Register nodes_save_image_promotable.py in comfy_extras loader

comfy_extras uses an explicit allowlist (extras_files) rather than glob,
so new files must be listed there to be loaded at server startup.
Verified live via /object_info and end-to-end prompt execution.

.spectral.yaml

@@ -89,3 +89,12 @@ rules:
     then:
       field: description
       function: truthy
+
+overrides:
+  # /ws uses HTTP 101 (Switching Protocols) — a legitimate response for a
+  # WebSocket upgrade, but not a 2xx, so operation-success-response fires
+  # as a false positive. OpenAPI 3.x has no native WebSocket support.
+  - files:
+      - "openapi.yaml#/paths/~1ws"
+    rules:
+      operation-success-response: off

comfy/supported_models.py

@@ -1443,7 +1443,7 @@ class HiDreamO1(supported_models_base.BASE):
     }
 
     latent_format = latent_formats.HiDreamO1Pixel
-    memory_usage_factor = 0.6
+    memory_usage_factor = 0.033
     # fp16 not supported: LM MLP down_proj activations fp16 overflow, causing NaNs
     supported_inference_dtypes = [torch.bfloat16, torch.float32]
 

comfy/utils.py

@@ -1164,12 +1164,18 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_am
 
             o = out
             o_d = out_div
+            ps_view = ps
+            mask_view = mask
             for d in range(dims):
-                o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2])
-                o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2])
+                l = min(ps_view.shape[d + 2], o.shape[d + 2] - upscaled[d])
+                o = o.narrow(d + 2, upscaled[d], l)
+                o_d = o_d.narrow(d + 2, upscaled[d], l)
+                if l < ps_view.shape[d + 2]:
+                    ps_view = ps_view.narrow(d + 2, 0, l)
+                    mask_view = mask_view.narrow(d + 2, 0, l)
 
-            o.add_(ps * mask)
-            o_d.add_(mask)
+            o.add_(ps_view * mask_view)
+            o_d.add_(mask_view)
 
             if pbar is not None:
                 pbar.update(1)

comfy_api/latest/_util/geometry_types.py

@@ -12,9 +12,24 @@ class VOXEL:
 
 
 class MESH:
-    def __init__(self, vertices: torch.Tensor, faces: torch.Tensor):
-        self.vertices = vertices
-        self.faces = faces
+    def __init__(self, vertices: torch.Tensor, faces: torch.Tensor,
+                 uvs: torch.Tensor | None = None,
+                 vertex_colors: torch.Tensor | None = None,
+                 texture: torch.Tensor | None = None,
+                 vertex_counts: torch.Tensor | None = None,
+                 face_counts: torch.Tensor | None = None):
+
+        assert (vertex_counts is None) == (face_counts is None), \
+            "vertex_counts and face_counts must be provided together (both or neither)"
+        self.vertices = vertices            # vertices: (B, N, 3)
+        self.faces = faces                  # faces: (B, M, 3)
+        self.uvs = uvs                      # uvs: (B, N, 2)
+        self.vertex_colors = vertex_colors  # vertex_colors: (B, N, 3 or 4)
+        self.texture = texture              # texture: (B, H, W, 3)
+        # When vertices/faces are zero-padded to a common N/M across the batch (variable-size mesh batch),
+        # these hold the real per-item lengths (B,). None means rows are uniform and no slicing is needed.
+        self.vertex_counts = vertex_counts
+        self.face_counts = face_counts
 
 
 class File3D:

comfy_api_nodes/apis/anthropic.py

@@ -0,0 +1,75 @@
+from enum import Enum
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class AnthropicRole(str, Enum):
+    user = "user"
+    assistant = "assistant"
+
+
+class AnthropicTextContent(BaseModel):
+    type: Literal["text"] = "text"
+    text: str = Field(...)
+
+
+class AnthropicImageSourceBase64(BaseModel):
+    type: Literal["base64"] = "base64"
+    media_type: str = Field(..., description="MIME type of the image, e.g. image/png, image/jpeg")
+    data: str = Field(..., description="Base64-encoded image data")
+
+
+class AnthropicImageSourceUrl(BaseModel):
+    type: Literal["url"] = "url"
+    url: str = Field(...)
+
+
+class AnthropicImageContent(BaseModel):
+    type: Literal["image"] = "image"
+    source: AnthropicImageSourceBase64 | AnthropicImageSourceUrl = Field(...)
+
+
+class AnthropicMessage(BaseModel):
+    role: AnthropicRole = Field(...)
+    content: list[AnthropicTextContent | AnthropicImageContent] = Field(...)
+
+
+class AnthropicMessagesRequest(BaseModel):
+    model: str = Field(...)
+    messages: list[AnthropicMessage] = Field(...)
+    max_tokens: int = Field(..., ge=1)
+    system: str | None = Field(None, description="Top-level system prompt")
+    temperature: float | None = Field(None, ge=0.0, le=1.0)
+    top_p: float | None = Field(None, ge=0.0, le=1.0)
+    top_k: int | None = Field(None, ge=0)
+    stop_sequences: list[str] | None = Field(None)
+
+
+class AnthropicResponseTextBlock(BaseModel):
+    type: Literal["text"] = "text"
+    text: str = Field(...)
+
+
+class AnthropicCacheCreationUsage(BaseModel):
+    ephemeral_5m_input_tokens: int | None = Field(None)
+    ephemeral_1h_input_tokens: int | None = Field(None)
+
+
+class AnthropicMessagesUsage(BaseModel):
+    input_tokens: int | None = Field(None)
+    output_tokens: int | None = Field(None)
+    cache_creation_input_tokens: int | None = Field(None)
+    cache_read_input_tokens: int | None = Field(None)
+    cache_creation: AnthropicCacheCreationUsage | None = Field(None)
+
+
+class AnthropicMessagesResponse(BaseModel):
+    id: str | None = Field(None)
+    type: str | None = Field(None)
+    role: str | None = Field(None)
+    model: str | None = Field(None)
+    content: list[AnthropicResponseTextBlock] | None = Field(None)
+    stop_reason: str | None = Field(None)
+    stop_sequence: str | None = Field(None)
+    usage: AnthropicMessagesUsage | None = Field(None)

