Track bundled vendor runtime sources

vendor/ComfyUI/comfy_api_nodes/nodes_hunyuan3d.py

@@ -0,0 +1,767 @@
+import zipfile
+from io import BytesIO
+
+import torch
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input, Types
+from comfy_api_nodes.apis.hunyuan3d import (
+    Hunyuan3DViewImage,
+    InputGenerateType,
+    ResultFile3D,
+    SmartTopologyRequest,
+    TaskFile3DInput,
+    TextureEditTaskRequest,
+    To3DPartTaskRequest,
+    To3DProTaskCreateResponse,
+    To3DProTaskQueryRequest,
+    To3DProTaskRequest,
+    To3DProTaskResultResponse,
+    To3DUVTaskRequest,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    bytesio_to_image_tensor,
+    download_url_to_bytesio,
+    download_url_to_file_3d,
+    download_url_to_image_tensor,
+    downscale_image_tensor_by_max_side,
+    poll_op,
+    sync_op,
+    upload_3d_model_to_comfyapi,
+    upload_image_to_comfyapi,
+    validate_image_dimensions,
+    validate_string,
+)
+
+
+def _is_tencent_rate_limited(status: int, body: object) -> bool:
+    return (
+        status == 400
+        and isinstance(body, dict)
+        and "RequestLimitExceeded" in str(body.get("Response", {}).get("Error", {}).get("Code", ""))
+    )
+
+
+class ObjZipResult:
+    __slots__ = ("obj", "texture", "metallic", "normal", "roughness")
+
+    def __init__(
+        self,
+        obj: Types.File3D,
+        texture: Input.Image | None = None,
+        metallic: Input.Image | None = None,
+        normal: Input.Image | None = None,
+        roughness: Input.Image | None = None,
+    ):
+        self.obj = obj
+        self.texture = texture
+        self.metallic = metallic
+        self.normal = normal
+        self.roughness = roughness
+
+
+async def download_and_extract_obj_zip(url: str) -> ObjZipResult:
+    """The Tencent API returns OBJ results as ZIP archives containing the .obj mesh, and texture images.
+
+    When PBR is enabled, the ZIP may contain additional metallic, normal, and roughness maps
+    identified by their filename suffixes.
+    """
+    data = BytesIO()
+    await download_url_to_bytesio(url, data)
+    data.seek(0)
+    if not zipfile.is_zipfile(data):
+        data.seek(0)
+        return ObjZipResult(obj=Types.File3D(source=data, file_format="obj"))
+    data.seek(0)
+    obj_bytes = None
+    textures: dict[str, Input.Image] = {}
+    with zipfile.ZipFile(data) as zf:
+        for name in zf.namelist():
+            lower = name.lower()
+            if lower.endswith(".obj"):
+                obj_bytes = zf.read(name)
+            elif any(lower.endswith(ext) for ext in (".png", ".jpg", ".jpeg", ".bmp", ".tiff", ".webp")):
+                stem = lower.rsplit(".", 1)[0]
+                tensor = bytesio_to_image_tensor(BytesIO(zf.read(name)), mode="RGB")
+                matched_key = "texture"
+                for suffix, key in {
+                    "_metallic": "metallic",
+                    "_normal": "normal",
+                    "_roughness": "roughness",
+                }.items():
+                    if stem.endswith(suffix):
+                        matched_key = key
+                        break
+                textures[matched_key] = tensor
+    if obj_bytes is None:
+        raise ValueError("ZIP archive does not contain an OBJ file.")
+    return ObjZipResult(
+        obj=Types.File3D(source=BytesIO(obj_bytes), file_format="obj"),
+        texture=textures.get("texture"),
+        metallic=textures.get("metallic"),
+        normal=textures.get("normal"),
+        roughness=textures.get("roughness"),
+    )
+
+
+def get_file_from_response(
+    response_objs: list[ResultFile3D], file_type: str, raise_if_not_found: bool = True
+) -> ResultFile3D | None:
+    for i in response_objs:
+        if i.Type.lower() == file_type.lower():
+            return i
+    if raise_if_not_found:
+        raise ValueError(f"'{file_type}' file type is not found in the response.")
