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feat(api-nodes): add Meshy 3D nodes (#11843)

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* feat(api-nodes): add Meshy 3D nodes

* rebased, added JSONata price badges
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160
comfy_api_nodes/apis/meshy.py Normal file
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from typing import TypedDict
from pydantic import BaseModel, Field
from comfy_api.latest import Input
class InputShouldRemesh(TypedDict):
should_remesh: str
topology: str
target_polycount: int
class InputShouldTexture(TypedDict):
should_texture: str
enable_pbr: bool
texture_prompt: str
texture_image: Input.Image | None
class MeshyTaskResponse(BaseModel):
result: str = Field(...)
class MeshyTextToModelRequest(BaseModel):
mode: str = Field("preview")
prompt: str = Field(..., max_length=600)
art_style: str = Field(..., description="'realistic' or 'sculpture'")
ai_model: str = Field(...)
topology: str | None = Field(..., description="'quad' or 'triangle'")
target_polycount: int | None = Field(..., ge=100, le=300000)
should_remesh: bool = Field(
True,
description="False returns the original mesh, ignoring topology and polycount.",
)
symmetry_mode: str = Field(..., description="'auto', 'off' or 'on'")
pose_mode: str = Field(...)
seed: int = Field(...)
moderation: bool = Field(False)
class MeshyRefineTask(BaseModel):
mode: str = Field("refine")
preview_task_id: str = Field(...)
enable_pbr: bool | None = Field(...)
texture_prompt: str | None = Field(...)
texture_image_url: str | None = Field(...)
ai_model: str = Field(...)
moderation: bool = Field(False)
class MeshyImageToModelRequest(BaseModel):
image_url: str = Field(...)
ai_model: str = Field(...)
topology: str | None = Field(..., description="'quad' or 'triangle'")
target_polycount: int | None = Field(..., ge=100, le=300000)
symmetry_mode: str = Field(..., description="'auto', 'off' or 'on'")
should_remesh: bool = Field(
True,
description="False returns the original mesh, ignoring topology and polycount.",
)
should_texture: bool = Field(...)
enable_pbr: bool | None = Field(...)
pose_mode: str = Field(...)
texture_prompt: str | None = Field(None, max_length=600)
texture_image_url: str | None = Field(None)
seed: int = Field(...)
moderation: bool = Field(False)
class MeshyMultiImageToModelRequest(BaseModel):
image_urls: list[str] = Field(...)
ai_model: str = Field(...)
topology: str | None = Field(..., description="'quad' or 'triangle'")
target_polycount: int | None = Field(..., ge=100, le=300000)
symmetry_mode: str = Field(..., description="'auto', 'off' or 'on'")
should_remesh: bool = Field(
True,
description="False returns the original mesh, ignoring topology and polycount.",
)
should_texture: bool = Field(...)
enable_pbr: bool | None = Field(...)
pose_mode: str = Field(...)
texture_prompt: str | None = Field(None, max_length=600)
texture_image_url: str | None = Field(None)
seed: int = Field(...)
moderation: bool = Field(False)
class MeshyRiggingRequest(BaseModel):
input_task_id: str = Field(...)
height_meters: float = Field(...)
texture_image_url: str | None = Field(...)
class MeshyAnimationRequest(BaseModel):
rig_task_id: str = Field(...)
action_id: int = Field(...)
class MeshyTextureRequest(BaseModel):
input_task_id: str = Field(...)
ai_model: str = Field(...)
enable_original_uv: bool = Field(...)
enable_pbr: bool = Field(...)
text_style_prompt: str | None = Field(...)
image_style_url: str | None = Field(...)
class MeshyModelsUrls(BaseModel):
glb: str = Field("")
class MeshyRiggedModelsUrls(BaseModel):
rigged_character_glb_url: str = Field("")
class MeshyAnimatedModelsUrls(BaseModel):
animation_glb_url: str = Field("")
class MeshyResultTextureUrls(BaseModel):
base_color: str = Field(...)