comfy_api_nodes/nodes_anthropic.py

@@ -0,0 +1,245 @@
+"""API Nodes for Anthropic Claude (Messages API). See: https://docs.anthropic.com/en/api/messages"""
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.anthropic import (
+    AnthropicImageContent,
+    AnthropicImageSourceUrl,
+    AnthropicMessage,
+    AnthropicMessagesRequest,
+    AnthropicMessagesResponse,
+    AnthropicRole,
+    AnthropicTextContent,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    get_number_of_images,
+    sync_op,
+    upload_images_to_comfyapi,
+    validate_string,
+)
+
+ANTHROPIC_MESSAGES_ENDPOINT = "/proxy/anthropic/v1/messages"
+ANTHROPIC_IMAGE_MAX_PIXELS = 1568 * 1568
+CLAUDE_MAX_IMAGES = 20
+
+CLAUDE_MODELS: dict[str, str] = {
+    "Opus 4.7": "claude-opus-4-7",
+    "Opus 4.6": "claude-opus-4-6",
+    "Sonnet 4.6": "claude-sonnet-4-6",
+    "Sonnet 4.5": "claude-sonnet-4-5-20250929",
+    "Haiku 4.5": "claude-haiku-4-5-20251001",
+}
+
+
+def _claude_model_inputs():
+    return [
+        IO.Int.Input(
+            "max_tokens",
+            default=16000,
+            min=32,
+            max=32000,
+            tooltip="Maximum number of tokens to generate before stopping.",
+            advanced=True,
+        ),
+        IO.Float.Input(
+            "temperature",
+            default=1.0,
+            min=0.0,
+            max=1.0,
+            step=0.01,
+            tooltip="Controls randomness. 0.0 is deterministic, 1.0 is most random.",
+            advanced=True,
+        ),
+    ]
+
+
+def _model_price_per_million(model: str) -> tuple[float, float] | None:
+    """Return (input_per_1M, output_per_1M) USD for a Claude model, or None if unknown."""
+    if "opus-4-7" in model or "opus-4-6" in model or "opus-4-5" in model:
+        return 5.0, 25.0
+    if "sonnet-4" in model:
+        return 3.0, 15.0
+    if "haiku-4-5" in model:
+        return 1.0, 5.0
+    return None
+
+
+def calculate_tokens_price(response: AnthropicMessagesResponse) -> float | None:
+    """Compute approximate USD price from response usage. Server-side billing is authoritative."""
+    if not response.usage or not response.model:
+        return None
+    rates = _model_price_per_million(response.model)
+    if rates is None:
+        return None
+    input_rate, output_rate = rates
+    input_tokens = response.usage.input_tokens or 0
+    output_tokens = response.usage.output_tokens or 0
+    cache_read = response.usage.cache_read_input_tokens or 0
+    cache_5m = 0
+    cache_1h = 0
+    if response.usage.cache_creation:
+        cache_5m = response.usage.cache_creation.ephemeral_5m_input_tokens or 0
+        cache_1h = response.usage.cache_creation.ephemeral_1h_input_tokens or 0
+    total = (
+        input_tokens * input_rate
+        + output_tokens * output_rate
+        + cache_read * input_rate * 0.1
+        + cache_5m * input_rate * 1.25
+        + cache_1h * input_rate * 2.0
+    )
+    return total / 1_000_000.0
+
+
+def _get_text_from_response(response: AnthropicMessagesResponse) -> str:
+    if not response.content:
+        return ""
+    return "\n".join(block.text for block in response.content if block.text)
+
+
+async def _build_image_content_blocks(
+    cls: type[IO.ComfyNode],
+    image_tensors: list[Input.Image],
+) -> list[AnthropicImageContent]:
+    urls = await upload_images_to_comfyapi(
+        cls,
+        image_tensors,
+        max_images=CLAUDE_MAX_IMAGES,
+        total_pixels=ANTHROPIC_IMAGE_MAX_PIXELS,
+        wait_label="Uploading reference images",
+    )
+    return [AnthropicImageContent(source=AnthropicImageSourceUrl(url=url)) for url in urls]
+
+
+class ClaudeNode(IO.ComfyNode):
+    """Generate text responses from an Anthropic Claude model."""
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ClaudeNode",
+            display_name="Anthropic Claude",
+            category="api node/text/Anthropic",
+            essentials_category="Text Generation",
+            description="Generate text responses with Anthropic's Claude models. "
+            "Provide a text prompt and optionally one or more images for multimodal context.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text input to the model.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[IO.DynamicCombo.Option(label, _claude_model_inputs()) for label in CLAUDE_MODELS],
+                    tooltip="The Claude model used to generate the response.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+                IO.Autogrow.Input(
+                    "images",
+                    template=IO.Autogrow.TemplateNames(
+                        IO.Image.Input("image"),
+                        names=[f"image_{i}" for i in range(1, CLAUDE_MAX_IMAGES + 1)],
+                        min=0,
+                    ),
+                    tooltip=f"Optional image(s) to use as context for the model. Up to {CLAUDE_MAX_IMAGES} images.",
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default="",
+                    optional=True,
+                    advanced=True,
+                    tooltip="Foundational instructions that dictate the model's behavior.",
+                ),
+            ],
+            outputs=[IO.String.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"]),
+                expr="""
+                (
+                  $m := widgets.model;
+                  $contains($m, "opus") ? {
+                    "type": "list_usd",
+                    "usd": [0.005, 0.025],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "sonnet") ? {
+                    "type": "list_usd",
+                    "usd": [0.003, 0.015],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "haiku") ? {
+                    "type": "list_usd",
+                    "usd": [0.001, 0.005],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : {"type":"text", "text":"Token-based"}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+        images: dict | None = None,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        model_label = model["model"]
+        max_tokens = model["max_tokens"]
+        temperature = model["temperature"]
+
+        image_tensors: list[Input.Image] = [t for t in (images or {}).values() if t is not None]
+        if sum(get_number_of_images(t) for t in image_tensors) > CLAUDE_MAX_IMAGES:
+            raise ValueError(f"Up to {CLAUDE_MAX_IMAGES} images are supported per request.")
+
+        content: list[AnthropicTextContent | AnthropicImageContent] = []
+        if image_tensors:
+            content.extend(await _build_image_content_blocks(cls, image_tensors))
+        content.append(AnthropicTextContent(text=prompt))
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=ANTHROPIC_MESSAGES_ENDPOINT, method="POST"),
+            response_model=AnthropicMessagesResponse,
+            data=AnthropicMessagesRequest(
+                model=CLAUDE_MODELS[model_label],
+                max_tokens=max_tokens,
+                messages=[AnthropicMessage(role=AnthropicRole.user, content=content)],
+                system=system_prompt or None,
+                temperature=temperature,
+            ),
+            price_extractor=calculate_tokens_price,
+        )
+        return IO.NodeOutput(_get_text_from_response(response) or "Empty response from Claude model.")
+
+
+class AnthropicExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [ClaudeNode]
+
+
+async def comfy_entrypoint() -> AnthropicExtension:
+    return AnthropicExtension()

comfy_extras/nodes_audio.py

@@ -297,6 +297,7 @@ class LoadAudio(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
         input_dir = folder_paths.get_input_directory()
+        os.makedirs(input_dir, exist_ok=True)
         files = folder_paths.filter_files_content_types(os.listdir(input_dir), ["audio", "video"])
         return IO.Schema(
             node_id="LoadAudio",

comfy_extras/nodes_hunyuan3d.py

@@ -1,12 +1,7 @@
 import torch
-import os
-import json
-import struct
-import numpy as np
 from comfy.ldm.modules.diffusionmodules.mmdit import get_1d_sincos_pos_embed_from_grid_torch
-import folder_paths
 import comfy.model_management
-from comfy.cli_args import args
+from comfy_extras.nodes_save_3d import pack_variable_mesh_batch
 from typing_extensions import override
 from comfy_api.latest import ComfyExtension, IO, Types
 from comfy_api.latest._util import MESH, VOXEL  # only for backward compatibility if someone import it from this file (will be removed later) # noqa
@@ -444,7 +439,9 @@ class VoxelToMeshBasic(IO.ComfyNode):
             vertices.append(v)
             faces.append(f)
 
-        return IO.NodeOutput(Types.MESH(torch.stack(vertices), torch.stack(faces)))
+        if vertices and all(v.shape == vertices[0].shape for v in vertices) and all(f.shape == faces[0].shape for f in faces):
+            return IO.NodeOutput(Types.MESH(torch.stack(vertices), torch.stack(faces)))
+        return IO.NodeOutput(pack_variable_mesh_batch(vertices, faces))
 
     decode = execute  # TODO: remove
 
@@ -481,206 +478,13 @@ class VoxelToMesh(IO.ComfyNode):
             vertices.append(v)
             faces.append(f)
 
