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Merge branch 'master' into dr-support-pip-cm

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Dr.Lt.Data 2025-10-05 07:36:33 +09:00
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30
README.md
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@ -206,14 +206,32 @@ Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
Put your VAE in: models/vae
### AMD GPUs (Linux only)
### AMD GPUs (Linux)
AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.4```
This is the command to install the nightly with ROCm 6.4 which might have some performance improvements:
This is the command to install the nightly with ROCm 7.0 which might have some performance improvements:
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.4```
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.0```
### AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only.
These have less hardware support than the builds above but they work on windows. You also need to install the pytorch version specific to your hardware.
RDNA 3 (RX 7000 series):
```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx110X-dgpu/```
RDNA 3.5 (Strix halo/Ryzen AI Max+ 365):
```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx1151/```
RDNA 4 (RX 9000 series):
```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/```
### Intel GPUs (Windows and Linux)
@ -270,12 +288,6 @@ You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS ve
> **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux).
#### DirectML (AMD Cards on Windows)
This is very badly supported and is not recommended. There are some unofficial builds of pytorch ROCm on windows that exist that will give you a much better experience than this. This readme will be updated once official pytorch ROCm builds for windows come out.
```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
#### Ascend NPUs
For models compatible with Ascend Extension for PyTorch (torch_npu). To get started, ensure your environment meets the prerequisites outlined on the [installation](https://ascend.github.io/docs/sources/ascend/quick_install.html) page. Here's a step-by-step guide tailored to your platform and installation method:

3
comfy_api_nodes/nodes_gemini.py
View File

@ -39,6 +39,7 @@ from comfy_api_nodes.apinode_utils import (
tensor_to_base64_string,
bytesio_to_image_tensor,
)
from comfy_api.util import VideoContainer, VideoCodec
GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini"
@ -310,7 +311,7 @@ class GeminiNode(ComfyNodeABC):
Returns:
List of GeminiPart objects containing the encoded video.
"""
from comfy_api.util import VideoContainer, VideoCodec
base_64_string = video_to_base64_string(
video_input,
container_format=VideoContainer.MP4,

49
comfy_api_nodes/nodes_moonvalley.py
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@ -2,11 +2,7 @@ import logging
from typing import Any, Callable, Optional, TypeVar
import torch
from typing_extensions import override
from comfy_api_nodes.util.validation_utils import (
get_image_dimensions,
validate_image_dimensions,
)
from comfy_api_nodes.util.validation_utils import validate_image_dimensions
from comfy_api_nodes.apis import (
MoonvalleyTextToVideoRequest,
@ -132,47 +128,6 @@ def validate_prompts(
return True
def validate_input_media(width, height, with_frame_conditioning, num_frames_in=None):
# inference validation
# T = num_frames
# in all cases, the following must be true: T divisible by 16 and H,W by 8. in addition...
# with image conditioning: H*W must be divisible by 8192
# without image conditioning: T divisible by 32
if num_frames_in and not num_frames_in % 16 == 0:
return False, ("The input video total frame count must be divisible by 16!")
if height % 8 != 0 or width % 8 != 0:
return False, (
f"Height ({height}) and width ({width}) must be " "divisible by 8"
)
if with_frame_conditioning:
if (height * width) % 8192 != 0:
return False, (
f"Height * width ({height * width}) must be "
"divisible by 8192 for frame conditioning"
)
else:
if num_frames_in and not num_frames_in % 32 == 0:
return False, ("The input video total frame count must be divisible by 32!")
def validate_input_image(
image: torch.Tensor, with_frame_conditioning: bool = False
) -> None:
"""
Validates the input image adheres to the expectations of the API:
- The image resolution should not be less than 300*300px
- The aspect ratio of the image should be between 1:2.5 ~ 2.5:1
"""
height, width = get_image_dimensions(image)
validate_input_media(width, height, with_frame_conditioning)
validate_image_dimensions(
image, min_width=300, min_height=300, max_height=MAX_HEIGHT, max_width=MAX_WIDTH
)
def validate_video_to_video_input(video: VideoInput) -> VideoInput:
"""
Validates and processes video input for Moonvalley Video-to-Video generation.