metallic: str | None = Field(None)
normal: str | None = Field(None)
roughness: str | None = Field(None)
class MeshyTaskError(BaseModel):
message: str | None = Field(None)
class MeshyModelResult(BaseModel):
id: str = Field(...)
type: str = Field(...)
model_urls: MeshyModelsUrls = Field(MeshyModelsUrls())
thumbnail_url: str = Field(...)
video_url: str | None = Field(None)
status: str = Field(...)
progress: int = Field(0)
texture_urls: list[MeshyResultTextureUrls] | None = Field([])
task_error: MeshyTaskError | None = Field(None)
class MeshyRiggedResult(BaseModel):
id: str = Field(...)
type: str = Field(...)
status: str = Field(...)
progress: int = Field(0)
result: MeshyRiggedModelsUrls = Field(MeshyRiggedModelsUrls())
task_error: MeshyTaskError | None = Field(None)
class MeshyAnimationResult(BaseModel):
id: str = Field(...)
type: str = Field(...)
status: str = Field(...)
progress: int = Field(0)
result: MeshyAnimatedModelsUrls = Field(MeshyAnimatedModelsUrls())
task_error: MeshyTaskError | None = Field(None)

790
comfy_api_nodes/nodes_meshy.py Normal file
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import os
from typing_extensions import override
from comfy_api.latest import IO, ComfyExtension, Input
from comfy_api_nodes.apis.meshy import (
InputShouldRemesh,
InputShouldTexture,
MeshyAnimationRequest,
MeshyAnimationResult,
MeshyImageToModelRequest,
MeshyModelResult,
MeshyMultiImageToModelRequest,
MeshyRefineTask,
MeshyRiggedResult,
MeshyRiggingRequest,
MeshyTaskResponse,
MeshyTextToModelRequest,
MeshyTextureRequest,
)
from comfy_api_nodes.util import (
ApiEndpoint,
download_url_to_bytesio,
poll_op,
sync_op,
upload_images_to_comfyapi,
validate_string,
)
from folder_paths import get_output_directory
class MeshyTextToModelNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="MeshyTextToModelNode",
display_name="Meshy: Text to Model",
category="api node/3d/Meshy",
inputs=[
IO.Combo.Input("model", options=["latest"]),
IO.String.Input("prompt", multiline=True, default=""),
IO.Combo.Input("style", options=["realistic", "sculpture"]),
IO.DynamicCombo.Input(
"should_remesh",
options=[
IO.DynamicCombo.Option(
"true",
[
IO.Combo.Input("topology", options=["triangle", "quad"]),
IO.Int.Input(
"target_polycount",
default=300000,
min=100,
max=300000,
display_mode=IO.NumberDisplay.number,
),
],
),
IO.DynamicCombo.Option("false", []),
],
tooltip="When set to false, returns an unprocessed triangular mesh.",
),
IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"]),
IO.Combo.Input(
"pose_mode",
options=["", "A-pose", "T-pose"],
tooltip="Specify the pose mode for the generated model.",
),
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"),
IO.Custom("MESHY_TASK_ID").Output(display_name="meshy_task_id"),
],
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(
expr="""{"type":"usd","usd":0.8}""",
),
)
@classmethod
async def execute(
cls,
model: str,
prompt: str,
style: str,
should_remesh: InputShouldRemesh,
symmetry_mode: str,
pose_mode: str,
seed: int,
) -> IO.NodeOutput:
validate_string(prompt, field_name="prompt", min_length=1, max_length=600)
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/meshy/openapi/v2/text-to-3d", method="POST"),
response_model=MeshyTaskResponse,
data=MeshyTextToModelRequest(
prompt=prompt,
art_style=style,
ai_model=model,
topology=should_remesh.get("topology", None),
target_polycount=should_remesh.get("target_polycount", None),
should_remesh=should_remesh["should_remesh"] == "true",
symmetry_mode=symmetry_mode,
pose_mode=pose_mode.lower(),
seed=seed,
),
)
result = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/meshy/openapi/v2/text-to-3d/{response.result}"),
response_model=MeshyModelResult,