+    return None
+
+
+class TencentTextToModelNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TencentTextToModelNode",
+            display_name="Hunyuan3D: Text to Model",
+            category="partner/3d/Tencent",
+            essentials_category="3D",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["3.0", "3.1"],
+                    tooltip="The LowPoly option is unavailable for the `3.1` model.",
+                ),
+                IO.String.Input("prompt", multiline=True, default="", tooltip="Supports up to 1024 characters."),
+                IO.Int.Input("face_count", default=500000, min=3000, max=1500000),
+                IO.DynamicCombo.Input(
+                    "generate_type",
+                    options=[
+                        IO.DynamicCombo.Option("Normal", [IO.Boolean.Input("pbr", default=False)]),
+                        IO.DynamicCombo.Option(
+                            "LowPoly",
+                            [
+                                IO.Combo.Input("polygon_type", options=["triangle", "quadrilateral"]),
+                                IO.Boolean.Input("pbr", default=False),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("Geometry", []),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.File3DGLB.Output(display_name="GLB"),
+                IO.File3DOBJ.Output(display_name="OBJ"),
+                IO.Image.Output(display_name="texture_image"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["generate_type", "generate_type.pbr", "face_count"]),
+                expr="""
+                (
+                  $base := widgets.generate_type = "normal" ? 25 : widgets.generate_type = "lowpoly" ? 30 : 15;
+                  $pbr := $lookup(widgets, "generate_type.pbr") ? 10 : 0;
+                  $face := widgets.face_count != 500000 ? 10 : 0;
+                  {"type":"usd","usd": ($base + $pbr + $face) * 0.02}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        face_count: int,
+        generate_type: InputGenerateType,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        validate_string(prompt, field_name="prompt", min_length=1, max_length=1024)
+        if model == "3.1" and generate_type["generate_type"].lower() == "lowpoly":
+            raise ValueError("The LowPoly option is currently unavailable for the 3.1 model.")
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-pro", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=To3DProTaskRequest(
+                Model=model,
+                Prompt=prompt,
+                FaceCount=face_count,
+                GenerateType=generate_type["generate_type"],
+                EnablePBR=generate_type.get("pbr", None),
+                PolygonType=generate_type.get("polygon_type", None),
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
+        task_id = response.JobId
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-pro/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=task_id),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        obj_file_response = get_file_from_response(result.ResultFile3Ds, "obj", raise_if_not_found=False)
+        obj_result = None
+        if obj_file_response:
+            obj_result = await download_and_extract_obj_zip(obj_file_response.Url)
+        return IO.NodeOutput(
+            f"{task_id}.glb",
+            await download_url_to_file_3d(
+                get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
+            ),
+            obj_result.obj if obj_result else None,
+            obj_result.texture if obj_result else None,
+        )
+
+
+class TencentImageToModelNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TencentImageToModelNode",
+            display_name="Hunyuan3D: Image(s) to Model",
+            category="partner/3d/Tencent",
+            essentials_category="3D",
+            inputs=[
+                IO.Combo.Input(
+                    "model",
+                    options=["3.0", "3.1"],
+                    tooltip="The LowPoly option is unavailable for the `3.1` model.",
+                ),
+                IO.Image.Input("image"),
+                IO.Image.Input("image_left", optional=True),
+                IO.Image.Input("image_right", optional=True),
+                IO.Image.Input("image_back", optional=True),
+                IO.Int.Input("face_count", default=500000, min=3000, max=1500000),
+                IO.DynamicCombo.Input(
+                    "generate_type",
+                    options=[
+                        IO.DynamicCombo.Option("Normal", [IO.Boolean.Input("pbr", default=False)]),
+                        IO.DynamicCombo.Option(
+                            "LowPoly",
+                            [
+                                IO.Combo.Input("polygon_type", options=["triangle", "quadrilateral"]),
+                                IO.Boolean.Input("pbr", default=False),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("Geometry", []),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(display_name="model_file"),  # for backward compatibility only
+                IO.File3DGLB.Output(display_name="GLB"),
+                IO.File3DOBJ.Output(display_name="OBJ"),
+                IO.Image.Output(display_name="texture_image"),
+                IO.Image.Output(display_name="optional_metallic"),
+                IO.Image.Output(display_name="optional_normal"),
+                IO.Image.Output(display_name="optional_roughness"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            is_output_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["generate_type", "generate_type.pbr", "face_count"],
+                    inputs=["image_left", "image_right", "image_back"],
+                ),
+                expr="""
+                (
+                  $base := widgets.generate_type = "normal" ? 25 : widgets.generate_type = "lowpoly" ? 30 : 15;
+                  $multiview := (
+                    inputs.image_left.connected or inputs.image_right.connected or inputs.image_back.connected
+                  ) ? 10 : 0;
+                  $pbr := $lookup(widgets, "generate_type.pbr") ? 10 : 0;
+                  $face := widgets.face_count != 500000 ? 10 : 0;
+                  {"type":"usd","usd": ($base + $multiview + $pbr + $face) * 0.02}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        face_count: int,
+        generate_type: InputGenerateType,
+        seed: int,
+        image_left: Input.Image | None = None,
+        image_right: Input.Image | None = None,
+        image_back: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        _ = seed
+        if model == "3.1" and generate_type["generate_type"].lower() == "lowpoly":
+            raise ValueError("The LowPoly option is currently unavailable for the 3.1 model.")