-        return IO.NodeOutput(Types.MESH(torch.stack(vertices), torch.stack(faces)))
+        if vertices and all(v.shape == vertices[0].shape for v in vertices) and all(f.shape == faces[0].shape for f in faces):
+            return IO.NodeOutput(Types.MESH(torch.stack(vertices), torch.stack(faces)))
+        return IO.NodeOutput(pack_variable_mesh_batch(vertices, faces))
 
     decode = execute  # TODO: remove
 
 
-def save_glb(vertices, faces, filepath, metadata=None):
-    """
-    Save PyTorch tensor vertices and faces as a GLB file without external dependencies.
-
-    Parameters:
-    vertices: torch.Tensor of shape (N, 3) - The vertex coordinates
-    faces: torch.Tensor of shape (M, 3) - The face indices (triangle faces)
-    filepath: str - Output filepath (should end with .glb)
-    """
-
-    # Convert tensors to numpy arrays
-    vertices_np = vertices.cpu().numpy().astype(np.float32)
-    faces_np = faces.cpu().numpy().astype(np.uint32)
-
-    vertices_buffer = vertices_np.tobytes()
-    indices_buffer = faces_np.tobytes()
-
-    def pad_to_4_bytes(buffer):
-        padding_length = (4 - (len(buffer) % 4)) % 4
-        return buffer + b'\x00' * padding_length
-
-    vertices_buffer_padded = pad_to_4_bytes(vertices_buffer)
-    indices_buffer_padded = pad_to_4_bytes(indices_buffer)
-
-    buffer_data = vertices_buffer_padded + indices_buffer_padded
-
-    vertices_byte_length = len(vertices_buffer)
-    vertices_byte_offset = 0
-    indices_byte_length = len(indices_buffer)
-    indices_byte_offset = len(vertices_buffer_padded)
-
-    gltf = {
-        "asset": {"version": "2.0", "generator": "ComfyUI"},
-        "buffers": [
-            {
-                "byteLength": len(buffer_data)
-            }
-        ],
-        "bufferViews": [
-            {
-                "buffer": 0,
-                "byteOffset": vertices_byte_offset,
-                "byteLength": vertices_byte_length,
-                "target": 34962  # ARRAY_BUFFER
-            },
-            {
-                "buffer": 0,
-                "byteOffset": indices_byte_offset,
-                "byteLength": indices_byte_length,
-                "target": 34963  # ELEMENT_ARRAY_BUFFER
-            }
-        ],
-        "accessors": [
-            {
-                "bufferView": 0,
-                "byteOffset": 0,
-                "componentType": 5126,  # FLOAT
-                "count": len(vertices_np),
-                "type": "VEC3",
-                "max": vertices_np.max(axis=0).tolist(),
-                "min": vertices_np.min(axis=0).tolist()
-            },
-            {
-                "bufferView": 1,
-                "byteOffset": 0,
-                "componentType": 5125,  # UNSIGNED_INT
-                "count": faces_np.size,
-                "type": "SCALAR"
-            }
-        ],
-        "meshes": [
-            {
-                "primitives": [
-                    {
-                        "attributes": {
-                            "POSITION": 0
-                        },
-                        "indices": 1,
-                        "mode": 4  # TRIANGLES
-                    }
-                ]
-            }
-        ],
-        "nodes": [
-            {
-                "mesh": 0
-            }
-        ],
-        "scenes": [
-            {
-                "nodes": [0]
-            }
-        ],
-        "scene": 0
-    }
-
-    if metadata is not None:
-        gltf["asset"]["extras"] = metadata
-
-    # Convert the JSON to bytes
-    gltf_json = json.dumps(gltf).encode('utf8')
-
-    def pad_json_to_4_bytes(buffer):
-        padding_length = (4 - (len(buffer) % 4)) % 4
-        return buffer + b' ' * padding_length
-
-    gltf_json_padded = pad_json_to_4_bytes(gltf_json)
-
-    # Create the GLB header
-    # Magic glTF
-    glb_header = struct.pack('<4sII', b'glTF', 2, 12 + 8 + len(gltf_json_padded) + 8 + len(buffer_data))
-
-    # Create JSON chunk header (chunk type 0)
-    json_chunk_header = struct.pack('<II', len(gltf_json_padded), 0x4E4F534A)  # "JSON" in little endian
-
-    # Create BIN chunk header (chunk type 1)
-    bin_chunk_header = struct.pack('<II', len(buffer_data), 0x004E4942)  # "BIN\0" in little endian
-
-    # Write the GLB file
-    with open(filepath, 'wb') as f:
-        f.write(glb_header)
-        f.write(json_chunk_header)
-        f.write(gltf_json_padded)
-        f.write(bin_chunk_header)
-        f.write(buffer_data)
-
-    return filepath
-
-
-class SaveGLB(IO.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="SaveGLB",
-            display_name="Save 3D Model",
-            search_aliases=["export 3d model", "save mesh"],
-            category="3d",
-            essentials_category="Basics",
-            is_output_node=True,
-            inputs=[
-                IO.MultiType.Input(
-                    IO.Mesh.Input("mesh"),
-                    types=[
-                        IO.File3DGLB,
-                        IO.File3DGLTF,
-                        IO.File3DOBJ,
-                        IO.File3DFBX,
-                        IO.File3DSTL,
-                        IO.File3DUSDZ,
-                        IO.File3DAny,
-                    ],
-                    tooltip="Mesh or 3D file to save",
-                ),
-                IO.String.Input("filename_prefix", default="3d/ComfyUI"),
-            ],
-            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo]
-        )
-
-    @classmethod
-    def execute(cls, mesh: Types.MESH | Types.File3D, filename_prefix: str) -> IO.NodeOutput:
-        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory())
-        results = []
-
-        metadata = {}
-        if not args.disable_metadata:
-            if cls.hidden.prompt is not None:
-                metadata["prompt"] = json.dumps(cls.hidden.prompt)
-            if cls.hidden.extra_pnginfo is not None:
-                for x in cls.hidden.extra_pnginfo:
-                    metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x])
-
-        if isinstance(mesh, Types.File3D):
-            # Handle File3D input - save BytesIO data to output folder
-            ext = mesh.format or "glb"
-            f = f"{filename}_{counter:05}_.{ext}"
-            mesh.save_to(os.path.join(full_output_folder, f))
-            results.append({
-                "filename": f,
-                "subfolder": subfolder,
-                "type": "output"
-            })
-        else:
-            # Handle Mesh input - save vertices and faces as GLB
-            for i in range(mesh.vertices.shape[0]):
-                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,
-                    "subfolder": subfolder,
-                    "type": "output"
-                })
-                counter += 1
-        return IO.NodeOutput(ui={"3d": results})
-
-
 class Hunyuan3dExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -691,7 +495,6 @@ class Hunyuan3dExtension(ComfyExtension):
             VAEDecodeHunyuan3D,
             VoxelToMeshBasic,
             VoxelToMesh,
-            SaveGLB,
         ]
 
 

comfy_extras/nodes_lt.py

@@ -338,8 +338,25 @@ class LTXVAddGuide(io.ComfyNode):
         noise_mask = get_noise_mask(latent)
 
         _, _, latent_length, latent_height, latent_width = latent_image.shape
+
+        # For mid-video multi-frame guides, prepend+strip a throwaway first frame so the VAE's "first latent = 1 pixel frame" asymmetry lands on the discarded slot
+        time_scale_factor = scale_factors[0]
+        num_frames_to_keep = ((image.shape[0] - 1) // time_scale_factor) * time_scale_factor + 1
+        resolved_frame_idx = frame_idx
+        if frame_idx < 0:
+            _, num_keyframes = get_keyframe_idxs(positive)
+            resolved_frame_idx = max((latent_length - num_keyframes - 1) * time_scale_factor + 1 + frame_idx, 0)
+        causal_fix = resolved_frame_idx == 0 or num_frames_to_keep == 1
+
+        if not causal_fix:
+            image = torch.cat([image[:1], image], dim=0)
+
         image, t = cls.encode(vae, latent_width, latent_height, image, scale_factors)
 
+        if not causal_fix:
+            t = t[:, :, 1:, :, :]
+            image = image[1:]
+
         frame_idx, latent_idx = cls.get_latent_index(positive, latent_length, len(image), frame_idx, scale_factors)
         assert latent_idx + t.shape[2] <= latent_length, "Conditioning frames exceed the length of the latent sequence."
 