@ -499,7 +454,7 @@ class MoonvalleyImg2VideoNode(comfy_io.ComfyNode):
seed: int,
steps: int,
) -> comfy_io.NodeOutput:
validate_input_image(image, True)
validate_image_dimensions(image, min_width=300, min_height=300, max_height=MAX_HEIGHT, max_width=MAX_WIDTH)
validate_prompts(prompt, negative_prompt, MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
width_height = parse_width_height_from_res(resolution)

801
comfy_api_nodes/nodes_rodin.py
View File

@ -7,14 +7,15 @@ Rodin API docs: https://developer.hyper3d.ai/
from __future__ import annotations
from inspect import cleandoc
from comfy.comfy_types.node_typing import IO
import folder_paths as comfy_paths
import aiohttp
import os
import asyncio
import io
import logging
import math
from typing import Optional
from io import BytesIO
from typing_extensions import override
from PIL import Image
from comfy_api_nodes.apis.rodin_api import (
Rodin3DGenerateRequest,
@ -31,186 +32,29 @@ from comfy_api_nodes.apis.client import (
SynchronousOperation,
PollingOperation,
)
from comfy_api.latest import ComfyExtension, io as comfy_io
COMMON_PARAMETERS = {
"Seed": (
IO.INT,
{
"default":0,
"min":0,
"max":65535,
"display":"number"
}
COMMON_PARAMETERS = [
comfy_io.Int.Input(
"Seed",
default=0,
min=0,
max=65535,
display_mode=comfy_io.NumberDisplay.number,
optional=True,
),
"Material_Type": (
IO.COMBO,
{
"options": ["PBR", "Shaded"],
"default": "PBR"
}
comfy_io.Combo.Input("Material_Type", options=["PBR", "Shaded"], default="PBR", optional=True),
comfy_io.Combo.Input(
"Polygon_count",
options=["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "200K-Triangle"],
default="18K-Quad",
optional=True,
),
"Polygon_count": (
IO.COMBO,
{
"options": ["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "200K-Triangle"],
"default": "18K-Quad"
}
)
}
def create_task_error(response: Rodin3DGenerateResponse):
"""Check if the response has error"""
return hasattr(response, "error")
]
class Rodin3DAPI:
"""
Generate 3D Assets using Rodin API
"""
RETURN_TYPES = (IO.STRING,)
RETURN_NAMES = ("3D Model Path",)
CATEGORY = "api node/3d/Rodin"
DESCRIPTION = cleandoc(__doc__ or "")
FUNCTION = "api_call"
API_NODE = True
def tensor_to_filelike(self, tensor, max_pixels: int = 2048*2048):
"""
Converts a PyTorch tensor to a file-like object.
Args:
- tensor (torch.Tensor): A tensor representing an image of shape (H, W, C)
where C is the number of channels (3 for RGB), H is height, and W is width.
Returns:
- io.BytesIO: A file-like object containing the image data.
"""
array = tensor.cpu().numpy()
array = (array * 255).astype('uint8')
image = Image.fromarray(array, 'RGB')
original_width, original_height = image.size
original_pixels = original_width * original_height
if original_pixels > max_pixels:
scale = math.sqrt(max_pixels / original_pixels)
new_width = int(original_width * scale)
new_height = int(original_height * scale)
else:
new_width, new_height = original_width, original_height
if new_width != original_width or new_height != original_height:
image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
img_byte_arr = io.BytesIO()
image.save(img_byte_arr, format='PNG') # PNG is used for lossless compression
img_byte_arr.seek(0)
return img_byte_arr
def check_rodin_status(self, response: Rodin3DCheckStatusResponse) -> str:
has_failed = any(job.status == JobStatus.Failed for job in response.jobs)
all_done = all(job.status == JobStatus.Done for job in response.jobs)
status_list = [str(job.status) for job in response.jobs]
logging.info(f"[ Rodin3D API - CheckStatus ] Generate Status: {status_list}")
if has_failed:
logging.error(f"[ Rodin3D API - CheckStatus ] Generate Failed: {status_list}, Please try again.")
raise Exception("[ Rodin3D API ] Generate Failed, Please Try again.")
elif all_done:
return "DONE"
else:
return "Generating"
async def create_generate_task(self, images=None, seed=1, material="PBR", quality_override=18000, tier="Regular", mesh_mode="Quad", TAPose = False, **kwargs):
if images is None:
raise Exception("Rodin 3D generate requires at least 1 image.")
