ComfyUI v0.22.2

* [Partner Nodes] add new Rodin2.5 nodes

Signed-off-by: bigcat88 <bigcat88@icloud.com>

* [Partner Nodes] fixed Quality Mesh Options

Signed-off-by: bigcat88 <bigcat88@icloud.com>

* [Partner Nodes] fix: remove non-supported "usdz"

Signed-off-by: bigcat88 <bigcat88@icloud.com>

* [Partner Nodes] fix: always pass seed to server

Signed-off-by: bigcat88 <bigcat88@icloud.com>

* [Partner Nodes] fix: set the default "material" value to "Shaded"

Signed-off-by: bigcat88 <bigcat88@icloud.com>

---------

Signed-off-by: bigcat88 <bigcat88@icloud.com>

comfy_api_nodes/apis/anthropic.py

@@ -35,6 +35,19 @@ class AnthropicMessage(BaseModel):
     content: list[AnthropicTextContent | AnthropicImageContent] = Field(...)
 
 
+class AnthropicThinkingConfig(BaseModel):
+    type: Literal["enabled", "disabled", "adaptive"] = Field(...)
+    budget_tokens: int | None = Field(
+        None, ge=1024,
+        description="Reasoning budget in tokens. Used when type is 'enabled'. Must be less than max_tokens.",
+    )
+
+
+class AnthropicOutputConfig(BaseModel):
+    """Used with `thinking.type='adaptive'` on models like Opus 4.7."""
+    effort: Literal["low", "medium", "high"] | None = Field(None)
+
+
 class AnthropicMessagesRequest(BaseModel):
     model: str = Field(...)
     messages: list[AnthropicMessage] = Field(...)
@@ -44,6 +57,8 @@ class AnthropicMessagesRequest(BaseModel):
     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)
+    thinking: AnthropicThinkingConfig | None = Field(None)
+    output_config: AnthropicOutputConfig | None = Field(None)
 
 
 class AnthropicResponseTextBlock(BaseModel):
@@ -51,6 +66,14 @@ class AnthropicResponseTextBlock(BaseModel):
     text: str = Field(...)
 
 
+class AnthropicResponseThinkingBlock(BaseModel):
+    type: Literal["thinking"] = "thinking"
+    thinking: str = Field(...)
+
+
+AnthropicResponseBlock = AnthropicResponseTextBlock | AnthropicResponseThinkingBlock
+
+
 class AnthropicCacheCreationUsage(BaseModel):
     ephemeral_5m_input_tokens: int | None = Field(None)
     ephemeral_1h_input_tokens: int | None = Field(None)
@@ -69,7 +92,7 @@ class AnthropicMessagesResponse(BaseModel):
     type: str | None = Field(None)
     role: str | None = Field(None)
     model: str | None = Field(None)
-    content: list[AnthropicResponseTextBlock] | None = Field(None)
+    content: list[AnthropicResponseBlock] | None = Field(None)
     stop_reason: str | None = Field(None)
     stop_sequence: str | None = Field(None)
     usage: AnthropicMessagesUsage | None = Field(None)

comfy_api_nodes/apis/openrouter.py

@@ -0,0 +1,93 @@
+"""Pydantic models for the OpenRouter chat completions API.
+
+See: https://openrouter.ai/docs/api/api-reference/chat/send-chat-completion-request
+"""
+
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class OpenRouterTextContent(BaseModel):
+    type: Literal["text"] = "text"
+    text: str = Field(...)
+
+
+class OpenRouterImageUrl(BaseModel):
+    url: str = Field(...)
+
+
+class OpenRouterImageContent(BaseModel):
+    type: Literal["image_url"] = "image_url"
+    image_url: OpenRouterImageUrl = Field(...)
+
+
+class OpenRouterVideoUrl(BaseModel):
+    url: str = Field(...)
+
+
+class OpenRouterVideoContent(BaseModel):
+    type: Literal["video_url"] = "video_url"
+    video_url: OpenRouterVideoUrl = Field(...)
+
+
+OpenRouterContentBlock = OpenRouterTextContent | OpenRouterImageContent | OpenRouterVideoContent
+
+
+class OpenRouterMessage(BaseModel):
+    role: Literal["system", "user", "assistant"] = Field(...)
+    content: str | list[OpenRouterContentBlock] = Field(...)
+
+
+class OpenRouterReasoningConfig(BaseModel):
+    effort: str | None = Field(None)
+    exclude: bool | None = Field(None, description="If true, model reasons but reasoning is excluded from response.")
+
+
+class OpenRouterWebSearchOptions(BaseModel):
+    search_context_size: str | None = Field(None)
+
+
+class OpenRouterChatRequest(BaseModel):
+    model: str = Field(...)
+    messages: list[OpenRouterMessage] = Field(...)
+    seed: int | None = Field(None)
+    reasoning: OpenRouterReasoningConfig | None = Field(None)
+    web_search_options: OpenRouterWebSearchOptions | None = Field(None)
+    stream: bool = Field(False)
+
+
+class OpenRouterUsage(BaseModel):
+    prompt_tokens: int | None = Field(None)
+    completion_tokens: int | None = Field(None)
+    total_tokens: int | None = Field(None)
+    cost: float | None = Field(None, description="Server-side authoritative USD cost of the call.")
+
+
+class OpenRouterResponseMessage(BaseModel):
+    role: str | None = Field(None)
+    content: str | None = Field(None)
+    reasoning: str | None = Field(None)
+    refusal: str | None = Field(None)
+
+
+class OpenRouterChoice(BaseModel):
+    index: int | None = Field(None)
+    message: OpenRouterResponseMessage | None = Field(None)
+    finish_reason: str | None = Field(None)
+
+
+class OpenRouterError(BaseModel):
+    code: int | str | None = Field(None)
+    message: str | None = Field(None)
+    metadata: dict | None = Field(None)
+
+
+class OpenRouterChatResponse(BaseModel):
+    id: str | None = Field(None)
+    model: str | None = Field(None)
+    object: str | None = Field(None)
+    provider: str | None = Field(None)
+    choices: list[OpenRouterChoice] | None = Field(None)
+    usage: OpenRouterUsage | None = Field(None)
+    error: OpenRouterError | None = Field(None)

comfy_api_nodes/apis/rodin.py

@@ -1,7 +1,5 @@
-from __future__ import annotations
-
 from enum import Enum
-from typing import Optional, List
+
 from pydantic import BaseModel, Field
 
 
@@ -11,44 +9,76 @@ class Rodin3DGenerateRequest(BaseModel):
     material: str = Field(..., description="The material type.")
     quality_override: int = Field(..., description="The poly count of the mesh.")
     mesh_mode: str = Field(..., description="It controls the type of faces of generated models.")
-    TAPose: Optional[bool] = Field(None, description="")
+    TAPose: bool | None = Field(None, description="")
+
+
+class Rodin3DGen25Request(BaseModel):
+
+    tier: str = Field(..., description="Gen-2.5 tier (e.g. Gen-2.5-High).")
+    prompt: str | None = Field(None, description="Required for Text-to-3D; ignored otherwise.")
+    seed: int | None = Field(None, description="0-65535.")
+    material: str | None = Field(None, description="PBR | Shaded | All | None.")
+    geometry_file_format: str | None = Field(None, description="glb | usdz | fbx | obj | stl.")
+    texture_mode: str | None = Field(None, description="legacy | extreme-low | low | medium | high.")
+    mesh_mode: str | None = Field(None, description="Raw (triangular) | Quad.")
+    quality_override: int | None = Field(None, description="Mesh face count override.")
+    geometry_instruct_mode: str | None = Field(None, description="faithful | creative.")
+    bbox_condition: list[int] | None = Field(None, description="Bounding box [Width(Y), Height(Z), Length(X)] in cm.")
+    height: int | None = Field(None, description="Approximate model height in cm.")
+    TAPose: bool | None = Field(None, description="T/A pose for human-like models.")
+    hd_texture: bool | None = Field(None, description="Enhanced texture quality.")
+    texture_delight: bool | None = Field(None, description="Remove baked lighting from textures.")
+    is_micro: bool | None = Field(None, description="Micro detail (Extreme-High only).")
+    use_original_alpha: bool | None = Field(None, description="Preserve image transparency.")
+    preview_render: bool | None = Field(None, description="Generate high-quality preview render.")
+    addons: list[str] | None = Field(None, description='Optional addons, e.g. ["HighPack"].')
+
 
 class GenerateJobsData(BaseModel):
-    uuids: List[str] = Field(..., description="str LIST")
+    uuids: list[str] = Field(..., description="str LIST")
     subscription_key: str = Field(..., description="subscription key")
 
