mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-09-04 04:27:04 +08:00
1348 lines
51 KiB
Python
1348 lines
51 KiB
Python
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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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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BFLFluxProGenerateRequest,
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BFLFluxProGenerateResponse,
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ImageRequest,
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Model
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)
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from comfy_api_nodes.apis.BFLPolling import BFLStatus
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from comfy_api_nodes.apis.luma_api import (
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LumaImageModel,
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LumaVideoModel,
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LumaVideoOutputResolution,
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LumaVideoModelOutputDuration,
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LumaAspectRatio,
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LumaState,
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LumaImageGenerationRequest,
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LumaGenerationRequest,
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LumaGeneration,
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LumaCharacterRef,
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LumaModifyImageRef,
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LumaImageIdentity,
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)
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from comfy_api_nodes.apis.client import ApiClient, ApiEndpoint, HttpMethod, SynchronousOperation, PollingOperation, EmptyRequest, UploadRequest, UploadResponse
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import numpy as np
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from PIL import Image
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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 time
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import uuid
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import folder_paths
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def downscale_input(image, total_pixels=1536*1024):
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samples = image.movedim(-1,1)
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#downscaling input images to roughly the same size as the outputs
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total = int(total_pixels)
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scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
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if scale_by >= 1:
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return image
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width = round(samples.shape[3] * scale_by)
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height = round(samples.shape[2] * scale_by)
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s = common_upscale(samples, width, height, "lanczos", "disabled")
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s = s.movedim(1,-1)
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return s
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def validate_and_cast_response(response):
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# validate raw JSON response
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data = response.data
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if not data or len(data) == 0:
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raise Exception("No images returned from API endpoint")
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# Initialize list to store image tensors
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image_tensors = []
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# Process each image in the data array
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for image_data in data:
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image_url = image_data.url
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b64_data = image_data.b64_json
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if not image_url and not b64_data:
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raise Exception("No image was generated in the response")
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if b64_data:
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img_data = base64.b64decode(b64_data)
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img = Image.open(io.BytesIO(img_data))
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elif image_url:
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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("RGBA")
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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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img_tensor = torch.from_numpy(img_array)
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# Add to list of tensors
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image_tensors.append(img_tensor)
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return torch.stack(image_tensors, dim=0)
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def validate_aspect_ratio(aspect_ratio: str, minimum_ratio: float, maximum_ratio: float, minimum_ratio_str: str, maximum_ratio_str: str):
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# get ratio values
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numbers = aspect_ratio.split(':')
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if len(numbers) != 2:
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raise Exception(f"Aspect ratio must be in the format X:Y, such as 16:9, but was {aspect_ratio}.")
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try:
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numerator = int(numbers[0])
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denominator = int(numbers[1])
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except ValueError:
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raise Exception(f"Aspect ratio must contain numbers separated by ':', such as 16:9, but was {aspect_ratio}.")
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calculated_ratio = numerator/denominator
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# if not close to minimum and maximum, check bounds
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if not math.isclose(calculated_ratio, minimum_ratio) or not math.isclose(calculated_ratio, maximum_ratio):
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if calculated_ratio < minimum_ratio:
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raise Exception(f"Aspect ratio cannot reduce to any less than {minimum_ratio_str} ({minimum_ratio}), but was {aspect_ratio} ({calculated_ratio}).")
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elif calculated_ratio > maximum_ratio:
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raise Exception(f"Aspect ratio cannot reduce to any greater than {maximum_ratio_str} ({maximum_ratio}), but was {aspect_ratio} ({calculated_ratio}).")
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return aspect_ratio
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def process_image_response(response: requests.Response):
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'''Uses content from a Response object and converts it to a torch.Tensor'''
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image = Image.open(io.BytesIO(response.content)).convert("RGBA")
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image_array = np.array(image).astype(np.float32) / 255.0
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return torch.from_numpy(image_array).unsqueeze(0)
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def convert_image_to_bytesio(image: torch.Tensor, name: str=None, allow_alpha=True, total_pixels=2048*2048):
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img_binary = None
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# only care about first image, if it is a batch
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if len(image.shape) > 3:
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image = image[0]
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# TODO: remove alpha if not allowed and present
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input_tensor = image.cpu()
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input_tensor = downscale_input(input_tensor.unsqueeze(0), total_pixels=total_pixels).squeeze()
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image_np = (input_tensor.numpy() * 255).astype(np.uint8)
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img = Image.fromarray(image_np)
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img_byte_arr = io.BytesIO()
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img.save(img_byte_arr, format='PNG')
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img_byte_arr.seek(0)
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img_binary = img_byte_arr
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img_binary.name = f"{name if name else uuid.uuid4()}.png"
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return img_binary
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def upload_images_to_comfyapi(image: torch.Tensor, max_images=8, auth_token=None) -> list[str]:
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# if batch, try to upload each file if max_images is greater than 0
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idx_image = 0
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download_urls: list[str] = []
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is_batch = len(image.shape) > 3
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batch_length = 1
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if is_batch:
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batch_length = image.shape[0]
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while True:
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curr_image = image
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if len(image.shape) > 3:
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curr_image = image[idx_image]
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# get BytesIO version of image
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img_binary = convert_image_to_bytesio(curr_image)
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# first, request upload/download urls from comfy API
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operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path="/customers/storage",
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method=HttpMethod.POST,
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request_model=UploadRequest,
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response_model=UploadResponse
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),
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request=UploadRequest(
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filename=img_binary.name
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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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upload_response = ApiClient.upload_file(response.upload_url, img_binary)
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# verify success
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try:
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upload_response.raise_for_status()
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except requests.exceptions.HTTPError as e:
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raise Exception(f"Could not upload one or more images: {e}")
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# add download_url to list
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download_urls.append(response.download_url)
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idx_image += 1
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# stop uploading additional files if done
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if is_batch and max_images > 0:
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if idx_image >= max_images:
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break
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if idx_image >= batch_length:
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break
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return download_urls
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class OpenAIDalle2(ComfyNodeABC):
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"""
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Generates images synchronously via OpenAI's DALL·E 2 endpoint.