if len(images) > 5:
raise Exception("Rodin 3D generate requires up to 5 image.")
path = "/proxy/rodin/api/v2/rodin"
operation = SynchronousOperation(
endpoint=ApiEndpoint(
path=path,
method=HttpMethod.POST,
request_model=Rodin3DGenerateRequest,
response_model=Rodin3DGenerateResponse,
),
request=Rodin3DGenerateRequest(
seed=seed,
tier=tier,
material=material,
quality_override=quality_override,
mesh_mode=mesh_mode,
TAPose=TAPose,
),
files=[
(
"images",
open(image, "rb") if isinstance(image, str) else self.tensor_to_filelike(image)
)
for image in images if image is not None
],
content_type = "multipart/form-data",
auth_kwargs=kwargs,
)
response = await operation.execute()
if create_task_error(response):
error_message = f"Rodin3D Create 3D generate Task Failed. Message: {response.message}, error: {response.error}"
logging.error(error_message)
raise Exception(error_message)
logging.info("[ Rodin3D API - Submit Jobs ] Submit Generate Task Success!")
subscription_key = response.jobs.subscription_key
task_uuid = response.uuid
logging.info(f"[ Rodin3D API - Submit Jobs ] UUID: {task_uuid}")
return task_uuid, subscription_key
async def poll_for_task_status(self, subscription_key, **kwargs) -> Rodin3DCheckStatusResponse:
path = "/proxy/rodin/api/v2/status"
poll_operation = PollingOperation(
poll_endpoint=ApiEndpoint(
path = path,
method=HttpMethod.POST,
request_model=Rodin3DCheckStatusRequest,
response_model=Rodin3DCheckStatusResponse,
),
request=Rodin3DCheckStatusRequest(
subscription_key = subscription_key
),
completed_statuses=["DONE"],
failed_statuses=["FAILED"],
status_extractor=self.check_rodin_status,
poll_interval=3.0,
auth_kwargs=kwargs,
)
logging.info("[ Rodin3D API - CheckStatus ] Generate Start!")
return await poll_operation.execute()
async def get_rodin_download_list(self, uuid, **kwargs) -> Rodin3DDownloadResponse:
logging.info("[ Rodin3D API - Downloading ] Generate Successfully!")
path = "/proxy/rodin/api/v2/download"
operation = SynchronousOperation(
endpoint=ApiEndpoint(
path=path,
method=HttpMethod.POST,
request_model=Rodin3DDownloadRequest,
response_model=Rodin3DDownloadResponse,
),
request=Rodin3DDownloadRequest(
task_uuid=uuid
),
auth_kwargs=kwargs
)
return await operation.execute()
def get_quality_mode(self, poly_count):
def get_quality_mode(poly_count):
polycount = poly_count.split("-")
poly = polycount[1]
count = polycount[0]
@ -242,7 +86,145 @@ class Rodin3DAPI:
return mesh_mode, quality_override
async def download_files(self, url_list, task_uuid):
def tensor_to_filelike(tensor, max_pixels: int = 2048*2048):
"""
Converts a PyTorch tensor to a file-like object.
Args:
- tensor (torch.Tensor): A tensor representing an image of shape (H, W, C)
where C is the number of channels (3 for RGB), H is height, and W is width.
Returns:
- io.BytesIO: A file-like object containing the image data.
"""
array = tensor.cpu().numpy()
array = (array * 255).astype('uint8')
image = Image.fromarray(array, 'RGB')
original_width, original_height = image.size
original_pixels = original_width * original_height
if original_pixels > max_pixels:
scale = math.sqrt(max_pixels / original_pixels)
new_width = int(original_width * scale)
new_height = int(original_height * scale)
else:
new_width, new_height = original_width, original_height
if new_width != original_width or new_height != original_height:
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
img_byte_arr.seek(0)
return img_byte_arr
async def create_generate_task(
images=None,
seed=1,
material="PBR",
quality_override=18000,
tier="Regular",
mesh_mode="Quad",
TAPose = False,
auth_kwargs: Optional[dict[str, str]] = None,
):
if images is None:
raise Exception("Rodin 3D generate requires at least 1 image.")
if len(images) > 5:
raise Exception("Rodin 3D generate requires up to 5 image.")