+
 class Rodin3DGenerateResponse(BaseModel):
-    message: Optional[str] = Field(None, description="Return message.")
+    message: str | None = Field(None, description="Return message.")
-    prompt: Optional[str] = Field(None, description="Generated Prompt from image.")
+    prompt: str | None = Field(None, description="Generated Prompt from image.")
-    submit_time: Optional[str] = Field(None, description="Submit Time")
+    submit_time: str | None = Field(None, description="Submit Time")
-    uuid: Optional[str] = Field(None, description="Task str")
+    uuid: str | None = Field(None, description="Task str")
-    jobs: Optional[GenerateJobsData] = Field(None, description="Details of jobs")
+    jobs: GenerateJobsData | None = Field(None, description="Details of jobs")
+
 
 class JobStatus(str, Enum):
     """
     Status for jobs
     """
+
     Done = "Done"
     Failed = "Failed"
     Generating = "Generating"
     Waiting = "Waiting"
 
+
 class Rodin3DCheckStatusRequest(BaseModel):
     subscription_key: str = Field(..., description="subscription from generate endpoint")
 
+
 class JobItem(BaseModel):
     uuid: str = Field(..., description="uuid")
-    status: JobStatus = Field(...,description="Status Currently")
+    status: JobStatus = Field(..., description="Status Currently")
+
 
 class Rodin3DCheckStatusResponse(BaseModel):
-    jobs: List[JobItem] = Field(..., description="Job status List")
+    jobs: list[JobItem] = Field(..., description="Job status List")
+
 
 class Rodin3DDownloadRequest(BaseModel):
     task_uuid: str = Field(..., description="Task str")
 
+
 class RodinResourceItem(BaseModel):
     url: str = Field(..., description="Download Url")
     name: str = Field(..., description="File name with ext")
 
+
 class Rodin3DDownloadResponse(BaseModel):
-    list: List[RodinResourceItem] = Field(..., description="Source List")
+    items: list[RodinResourceItem] = Field(..., alias="list", description="Source List")

comfy_api_nodes/nodes_anthropic.py

@@ -9,8 +9,11 @@ from comfy_api_nodes.apis.anthropic import (
     AnthropicMessage,
     AnthropicMessagesRequest,
     AnthropicMessagesResponse,
+    AnthropicOutputConfig,
+    AnthropicResponseTextBlock,
     AnthropicRole,
     AnthropicTextContent,
+    AnthropicThinkingConfig,
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
@@ -32,15 +35,29 @@ CLAUDE_MODELS: dict[str, str] = {
     "Haiku 4.5": "claude-haiku-4-5-20251001",
 }
 
+_THINKING_UNSUPPORTED = {"Haiku 4.5"}
+# Models that use the newer "adaptive" thinking mode (Opus 4.7 requires it; older models keep the explicit budget API).
+# Anthropic decides the actual budget when adaptive is used, based on the `output_config.effort` hint.
+_ADAPTIVE_THINKING_MODELS = {"Opus 4.7", "Opus 4.6", "Sonnet 4.6"}
 
-def _claude_model_inputs():
+# Budget mode (Sonnet 4.5): effort -> reasoning budget in tokens. Must be < max_tokens.
-    return [
+# Sized so even the "high" budget fits comfortably under the default max_tokens=32768.
+_REASONING_BUDGET: dict[str, int] = {
+    "low": 2048,
+    "medium": 8192,
+    "high": 16384,
+}
+_REASONING_EFFORTS = ["off", "low", "medium", "high"]
+
+
+def _claude_model_inputs(model_label: str):
+    inputs: list = [
         IO.Int.Input(
             "max_tokens",
-            default=16000,
+            default=32768,
-            min=32,
+            min=4096,
-            max=32000,
+            max=64000,
-            tooltip="Maximum number of tokens to generate before stopping.",
+            tooltip="Maximum number of tokens to generate (includes reasoning tokens when enabled).",
             advanced=True,
         ),
         IO.Float.Input(
@@ -49,10 +66,24 @@ def _claude_model_inputs():
             min=0.0,
             max=1.0,
             step=0.01,
-            tooltip="Controls randomness. 0.0 is deterministic, 1.0 is most random. Ignored for Opus 4.7.",
+            tooltip=(
+                "Controls randomness. 0.0 is deterministic, 1.0 is most random. "
+                "Ignored for Opus 4.7 and any model when reasoning_effort is set."
+            ),
             advanced=True,
         ),
     ]
+    if model_label not in _THINKING_UNSUPPORTED:
+        inputs.append(
+            IO.Combo.Input(
+                "reasoning_effort",
+                options=_REASONING_EFFORTS,
+                default="off",
+                tooltip="Extended thinking effort. 'off' disables reasoning.",
+                advanced=True,
+            )
+        )
+    return inputs
 
 
 def _model_price_per_million(model: str) -> tuple[float, float] | None:
@@ -95,7 +126,11 @@ def calculate_tokens_price(response: AnthropicMessagesResponse) -> float | None:
 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)
+    # Thinking blocks are silently dropped — we never want reasoning in the output.
+    return "\n".join(
+        block.text for block in response.content
+        if isinstance(block, AnthropicResponseTextBlock) and block.text
+    )
 
 
 async def _build_image_content_blocks(
@@ -133,7 +168,10 @@ class ClaudeNode(IO.ComfyNode):
                 ),
                 IO.DynamicCombo.Input(
                     "model",
-                    options=[IO.DynamicCombo.Option(label, _claude_model_inputs()) for label in CLAUDE_MODELS],
+                    options=[
+                        IO.DynamicCombo.Option(label, _claude_model_inputs(label))
+                        for label in CLAUDE_MODELS
+                    ],
                     tooltip="The Claude model used to generate the response.",
                 ),
                 IO.Int.Input(
@@ -207,8 +245,29 @@ class ClaudeNode(IO.ComfyNode):
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
         model_label = model["model"]
-        max_tokens = model["max_tokens"]
+        max_tokens = model.get("max_tokens", 32768)
-        temperature = None if model_label == "Opus 4.7" else model["temperature"]
+        reasoning_effort = model.get("reasoning_effort", "off")
+        thinking_enabled = reasoning_effort not in ("off", None) and model_label not in _THINKING_UNSUPPORTED
+
+        # Anthropic requires temperature to be unset (defaults to 1.0) when thinking is enabled.
+        # Opus 4.7 also rejects user-supplied temperature.
+        if thinking_enabled or model_label == "Opus 4.7":
+            temperature = None
+        else:
+            temperature = model.get("temperature", 1.0)
+
+        thinking_cfg: AnthropicThinkingConfig | None = None
+        output_cfg: AnthropicOutputConfig | None = None
+        if thinking_enabled:
+            if model_label in _ADAPTIVE_THINKING_MODELS:
+                # Adaptive mode - Anthropic chooses the budget based on effort hint
+                thinking_cfg = AnthropicThinkingConfig(type="adaptive")
+                output_cfg = AnthropicOutputConfig(effort=reasoning_effort)
+            else:
+                # Budget mode (Sonnet 4.5). Leave at least 1024 tokens for the actual response
+                budget = _REASONING_BUDGET[reasoning_effort]
+                budget = min(budget, max(1024, max_tokens - 1024))
+                thinking_cfg = AnthropicThinkingConfig(type="enabled", budget_tokens=budget)
 
