation Mode (pose_video_mask has black background, reference_image_mask has white background). " + "True = Replacement Mode (pose_video_mask has white background, reference_image_mask has black background)."), ], outputs=[ io.Image.Output("pose_video_mask"), @@ -282,11 +306,11 @@ class SCAIL2ColoredMask(io.ComfyNode): def _prep(td): masks_bool = _unpack(td) if sort_by != "none" and masks_bool is not None: - cx, area = _first_frame_cx_area(masks_bool) + first_t, cx, area = _first_appearance_cx_area(masks_bool) if sort_by == "left_to_right": - order = sorted(range(len(cx)), key=lambda i: cx[i]) + order = sorted(range(len(cx)), key=lambda i: (first_t[i], cx[i])) else: # "area" - order = sorted(range(len(area)), key=lambda i: -area[i]) + order = sorted(range(len(area)), key=lambda i: (first_t[i], -area[i])) td = _subset_track_data(td, order) if object_indices.strip(): indices = [int(i.strip()) for i in object_indices.split(",") if i.strip().isdigit()] @@ -302,8 +326,10 @@ class SCAIL2ColoredMask(io.ComfyNode): ref_bg = "black" if replacement_mode else "white" if ref_track_data is not None: - ref = _prep(ref_track_data) - reference_image_mask = _render_colored_masks(ref, ref_bg) + if isinstance(ref_track_data, torch.Tensor): # plain comfy MASK + reference_image_mask = _render_mask_as_identity(ref_track_data, ref_bg) + else: + reference_image_mask = _render_colored_masks(_prep(ref_track_data), ref_bg) else: H, W = drv["orig_size"] fill_value = 1.0 if ref_bg == "white" else 0.0 diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py index 38cbf117b..40e84656b 100644 --- a/comfy_extras/nodes_sd3.py +++ b/comfy_extras/nodes_sd3.py @@ -13,8 +13,9 @@ class TripleCLIPLoader(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="TripleCLIPLoader", - category="advanced/loaders", - description="[Recipes]\n\nsd3: clip-l, clip-g, t5", + display_name="Load CLIP (Triple)", + category="model/loaders", + description="Recipes:\nsd3: clip-l, clip-g, t5", inputs=[ io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")), io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")), @@ -41,7 +42,7 @@ class EmptySD3LatentImage(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="EmptySD3LatentImage", - category="model/latent/sd3", + category="model/latent/stable diffusion", inputs=[ io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), @@ -66,7 +67,8 @@ class CLIPTextEncodeSD3(io.ComfyNode): return io.Schema( node_id="CLIPTextEncodeSD3", search_aliases=["sd3 prompt"], - category="advanced/conditioning", + display_name="CLIP Text Encode (SD3)", + category="model/conditioning/stable diffusion", inputs=[ io.Clip.Input("clip"), io.String.Input("clip_l", multiline=True, dynamic_prompts=True), diff --git a/comfy_extras/nodes_sdpose.py b/comfy_extras/nodes_sdpose.py index 20d459b00..d1cbff2a6 100644 --- a/comfy_extras/nodes_sdpose.py +++ b/comfy_extras/nodes_sdpose.py @@ -96,8 +96,12 @@ class KeypointDraw: # Body connections - matching DWPose limbSeq (1-indexed, converted to 0-indexed) self.body_limbSeq = [ [2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], - [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], - [1, 16], [16, 18] + [10, 11], [2, 12], [12, 13], [13, 14] + ] + + # Head connections (1-indexed, converted to 0-indexed) + self.head_edges = [ + [2, 1], [1, 15], [15, 17], [1, 16], [16, 18] ] # Colors matching DWPose @@ -215,7 +219,7 @@ class KeypointDraw: return unique_pts if len(unique_pts) > 1 else [[center[0], center[1]], [center[0], center[1]]] def draw_wholebody_keypoints(self, canvas, keypoints, scores=None, threshold=0.3, - draw_body=True, draw_feet=True, draw_face=True, draw_hands=True, stick_width=4, face_point_size=3): + draw_body=True, draw_head=True, draw_feet=True, draw_face=True, draw_hands=True, stick_width=4, face_point_size=3): """ Draw wholebody keypoints (134 keypoints after processing) in DWPose style. @@ -237,9 +241,17 @@ class KeypointDraw: """ H, W, C = canvas.shape - # Draw body limbs - if draw_body and len(keypoints) >= 18: - for i, limb in enumerate(self.body_limbSeq): + # Draw body limbs & head connections + if (draw_body or draw_head) and len(keypoints) >= 18: + colorIndexOffset = 0 + edges = [] + if draw_body: + edges += self.body_limbSeq + else: + colorIndexOffset += len(self.body_limbSeq) + if draw_head: + edges += self.head_edges + for i, limb in enumerate(edges): # Convert from 1-indexed to 0-indexed idx1, idx2 = limb[0] - 1, limb[1] - 1 @@ -262,11 +274,17 @@ class KeypointDraw: polygon = self.draw.