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Uses the proxy at /proxy/openai/images/generations. Returned URLs are short‑lived,
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so download or cache results if you need to keep them.
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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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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": "Text prompt for DALL·E",
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}),
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},
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"optional": {
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"seed": (IO.INT, {
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"default": 0,
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"min": 0,
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"max": 2**31-1,
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"step": 1,
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"display": "number",
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"tooltip": "not implemented yet in backend",
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}),
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"size": (IO.COMBO, {
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"options": ["256x256", "512x512", "1024x1024"],
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"default": "1024x1024",
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"tooltip": "Image size",
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}),
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"n": (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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"tooltip": "How many images to generate",
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}),
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"image": (IO.IMAGE, {
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"default": None,
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"tooltip": "Optional reference image for image editing.",
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}),
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"mask": (IO.MASK, {
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"default": None,
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"tooltip": "Optional mask for inpainting (white areas will be replaced)",
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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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FUNCTION = "api_call"
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CATEGORY = "api node"
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DESCRIPTION = cleandoc(__doc__ or "")
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API_NODE = True
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def api_call(self, prompt, seed=0, image=None, mask=None, n=1, size="1024x1024", auth_token=None):
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model = "dall-e-2"
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path = "/proxy/openai/images/generations"
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request_class = OpenAIImageGenerationRequest
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img_binary = None
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if image is not None and mask is not None:
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path = "/proxy/openai/images/edits"
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request_class = OpenAIImageEditRequest
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input_tensor = image.squeeze().cpu()
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height, width, channels = input_tensor.shape
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rgba_tensor = torch.ones(height, width, 4, device="cpu")
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rgba_tensor[:, :, :channels] = input_tensor
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if mask.shape[1:] != image.shape[1:-1]:
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raise Exception("Mask and Image must be the same size")
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rgba_tensor[:,:,3] = (1-mask.squeeze().cpu())
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rgba_tensor = downscale_input(rgba_tensor.unsqueeze(0)).squeeze()
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image_np = (rgba_tensor.numpy() * 255).astype(np.uint8)
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img = Image.fromarray(image_np)
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img_byte_arr = io.BytesIO()
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img.save(img_byte_arr, format='PNG')
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img_byte_arr.seek(0)
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img_binary = img_byte_arr#.getvalue()
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img_binary.name = "image.png"
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elif image is not None or mask is not None:
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raise Exception("Dall-E 2 image editing requires an image AND a mask")
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# Build the operation
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operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path=path,
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method=HttpMethod.POST,
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request_model=request_class,
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response_model=OpenAIImageGenerationResponse
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),
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request=request_class(
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model=model,
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prompt=prompt,
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n=n,
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size=size,
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seed=seed,
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),
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files={
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"image": img_binary,
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} if img_binary else None,
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auth_token=auth_token
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)
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response = operation.execute()
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img_tensor = validate_and_cast_response(response)
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return (img_tensor,)
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class OpenAIDalle3(ComfyNodeABC):
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"""
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Generates images synchronously via OpenAI's DALL·E 3 endpoint.
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Uses the proxy at /proxy/openai/images/generations. Returned URLs are short‑lived,
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so download or cache results if you need to keep them.
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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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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": "Text prompt for DALL·E",
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}),
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},
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"optional": {
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"seed": (IO.INT, {
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"default": 0,
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"min": 0,
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"max": 2**31-1,
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"step": 1,
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"display": "number",
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"tooltip": "not implemented yet in backend",
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}),
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"quality" : (IO.COMBO, {
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"options": ["standard","hd"],
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"default": "standard",
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"tooltip": "Image quality",
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}),
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"style": (IO.COMBO, {
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"options": ["natural","vivid"],
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"default": "natural",
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"tooltip": "Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images.",
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}),
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"size": (IO.COMBO, {
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"options": ["1024x1024", "1024x1792", "1792x1024"],
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"default": "1024x1024",
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"tooltip": "Image size",
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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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FUNCTION = "api_call"
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CATEGORY = "api node"
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DESCRIPTION = cleandoc(__doc__ or "")
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API_NODE = True
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def api_call(self, prompt, seed=0, style="natural", quality="standard", size="1024x1024", auth_token=None):
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model = "dall-e-3"
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# build the operation
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operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path="/proxy/openai/images/generations",
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method=HttpMethod.POST,
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request_model=OpenAIImageGenerationRequest,
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response_model=OpenAIImageGenerationResponse
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),
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request=OpenAIImageGenerationRequest(
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model=model,
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prompt=prompt,
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quality=quality,
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size=size,
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style=style,
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seed=seed,
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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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img_tensor = validate_and_cast_response(response)
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return (img_tensor,)
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|
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class OpenAIGPTImage1(ComfyNodeABC):
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"""
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Generates images synchronously via OpenAI's GPT Image 1 endpoint.