path = "/proxy/rodin/api/v2/rodin"
operation = SynchronousOperation(
endpoint=ApiEndpoint(
path=path,
method=HttpMethod.POST,
request_model=Rodin3DGenerateRequest,
response_model=Rodin3DGenerateResponse,
),
request=Rodin3DGenerateRequest(
seed=seed,
tier=tier,
material=material,
quality_override=quality_override,
mesh_mode=mesh_mode,
TAPose=TAPose,
),
files=[
(
"images",
open(image, "rb") if isinstance(image, str) else tensor_to_filelike(image)
)
for image in images if image is not None
],
content_type="multipart/form-data",
auth_kwargs=auth_kwargs,
)
response = await operation.execute()
if hasattr(response, "error"):
error_message = f"Rodin3D Create 3D generate Task Failed. Message: {response.message}, error: {response.error}"
logging.error(error_message)
raise Exception(error_message)
logging.info("[ Rodin3D API - Submit Jobs ] Submit Generate Task Success!")
subscription_key = response.jobs.subscription_key
task_uuid = response.uuid
logging.info(f"[ Rodin3D API - Submit Jobs ] UUID: {task_uuid}")
return task_uuid, subscription_key
def check_rodin_status(response: Rodin3DCheckStatusResponse) -> str:
all_done = all(job.status == JobStatus.Done for job in response.jobs)
status_list = [str(job.status) for job in response.jobs]
logging.info(f"[ Rodin3D API - CheckStatus ] Generate Status: {status_list}")
if any(job.status == JobStatus.Failed for job in response.jobs):
logging.error(f"[ Rodin3D API - CheckStatus ] Generate Failed: {status_list}, Please try again.")
raise Exception("[ Rodin3D API ] Generate Failed, Please Try again.")
if all_done:
return "DONE"
return "Generating"
async def poll_for_task_status(
subscription_key, auth_kwargs: Optional[dict[str, str]] = None,
) -> Rodin3DCheckStatusResponse:
poll_operation = PollingOperation(
poll_endpoint=ApiEndpoint(
path="/proxy/rodin/api/v2/status",
method=HttpMethod.POST,
request_model=Rodin3DCheckStatusRequest,
response_model=Rodin3DCheckStatusResponse,
),
request=Rodin3DCheckStatusRequest(subscription_key=subscription_key),
completed_statuses=["DONE"],
failed_statuses=["FAILED"],
status_extractor=check_rodin_status,
poll_interval=3.0,
auth_kwargs=auth_kwargs,
)
logging.info("[ Rodin3D API - CheckStatus ] Generate Start!")
return await poll_operation.execute()
async def get_rodin_download_list(uuid, auth_kwargs: Optional[dict[str, str]] = None) -> Rodin3DDownloadResponse:
logging.info("[ Rodin3D API - Downloading ] Generate Successfully!")
operation = SynchronousOperation(
endpoint=ApiEndpoint(
path="/proxy/rodin/api/v2/download",
method=HttpMethod.POST,
request_model=Rodin3DDownloadRequest,
response_model=Rodin3DDownloadResponse,
),
request=Rodin3DDownloadRequest(task_uuid=uuid),
auth_kwargs=auth_kwargs,
)
return await operation.execute()
async def download_files(url_list, task_uuid):
save_path = os.path.join(comfy_paths.get_output_directory(), f"Rodin3D_{task_uuid}")
os.makedirs(save_path, exist_ok=True)
model_file_path = None
@ -274,185 +256,212 @@ class Rodin3DAPI:
file_path,
max_retries,
)
return model_file_path
class Rodin3D_Regular(Rodin3DAPI):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"Images":
(
IO.IMAGE,
{
"forceInput":True,
}
)
},
"optional": {
**COMMON_PARAMETERS
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
"comfy_api_key": "API_KEY_COMFY_ORG",
},
}
class Rodin3D_Regular(comfy_io.ComfyNode):
"""Generate 3D Assets using Rodin API"""
async def api_call(
self,
@classmethod
def define_schema(cls) -> comfy_io.Schema:
return comfy_io.Schema(
node_id="Rodin3D_Regular",
display_name="Rodin 3D Generate - Regular Generate",
category="api node/3d/Rodin",
description=cleandoc(cls.__doc__ or ""),
inputs=[
comfy_io.Image.Input("Images"),
*COMMON_PARAMETERS,
],
outputs=[comfy_io.String.Output(display_name="3D Model Path")],
hidden=[
comfy_io.Hidden.auth_token_comfy_org,
comfy_io.Hidden.api_key_comfy_org,
],
is_api_node=True,
)
@classmethod
async def execute(
cls,
Images,
Seed,
Material_Type,
Polygon_count,
**kwargs
):
) -> comfy_io.NodeOutput:
tier = "Regular"
num_images = Images.shape[0]
m_images = []