status_extractor=lambda r: r.status,
progress_extractor=lambda r: r.progress,
)
model_file = f"meshy_model_{response.result}.glb"
await download_url_to_bytesio(result.model_urls.glb, os.path.join(get_output_directory(), model_file))
return IO.NodeOutput(model_file, response.result)
class MeshyRefineNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="MeshyRefineNode",
display_name="Meshy: Refine Draft Model",
category="api node/3d/Meshy",
description="Refine a previously created draft model.",
inputs=[
IO.Combo.Input("model", options=["latest"]),
IO.Custom("MESHY_TASK_ID").Input("meshy_task_id"),
IO.Boolean.Input(
"enable_pbr",
default=False,
tooltip="Generate PBR Maps (metallic, roughness, normal) in addition to the base color. "
"Note: this should be set to false when using Sculpture style, "
"as Sculpture style generates its own set of PBR maps.",
),
IO.String.Input(
"texture_prompt",
default="",
multiline=True,
tooltip="Provide a text prompt to guide the texturing process. "
"Maximum 600 characters. Cannot be used at the same time as 'texture_image'.",
),
IO.Image.Input(
"texture_image",
tooltip="Only one of 'texture_image' or 'texture_prompt' may be used at the same time.",
optional=True,
),
],
outputs=[
IO.String.Output(display_name="model_file"),
IO.Custom("MESHY_TASK_ID").Output(display_name="meshy_task_id"),
],
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(
expr="""{"type":"usd","usd":0.4}""",
),
)
@classmethod
async def execute(
cls,
model: str,
meshy_task_id: str,
enable_pbr: bool,
texture_prompt: str,
texture_image: Input.Image | None = None,
) -> IO.NodeOutput:
if texture_prompt and texture_image is not None:
raise ValueError("texture_prompt and texture_image cannot be used at the same time")
texture_image_url = None
if texture_prompt:
validate_string(texture_prompt, field_name="texture_prompt", max_length=600)
if texture_image is not None:
texture_image_url = (await upload_images_to_comfyapi(cls, texture_image, wait_label="Uploading texture"))[0]
response = await sync_op(
cls,
endpoint=ApiEndpoint(path="/proxy/meshy/openapi/v2/text-to-3d", method="POST"),
response_model=MeshyTaskResponse,
data=MeshyRefineTask(
preview_task_id=meshy_task_id,
enable_pbr=enable_pbr,
texture_prompt=texture_prompt if texture_prompt else None,
texture_image_url=texture_image_url,
ai_model=model,
),
)
result = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/meshy/openapi/v2/text-to-3d/{response.result}"),
response_model=MeshyModelResult,
status_extractor=lambda r: r.status,
progress_extractor=lambda r: r.progress,
)
model_file = f"meshy_model_{response.result}.glb"
await download_url_to_bytesio(result.model_urls.glb, os.path.join(get_output_directory(), model_file))
return IO.NodeOutput(model_file, response.result)
class MeshyImageToModelNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="MeshyImageToModelNode",
display_name="Meshy: Image to Model",
category="api node/3d/Meshy",
inputs=[
IO.Combo.Input("model", options=["latest"]),
IO.Image.Input("image"),
IO.DynamicCombo.Input(
"should_remesh",
options=[
IO.DynamicCombo.Option(
"true",
[
IO.Combo.Input("topology", options=["triangle", "quad"]),
IO.Int.Input(
"target_polycount",
default=300000,
min=100,
max=300000,
display_mode=IO.NumberDisplay.number,
),
],
),
IO.DynamicCombo.Option("false", []),
],
tooltip="When set to false, returns an unprocessed triangular mesh.",
),
IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"]),
IO.DynamicCombo.Input(
"should_texture",
options=[
IO.DynamicCombo.Option(
"true",
[
IO.Boolean.Input(
"enable_pbr",
default=False,
tooltip="Generate PBR Maps (metallic, roughness, normal) "
"in addition to the base color.",
