Ruff fix

Tests cover:

- Orphaned seed assets pruned when file removed

- Seed assets with valid files survive

- Hashed assets not pruned even without file

- Multi-root pruning

- SQL LIKE escape handling for %, _, spaces

Amp-Thread-ID: https://ampcode.com/threads/T-019c0c7a-5c8a-7548-b6c3-823e9829ce74
Co-authored-by: Amp <amp@ampcode.com>

.github/workflows/test-assets.yml

@@ -0,0 +1,30 @@
+name: Assets Tests
+
+on:
+  push:
+    branches: [ main, master, release/** ]
+  pull_request:
+    branches: [ main, master, release/** ]
+
+jobs:
+  test:
+    strategy:
+      matrix:
+        os: [ubuntu-latest, windows-latest, macos-latest]
+    runs-on: ${{ matrix.os }}
+    continue-on-error: true
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.12'
+    - name: Install requirements
+      run: |
+        python -m pip install --upgrade pip
+        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+        pip install -r requirements.txt
+    - name: Run Assets Tests
+      run: |
+        pip install -r tests-assets/requirements.txt
+        python -m pytest tests-assets -v

.github/workflows/windows_release_dependencies.yml

@@ -29,7 +29,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "11"
+        default: "9"
 #  push:
 #    branches:
 #      - master

comfy/ldm/cosmos/predict2.py

@@ -13,7 +13,6 @@ from torchvision import transforms
 
 import comfy.patcher_extension
 from comfy.ldm.modules.attention import optimized_attention
-import comfy.ldm.common_dit
 
 def apply_rotary_pos_emb(
     t: torch.Tensor,
@@ -836,8 +835,6 @@ class MiniTrainDIT(nn.Module):
         padding_mask: Optional[torch.Tensor] = None,
         **kwargs,
     ):
-        orig_shape = list(x.shape)
-        x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_temporal, self.patch_spatial, self.patch_spatial))
         x_B_C_T_H_W = x
         timesteps_B_T = timesteps
         crossattn_emb = context
@@ -885,5 +882,5 @@ class MiniTrainDIT(nn.Module):
             )
 
         x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D, t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D)
-        x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O)[:, :, :orig_shape[-3], :orig_shape[-2], :orig_shape[-1]]
+        x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O)
         return x_B_C_Tt_Hp_Wp

comfy/text_encoders/llama.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 from dataclasses import dataclass
-from typing import Optional, Any, Tuple
+from typing import Optional, Any
 import math
 
 from comfy.ldm.modules.attention import optimized_attention_for_device
@@ -32,7 +32,6 @@ class Llama2Config:
     k_norm = None
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Mistral3Small24BConfig:
@@ -55,7 +54,6 @@ class Mistral3Small24BConfig:
     k_norm = None
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Qwen25_3BConfig:
@@ -78,7 +76,6 @@ class Qwen25_3BConfig:
     k_norm = None
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Qwen3_06BConfig:
@@ -101,7 +98,6 @@ class Qwen3_06BConfig:
     k_norm = "gemma3"
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Qwen3_4BConfig:
@@ -124,7 +120,6 @@ class Qwen3_4BConfig:
     k_norm = "gemma3"
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Qwen3_8BConfig:
@@ -147,7 +142,6 @@ class Qwen3_8BConfig:
     k_norm = "gemma3"
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Ovis25_2BConfig:
@@ -170,7 +164,6 @@ class Ovis25_2BConfig:
     k_norm = "gemma3"
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Qwen25_7BVLI_Config:
@@ -193,7 +186,6 @@ class Qwen25_7BVLI_Config:
     k_norm = None
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Gemma2_2B_Config:
@@ -217,7 +209,6 @@ class Gemma2_2B_Config:
     sliding_attention = None
     rope_scale = None
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Gemma3_4B_Config:
@@ -241,7 +232,6 @@ class Gemma3_4B_Config:
     sliding_attention = [1024, 1024, 1024, 1024, 1024, False]
     rope_scale = [8.0, 1.0]
     final_norm: bool = True
-    lm_head: bool = False
 
 @dataclass
 class Gemma3_12B_Config:
@@ -265,7 +255,6 @@ class Gemma3_12B_Config:
     sliding_attention = [1024, 1024, 1024, 1024, 1024, False]
     rope_scale = [8.0, 1.0]
     final_norm: bool = True
-    lm_head: bool = False
     vision_config = {"num_channels": 3, "hidden_act": "gelu_pytorch_tanh", "hidden_size": 1152, "image_size": 896, "intermediate_size": 4304, "model_type": "siglip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 14}
     mm_tokens_per_image = 256
 
@@ -367,7 +356,6 @@ class Attention(nn.Module):
         attention_mask: Optional[torch.Tensor] = None,
         freqs_cis: Optional[torch.Tensor] = None,
         optimized_attention=None,
-        past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ):
         batch_size, seq_length, _ = hidden_states.shape
         xq = self.q_proj(hidden_states)
@@ -385,30 +373,11 @@ class Attention(nn.Module):
 
         xq, xk = apply_rope(xq, xk, freqs_cis=freqs_cis)
 
-        present_key_value = None
-        if past_key_value is not None:
-            index = 0
-            num_tokens = xk.shape[2]
-            if len(past_key_value) > 0:
-                past_key, past_value, index = past_key_value
-                if past_key.shape[2] >= (index + num_tokens):
-                    past_key[:, :, index:index + xk.shape[2]] = xk
-                    past_value[:, :, index:index + xv.shape[2]] = xv
-                    xk = past_key[:, :, :index + xk.shape[2]]
-                    xv = past_value[:, :, :index + xv.shape[2]]
-                    present_key_value = (past_key, past_value, index + num_tokens)
-                else:
-                    xk = torch.cat((past_key[:, :, :index], xk), dim=2)
-                    xv = torch.cat((past_value[:, :, :index], xv), dim=2)
-                    present_key_value = (xk, xv, index + num_tokens)
-            else:
-                present_key_value = (xk, xv, index + num_tokens)
-
         xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
         xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
 
         output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True)
-        return self.o_proj(output), present_key_value
+        return self.o_proj(output)
 
 class MLP(nn.Module):
     def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
@@ -439,17 +408,15 @@ class TransformerBlock(nn.Module):
         attention_mask: Optional[torch.Tensor] = None,
         freqs_cis: Optional[torch.Tensor] = None,
         optimized_attention=None,
-        past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ):
         # Self Attention
         residual = x
         x = self.input_layernorm(x)
-        x, present_key_value = self.self_attn(
+        x = self.self_attn(
             hidden_states=x,
             attention_mask=attention_mask,
             freqs_cis=freqs_cis,
             optimized_attention=optimized_attention,
-            past_key_value=past_key_value,
         )
         x = residual + x
 
@@ -459,7 +426,7 @@ class TransformerBlock(nn.Module):
         x = self.mlp(x)
         x = residual + x
 