for i in range(num_images):
m_images.append(Images[i])
mesh_mode, quality_override = self.get_quality_mode(Polygon_count)
task_uuid, subscription_key = await self.create_generate_task(images=m_images, seed=Seed, material=Material_Type,
quality_override=quality_override, tier=tier, mesh_mode=mesh_mode,
**kwargs)
await self.poll_for_task_status(subscription_key, **kwargs)
download_list = await self.get_rodin_download_list(task_uuid, **kwargs)
model = await self.download_files(download_list, task_uuid)
return (model,)
class Rodin3D_Detail(Rodin3DAPI):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"Images":
(
IO.IMAGE,
{
"forceInput":True,
mesh_mode, quality_override = get_quality_mode(Polygon_count)
auth = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
task_uuid, subscription_key = await create_generate_task(
images=m_images,
seed=Seed,
material=Material_Type,
quality_override=quality_override,
tier=tier,
mesh_mode=mesh_mode,
auth_kwargs=auth,
)
},
"optional": {
**COMMON_PARAMETERS
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
"comfy_api_key": "API_KEY_COMFY_ORG",
},
}
await poll_for_task_status(subscription_key, auth_kwargs=auth)
download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
model = await download_files(download_list, task_uuid)
async def api_call(
self,
return comfy_io.NodeOutput(model)
class Rodin3D_Detail(comfy_io.ComfyNode):
"""Generate 3D Assets using Rodin API"""
@classmethod
def define_schema(cls) -> comfy_io.Schema:
return comfy_io.Schema(
node_id="Rodin3D_Detail",
display_name="Rodin 3D Generate - Detail Generate",
category="api node/3d/Rodin",
description=cleandoc(cls.__doc__ or ""),
inputs=[
comfy_io.Image.Input("Images"),
*COMMON_PARAMETERS,
],
outputs=[comfy_io.String.Output(display_name="3D Model Path")],
hidden=[
comfy_io.Hidden.auth_token_comfy_org,
comfy_io.Hidden.api_key_comfy_org,
],
is_api_node=True,
)
@classmethod
async def execute(
cls,
Images,
Seed,
Material_Type,
Polygon_count,
**kwargs
):
) -> comfy_io.NodeOutput:
tier = "Detail"
num_images = Images.shape[0]
m_images = []
for i in range(num_images):
m_images.append(Images[i])
mesh_mode, quality_override = self.get_quality_mode(Polygon_count)
task_uuid, subscription_key = await self.create_generate_task(images=m_images, seed=Seed, material=Material_Type,
quality_override=quality_override, tier=tier, mesh_mode=mesh_mode,
**kwargs)
await self.poll_for_task_status(subscription_key, **kwargs)
download_list = await self.get_rodin_download_list(task_uuid, **kwargs)
model = await self.download_files(download_list, task_uuid)
return (model,)
class Rodin3D_Smooth(Rodin3DAPI):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"Images":
(
IO.IMAGE,
{
"forceInput":True,
mesh_mode, quality_override = get_quality_mode(Polygon_count)
auth = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
task_uuid, subscription_key = await create_generate_task(
images=m_images,
seed=Seed,
material=Material_Type,
quality_override=quality_override,
tier=tier,
mesh_mode=mesh_mode,
auth_kwargs=auth,
)
},
"optional": {
**COMMON_PARAMETERS
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
"comfy_api_key": "API_KEY_COMFY_ORG",
},
}
await poll_for_task_status(subscription_key, auth_kwargs=auth)
download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
model = await download_files(download_list, task_uuid)
async def api_call(
self,
return comfy_io.NodeOutput(model)
class Rodin3D_Smooth(comfy_io.ComfyNode):
"""Generate 3D Assets using Rodin API"""
@classmethod
def define_schema(cls) -> comfy_io.Schema:
return comfy_io.Schema(
node_id="Rodin3D_Smooth",
display_name="Rodin 3D Generate - Smooth Generate",
category="api node/3d/Rodin",
description=cleandoc(cls.__doc__ or ""),
inputs=[
comfy_io.Image.Input("Images"),
*COMMON_PARAMETERS,
],
outputs=[comfy_io.String.Output(display_name="3D Model Path")],
hidden=[
comfy_io.Hidden.auth_token_comfy_org,
comfy_io.Hidden.api_key_comfy_org,
],
is_api_node=True,
)
@classmethod
async def execute(
cls,
Images,
Seed,
Material_Type,
Polygon_count,