@@ -352,6 +369,7 @@ class LTXVAddGuide(io.ComfyNode):
             t,
             strength,
             scale_factors,
+            causal_fix=causal_fix,
         )
 
         # Track this guide for per-reference attention control.

comfy_extras/nodes_mask.py

@@ -40,23 +40,13 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou
 
     inverse_mask = torch.ones_like(mask) - mask
 
-    source_rgb = source[:, :3, :visible_height, :visible_width]
-    dest_slice = destination[..., top:bottom, left:right]
-
-    if destination.shape[1] == 4:
-        if torch.max(dest_slice) == 0:
-            destination[:, :3, top:bottom, left:right] = source_rgb
-            destination[:, 3:4, top:bottom, left:right] = mask
-        else:
-            destination[:, :3, top:bottom, left:right] = (mask * source_rgb) + (inverse_mask * dest_slice[:, :3])
-            destination[:, 3:4, top:bottom, left:right] = torch.max(mask, dest_slice[:, 3:4])
-    else:
-        source_portion = mask * source_rgb
-        destination_portion = inverse_mask * dest_slice
-        destination[..., top:bottom, left:right] = source_portion + destination_portion
+    source_portion = mask * source[..., :visible_height, :visible_width]
+    destination_portion = inverse_mask  * destination[..., top:bottom, left:right]
 
+    destination[..., top:bottom, left:right] = source_portion + destination_portion
     return destination
 
+
 class LatentCompositeMasked(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -95,23 +85,18 @@ class ImageCompositeMasked(IO.ComfyNode):
             display_name="Image Composite Masked",
             category="image",
             inputs=[
+                IO.Image.Input("destination"),
                 IO.Image.Input("source"),
                 IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
                 IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
                 IO.Boolean.Input("resize_source", default=False),
-                IO.Image.Input("destination", optional=True),
                 IO.Mask.Input("mask", optional=True),
             ],
             outputs=[IO.Image.Output()],
         )
 
     @classmethod
-    def execute(cls, source, x, y, resize_source, destination = None, mask = None) -> IO.NodeOutput:
-        if destination is None: # transparent rgba
-            B, H, W, C = source.shape
-            destination = torch.zeros((B, H, W, 4), dtype=source.dtype, device=source.device)
-            if C == 3:
-                source = torch.nn.functional.pad(source, (0, 1), value=1.0)
+    def execute(cls, destination, source, x, y, resize_source, mask = None) -> IO.NodeOutput:
         destination, source = node_helpers.image_alpha_fix(destination, source)
         destination = destination.clone().movedim(-1, 1)
         output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)