-        return x, present_key_value
+        return x
 
 class TransformerBlockGemma2(nn.Module):
     def __init__(self, config: Llama2Config, index, device=None, dtype=None, ops: Any = None):
@@ -484,7 +451,6 @@ class TransformerBlockGemma2(nn.Module):
         attention_mask: Optional[torch.Tensor] = None,
         freqs_cis: Optional[torch.Tensor] = None,
         optimized_attention=None,
-        past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ):
         if self.transformer_type == 'gemma3':
             if self.sliding_attention:
@@ -502,12 +468,11 @@ class TransformerBlockGemma2(nn.Module):
         # Self Attention
         residual = x
         x = self.input_layernorm(x)
-        x, present_key_value = self.self_attn(
+        x = self.self_attn(
             hidden_states=x,
             attention_mask=attention_mask,
             freqs_cis=freqs_cis,
             optimized_attention=optimized_attention,
-            past_key_value=past_key_value,
         )
 
         x = self.post_attention_layernorm(x)
@@ -520,7 +485,7 @@ class TransformerBlockGemma2(nn.Module):
         x = self.post_feedforward_layernorm(x)
         x = residual + x
 
-        return x, present_key_value
+        return x
 
 class Llama2_(nn.Module):
     def __init__(self, config, device=None, dtype=None, ops=None):
@@ -551,10 +516,9 @@ class Llama2_(nn.Module):
         else:
             self.norm = None
 
-        if config.lm_head:
-            self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
+        # self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
 
-    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):
+    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=[]):
         if embeds is not None:
             x = embeds
         else:
@@ -563,13 +527,8 @@ class Llama2_(nn.Module):
         if self.normalize_in:
             x *= self.config.hidden_size ** 0.5
 
-        seq_len = x.shape[1]
-        past_len = 0
-        if past_key_values is not None and len(past_key_values) > 0:
-            past_len = past_key_values[0][2]
-
         if position_ids is None:
-            position_ids = torch.arange(past_len, past_len + seq_len, device=x.device).unsqueeze(0)
+            position_ids = torch.arange(0, x.shape[1], device=x.device).unsqueeze(0)
 
         freqs_cis = precompute_freqs_cis(self.config.head_dim,
                                          position_ids,
@@ -580,16 +539,14 @@ class Llama2_(nn.Module):
 
         mask = None
         if attention_mask is not None:
-            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, seq_len, attention_mask.shape[-1])
+            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
             mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
 
-        if seq_len > 1:
-            causal_mask = torch.empty(past_len + seq_len, past_len + seq_len, dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
-            if mask is not None:
-                mask += causal_mask
-            else:
-                mask = causal_mask
-
+        causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
+        if mask is not None:
+            mask += causal_mask
+        else:
+            mask = causal_mask
         optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
 
         intermediate = None
@@ -605,27 +562,16 @@ class Llama2_(nn.Module):
             elif intermediate_output < 0:
                 intermediate_output = len(self.layers) + intermediate_output
 
-        next_key_values = []
         for i, layer in enumerate(self.layers):
             if all_intermediate is not None:
                 if only_layers is None or (i in only_layers):
                     all_intermediate.append(x.unsqueeze(1).clone())
-
-            past_kv = None
-            if past_key_values is not None:
-                past_kv = past_key_values[i] if len(past_key_values) > 0 else []
-
-            x, current_kv = layer(
+            x = layer(
                 x=x,
                 attention_mask=mask,
                 freqs_cis=freqs_cis,
                 optimized_attention=optimized_attention,
-                past_key_value=past_kv,
             )
-
-            if current_kv is not None:
-                next_key_values.append(current_kv)
-
             if i == intermediate_output:
                 intermediate = x.clone()
 
@@ -642,10 +588,7 @@ class Llama2_(nn.Module):
         if intermediate is not None and final_layer_norm_intermediate and self.norm is not None:
             intermediate = self.norm(intermediate)
 
-        if len(next_key_values) > 0:
-            return x, intermediate, next_key_values
-        else:
-            return x, intermediate
+        return x, intermediate
 
 
 class Gemma3MultiModalProjector(torch.nn.Module):

comfy_api/latest/_io.py

@@ -1146,20 +1146,6 @@ class ImageCompare(ComfyTypeI):
       def as_dict(self):
           return super().as_dict()
 
-
-@comfytype(io_type="COLOR")
-class Color(ComfyTypeIO):
-  Type = str
-
-  class Input(WidgetInput):
-      def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
-                   socketless: bool=True, advanced: bool=None, default: str="#ffffff"):
-          super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
-          self.default: str
-
-      def as_dict(self):
-          return super().as_dict()
-
 DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {}
 def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]):
     DYNAMIC_INPUT_LOOKUP[io_type] = func
@@ -1248,7 +1234,6 @@ class Hidden(str, Enum):
 class NodeInfoV1:
     input: dict=None
     input_order: dict[str, list[str]]=None
-    is_input_list: bool=None
     output: list[str]=None
     output_is_list: list[bool]=None
     output_name: list[str]=None
@@ -1267,6 +1252,23 @@ class NodeInfoV1:
     price_badge: dict | None = None
     search_aliases: list[str]=None
 
+@dataclass
+class NodeInfoV3:
+    input: dict=None
+    output: dict=None
+    hidden: list[str]=None
+    name: str=None
+    display_name: str=None
+    description: str=None
+    python_module: Any = None
+    category: str=None
+    output_node: bool=None
+    deprecated: bool=None
+    experimental: bool=None
+    dev_only: bool=None
+    api_node: bool=None
+    price_badge: dict | None = None
+
 
 @dataclass
 class PriceBadgeDepends:
@@ -1475,7 +1477,6 @@ class Schema:
         info = NodeInfoV1(
             input=input,
             input_order={key: list(value.keys()) for (key, value) in input.items()},
-            is_input_list=self.is_input_list,
             output=output,
             output_is_list=output_is_list,
             output_name=output_name,
@@ -1496,6 +1497,40 @@ class Schema:
         )
         return info
 
+
+    def get_v3_info(self, cls) -> NodeInfoV3:
+        input_dict = {}
+        output_dict = {}
+        hidden_list = []
+        # TODO: make sure dynamic types will be handled correctly
+        if self.inputs:
+            for input in self.inputs:
+                add_to_dict_v3(input, input_dict)
+        if self.outputs:
+            for output in self.outputs:
+                add_to_dict_v3(output, output_dict)
+        if self.hidden:
+            for hidden in self.hidden:
+                hidden_list.append(hidden.value)
+
+        info = NodeInfoV3(
+            input=input_dict,
+            output=output_dict,
+            hidden=hidden_list,
+            name=self.node_id,
+            display_name=self.display_name,
+            description=self.description,
+            category=self.category,
+            output_node=self.is_output_node,
+            deprecated=self.is_deprecated,
+            experimental=self.is_experimental,
+            dev_only=self.is_dev_only,
+            api_node=self.is_api_node,
+            python_module=getattr(cls, "RELATIVE_PYTHON_MODULE", "nodes"),
+            price_badge=self.price_badge.as_dict(self.inputs) if self.price_badge is not None else None,
+        )
+        return info
+
 def get_finalized_class_inputs(d: dict[str, Any], live_inputs: dict[str, Any], include_hidden=False) -> tuple[dict[str, Any], V3Data]:
     out_dict = {
         "required": {},
@@ -1550,6 +1585,9 @@ def add_to_dict_v1(i: Input, d: dict):
     as_dict.pop("optional", None)
     d.setdefault(key, {})[i.id] = (i.get_io_type(), as_dict)
 
+def add_to_dict_v3(io: Input | Output, d: dict):
+    d[io.id] = (io.get_io_type(), io.as_dict())
+
 class DynamicPathsDefaultValue:
     EMPTY_DICT = "empty_dict"
 
