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Remove nodes.

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This commit is contained in:
Robin Huang 2025-04-22 21:57:11 -07:00
parent 7c41723ef3
commit b1db184a9e
6 changed files with 0 additions and 2206 deletions
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47
.github/workflows/update-api-stubs.yml vendored
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name: Generate Pydantic Stubs from api.comfy.org
on:
schedule:
- cron: '0 0 * * 1'
workflow_dispatch:
jobs:
generate-models:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.10'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install 'datamodel-code-generator[http]'
- name: Generate API models
run: |
datamodel-codegen --use-subclass-enum --url https://api.comfy.org/openapi --output comfy_api_nodes/apis --output-model-type pydantic_v2.BaseModel
- name: Check for changes
id: git-check
run: |
git diff --exit-code comfy_api_nodes/apis || echo "changes=true" >> $GITHUB_OUTPUT
- name: Create Pull Request
if: steps.git-check.outputs.changes == 'true'
uses: peter-evans/create-pull-request@v5
with:
commit-message: 'chore: update API models from OpenAPI spec'
title: 'Update API models from api.comfy.org'
body: |
This PR updates the API models based on the latest api.comfy.org OpenAPI specification.
Generated automatically by the a Github workflow.
branch: update-api-stubs
delete-branch: true
base: main

17
comfy_api_nodes/apis/PixverseController.py
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# generated by datamodel-codegen:
# filename: http://localhost:8080/openapi
# timestamp: 2025-04-22T20:42:39+00:00
from __future__ import annotations
from typing import Optional
from pydantic import BaseModel
from . import PixverseDto
class ResponseData(BaseModel):
ErrCode: Optional[int] = None
ErrMsg: Optional[str] = None
Resp: Optional[PixverseDto.V2OpenAPII2VResp] = None

57
comfy_api_nodes/apis/PixverseDto.py
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# generated by datamodel-codegen:
# filename: http://localhost:8080/openapi
# timestamp: 2025-04-22T20:42:39+00:00
from __future__ import annotations
from typing import Optional
from pydantic import BaseModel, Field, constr
class V2OpenAPII2VResp(BaseModel):
video_id: Optional[int] = Field(None, description='Video_id')
class V2OpenAPIT2VReq(BaseModel):
aspect_ratio: str = Field(
..., description='Aspect ratio (16:9, 4:3, 1:1, 3:4, 9:16)', examples=['16:9']
)
duration: int = Field(
...,
description='Video duration (5, 8 seconds, --model=v3.5 only allows 5,8; --quality=1080p does not support 8s)',
examples=[5],
)
model: str = Field(
..., description='Model version (only supports v3.5)', examples=['v3.5']
)
motion_mode: Optional[str] = Field(
'normal',
description='Motion mode (normal, fast, --fast only available when duration=5; --quality=1080p does not support fast)',
examples=['normal'],
)
negative_prompt: Optional[constr(max_length=2048)] = Field(
None, description='Negative prompt\n'
)
prompt: constr(max_length=2048) = Field(..., description='Prompt')
quality: str = Field(
...,
description='Video quality ("360p"(Turbo model), "540p", "720p", "1080p")',
examples=['540p'],
)
seed: Optional[int] = Field(None, description='Random seed, range: 0 - 2147483647')
style: Optional[str] = Field(
None,
description='Style (effective when model=v3.5, "anime", "3d_animation", "clay", "comic", "cyberpunk") Do not include style parameter unless needed',
examples=['anime'],
)
template_id: Optional[int] = Field(
None,
description='Template ID (template_id must be activated before use)',
examples=[302325299692608],
)
water_mark: Optional[bool] = Field(
False,
description='Watermark (true: add watermark, false: no watermark)',
examples=[False],
)