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stick_width), int(angle), 0, 360, 1) - self.draw.fillConvexPoly(canvas, polygon, self.colors[i % len(self.colors)]) + self.draw.fillConvexPoly(canvas, polygon, self.colors[(i + colorIndexOffset) % len(self.colors)]) - # Draw body keypoints - if draw_body and len(keypoints) >= 18: + # Draw body & head keypoints + if (draw_body or draw_head) and len(keypoints) >= 18: + head_keypoints = {0, 14, 15, 16, 17} # nose, eyes, ears + neck_point = 1 for i in range(18): + if not draw_head and i in head_keypoints: + continue + if not draw_body and i not in head_keypoints and i != neck_point: + continue if scores is not None and scores[i] < threshold: continue x, y = int(keypoints[i][0]), int(keypoints[i][1]) @@ -365,6 +383,7 @@ class SDPoseDrawKeypoints(io.ComfyNode): io.Int.Input("stick_width", default=4, min=1, max=10, step=1), io.Int.Input("face_point_size", default=3, min=1, max=10, step=1), io.Float.Input("score_threshold", default=0.3, min=0.0, max=1.0, step=0.01), + io.Boolean.Input("draw_head", default=True), ], outputs=[ io.Image.Output(), @@ -372,7 +391,7 @@ class SDPoseDrawKeypoints(io.ComfyNode): ) @classmethod - def execute(cls, keypoints, draw_body, draw_hands, draw_face, draw_feet, stick_width, face_point_size, score_threshold) -> io.NodeOutput: + def execute(cls, keypoints, draw_body, draw_hands, draw_face, draw_feet, stick_width, face_point_size, score_threshold, draw_head) -> io.NodeOutput: if not keypoints: return io.NodeOutput(torch.zeros((1, 64, 64, 3), dtype=torch.float32)) height = keypoints[0]["canvas_height"] @@ -405,7 +424,7 @@ class SDPoseDrawKeypoints(io.ComfyNode): canvas = drawer.draw_wholebody_keypoints( canvas, kp, sc, threshold=score_threshold, - draw_body=draw_body, draw_feet=draw_feet, + draw_body=draw_body, draw_head=draw_head, draw_feet=draw_feet, draw_face=draw_face, draw_hands=draw_hands, stick_width=stick_width, face_point_size=face_point_size, ) diff --git a/comfy_extras/nodes_sdupscale.py b/comfy_extras/nodes_sdupscale.py index ea283e971..5c247fb49 100644 --- a/comfy_extras/nodes_sdupscale.py +++ b/comfy_extras/nodes_sdupscale.py @@ -9,7 +9,7 @@ class SD_4XUpscale_Conditioning(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="SD_4XUpscale_Conditioning", - category="model/conditioning/upscale_diffusion", + category="model/conditioning/stable diffusion upscaler", inputs=[ io.Image.Input("images"), io.Conditioning.Input("positive"), diff --git a/comfy_extras/nodes_stable3d.py b/comfy_extras/nodes_stable3d.py index 8a6e5b726..b0eba819b 100644 --- a/comfy_extras/nodes_stable3d.py +++ b/comfy_extras/nodes_stable3d.py @@ -27,7 +27,7 @@ class StableZero123_Conditioning(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StableZero123_Conditioning", - category="model/conditioning/3d_models", + category="model/conditioning/stable zero123", inputs=[ io.ClipVision.Input("clip_vision"), io.Image.Input("init_image"), @@ -65,7 +65,7 @@ class StableZero123_Conditioning_Batched(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StableZero123_Conditioning_Batched", - category="model/conditioning/3d_models", + category="model/conditioning/stable zero123", inputs=[ io.ClipVision.Input("clip_vision"), io.Image.Input("init_image"), @@ -112,7 +112,7 @@ class SV3D_Conditioning(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="SV3D_Conditioning", - category="model/conditioning/3d_models", + category="model/conditioning/stable video 3d", inputs=[ io.ClipVision.Input("clip_vision"), io.Image.Input("init_image"), diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py index e55f248ae..6a78ffb47 100644 --- a/comfy_extras/nodes_stable_cascade.py +++ b/comfy_extras/nodes_stable_cascade.py @@ -29,7 +29,7 @@ class StableCascade_EmptyLatentImage(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StableCascade_EmptyLatentImage", - category="model/latent/stable_cascade", + category="model/latent/stable cascade", inputs=[ io.Int.Input("width", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8), io.Int.Input("height", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8), @@ -58,7 +58,7 @@ class StableCascade_StageC_VAEEncode(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StableCascade_StageC_VAEEncode", - category="model/latent/stable_cascade", + category="model/latent/stable cascade", inputs=[ io.Image.Input("image"), io.Vae.Input("vae"), @@ -93,7 +93,7 @@ class StableCascade_StageB_Conditioning(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StableCascade_StageB_Conditioning", - category="model/conditioning/stable_cascade", + category="model/conditioning/stable cascade", inputs=[ io.Conditioning.Input("conditioning"), io.Latent.Input("stage_c"), diff --git a/comfy_extras/nodes_textgen.py b/comfy_extras/nodes_textgen.py index d52faf815..5a947d5c5 100644 --- a/comfy_extras/nodes_textgen.py +++ b/comfy_extras/nodes_textgen.py @@ -35,7 +35,7 @@ class TextGenerate(io.ComfyNode): io.Image.Input("image", optional=True), io.Image.Input("video", optional=True, tooltip="Video frames as image batch. Assumed to be 24 FPS; subsampled to 1 FPS internally."), io.Audio.Input("audio", optional=True), - io.Int.Input("max_length", default=256, min=1, max=2048), + io.Int.Input("max_length", default=512, min=1, max=32768), io.DynamicCombo.Input("sampling_mode", options=sampling_options, display_name="Sampling Mode"), io.Boolean.Input("thinking", optional=True, default=False, tooltip="Operate in thinking mode if the model supports it."), io.Boolean.Input("use_default_template", optional=True, default=True, tooltip="Use the built in system prompt/template if the model has one.", advanced=True), diff --git a/comfy_extras/nodes_train.py b/comfy_extras/nodes_train.py index bb68da6fa..a27217b80 100644 --- a/comfy_extras/nodes_train.py +++ b/comfy_extras/nodes_train.py @@ -1367,7 +1367,7 @@ class SaveLoRA(io.ComfyNode): node_id="SaveLoRA", search_aliases=["export lora"], display_name="Save LoRA Weights", - category="advanced/model_merging", + category="model/merging", is_experimental=True, is_output_node=True, inputs=[ diff --git a/comfy_extras/nodes_triposplat.py b/comfy_extras/nodes_triposplat.py index 1848ad31a..7bf4703fe 100644 --- a/comfy_extras/nodes_triposplat.py +++ b/comfy_extras/nodes_triposplat.py @@ -65,7 +65,7 @@ class TripoSplatPreprocessImage(IO.ComfyNode): return IO.Schema( node_id="TripoSplatPreprocessImage", display_name="TripoSplat Preprocess Image", - category="3d/conditioning", + category="model/conditioning/triposplat", description="Crop center each image to a square canvas on a black background and add padding.", inputs=[ IO.Image.Input("image"), @@ -95,7 +95,7 @@ class TripoSplatConditioning(IO.ComfyNode): return IO.Schema( node_id="TripoSplatConditioning", display_name="TripoSplat Conditioning", - category="3d/conditioning", + category="model/conditioning/triposplat", description="Encode the image with DINOv3 and the Flux2 VAE into TripoSplat positive/negative " "conditioning, and create the fixed size noise target (latent + camera) for the KSampler", inputs=[ diff --git a/comfy_extras/nodes_video_model.py b/comfy_extras/nodes_video_model.py index 0d6cae6a8..01d48d4d4 100644 --- a/comfy_extras/nodes_video_model.py +++ b/comfy_extras/nodes_video_model.py @@ -41,7 +41,7 @@ class SVD_img2vid_Conditioning: FUNCTION = "encode" - CATEGORY = "model/conditioning/video_models" + CATEGORY = "model/conditioning/stable video" def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level): output = clip_vision.encode_image(init_image) @@ -108,7 +108,7 @@ class VideoTriangleCFGGuidance: return (m, ) class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave): - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" @classmethod def INPUT_TYPES(s): @@ -138,7 +138,7 @@ class ConditioningSetAreaPercentageVideo: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" def append(self, conditioning, width, height, temporal, x, y, z, strength): c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", temporal, height, width, z, y, x), @@ -160,4 +160,5 @@ NODE_DISPLAY_NAME_MAPPINGS = { "ImageOnlyCheckpointLoader": "Load Checkpoint Image Only (img2vid model)", "VideoLinearCFGGuidance": "Video Linear CFG Guidance", "VideoTriangleCFGGuidance": "Video Triangle CFG Guidance", + "ConditioningSetAreaPercentageVideo": "Conditioning (Set Area with Percentage for Video)", } diff --git