comfy_extras/nodes_save_3d.py

@@ -0,0 +1,396 @@
+"""Save-side 3D nodes: mesh packing/slicing helpers + GLB writer + SaveGLB node."""
+
+import json
+import logging
+import os
+import struct
+from io import BytesIO
+
+import numpy as np
+from PIL import Image
+import torch
+from typing_extensions import override
+
+import folder_paths
+from comfy.cli_args import args
+from comfy_api.latest import ComfyExtension, IO, Types
+
+
+def pack_variable_mesh_batch(vertices, faces, colors=None, uvs=None, texture=None):
+    # Pack lists of (Nᵢ, *) vertex/face/color/uv tensors into padded batched tensors,
+    # stashing per-item lengths as runtime attrs so consumers can recover the real slice.
+    # colors and uvs are 1:1 with vertices, so they're padded to max_vertices and read with vertex_counts.
+    # texture is (B, H, W, 3) — passed through unchanged
+    batch_size = len(vertices)
+    max_vertices = max(v.shape[0] for v in vertices)
+    max_faces = max(f.shape[0] for f in faces)
+
+    packed_vertices = vertices[0].new_zeros((batch_size, max_vertices, vertices[0].shape[1]))
+    packed_faces = faces[0].new_zeros((batch_size, max_faces, faces[0].shape[1]))
+    vertex_counts = torch.tensor([v.shape[0] for v in vertices], device=vertices[0].device, dtype=torch.int64)
+    face_counts = torch.tensor([f.shape[0] for f in faces], device=faces[0].device, dtype=torch.int64)
+
+    for i, (v, f) in enumerate(zip(vertices, faces)):
+        packed_vertices[i, :v.shape[0]] = v
+        packed_faces[i, :f.shape[0]] = f
+
+    packed_colors = None
+    if colors is not None:
+        packed_colors = colors[0].new_zeros((batch_size, max_vertices, colors[0].shape[1]))
+        for i, c in enumerate(colors):
+            assert c.shape[0] == vertices[i].shape[0], (
+                f"vertex_colors[{i}] has {c.shape[0]} entries, expected {vertices[i].shape[0]} (1:1 with vertices)"
+            )
+            packed_colors[i, :c.shape[0]] = c
+
+    packed_uvs = None
+    if uvs is not None:
+        packed_uvs = uvs[0].new_zeros((batch_size, max_vertices, uvs[0].shape[1]))
+        for i, u in enumerate(uvs):
+            assert u.shape[0] == vertices[i].shape[0], (
+                f"uvs[{i}] has {u.shape[0]} entries, expected {vertices[i].shape[0]} (1:1 with vertices)"
+            )
+            packed_uvs[i, :u.shape[0]] = u
+
+    return Types.MESH(packed_vertices, packed_faces,
+                      uvs=packed_uvs, vertex_colors=packed_colors, texture=texture,
+                      vertex_counts=vertex_counts, face_counts=face_counts)
+
+
+def get_mesh_batch_item(mesh, index):
+    # Returns (vertices, faces, colors, uvs) for batch index, slicing to real lengths
+    # if the mesh carries per-item counts (variable-size batch).
+    v_colors = getattr(mesh, "vertex_colors", None)
+    v_uvs = getattr(mesh, "uvs", None)
+    if getattr(mesh, "vertex_counts", None) is not None:
+        vertex_count = int(mesh.vertex_counts[index].item())
+        face_count = int(mesh.face_counts[index].item())
+        vertices = mesh.vertices[index, :vertex_count]
+        faces = mesh.faces[index, :face_count]
+        colors = v_colors[index, :vertex_count] if v_colors is not None else None
+        uvs = v_uvs[index, :vertex_count] if v_uvs is not None else None
+        return vertices, faces, colors, uvs
+
+    colors = v_colors[index] if v_colors is not None else None
+    uvs = v_uvs[index] if v_uvs is not None else None
+    return mesh.vertices[index], mesh.faces[index], colors, uvs
+
+
+def save_glb(vertices, faces, filepath, metadata=None,
+             uvs=None, vertex_colors=None, texture_image=None):
+    """
+    Save PyTorch tensor vertices and faces as a GLB file without external dependencies.
+
+    Parameters:
+    vertices: torch.Tensor of shape (N, 3) - The vertex coordinates
+    faces: torch.Tensor of shape (M, 3) - The face indices (triangle faces)
+    filepath: str - Output filepath (should end with .glb)
+    metadata: dict - Optional asset.extras metadata
+    uvs: torch.Tensor of shape (N, 2) - Optional per-vertex texture coordinates
+    vertex_colors: torch.Tensor of shape (N, 3) or (N, 4) - Optional per-vertex colors in [0, 1]
+    texture_image: PIL.Image - Optional baseColor texture, embedded as PNG
+    """
+
+    # Convert tensors to numpy arrays
+    vertices_np = vertices.cpu().numpy().astype(np.float32)
+    faces_signed = faces.cpu().numpy().astype(np.int64)
+    uvs_np = uvs.cpu().numpy().astype(np.float32) if uvs is not None else None
+    colors_np = vertex_colors.cpu().numpy().astype(np.float32) if vertex_colors is not None else None
+    if colors_np is not None:
+        colors_np = np.clip(colors_np, 0.0, 1.0)
+
+    n_verts = vertices_np.shape[0]
+    if n_verts == 0:
+        raise ValueError("save_glb: vertices is empty")
+    if faces_signed.size > 0:
+        fmin = int(faces_signed.min())
+        fmax = int(faces_signed.max())
+        if fmin < 0 or fmax >= n_verts:
+            raise ValueError(
+                f"save_glb: face index out of range [0, {n_verts}): min={fmin}, max={fmax}"
+            )
+    if uvs_np is not None and uvs_np.shape[0] != n_verts:
+        raise ValueError(
+            f"save_glb: uvs has {uvs_np.shape[0]} entries but vertex count is {n_verts}"
+        )
+    if colors_np is not None and colors_np.shape[0] != n_verts:
+        raise ValueError(
+            f"save_glb: vertex_colors has {colors_np.shape[0]} entries but vertex count is {n_verts}"
+        )
+    faces_np = faces_signed.astype(np.uint32)
+    texture_png_bytes = None
+    if texture_image is not None:
+        buf = BytesIO()
+        texture_image.save(buf, format="PNG")
+        texture_png_bytes = buf.getvalue()
+
+    vertices_buffer = vertices_np.tobytes()
+    indices_buffer = faces_np.tobytes()
+    uvs_buffer = uvs_np.tobytes() if uvs_np is not None else b""
+    colors_buffer = colors_np.tobytes() if colors_np is not None else b""
+    texture_buffer = texture_png_bytes if texture_png_bytes is not None else b""
+
+    def pad_to_4_bytes(buffer):
+        padding_length = (4 - (len(buffer) % 4)) % 4
+        return buffer + b'\x00' * padding_length
+
+    vertices_buffer_padded = pad_to_4_bytes(vertices_buffer)
+    indices_buffer_padded = pad_to_4_bytes(indices_buffer)
+    uvs_buffer_padded = pad_to_4_bytes(uvs_buffer)
+    colors_buffer_padded = pad_to_4_bytes(colors_buffer)
+    texture_buffer_padded = pad_to_4_bytes(texture_buffer)
+
+    buffer_data = b"".join([
+        vertices_buffer_padded,
+        indices_buffer_padded,
+        uvs_buffer_padded,
+        colors_buffer_padded,
+        texture_buffer_padded,
+    ])
+
+    vertices_byte_length = len(vertices_buffer)
+    vertices_byte_offset = 0
+    indices_byte_length = len(indices_buffer)
+    indices_byte_offset = len(vertices_buffer_padded)
+    uvs_byte_offset = indices_byte_offset + len(indices_buffer_padded)
+    colors_byte_offset = uvs_byte_offset + len(uvs_buffer_padded)
+    texture_byte_offset = colors_byte_offset + len(colors_buffer_padded)
+
+    buffer_views = [
+        {
+            "buffer": 0,
+            "byteOffset": vertices_byte_offset,
+            "byteLength": vertices_byte_length,
+            "target": 34962  # ARRAY_BUFFER
+        },
+        {
+            "buffer": 0,
+            "byteOffset": indices_byte_offset,
+            "byteLength": indices_byte_length,
+            "target": 34963  # ELEMENT_ARRAY_BUFFER
+        }
+    ]
+    accessors = [
+        {
+            "bufferView": 0,
+            "byteOffset": 0,
+            "componentType": 5126,  # FLOAT
+            "count": len(vertices_np),
+            "type": "VEC3",
+            "max": vertices_np.max(axis=0).tolist(),
+            "min": vertices_np.min(axis=0).tolist()
+        },
+        {
+            "bufferView": 1,
+            "byteOffset": 0,
+            "componentType": 5125,  # UNSIGNED_INT
+            "count": faces_np.size,
+            "type": "SCALAR"
+        }
+    ]
+    primitive_attributes = {"POSITION": 0}
+
+    if uvs_np is not None and len(uvs_np) > 0:
+        buffer_views.append({
+            "buffer": 0,
+            "byteOffset": uvs_byte_offset,
+            "byteLength": len(uvs_buffer),
+            "target": 34962
+        })
+        accessor_idx = len(accessors)
+        accessors.append({
+            "bufferView": len(buffer_views) - 1,
+            "byteOffset": 0,
+            "componentType": 5126,
+            "count": len(uvs_np),
+            "type": "VEC2",
+        })
+        primitive_attributes["TEXCOORD_0"] = accessor_idx
+
+    if colors_np is not None and len(colors_np) > 0:
+        buffer_views.append({
+            "buffer": 0,
+            "byteOffset": colors_byte_offset,
+            "byteLength": len(colors_buffer),
+            "target": 34962
+        })
+        accessor_idx = len(accessors)
+        accessors.append({
+            "bufferView": len(buffer_views) - 1,
+            "byteOffset": 0,
+            "componentType": 5126,
+            "count": len(colors_np),
+            "type": "VEC3" if colors_np.shape[1] == 3 else "VEC4",
+        })
+        primitive_attributes["COLOR_0"] = accessor_idx
+
+    primitive = {
+        "attributes": primitive_attributes,
+        "indices": 1,
+        "mode": 4  # TRIANGLES
+    }
+
+    images = []
+    textures = []
+    samplers = []
+    materials = []
+    if texture_png_bytes is not None and "TEXCOORD_0" in primitive_attributes:
+        buffer_views.append({
+            "buffer": 0,
+            "byteOffset": texture_byte_offset,
+            "byteLength": len(texture_buffer),
+        })
+        images.append({"bufferView": len(buffer_views) - 1, "mimeType": "image/png"})
+        samplers.append({"magFilter": 9729, "minFilter": 9729, "wrapS": 33071, "wrapT": 33071})
+        textures.append({"source": 0, "sampler": 0})
+        materials.append({
+            "pbrMetallicRoughness": {
+                "baseColorTexture": {"index": 0, "texCoord": 0},
+                "metallicFactor": 0.0,
+                "roughnessFactor": 1.0,
+            },
+            "doubleSided": True,
+        })
+        primitive["material"] = 0
+
+    gltf = {
+        "asset": {"version": "2.0", "generator": "ComfyUI"},
+        "buffers": [{"byteLength": len(buffer_data)}],
+        "bufferViews": buffer_views,
+        "accessors": accessors,
+        "meshes": [{"primitives": [primitive]}],
+        "nodes": [{"mesh": 0}],
+        "scenes": [{"nodes": [0]}],
+        "scene": 0,
+    }
+    if images:
+        gltf["images"] = images
+    if samplers:
+        gltf["samplers"] = samplers
+    if textures:
+        gltf["textures"] = textures
+    if materials:
+        gltf["materials"] = materials
+
+    if metadata:
+        gltf["asset"]["extras"] = metadata
+
+    # Convert the JSON to bytes
+    gltf_json = json.dumps(gltf).encode('utf8')
+
+    def pad_json_to_4_bytes(buffer):
+        padding_length = (4 - (len(buffer) % 4)) % 4
+        return buffer + b' ' * padding_length
+
+    gltf_json_padded = pad_json_to_4_bytes(gltf_json)
+
+    # Create the GLB header (a 4-byte ASCII magic identifier glTF)
+    glb_header = struct.pack('<4sII', b'glTF', 2, 12 + 8 + len(gltf_json_padded) + 8 + len(buffer_data))
+
+    # Create JSON chunk header (chunk type 0)
+    json_chunk_header = struct.pack('<II', len(gltf_json_padded), 0x4E4F534A)  # "JSON" in little endian
+
+    # Create BIN chunk header (chunk type 1)
+    bin_chunk_header = struct.pack('<II', len(buffer_data), 0x004E4942)  # "BIN\0" in little endian
+
+    # Write the GLB file
+    with open(filepath, 'wb') as f:
+        f.write(glb_header)
+        f.write(json_chunk_header)
+        f.write(gltf_json_padded)
+        f.write(bin_chunk_header)
+        f.write(buffer_data)
+
+    return filepath
+
+
+class SaveGLB(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveGLB",
+            display_name="Save 3D Model",
+            search_aliases=["export 3d model", "save mesh"],
+            category="3d",
+            essentials_category="Basics",
+            is_output_node=True,
+            inputs=[
+                IO.MultiType.Input(
+                    IO.Mesh.Input("mesh"),
+                    types=[
+                        IO.File3DGLB,
+                        IO.File3DGLTF,
+                        IO.File3DOBJ,
+                        IO.File3DFBX,
+                        IO.File3DSTL,
+                        IO.File3DUSDZ,
+                        IO.File3DAny,
+                    ],
+                    tooltip="Mesh or 3D file to save",
+                ),
+                IO.String.Input("filename_prefix", default="3d/ComfyUI"),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo]
+        )
+
+    @classmethod
+    def execute(cls, mesh: Types.MESH | Types.File3D, filename_prefix: str) -> IO.NodeOutput:
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory())
+        results = []
+
+        metadata = {}
+        if not args.disable_metadata:
+            if cls.hidden.prompt is not None:
+                metadata["prompt"] = json.dumps(cls.hidden.prompt)
+            if cls.hidden.extra_pnginfo is not None:
+                for x in cls.hidden.extra_pnginfo:
+                    metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x])
+
+        if isinstance(mesh, Types.File3D):
+            # Handle File3D input - save BytesIO data to output folder
+            ext = mesh.format or "glb"
+            f = f"{filename}_{counter:05}_.{ext}"
+            mesh.save_to(os.path.join(full_output_folder, f))
+            results.append({
+                "filename": f,
+                "subfolder": subfolder,
+                "type": "output"
+            })
+            counter += 1
+        else:
+            # Handle Mesh input - save vertices and faces as GLB; carry optional UVs / colors / texture.
+            texture_b = getattr(mesh, "texture", None)
+            texture_np = None
+            if texture_b is not None:
+                texture_np = (texture_b.clamp(0.0, 1.0).cpu().numpy() * 255).astype(np.uint8)
+                assert texture_np.ndim == 4 and texture_np.shape[-1] == 3, (
+                    f"texture must be (B, H, W, 3) RGB, got shape {tuple(texture_np.shape)}"
+                )
+            for i in range(mesh.vertices.shape[0]):
+                vertices_i, faces_i, v_colors, uvs_i = get_mesh_batch_item(mesh, i)
+                if vertices_i.shape[0] == 0 or faces_i.shape[0] == 0:
+                    logging.warning(f"SaveGLB: skipping empty mesh at batch index {i}")
+                    continue
+                tex_img = Image.fromarray(texture_np[i], mode="RGB") if texture_np is not None else None
+                f = f"{filename}_{counter:05}_.glb"
+                save_glb(vertices_i, faces_i, os.path.join(full_output_folder, f), metadata,
+                         uvs=uvs_i,
+                         vertex_colors=v_colors,
+                         texture_image=tex_img)
+                results.append({
+                    "filename": f,
+                    "subfolder": subfolder,
+                    "type": "output"
+                })
+                counter += 1
+        return IO.NodeOutput(ui={"3d": results})
+
+
+class Save3DExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [SaveGLB]
+
+
+async def comfy_entrypoint() -> Save3DExtension:
+    return Save3DExtension()