+        validate_image_dimensions(image, min_width=128, min_height=128)
+        multiview_images = []
+        for k, v in {
+            "left": image_left,
+            "right": image_right,
+            "back": image_back,
+        }.items():
+            if v is None:
+                continue
+            validate_image_dimensions(v, min_width=128, min_height=128)
+            multiview_images.append(
+                Hunyuan3DViewImage(
+                    ViewType=k,
+                    ViewImageUrl=await upload_image_to_comfyapi(
+                        cls,
+                        downscale_image_tensor_by_max_side(v, max_side=4900),
+                        mime_type="image/webp",
+                        total_pixels=24_010_000,
+                    ),
+                )
+            )
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-pro", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=To3DProTaskRequest(
+                Model=model,
+                FaceCount=face_count,
+                GenerateType=generate_type["generate_type"],
+                ImageUrl=await upload_image_to_comfyapi(
+                    cls,
+                    downscale_image_tensor_by_max_side(image, max_side=4900),
+                    mime_type="image/webp",
+                    total_pixels=24_010_000,
+                ),
+                MultiViewImages=multiview_images if multiview_images else None,
+                EnablePBR=generate_type.get("pbr", None),
+                PolygonType=generate_type.get("polygon_type", None),
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
+        task_id = response.JobId
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-pro/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=task_id),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        obj_file_response = get_file_from_response(result.ResultFile3Ds, "obj", raise_if_not_found=False)
+        if obj_file_response:
+            obj_result = await download_and_extract_obj_zip(obj_file_response.Url)
+            return IO.NodeOutput(
+                f"{task_id}.glb",
+                await download_url_to_file_3d(
+                    get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
+                ),
+                obj_result.obj,
+                obj_result.texture,
+                obj_result.metallic if obj_result.metallic is not None else torch.zeros(1, 1, 1, 3),
+                obj_result.normal if obj_result.normal is not None else torch.zeros(1, 1, 1, 3),
+                obj_result.roughness if obj_result.roughness is not None else torch.zeros(1, 1, 1, 3),
+            )
+        return IO.NodeOutput(
+            f"{task_id}.glb",
+            await download_url_to_file_3d(
+                get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id
+            ),
+            None,
+            None,
+            None,
+            None,
+            None,
+        )
+
+
+class TencentModelTo3DUVNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TencentModelTo3DUVNode",
+            display_name="Hunyuan3D: Model to UV",
+            category="partner/3d/Tencent",
+            description="Perform UV unfolding on a 3D model to generate UV texture. "
+            "Input model must have less than 30000 faces.",
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[IO.File3DGLB, IO.File3DOBJ, IO.File3DFBX, IO.File3DAny],
+                    tooltip="Input 3D model (GLB, OBJ, or FBX)",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.File3DOBJ.Output(display_name="OBJ"),
+                IO.File3DFBX.Output(display_name="FBX"),
+                IO.Image.Output(display_name="uv_image"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(expr='{"type":"usd","usd":0.2}'),
+        )
+
+    SUPPORTED_FORMATS = {"glb", "obj", "fbx"}
+
+    @classmethod
+    async def execute(
+        cls,
+        model_3d: Types.File3D,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        file_format = model_3d.format.lower()
+        if file_format not in cls.SUPPORTED_FORMATS:
+            raise ValueError(
+                f"Unsupported file format: '{file_format}'. "
+                f"Supported formats: {', '.join(sorted(cls.SUPPORTED_FORMATS))}."
+            )
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-uv", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=To3DUVTaskRequest(
+                File=TaskFile3DInput(
+                    Type=file_format.upper(),
+                    Url=await upload_3d_model_to_comfyapi(cls, model_3d, file_format),
+                )
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-uv/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=response.JobId),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        uv_image_file = get_file_from_response(result.ResultFile3Ds, "uv_image", raise_if_not_found=False)
+        uv_image = (
+            await download_url_to_image_tensor(uv_image_file.Url)
+            if uv_image_file is not None
+            else torch.zeros(1, 1, 1, 3)
+        )
+        return IO.NodeOutput(
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
+            uv_image,
+        )
+
+
+class Tencent3DTextureEditNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Tencent3DTextureEditNode",
+            display_name="Hunyuan3D: 3D Texture Edit",
+            category="partner/3d/Tencent",
+            description="After inputting the 3D model, perform 3D model texture redrawing.",
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[IO.File3DFBX, IO.File3DAny],
+                    tooltip="3D model in FBX format. Model should have less than 100000 faces.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Describes texture editing. Supports up to 1024 UTF-8 characters.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.File3DGLB.Output(display_name="GLB"),
+                IO.File3DOBJ.Output(display_name="OBJ"),
+                IO.Image.Output(display_name="texture_image"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd": 0.6}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_3d: Types.File3D,
+        prompt: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        file_format = model_3d.format.lower()
+        if file_format != "fbx":
+            raise ValueError(f"Unsupported file format: '{file_format}'. Only FBX format is supported.")