),
IO.String.Input(
"texture_prompt",
default="",
multiline=True,
tooltip="Provide a text prompt to guide the texturing process. "
"Maximum 600 characters. Cannot be used at the same time as 'texture_image'.",
),
IO.Image.Input(
"texture_image",
tooltip="Only one of 'texture_image' or 'texture_prompt' "
"may be used at the same time.",
optional=True,
),
],
),
IO.DynamicCombo.Option("false", []),
],
tooltip="Determines whether textures are generated. "
"Setting it to false skips the texture phase and returns a mesh without textures.",
),
IO.Combo.Input(
"pose_mode",
options=["", "A-pose", "T-pose"],
tooltip="Specify the pose mode for the generated model.",
),
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"),
IO.Custom("MESHY_TASK_ID").Output(display_name="meshy_task_id"),
],
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=["should_texture"]),
expr="""
(
$prices := {"true": 1.2, "false": 0.8};
{"type":"usd","usd": $lookup($prices, widgets.should_texture)}
)
""",
),
)
@classmethod
async def execute(
cls,
model: str,
image: Input.Image,
should_remesh: InputShouldRemesh,
symmetry_mode: str,
should_texture: InputShouldTexture,
pose_mode: str,
seed: int,
) -> IO.NodeOutput:
texture = should_texture["should_texture"] == "true"
texture_image_url = texture_prompt = None
if texture:
if should_texture["texture_prompt"] and should_texture["texture_image"] is not None:
raise ValueError("texture_prompt and texture_image cannot be used at the same time")
if should_texture["texture_prompt"]:
validate_string(should_texture["texture_prompt"], field_name="texture_prompt", max_length=600)
texture_prompt = should_texture["texture_prompt"]
if should_texture["texture_image"] is not None:
texture_image_url = (
await upload_images_to_comfyapi(
cls, should_texture["texture_image"], wait_label="Uploading texture"
)
)[0]
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/meshy/openapi/v1/image-to-3d", method="POST"),
response_model=MeshyTaskResponse,
data=MeshyImageToModelRequest(
image_url=(await upload_images_to_comfyapi(cls, image, wait_label="Uploading base image"))[0],
ai_model=model,
topology=should_remesh.get("topology", None),
target_polycount=should_remesh.get("target_polycount", None),
symmetry_mode=symmetry_mode,
should_remesh=should_remesh["should_remesh"] == "true",
should_texture=texture,
enable_pbr=should_texture.get("enable_pbr", None),
pose_mode=pose_mode.lower(),
texture_prompt=texture_prompt,
texture_image_url=texture_image_url,
seed=seed,
),
)
result = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/meshy/openapi/v1/image-to-3d/{response.result}"),
response_model=MeshyModelResult,
status_extractor=lambda r: r.status,
progress_extractor=lambda r: r.progress,
)
model_file = f"meshy_model_{response.result}.glb"
await download_url_to_bytesio(result.model_urls.glb, os.path.join(get_output_directory(), model_file))
return IO.NodeOutput(model_file, response.result)
class MeshyMultiImageToModelNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="MeshyMultiImageToModelNode",
display_name="Meshy: Multi-Image to Model",
category="api node/3d/Meshy",
inputs=[
IO.Combo.Input("model", options=["latest"]),
IO.Autogrow.Input(
"images",
template=IO.Autogrow.TemplatePrefix(IO.Image.Input("image"), prefix="image", min=2, max=4),
),
IO.DynamicCombo.Input(
"should_remesh",
options=[
IO.DynamicCombo.Option(
"true",
[
IO.Combo.Input("topology", options=["triangle", "quad"]),
IO.Int.Input(
"target_polycount",
default=300000,
min=100,
max=300000,
display_mode=IO.NumberDisplay.number,
),
],
),
IO.DynamicCombo.Option("false", []),
],
tooltip="When set to false, returns an unprocessed triangular mesh.",
),