**kwargs
):
) -> comfy_io.NodeOutput:
tier = "Smooth"
num_images = Images.shape[0]
m_images = []
for i in range(num_images):
m_images.append(Images[i])
mesh_mode, quality_override = self.get_quality_mode(Polygon_count)
task_uuid, subscription_key = await self.create_generate_task(images=m_images, seed=Seed, material=Material_Type,
quality_override=quality_override, tier=tier, mesh_mode=mesh_mode,
**kwargs)
await self.poll_for_task_status(subscription_key, **kwargs)
download_list = await self.get_rodin_download_list(task_uuid, **kwargs)
model = await self.download_files(download_list, task_uuid)
mesh_mode, quality_override = get_quality_mode(Polygon_count)
auth = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
task_uuid, subscription_key = await create_generate_task(
images=m_images,
seed=Seed,
material=Material_Type,
quality_override=quality_override,
tier=tier,
mesh_mode=mesh_mode,
auth_kwargs=auth,
)
await poll_for_task_status(subscription_key, auth_kwargs=auth)
download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
model = await download_files(download_list, task_uuid)
return (model,)
return comfy_io.NodeOutput(model)
class Rodin3D_Sketch(Rodin3DAPI):
class Rodin3D_Sketch(comfy_io.ComfyNode):
"""Generate 3D Assets using Rodin API"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"Images":
(
IO.IMAGE,
{
"forceInput":True,
}
def define_schema(cls) -> comfy_io.Schema:
return comfy_io.Schema(
node_id="Rodin3D_Sketch",
display_name="Rodin 3D Generate - Sketch Generate",
category="api node/3d/Rodin",
description=cleandoc(cls.__doc__ or ""),
inputs=[
comfy_io.Image.Input("Images"),
comfy_io.Int.Input(
"Seed",
default=0,
min=0,
max=65535,
display_mode=comfy_io.NumberDisplay.number,
optional=True,
),
],
outputs=[comfy_io.String.Output(display_name="3D Model Path")],
hidden=[
comfy_io.Hidden.auth_token_comfy_org,
comfy_io.Hidden.api_key_comfy_org,
],
is_api_node=True,
)
},
"optional": {
"Seed":
(
IO.INT,
{
"default":0,
"min":0,
"max":65535,
"display":"number"
}
)
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
"comfy_api_key": "API_KEY_COMFY_ORG",
},
}
async def api_call(
self,
@classmethod
async def execute(
cls,
Images,
Seed,
**kwargs
):
) -> comfy_io.NodeOutput:
tier = "Sketch"
num_images = Images.shape[0]
m_images = []
@ -461,104 +470,110 @@ class Rodin3D_Sketch(Rodin3DAPI):
material_type = "PBR"
quality_override = 18000
mesh_mode = "Quad"
task_uuid, subscription_key = await self.create_generate_task(
images=m_images, seed=Seed, material=material_type, quality_override=quality_override, tier=tier, mesh_mode=mesh_mode, **kwargs
auth = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
task_uuid, subscription_key = await create_generate_task(
images=m_images,
seed=Seed,
material=material_type,
quality_override=quality_override,
tier=tier,
mesh_mode=mesh_mode,
auth_kwargs=auth,
)
await self.poll_for_task_status(subscription_key, **kwargs)
download_list = await self.get_rodin_download_list(task_uuid, **kwargs)
model = await self.download_files(download_list, task_uuid)
await poll_for_task_status(subscription_key, auth_kwargs=auth)
download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
model = await download_files(download_list, task_uuid)
return (model,)
return comfy_io.NodeOutput(model)
class Rodin3D_Gen2(comfy_io.ComfyNode):
"""Generate 3D Assets using Rodin API"""
class Rodin3D_Gen2(Rodin3DAPI):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"Images":
(
IO.IMAGE,
{
"forceInput":True,
}
def define_schema(cls) -> comfy_io.Schema:
return comfy_io.Schema(
node_id="Rodin3D_Gen2",
display_name="Rodin 3D Generate - Gen-2 Generate",
category="api node/3d/Rodin",
description=cleandoc(cls.__doc__ or ""),
inputs=[
comfy_io.Image.Input("Images"),
comfy_io.Int.Input(
"Seed",
default=0,
min=0,
max=65535,
display_mode=comfy_io.NumberDisplay.number,
optional=True,
),
comfy_io.Combo.Input("Material_Type", options=["PBR", "Shaded"], default="PBR", optional=True),
comfy_io.Combo.Input(
"Polygon_count",