a/comfy_extras/nodes_void.py b/comfy_extras/nodes_void.py index b43154b8d..7527baf43 100644 --- a/comfy_extras/nodes_void.py +++ b/comfy_extras/nodes_void.py @@ -175,7 +175,7 @@ class VOIDInpaintConditioning(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="VOIDInpaintConditioning", - category="model/conditioning/video_models", + category="model/conditioning/void", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -288,7 +288,7 @@ class VOIDWarpedNoise(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="VOIDWarpedNoise", - category="model/latent/video", + category="model/latent/void", inputs=[ OpticalFlow.Input( "optical_flow", @@ -393,7 +393,7 @@ class VOIDWarpedNoiseSource(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="VOIDWarpedNoiseSource", - category="model/sampling/noise", + category="model/latent/void", inputs=[ io.Latent.Input("warped_noise", tooltip="Warped noise latent from VOIDWarpedNoise"), diff --git a/comfy_extras/nodes_wan.py b/comfy_extras/nodes_wan.py index d73be8e00..0e47a58df 100644 --- a/comfy_extras/nodes_wan.py +++ b/comfy_extras/nodes_wan.py @@ -18,7 +18,7 @@ class WanImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanImageToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -66,7 +66,7 @@ class WanFunControlToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanFunControlToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/fun control", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -119,7 +119,7 @@ class Wan22FunControlToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="Wan22FunControlToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/fun control", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -184,7 +184,7 @@ class WanFirstLastFrameToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanFirstLastFrameToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -256,7 +256,7 @@ class WanFunInpaintToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanFunInpaintToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/fun inpaint", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -288,7 +288,7 @@ class WanVaceToVideo(io.ComfyNode): return io.Schema( node_id="WanVaceToVideo", search_aliases=["video conditioning", "video control"], - category="model/conditioning/video_models", + category="model/conditioning/wan/vace", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -375,7 +375,8 @@ class TrimVideoLatent(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="TrimVideoLatent", - category="model/latent/video", + display_name="Trim Video Latent", + category="model/latent", inputs=[ io.Latent.Input("samples"), io.Int.Input("trim_amount", default=0, min=0, max=99999), @@ -398,7 +399,7 @@ class WanCameraImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanCameraImageToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/camera", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -452,7 +453,7 @@ class WanPhantomSubjectToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanPhantomSubjectToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/phantom subject", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -707,7 +708,7 @@ class WanTrackToVideo(io.ComfyNode): return io.Schema( node_id="WanTrackToVideo", search_aliases=["motion tracking", "trajectory video", "point tracking", "keypoint animation"], - category="model/conditioning/video_models", + category="model/conditioning/wan/move", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -951,7 +952,7 @@ class WanSoundImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanSoundImageToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/sound", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -984,7 +985,7 @@ class WanSoundImageToVideoExtend(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanSoundImageToVideoExtend", - category="model/conditioning/video_models", + category="model/conditioning/wan/sound", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -1046,7 +1047,7 @@ class WanHuMoImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanHuMoImageToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/humo", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -1112,7 +1113,7 @@ class WanAnimateToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanAnimateToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/animate", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -1252,7 +1253,7 @@ class Wan22ImageToVideoLatent(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="Wan22ImageToVideoLatent", - category="model/conditioning/inpaint", + category="model/conditioning/wan", inputs=[ io.Vae.Input("vae"), io.Int.Input("width", default=1280, min=32, max=nodes.MAX_RESOLUTION, step=32), @@ -1302,7 +1303,7 @@ class WanInfiniteTalkToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanInfiniteTalkToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/infinite talk", inputs=[ io.DynamicCombo.Input("mode", options=[ io.DynamicCombo.Option("single_speaker", []), diff --git a/comfy_extras/nodes_wandancer.py b/comfy_extras/nodes_wandancer.py index a96885745..fdb2b5e57 100644 --- a/comfy_extras/nodes_wandancer.py +++ b/comfy_extras/nodes_wandancer.py @@ -713,7 +713,7 @@ class WanDancerEncodeAudio(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanDancerEncodeAudio", - category="model/conditioning/video_models", + category="model/conditioning/wan/dancer", inputs=[ io.Audio.Input("audio"), io.Int.Input("video_frames", default=149, min=1, max=nodes.MAX_RESOLUTION, step=4), @@ -787,7 +787,7 @@ class WanDancerVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanDancerVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/dancer", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), diff --git a/comfy_extras/nodes_wanmove.py b/comfy_extras/nodes_wanmove.py index 2db064922..d1f924a40 100644 --- a/comfy_extras/nodes_wanmove.py +++ b/comfy_extras/nodes_wanmove.py @@ -247,7 +247,7 @@ class WanMoveVisualizeTracks(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanMoveVisualizeTracks", - category="model/conditioning/video_models", + category="model/conditioning/wan/move", inputs=[ io.Image.Input("images"), io.Tracks.Input("tracks", optional=True), @@ -283,7 +283,7 @@ class WanMoveTracksFromCoords(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanMoveTracksFromCoords", - category="model/conditioning/video_models", + category="model/conditioning/wan/move", inputs=[ io.String.Input("track_coords", force_input=True, default="[]", optional=True), io.Mask.Input("track_mask", optional=True), @@ -325,7 +325,8 @@ class GenerateTracks(io.ComfyNode): return io.Schema( node_id="GenerateTracks", search_aliases=["motion paths", "camera movement", "trajectory"], - category="model/conditioning/video_models", + display_name="Generate Video Tracks", + category="model/conditioning/wan/move", inputs=[ io.Int.Input("width", default=832, min=16, max=4096, step=16), io.Int.Input("height", default=480, min=16, max=4096, step=16), @@ -434,7 +435,7 @@ class WanMoveConcatTrack(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanMoveConcatTrack", - category="model/conditioning/video_models", + category="model/conditioning/wan/move", inputs=[ io.Tracks.Input("tracks_1"), io.Tracks.Input("tracks_2", optional=True), @@ -463,7 +464,7 @@ class WanMoveTrackToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanMoveTrackToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/move", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), diff --git a/comfy_extras/nodes_zimage.py b/comfy_extras/nodes_zimage.py index 70ddc4afa..ce946b377 100644 --- a/comfy_extras/nodes_zimage.py +++ b/comfy_extras/nodes_zimage.py @@ -10,7 +10,7 @@ class TextEncodeZImageOmni(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="TextEncodeZImageOmni", - category="advanced/conditioning", + category="model/conditioning/z-image", is_experimental=True, inputs=[ io.Clip.Input("clip"), diff --git a/comfyui_version.py b/comfyui_version.py index 4e3c924e6..cee317f3d 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.24.0" +__version__ = "0.25.0" diff --git a/main.py b/main.py index 2cdb9caad..ad5c11e16 100644 --- a/main.py +++ b/main.py @@ -55,7 +55,11 @@ if __name__ == "__main__" and args.debug_hang: import comfy_aimdo.control if enables_dynamic_vram(): - comfy_aimdo.control.init(simple_vram_headroom=None if args.reserve_vram is None else int(args.reserve_vram * 1024 ** 3)) + try: + comfy_aimdo.control.init(simple_vram_headroom=None if args.reserve_vram is None else int(args.reserve_vram * 1024 ** 3)) + except TypeError: + # comfy-aimdo 0.4.9 