1769
comfy_api_nodes/apis/__init__.py
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307
comfy_api_nodes/nodes_api.py
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import io
from inspect import cleandoc
from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict
from comfy_api_nodes.apis import (
IdeogramGenerateRequest,
IdeogramGenerateResponse,
ImageRequest,
OpenAIImageGenerationRequest,
OpenAIImageGenerationResponse
)
from comfy_api_nodes.apis.client import ApiEndpoint, HttpMethod, SynchronousOperation
class IdeogramTextToImage(ComfyNodeABC):
"""
Generates images synchronously based on a given prompt and optional parameters.
Images links are available for a limited period of time; if you would like to keep the image, you must download it.
"""
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls) -> InputTypeDict:
"""
Return a dictionary which contains config for all input fields.
Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
The type can be a list for selection.
Returns: `dict`:
- Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
- Value input_fields (`dict`): Contains input fields config:
* Key field_name (`string`): Name of a entry-point method's argument
* Value field_config (`tuple`):
+ First value is a string indicate the type of field or a list for selection.
+ Secound value is a config for type "INT", "STRING" or "FLOAT".
"""
return {
"required": {
"prompt": (IO.STRING, {
"multiline": True,
"default": "",
"tooltip": "Prompt for the image generation",
}),
"model": (IO.COMBO, { "options": ["V_2", "V_2_TURBO", "V_1", "V_1_TURBO"], "default": "V_2", "tooltip": "Model to use for image generation"}),
},
"optional": {
"aspect_ratio": (IO.COMBO, { "options": ["ASPECT_1_1", "ASPECT_4_3", "ASPECT_3_4", "ASPECT_16_9", "ASPECT_9_16", "ASPECT_2_1", "ASPECT_1_2", "ASPECT_3_2", "ASPECT_2_3", "ASPECT_4_5", "ASPECT_5_4"], "default": "ASPECT_1_1", "tooltip": "The aspect ratio for image generation. Cannot be used with resolution"
}),
"resolution": (IO.COMBO, { "options": ["1024x1024", "1024x1792", "1792x1024"],
"default": "1024x1024",
"tooltip": "The resolution for image generation (V2 only). Cannot be used with aspect_ratio"
}),
"magic_prompt_option": (IO.COMBO, { "options": ["AUTO", "ON", "OFF"],
"default": "AUTO",
"tooltip": "Determine if MagicPrompt should be used in generation"
}),
"seed": (IO.INT, {
"default": 0,
"min": 0,
"max": 2147483647,
"step": 1,
"display": "number"
}),
"style_type": (IO.COMBO, { "options": ["NONE", "ANIME", "CINEMATIC", "CREATIVE", "DIGITAL_ART", "PHOTOGRAPHIC"],
"default": "NONE",
"tooltip": "Style type for generation (V2+ only)"
}),
"negative_prompt": (IO.STRING, {
"multiline": True,
"default": "",
"tooltip": "Description of what to exclude from the image (V1/V2 only)"
}),
"num_images": (IO.INT, {
"default": 1,
"min": 1,
"max": 8,
"step": 1,
"display": "number"
}),
"color_palette": (IO.STRING, {
"multiline": False,
"default": "",
"tooltip": "Color palette preset name or hex colors with weights (V2/V2_TURBO only)"
}),
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG"
}
}
RETURN_TYPES = (IO.IMAGE,)
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
FUNCTION = "api_call"
API_NODE = True
CATEGORY = "Example"
def api_call(self, prompt, model, aspect_ratio=None, resolution=None,
magic_prompt_option="AUTO", seed=0, style_type="NONE",
negative_prompt="", num_images=1, color_palette="", auth_token=None):
import numpy as np
import requests
import torch
from PIL import Image
operation = SynchronousOperation(
endpoint=ApiEndpoint(
path="/proxy/ideogram/generate",
method=HttpMethod.POST,
request_model=IdeogramGenerateRequest,
response_model=IdeogramGenerateResponse
),
request=IdeogramGenerateRequest(
image_request=ImageRequest(
prompt=prompt,
model=model,
num_images=num_images,
seed=seed,
aspect_ratio=aspect_ratio if aspect_ratio != "ASPECT_1_1" else None,
resolution=resolution if resolution != "1024x1024" else None,
magic_prompt_option=magic_prompt_option if magic_prompt_option != "AUTO" else None,
style_type=style_type if style_type != "NONE" else None,
negative_prompt=negative_prompt if negative_prompt else None,
color_palette=None
)
),
auth_token=auth_token
)
response = operation.execute()
if not response.data or len(response.data) == 0:
raise Exception("No images were generated in the response")
image_url = response.data[0].url
if not image_url:
raise Exception("No image URL was generated in the response")
img_response = requests.get(image_url)
if img_response.status_code != 200:
raise Exception("Failed to download the image")
img = Image.open(io.BytesIO(img_response.content))
img = img.convert("RGB") # Ensure RGB format
# Convert to numpy array, normalize to float32 between 0 and 1
img_array = np.array(img).astype(np.float32) / 255.0