options=["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "2K-Triangle", "20K-Triangle", "150K-Triangle", "500K-Triangle"],
default="500K-Triangle",
optional=True,
),
comfy_io.Boolean.Input("TAPose", default=False),
],
outputs=[comfy_io.String.Output(display_name="3D Model Path")],
hidden=[
comfy_io.Hidden.auth_token_comfy_org,
comfy_io.Hidden.api_key_comfy_org,
],
is_api_node=True,
)
},
"optional": {
"Seed": (
IO.INT,
{
"default":0,
"min":0,
"max":65535,
"display":"number"
}
),
"Material_Type": (
IO.COMBO,
{
"options": ["PBR", "Shaded"],
"default": "PBR"
}
),
"Polygon_count": (
IO.COMBO,
{
"options": ["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "2K-Triangle", "20K-Triangle", "150K-Triangle", "500K-Triangle"],
"default": "500K-Triangle"
}
),
"TAPose": (
IO.BOOLEAN,
{
"default": False,
}
)
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
"comfy_api_key": "API_KEY_COMFY_ORG",
},
}
async def api_call(
self,
@classmethod
async def execute(
cls,
Images,
Seed,
Material_Type,
Polygon_count,
TAPose,
**kwargs
):
) -> comfy_io.NodeOutput:
tier = "Gen-2"
num_images = Images.shape[0]
m_images = []
for i in range(num_images):
m_images.append(Images[i])
mesh_mode, quality_override = self.get_quality_mode(Polygon_count)
task_uuid, subscription_key = await self.create_generate_task(images=m_images, seed=Seed, material=Material_Type,
quality_override=quality_override, tier=tier, mesh_mode=mesh_mode, TAPose=TAPose,
**kwargs)
await self.poll_for_task_status(subscription_key, **kwargs)
download_list = await self.get_rodin_download_list(task_uuid, **kwargs)
model = await self.download_files(download_list, task_uuid)
mesh_mode, quality_override = get_quality_mode(Polygon_count)
auth = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
task_uuid, subscription_key = await create_generate_task(
images=m_images,
seed=Seed,
material=Material_Type,
quality_override=quality_override,
tier=tier,
mesh_mode=mesh_mode,
TAPose=TAPose,
auth_kwargs=auth,
)
await poll_for_task_status(subscription_key, auth_kwargs=auth)
download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
model = await download_files(download_list, task_uuid)
return (model,)
return comfy_io.NodeOutput(model)
# A dictionary that contains all nodes you want to export with their names
# NOTE: names should be globally unique
NODE_CLASS_MAPPINGS = {
"Rodin3D_Regular": Rodin3D_Regular,
"Rodin3D_Detail": Rodin3D_Detail,
"Rodin3D_Smooth": Rodin3D_Smooth,
"Rodin3D_Sketch": Rodin3D_Sketch,
"Rodin3D_Gen2": Rodin3D_Gen2,
}
# A dictionary that contains the friendly/humanly readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
"Rodin3D_Regular": "Rodin 3D Generate - Regular Generate",
"Rodin3D_Detail": "Rodin 3D Generate - Detail Generate",
"Rodin3D_Smooth": "Rodin 3D Generate - Smooth Generate",
"Rodin3D_Sketch": "Rodin 3D Generate - Sketch Generate",
"Rodin3D_Gen2": "Rodin 3D Generate - Gen-2 Generate",
}
class Rodin3DExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
return [
Rodin3D_Regular,
Rodin3D_Detail,
Rodin3D_Smooth,
Rodin3D_Sketch,
Rodin3D_Gen2,
]
async def comfy_entrypoint() -> Rodin3DExtension:
return Rodin3DExtension()

149
comfy_extras/nodes_stable3d.py
View File

@ -1,6 +1,8 @@
import torch
import nodes
import comfy.utils
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
def camera_embeddings(elevation, azimuth):
elevation = torch.as_tensor([elevation])
@ -20,26 +22,31 @@ def camera_embeddings(elevation, azimuth):
return embeddings
class StableZero123_Conditioning:
class StableZero123_Conditioning(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
def define_schema(cls):
return io.Schema(
node_id="StableZero123_Conditioning",
category="conditioning/3d_models",
inputs=[
io.ClipVision.Input("clip_vision"),
io.Image.Input("init_image"),
io.Vae.Input("vae"),
io.Int.Input("width", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("height", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("batch_size", default=1, min=1, max=4096),
io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False)
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent")
