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https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-09-04 15:57:10 +08:00
Added Ideogram and Minimax back in.
This commit is contained in:
Robin Huang
parent
ac559a2b76
commit
1d24f0a68f
@ -308,6 +308,12 @@ class IdeogramGenerateRequest(BaseModel):
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)
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class IdeogramGenerateRequest(BaseModel):
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image_request: ImageRequest = Field(
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..., description='The image generation request parameters.'
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)
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class Datum(BaseModel):
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prompt: Optional[str] = Field(
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None, description='The prompt used to generate this image.'
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@ -845,16 +851,75 @@ class KlingVirtualTryOnResponse(BaseModel):
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data: Optional[Data6] = None
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class ResourcePackType(str, Enum):
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decreasing_total = 'decreasing_total'
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constant_period = 'constant_period'
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class KlingRequestError(KlingErrorResponse):
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code: Optional[Code2] = Field(
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None,
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description='- 1200: Invalid request parameters\n- 1201: Invalid parameters\n- 1202: Invalid request method\n- 1203: Requested resource does not exist\n',
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)
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class Code3(Enum):
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int_5000 = 5000
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int_5001 = 5001
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int_5002 = 5002
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class KlingServerError(KlingErrorResponse):
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code: Optional[Code3] = Field(
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None,
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description='- 5000: Internal server error\n- 5001: Service temporarily unavailable\n- 5002: Server internal timeout\n',
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)
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class Code4(Enum):
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int_1300 = 1300
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int_1301 = 1301
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int_1302 = 1302
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int_1303 = 1303
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int_1304 = 1304
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class KlingStrategyError(KlingErrorResponse):
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code: Optional[Code4] = Field(
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None,
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description='- 1300: Trigger platform strategy\n- 1301: Trigger content security policy\n- 1302: API request too frequent\n- 1303: Concurrency/QPS exceeds limit\n- 1304: Trigger IP whitelist policy\n',
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)
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class MinimaxBaseResponse(BaseModel):
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status_code: int = Field(
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...,
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description='Status code. 0 indicates success, other values indicate errors.',
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)
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status_msg: str = Field(
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..., description='Specific error details or success message.'
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)
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class File(BaseModel):
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bytes: Optional[int] = Field(None, description='File size in bytes')
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created_at: Optional[int] = Field(
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None, description='Unix timestamp when the file was created, in seconds'
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)
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download_url: Optional[str] = Field(
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None, description='The URL to download the video'
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)
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file_id: Optional[int] = Field(None, description='Unique identifier for the file')
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filename: Optional[str] = Field(None, description='The name of the file')
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purpose: Optional[str] = Field(None, description='The purpose of using the file')
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class MinimaxFileRetrieveResponse(BaseModel):
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base_resp: MinimaxBaseResponse
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file: File
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class Status(str, Enum):
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toBeOnline = 'toBeOnline'
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online = 'online'
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expired = 'expired'
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runOut = 'runOut'
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Queueing = 'Queueing'
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Preparing = 'Preparing'
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Processing = 'Processing'
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Success = 'Success'
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Fail = 'Fail'
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class ResourcePackSubscribeInfo(BaseModel):
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@ -97,7 +97,6 @@ import io
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import socket
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from typing import Dict, Type, Optional, Any, TypeVar, Generic, Callable, Tuple
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from enum import Enum
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import time
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import json
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import requests
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from urllib.parse import urljoin, urlparse
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@ -1,14 +1,23 @@
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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 FileLocator
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from typing import Literal
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from comfy.utils import common_upscale
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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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OpenAIImageGenerationRequest,
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OpenAIImageEditRequest,
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OpenAIImageGenerationResponse
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OpenAIImageGenerationResponse,
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MinimaxVideoGenerationRequest,
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MinimaxVideoGenerationResponse,
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MinimaxFileRetrieveResponse,
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MinimaxTaskResultResponse,
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IdeogramGenerateRequest,
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IdeogramGenerateResponse,
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ImageRequest,
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Model
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)
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from comfy_api_nodes.apis.client import ApiEndpoint, HttpMethod, SynchronousOperation
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from comfy_api_nodes.apis.client import ApiEndpoint, HttpMethod, SynchronousOperation, PollingOperation, EmptyRequest
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import numpy as np
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from PIL import Image
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@ -16,6 +25,11 @@ import requests
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import torch
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import math
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import base64
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import logging
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import json
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import av
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import os
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import folder_paths
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def downscale_input(image):
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samples = image.movedim(-1,1)
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@ -428,14 +442,168 @@ class OpenAIGPTImage1(ComfyNodeABC):
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return (img_tensor,)
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class MinimaxVideoNode:
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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 torch
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from PIL import Image
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import io
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import numpy as np
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import requests
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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 MinimaxTextToVideoNode:
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"""
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Generates videos synchronously based on a prompt, and optional parameters using Minimax's API.
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"""
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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self.type = "output"
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self.type: Literal["output"] = "output"
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@classmethod
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def INPUT_TYPES(s):
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@ -597,13 +765,14 @@ class MinimaxVideoNode:
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return {"ui": {"images": results, "animated": (True,)}}
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# A dictionary that contains all nodes you want to export with their names
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# NOTE: names should be globally unique
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NODE_CLASS_MAPPINGS = {
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"OpenAIDalle2": OpenAIDalle2,
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"OpenAIDalle3": OpenAIDalle3,
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"OpenAIGPTImage1": OpenAIGPTImage1,
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"IdeogramTextToImage": IdeogramTextToImage,
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"MinimaxTextToVideoNode": MinimaxTextToVideoNode,
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}
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# A dictionary that contains the friendly/humanly readable titles for the nodes
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@ -611,4 +780,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"OpenAIDalle2": "OpenAI DALL·E 2",
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"OpenAIDalle3": "OpenAI DALL·E 3",
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"OpenAIGPTImage1": "OpenAI GPT Image 1",
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"IdeogramTextToImage": "Ideogram Text to Image",
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"MinimaxTextToVideoNode": "Minimax Text to Video",
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}
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