@@ -1710,6 +1748,13 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
         # set hidden
         type_clone.hidden = HiddenHolder.from_v3_data(v3_data)
         return type_clone
+
+    @final
+    @classmethod
+    def GET_NODE_INFO_V3(cls) -> dict[str, Any]:
+        schema = cls.GET_SCHEMA()
+        info = schema.get_v3_info(cls)
+        return asdict(info)
     #############################################
     # V1 Backwards Compatibility code
     #--------------------------------------------
@@ -2054,7 +2099,6 @@ __all__ = [
     "AnyType",
     "MultiType",
     "Tracks",
-    "Color",
     # Dynamic Types
     "MatchType",
     "DynamicCombo",
@@ -2063,10 +2107,12 @@ __all__ = [
     "HiddenHolder",
     "Hidden",
     "NodeInfoV1",
+    "NodeInfoV3",
     "Schema",
     "ComfyNode",
     "NodeOutput",
     "add_to_dict_v1",
+    "add_to_dict_v3",
     "V3Data",
     "ImageCompare",
     "PriceBadgeDepends",

comfy_api/latest/_ui.py

@@ -146,7 +146,8 @@ class ImageSaveHelper:
         metadata = ImageSaveHelper._create_png_metadata(cls)
         for batch_number, image_tensor in enumerate(images):
             img = ImageSaveHelper._convert_tensor_to_pil(image_tensor)
-            file = folder_paths.format_output_filename(filename, counter, "png", batch_num=str(batch_number))
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.png"
             img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level)
             results.append(SavedResult(file, subfolder, folder_type))
             counter += 1
@@ -175,7 +176,7 @@ class ImageSaveHelper:
         )
         pil_images = [ImageSaveHelper._convert_tensor_to_pil(img) for img in images]
         metadata = ImageSaveHelper._create_animated_png_metadata(cls)
-        file = folder_paths.format_output_filename(filename, counter, "png")
+        file = f"{filename}_{counter:05}_.png"
         save_path = os.path.join(full_output_folder, file)
         pil_images[0].save(
             save_path,
@@ -219,7 +220,7 @@ class ImageSaveHelper:
         )
         pil_images = [ImageSaveHelper._convert_tensor_to_pil(img) for img in images]
         pil_exif = ImageSaveHelper._create_webp_metadata(pil_images[0], cls)
-        file = folder_paths.format_output_filename(filename, counter, "webp")
+        file = f"{filename}_{counter:05}_.webp"
         pil_images[0].save(
             os.path.join(full_output_folder, file),
             save_all=True,
@@ -283,7 +284,8 @@ class AudioSaveHelper:
 
         results = []
         for batch_number, waveform in enumerate(audio["waveform"].cpu()):
-            file = folder_paths.format_output_filename(filename, counter, format, batch_num=str(batch_number))
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.{format}"
             output_path = os.path.join(full_output_folder, file)
 
             # Use original sample rate initially

comfy_api_nodes/apis/recraft.py

@@ -1,8 +1,11 @@
 from __future__ import annotations
 
-from enum import Enum
 
-from pydantic import BaseModel, Field
+
+from enum import Enum
+from typing import Optional
+
+from pydantic import BaseModel, Field, conint, confloat
 
 
 class RecraftColor:
@@ -226,24 +229,24 @@ class RecraftColorObject(BaseModel):
 
 
 class RecraftControlsObject(BaseModel):
-    colors: list[RecraftColorObject] | None = Field(None, description='An array of preferable colors')
-    background_color: RecraftColorObject | None = Field(None, description='Use given color as a desired background color')
-    no_text: bool | None = Field(None, description='Do not embed text layouts')
-    artistic_level: int | None = Field(None, description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity. The value should be in range [0..5].')
+    colors: Optional[list[RecraftColorObject]] = Field(None, description='An array of preferable colors')
+    background_color: Optional[RecraftColorObject] = Field(None, description='Use given color as a desired background color')
+    no_text: Optional[bool] = Field(None, description='Do not embed text layouts')
+    artistic_level: Optional[conint(ge=0, le=5)] = Field(None, description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity. The value should be in range [0..5].')
 
 
 class RecraftImageGenerationRequest(BaseModel):
     prompt: str = Field(..., description='The text prompt describing the image to generate')
-    size: RecraftImageSize | None = Field(None, description='The size of the generated image (e.g., "1024x1024")')
-    n: int = Field(..., description='The number of images to generate')
-    negative_prompt: str | None = Field(None, description='A text description of undesired elements on an image')
-    model: RecraftModel | None = Field(RecraftModel.recraftv3, description='The model to use for generation (e.g., "recraftv3")')
-    style: str | None = Field(None, description='The style to apply to the generated image (e.g., "digital_illustration")')
-    substyle: str | None = Field(None, description='The substyle to apply to the generated image, depending on the style input')
-    controls: RecraftControlsObject | None = Field(None, description='A set of custom parameters to tweak generation process')
-    style_id: str | None = Field(None, description='Use a previously uploaded style as a reference; UUID')
-    strength: float | None = Field(None, description='Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity')
-    random_seed: int | None = Field(None, description="Seed for video generation")
+    size: Optional[RecraftImageSize] = Field(None, description='The size of the generated image (e.g., "1024x1024")')
+    n: conint(ge=1, le=6) = Field(..., description='The number of images to generate')
+    negative_prompt: Optional[str] = Field(None, description='A text description of undesired elements on an image')
+    model: Optional[RecraftModel] = Field(RecraftModel.recraftv3, description='The model to use for generation (e.g., "recraftv3")')
+    style: Optional[str] = Field(None, description='The style to apply to the generated image (e.g., "digital_illustration")')
+    substyle: Optional[str] = Field(None, description='The substyle to apply to the generated image, depending on the style input')
+    controls: Optional[RecraftControlsObject] = Field(None, description='A set of custom parameters to tweak generation process')
+    style_id: Optional[str] = Field(None, description='Use a previously uploaded style as a reference; UUID')
+    strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None, description='Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity')
+    random_seed: Optional[int] = Field(None, description="Seed for video generation")
     # text_layout
 
 
@@ -255,13 +258,5 @@ class RecraftReturnedObject(BaseModel):
 class RecraftImageGenerationResponse(BaseModel):
     created: int = Field(..., description='Unix timestamp when the generation was created')
     credits: int = Field(..., description='Number of credits used for the generation')
-    data: list[RecraftReturnedObject] | None = Field(None, description='Array of generated image information')
-    image: RecraftReturnedObject | None = Field(None, description='Single generated image')
-
-
-class RecraftCreateStyleRequest(BaseModel):
-    style: str = Field(..., description="realistic_image, digital_illustration, vector_illustration, or icon")
-
-
-class RecraftCreateStyleResponse(BaseModel):
-    id: str = Field(..., description="UUID of the created style")
+    data: Optional[list[RecraftReturnedObject]] = Field(None, description='Array of generated image information')
+    image: Optional[RecraftReturnedObject] = Field(None, description='Single generated image')

comfy_api_nodes/apis/vidu.py

@@ -6,30 +6,6 @@ class SubjectReference(BaseModel):
     images: list[str] = Field(...)
 