         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:
@@ -229,6 +288,8 @@ class ClaudeNode(IO.ComfyNode):
                 messages=[AnthropicMessage(role=AnthropicRole.user, content=content)],
                 system=system_prompt or None,
                 temperature=temperature,
+                thinking=thinking_cfg,
+                output_config=output_cfg,
             ),
             price_extractor=calculate_tokens_price,
         )

comfy_api_nodes/nodes_bytedance.py

@@ -43,15 +43,16 @@ from comfy_api_nodes.util import (
     ApiEndpoint,
     download_url_to_image_tensor,
     download_url_to_video_output,
+    downscale_video_to_max_pixels,
     get_number_of_images,
     image_tensor_pair_to_batch,
     poll_op,
-    resize_video_to_pixel_budget,
     sync_op,
     upload_audio_to_comfyapi,
     upload_image_to_comfyapi,
     upload_images_to_comfyapi,
     upload_video_to_comfyapi,
+    upscale_video_to_min_pixels,
     validate_image_aspect_ratio,
     validate_image_dimensions,
     validate_string,
@@ -110,12 +111,13 @@ def _validate_ref_video_pixels(video: Input.Video, model_id: str, resolution: st
     max_px = limits.get("max")
     if min_px and pixels < min_px:
         raise ValueError(
-            f"Reference video {index} is too small: {w}x{h} = {pixels:,}px. " f"Minimum is {min_px:,}px for this model."
+            f"Reference video {index} is too small: {w}x{h} = {pixels:,} total pixels. "
+            f"Minimum for this model is {min_px:,} total pixels."
         )
     if max_px and pixels > max_px:
         raise ValueError(
-            f"Reference video {index} is too large: {w}x{h} = {pixels:,}px. "
+            f"Reference video {index} is too large: {w}x{h} = {pixels:,} total pixels. "
-            f"Maximum is {max_px:,}px for this model. Try downscaling the video."
+            f"Maximum for this model is {max_px:,} total pixels. Try downscaling the video."
         )
 
 
@@ -1676,14 +1678,14 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
                     "first_frame_asset_id",
                     default="",
                     tooltip="Seedance asset_id to use as the first frame. "
-                            "Mutually exclusive with the first_frame image input.",
+                    "Mutually exclusive with the first_frame image input.",
                     optional=True,
                 ),
                 IO.String.Input(
                     "last_frame_asset_id",
                     default="",
                     tooltip="Seedance asset_id to use as the last frame. "
-                            "Mutually exclusive with the last_frame image input.",
+                    "Mutually exclusive with the last_frame image input.",
                     optional=True,
                 ),
                 IO.Int.Input(
@@ -1865,11 +1867,20 @@ def _seedance2_reference_inputs(resolutions: list[str], default_ratio: str = "16
         IO.Boolean.Input(
             "auto_downscale",
             default=False,
-            advanced=True,
             optional=True,
             tooltip="Automatically downscale reference videos that exceed the model's pixel budget "
             "for the selected resolution. Aspect ratio is preserved; videos already within limits are untouched.",
         ),
+        IO.Boolean.Input(
+            "auto_upscale",
+            default=False,
+            advanced=True,
+            optional=True,
+            tooltip="Automatically upscale reference videos that are below the model's minimum pixel count "
+            "for the selected resolution. Aspect ratio is preserved; videos already meeting the minimum are "
+            "untouched. Note: upscaling a low-resolution source does not add real detail and may produce "
+            "lower-quality generations.",
+        ),
         IO.Autogrow.Input(
             "reference_assets",
             template=IO.Autogrow.TemplateNames(
@@ -2030,7 +2041,13 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
             max_px = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {}).get(model["resolution"], {}).get("max")
             if max_px:
                 for key in reference_videos:
-                    reference_videos[key] = resize_video_to_pixel_budget(reference_videos[key], max_px)
+                    reference_videos[key] = downscale_video_to_max_pixels(reference_videos[key], max_px)
+
+        if model.get("auto_upscale") and reference_videos:
+            min_px = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {}).get(model["resolution"], {}).get("min")
+            if min_px:
+                for key in reference_videos:
+                    reference_videos[key] = upscale_video_to_min_pixels(reference_videos[key], min_px)
 
         total_video_duration = 0.0
         for i, key in enumerate(reference_videos, 1):