IO.Combo.Input("symmetry_mode", options=["auto", "on", "off"]),
IO.DynamicCombo.Input(
"should_texture",
options=[
IO.DynamicCombo.Option(
"true",
[
IO.Boolean.Input(
"enable_pbr",
default=False,
tooltip="Generate PBR Maps (metallic, roughness, normal) "
"in addition to the base color.",
),
IO.String.Input(
"texture_prompt",
default="",
multiline=True,
tooltip="Provide a text prompt to guide the texturing process. "
"Maximum 600 characters. Cannot be used at the same time as 'texture_image'.",
),
IO.Image.Input(
"texture_image",
tooltip="Only one of 'texture_image' or 'texture_prompt' "
"may be used at the same time.",
optional=True,
),
],
),
IO.DynamicCombo.Option("false", []),
],
tooltip="Determines whether textures are generated. "
"Setting it to false skips the texture phase and returns a mesh without textures.",
),
IO.Combo.Input(
"pose_mode",
options=["", "A-pose", "T-pose"],
tooltip="Specify the pose mode for the generated model.",
),
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"),
IO.Custom("MESHY_TASK_ID").Output(display_name="meshy_task_id"),
],
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=["should_texture"]),
expr="""
(
$prices := {"true": 0.6, "false": 0.2};
{"type":"usd","usd": $lookup($prices, widgets.should_texture)}
)
""",
),
)
@classmethod
async def execute(
cls,
model: str,
images: IO.Autogrow.Type,
should_remesh: InputShouldRemesh,
symmetry_mode: str,
should_texture: InputShouldTexture,
pose_mode: str,
seed: int,
) -> IO.NodeOutput:
texture = should_texture["should_texture"] == "true"
texture_image_url = texture_prompt = None
if texture:
if should_texture["texture_prompt"] and should_texture["texture_image"] is not None:
raise ValueError("texture_prompt and texture_image cannot be used at the same time")
if should_texture["texture_prompt"]:
validate_string(should_texture["texture_prompt"], field_name="texture_prompt", max_length=600)
texture_prompt = should_texture["texture_prompt"]
if should_texture["texture_image"] is not None:
texture_image_url = (
await upload_images_to_comfyapi(
cls, should_texture["texture_image"], wait_label="Uploading texture"
)
)[0]
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/meshy/openapi/v1/multi-image-to-3d", method="POST"),
response_model=MeshyTaskResponse,
data=MeshyMultiImageToModelRequest(
image_urls=await upload_images_to_comfyapi(
cls, list(images.values()), wait_label="Uploading base images"
),
ai_model=model,
topology=should_remesh.get("topology", None),
target_polycount=should_remesh.get("target_polycount", None),
symmetry_mode=symmetry_mode,
should_remesh=should_remesh["should_remesh"] == "true",
should_texture=texture,
enable_pbr=should_texture.get("enable_pbr", None),
pose_mode=pose_mode.lower(),
texture_prompt=texture_prompt,
texture_image_url=texture_image_url,
seed=seed,
),
)
result = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/meshy/openapi/v1/multi-image-to-3d/{response.result}"),
response_model=MeshyModelResult,
status_extractor=lambda r: r.status,
progress_extractor=lambda r: r.progress,
)
model_file = f"meshy_model_{response.result}.glb"
await download_url_to_bytesio(result.model_urls.glb, os.path.join(get_output_directory(), model_file))
return IO.NodeOutput(model_file, response.result)
class MeshyRigModelNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="MeshyRigModelNode",
display_name="Meshy: Rig Model",
category="api node/3d/Meshy",
description="Provides a rigged character in standard formats. "
"Auto-rigging is currently not suitable for untextured meshes, non-humanoid assets, "
"or humanoid assets with unclear limb and body structure.",
inputs=[
IO.Custom("MESHY_TASK_ID").Input("meshy_task_id"),
IO.Float.Input(