 
-class FrameSetting(BaseModel):
-    prompt: str = Field(...)
-    key_image: str = Field(...)
-    duration: int = Field(...)
-
-
-class TaskMultiFrameCreationRequest(BaseModel):
-    model: str = Field(...)
-    seed: int = Field(..., ge=0, le=2147483647)
-    resolution: str = Field(...)
-    start_image: str = Field(...)
-    image_settings: list[FrameSetting] = Field(...)
-
-
-class TaskExtendCreationRequest(BaseModel):
-    model: str = Field(...)
-    prompt: str = Field(..., max_length=2000)
-    duration: int = Field(...)
-    seed: int = Field(..., ge=0, le=2147483647)
-    resolution: str = Field(...)
-    images: list[str] | None = Field(None, description="Base64 encoded string or image URL")
-    video_url: str = Field(..., description="URL of the video to extend")
-
-
 class TaskCreationRequest(BaseModel):
     model: str = Field(...)
     prompt: str = Field(..., max_length=2000)

comfy_api_nodes/nodes_recraft.py

@@ -12,8 +12,6 @@ from comfy_api_nodes.apis.recraft import (
     RecraftColor,
     RecraftColorChain,
     RecraftControls,
-    RecraftCreateStyleRequest,
-    RecraftCreateStyleResponse,
     RecraftImageGenerationRequest,
     RecraftImageGenerationResponse,
     RecraftImageSize,
@@ -325,75 +323,6 @@ class RecraftStyleInfiniteStyleLibrary(IO.ComfyNode):
         return IO.NodeOutput(RecraftStyle(style_id=style_id))
 
 
-class RecraftCreateStyleNode(IO.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="RecraftCreateStyleNode",
-            display_name="Recraft Create Style",
-            category="api node/image/Recraft",
-            description="Create a custom style from reference images. "
-            "Upload 1-5 images to use as style references. "
-            "Total size of all images is limited to 5 MB.",
-            inputs=[
-                IO.Combo.Input(
-                    "style",
-                    options=["realistic_image", "digital_illustration"],
-                    tooltip="The base style of the generated images.",
-                ),
-                IO.Autogrow.Input(
-                    "images",
-                    template=IO.Autogrow.TemplatePrefix(
-                        IO.Image.Input("image"),
-                        prefix="image",
-                        min=1,
-                        max=5,
-                    ),
-                ),
-            ],
-            outputs=[
-                IO.String.Output(display_name="style_id"),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                expr="""{"type":"usd","usd": 0.04}""",
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        style: str,
-        images: IO.Autogrow.Type,
-    ) -> IO.NodeOutput:
-        files = []
-        total_size = 0
-        max_total_size = 5 * 1024 * 1024  # 5 MB limit
-        for i, img in enumerate(list(images.values())):
-            file_bytes = tensor_to_bytesio(img, total_pixels=2048 * 2048, mime_type="image/webp").read()
-            total_size += len(file_bytes)
-            if total_size > max_total_size:
-                raise Exception("Total size of all images exceeds 5 MB limit.")
-            files.append((f"file{i + 1}", file_bytes))
-
-        response = await sync_op(
-            cls,
-            endpoint=ApiEndpoint(path="/proxy/recraft/styles", method="POST"),
-            response_model=RecraftCreateStyleResponse,
-            files=files,
-            data=RecraftCreateStyleRequest(style=style),
-            content_type="multipart/form-data",
-            max_retries=1,
-        )
-
-        return IO.NodeOutput(response.id)
-
-
 class RecraftTextToImageNode(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -466,7 +395,7 @@ class RecraftTextToImageNode(IO.ComfyNode):
         negative_prompt: str = None,
         recraft_controls: RecraftControls = None,
     ) -> IO.NodeOutput:
-        validate_string(prompt, strip_whitespace=False, min_length=1, max_length=1000)
+        validate_string(prompt, strip_whitespace=False, max_length=1000)
         default_style = RecraftStyle(RecraftStyleV3.realistic_image)
         if recraft_style is None:
             recraft_style = default_style
@@ -1095,7 +1024,6 @@ class RecraftExtension(ComfyExtension):
             RecraftStyleV3DigitalIllustrationNode,
             RecraftStyleV3LogoRasterNode,
             RecraftStyleInfiniteStyleLibrary,
-            RecraftCreateStyleNode,
             RecraftColorRGBNode,
             RecraftControlsNode,
         ]

comfy_api_nodes/nodes_vidu.py

@@ -2,12 +2,9 @@ from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input
 from comfy_api_nodes.apis.vidu import (
-    FrameSetting,
     SubjectReference,
     TaskCreationRequest,
     TaskCreationResponse,
-    TaskExtendCreationRequest,
-    TaskMultiFrameCreationRequest,
     TaskResult,
     TaskStatusResponse,
 )
@@ -17,14 +14,11 @@ from comfy_api_nodes.util import (
     get_number_of_images,
     poll_op,
     sync_op,
-    upload_image_to_comfyapi,
     upload_images_to_comfyapi,
-    upload_video_to_comfyapi,
     validate_image_aspect_ratio,
     validate_image_dimensions,
     validate_images_aspect_ratio_closeness,
     validate_string,
-    validate_video_duration,
 )
 
 VIDU_TEXT_TO_VIDEO = "/proxy/vidu/text2video"
@@ -37,8 +31,7 @@ VIDU_GET_GENERATION_STATUS = "/proxy/vidu/tasks/%s/creations"
 async def execute_task(
     cls: type[IO.ComfyNode],
     vidu_endpoint: str,
-    payload: TaskCreationRequest | TaskExtendCreationRequest | TaskMultiFrameCreationRequest,
-    max_poll_attempts: int = 320,
+    payload: TaskCreationRequest,
 ) -> list[TaskResult]:
     task_creation_response = await sync_op(
         cls,
@@ -54,7 +47,7 @@ async def execute_task(
         response_model=TaskStatusResponse,
         status_extractor=lambda r: r.state,
         progress_extractor=lambda r: r.progress,
-        max_poll_attempts=max_poll_attempts,
+        max_poll_attempts=320,
     )
     if not response.creations:
         raise RuntimeError(
@@ -947,540 +940,6 @@ class Vidu2StartEndToVideoNode(IO.ComfyNode):
         return IO.NodeOutput(await download_url_to_video_output(results[0].url))
 