+        validate_string(prompt, field_name="prompt", min_length=1, max_length=1024)
+        model_url = await upload_3d_model_to_comfyapi(cls, model_3d, file_format)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-texture-edit", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=TextureEditTaskRequest(
+                File3D=TaskFile3DInput(Type=file_format.upper(), Url=model_url),
+                Prompt=prompt,
+                EnablePBR=True,
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
+
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-texture-edit/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=response.JobId),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        return IO.NodeOutput(
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb"),
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
+            await download_url_to_image_tensor(get_file_from_response(result.ResultFile3Ds, "texture_image").Url),
+        )
+
+
+class Tencent3DPartNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Tencent3DPartNode",
+            display_name="Hunyuan3D: 3D Part",
+            category="partner/3d/Tencent",
+            description="Automatically perform component identification and generation based on the model structure.",
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[IO.File3DFBX, IO.File3DAny],
+                    tooltip="3D model in FBX format. Model should have less than 30000 faces.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.File3DFBX.Output(display_name="FBX"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(expr='{"type":"usd","usd":0.6}'),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model_3d: Types.File3D,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        file_format = model_3d.format.lower()
+        if file_format != "fbx":
+            raise ValueError(f"Unsupported file format: '{file_format}'. Only FBX format is supported.")
+        model_url = await upload_3d_model_to_comfyapi(cls, model_3d, file_format)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-part", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=To3DPartTaskRequest(
+                File=TaskFile3DInput(Type=file_format.upper(), Url=model_url),
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed with code {response.Error.Code}: {response.Error.Message}")
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-part/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=response.JobId),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        return IO.NodeOutput(
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "fbx").Url, "fbx"),
+        )
+
+
+class TencentSmartTopologyNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TencentSmartTopologyNode",
+            display_name="Hunyuan3D: Smart Topology",
+            category="partner/3d/Tencent",
+            description="Perform smart retopology on a 3D model. "
+            "Supports GLB/OBJ formats; max 200MB; recommended for high-poly models.",
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[IO.File3DGLB, IO.File3DOBJ, IO.File3DAny],
+                    tooltip="Input 3D model (GLB or OBJ)",
+                ),
+                IO.Combo.Input(
+                    "polygon_type",
+                    options=["triangle", "quadrilateral"],
+                    tooltip="Surface composition type.",
+                ),
+                IO.Combo.Input(
+                    "face_level",
+                    options=["medium", "high", "low"],
+                    tooltip="Polygon reduction level.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+            ],
+            outputs=[
+                IO.File3DOBJ.Output(display_name="OBJ"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(expr='{"type":"usd","usd":1.0}'),
+        )
+
+    SUPPORTED_FORMATS = {"glb", "obj"}
+
+    @classmethod
+    async def execute(
+        cls,
+        model_3d: Types.File3D,
+        polygon_type: str,
+        face_level: str,
+        seed: int,
+    ) -> IO.NodeOutput:
+        _ = seed
+        file_format = model_3d.format.lower()
+        if file_format not in cls.SUPPORTED_FORMATS:
+            raise ValueError(
+                f"Unsupported file format: '{file_format}'. " f"Supported: {', '.join(sorted(cls.SUPPORTED_FORMATS))}."
+            )
+        model_url = await upload_3d_model_to_comfyapi(cls, model_3d, file_format)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-smart-topology", method="POST"),
+            response_model=To3DProTaskCreateResponse,
+            data=SmartTopologyRequest(
+                File3D=TaskFile3DInput(Type=file_format.upper(), Url=model_url),
+                PolygonType=polygon_type,
+                FaceLevel=face_level,
+            ),
+            is_rate_limited=_is_tencent_rate_limited,
+        )
+        if response.Error:
+            raise ValueError(f"Task creation failed: [{response.Error.Code}] {response.Error.Message}")
+        result = await poll_op(
+            cls,
+            ApiEndpoint(path="/proxy/tencent/hunyuan/3d-smart-topology/query", method="POST"),
+            data=To3DProTaskQueryRequest(JobId=response.JobId),
+            response_model=To3DProTaskResultResponse,
+            status_extractor=lambda r: r.Status,
+        )
+        return IO.NodeOutput(
+            await download_url_to_file_3d(get_file_from_response(result.ResultFile3Ds, "obj").Url, "obj"),
+        )
+
+
+class TencentHunyuan3DExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            TencentTextToModelNode,
+            TencentImageToModelNode,
+            TencentModelTo3DUVNode,
+            Tencent3DTextureEditNode,
+            Tencent3DPartNode,
+            TencentSmartTopologyNode,
+        ]
+
+
+async def comfy_entrypoint() -> TencentHunyuan3DExtension:
+    return TencentHunyuan3DExtension()