|
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Uses the proxy at /proxy/openai/images/generations. Returned URLs are short‑lived,
|
||
so download or cache results if you need to keep them.
|
||
"""
|
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|
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
|
||
self.type = "output"
|
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|
||
@classmethod
|
||
def INPUT_TYPES(cls) -> InputTypeDict:
|
||
return {
|
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"required": {
|
||
"prompt": (IO.STRING, {
|
||
"multiline": True,
|
||
"default": "",
|
||
"tooltip": "Text prompt for GPT Image 1",
|
||
}),
|
||
},
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"optional": {
|
||
"seed": (IO.INT, {
|
||
"default": 0,
|
||
"min": 0,
|
||
"max": 2**31-1,
|
||
"step": 1,
|
||
"display": "number",
|
||
"tooltip": "not implemented yet in backend",
|
||
}),
|
||
"quality": (IO.COMBO, {
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||
"options": ["low","medium","high"],
|
||
"default": "low",
|
||
"tooltip": "Image quality, affects cost and generation time.",
|
||
}),
|
||
"background": (IO.COMBO, {
|
||
"options": ["opaque","transparent"],
|
||
"default": "opaque",
|
||
"tooltip": "Return image with or without background",
|
||
}),
|
||
"size": (IO.COMBO, {
|
||
"options": ["auto", "1024x1024", "1024x1536", "1536x1024"],
|
||
"default": "auto",
|
||
"tooltip": "Image size",
|
||
}),
|
||
"n": (IO.INT, {
|
||
"default": 1,
|
||
"min": 1,
|
||
"max": 8,
|
||
"step": 1,
|
||
"display": "number",
|
||
"tooltip": "How many images to generate",
|
||
}),
|
||
"image": (IO.IMAGE, {
|
||
"default": None,
|
||
"tooltip": "Optional reference image for image editing.",
|
||
}),
|
||
"mask": (IO.MASK, {
|
||
"default": None,
|
||
"tooltip": "Optional mask for inpainting (white areas will be replaced)",
|
||
}),
|
||
},
|
||
"hidden": {
|
||
"auth_token": "AUTH_TOKEN_COMFY_ORG"
|
||
}
|
||
}
|
||
|
||
RETURN_TYPES = (IO.IMAGE,)
|
||
FUNCTION = "api_call"
|
||
CATEGORY = "api node"
|
||
DESCRIPTION = cleandoc(__doc__ or "")
|
||
API_NODE = True
|
||
|
||
def api_call(self, prompt, seed=0, quality="low", background="opaque", image=None, mask=None, n=1, size="1024x1024", auth_token=None):
|
||
model = "gpt-image-1"
|
||
path = "/proxy/openai/images/generations"
|
||
request_class = OpenAIImageGenerationRequest
|
||
img_binaries = []
|
||
mask_binary = None
|
||
files = []
|
||
|
||
if image is not None:
|
||
path = "/proxy/openai/images/edits"
|
||
request_class = OpenAIImageEditRequest
|
||
|
||
batch_size = image.shape[0]
|
||
|
||
|
||
for i in range(batch_size):
|
||
single_image = image[i:i+1]
|
||
scaled_image = downscale_input(single_image).squeeze()
|
||
|
||
image_np = (scaled_image.numpy() * 255).astype(np.uint8)
|
||
img = Image.fromarray(image_np)
|
||
img_byte_arr = io.BytesIO()
|
||
img.save(img_byte_arr, format='PNG')
|
||
img_byte_arr.seek(0)
|
||
img_binary = img_byte_arr
|
||
img_binary.name = f"image_{i}.png"
|
||
|
||
img_binaries.append(img_binary)
|
||
if batch_size == 1:
|
||
files.append(("image", img_binary))
|
||
else:
|
||
files.append(("image[]", img_binary))
|
||
|
||
if mask is not None:
|
||
if image.shape[0] != 1:
|
||
raise Exception("Cannot use a mask with multiple image")
|
||
if image is None:
|
||
raise Exception("Cannot use a mask without an input image")
|
||
if mask.shape[1:] != image.shape[1:-1]:
|
||
raise Exception("Mask and Image must be the same size")
|
||
batch, height, width = mask.shape
|
||
rgba_mask = torch.zeros(height, width, 4, device="cpu")
|
||
rgba_mask[:,:,3] = (1-mask.squeeze().cpu())
|
||
|
||
scaled_mask = downscale_input(rgba_mask.unsqueeze(0)).squeeze()
|
||
|
||
mask_np = (scaled_mask.numpy() * 255).astype(np.uint8)
|
||
mask_img = Image.fromarray(mask_np)
|
||
mask_img_byte_arr = io.BytesIO()
|
||
mask_img.save(mask_img_byte_arr, format='PNG')
|
||
mask_img_byte_arr.seek(0)
|
||
mask_binary = mask_img_byte_arr
|
||
mask_binary.name = "mask.png"
|
||
files.append(("mask", mask_binary))
|
||
|
||
|
||
# Build the operation
|
||
operation = SynchronousOperation(
|
||
endpoint=ApiEndpoint(
|
||
path=path,
|
||
method=HttpMethod.POST,
|
||
request_model=request_class,
|
||
response_model=OpenAIImageGenerationResponse
|
||
),
|
||
request=request_class(
|
||
model=model,
|
||
prompt=prompt,
|
||
quality=quality,
|
||
background=background,
|
||
n=n,
|
||
seed=seed,
|
||
size=size,
|
||
),
|
||
files=files if files else None,
|
||
auth_token=auth_token
|
||
)
|
||
|
||
response = operation.execute()
|
||
|
||
img_tensor = validate_and_cast_response(response)
|
||
return (img_tensor,)
|
||
|
||
|
||
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 torch
|
||
from PIL import Image
|
||
import io
|
||
import numpy as np
|
||
import requests
|
||
|
||
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 FluxProUltraImageNode(ComfyNodeABC):
|
||
"""
|
||
Generates images synchronously based on prompt and resolution.