"height_meters",
min=0.1,
max=15.0,
default=1.7,
tooltip="The approximate height of the character model in meters. "
"This aids in scaling and rigging accuracy.",
),
IO.Image.Input(
"texture_image",
tooltip="The model's UV-unwrapped base color texture image.",
optional=True,
),
],
outputs=[
IO.String.Output(display_name="model_file"),
IO.Custom("MESHY_RIGGED_TASK_ID").Output(display_name="rig_task_id"),
],
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(
expr="""{"type":"usd","usd":0.2}""",
),
)
@classmethod
async def execute(
cls,
meshy_task_id: str,
height_meters: float,
texture_image: Input.Image | None = None,
) -> IO.NodeOutput:
texture_image_url = None
if texture_image is not None:
texture_image_url = (await upload_images_to_comfyapi(cls, texture_image, wait_label="Uploading texture"))[0]
response = await sync_op(
cls,
endpoint=ApiEndpoint(path="/proxy/meshy/openapi/v1/rigging", method="POST"),
response_model=MeshyTaskResponse,
data=MeshyRiggingRequest(
input_task_id=meshy_task_id,
height_meters=height_meters,
texture_image_url=texture_image_url,
),
)
result = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/meshy/openapi/v1/rigging/{response.result}"),
response_model=MeshyRiggedResult,
status_extractor=lambda r: r.status,
progress_extractor=lambda r: r.progress,
)
model_file = f"meshy_model_{response.result}.glb"
await download_url_to_bytesio(
result.result.rigged_character_glb_url, os.path.join(get_output_directory(), model_file)
)
return IO.NodeOutput(model_file, response.result)
class MeshyAnimateModelNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="MeshyAnimateModelNode",
display_name="Meshy: Animate Model",
category="api node/3d/Meshy",
description="Apply a specific animation action to a previously rigged character.",
inputs=[
IO.Custom("MESHY_RIGGED_TASK_ID").Input("rig_task_id"),
IO.Int.Input(
"action_id",
default=0,
min=0,
max=696,
tooltip="Visit https://docs.meshy.ai/en/api/animation-library for a list of available values.",
),
],
outputs=[
IO.String.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,
is_output_node=True,
price_badge=IO.PriceBadge(
expr="""{"type":"usd","usd":0.12}""",
),
)
@classmethod
async def execute(
cls,
rig_task_id: str,
action_id: int,
) -> IO.NodeOutput:
response = await sync_op(
cls,
endpoint=ApiEndpoint(path="/proxy/meshy/openapi/v1/animations", method="POST"),
response_model=MeshyTaskResponse,
data=MeshyAnimationRequest(
rig_task_id=rig_task_id,
action_id=action_id,
),
)
result = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/meshy/openapi/v1/animations/{response.result}"),
response_model=MeshyAnimationResult,
status_extractor=lambda r: r.status,
progress_extractor=lambda r: r.progress,
)
model_file = f"meshy_model_{response.result}.glb"
await download_url_to_bytesio(result.result.animation_glb_url, os.path.join(get_output_directory(), model_file))
return IO.NodeOutput(model_file, response.result)
class MeshyTextureNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="MeshyTextureNode",
display_name="Meshy: Texture Model",
category="api node/3d/Meshy",
inputs=[
IO.Combo.Input("model", options=["latest"]),
IO.Custom("MESHY_TASK_ID").Input("meshy_task_id"),
IO.Boolean.Input(
"enable_original_uv",
default=True,
tooltip="Use the original UV of the model instead of generating new UVs. "
"When enabled, Meshy preserves existing textures from the uploaded model. "
"If the model has no original UV, the quality of the output might not be as good.",
),
IO.Boolean.Input("pbr", default=False),
IO.String.Input(
"text_style_prompt",
default="",
multiline=True,
tooltip="Describe your desired texture style of the object using text. Maximum 600 characters."
