mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-09-08 20:47:07 +08:00
Remove nodes.
This commit is contained in:
Robin Huang
parent
7c41723ef3
commit
b1db184a9e
47
.github/workflows/update-api-stubs.yml
vendored
47
.github/workflows/update-api-stubs.yml
vendored
@ -1,47 +0,0 @@
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name: Generate Pydantic Stubs from api.comfy.org
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on:
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schedule:
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- cron: '0 0 * * 1'
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workflow_dispatch:
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jobs:
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generate-models:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout repository
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uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.10'
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install 'datamodel-code-generator[http]'
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- name: Generate API models
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run: |
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datamodel-codegen --use-subclass-enum --url https://api.comfy.org/openapi --output comfy_api_nodes/apis --output-model-type pydantic_v2.BaseModel
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- name: Check for changes
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id: git-check
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run: |
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git diff --exit-code comfy_api_nodes/apis || echo "changes=true" >> $GITHUB_OUTPUT
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- name: Create Pull Request
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if: steps.git-check.outputs.changes == 'true'
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uses: peter-evans/create-pull-request@v5
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with:
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commit-message: 'chore: update API models from OpenAPI spec'
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title: 'Update API models from api.comfy.org'
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body: |
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This PR updates the API models based on the latest api.comfy.org OpenAPI specification.
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Generated automatically by the a Github workflow.
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branch: update-api-stubs
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delete-branch: true
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base: main
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@ -1,17 +0,0 @@
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# generated by datamodel-codegen:
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# filename: http://localhost:8080/openapi
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# timestamp: 2025-04-22T20:42:39+00:00
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from __future__ import annotations
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from typing import Optional
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from pydantic import BaseModel
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from . import PixverseDto
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class ResponseData(BaseModel):
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ErrCode: Optional[int] = None
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ErrMsg: Optional[str] = None
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Resp: Optional[PixverseDto.V2OpenAPII2VResp] = None
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@ -1,57 +0,0 @@
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# generated by datamodel-codegen:
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# filename: http://localhost:8080/openapi
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# timestamp: 2025-04-22T20:42:39+00:00
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from __future__ import annotations
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from typing import Optional
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from pydantic import BaseModel, Field, constr
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class V2OpenAPII2VResp(BaseModel):
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video_id: Optional[int] = Field(None, description='Video_id')
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class V2OpenAPIT2VReq(BaseModel):
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aspect_ratio: str = Field(
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..., description='Aspect ratio (16:9, 4:3, 1:1, 3:4, 9:16)', examples=['16:9']
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)
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duration: int = Field(
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...,
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description='Video duration (5, 8 seconds, --model=v3.5 only allows 5,8; --quality=1080p does not support 8s)',
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examples=[5],
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)
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model: str = Field(
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..., description='Model version (only supports v3.5)', examples=['v3.5']
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)
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motion_mode: Optional[str] = Field(
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'normal',
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description='Motion mode (normal, fast, --fast only available when duration=5; --quality=1080p does not support fast)',
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examples=['normal'],
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)
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negative_prompt: Optional[constr(max_length=2048)] = Field(
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None, description='Negative prompt\n'
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)
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prompt: constr(max_length=2048) = Field(..., description='Prompt')
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quality: str = Field(
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...,
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description='Video quality ("360p"(Turbo model), "540p", "720p", "1080p")',
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examples=['540p'],
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)
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seed: Optional[int] = Field(None, description='Random seed, range: 0 - 2147483647')
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style: Optional[str] = Field(
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None,
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description='Style (effective when model=v3.5, "anime", "3d_animation", "clay", "comic", "cyberpunk") Do not include style parameter unless needed',
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examples=['anime'],
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)
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template_id: Optional[int] = Field(
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None,
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description='Template ID (template_id must be activated before use)',
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examples=[302325299692608],
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)
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water_mark: Optional[bool] = Field(
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False,
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description='Watermark (true: add watermark, false: no watermark)',
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examples=[False],
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)
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File diff suppressed because it is too large
Load Diff
@ -1,307 +0,0 @@
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import io
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from inspect import cleandoc
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from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict
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from comfy_api_nodes.apis import (
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IdeogramGenerateRequest,
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IdeogramGenerateResponse,
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ImageRequest,
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OpenAIImageGenerationRequest,
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OpenAIImageGenerationResponse
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)
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from comfy_api_nodes.apis.client import ApiEndpoint, HttpMethod, SynchronousOperation
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class IdeogramTextToImage(ComfyNodeABC):
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"""
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Generates images synchronously based on a given prompt and optional parameters.
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Images links are available for a limited period of time; if you would like to keep the image, you must download it.
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"""
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls) -> InputTypeDict:
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"""
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Return a dictionary which contains config for all input fields.
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Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
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Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
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The type can be a list for selection.
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Returns: `dict`:
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- Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
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- Value input_fields (`dict`): Contains input fields config:
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* Key field_name (`string`): Name of a entry-point method's argument
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* Value field_config (`tuple`):
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+ First value is a string indicate the type of field or a list for selection.
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+ Secound value is a config for type "INT", "STRING" or "FLOAT".