comfy_extras/nodes_save_image_promotable.py

@@ -0,0 +1,249 @@
+"""
+SaveImagePromotable: a pass-through SaveImage variant with accumulating previews
+and a "promote/lock" feature.
+
+Modes:
+- Pass-through (default): saves incoming images, emits preview UI, returns the
+  input tensor as output. With `accumulate=True`, the frontend appends previews
+  to a gallery instead of replacing it.
+- Locked: when `promoted_asset_ref` is a non-empty JSON ref to a saved asset,
+  the node skips saving, loads the referenced image, and outputs that image.
+  The frontend is expected to write the ref into the widget when the user
+  clicks the "lock" UI on a preview.
+
+Caching: IS_CHANGED returns a stable key derived from the ref (+ file mtime)
+when locked, so re-queues with the same lock are cache hits and upstream
+ancestors are skipped. Unlocked, IS_CHANGED returns False to defer to normal
+input-signature caching.
+"""
+
+from __future__ import annotations
+
+import json
+import os
+
+import numpy as np
+import torch
+from PIL import Image, ImageOps, ImageSequence
+from PIL.PngImagePlugin import PngInfo
+
+import folder_paths
+import node_helpers
+from comfy.cli_args import args
+
+
+def _parse_promoted_ref(promoted_asset_ref: str) -> dict | None:
+    if not promoted_asset_ref:
+        return None
+    try:
+        ref = json.loads(promoted_asset_ref)
+    except (json.JSONDecodeError, TypeError):
+        return None
+    if not isinstance(ref, dict):
+        return None
+    filename = ref.get("filename")
+    if not isinstance(filename, str) or not filename:
+        return None
+    subfolder = ref.get("subfolder", "") or ""
+    asset_type = ref.get("type", "output") or "output"
+    if not isinstance(subfolder, str) or not isinstance(asset_type, str):
+        return None
+    # Reject anything that could escape the base directory.
+    if os.path.isabs(subfolder) or ".." in subfolder.split(os.sep):
+        return None
+    if os.path.isabs(filename) or ".." in filename.split(os.sep):
+        return None
+    return {"filename": filename, "subfolder": subfolder, "type": asset_type}
+
+
+def _resolve_ref_path(ref: dict) -> str | None:
+    asset_type = ref["type"]
+    if asset_type == "output":
+        base = folder_paths.get_output_directory()
+    elif asset_type == "input":
+        base = folder_paths.get_input_directory()
+    elif asset_type == "temp":
+        base = folder_paths.get_temp_directory()
+    else:
+        return None
+    path = os.path.join(base, ref["subfolder"], ref["filename"])
+    # Defense-in-depth: ensure the resolved path stays inside the base dir.
+    base_real = os.path.realpath(base)
+    path_real = os.path.realpath(path)
+    if not path_real.startswith(base_real + os.sep) and path_real != base_real:
+        return None
+    if not os.path.isfile(path_real):
+        return None
+    return path_real
+
+
+def _load_image_tensor(path: str) -> torch.Tensor:
+    img = node_helpers.pillow(Image.open, path)
+    output_images: list[torch.Tensor] = []
+    w: int | None = None
+    h: int | None = None
+    for frame in ImageSequence.Iterator(img):
+        frame = node_helpers.pillow(ImageOps.exif_transpose, frame)
+        image = frame.convert("RGB")
+        if not output_images:
+            w, h = image.size
+        if image.size != (w, h):
+            continue
+        arr = np.array(image).astype(np.float32) / 255.0
+        output_images.append(torch.from_numpy(arr)[None,])
+    if not output_images:
+        raise RuntimeError(f"Failed to decode any frames from {path}")
+    return torch.cat(output_images, dim=0)
+
+
+class SaveImagePromotable:
+    """Pass-through SaveImage with accumulating previews and promote/lock.
+
+    Inputs:
+        images: IMAGE tensor to save + pass through (ignored when locked).
+        filename_prefix: STRING prefix for saved files.
+        accumulate: BOOLEAN — when True, frontend appends previews to gallery.
+        promoted_asset_ref: STRING — JSON ref written by the frontend on lock.
+            Empty string means "not locked, normal pass-through".
+    Output:
+        IMAGE — input pass-through, or the loaded promoted image when locked.
+    """
+
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": (
+                    "IMAGE",
+                    {
+                        "tooltip": "Images to save and pass through. Ignored when a promoted asset is locked."
+                    },
+                ),
+                "filename_prefix": (
+                    "STRING",
+                    {"default": "ComfyUI", "tooltip": "Prefix for saved files."},
+                ),
+                "accumulate": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "When enabled, previews append to a per-node gallery instead of replacing it.",
+                    },
+                ),
+            },
+            "optional": {
+                "promoted_asset_ref": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": False,
+                        "tooltip": "JSON ref to a saved asset. Set by the UI; do not edit manually.",
+                    },
+                ),
+            },
+            "hidden": {
+                "prompt": "PROMPT",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("images",)
+    FUNCTION = "execute"
+    OUTPUT_NODE = True
+    CATEGORY = "image"
+    DESCRIPTION = "Saves images, shows accumulating previews, and passes the input through. A promoted (locked) preview overrides pass-through to output the chosen image."
+
+    def _save_images(self, images, filename_prefix, prompt, extra_pnginfo):
+        full_output_folder, filename, counter, subfolder, _ = (
+            folder_paths.get_save_image_path(
+                filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]
+            )
+        )
+        results: list[dict] = []
+        for batch_number, image in enumerate(images):
+            arr = 255.0 * image.cpu().numpy()
+            img = Image.fromarray(np.clip(arr, 0, 255).astype(np.uint8))
+            metadata: PngInfo | None = None
+            if not args.disable_metadata:
+                metadata = PngInfo()
+                if prompt is not None:
+                    metadata.add_text("prompt", json.dumps(prompt))
+                if extra_pnginfo is not None:
+                    for key in extra_pnginfo:
+                        metadata.add_text(key, json.dumps(extra_pnginfo[key]))
+            filename_with_batch = filename.replace("%batch_num%", str(batch_number))
+            out_name = f"{filename_with_batch}_{counter:05}_.png"
+            img.save(
+                os.path.join(full_output_folder, out_name),
+                pnginfo=metadata,
+                compress_level=self.compress_level,
+            )
+            results.append(
+                {"filename": out_name, "subfolder": subfolder, "type": self.type}
+            )
+            counter += 1
+        return results
+
+    def execute(
+        self,
+        images,
+        filename_prefix="ComfyUI",
+        accumulate=False,  # noqa: ARG002
+        promoted_asset_ref="",
+        prompt=None,
+        extra_pnginfo=None,
+    ):
+        ref = _parse_promoted_ref(promoted_asset_ref)
+        if ref is not None:
+            path = _resolve_ref_path(ref)
+            if path is not None:
+                tensor = _load_image_tensor(path)
+                tensor = tensor.to(device=images.device, dtype=images.dtype)
+                return {
+                    "ui": {"images": [ref]},
+                    "result": (tensor,),
+                }
+            # Ref is set but stale (file deleted / failed validation): fall
+            # through to pass-through so the user gets a working graph rather
+            # than an execution error.
+
+        saved = self._save_images(images, filename_prefix, prompt, extra_pnginfo)
+        return {"ui": {"images": saved}, "result": (images,)}
+
+    @classmethod
+    def IS_CHANGED(
+        cls,
+        images,  # noqa: ARG003
+        filename_prefix="ComfyUI",
+        accumulate=False,  # noqa: ARG003
+        promoted_asset_ref="",
+        prompt=None,  # noqa: ARG003
+        extra_pnginfo=None,  # noqa: ARG003
+    ):
+        ref = _parse_promoted_ref(promoted_asset_ref)
+        if ref is None:
+            return False
+        path = _resolve_ref_path(ref)
+        if path is None:
+            return f"PROMOTED::MISSING::{promoted_asset_ref}"
+        try:
+            stat = os.stat(path)
+            sig = f"{stat.st_size}:{stat.st_mtime_ns}"
+        except OSError:
+            sig = "NOSTAT"
+        return f"PROMOTED::{promoted_asset_ref}::{sig}"
+
+
+NODE_CLASS_MAPPINGS = {
+    "SaveImagePromotable": SaveImagePromotable,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "SaveImagePromotable": "Save Image (Promotable, PoC)",
+}