|
||
"""
|
||
MINIMUM_RATIO = 1/4
|
||
MAXIMUM_RATIO = 4/1
|
||
MINIMUM_RATIO_STR = "1:4"
|
||
MAXIMUM_RATIO_STR = "4:1"
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
return {
|
||
"required": {
|
||
"prompt": (IO.STRING, {
|
||
"multiline": True,
|
||
"default": "",
|
||
"tooltip": "Prompt for the image generation",
|
||
}),
|
||
"prompt_upsampling": (IO.BOOLEAN, {
|
||
"default": False,
|
||
"tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result)."
|
||
}),
|
||
"seed": (IO.INT, {
|
||
"default": 0,
|
||
"min": 0,
|
||
"max": 0xFFFFFFFFFFFFFFFF,
|
||
"control_after_generate": True,
|
||
"tooltip": "The random seed used for creating the noise.",
|
||
}),
|
||
"aspect_ratio": (IO.STRING, {
|
||
"default": "16:9",
|
||
"tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.",
|
||
}),
|
||
"raw": (IO.BOOLEAN, {
|
||
"default": False,
|
||
"tooltip": "When True, generate less processed, more natural-looking images."
|
||
}),
|
||
},
|
||
"optional": {
|
||
"image_prompt": (IO.IMAGE, ),
|
||
"image_prompt_strength": (IO.FLOAT, {
|
||
"default": 0.1,
|
||
"min": 0.0,
|
||
"max": 1.0,
|
||
"step": 0.01,
|
||
"tooltip": "Blend between the prompt and the image prompt.",
|
||
}),
|
||
},
|
||
"hidden": {
|
||
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
||
}
|
||
}
|
||
|
||
@classmethod
|
||
def VALIDATE_INPUTS(cls, aspect_ratio: str):
|
||
try:
|
||
validate_aspect_ratio(aspect_ratio, minimum_ratio=cls.MINIMUM_RATIO, maximum_ratio=cls.MAXIMUM_RATIO,
|
||
minimum_ratio_str=cls.MINIMUM_RATIO_STR, maximum_ratio_str=cls.MAXIMUM_RATIO_STR)
|
||
except Exception as e:
|
||
return str(e)
|
||
return True
|
||
|
||
RETURN_TYPES = (IO.IMAGE,)
|
||
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
|
||
FUNCTION = "api_call"
|
||
API_NODE = True
|
||
CATEGORY = "api node"
|
||
|
||
def api_call(self, prompt: str, aspect_ratio: str, prompt_upsampling=False, raw=False, seed=0, image_prompt=None, image_prompt_strength=0.1, auth_token=None, **kwargs):
|
||
operation = SynchronousOperation(
|
||
endpoint=ApiEndpoint(
|
||
path="/proxy/bfl/flux-pro-1.1-ultra/generate",
|
||
method=HttpMethod.POST,
|
||
request_model=BFLFluxProGenerateRequest,
|
||
response_model=BFLFluxProGenerateResponse
|
||
),
|
||
request=BFLFluxProGenerateRequest(
|
||
prompt=prompt,
|
||
prompt_upsampling=prompt_upsampling,
|
||
seed=seed,
|
||
aspect_ratio=validate_aspect_ratio(aspect_ratio, minimum_ratio=self.MINIMUM_RATIO, maximum_ratio=self.MAXIMUM_RATIO,
|
||
minimum_ratio_str=self.MINIMUM_RATIO_STR, maximum_ratio_str=self.MAXIMUM_RATIO_STR),
|
||
raw=raw,
|
||
image_prompt=image_prompt if image_prompt is None else self._convert_image_to_base64(image_prompt),
|
||
image_prompt_strength=None if image_prompt is None else round(image_prompt_strength, 2),
|
||
),
|
||
auth_token=auth_token
|
||
)
|
||
output_image = self._handle_bfl_synchronous_operation(operation)
|
||
return (output_image,)
|
||
|
||
def _handle_bfl_synchronous_operation(self, operation: SynchronousOperation, timeout_bfl_calls=360):
|
||
response_api: BFLFluxProGenerateResponse = operation.execute()
|
||
return self._poll_until_generated(response_api.polling_url, timeout=timeout_bfl_calls)
|
||
|
||
def _poll_until_generated(self, polling_url: str, timeout=360):
|
||
# used bfl-comfy-nodes to verify code implementation:
|
||
# https://github.com/black-forest-labs/bfl-comfy-nodes/tree/main
|
||
start_time = time.time()
|
||
retries_404 = 0
|
||
max_retries_404 = 5
|
||
retry_404_seconds = 2
|
||
retry_202_seconds = 2
|
||
retry_pending_seconds = 1
|
||
request = requests.Request(method=HttpMethod.GET, url=polling_url)