comfy_api_nodes/nodes_openrouter.py

@@ -0,0 +1,374 @@
+"""API Nodes for OpenRouter LLM chat completions."""
+
+from dataclasses import dataclass
+from typing import Literal
+
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.openrouter import (
+    OpenRouterChatRequest,
+    OpenRouterChatResponse,
+    OpenRouterContentBlock,
+    OpenRouterImageContent,
+    OpenRouterImageUrl,
+    OpenRouterMessage,
+    OpenRouterReasoningConfig,
+    OpenRouterTextContent,
+    OpenRouterVideoContent,
+    OpenRouterVideoUrl,
+    OpenRouterWebSearchOptions,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    get_number_of_images,
+    sync_op,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_string,
+)
+
+OPENROUTER_CHAT_ENDPOINT = "/proxy/openrouter/api/v1/chat/completions"
+
+
+Profile = Literal["standard", "reasoning", "frontier_reasoning", "perplexity", "perplexity_reasoning"]
+
+
+@dataclass(frozen=True)
+class _ModelSpec:
+    slug: str  # exact OpenRouter model id
+    profile: Profile
+    price_in: float  # USD per token (prompt)
+    price_out: float  # USD per token (completion)
+    max_images: int = 0  # 0 = no image input; otherwise max URL-passed images supported
+    max_videos: int = 0  # 0 = no video input; otherwise max URL-passed videos supported
+
+
+MODELS: list[_ModelSpec] = [
+    _ModelSpec("anthropic/claude-opus-4.7", "frontier_reasoning", 0.000005, 0.000025, max_images=20),
+    _ModelSpec("openai/gpt-5.5-pro", "frontier_reasoning", 0.00003, 0.00018, max_images=20),
+    _ModelSpec("openai/gpt-5.5", "frontier_reasoning", 0.000005, 0.00003, max_images=20),
+    _ModelSpec("google/gemini-3.5-flash", "reasoning", 0.0000015, 0.000009, max_images=20, max_videos=4),
+    _ModelSpec("x-ai/grok-4.20", "reasoning", 0.00000125, 0.0000025, max_images=20),
+    _ModelSpec("x-ai/grok-4.3", "reasoning", 0.00000125, 0.0000025, max_images=20),
+    _ModelSpec("deepseek/deepseek-v4-pro", "reasoning", 0.000000435, 0.00000087),
+    _ModelSpec("deepseek/deepseek-v4-flash", "reasoning", 0.000000112, 0.000000224),
+    _ModelSpec("deepseek/deepseek-v3.2", "reasoning", 0.000000252, 0.000000378),
+    _ModelSpec("qwen/qwen3.6-max-preview", "reasoning", 0.00000104, 0.00000624),
+    _ModelSpec("qwen/qwen3.6-plus", "reasoning", 0.000000325, 0.00000195, max_images=10, max_videos=4),
+    _ModelSpec("qwen/qwen3.6-flash", "reasoning", 0.0000001875, 0.000001125, max_images=10, max_videos=4),
+    _ModelSpec("mistralai/mistral-large-2512", "standard", 0.0000005, 0.0000015, max_images=8),
+    _ModelSpec("mistralai/mistral-medium-3-5", "reasoning", 0.0000015, 0.0000075, max_images=8),
+    _ModelSpec("z-ai/glm-4.6", "reasoning", 0.00000043, 0.00000174),
+    _ModelSpec("z-ai/glm-5", "reasoning", 0.0000006, 0.00000192),
+    _ModelSpec("moonshotai/kimi-k2.6", "reasoning", 0.00000073, 0.00000349, max_images=10),
+    _ModelSpec("moonshotai/kimi-k2-thinking", "reasoning", 0.0000006, 0.0000025),
+    _ModelSpec("perplexity/sonar-pro", "perplexity", 0.000003, 0.000015),
+    _ModelSpec("perplexity/sonar-reasoning-pro", "perplexity_reasoning", 0.000002, 0.000008),
+    _ModelSpec("perplexity/sonar-deep-research", "perplexity_reasoning", 0.000002, 0.000008),
+]
+
+_MODELS_BY_SLUG: dict[str, _ModelSpec] = {m.slug: m for m in MODELS}
+_REASONING_EFFORTS = ["off", "low", "medium", "high"]
+_SEARCH_CONTEXT_SIZES = ["low", "medium", "high"]
+
+
+def _reasoning_extra_inputs() -> list:
+    return [
+        IO.Combo.Input(
+            "reasoning_effort",
+            options=_REASONING_EFFORTS,
+            default="off",
+            tooltip="Reasoning effort. 'off' disables reasoning entirely.",
+            advanced=True,
+        ),
+    ]
+
+
+def _perplexity_extra_inputs() -> list:
+    return [
+        IO.Combo.Input(
+            "search_context_size",
+            options=_SEARCH_CONTEXT_SIZES,
+            default="medium",
+            tooltip="How much web search context to retrieve. Larger = more grounded but slower/pricier.",
+            advanced=True,
+        ),
+    ]
+
+
+def _profile_inputs(profile: Profile) -> list:
+    if profile == "standard":
+        return []
+    if profile in ("reasoning", "frontier_reasoning"):
+        return _reasoning_extra_inputs()
+    if profile == "perplexity":
+        return _perplexity_extra_inputs()
+    if profile == "perplexity_reasoning":
+        return _perplexity_extra_inputs() + _reasoning_extra_inputs()
+    raise ValueError(f"Unknown profile: {profile}")
+
+
+def _media_inputs(spec: _ModelSpec) -> list:
+    extras: list = []
+    if spec.max_images > 0:
+        extras.append(
+            IO.Autogrow.Input(
+                "images",
+                template=IO.Autogrow.TemplateNames(
+                    IO.Image.Input("image"),
+                    names=[f"image_{i}" for i in range(1, spec.max_images + 1)],
+                    min=0,
+                ),
+                tooltip=f"Optional reference image(s) — up to {spec.max_images}. Sent as URLs.",
+            )
+        )
+    if spec.max_videos > 0:
+        extras.append(
+            IO.Autogrow.Input(
+                "videos",
+                template=IO.Autogrow.TemplateNames(
+                    IO.Video.Input("video"),
+                    names=[f"video_{i}" for i in range(1, spec.max_videos + 1)],
+                    min=0,
+                ),
+                tooltip=f"Optional reference video(s) — up to {spec.max_videos}. Sent as URLs.",
+            )
+        )
+    return extras
+
+
+def _inputs_for_model(spec: _ModelSpec) -> list:
+    return _profile_inputs(spec.profile) + _media_inputs(spec)
+
+
+def _build_model_options() -> list[IO.DynamicCombo.Option]:
+    return [IO.DynamicCombo.Option(spec.slug, _inputs_for_model(spec)) for spec in MODELS]
+
+
+def _calculate_price(response: OpenRouterChatResponse) -> float | None:
+    if response.usage and response.usage.cost is not None:
+        return float(response.usage.cost)
+    return None
+
+
+def _price_badge_jsonata() -> str:
+    rates_pairs = []
+    for spec in MODELS:
+        prompt_per_1k = spec.price_in * 1000
+        completion_per_1k = spec.price_out * 1000
+        rates_pairs.append(f'  "{spec.slug}": [{prompt_per_1k:.8g}, {completion_per_1k:.8g}]')
+    rates_block = ",\n".join(rates_pairs)
+    return (
+        "(\n"
+        "  $rates := {\n"
+        f"{rates_block}\n"
+        "  };\n"
+        "  $r := $lookup($rates, widgets.model);\n"
+        "  $r ? {\n"
+        '    "type": "list_usd",\n'
+        '    "usd": $r,\n'
+        '    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }\n'
+        '  } : {"type": "text", "text": "Token-based"}\n'
+        ")"
+    )
+
+
+async def _build_image_blocks(
+    cls: type[IO.ComfyNode], spec: _ModelSpec, images: list[Input.Image]
+) -> list[OpenRouterImageContent]:
+    urls = await upload_images_to_comfyapi(
+        cls,
+        images,
+        max_images=spec.max_images,
+        total_pixels=2048 * 2048,
+        mime_type="image/png",
+        wait_label="Uploading reference images",
+    )
+    return [OpenRouterImageContent(image_url=OpenRouterImageUrl(url=url)) for url in urls]
+
+
+async def _build_video_blocks(cls: type[IO.ComfyNode], videos: list[Input.Video]) -> list[OpenRouterVideoContent]:
+    blocks: list[OpenRouterVideoContent] = []
+    total = len(videos)
+    for idx, video in enumerate(videos):
+        label = "Uploading reference video"
+        if total > 1:
+            label = f"{label} ({idx + 1}/{total})"
+        url = await upload_video_to_comfyapi(cls, video, wait_label=label)
+        blocks.append(OpenRouterVideoContent(video_url=OpenRouterVideoUrl(url=url)))
+    return blocks
+
+
+def _user_message(prompt: str, media_blocks: list[OpenRouterContentBlock]) -> OpenRouterMessage:
+    if not media_blocks:
+        return OpenRouterMessage(role="user", content=prompt)
+    blocks: list[OpenRouterContentBlock] = list(media_blocks)
+    blocks.append(OpenRouterTextContent(text=prompt))
+    return OpenRouterMessage(role="user", content=blocks)
+
+
+def _build_messages(
+    system_prompt: str, prompt: str, media_blocks: list[OpenRouterContentBlock]
+) -> list[OpenRouterMessage]:
+    messages: list[OpenRouterMessage] = []
+    if system_prompt:
+        messages.append(OpenRouterMessage(role="system", content=system_prompt))
+    messages.append(_user_message(prompt, media_blocks))
+    return messages
+
+
+def _build_request(
+    slug: str,
+    system_prompt: str,
+    prompt: str,
+    media_blocks: list[OpenRouterContentBlock],
+    *,
+    seed: int,
+    reasoning_effort: str | None,
+    search_context_size: str | None,
+) -> OpenRouterChatRequest:
+    reasoning_cfg: OpenRouterReasoningConfig | None = None
+    if reasoning_effort and reasoning_effort != "off":
+        # exclude=True asks providers to reason internally but not return the trace
+        reasoning_cfg = OpenRouterReasoningConfig(effort=reasoning_effort, exclude=True)
+    web_search_cfg: OpenRouterWebSearchOptions | None = None
+    if search_context_size:
+        web_search_cfg = OpenRouterWebSearchOptions(search_context_size=search_context_size)
+    return OpenRouterChatRequest(
+        model=slug,
+        messages=_build_messages(system_prompt, prompt, media_blocks),
+        seed=seed if seed > 0 else None,
+        reasoning=reasoning_cfg,
+        web_search_options=web_search_cfg,
+    )
+
+
+def _extract_text(response: OpenRouterChatResponse) -> str:
+    if response.error:
+        code = response.error.code if response.error.code is not None else "unknown"
+        raise ValueError(f"OpenRouter error ({code}): {response.error.message or 'no message'}")
+    if not response.choices:
+        raise ValueError("Empty response from OpenRouter (no choices).")
+    message = response.choices[0].message
+    if not message:
+        raise ValueError("Empty response from OpenRouter (no message).")
+    if message.refusal:
+        raise ValueError(f"Model refused to respond: {message.refusal}")
+    return message.content or ""
+
+
+class OpenRouterLLMNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="OpenRouterLLMNode",
+            display_name="OpenRouter LLM",
+            category="api node/text/OpenRouter",
+            essentials_category="Text Generation",
+            description=(
+                "Generate text responses through OpenRouter. Routes to a curated set of popular "
+                "models from xAI, DeepSeek, Qwen, Mistral, Z.AI (GLM), Moonshot (Kimi), and "
+                "Perplexity Sonar."
+            ),
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text input to the model.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=_build_model_options(),
+                    tooltip="The OpenRouter model used to generate the response.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed for sampling. Set to 0 to omit. Most models treat this as a hint only.",
+                ),
+                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=_price_badge_jsonata(),
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        slug: str = model["model"]
+        spec = _MODELS_BY_SLUG.get(slug)
+        if spec is None:
+            raise ValueError(f"Unknown OpenRouter model: {slug}")
+
+        reasoning_effort: str | None = model.get("reasoning_effort")
+        search_context_size: str | None = model.get("search_context_size")
+
+        image_tensors: list[Input.Image] = [t for t in (model.get("images") or {}).values() if t is not None]
+        if image_tensors and sum(get_number_of_images(t) for t in image_tensors) > spec.max_images:
+            raise ValueError(f"Up to {spec.max_images} images are supported for {slug}.")
+        video_inputs: list[Input.Video] = [v for v in (model.get("videos") or {}).values() if v is not None]
+        if video_inputs and len(video_inputs) > spec.max_videos:
+            raise ValueError(f"Up to {spec.max_videos} videos are supported for {slug}.")
+
+        media_blocks: list[OpenRouterContentBlock] = []
+        if image_tensors:
+            media_blocks.extend(await _build_image_blocks(cls, spec, image_tensors))
+        if video_inputs:
+            media_blocks.extend(await _build_video_blocks(cls, video_inputs))
+
+        request = _build_request(
+            slug,
+            system_prompt,
+            prompt,
+            media_blocks,
+            seed=seed,
+            reasoning_effort=reasoning_effort,
+            search_context_size=search_context_size,
+        )
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=OPENROUTER_CHAT_ENDPOINT, method="POST"),
+            response_model=OpenRouterChatResponse,
+            data=request,
+            price_extractor=_calculate_price,
+        )
+        return IO.NodeOutput(_extract_text(response))
+
+
+class OpenRouterExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [OpenRouterLLMNode]
+
+
+async def comfy_entrypoint() -> OpenRouterExtension:
+    return OpenRouterExtension()