]
)
FUNCTION = "encode"
CATEGORY = "conditioning/3d_models"
def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth):
@classmethod
def execute(cls, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth) -> io.NodeOutput:
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
@ -51,30 +58,35 @@ class StableZero123_Conditioning:
positive = [[cond, {"concat_latent_image": t}]]
negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent})
return io.NodeOutput(positive, negative, {"samples":latent})
class StableZero123_Conditioning_Batched:
class StableZero123_Conditioning_Batched(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
def define_schema(cls):
return io.Schema(
node_id="StableZero123_Conditioning_Batched",
category="conditioning/3d_models",
inputs=[
io.ClipVision.Input("clip_vision"),
io.Image.Input("init_image"),
io.Vae.Input("vae"),
io.Int.Input("width", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("height", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("batch_size", default=1, min=1, max=4096),
io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
io.Float.Input("elevation_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
io.Float.Input("azimuth_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False)
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent")
]
)
FUNCTION = "encode"
CATEGORY = "conditioning/3d_models"
def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment):
@classmethod
def execute(cls, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment) -> io.NodeOutput:
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
@ -93,27 +105,32 @@ class StableZero123_Conditioning_Batched:
positive = [[cond, {"concat_latent_image": t}]]
negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size})
return io.NodeOutput(positive, negative, {"samples":latent, "batch_index": [0] * batch_size})
class SV3D_Conditioning:
class SV3D_Conditioning(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"video_frames": ("INT", {"default": 21, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -90.0, "max": 90.0, "step": 0.1, "round": False}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
def define_schema(cls):
return io.Schema(
node_id="SV3D_Conditioning",
category="conditioning/3d_models",
inputs=[
io.ClipVision.Input("clip_vision"),
io.Image.Input("init_image"),
io.Vae.Input("vae"),
io.Int.Input("width", default=576, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("height", default=576, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("video_frames", default=21, min=1, max=4096),
io.Float.Input("elevation", default=0.0, min=-90.0, max=90.0, step=0.1, round=False)
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent")
]
)
FUNCTION = "encode"
CATEGORY = "conditioning/3d_models"
def encode(self, clip_vision, init_image, vae, width, height, video_frames, elevation):
@classmethod
def execute(cls, clip_vision, init_image, vae, width, height, video_frames, elevation) -> io.NodeOutput:
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
@ -133,11 +150,17 @@ class SV3D_Conditioning:
positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]]
negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]]
latent = torch.zeros([video_frames, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent})
return io.NodeOutput(positive, negative, {"samples":latent})
NODE_CLASS_MAPPINGS = {
"StableZero123_Conditioning": StableZero123_Conditioning,
"StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched,
"SV3D_Conditioning": SV3D_Conditioning,
}
class Stable3DExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
StableZero123_Conditioning,
StableZero123_Conditioning_Batched,
SV3D_Conditioning,
]
async def comfy_entrypoint() -> Stable3DExtension:
return Stable3DExtension()

2
pyproject.toml
View File

@ -70,7 +70,5 @@ messages_control.disable = [
"invalid-overridden-method",
"unused-variable",
"pointless-string-statement",
"inconsistent-return-statements",
"import-outside-toplevel",
"redefined-outer-name",
]