|
||
# NOTE: should True loop be replaced with checking if workflow has been interrupted?
|
||
while True:
|
||
response = requests.Session().send(request.prepare())
|
||
if response.status_code == 200:
|
||
result = response.json()
|
||
if result["status"] == BFLStatus.ready:
|
||
img_url = result["result"]["sample"]
|
||
img_response = requests.get(img_url)
|
||
return process_image_response(img_response)
|
||
elif result["status"] in [BFLStatus.request_moderated, BFLStatus.content_moderated]:
|
||
status = result["status"]
|
||
raise Exception(f"BFL API did not return an image due to: {status}.")
|
||
elif result["status"] == BFLStatus.error:
|
||
raise Exception(f"BFL API encountered an error: {result}.")
|
||
elif result["status"] == BFLStatus.pending:
|
||
time.sleep(retry_pending_seconds)
|
||
continue
|
||
elif response.status_code == 404:
|
||
if retries_404 < max_retries_404:
|
||
retries_404 += 1
|
||
time.sleep(retry_404_seconds)
|
||
continue
|
||
raise Exception(f"BFL API could not find task after {max_retries_404} tries.")
|
||
elif response.status_code == 202:
|
||
time.sleep(retry_202_seconds)
|
||
elif time.time() - start_time > timeout:
|
||
raise Exception(f"BFL API experienced a timeout; could not return request under {timeout} seconds.")
|
||
else:
|
||
raise Exception(f"BFL API encountered an error: {response.json()}")
|
||
|
||
def _convert_image_to_base64(self, image: torch.Tensor):
|
||
scaled_image = downscale_input(image, total_pixels=2048*2048)
|
||
# remove batch dimension if present
|
||
if len(scaled_image.shape) > 3:
|
||
scaled_image = scaled_image[0]
|
||
image_np = (scaled_image.numpy() * 255).astype(np.uint8)
|
||
img = Image.fromarray(image_np)
|
||
img_byte_arr = io.BytesIO()
|
||
img.save(img_byte_arr, format='PNG')
|
||
return base64.b64encode(img_byte_arr.getvalue()).decode()
|
||
|
||
class LumaImageGenerationNode:
|
||
"""
|
||
Generates images synchronously based on prompt and aspect ratio.
|
||
"""
|
||
RETURN_TYPES = (IO.IMAGE,)
|
||
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
|
||
FUNCTION = "api_call"
|
||
API_NODE = True
|
||
CATEGORY = "api node"
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
return {
|
||
"required": {
|
||
"prompt": (IO.STRING, {
|
||
"multiline": True,
|
||
"default": "",
|
||
"tooltip": "Prompt for the image generation",
|
||
}),
|
||
"model": ([model.value for model in LumaImageModel],),
|
||
"aspect_ratio": ([ratio.value for ratio in LumaAspectRatio], {
|
||
"default": LumaAspectRatio.ratio_16_9,
|
||
}),
|
||
"seed": (IO.INT, {
|
||
"default": 0,
|
||
"min": 0,
|
||
"max": 0xFFFFFFFFFFFFFFFF,
|
||
"control_after_generate": True,
|
||
"tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
|
||
}),
|
||
},
|
||
"optional": {
|
||
"character_ref_image": (IO.IMAGE, {
|
||
"tooltip": "Character reference images; can be a batch of multiple, only the first 4 images will be considered."
|
||
})
|
||
},
|
||
"hidden": {
|
||
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
||
}
|
||
}
|
||
|
||
def api_call(self, prompt: str, model: str, aspect_ratio: str, seed, character_ref_image: torch.Tensor=None, auth_token=None, **kwargs):
|
||
# handle character_ref images
|
||
character_ref = None
|
||
if character_ref_image is not None:
|
||
download_urls = upload_images_to_comfyapi(character_ref_image, max_images=4, auth_token=auth_token)
|
||
character_ref = LumaCharacterRef(identity0=LumaImageIdentity(images=download_urls))
|
||
|
||
operation = SynchronousOperation(
|
||
endpoint=ApiEndpoint(
|
||
path="/proxy/luma/generations/image",
|
||
method=HttpMethod.POST,
|
||
request_model=LumaImageGenerationRequest,
|
||
response_model=LumaGeneration
|
||
),
|
||
request=LumaImageGenerationRequest(
|
||
prompt=prompt,
|
||
model=model,
|
||
aspect_ratio=aspect_ratio,
|
||
character_ref=character_ref
|
||
),
|
||
auth_token=auth_token
|
||
)
|
||
response_api: LumaGeneration = operation.execute()
|
||
|
||
operation = PollingOperation(
|
||
poll_endpoint=ApiEndpoint(
|
||
path=f"/proxy/luma/generations/{response_api.id}",
|
||
method=HttpMethod.GET,
|
||
request_model=EmptyRequest,
|
||
response_model=LumaGeneration,
|
||
),
|
||
completed_statuses=[LumaState.completed],
|
||
failed_statuses=[LumaState.failed],
|
||
status_extractor=lambda x: x.state,
|
||
auth_token=auth_token,
|
||
)
|
||
response_poll = operation.execute()
|
||
|
||
img_response = requests.get(response_poll.assets.image)
|
||
img = process_image_response(img_response)
|
||
return (img,)
|
||
|
||
class LumaImageModifyNode:
|
||
"""
|
||
Modifies images synchronously based on prompt and aspect ratio.