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"""
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return {
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"required": {
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"prompt": (IO.STRING, {
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"multiline": True,
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"default": "",
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"tooltip": "Prompt for the image generation",
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}),
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"model": (IO.COMBO, { "options": ["V_2", "V_2_TURBO", "V_1", "V_1_TURBO"], "default": "V_2", "tooltip": "Model to use for image generation"}),
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},
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"optional": {
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"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"
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}),
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"resolution": (IO.COMBO, { "options": ["1024x1024", "1024x1792", "1792x1024"],
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"default": "1024x1024",
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"tooltip": "The resolution for image generation (V2 only). Cannot be used with aspect_ratio"
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}),
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"magic_prompt_option": (IO.COMBO, { "options": ["AUTO", "ON", "OFF"],
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"default": "AUTO",
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"tooltip": "Determine if MagicPrompt should be used in generation"
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}),
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"seed": (IO.INT, {
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"default": 0,
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"min": 0,
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"max": 2147483647,
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"step": 1,
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"display": "number"
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}),
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"style_type": (IO.COMBO, { "options": ["NONE", "ANIME", "CINEMATIC", "CREATIVE", "DIGITAL_ART", "PHOTOGRAPHIC"],
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"default": "NONE",
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"tooltip": "Style type for generation (V2+ only)"
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}),
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"negative_prompt": (IO.STRING, {
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"multiline": True,
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"default": "",
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"tooltip": "Description of what to exclude from the image (V1/V2 only)"
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}),
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"num_images": (IO.INT, {
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"default": 1,
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"min": 1,
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"max": 8,
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"step": 1,
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"display": "number"
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}),
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"color_palette": (IO.STRING, {
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"multiline": False,
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"default": "",
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"tooltip": "Color palette preset name or hex colors with weights (V2/V2_TURBO only)"
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}),
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},
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"hidden": {
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"auth_token": "AUTH_TOKEN_COMFY_ORG"
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}
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}
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RETURN_TYPES = (IO.IMAGE,)
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DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
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FUNCTION = "api_call"
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API_NODE = True
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CATEGORY = "Example"
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def api_call(self, prompt, model, aspect_ratio=None, resolution=None,
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magic_prompt_option="AUTO", seed=0, style_type="NONE",
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negative_prompt="", num_images=1, color_palette="", auth_token=None):
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import numpy as np
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import requests
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import torch
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from PIL import Image
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operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path="/proxy/ideogram/generate",
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method=HttpMethod.POST,
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request_model=IdeogramGenerateRequest,
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response_model=IdeogramGenerateResponse
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),
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request=IdeogramGenerateRequest(
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image_request=ImageRequest(
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prompt=prompt,
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model=model,
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num_images=num_images,
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seed=seed,
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aspect_ratio=aspect_ratio if aspect_ratio != "ASPECT_1_1" else None,
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resolution=resolution if resolution != "1024x1024" else None,
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magic_prompt_option=magic_prompt_option if magic_prompt_option != "AUTO" else None,
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style_type=style_type if style_type != "NONE" else None,
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negative_prompt=negative_prompt if negative_prompt else None,
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color_palette=None
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)
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),
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auth_token=auth_token
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)
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response = operation.execute()
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if not response.data or len(response.data) == 0:
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raise Exception("No images were generated in the response")
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image_url = response.data[0].url
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if not image_url:
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raise Exception("No image URL was generated in the response")
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img_response = requests.get(image_url)
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if img_response.status_code != 200:
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raise Exception("Failed to download the image")
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img = Image.open(io.BytesIO(img_response.content))
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img = img.convert("RGB") # Ensure RGB format
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# Convert to numpy array, normalize to float32 between 0 and 1
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img_array = np.array(img).astype(np.float32) / 255.0
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# Convert to torch tensor and add batch dimension
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img_tensor = torch.from_numpy(img_array)[None,]
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return (img_tensor,)
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"""
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The node will always be re executed if any of the inputs change but
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this method can be used to force the node to execute again even when the inputs don't change.
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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
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executed, if it is different the node will be executed again.
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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
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changes between executions the LoadImage node is executed again.
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"""
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#@classmethod
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#def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen):
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# return ""
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class OpenAITextToImage(ComfyNodeABC):
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"""
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Generates images synchronously via OpenAI's DALL·E 3 endpoint.
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||||||
|
|
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Uses the proxy at /proxy/dalle-3/generate. Returned URLs are short‑lived,
|
|
||||||
so download or cache results if you need to keep them.
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|
||||||
"""
|
|
||||||
def __init__(self):
|
|
||||||
pass
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|
||||||
|
|
||||||
@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",
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|
||||||
}),
|
|
||||||
},
|
|
||||||
"hidden": {
|
|
||||||
"auth_token": "AUTH_TOKEN_COMFY_ORG"
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
RETURN_TYPES = (IO.IMAGE,)
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|
||||||
FUNCTION = "api_call"
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|
||||||
CATEGORY = "Example"
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|
||||||
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
9
nodes.py
@ -2260,20 +2260,11 @@ def init_builtin_extra_nodes():
|
|||||||
"nodes_fresca.py",
|
"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 = []
|
import_failed = []
|
||||||
for node_file in extras_files:
|
for node_file in extras_files:
|
||||||
if not load_custom_node(os.path.join(extras_dir, node_file), module_parent="comfy_extras"):
|
if not load_custom_node(os.path.join(extras_dir, node_file), module_parent="comfy_extras"):
|
||||||
import_failed.append(node_file)
|
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
|
return import_failed
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user