 
-class ViduExtendVideoNode(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="ViduExtendVideoNode",
-            display_name="Vidu Video Extension",
-            category="api node/video/Vidu",
-            description="Extend an existing video by generating additional frames.",
-            inputs=[
-                IO.DynamicCombo.Input(
-                    "model",
-                    options=[
-                        IO.DynamicCombo.Option(
-                            "viduq2-pro",
-                            [
-                                IO.Int.Input(
-                                    "duration",
-                                    default=4,
-                                    min=1,
-                                    max=7,
-                                    step=1,
-                                    display_mode=IO.NumberDisplay.slider,
-                                    tooltip="Duration of the extended video in seconds.",
-                                ),
-                                IO.Combo.Input(
-                                    "resolution",
-                                    options=["720p", "1080p"],
-                                    tooltip="Resolution of the output video.",
-                                ),
-                            ],
-                        ),
-                        IO.DynamicCombo.Option(
-                            "viduq2-turbo",
-                            [
-                                IO.Int.Input(
-                                    "duration",
-                                    default=4,
-                                    min=1,
-                                    max=7,
-                                    step=1,
-                                    display_mode=IO.NumberDisplay.slider,
-                                    tooltip="Duration of the extended video in seconds.",
-                                ),
-                                IO.Combo.Input(
-                                    "resolution",
-                                    options=["720p", "1080p"],
-                                    tooltip="Resolution of the output video.",
-                                ),
-                            ],
-                        ),
-                    ],
-                    tooltip="Model to use for video extension.",
-                ),
-                IO.Video.Input(
-                    "video",
-                    tooltip="The source video to extend.",
-                ),
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="An optional text prompt for the extended video (max 2000 characters).",
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=1,
-                    min=0,
-                    max=2147483647,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    control_after_generate=True,
-                ),
-                IO.Image.Input("end_frame", optional=True),
-            ],
-            outputs=[
-                IO.Video.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.duration", "model.resolution"]),
-                expr="""
-                (
-                  $m := widgets.model;
-                  $d := $lookup(widgets, "model.duration");
-                  $res := $lookup(widgets, "model.resolution");
-                  $contains($m, "pro")
-                    ? (
-                        $base := $lookup({"720p": 0.15, "1080p": 0.3}, $res);
-                        $perSec := $lookup({"720p": 0.05, "1080p": 0.075}, $res);
-                        {"type":"usd","usd": $base + $perSec * ($d - 1)}
-                      )
-                    : (
-                        $base := $lookup({"720p": 0.075, "1080p": 0.2}, $res);
-                        $perSec := $lookup({"720p": 0.025, "1080p": 0.05}, $res);
-                        {"type":"usd","usd": $base + $perSec * ($d - 1)}
-                      )
-                )
-                """,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        model: dict,
-        video: Input.Video,
-        prompt: str,
-        seed: int,
-        end_frame: Input.Image | None = None,
-    ) -> IO.NodeOutput:
-        validate_string(prompt, max_length=2000)
-        validate_video_duration(video, min_duration=4, max_duration=55)
-        image_url = None
-        if end_frame is not None:
-            validate_image_aspect_ratio(end_frame, (1, 4), (4, 1))
-            validate_image_dimensions(end_frame, min_width=128, min_height=128)
-            image_url = await upload_image_to_comfyapi(cls, end_frame, wait_label="Uploading end frame")
-        results = await execute_task(
-            cls,
-            "/proxy/vidu/extend",
-            TaskExtendCreationRequest(
-                model=model["model"],
-                prompt=prompt,
-                duration=model["duration"],
-                seed=seed,
-                resolution=model["resolution"],
-                video_url=await upload_video_to_comfyapi(cls, video, wait_label="Uploading video"),
-                images=[image_url] if image_url else None,
-            ),
-            max_poll_attempts=480,
-        )
-        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
-
-
-def _generate_frame_inputs(count: int) -> list:
-    """Generate input widgets for a given number of frames."""
-    inputs = []
-    for i in range(1, count + 1):
-        inputs.extend(
-            [
-                IO.String.Input(
-                    f"prompt{i}",
-                    multiline=True,
-                    default="",
-                    tooltip=f"Text prompt for frame {i} transition.",
-                ),
-                IO.Image.Input(
-                    f"end_image{i}",
-                    tooltip=f"End frame image for segment {i}. Aspect ratio must be between 1:4 and 4:1.",
-                ),
-                IO.Int.Input(
-                    f"duration{i}",
-                    default=4,
-                    min=2,
-                    max=7,
-                    step=1,
-                    display_mode=IO.NumberDisplay.slider,
-                    tooltip=f"Duration for segment {i} in seconds.",
-                ),
-            ]
-        )
-    return inputs
-
-
-class ViduMultiFrameVideoNode(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="ViduMultiFrameVideoNode",
-            display_name="Vidu Multi-Frame Video Generation",
-            category="api node/video/Vidu",
-            description="Generate a video with multiple keyframe transitions.",
-            inputs=[
-                IO.Combo.Input("model", options=["viduq2-pro", "viduq2-turbo"]),
-                IO.Image.Input(
-                    "start_image",
-                    tooltip="The starting frame image. Aspect ratio must be between 1:4 and 4:1.",
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=1,
-                    min=0,
-                    max=2147483647,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    control_after_generate=True,
-                ),
-                IO.Combo.Input("resolution", options=["720p", "1080p"]),
-                IO.DynamicCombo.Input(
-                    "frames",
-                    options=[
-                        IO.DynamicCombo.Option("2", _generate_frame_inputs(2)),
-                        IO.DynamicCombo.Option("3", _generate_frame_inputs(3)),
-                        IO.DynamicCombo.Option("4", _generate_frame_inputs(4)),
-                        IO.DynamicCombo.Option("5", _generate_frame_inputs(5)),
-                        IO.DynamicCombo.Option("6", _generate_frame_inputs(6)),
-                        IO.DynamicCombo.Option("7", _generate_frame_inputs(7)),
-                        IO.DynamicCombo.Option("8", _generate_frame_inputs(8)),
-                        IO.DynamicCombo.Option("9", _generate_frame_inputs(9)),
-                    ],
-                    tooltip="Number of keyframe transitions (2-9).",
-                ),
-            ],
-            outputs=[
-                IO.Video.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(
-                    widgets=[
-                        "model",
-                        "resolution",
-                        "frames",
-                        "frames.duration1",
-                        "frames.duration2",
-                        "frames.duration3",
-                        "frames.duration4",
-                        "frames.duration5",
-                        "frames.duration6",
-                        "frames.duration7",
-                        "frames.duration8",
-                        "frames.duration9",
-                    ]
-                ),
-                expr="""
-                (
-                  $m := widgets.model;
-                  $n := $number(widgets.frames);
-                  $is1080 := widgets.resolution = "1080p";
-                  $d1 := $lookup(widgets, "frames.duration1");
-                  $d2 := $lookup(widgets, "frames.duration2");
-                  $d3 := $n >= 3 ? $lookup(widgets, "frames.duration3") : 0;
-                  $d4 := $n >= 4 ? $lookup(widgets, "frames.duration4") : 0;
-                  $d5 := $n >= 5 ? $lookup(widgets, "frames.duration5") : 0;
-                  $d6 := $n >= 6 ? $lookup(widgets, "frames.duration6") : 0;
-                  $d7 := $n >= 7 ? $lookup(widgets, "frames.duration7") : 0;
-                  $d8 := $n >= 8 ? $lookup(widgets, "frames.duration8") : 0;
-                  $d9 := $n >= 9 ? $lookup(widgets, "frames.duration9") : 0;
-                  $totalDuration := $d1 + $d2 + $d3 + $d4 + $d5 + $d6 + $d7 + $d8 + $d9;
-                  $contains($m, "pro")
-                    ? (
-                        $base := $is1080 ? 0.3 : 0.15;
-                        $perSec := $is1080 ? 0.075 : 0.05;
-                        {"type":"usd","usd": $n * $base + $perSec * $totalDuration}
-                      )
-                    : (
-                        $base := $is1080 ? 0.2 : 0.075;