|
||
"""
|
||
RETURN_TYPES = (IO.IMAGE,)
|
||
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
|
||
FUNCTION = "api_call"
|
||
API_NODE = True
|
||
CATEGORY = "api node"
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
return {
|
||
"required": {
|
||
"image": (IO.IMAGE,),
|
||
"prompt": (IO.STRING, {
|
||
"multiline": True,
|
||
"default": "",
|
||
"tooltip": "Prompt for the image generation",
|
||
}),
|
||
"image_weight": (IO.FLOAT, {
|
||
"default": 1.0,
|
||
"min": 0.0,
|
||
"max": 1.0,
|
||
"step": 0.01,
|
||
"tooltip": "Weight of the image; the closer to 0.0, the less the image will be modified."
|
||
}),
|
||
"model": ([model.value for model in LumaImageModel],),
|
||
"seed": (IO.INT, {
|
||
"default": 0,
|
||
"min": 0,
|
||
"max": 0xFFFFFFFFFFFFFFFF,
|
||
"control_after_generate": True,
|
||
"tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
|
||
}),
|
||
},
|
||
"optional": {
|
||
},
|
||
"hidden": {
|
||
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
||
}
|
||
}
|
||
|
||
def api_call(self, prompt: str, model: str, image: torch.Tensor, image_weight: float, seed, auth_token=None, **kwargs):
|
||
# first, upload image
|
||
download_urls = upload_images_to_comfyapi(image, max_images=1, auth_token=auth_token)
|
||
image_url = download_urls[0]
|
||
# next, make Luma call with download url provided
|
||
operation = SynchronousOperation(
|
||
endpoint=ApiEndpoint(
|
||
path="/proxy/luma/generations/image",
|
||
method=HttpMethod.POST,
|
||
request_model=LumaImageGenerationRequest,
|
||
response_model=LumaGeneration
|
||
),
|
||
request=LumaImageGenerationRequest(
|
||
prompt=prompt,
|
||
model=model,
|
||
modify_image_ref=LumaModifyImageRef(
|
||
url=image_url,
|
||
weight=round(image_weight, 2)
|
||
),
|
||
),
|
||
auth_token=auth_token
|
||
)
|
||
response_api: LumaGeneration = operation.execute()
|
||
|
||
operation = PollingOperation(
|
||
poll_endpoint=ApiEndpoint(
|
||
path=f"/proxy/luma/generations/{response_api.id}",
|
||
method=HttpMethod.GET,
|
||
request_model=EmptyRequest,
|
||
response_model=LumaGeneration,
|
||
),
|
||
completed_statuses=[LumaState.completed],
|
||
failed_statuses=[LumaState.failed],
|
||
status_extractor=lambda x: x.state,
|
||
auth_token=auth_token,
|
||
)
|
||
response_poll = operation.execute()
|
||
|
||
img_response = requests.get(response_poll.assets.image)
|
||
img = process_image_response(img_response)
|
||
return (img,)
|
||
|
||
class LumaVideoGenerationNode:
|
||
"""
|
||
Generates videos synchronously based on prompt and output_size.
|
||
"""
|
||
def __init__(self):
|
||
self.output_dir = folder_paths.get_output_directory()
|
||
self.type: Literal["output"] = "output"
|
||
|
||
RETURN_TYPES = ("IMAGE",)
|
||
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
|
||
FUNCTION = "api_call"
|
||
API_NODE = True
|
||
CATEGORY = "api node"
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
return {
|
||
"required": {
|
||
"prompt": (IO.STRING, {
|
||
"multiline": True,
|
||
"default": "",
|
||
"tooltip": "Prompt for the video generation",
|
||
}),
|
||
"model": ([model.value for model in LumaVideoModel],),
|
||
"aspect_ratio": ([ratio.value for ratio in LumaAspectRatio], {
|
||
"default": LumaAspectRatio.ratio_16_9,
|
||
}),
|
||
"resolution": ([resolution.value for resolution in LumaVideoOutputResolution], {
|
||
"default": LumaVideoOutputResolution.res_540p,
|
||
}),
|
||
"duration": ([dur.value for dur in LumaVideoModelOutputDuration],),
|
||
"seed": (IO.INT, {
|
||
"default": 0,
|
||
"min": 0,
|
||
"max": 0xFFFFFFFFFFFFFFFF,
|
||
"control_after_generate": True,
|
||
"tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
|
||
}),
|
||
"filename_prefix": ("STRING", {"default": "ComfyUI"}),
|
||
},
|
||
"optional": {
|
||
},
|
||
"hidden": {
|
||
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
||
}
|
||
}
|
||
|
||
def api_call(self, prompt: str, model: str, aspect_ratio: str, resolution: str, duration: str, seed, filename_prefix: str,
|
||
auth_token=None, **kwargs):
|
||
extra_pnginfo = None
|
||
operation = SynchronousOperation(
|
||
endpoint=ApiEndpoint(
|
||
path="/proxy/luma/generations",
|
||
method=HttpMethod.POST,
|
||
request_model=LumaGenerationRequest,
|
||
response_model=LumaGeneration
|
||
),
|
||
request=LumaGenerationRequest(
|
||
prompt=prompt,
|
||
model=model,
|
||
resolution=resolution,
|
||
aspect_ratio=aspect_ratio,
|
||
duration=duration,
|
||
),
|
||
auth_token=auth_token
|
||
)
|
||
response_api: LumaGeneration = operation.execute()
|
||
|
||
operation = PollingOperation(
|
||
poll_endpoint=ApiEndpoint(
|
||
path=f"/proxy/luma/generations/{response_api.id}",
|
||
method=HttpMethod.GET,
|
||
request_model=EmptyRequest,
|
||
response_model=LumaGeneration,
|
||
),
|
||
completed_statuses=[LumaState.completed],
|
||