comfy_api_nodes/nodes_rodin.py

@@ -5,32 +5,37 @@ Rodin API docs: https://developer.hyper3d.ai/
 
 """
 
-from inspect import cleandoc
-import folder_paths as comfy_paths
-import os
 import logging
 import math
+import os
+from inspect import cleandoc
 from io import BytesIO
-from typing_extensions import override
+from typing import Any
+
+import aiohttp
 from PIL import Image
+from typing_extensions import override
+
+import folder_paths as comfy_paths
+from comfy_api.latest import IO, ComfyExtension, Types
 from comfy_api_nodes.apis.rodin import (
-    Rodin3DGenerateRequest,
+    JobStatus,
-    Rodin3DGenerateResponse,
     Rodin3DCheckStatusRequest,
     Rodin3DCheckStatusResponse,
     Rodin3DDownloadRequest,
     Rodin3DDownloadResponse,
-    JobStatus,
+    Rodin3DGen25Request,
+    Rodin3DGenerateRequest,
+    Rodin3DGenerateResponse,
 )
 from comfy_api_nodes.util import (
-    sync_op,
-    poll_op,
     ApiEndpoint,
     download_url_to_bytesio,
     download_url_to_file_3d,
+    poll_op,
+    sync_op,
+    validate_string,
 )
-from comfy_api.latest import ComfyExtension, IO, Types
-
 
 COMMON_PARAMETERS = [
     IO.Int.Input(
@@ -51,40 +56,30 @@ COMMON_PARAMETERS = [
 ]
 
 
-def get_quality_mode(poly_count):
+_QUALITY_MESH_OPTIONS: dict[str, tuple[str, int]] = {
-    polycount = poly_count.split("-")
+    "4K-Quad":       ("Quad", 4000),
-    poly = polycount[1]
+    "8K-Quad":       ("Quad", 8000),
-    count = polycount[0]
+    "18K-Quad":      ("Quad", 18000),
-    if poly == "Triangle":
+    "50K-Quad":      ("Quad", 50000),
-        mesh_mode = "Raw"
+    "200K-Quad":     ("Quad", 200000),
-    elif poly == "Quad":
+    "2K-Triangle":   ("Raw", 2000),
-        mesh_mode = "Quad"
+    "20K-Triangle":  ("Raw", 20000),
-    else:
+    "150K-Triangle": ("Raw", 150000),
-        mesh_mode = "Quad"
+    "200K-Triangle": ("Raw", 200000),
-
+    "500K-Triangle": ("Raw", 500000),
-    if count == "4K":
+    "1M-Triangle":   ("Raw", 1000000),
-        quality_override = 4000
+}
-    elif count == "8K":
-        quality_override = 8000
-    elif count == "18K":
-        quality_override = 18000
-    elif count == "50K":
-        quality_override = 50000
-    elif count == "2K":
-        quality_override = 2000
-    elif count == "20K":
-        quality_override = 20000
-    elif count == "150K":
-        quality_override = 150000
-    elif count == "500K":
-        quality_override = 500000
-    else:
-        quality_override = 18000
-
-    return mesh_mode, quality_override
 
 
-def tensor_to_filelike(tensor, max_pixels: int = 2048*2048):
+def get_quality_mode(poly_count: str) -> tuple[str, int]:
+    """Map a polygon-count preset like '18K-Quad' to (mesh_mode, quality_override).
+
+    Falls back to ('Quad', 18000) for unknown labels; legacy parity.
+    """
+    return _QUALITY_MESH_OPTIONS.get(poly_count, ("Quad", 18000))
+
+
+def tensor_to_filelike(tensor, max_pixels: int = 2048 * 2048):
     """
     Converts a PyTorch tensor to a file-like object.
 
@@ -96,8 +91,8 @@ def tensor_to_filelike(tensor, max_pixels: int = 2048*2048):
     - io.BytesIO: A file-like object containing the image data.
     """
     array = tensor.cpu().numpy()
-    array = (array * 255).astype('uint8')
+    array = (array * 255).astype("uint8")
-    image = Image.fromarray(array, 'RGB')
+    image = Image.fromarray(array, "RGB")
 
     original_width, original_height = image.size
     original_pixels = original_width * original_height
@@ -112,7 +107,7 @@ def tensor_to_filelike(tensor, max_pixels: int = 2048*2048):
         image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
 
     img_byte_arr = BytesIO()
-    image.save(img_byte_arr, format='PNG')  # PNG is used for lossless compression
+    image.save(img_byte_arr, format="PNG")  # PNG is used for lossless compression
     img_byte_arr.seek(0)
     return img_byte_arr
 
@@ -145,11 +140,9 @@ async def create_generate_task(
             TAPose=ta_pose,
         ),
         files=[
-            (
+            ("images", open(image, "rb") if isinstance(image, str) else tensor_to_filelike(image))
-                "images",
+            for image in images
-                open(image, "rb") if isinstance(image, str) else tensor_to_filelike(image)
+            if image is not None
-            )
-            for image in images if image is not None
         ],
         content_type="multipart/form-data",
     )
@@ -177,6 +170,7 @@ def check_rodin_status(response: Rodin3DCheckStatusResponse) -> str:
         return "DONE"
     return "Generating"
 
+
 def extract_progress(response: Rodin3DCheckStatusResponse) -> int | None:
     if not response.jobs:
         return None
@@ -214,7 +208,7 @@ async def download_files(url_list, task_uuid: str) -> tuple[str | None, Types.Fi
     model_file_path = None
     file_3d = None
 