-                        $perSec := $is1080 ? 0.05 : 0.025;
-                        {"type":"usd","usd": $n * $base + $perSec * $totalDuration}
-                      )
-                )
-                """,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        model: str,
-        start_image: Input.Image,
-        seed: int,
-        resolution: str,
-        frames: dict,
-    ) -> IO.NodeOutput:
-        validate_image_aspect_ratio(start_image, (1, 4), (4, 1))
-        frame_count = int(frames["frames"])
-        image_settings: list[FrameSetting] = []
-        for i in range(1, frame_count + 1):
-            validate_image_aspect_ratio(frames[f"end_image{i}"], (1, 4), (4, 1))
-            validate_string(frames[f"prompt{i}"], max_length=2000)
-        start_image_url = await upload_image_to_comfyapi(
-            cls,
-            start_image,
-            mime_type="image/png",
-            wait_label="Uploading start image",
-        )
-        for i in range(1, frame_count + 1):
-            image_settings.append(
-                FrameSetting(
-                    prompt=frames[f"prompt{i}"],
-                    key_image=await upload_image_to_comfyapi(
-                        cls,
-                        frames[f"end_image{i}"],
-                        mime_type="image/png",
-                        wait_label=f"Uploading end image({i})",
-                    ),
-                    duration=frames[f"duration{i}"],
-                )
-            )
-        results = await execute_task(
-            cls,
-            "/proxy/vidu/multiframe",
-            TaskMultiFrameCreationRequest(
-                model=model,
-                seed=seed,
-                resolution=resolution,
-                start_image=start_image_url,
-                image_settings=image_settings,
-            ),
-            max_poll_attempts=480 * frame_count,
-        )
-        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
-
-
-class Vidu3TextToVideoNode(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="Vidu3TextToVideoNode",
-            display_name="Vidu Q3 Text-to-Video Generation",
-            category="api node/video/Vidu",
-            description="Generate video from a text prompt.",
-            inputs=[
-                IO.DynamicCombo.Input(
-                    "model",
-                    options=[
-                        IO.DynamicCombo.Option(
-                            "viduq3-pro",
-                            [
-                                IO.Combo.Input(
-                                    "aspect_ratio",
-                                    options=["16:9", "9:16", "3:4", "4:3", "1:1"],
-                                    tooltip="The aspect ratio of the output video.",
-                                ),
-                                IO.Combo.Input(
-                                    "resolution",
-                                    options=["720p", "1080p"],
-                                    tooltip="Resolution of the output video.",
-                                ),
-                                IO.Int.Input(
-                                    "duration",
-                                    default=5,
-                                    min=1,
-                                    max=16,
-                                    step=1,
-                                    display_mode=IO.NumberDisplay.slider,
-                                    tooltip="Duration of the output video in seconds.",
-                                ),
-                                IO.Boolean.Input(
-                                    "audio",
-                                    default=False,
-                                    tooltip="When enabled, outputs video with sound "
-                                    "(including dialogue and sound effects).",
-                                ),
-                            ],
-                        ),
-                    ],
-                    tooltip="Model to use for video generation.",
-                ),
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    tooltip="A textual description for video generation, with a maximum length of 2000 characters.",
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=1,
-                    min=0,
-                    max=2147483647,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    control_after_generate=True,
-                ),
-            ],
-            outputs=[
-                IO.Video.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model.duration", "model.resolution"]),
-                expr="""
-                (
-                  $res := $lookup(widgets, "model.resolution");
-                  $base := $lookup({"720p": 0.075, "1080p": 0.1}, $res);
-                  $perSec := $lookup({"720p": 0.025, "1080p": 0.05}, $res);
-                  {"type":"usd","usd": $base + $perSec * ($lookup(widgets, "model.duration") - 1)}
-                )
-                """,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        model: dict,
-        prompt: str,
-        seed: int,
-    ) -> IO.NodeOutput:
-        validate_string(prompt, min_length=1, max_length=2000)
-        results = await execute_task(
-            cls,
-            VIDU_TEXT_TO_VIDEO,
-            TaskCreationRequest(
-                model=model["model"],
-                prompt=prompt,
-                duration=model["duration"],
-                seed=seed,
-                aspect_ratio=model["aspect_ratio"],
-                resolution=model["resolution"],
-                audio=model["audio"],
-            ),
-            max_poll_attempts=640,
-        )
-        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
-
-
-class Vidu3ImageToVideoNode(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="Vidu3ImageToVideoNode",
-            display_name="Vidu Q3 Image-to-Video Generation",
-            category="api node/video/Vidu",
-            description="Generate a video from an image and an optional prompt.",
-            inputs=[
-                IO.DynamicCombo.Input(
-                    "model",
-                    options=[
-                        IO.DynamicCombo.Option(
-                            "viduq3-pro",
-                            [
-                                IO.Combo.Input(
-                                    "resolution",
-                                    options=["720p", "1080p", "2K"],
-                                    tooltip="Resolution of the output video.",
-                                ),
-                                IO.Int.Input(
-                                    "duration",
-                                    default=5,
-                                    min=1,
-                                    max=16,
-                                    step=1,
-                                    display_mode=IO.NumberDisplay.slider,
-                                    tooltip="Duration of the output video in seconds.",
-                                ),
-                                IO.Boolean.Input(
-                                    "audio",
-                                    default=False,
-                                    tooltip="When enabled, outputs video with sound "
-                                    "(including dialogue and sound effects).",
-                                ),
-                            ],
-                        ),
-                    ],
-                    tooltip="Model to use for video generation.",
-                ),
-                IO.Image.Input(
-                    "image",
-                    tooltip="An image to be used as the start frame of the generated video.",
-                ),
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="An optional text prompt for video generation (max 2000 characters).",
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=1,
-                    min=0,
-                    max=2147483647,
-                    step=1,
-                    display_mode=IO.NumberDisplay.number,
-                    control_after_generate=True,
-                ),
-            ],
-            outputs=[
-                IO.Video.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model.duration", "model.resolution"]),
-                expr="""
-                (
-                  $res := $lookup(widgets, "model.resolution");
-                  $base := $lookup({"720p": 0.075, "1080p": 0.275, "2k": 0.35}, $res);
-                  $perSec := $lookup({"720p": 0.05, "1080p": 0.075, "2k": 0.075}, $res);
-                  {"type":"usd","usd": $base + $perSec * ($lookup(widgets, "model.duration") - 1)}
-                )
-                """,
-            ),
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        model: dict,
-        image: Input.Image,
-        prompt: str,
-        seed: int,
-    ) -> IO.NodeOutput:
-        validate_image_aspect_ratio(image, (1, 4), (4, 1))
-        validate_string(prompt, max_length=2000)
-        results = await execute_task(
-            cls,
-            VIDU_IMAGE_TO_VIDEO,
-            TaskCreationRequest(
-                model=model["model"],
-                prompt=prompt,
-                duration=model["duration"],
-                seed=seed,
-                resolution=model["resolution"],
-                audio=model["audio"],
-                images=[await upload_image_to_comfyapi(cls, image)],
-            ),
-            max_poll_attempts=720,
-        )
-        return IO.NodeOutput(await download_url_to_video_output(results[0].url))
-
-
 class ViduExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -1493,10 +952,6 @@ class ViduExtension(ComfyExtension):
             Vidu2ImageToVideoNode,
             Vidu2ReferenceVideoNode,
             Vidu2StartEndToVideoNode,
-            ViduExtendVideoNode,
-            ViduMultiFrameVideoNode,
-            Vidu3TextToVideoNode,
-            Vidu3ImageToVideoNode,
         ]
 