"Maximum 600 characters. Cannot be used at the same time as 'image_style'.",
),
IO.Image.Input(
"image_style",
optional=True,
tooltip="A 2d image to guide the texturing process. "
"Can not be used at the same time with 'text_style_prompt'.",
),
],
outputs=[
IO.String.Output(display_name="model_file"),
IO.Custom("MODEL_TASK_ID").Output(display_name="meshy_task_id"),
],
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(
expr="""{"type":"usd","usd":0.4}""",
),
)
@classmethod
async def execute(
cls,
model: str,
meshy_task_id: str,
enable_original_uv: bool,
pbr: bool,
text_style_prompt: str,
image_style: Input.Image | None = None,
) -> IO.NodeOutput:
if text_style_prompt and image_style is not None:
raise ValueError("text_style_prompt and image_style cannot be used at the same time")
if not text_style_prompt and image_style is None:
raise ValueError("Either text_style_prompt or image_style is required")
image_style_url = None
if image_style is not None:
image_style_url = (await upload_images_to_comfyapi(cls, image_style, wait_label="Uploading style"))[0]
response = await sync_op(
cls,
endpoint=ApiEndpoint(path="/proxy/meshy/openapi/v1/retexture", method="POST"),
response_model=MeshyTaskResponse,
data=MeshyTextureRequest(
input_task_id=meshy_task_id,
ai_model=model,
enable_original_uv=enable_original_uv,
enable_pbr=pbr,
text_style_prompt=text_style_prompt if text_style_prompt else None,
image_style_url=image_style_url,
),
)
result = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/meshy/openapi/v1/retexture/{response.result}"),
response_model=MeshyModelResult,
status_extractor=lambda r: r.status,
progress_extractor=lambda r: r.progress,
)
model_file = f"meshy_model_{response.result}.glb"
await download_url_to_bytesio(result.model_urls.glb, os.path.join(get_output_directory(), model_file))
return IO.NodeOutput(model_file, response.result)
class MeshyExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
return [
MeshyTextToModelNode,
MeshyRefineNode,
MeshyImageToModelNode,
MeshyMultiImageToModelNode,
MeshyRigModelNode,
MeshyAnimateModelNode,
MeshyTextureNode,
]
async def comfy_entrypoint() -> MeshyExtension:
return MeshyExtension()

23
comfy_api_nodes/util/upload_helpers.py
View File

@ -43,7 +43,7 @@ class UploadResponse(BaseModel):
async def upload_images_to_comfyapi(
cls: type[IO.ComfyNode],
image: torch.Tensor,
image: torch.Tensor | list[torch.Tensor],
*,
max_images: int = 8,
mime_type: str | None = None,
@ -55,15 +55,28 @@ async def upload_images_to_comfyapi(
Uploads images to ComfyUI API and returns download URLs.
To upload multiple images, stack them in the batch dimension first.
"""
tensors: list[torch.Tensor] = []
if isinstance(image, list):
for img in image:
is_batch = len(img.shape) > 3
if is_batch:
tensors.extend(img[i] for i in range(img.shape[0]))
else:
tensors.append(img)
else:
is_batch = len(image.shape) > 3
if is_batch:
tensors.extend(image[i] for i in range(image.shape[0]))
else:
tensors.append(image)
# if batched, try to upload each file if max_images is greater than 0
download_urls: list[str] = []
is_batch = len(image.shape) > 3
batch_len = image.shape[0] if is_batch else 1
num_to_upload = min(batch_len, max_images)
num_to_upload = min(len(tensors), max_images)
batch_start_ts = time.monotonic()
for idx in range(num_to_upload):
tensor = image[idx] if is_batch else image
tensor = tensors[idx]
img_io = tensor_to_bytesio(tensor, total_pixels=total_pixels, mime_type=mime_type)
effective_label = wait_label

1
nodes.py
View File

@ -2401,6 +2401,7 @@ async def init_builtin_api_nodes():
"nodes_sora.py",
"nodes_topaz.py",
"nodes_tripo.py",
"nodes_meshy.py",
"nodes_moonvalley.py",
"nodes_rodin.py",
"nodes_gemini.py",