-    for i in url_list.list:
+    for i in url_list.items:
         file_path = os.path.join(save_path, i.name)
         if i.name.lower().endswith(".glb"):
             model_file_path = os.path.join(result_folder_name, i.name)
@@ -489,7 +483,16 @@ class Rodin3D_Gen2(IO.ComfyNode):
                 IO.Combo.Input("Material_Type", options=["PBR", "Shaded"], default="PBR", optional=True),
                 IO.Combo.Input(
                     "Polygon_count",
-                    options=["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "2K-Triangle", "20K-Triangle", "150K-Triangle", "500K-Triangle"],
+                    options=[
+                        "4K-Quad",
+                        "8K-Quad",
+                        "18K-Quad",
+                        "50K-Quad",
+                        "2K-Triangle",
+                        "20K-Triangle",
+                        "150K-Triangle",
+                        "500K-Triangle",
+                    ],
                     default="500K-Triangle",
                     optional=True,
                 ),
@@ -542,6 +545,566 @@ class Rodin3D_Gen2(IO.ComfyNode):
         return IO.NodeOutput(model_path, file_3d)
 
 
+def _rodin_multipart_parser(data: dict[str, Any]) -> aiohttp.FormData:
+    """Convert a Rodin request dict to an aiohttp form, fixing bool/list serialization.
+
+    Booleans --> "true"/"false". Lists --> one field per element.
+    """
+    form = aiohttp.FormData(default_to_multipart=True)
+    for key, value in data.items():
+        if value is None:
+            continue
+        if isinstance(value, bool):
+            form.add_field(key, "true" if value else "false")
+        elif isinstance(value, list):
+            for item in value:
+                form.add_field(key, str(item))
+        elif isinstance(value, (bytes, bytearray)):
+            form.add_field(key, value)
+        else:
+            form.add_field(key, str(value))
+    return form
+
+
+async def _create_gen25_task(
+    cls: type[IO.ComfyNode],
+    request: Rodin3DGen25Request,
+    images: list | None,
+) -> tuple[str, str]:
+    """Submit a Gen-2.5 generate job; returns (task_uuid, subscription_key)."""
+
+    if images is not None and len(images) > 5:
+        raise ValueError("Rodin Gen-2.5 supports at most 5 input images.")
+
+    files = None
+    if images:
+        files = [
+            (
+                "images",
+                open(image, "rb") if isinstance(image, str) else tensor_to_filelike(image),
+            )
+            for image in images
+            if image is not None
+        ]
+
+    response = await sync_op(
+        cls,
+        ApiEndpoint(path="/proxy/rodin/api/v2/rodin", method="POST"),
+        response_model=Rodin3DGenerateResponse,
+        data=request,
+        files=files,
+        content_type="multipart/form-data",
+        multipart_parser=_rodin_multipart_parser,
+    )
+
+    if not response.uuid or not response.jobs or not response.jobs.subscription_key:
+        raise RuntimeError(f"Rodin Gen-2.5 submit failed: message={response.message!r}")
+    return response.uuid, response.jobs.subscription_key
+
+
+_PREVIEWABLE_3D_EXTS = {".glb", ".obj", ".fbx", ".stl", ".gltf"}
+
+
+async def _download_gen25_files(
+    download_list: Rodin3DDownloadResponse,
+    task_uuid: str,
+    geometry_file_format: str,
+) -> Types.File3D | None:
+    """Download every file in the list; return the File3D matching the chosen format."""
+
+    folder_name = f"Rodin3D_Gen25_{task_uuid}"
+    save_dir = os.path.join(comfy_paths.get_output_directory(), folder_name)
+    os.makedirs(save_dir, exist_ok=True)
+
+    target_ext = f".{geometry_file_format.lower().lstrip('.')}"
+    file_3d: Types.File3D | None = None
+
+    for item in download_list.items:
+        file_path = os.path.join(save_dir, item.name)
+        ext = os.path.splitext(item.name.lower())[1]
+        # Prefer the file matching the user's chosen format; fall back below.
+        if file_3d is None and ext == target_ext and ext in _PREVIEWABLE_3D_EXTS:
+            file_3d = await download_url_to_file_3d(item.url, target_ext.lstrip("."))
+            with open(file_path, "wb") as f:
+                f.write(file_3d.get_bytes())
+            continue
+        await download_url_to_bytesio(item.url, file_path)
+
+    # If the chosen format wasn't found, surface any model file we did get.
+    if file_3d is None:
+        for item in download_list.items:
+            ext = os.path.splitext(item.name.lower())[1]
+            if ext in _PREVIEWABLE_3D_EXTS:
+                file_3d = await download_url_to_file_3d(item.url, ext.lstrip("."))
+                break
+    return file_3d
+
+
+_MODE_REGULAR = "Regular"
+_MODE_FAST = "Fast"
+_MODE_EXTREME_HIGH = "Extreme-High"
+
+_REGULAR_POLY_OPTIONS = [
+    "Default",
+    "4K-Quad",
+    "8K-Quad",
+    "18K-Quad",
+    "50K-Quad",
+    "2K-Triangle",
+    "20K-Triangle",
+    "150K-Triangle",
+    "500K-Triangle",
+    "1M-Triangle",
+]
+
+_TEXTURE_MODE_OPTIONS = ["Default", "legacy", "extreme-low", "low", "medium", "high"]
+_GEOMETRY_FORMAT_OPTIONS = ["glb", "fbx", "obj", "stl"]
+_MATERIAL_OPTIONS = ["PBR", "Shaded", "All", "None"]
+
+
+def _build_mode_input(name: str = "mode") -> IO.DynamicCombo.Input:
+    return IO.DynamicCombo.Input(
+        name,
+        options=[
+            IO.DynamicCombo.Option(
+                _MODE_REGULAR,
+                [
+                    IO.Combo.Input(
+                        "tier",
+                        options=["Gen-2.5-Low", "Gen-2.5-Medium", "Gen-2.5-High"],
+                        default="Gen-2.5-High",
+                        tooltip="Quality tier. Higher tiers produce higher-fidelity geometry.",
+                    ),
+                    IO.Combo.Input(
+                        "polygon_count",
+                        options=_REGULAR_POLY_OPTIONS,
+                        default="Default",
+                        tooltip="Preset face count. 'Default' uses the server's default for the selected tier.",
+                    ),
+                    IO.Boolean.Input(
+                        "creative",
+                        default=False,
+                        tooltip="Creative mode (Medium/High only). Enhances generative robustness.",
+                    ),
+                ],
+            ),
+            IO.DynamicCombo.Option(
+                _MODE_FAST,
+                [
+                    IO.Combo.Input(
+                        "tier",
+                        options=[
+                            "Gen-2.5-Extreme-Low",
+                            "Gen-2.5-Low",
+                            "Gen-2.5-Medium",
+                            "Gen-2.5-High",
+                        ],
+                        default="Gen-2.5-Low",
+                    ),
+                    IO.Int.Input(
+                        "mesh_faces",
+                        default=20000,
+                        min=1000,
+                        max=20000,
+                        display_mode=IO.NumberDisplay.number,
+                        tooltip="Mesh face count (1K-20K in Fast mode).",
+                    ),
+                ],
+            ),
+            IO.DynamicCombo.Option(
+                _MODE_EXTREME_HIGH,
+                [
+                    IO.Combo.Input("mesh_mode", options=["Raw", "Quad"], default="Raw"),
+                    IO.Int.Input(
+                        "mesh_faces",
+                        default=1000000,
+                        min=20000,
+                        max=2000000,
+                        display_mode=IO.NumberDisplay.number,
+                        tooltip=(
+                            "Mesh face count. Raw mode: 20K-2M. "
+                            "Quad mode: keep under 200K (upstream may reject higher values)."
+                        ),
+                    ),
+                    IO.Boolean.Input(
+                        "is_micro",
+                        default=False,
+                        tooltip="Enable micro detail (Extreme-High only).",
+                    ),
+                    IO.Boolean.Input(
+                        "creative",
+                        default=False,
+                        tooltip="Creative mode. Enhances generative robustness.",
+                    ),
+                ],
+            ),
+        ],
+        tooltip=(
+            "Generation mode. Regular = balanced. Fast = 1K-20K faces for rapid prototyping. "
+            "Extreme-High = 20K-2M faces with optional micro details."
+        ),
+    )
+
+
+def _build_common_inputs(*, include_image_only: bool) -> list:
+    inputs: list = [
+        IO.Combo.Input("material", options=_MATERIAL_OPTIONS, default="Shaded"),
+        IO.Combo.Input("geometry_file_format", options=_GEOMETRY_FORMAT_OPTIONS, default="glb"),
+        IO.Combo.Input(
+            "texture_mode",
+            options=_TEXTURE_MODE_OPTIONS,
+            default="Default",
+            optional=True,
+            tooltip="Texture quality preset. 'Default' uses the server's default for the selected tier.",
+        ),
+        IO.Int.Input(
+            "seed",
+            default=0,
+            min=0,
+            max=65535,
+            display_mode=IO.NumberDisplay.number,
+            control_after_generate=True,
+            optional=True,
+        ),
+        IO.Boolean.Input(
+            "TAPose", default=False, optional=True, advanced=True, tooltip="T/A pose for human-like models."
+        ),
+        IO.Boolean.Input(
+            "hd_texture", default=False, optional=True, advanced=True, tooltip="High-quality texture enhancement."
+        ),
+        IO.Boolean.Input(
+            "texture_delight",
+            default=False,
+            optional=True,
+            advanced=True,
+            tooltip="Remove baked lighting from textures.",
+        ),
+    ]
+    if include_image_only:
+        inputs.append(
+            IO.Boolean.Input(
+                "use_original_alpha",
+                default=False,
+                optional=True,
+                advanced=True,
+                tooltip="Preserve image transparency.",
+            )
+        )
+    inputs.extend(
+        [
+            IO.Boolean.Input(
+                "addon_highpack",
+                default=False,
+                optional=True,