protocol. + comfy_aimdo.control.init() if os.name == "nt": os.environ['MIMALLOC_PURGE_DELAY'] = '0' @@ -123,6 +127,10 @@ def apply_custom_paths(): for config_path in itertools.chain(*args.extra_model_paths_config): utils.extra_config.load_extra_path_config(config_path) + # --base-directory + if args.base_directory: + logging.info(f"Setting base directory to: {folder_paths.base_path}") + # --output-directory, --input-directory, --user-directory if args.output_directory: output_dir = os.path.abspath(args.output_directory) @@ -231,23 +239,30 @@ import comfy.model_patcher if args.enable_dynamic_vram or (enables_dynamic_vram() and comfy.model_management.is_nvidia() and not comfy.model_management.is_wsl()): if (not args.enable_dynamic_vram) and (comfy.model_management.torch_version_numeric < (2, 8)): logging.warning("Unsupported Pytorch detected. DynamicVRAM support requires Pytorch version 2.8 or later. Falling back to legacy ModelPatcher. VRAM estimates may be unreliable especially on Windows") - elif comfy_aimdo.control.init_devices((d.index, int(args.vram_headroom * 1024 ** 3)) for d in comfy.model_management.get_all_torch_devices()): - if args.verbose == 'DEBUG': - comfy_aimdo.control.set_log_debug() - elif args.verbose == 'CRITICAL': - comfy_aimdo.control.set_log_critical() - elif args.verbose == 'ERROR': - comfy_aimdo.control.set_log_error() - elif args.verbose == 'WARNING': - comfy_aimdo.control.set_log_warning() - else: #INFO - comfy_aimdo.control.set_log_info() - - comfy.model_patcher.CoreModelPatcher = comfy.model_patcher.ModelPatcherDynamic - comfy.memory_management.aimdo_enabled = True - logging.info("DynamicVRAM support detected and enabled") else: - logging.warning("No working comfy-aimdo install detected. DynamicVRAM support disabled. Falling back to legacy ModelPatcher. VRAM estimates may be unreliable especially on Windows") + try: + aimdo_initialized = comfy_aimdo.control.init_devices((d.index, int(args.vram_headroom * 1024 ** 3)) for d in comfy.model_management.get_all_torch_devices()) + except TypeError: + # comfy-aimdo 0.4.9 protocol. + aimdo_initialized = comfy_aimdo.control.init_devices(d.index for d in comfy.model_management.get_all_torch_devices()) + + if aimdo_initialized: + if args.verbose == 'DEBUG': + comfy_aimdo.control.set_log_debug() + elif args.verbose == 'CRITICAL': + comfy_aimdo.control.set_log_critical() + elif args.verbose == 'ERROR': + comfy_aimdo.control.set_log_error() + elif args.verbose == 'WARNING': + comfy_aimdo.control.set_log_warning() + else: #INFO + comfy_aimdo.control.set_log_info() + + comfy.model_patcher.CoreModelPatcher = comfy.model_patcher.ModelPatcherDynamic + comfy.memory_management.aimdo_enabled = True + logging.info("DynamicVRAM support detected and enabled") + else: + logging.warning("No working comfy-aimdo install detected. DynamicVRAM support disabled. Falling back to legacy ModelPatcher. VRAM estimates may be unreliable especially on Windows") def cuda_malloc_warning(): diff --git a/nodes.py b/nodes.py index 0d422d418..bb4649478 100644 --- a/nodes.py +++ b/nodes.py @@ -87,7 +87,7 @@ class ConditioningCombine: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "combine" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" SEARCH_ALIASES = ["combine", "merge conditioning", "combine prompts", "merge prompts", "mix prompts", "add prompt"] def combine(self, conditioning_1, conditioning_2): @@ -104,7 +104,7 @@ class ConditioningAverage : RETURN_TYPES = ("CONDITIONING",) FUNCTION = "addWeighted" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength): out = [] @@ -143,7 +143,7 @@ class ConditioningConcat: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "concat" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" def concat(self, conditioning_to, conditioning_from): out = [] @@ -176,7 +176,7 @@ class ConditioningSetArea: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" def append(self, conditioning, width, height, x, y, strength): c = node_helpers.conditioning_set_values(conditioning, {"area": (height // 8, width // 8, y // 8, x // 8), @@ -197,7 +197,7 @@ class ConditioningSetAreaPercentage: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" def append(self, conditioning, width, height, x, y, strength): c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", height, width, y, x), @@ -214,7 +214,7 @@ class ConditioningSetAreaStrength: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" def append(self, conditioning, strength): c = node_helpers.conditioning_set_values(conditioning, {"strength": strength}) @@ -234,7 +234,7 @@ class ConditioningSetMask: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" def append(self, conditioning, mask, set_cond_area, strength): set_area_to_bounds = False @@ -257,7 +257,7 @@ class ConditioningZeroOut: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "zero_out" - CATEGORY = "advanced/conditioning" + CATEGORY = "model/conditioning/transform" def zero_out(self, conditioning): c = [] @@ -283,11 +283,10 @@ class ConditioningSetTimestepRange: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "set_range" - CATEGORY = "advanced/conditioning" + CATEGORY = "model/conditioning/transform" def set_range(self, conditioning, start, end): - c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start, - "end_percent": end}) + c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start, "end_percent": end}) return (c, ) class VAEDecode: @@ -389,7 +388,7 @@ class VAEEncodeForInpaint: RETURN_TYPES = ("LATENT",) FUNCTION = "encode" - CATEGORY = "model/latent/inpaint" + CATEGORY = "model/latent" def encode(self, vae, pixels, mask, grow_mask_by=6): downscale_ratio = vae.spacial_compression_encode() @@ -438,7 +437,7 @@ class InpaintModelConditioning: RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" - CATEGORY = "model/conditioning/inpaint" + CATEGORY = "model/conditioning" def encode(self, positive, negative, pixels, vae, mask, noise_mask=True): x = (pixels.shape[1] // 8) * 8 @@ -576,7 +575,7 @@ class CheckpointLoader: RETURN_TYPES = ("MODEL", "CLIP", "VAE") FUNCTION = "load_checkpoint" - CATEGORY = "advanced/loaders" + CATEGORY = "model/loaders" DEPRECATED = True def load_checkpoint(self, config_name, ckpt_name): @@ -622,8 +621,9 @@ class DiffusersLoader: return {"required": {"model_path": (paths,), }} RETURN_TYPES = ("MODEL", "CLIP", "VAE") FUNCTION = "load_checkpoint" + DEPRECATED = True - CATEGORY = "advanced/loaders/deprecated" + CATEGORY = "model/loaders" def load_checkpoint(self, model_path, output_vae=True, output_clip=True): for search_path in folder_paths.get_folder_paths("diffusers"): @@ -949,7 +949,7 @@ class UNETLoader: RETURN_TYPES = ("MODEL",) FUNCTION = "load_unet" - CATEGORY = "advanced/loaders" + CATEGORY = "model/loaders" def load_unet(self, unet_name, weight_dtype): model_options = {} @@ -977,9 +977,9 @@ class CLIPLoader: RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" - CATEGORY = "advanced/loaders" + CATEGORY = "model/loaders" - DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B\nlens: gpt-oss-20b\n pixeldit: gemma 2 2B elm" + DESCRIPTION = "Recipes:\nsd: clip-l\nstable cascade: clip-g\nsd3: t5 xxl / clip-g / clip-l\nstable audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\nhidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B\nlens: gpt-oss-20b\npixeldit: gemma 2 2B elm" def load_clip(self, clip_name, type="stable_diffusion", device="default"): clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION) @@ -1005,9 +1005,9 @@ class DualCLIPLoader: RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" - CATEGORY = "advanced/loaders" + CATEGORY = "model/loaders" - DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5\nhidream: at least one of t5 or llama, recommended t5 and llama\nhunyuan_image: qwen2.5vl 7b and byt5 small\nnewbie: gemma-3-4b-it, jina clip v2" + DESCRIPTION = "Recipes:\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5\nhidream: at least one of t5 or llama, recommended t5 and llama\nhunyuan_image: qwen2.5vl 7b and byt5 small\nnewbie: gemma-3-4b-it, jina clip v2" def load_clip(self, clip_name1, clip_name2, type, device="default"): clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION) @@ -1088,7 +1088,7 @@ class StyleModelApply: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "apply_stylemodel" - CATEGORY = "model/conditioning/style_model" + CATEGORY = "model/conditioning" def apply_stylemodel(self, conditioning, style_model, clip_vision_output, strength, strength_type): cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0) @@ -1518,13 +1518,11 @@ class LatentCrop: class