comfy_extras/nodes_video.py

@@ -123,6 +123,7 @@ class CreateVideo(io.ComfyNode):
             search_aliases=["images to video"],
             display_name="Create Video",
             category="video",
+            essentials_category="Video Tools",
             description="Create a video from images.",
             inputs=[
                 io.Image.Input("images", tooltip="The images to create a video from."),

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.21.0"
+__version__ = "0.21.1"

nodes.py

@@ -2397,6 +2397,7 @@ async def init_builtin_extra_nodes():
         "nodes_fresca.py",
         "nodes_apg.py",
         "nodes_preview_any.py",
+        "nodes_save_image_promotable.py",
         "nodes_ace.py",
         "nodes_string.py",
         "nodes_camera_trajectory.py",
@@ -2436,6 +2437,7 @@ async def init_builtin_extra_nodes():
         "nodes_void.py",
         "nodes_wandancer.py",
         "nodes_hidream_o1.py",
+        "nodes_save_3d.py",
     ]
 
     import_failed = []

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.21.0"
+version = "0.21.1"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,6 +1,6 @@
 comfyui-frontend-package==1.43.18
-comfyui-workflow-templates==0.9.73
-comfyui-embedded-docs==0.4.4
+comfyui-workflow-templates==0.9.75
+comfyui-embedded-docs==0.5.0
 torch
 torchsde
 torchvision