failed_statuses=[LumaState.failed],
|
||
status_extractor=lambda x: x.state,
|
||
auth_token=auth_token,
|
||
)
|
||
response_poll = operation.execute()
|
||
|
||
vid_response = requests.get(response_poll.assets.video)
|
||
self._save_video_locally(vid_response, filename_prefix, extra_pnginfo)
|
||
|
||
return (None,)
|
||
#return {"ui": {"images": results, "animated": (True,)}}
|
||
|
||
def _save_video_locally(self, response: requests.Response, filename_prefix: str, extra_pnginfo):
|
||
# Construct the save path
|
||
full_output_folder, filename, counter, subfolder, filename_prefix = (
|
||
folder_paths.get_save_image_path(filename_prefix, self.output_dir)
|
||
)
|
||
file_basename = f"{filename}_{counter:05}_.mp4"
|
||
save_path = os.path.join(full_output_folder, file_basename)
|
||
|
||
video_data = response.content
|
||
|
||
# Save the video data to a file
|
||
with open(save_path, "wb") as video_file:
|
||
video_file.write(video_data)
|
||
|
||
# Add workflow metadata to the video container
|
||
#if prompt is not None or extra_pnginfo is not None:
|
||
if extra_pnginfo is not None:
|
||
try:
|
||
container = av.open(save_path, mode="r+")
|
||
# if prompt is not None:
|
||
# container.metadata["prompt"] = json.dumps(prompt)
|
||
if extra_pnginfo is not None:
|
||
for x in extra_pnginfo:
|
||
container.metadata[x] = json.dumps(extra_pnginfo[x])
|
||
container.close()
|
||
except Exception as e:
|
||
logging.warning(f"Failed to add metadata to video: {e}")
|
||
|
||
# Create a FileLocator for the frontend to use for the preview
|
||
results: list[FileLocator] = [
|
||
{
|
||
"filename": file_basename,
|
||
"subfolder": subfolder,
|
||
"type": self.type,
|
||
}
|
||
]
|
||
|
||
return results
|
||
|
||
def _get_output_type(self, output_size: str):
|
||
if output_size in [resolution.value for resolution in LumaVideoOutputResolution]:
|
||
return LumaVideoOutputResolution
|
||
else:
|
||
return LumaAspectRatio
|
||
|
||
class MinimaxTextToVideoNode:
|
||
"""
|
||
Generates videos synchronously based on a prompt, and optional parameters using Minimax's API.
|
||
"""
|
||
|
||
def __init__(self):
|
||
self.output_dir = folder_paths.get_output_directory()
|
||
self.type: Literal["output"] = "output"
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
return {
|
||
"required": {
|
||
"prompt_text": (
|
||
"STRING",
|
||
{
|
||
"multiline": True,
|
||
"default": "",
|
||
"tooltip": "Text prompt to guide the video generation",
|
||
},
|
||
),
|
||
"filename_prefix": ("STRING", {"default": "ComfyUI"}),
|
||
"model": (
|
||
[
|
||
"T2V-01",
|
||
"I2V-01-Director",
|
||
"S2V-01",
|
||
"I2V-01",
|
||
"I2V-01-live",
|
||
"T2V-01",
|
||
],
|
||
{
|
||
"default": "T2V-01",
|
||
"tooltip": "Model to use for video generation",
|
||
},
|
||
),
|
||
},
|
||
"optional": {
|
||
"seed": (
|
||
IO.INT,
|
||
{
|
||
"default": 0,
|
||
"min": 0,
|
||
"max": 0xFFFFFFFFFFFFFFFF,
|
||
"control_after_generate": True,
|
||
"tooltip": "The random seed used for creating the noise.",
|
||
},
|
||
),
|
||
},
|
||
"hidden": {
|
||
"prompt": "PROMPT",
|
||
"extra_pnginfo": "EXTRA_PNGINFO",
|
||
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
||
},
|
||
}
|
||
|
||
RETURN_TYPES = ("VIDEO",)
|
||
DESCRIPTION = "Generates videos from prompts using Minimax's API"
|
||
FUNCTION = "generate_video"
|
||
CATEGORY = "video"
|
||
API_NODE = True
|
||
OUTPUT_NODE = True
|
||
|
||
def generate_video(
|
||
self,
|
||
prompt_text,
|
||
filename_prefix,
|
||
seed=0,
|
||
model="T2V-01",
|
||
prompt=None,
|
||
extra_pnginfo=None,
|
||
auth_token=None,
|
||
):
|
||
video_generate_operation = SynchronousOperation(
|
||
endpoint=ApiEndpoint(
|
||
path="/proxy/minimax/video_generation",
|
||
method=HttpMethod.POST,
|
||
request_model=MinimaxVideoGenerationRequest,
|
||
response_model=MinimaxVideoGenerationResponse,
|
||
),
|
||
request=MinimaxVideoGenerationRequest(
|
||
model=Model(model),
|
||
prompt=prompt_text,
|
||
callback_url=None,
|
||
first_frame_image=None,
|
||
subject_reference=None,
|
||
prompt_optimizer=None,
|
||
),
|
||
auth_token=auth_token,
|
||
)
|
||
response = video_generate_operation.execute()
|
||
|
||
task_id = response.task_id
|
||
|
||
video_generate_operation = PollingOperation(
|
||
poll_endpoint=ApiEndpoint(
|
||
path="/proxy/minimax/query/video_generation",
|
||
method=HttpMethod.GET,
|
||
request_model=EmptyRequest,
|
||
response_model=MinimaxTaskResultResponse,
|
||
query_params={"task_id": task_id},
|
||
),
|
||
completed_statuses=["Success"],
|
||
failed_statuses=["Fail"],
|
||
status_extractor=lambda x: x.status.value,
|
||
auth_token=auth_token,
|
||
)
|
||
task_result = video_generate_operation.execute()
|
||
|
||
file_id = task_result.file_id
|
||
if file_id is None:
|
||
raise Exception("Request was not successful. Missing file ID.")