# Convert to torch tensor and add batch dimension
img_tensor = torch.from_numpy(img_array)[None,]
return (img_tensor,)
"""
The node will always be re executed if any of the inputs change but
this method can be used to force the node to execute again even when the inputs don't change.
You can make this node return a number or a string. This value will be compared to the one returned the last time the node was
executed, if it is different the node will be executed again.
This method is used in the core repo for the LoadImage node where they return the image hash as a string, if the image hash
changes between executions the LoadImage node is executed again.
"""
#@classmethod
#def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen):
# return ""
class OpenAITextToImage(ComfyNodeABC):
"""
Generates images synchronously via OpenAI's DALL·E 3 endpoint.
Uses the proxy at /proxy/dalle-3/generate. Returned URLs are short‑lived,
so download or cache results if you need to keep them.
"""
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls) -> InputTypeDict:
return {
"required": {
"prompt": (IO.STRING, {
"multiline": True,
"default": "",
"tooltip": "Text prompt for DALL·E",
}),
# TODO: add NEW MODEL
"model": (IO.COMBO, {
"options": ["dall-e-3", "dall-e-2"],
"default": "dall-e-3",
"tooltip": "OpenAI model name",
}),
},
"optional": {
"n": (IO.INT, {
"default": 1,
"min": 1,
"max": 8,
"step": 1,
"display": "number",
"tooltip": "How many images to generate",
}),
"size": (IO.COMBO, {
"options": ["256x256", "512x512", "1024x1792", "1792x1024", "1024x1024", "1536x1024", "1024x1536", "auto"],
"default": "auto",
"tooltip": "Image size",
}),
"seed": (IO.INT, {
"default": 0,
"min": 0,
"max": 2**31-1,
"step": 1,
"display": "number",
"tooltip": "Optional random seed",
}),
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG"
}
}
RETURN_TYPES = (IO.IMAGE,)
FUNCTION = "api_call"
CATEGORY = "Example"
DESCRIPTION = cleandoc(__doc__ or "")
API_NODE = True
def api_call(self, prompt, model, n=1, size="1024x1024", seed=0, auth_token=None):
# Validate size based on model
if model == "dall-e-2":
if size == "auto":
size = "1024x1024"
valid_sizes = ["256x256", "512x512", "1024x1024"]
if size not in valid_sizes:
raise ValueError(f"Size {size} not valid for dall-e-2. Must be one of: {', '.join(valid_sizes)}")
elif model == "dall-e-3":
if size == "auto":
size = "1024x1024"
valid_sizes = ["1024x1024", "1792x1024", "1024x1792"]
if size not in valid_sizes:
raise ValueError(f"Size {size} not valid for dall-e-3. Must be one of: {', '.join(valid_sizes)}")
# TODO: add NEW MODEL
import numpy as np
import torch
from PIL import Image
import requests
# build the operation
operation = SynchronousOperation(
endpoint=ApiEndpoint(
path="/proxy/openai/images/generations",
method=HttpMethod.POST,
request_model=OpenAIImageGenerationRequest,
response_model=OpenAIImageGenerationResponse
),
request=OpenAIImageGenerationRequest(
model=model,
prompt=prompt,
n=n,
size=size,
seed=seed if seed != 0 else None
),
auth_token=auth_token
)
response = operation.execute()
# validate raw JSON response
data = response.data
if not data or len(data) == 0:
raise Exception("No images returned from OpenAI endpoint")
# Get base64 image data
image_url = data[0].url
if not image_url:
raise Exception("No image URL was generated in the response")
img_response = requests.get(image_url)
if img_response.status_code != 200:
raise Exception("Failed to download the image")
img = Image.open(io.BytesIO(img_response.content))
img = img.convert("RGB") # Ensure RGB format
# Convert to numpy array, normalize to float32 between 0 and 1
img_array = np.array(img).astype(np.float32) / 255.0
# Convert to torch tensor and add batch dimension
img_tensor = torch.from_numpy(img_array)[None,]
return (img_tensor,)
# A dictionary that contains all nodes you want to export with their names
# NOTE: names should be globally unique
NODE_CLASS_MAPPINGS = {
"IdeogramTextToImage": IdeogramTextToImage,
"OpenAIDalleTextToImage": OpenAITextToImage,
}
# A dictionary that contains the friendly/humanly readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
"IdeogramTextToImage": "Ideogram Text to Image",
"OpenAIDalleTextToImage": "OpenAI DALL·E 3 Text to Image",
}

9
nodes.py
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@ -2260,20 +2260,11 @@ def init_builtin_extra_nodes():
"nodes_fresca.py",
]
api_nodes_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_api_nodes")
api_nodes_files = [
"nodes_api.py",
]
import_failed = []
for node_file in extras_files:
if not load_custom_node(os.path.join(extras_dir, node_file), module_parent="comfy_extras"):
import_failed.append(node_file)
for node_file in api_nodes_files:
if not load_custom_node(os.path.join(api_nodes_dir, node_file), module_parent="comfy_api_nodes"):
import_failed.append(node_file)
return import_failed