tests-unit/comfy_extras_test/save_image_promotable_test.py

@@ -0,0 +1,289 @@
+import json
+import os
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+import torch
+from PIL import Image
+
+mock_nodes = MagicMock()
+mock_nodes.MAX_RESOLUTION = 16384
+mock_server = MagicMock()
+
+with patch.dict("sys.modules", {"nodes": mock_nodes, "server": mock_server}):
+    from comfy_extras import nodes_save_image_promotable as mod
+
+
+def _make_image(width=8, height=4):
+    return torch.rand(1, height, width, 3)
+
+
+def _write_png(path: str, width=8, height=4):
+    arr = (np.random.rand(height, width, 3) * 255).astype(np.uint8)
+    Image.fromarray(arr).save(path)
+
+
+class TestParseRef:
+    def test_empty(self):
+        assert mod._parse_promoted_ref("") is None
+
+    def test_invalid_json(self):
+        assert mod._parse_promoted_ref("{not json") is None
+
+    def test_non_object(self):
+        assert mod._parse_promoted_ref('"a string"') is None
+        assert mod._parse_promoted_ref("[]") is None
+
+    def test_missing_filename(self):
+        assert mod._parse_promoted_ref('{"subfolder":"x","type":"output"}') is None
+
+    def test_path_traversal_filename(self):
+        ref = json.dumps(
+            {"filename": "../etc/passwd", "subfolder": "", "type": "output"}
+        )
+        assert mod._parse_promoted_ref(ref) is None
+
+    def test_path_traversal_subfolder(self):
+        ref = json.dumps({"filename": "x.png", "subfolder": "../..", "type": "output"})
+        assert mod._parse_promoted_ref(ref) is None
+
+    def test_absolute_filename(self):
+        ref = json.dumps({"filename": "/etc/passwd", "subfolder": "", "type": "output"})
+        assert mod._parse_promoted_ref(ref) is None
+
+    def test_valid(self):
+        ref = json.dumps({"filename": "x.png", "subfolder": "sub", "type": "output"})
+        parsed = mod._parse_promoted_ref(ref)
+        assert parsed == {"filename": "x.png", "subfolder": "sub", "type": "output"}
+
+    def test_defaults_applied(self):
+        ref = json.dumps({"filename": "x.png"})
+        parsed = mod._parse_promoted_ref(ref)
+        assert parsed == {"filename": "x.png", "subfolder": "", "type": "output"}
+
+
+class TestResolveRefPath:
+    def test_unknown_type(self, tmp_path):
+        with patch.object(
+            mod.folder_paths, "get_output_directory", return_value=str(tmp_path)
+        ):
+            assert (
+                mod._resolve_ref_path(
+                    {"filename": "x.png", "subfolder": "", "type": "garbage"}
+                )
+                is None
+            )
+
+    def test_missing_file(self, tmp_path):
+        with patch.object(
+            mod.folder_paths, "get_output_directory", return_value=str(tmp_path)
+        ):
+            assert (
+                mod._resolve_ref_path(
+                    {"filename": "missing.png", "subfolder": "", "type": "output"}
+                )
+                is None
+            )
+
+    def test_resolves_file(self, tmp_path):
+        target = tmp_path / "img.png"
+        _write_png(str(target))
+        with patch.object(
+            mod.folder_paths, "get_output_directory", return_value=str(tmp_path)
+        ):
+            resolved = mod._resolve_ref_path(
+                {"filename": "img.png", "subfolder": "", "type": "output"}
+            )
+        assert resolved is not None
+        assert os.path.realpath(resolved) == os.path.realpath(str(target))
+
+    def test_resolves_file_in_subfolder(self, tmp_path):
+        sub = tmp_path / "nested"
+        sub.mkdir()
+        target = sub / "img.png"
+        _write_png(str(target))
+        with patch.object(
+            mod.folder_paths, "get_output_directory", return_value=str(tmp_path)
+        ):
+            resolved = mod._resolve_ref_path(
+                {"filename": "img.png", "subfolder": "nested", "type": "output"}
+            )
+        assert resolved is not None
+        assert os.path.realpath(resolved) == os.path.realpath(str(target))
+
+    def test_symlink_escape_rejected(self, tmp_path):
+        outside = tmp_path / "outside"
+        outside.mkdir()
+        secret = outside / "secret.png"
+        _write_png(str(secret))
+        base = tmp_path / "base"
+        base.mkdir()
+        link = base / "link.png"
+        os.symlink(str(secret), str(link))
+        with patch.object(
+            mod.folder_paths, "get_output_directory", return_value=str(base)
+        ):
+            resolved = mod._resolve_ref_path(
+                {"filename": "link.png", "subfolder": "", "type": "output"}
+            )
+        assert resolved is None
+
+
+class TestNodeContract:
+    def test_input_types_shape(self):
+        inp = mod.SaveImagePromotable.INPUT_TYPES()
+        assert set(inp["required"].keys()) == {
+            "images",
+            "filename_prefix",
+            "accumulate",
+        }
+        assert set(inp["optional"].keys()) == {"promoted_asset_ref"}
+        assert set(inp["hidden"].keys()) == {"prompt", "extra_pnginfo"}
+        assert inp["required"]["accumulate"][0] == "BOOLEAN"
+        assert inp["required"]["accumulate"][1]["default"] is False
+
+    def test_class_metadata(self):
+        cls = mod.SaveImagePromotable
+        assert cls.RETURN_TYPES == ("IMAGE",)
+        assert cls.RETURN_NAMES == ("images",)
+        assert cls.OUTPUT_NODE is True
+        assert cls.FUNCTION == "execute"
+        assert "SaveImagePromotable" in mod.NODE_CLASS_MAPPINGS
+        assert mod.NODE_CLASS_MAPPINGS["SaveImagePromotable"] is cls
+
+
+class TestExecutePassthrough:
+    def test_passthrough_saves_and_returns_input(self, tmp_path):
+        node = mod.SaveImagePromotable()
+        node.output_dir = str(tmp_path)
+        images = _make_image()
+
+        with (
+            patch.object(mod.args, "disable_metadata", True),
+            patch.object(mod.folder_paths, "get_save_image_path") as get_path,
+        ):
+            get_path.return_value = (str(tmp_path), "ComfyUI", 1, "", "ComfyUI")
+            result = node.execute(
+                images,
+                filename_prefix="ComfyUI",
+                accumulate=False,
+                promoted_asset_ref="",
+            )
+
+        assert "ui" in result
+        assert "result" in result
+        assert torch.equal(result["result"][0], images)
+        assert len(result["ui"]["images"]) == 1
+        saved_name = result["ui"]["images"][0]["filename"]
+        assert os.path.isfile(os.path.join(str(tmp_path), saved_name))
+
+    def test_stale_ref_falls_through_to_passthrough(self, tmp_path):
+        node = mod.SaveImagePromotable()
+        node.output_dir = str(tmp_path)
+        images = _make_image()
+        ref = json.dumps(
+            {"filename": "does_not_exist.png", "subfolder": "", "type": "output"}
+        )
+
+        with (
+            patch.object(mod.args, "disable_metadata", True),
+            patch.object(mod.folder_paths, "get_save_image_path") as get_path,
+            patch.object(
+                mod.folder_paths, "get_output_directory", return_value=str(tmp_path)
+            ),
+        ):
+            get_path.return_value = (str(tmp_path), "ComfyUI", 1, "", "ComfyUI")
+            result = node.execute(images, promoted_asset_ref=ref)
+
+        assert torch.equal(result["result"][0], images)
+
+
+class TestExecuteLocked:
+    def test_locked_outputs_loaded_image(self, tmp_path):
+        target = tmp_path / "promoted.png"
+        _write_png(str(target), width=8, height=4)
+        ref = json.dumps(
+            {"filename": "promoted.png", "subfolder": "", "type": "output"}
+        )
+        node = mod.SaveImagePromotable()
+        node.output_dir = str(tmp_path)
+        upstream = _make_image(width=8, height=4)
+
+        with patch.object(
+            mod.folder_paths, "get_output_directory", return_value=str(tmp_path)
+        ):
+            result = node.execute(upstream, promoted_asset_ref=ref)
+
+        assert result["ui"]["images"] == [
+            {"filename": "promoted.png", "subfolder": "", "type": "output"}
+        ]
+        out = result["result"][0]
+        assert out.shape == upstream.shape
+        assert out.dtype == upstream.dtype
+        assert not torch.equal(out, upstream)
+
+    def test_locked_does_not_save(self, tmp_path):
+        target = tmp_path / "promoted.png"
+        _write_png(str(target))
+        ref = json.dumps(
+            {"filename": "promoted.png", "subfolder": "", "type": "output"}
+        )
+        node = mod.SaveImagePromotable()
+        node.output_dir = str(tmp_path)
+        images = _make_image()
+
+        with (
+            patch.object(
+                mod.folder_paths, "get_output_directory", return_value=str(tmp_path)
+            ),
+            patch.object(node, "_save_images") as save_mock,
+        ):
+            node.execute(images, promoted_asset_ref=ref)
+
+        save_mock.assert_not_called()
+
+
+class TestIsChanged:
+    def test_unlocked_returns_false(self):
+        assert (
+            mod.SaveImagePromotable.IS_CHANGED(images=None, promoted_asset_ref="")
+            is False
+        )
+
+    def test_locked_missing_file(self, tmp_path):
+        ref = json.dumps({"filename": "missing.png", "subfolder": "", "type": "output"})
+        with patch.object(
+            mod.folder_paths, "get_output_directory", return_value=str(tmp_path)
+        ):
+            key = mod.SaveImagePromotable.IS_CHANGED(
+                images=None, promoted_asset_ref=ref
+            )
+        assert isinstance(key, str)
+        assert key.startswith("PROMOTED::MISSING::")
+
+    def test_locked_stable_key(self, tmp_path):
+        target = tmp_path / "p.png"
+        _write_png(str(target))
+        ref = json.dumps({"filename": "p.png", "subfolder": "", "type": "output"})
+        with patch.object(
+            mod.folder_paths, "get_output_directory", return_value=str(tmp_path)
+        ):
+            k1 = mod.SaveImagePromotable.IS_CHANGED(images=None, promoted_asset_ref=ref)
+            k2 = mod.SaveImagePromotable.IS_CHANGED(images=None, promoted_asset_ref=ref)
+        assert k1 == k2
+        assert k1.startswith("PROMOTED::")
+
+    def test_locked_key_changes_when_file_changes(self, tmp_path):
+        target = tmp_path / "p.png"
+        _write_png(str(target), width=8, height=4)
+        ref = json.dumps({"filename": "p.png", "subfolder": "", "type": "output"})
+        with patch.object(
+            mod.folder_paths, "get_output_directory", return_value=str(tmp_path)
+        ):
+            k1 = mod.SaveImagePromotable.IS_CHANGED(images=None, promoted_asset_ref=ref)
+        os.utime(str(target), (1234567890, 1234567890))
+        with patch.object(
+            mod.folder_paths, "get_output_directory", return_value=str(tmp_path)
+        ):
+            k2 = mod.SaveImagePromotable.IS_CHANGED(images=None, promoted_asset_ref=ref)
+        assert k1 != k2