+                advanced=True,
+                tooltip="HighPack addon: 4K textures and ~16x faces in Quad mode.",
+            ),
+            IO.Int.Input(
+                "bbox_width",
+                default=0,
+                min=0,
+                max=300,
+                display_mode=IO.NumberDisplay.number,
+                optional=True,
+                advanced=True,
+                tooltip="Bounding-box width (Y axis). Set to 0 with the others to skip bbox.",
+            ),
+            IO.Int.Input(
+                "bbox_height",
+                default=0,
+                min=0,
+                max=300,
+                display_mode=IO.NumberDisplay.number,
+                optional=True,
+                advanced=True,
+                tooltip="Bounding-box height (Z axis).",
+            ),
+            IO.Int.Input(
+                "bbox_length",
+                default=0,
+                min=0,
+                max=300,
+                display_mode=IO.NumberDisplay.number,
+                optional=True,
+                advanced=True,
+                tooltip="Bounding-box length (X axis).",
+            ),
+            IO.Int.Input(
+                "height_cm",
+                default=0,
+                min=0,
+                max=10000,
+                display_mode=IO.NumberDisplay.number,
+                optional=True,
+                advanced=True,
+                tooltip="Approximate model height in centimeters (0 to skip).",
+            ),
+        ]
+    )
+    return inputs
+
+
+_PRICE_EXPR = """
+(
+  $baseCredits := widgets.mode = "extreme-high" ? 1.0 : 0.5;
+  $addonCredits := widgets.addon_highpack ? 1.0 : 0.0;
+  $total := ($baseCredits * 1.5) + ($addonCredits * 0.8);
+  {"type":"usd","usd": $total}
+)
+"""
+
+
+def _resolve_mode_params(mode_input: dict) -> dict:
+    """Translate the DynamicCombo `mode` payload into Gen-2.5 request fields.
+
+    Returns a dict with: tier, quality_override, mesh_mode, geometry_instruct_mode, is_micro.
+    Missing keys mean "do not send" (so we don't override server defaults).
+    """
+    selected = mode_input["mode"]
+    out: dict = {}
+
+    if selected == _MODE_REGULAR:
+        out["tier"] = mode_input["tier"]
+        polygon = mode_input.get("polygon_count", "Default")
+        if polygon != "Default":
+            mesh_mode, faces = get_quality_mode(polygon)
+            out["mesh_mode"] = mesh_mode
+            out["quality_override"] = faces
+        if mode_input.get("creative"):
+            out["geometry_instruct_mode"] = "creative"
+
+    elif selected == _MODE_FAST:
+        out["tier"] = mode_input["tier"]
+        out["mesh_mode"] = "Raw"
+        out["quality_override"] = int(mode_input["mesh_faces"])
+
+    elif selected == _MODE_EXTREME_HIGH:
+        out["tier"] = "Gen-2.5-Extreme-High"
+        out["mesh_mode"] = mode_input["mesh_mode"]
+        out["quality_override"] = int(mode_input["mesh_faces"])
+        if mode_input.get("is_micro"):
+            out["is_micro"] = True
+        if mode_input.get("creative"):
+            out["geometry_instruct_mode"] = "creative"
+    return out
+
+
+def _build_request(
+    *,
+    mode_input: dict,
+    material: str,
+    geometry_file_format: str,
+    texture_mode: str,
+    seed: int,
+    TAPose: bool,
+    hd_texture: bool,
+    texture_delight: bool,
+    addon_highpack: bool,
+    bbox_width: int,
+    bbox_height: int,
+    bbox_length: int,
+    height_cm: int,
+    prompt: str | None = None,
+    use_original_alpha: bool = False,
+) -> Rodin3DGen25Request:
+    mode_params = _resolve_mode_params(mode_input)
+
+    bbox = None
+    if bbox_width and bbox_height and bbox_length:
+        bbox = [bbox_width, bbox_height, bbox_length]
+
+    return Rodin3DGen25Request(
+        tier=mode_params["tier"],
+        prompt=prompt or None,
+        seed=seed,
+        material=material,
+        geometry_file_format=geometry_file_format,
+        texture_mode=None if texture_mode == "Default" else texture_mode,
+        mesh_mode=mode_params.get("mesh_mode"),
+        quality_override=mode_params.get("quality_override"),
+        geometry_instruct_mode=mode_params.get("geometry_instruct_mode"),
+        bbox_condition=bbox,
+        height=height_cm or None,
+        TAPose=TAPose or None,
+        hd_texture=hd_texture or None,
+        texture_delight=texture_delight or None,
+        is_micro=mode_params.get("is_micro"),
+        use_original_alpha=use_original_alpha or None,
+        addons=["HighPack"] if addon_highpack else None,
+    )
+
+
+class Rodin3D_Gen25_Image(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Rodin3D_Gen25_Image",
+            display_name="Rodin 3D Gen-2.5 - Image to 3D",
+            category="api node/3d/Rodin",
+            description=(
+                "Generate a 3D model from 1-5 reference images via Rodin Gen-2.5. "
+                "Pick a mode (Fast / Regular / Extreme-High) to tune quality vs. cost."
+            ),
+            inputs=[
+                IO.Autogrow.Input(
+                    "images",
+                    template=IO.Autogrow.TemplatePrefix(IO.Image.Input("image"), prefix="image", min=1, max=5),
+                    tooltip="1-5 images. The first image is used for materials when multi-view.",
+                ),
+                _build_mode_input(),
+                *_build_common_inputs(include_image_only=True),
+            ],
+            outputs=[IO.File3DAny.Output(display_name="model_file")],
+            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=["mode", "addon_highpack"]),
+                expr=_PRICE_EXPR,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        images: IO.Autogrow.Type,
+        mode: dict,
+        material: str,
+        geometry_file_format: str,
+        texture_mode: str,
+        seed: int,
+        TAPose: bool,
+        hd_texture: bool,
+        texture_delight: bool,
+        use_original_alpha: bool,
+        addon_highpack: bool,
+        bbox_width: int,
+        bbox_height: int,
+        bbox_length: int,
+        height_cm: int,
+    ) -> IO.NodeOutput:
+        image_tensors = [img for img in images.values() if img is not None]
+        if not image_tensors:
+            raise ValueError("Rodin Gen-2.5 Image-to-3D requires at least one image.")
+
+        # Flatten multi-image tensors into individual frames; the API accepts each as a separate part.
+        flat_images: list = []
+        for tensor in image_tensors:
+            if hasattr(tensor, "shape") and len(tensor.shape) == 4:
+                for i in range(tensor.shape[0]):
+                    flat_images.append(tensor[i])
+            else:
+                flat_images.append(tensor)
+
+        if len(flat_images) > 5:
+            raise ValueError(f"Rodin Gen-2.5 accepts at most 5 images; received {len(flat_images)}.")
+
+        request = _build_request(
+            mode_input=mode,
+            material=material,
+            geometry_file_format=geometry_file_format,
+            texture_mode=texture_mode,
+            seed=seed,
+            TAPose=TAPose,
+            hd_texture=hd_texture,
+            texture_delight=texture_delight,
+            addon_highpack=addon_highpack,
+            bbox_width=bbox_width,
+            bbox_height=bbox_height,
+            bbox_length=bbox_length,
+            height_cm=height_cm,
+            prompt=None,
+            use_original_alpha=use_original_alpha,
+        )
+
+        task_uuid, subscription_key = await _create_gen25_task(cls, request, flat_images)
+        await poll_for_task_status(subscription_key, cls)
+        download_list = await get_rodin_download_list(task_uuid, cls)
+        file_3d = await _download_gen25_files(download_list, task_uuid, geometry_file_format)
+        return IO.NodeOutput(file_3d)
+
+
+class Rodin3D_Gen25_Text(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Rodin3D_Gen25_Text",
+            display_name="Rodin 3D Gen-2.5 - Text to 3D",
+            category="api node/3d/Rodin",
+            description=(
+                "Generate a 3D model from a text prompt via Rodin Gen-2.5. "
+                "Pick a mode (Fast / Regular / Extreme-High) to tune quality vs. cost."
+            ),
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for the 3D model.",
+                ),
+                _build_mode_input(),
+                *_build_common_inputs(include_image_only=False),
+            ],
+            outputs=[IO.File3DAny.Output(display_name="model_file")],
+            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=["mode", "addon_highpack"]),
+                expr=_PRICE_EXPR,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        mode: dict,
+        material: str,
+        geometry_file_format: str,
+        texture_mode: str,
+        seed: int,
+        TAPose: bool,
+        hd_texture: bool,
+        texture_delight: bool,
+        addon_highpack: bool,
+        bbox_width: int,
+        bbox_height: int,
+        bbox_length: int,
+        height_cm: int,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, field_name="prompt", min_length=1, max_length=2500)
+        request = _build_request(
+            mode_input=mode,
+            material=material,
+            geometry_file_format=geometry_file_format,
+            texture_mode=texture_mode,
+            seed=seed,
+            TAPose=TAPose,
+            hd_texture=hd_texture,
+            texture_delight=texture_delight,
+            addon_highpack=addon_highpack,
+            bbox_width=bbox_width,
+            bbox_height=bbox_height,
+            bbox_length=bbox_length,
+            height_cm=height_cm,
+            prompt=prompt,
+        )
+        task_uuid, subscription_key = await _create_gen25_task(cls, request, images=None)
+        await poll_for_task_status(subscription_key, cls)
+        download_list = await get_rodin_download_list(task_uuid, cls)
+        file_3d = await _download_gen25_files(download_list, task_uuid, geometry_file_format)
+        return IO.NodeOutput(file_3d)
+
+
 class Rodin3DExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -551,6 +1114,8 @@ class Rodin3DExtension(ComfyExtension):
             Rodin3D_Smooth,
             Rodin3D_Sketch,
             Rodin3D_Gen2,
+            Rodin3D_Gen25_Image,
+            Rodin3D_Gen25_Text,
         ]
 