|
||
file_retrieve_operation = SynchronousOperation(
|
||
endpoint=ApiEndpoint(
|
||
path="/proxy/minimax/files/retrieve",
|
||
method=HttpMethod.GET,
|
||
request_model=EmptyRequest,
|
||
response_model=MinimaxFileRetrieveResponse,
|
||
query_params={"file_id": int(file_id)},
|
||
),
|
||
request=EmptyRequest(),
|
||
auth_token=auth_token,
|
||
)
|
||
file_result = file_retrieve_operation.execute()
|
||
|
||
file_url = file_result.file.download_url
|
||
if file_url is None:
|
||
raise Exception(f"No video was found in the response. Full response: {file_result.model_dump()}")
|
||
logging.info(f"Generated video URL: {file_url}")
|
||
|
||
# Construct the save path
|
||
full_output_folder, filename, counter, subfolder, filename_prefix = (
|
||
folder_paths.get_save_image_path(filename_prefix, self.output_dir)
|
||
)
|
||
file_basename = f"{filename}_{counter:05}_.mp4"
|
||
save_path = os.path.join(full_output_folder, file_basename)
|
||
|
||
# Download the video data
|
||
video_response = requests.get(file_url)
|
||
video_data = video_response.content
|
||
|
||
# Save the video data to a file
|
||
with open(save_path, "wb") as video_file:
|
||
video_file.write(video_data)
|
||
|
||
# Add workflow metadata to the video container
|
||
if prompt is not None or extra_pnginfo is not None:
|
||
try:
|
||
container = av.open(save_path, mode="r+")
|
||
if prompt is not None:
|
||
container.metadata["prompt"] = json.dumps(prompt)
|
||
if extra_pnginfo is not None:
|
||
for x in extra_pnginfo:
|
||
container.metadata[x] = json.dumps(extra_pnginfo[x])
|
||
container.close()
|
||
except Exception as e:
|
||
logging.warning(f"Failed to add metadata to video: {e}")
|
||
|
||
# Create a FileLocator for the frontend to use for the preview
|
||
results: list[FileLocator] = [
|
||
{
|
||
"filename": file_basename,
|
||
"subfolder": subfolder,
|
||
"type": self.type,
|
||
}
|
||
]
|
||
|
||
return {"ui": {"images": results, "animated": (True,)}}
|
||
|
||
# A dictionary that contains all nodes you want to export with their names
|
||
# NOTE: names should be globally unique
|
||
NODE_CLASS_MAPPINGS = {
|
||
"OpenAIDalle2": OpenAIDalle2,
|
||
"OpenAIDalle3": OpenAIDalle3,
|
||
"OpenAIGPTImage1": OpenAIGPTImage1,
|
||
"IdeogramTextToImage": IdeogramTextToImage,
|
||
"FluxProUltraImageNode": FluxProUltraImageNode,
|
||
"LumaImageNode": LumaImageGenerationNode,
|
||
"LumaImageModifyNode": LumaImageModifyNode,
|
||
"LumaVideoNode": LumaVideoGenerationNode,
|
||
"MinimaxTextToVideoNode": MinimaxTextToVideoNode,
|
||
}
|
||
|
||
# A dictionary that contains the friendly/humanly readable titles for the nodes
|
||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||
"OpenAIDalle2": "OpenAI DALL·E 2",
|
||
"OpenAIDalle3": "OpenAI DALL·E 3",
|
||
"OpenAIGPTImage1": "OpenAI GPT Image 1",
|
||
"IdeogramTextToImage": "Ideogram Text to Image",
|
||
"FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image",
|
||
"LumaImageNode": "Luma Generate Image",
|
||
"LumaImageModifyNode": "Luma Modify Image",
|
||
"LumaVideoNode": "Luma Generate Video",
|
||
"MinimaxTextToVideoNode": "Minimax Text to Video",
|
||
}
|