 

comfy_execution/jobs.py

@@ -171,10 +171,9 @@ def get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
                 continue
 
             for item in items:
-                count += 1
-
                 if not isinstance(item, dict):
                     continue
+                count += 1
 
                 if preview_output is None and is_previewable(media_type, item):
                     enriched = {

comfy_extras/nodes_color.py

@@ -1,42 +0,0 @@
-from typing_extensions import override
-from comfy_api.latest import ComfyExtension, io
-
-
-class ColorToRGBInt(io.ComfyNode):
-    @classmethod
-    def define_schema(cls) -> io.Schema:
-        return io.Schema(
-            node_id="ColorToRGBInt",
-            display_name="Color to RGB Int",
-            category="utils",
-            description="Convert a color to a RGB integer value.",
-            inputs=[
-                io.Color.Input("color"),
-            ],
-            outputs=[
-                io.Int.Output(display_name="rgb_int"),
-            ],
-        )
-
-    @classmethod
-    def execute(
-        cls,
-        color: str,
-    ) -> io.NodeOutput:
-        # expect format #RRGGBB
-        if len(color) != 7 or color[0] != "#":
-            raise ValueError("Color must be in format #RRGGBB")
-        r = int(color[1:3], 16)
-        g = int(color[3:5], 16)
-        b = int(color[5:7], 16)
-        return io.NodeOutput(r * 256 * 256 + g * 256 + b)
-
-
-class ColorExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-        return [ColorToRGBInt]
-
-
-async def comfy_entrypoint() -> ColorExtension:
-    return ColorExtension()

comfy_extras/nodes_hunyuan.py

@@ -56,7 +56,7 @@ class EmptyHunyuanLatentVideo(io.ComfyNode):
     @classmethod
     def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
         latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-        return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 8})
+        return io.NodeOutput({"samples":latent})
 
     generate = execute  # TODO: remove
 
@@ -73,7 +73,7 @@ class EmptyHunyuanVideo15Latent(EmptyHunyuanLatentVideo):
     def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
         # Using scale factor of 16 instead of 8
         latent = torch.zeros([batch_size, 32, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device())
-        return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 16})
+        return io.NodeOutput({"samples": latent})
 
 
 class HunyuanVideo15ImageToVideo(io.ComfyNode):

comfy_extras/nodes_hunyuan3d.py

@@ -642,7 +642,7 @@ class SaveGLB(IO.ComfyNode):
                     metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x])
 
         for i in range(mesh.vertices.shape[0]):
-            f = folder_paths.format_output_filename(filename, counter, "glb")
+            f = f"{filename}_{counter:05}_.glb"
             save_glb(mesh.vertices[i], mesh.faces[i], os.path.join(full_output_folder, f), metadata)
             results.append({
                 "filename": f,

comfy_extras/nodes_images.py

@@ -460,7 +460,8 @@ class SaveSVGNode(IO.ComfyNode):
 
 
         for batch_number, svg_bytes in enumerate(svg.data):
-            file = folder_paths.format_output_filename(filename, counter, "svg", batch_num=str(batch_number))
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.svg"
 
             # Read SVG content
             svg_bytes.seek(0)

comfy_extras/nodes_lora_extract.py

@@ -115,7 +115,7 @@ class LoraSave(io.ComfyNode):
         if text_encoder_diff is not None:
             output_sd = calc_lora_model(text_encoder_diff.patcher, rank, "", "text_encoders.", output_sd, lora_type, bias_diff=bias_diff)
 
-        output_checkpoint = folder_paths.format_output_filename(filename, counter, "safetensors")
+        output_checkpoint = f"{filename}_{counter:05}_.safetensors"
         output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
 
         comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None)

comfy_extras/nodes_model_merging.py

@@ -221,7 +221,7 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi
             for x in extra_pnginfo:
                 metadata[x] = json.dumps(extra_pnginfo[x])
 
-    output_checkpoint = folder_paths.format_output_filename(filename, counter, "safetensors")
+    output_checkpoint = f"{filename}_{counter:05}_.safetensors"
     output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
 
     comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata, extra_keys=extra_keys)
@@ -297,7 +297,7 @@ class CLIPSave:
 
             full_output_folder, filename, counter, subfolder, filename_prefix_ = folder_paths.get_save_image_path(filename_prefix_, self.output_dir)
 
-            output_checkpoint = folder_paths.format_output_filename(filename, counter, "safetensors")
+            output_checkpoint = f"{filename}_{counter:05}_.safetensors"
             output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
 
             current_clip_sd = comfy.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix)
@@ -333,7 +333,7 @@ class VAESave:
                 for x in extra_pnginfo:
                     metadata[x] = json.dumps(extra_pnginfo[x])
 
-        output_checkpoint = folder_paths.format_output_filename(filename, counter, "safetensors")
+        output_checkpoint = f"{filename}_{counter:05}_.safetensors"
         output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
 
         comfy.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata)

comfy_extras/nodes_train.py

@@ -1221,9 +1221,9 @@ class SaveLoRA(io.ComfyNode):
             folder_paths.get_save_image_path(prefix, output_dir)
         )
         if steps is None:
-            output_checkpoint = folder_paths.format_output_filename(filename, counter, "safetensors")
+            output_checkpoint = f"{filename}_{counter:05}_.safetensors"
         else:
-            output_checkpoint = folder_paths.format_output_filename(f"{filename}_{steps}_steps", counter, "safetensors")
+            output_checkpoint = f"{filename}_{steps}_steps_{counter:05}_.safetensors"
         output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
         safetensors.torch.save_file(lora, output_checkpoint)
         return io.NodeOutput()

comfy_extras/nodes_video.py

@@ -36,7 +36,7 @@ class SaveWEBM(io.ComfyNode):
             filename_prefix, folder_paths.get_output_directory(), images[0].shape[1], images[0].shape[0]
         )
 
-        file = folder_paths.format_output_filename(filename, counter, "webm")
+        file = f"{filename}_{counter:05}_.webm"
         container = av.open(os.path.join(full_output_folder, file), mode="w")
 
         if cls.hidden.prompt is not None:
@@ -102,7 +102,7 @@ class SaveVideo(io.ComfyNode):
                 metadata["prompt"] = cls.hidden.prompt
             if len(metadata) > 0:
                 saved_metadata = metadata
-        file = folder_paths.format_output_filename(filename, counter, Types.VideoContainer.get_extension(format))
+        file = f"{filename}_{counter:05}_.{Types.VideoContainer.get_extension(format)}"
         video.save_to(
             os.path.join(full_output_folder, file),
             format=Types.VideoContainer(format),

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.11.1"
+__version__ = "0.11.0"

folder_paths.py

@@ -4,7 +4,6 @@ import os
 import time
 import mimetypes
 import logging
-from datetime import datetime, timezone
 from typing import Literal, List
 from collections.abc import Collection
 
@@ -12,46 +11,6 @@ from comfy.cli_args import args
 
 supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.pt2', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
 