 

comfy_api_nodes/util/__init__.py

@@ -16,16 +16,17 @@ from .conversions import (
     convert_mask_to_image,
     downscale_image_tensor,
     downscale_image_tensor_by_max_side,
+    downscale_video_to_max_pixels,
     image_tensor_pair_to_batch,
     pil_to_bytesio,
     resize_mask_to_image,
-    resize_video_to_pixel_budget,
     tensor_to_base64_string,
     tensor_to_bytesio,
     tensor_to_pil,
     text_filepath_to_base64_string,
     text_filepath_to_data_uri,
     trim_video,
+    upscale_video_to_min_pixels,
     video_to_base64_string,
 )
 from .download_helpers import (
@@ -88,16 +89,17 @@ __all__ = [
     "convert_mask_to_image",
     "downscale_image_tensor",
     "downscale_image_tensor_by_max_side",
+    "downscale_video_to_max_pixels",
     "image_tensor_pair_to_batch",
     "pil_to_bytesio",
     "resize_mask_to_image",
-    "resize_video_to_pixel_budget",
     "tensor_to_base64_string",
     "tensor_to_bytesio",
     "tensor_to_pil",
     "text_filepath_to_base64_string",
     "text_filepath_to_data_uri",
     "trim_video",
+    "upscale_video_to_min_pixels",
     "video_to_base64_string",
     # Validation utilities
     "get_image_dimensions",

comfy_api_nodes/util/conversions.py

@@ -415,14 +415,48 @@ def trim_video(video: Input.Video, duration_sec: float) -> Input.Video:
         raise RuntimeError(f"Failed to trim video: {str(e)}") from e
 
 
-def resize_video_to_pixel_budget(video: Input.Video, total_pixels: int) -> Input.Video:
+def downscale_video_to_max_pixels(video: Input.Video, max_pixels: int) -> Input.Video:
-    """Downscale a video to fit within ``total_pixels`` (w * h), preserving aspect ratio.
+    """Downscale a video to fit within ``max_pixels`` (w * h), preserving aspect ratio.
 
     Returns the original video object untouched when it already fits. Preserves frame rate, duration, and audio.
     Aspect ratio is preserved up to a fraction of a percent (even-dim rounding).
     """
     src_w, src_h = video.get_dimensions()
-    scale_dims = _compute_downscale_dims(src_w, src_h, total_pixels)
+    scale_dims = _compute_downscale_dims(src_w, src_h, max_pixels)
+    if scale_dims is None:
+        return video
+    return _apply_video_scale(video, scale_dims)
+
+
+def _compute_upscale_dims(src_w: int, src_h: int, total_pixels: int) -> tuple[int, int] | None:
+    """Return upscaled (w, h) with even dims meeting at least ``total_pixels``, or None if already large enough.
+
+    Source aspect ratio is preserved; output may drift by a fraction of a percent because both dimensions
+    are rounded up to even values (many codecs require divisible-by-2). The result is guaranteed to be at
+    least ``total_pixels``.
+    """
+    pixels = src_w * src_h
+    if pixels >= total_pixels:
+        return None
+    scale = math.sqrt(total_pixels / pixels)
+    new_w = math.ceil(src_w * scale)
+    new_h = math.ceil(src_h * scale)
+    if new_w % 2:
+        new_w += 1
+    if new_h % 2:
+        new_h += 1
+    return new_w, new_h
+
+
+def upscale_video_to_min_pixels(video: Input.Video, min_pixels: int) -> Input.Video:
+    """Upscale a video to meet at least ``min_pixels`` (w * h), preserving aspect ratio.
+
+    Returns the original video object untouched when it already meets the minimum. Preserves frame rate,
+    duration, and audio. Aspect ratio is preserved up to a fraction of a percent (even-dim rounding).
+    Note: upscaling a low-resolution source does not add real detail; downstream model quality may suffer.
+    """
+    src_w, src_h = video.get_dimensions()
+    scale_dims = _compute_upscale_dims(src_w, src_h, min_pixels)
     if scale_dims is None:
         return video
     return _apply_video_scale(video, scale_dims)

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.22.0"
+__version__ = "0.22.2"

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.22.0"
+version = "0.22.2"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.43.18
-comfyui-workflow-templates==0.9.79
+comfyui-workflow-templates==0.9.82
 comfyui-embedded-docs==0.5.0
 torch
 torchsde