SetLatentNoiseMask: @classmethod def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), - "mask": ("MASK",), - }} + return {"required": { "samples": ("LATENT",), "mask": ("MASK",), }} RETURN_TYPES = ("LATENT",) FUNCTION = "set_mask" - CATEGORY = "model/latent/inpaint" + CATEGORY = "model/latent" def set_mask(self, samples, mask): s = samples.copy() @@ -2045,7 +2043,7 @@ NODE_CLASS_MAPPINGS = { "ImageBatch": ImageBatch, "ImagePadForOutpaint": ImagePadForOutpaint, "EmptyImage": EmptyImage, - "ConditioningAverage": ConditioningAverage , + "ConditioningAverage": ConditioningAverage, "ConditioningCombine": ConditioningCombine, "ConditioningConcat": ConditioningConcat, "ConditioningSetArea": ConditioningSetArea, @@ -2101,6 +2099,7 @@ NODE_DISPLAY_NAME_MAPPINGS = { "LoraLoader": "Load LoRA (Model and CLIP)", "LoraLoaderModelOnly": "Load LoRA", "CLIPLoader": "Load CLIP", + "DualCLIPLoader": "Load CLIP (Dual)", "ControlNetLoader": "Load ControlNet Model", "DiffControlNetLoader": "Load ControlNet Model (diff)", "StyleModelLoader": "Load Style Model", @@ -2108,6 +2107,7 @@ NODE_DISPLAY_NAME_MAPPINGS = { "UNETLoader": "Load Diffusion Model", "unCLIPCheckpointLoader": "Load unCLIP Checkpoint", "GLIGENLoader": "Load GLIGEN Model", + "DiffusersLoader": "Load Diffusers Model (DEPRECATED)", # Conditioning "CLIPVisionEncode": "CLIP Vision Encode", "StyleModelApply": "Apply Style Model", @@ -2115,12 +2115,16 @@ NODE_DISPLAY_NAME_MAPPINGS = { "CLIPSetLastLayer": "CLIP Set Last Layer", "ConditioningCombine": "Conditioning (Combine)", "ConditioningAverage ": "Conditioning (Average)", + "ConditioningAverage": "Conditioning (Average)", "ConditioningConcat": "Conditioning (Concat)", "ConditioningSetArea": "Conditioning (Set Area)", "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)", + "ConditioningSetAreaStrength": "Conditioning (Set Area Strength)", "ConditioningSetMask": "Conditioning (Set Mask)", "ControlNetApply": "Apply ControlNet (DEPRECATED)", "ControlNetApplyAdvanced": "Apply ControlNet", + "GLIGENTextBoxApply": "Apply GLIGEN Text Box", + "ConditioningZeroOut": "Conditioning Zero Out", # Latent "VAEEncodeForInpaint": "VAE Encode (for Inpainting)", "SetLatentNoiseMask": "Set Latent Noise Mask", @@ -2134,7 +2138,7 @@ NODE_DISPLAY_NAME_MAPPINGS = { "LatentUpscaleBy": "Upscale Latent By", "LatentComposite": "Latent Composite", "LatentBlend": "Latent Blend", - "LatentFromBatch" : "Latent From Batch", + "LatentFromBatch" : "Get Latent From Batch", "RepeatLatentBatch": "Repeat Latent Batch", # Image "EmptyImage": "Empty Image", diff --git a/openapi.yaml b/openapi.yaml index c1263e7ed..5193e1773 100644 --- a/openapi.yaml +++ b/openapi.yaml @@ -939,11 +939,6 @@ components: additionalProperties: true description: The workflow graph to execute type: object - prompt_id: - description: Optional client-supplied job id. Must be a UUID in canonical lowercase hyphenated form; it is echoed back in the response. Omitted or null means the server generates one. - format: uuid - nullable: true - type: string workflow_id: description: UUID identifying the cloud workflow entity to associate with this job type: string @@ -1843,7 +1838,9 @@ paths: application/json: schema: $ref: '#/components/schemas/ErrorResponse' - description: Invalid request (no fields provided) + description: | + Invalid request — no fields provided, or `preview_id` is the zero UUID + (`INVALID_PREVIEW_ID`). "401": content: application/json: @@ -1855,7 +1852,10 @@ paths: application/json: schema: $ref: '#/components/schemas/ErrorResponse' - description: Asset not found + description: | + Asset not found — returned both when the asset being updated does + not exist and when `preview_id` does not reference an asset + accessible to the caller. "500": content: application/json: @@ -3143,6 +3143,12 @@ paths: schema: $ref: '#/components/schemas/PromptErrorResponse' description: Payment required - Insufficient credits + "413": + content: + application/json: + schema: + $ref: '#/components/schemas/PromptErrorResponse' + description: Workflow JSON too large "429": content: application/json: diff --git a/pyproject.toml b/pyproject.toml index 4107b4911..54f11d7fa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.24.0" +version = "0.25.0" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.10"