-
-def get_timestamp() -> str:
-    """Generate a filesystem-safe timestamp string for output filenames.
-
-    Returns a UTC timestamp in the format YYYYMMDD-HHMMSS-ffffff (microseconds)
-    which is human-readable, lexicographically sortable, and Windows-safe.
-    """
-    now = datetime.now(timezone.utc)
-    return now.strftime("%Y%m%d-%H%M%S-%f")
-
-
-def format_output_filename(
-    filename: str,
-    counter: int,
-    ext: str,
-    *,
-    batch_num: str | None = None,
-    timestamp: str | None = None,
-) -> str:
-    """Format an output filename with counter and timestamp for cache-busting.
-
-    Args:
-        filename: The base filename prefix
-        counter: The numeric counter for uniqueness
-        ext: The file extension (with or without leading dot)
-        batch_num: Optional batch number to replace %batch_num% placeholder
-        timestamp: Optional timestamp string (defaults to current UTC time)
-
-    Returns:
-        Formatted filename like: filename_00001_20260131-123456-789012_.ext
-    """
-    ext = ext.lstrip(".")
-    if timestamp is None:
-        timestamp = get_timestamp()
-
-    if batch_num is not None:
-        filename = filename.replace("%batch_num%", batch_num)
-
-    return f"{filename}_{counter:05}_{timestamp}_.{ext}"
-
 folder_names_and_paths: dict[str, tuple[list[str], set[str]]] = {}
 
 # --base-directory - Resets all default paths configured in folder_paths with a new base path

nodes.py

@@ -508,7 +508,7 @@ class SaveLatent:
                 for x in extra_pnginfo:
                     metadata[x] = json.dumps(extra_pnginfo[x])
 
-        file = folder_paths.format_output_filename(filename, counter, "latent")
+        file = f"{filename}_{counter:05}_.latent"
 
         results: list[FileLocator] = []
         results.append({
@@ -1667,7 +1667,8 @@ class SaveImage:
                     for x in extra_pnginfo:
                         metadata.add_text(x, json.dumps(extra_pnginfo[x]))
 
-            file = folder_paths.format_output_filename(filename, counter, "png", batch_num=str(batch_number))
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.png"
             img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
             results.append({
                 "filename": file,
@@ -2431,8 +2432,7 @@ async def init_builtin_extra_nodes():
         "nodes_wanmove.py",
         "nodes_image_compare.py",
         "nodes_zimage.py",
-        "nodes_lora_debug.py",
-        "nodes_color.py"
+        "nodes_lora_debug.py"
     ]
 
     import_failed = []

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.11.1"
+version = "0.11.0"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

server.py

@@ -656,7 +656,6 @@ class PromptServer():
             info = {}
             info['input'] = obj_class.INPUT_TYPES()
             info['input_order'] = {key: list(value.keys()) for (key, value) in obj_class.INPUT_TYPES().items()}
-            info['is_input_list'] = getattr(obj_class, "INPUT_IS_LIST", False)
             info['output'] = obj_class.RETURN_TYPES
             info['output_is_list'] = obj_class.OUTPUT_IS_LIST if hasattr(obj_class, 'OUTPUT_IS_LIST') else [False] * len(obj_class.RETURN_TYPES)
             info['output_name'] = obj_class.RETURN_NAMES if hasattr(obj_class, 'RETURN_NAMES') else info['output']

tests-assets/conftest.py

@@ -98,7 +98,7 @@ def comfy_url_and_proc(comfy_tmp_base_dir: Path, request: pytest.FixtureRequest)
     out_log = open(logs_dir / "stdout.log", "w", buffering=1)
     err_log = open(logs_dir / "stderr.log", "w", buffering=1)
 
-    comfy_root = Path(__file__).resolve().parent.parent.parent
+    comfy_root = Path(__file__).resolve().parent.parent
     if not (comfy_root / "main.py").is_file():
         raise FileNotFoundError(f"main.py not found under {comfy_root}")
 

tests-assets/requirements.txt

@@ -0,0 +1,2 @@
+pytest>=7.8.0
+blake3

tests-unit/folder_paths_test/output_filename_test.py

@@ -1,83 +0,0 @@
-"""Tests for folder_paths.format_output_filename and get_timestamp functions."""
-
-import sys
-import os
-import unittest
-
-# Add the ComfyUI root to the path for imports
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
-
-import folder_paths
-
-
-class TestGetTimestamp(unittest.TestCase):
-    """Tests for get_timestamp function."""
-
-    def test_returns_string(self):
-        """Should return a string."""
-        result = folder_paths.get_timestamp()
-        self.assertIsInstance(result, str)
-
-    def test_format_matches_expected_pattern(self):
-        """Should return format YYYYMMDD-HHMMSS-ffffff."""
-        result = folder_paths.get_timestamp()
-        # Pattern: 8 digits, hyphen, 6 digits, hyphen, 6 digits
-        pattern = r"^\d{8}-\d{6}-\d{6}$"
-        self.assertRegex(result, pattern)
-
-    def test_is_filesystem_safe(self):
-        """Should not contain characters that are unsafe for filenames."""
-        result = folder_paths.get_timestamp()
-        unsafe_chars = ['/', '\\', ':', '*', '?', '"', '<', '>', '|', ' ']
-        for char in unsafe_chars:
-            self.assertNotIn(char, result)
-
-
-class TestFormatOutputFilename(unittest.TestCase):
-    """Tests for format_output_filename function."""
-
-    def test_basic_format(self):
-        """Should format filename with counter and timestamp."""
-        result = folder_paths.format_output_filename("test", 1, "png")
-        # Pattern: test_00001_YYYYMMDD-HHMMSS-ffffff_.png
-        pattern = r"^test_00001_\d{8}-\d{6}-\d{6}_\.png$"
-        self.assertRegex(result, pattern)
-
-    def test_counter_padding(self):
-        """Should pad counter to 5 digits."""
-        result = folder_paths.format_output_filename("test", 42, "png")
-        self.assertIn("_00042_", result)
-
-    def test_extension_with_leading_dot(self):
-        """Should handle extension with leading dot."""
-        result = folder_paths.format_output_filename("test", 1, ".png")
-        self.assertTrue(result.endswith("_.png"))
-        self.assertNotIn("..png", result)
-
-    def test_extension_without_leading_dot(self):
-        """Should handle extension without leading dot."""
-        result = folder_paths.format_output_filename("test", 1, "webm")
-        self.assertTrue(result.endswith("_.webm"))
-
-    def test_batch_num_replacement(self):
-        """Should replace %batch_num% placeholder."""
-        result = folder_paths.format_output_filename("test_%batch_num%", 1, "png", batch_num="3")
-        self.assertIn("test_3_", result)
-        self.assertNotIn("%batch_num%", result)
-
-    def test_custom_timestamp(self):
-        """Should use provided timestamp instead of generating one."""
-        custom_ts = "20260101-120000-000000"
-        result = folder_paths.format_output_filename("test", 1, "png", timestamp=custom_ts)
-        self.assertIn(custom_ts, result)
-
-    def test_different_extensions(self):
-        """Should work with various extensions."""
-        extensions = ["png", "webp", "webm", "svg", "glb", "safetensors", "latent"]
-        for ext in extensions:
-            result = folder_paths.format_output_filename("test", 1, ext)
-            self.assertTrue(result.endswith(f"_.{ext}"))
-
-
-if __name__ == "__main__":
-    unittest.main()

tests-unit/requirements.txt

@@ -2,4 +2,3 @@ pytest>=7.8.0
 pytest-aiohttp
 pytest-asyncio
 websocket-client
-blake3