774 lines
30 KiB
Python
774 lines
30 KiB
Python
import os
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import json
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import base64
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import requests
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import torch
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import numpy as np
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from PIL import Image
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from io import BytesIO
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from typing import Optional, List, Dict, Any, Tuple
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from comfy_api.latest import io
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try:
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from ..utils.config_utils import get_config_section
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from ..utils.image_utils import downscale_image_tensor, common_upscale
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except (ImportError, ValueError):
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try:
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from utils.config_utils import get_config_section
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from utils.image_utils import downscale_image_tensor, common_upscale
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except (ImportError, ValueError):
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import sys
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from pathlib import Path
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_root = str(Path(__file__).resolve().parent.parent)
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if _root not in sys.path:
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sys.path.insert(0, _root)
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from utils.config_utils import get_config_section
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from utils.image_utils import downscale_image_tensor, common_upscale
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DEFAULT_MODELS = [
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"gpt-image-2",
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"gpt-image-1.5",
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"gpt-image-1",
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"gpt-image-1-mini",
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]
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class OpenAIImageAPI(io.ComfyNode):
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"""
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OpenAI Image API node for generating and editing images using the gpt-image model series.
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Supports text-to-image generation (/images/generations) and image editing/inpainting (/images/edits).
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"""
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@classmethod
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def _load_models_from_config(cls) -> List[str]:
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"""
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Load model list from config.json 'openai-image' section.
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Falls back to DEFAULT_MODELS if not configured.
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"""
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try:
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config_path = os.path.join(os.path.dirname(__file__), '..', "config.json")
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if not os.path.exists(config_path):
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return DEFAULT_MODELS
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with open(config_path, 'r', encoding='utf-8') as f:
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config = json.load(f)
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if 'openai-image' in config and 'models' in config['openai-image']:
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models = config['openai-image']['models']
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if isinstance(models, list) and len(models) > 0:
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clean_models = [str(m).strip() for m in models if str(m).strip()]
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if clean_models:
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return clean_models
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return DEFAULT_MODELS
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except Exception:
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return DEFAULT_MODELS
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@classmethod
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def _load_config_credentials(cls, config_options: Optional[dict] = None) -> Tuple[str, str, int]:
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"""
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Load API credentials from config_options or config.json.
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Returns (base_url, api_key, timeout) tuple.
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"""
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# 1. Check runtime overrides from config_options
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if config_options is not None:
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base_url = str(config_options.get('base_url', '')).strip()
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api_key = str(config_options.get('api_key', '')).strip()
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timeout = config_options.get('timeout', 120)
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if base_url and api_key:
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try:
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timeout = int(timeout) if int(timeout) > 0 else 120
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except (ValueError, TypeError):
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timeout = 120
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return base_url, api_key, timeout
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# 2. Check config.json 'openai-image' section
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config_path = os.path.join(os.path.dirname(__file__), '..', "config.json")
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base_url = "https://api.openai.com/v1"
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api_key = ""
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timeout = 120
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if os.path.exists(config_path):
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try:
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with open(config_path, 'r', encoding='utf-8') as f:
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config = json.load(f)
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if 'openai-image' in config and isinstance(config['openai-image'], dict):
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img_cfg = config['openai-image']
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raw_base = img_cfg.get('base_url', '').strip()
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if raw_base:
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base_url = raw_base
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raw_key = img_cfg.get('api_key', '').strip()
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if raw_key:
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api_key = raw_key
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raw_timeout = img_cfg.get('timeout', 120)
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try:
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timeout = int(raw_timeout) if int(raw_timeout) > 0 else 120
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except (ValueError, TypeError):
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timeout = 120
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# Fallback to openai-text config if api_key not set
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if not api_key and 'openai-text' in config:
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text_cfg = config['openai-text']
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candidates = text_cfg if isinstance(text_cfg, list) else [text_cfg]
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for item in candidates:
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if isinstance(item, dict) and item.get('api_key', '').strip():
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api_key = item['api_key'].strip()
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# If openai-image base_url was default and text item has a base_url, adopt it
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if base_url == "https://api.openai.com/v1" and item.get('base_url', '').strip():
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base_url = item['base_url'].strip()
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break
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except Exception as e:
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raise ValueError(f"Config loading error: {str(e)}")
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# 3. Check environment variable fallback
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if not api_key:
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api_key = os.environ.get("OPENAI_API_KEY", "").strip()
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# 4. Apply any partial config_options override
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if config_options is not None:
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if config_options.get('base_url', '').strip():
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base_url = config_options['base_url'].strip()
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if config_options.get('api_key', '').strip():
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api_key = config_options['api_key'].strip()
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if config_options.get('timeout'):
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try:
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timeout = int(config_options['timeout'])
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except (ValueError, TypeError):
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pass
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if not api_key:
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raise ValueError("OpenAI API key not found. Please provide an api_key in config.json ('openai-image') or via API Config Options.")
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return base_url, api_key, timeout
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@classmethod
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def _get_proxy_config(cls, proxy_options: Optional[dict] = None) -> Optional[Dict[str, str]]:
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"""
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Get proxy settings from proxy_options or config.json.
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"""
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if proxy_options is not None:
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if not proxy_options.get('enable', False):
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return None
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proxies = {}
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if proxy_options.get('http', '').strip():
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proxies['http'] = proxy_options['http'].strip()
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if proxy_options.get('https', '').strip():
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proxies['https'] = proxy_options['https'].strip()
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return proxies if proxies else None
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try:
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proxy_config = get_config_section('proxy')
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if not proxy_config or not proxy_config.get('enable', False):
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return None
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proxies = {}
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if proxy_config.get('http'):
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proxies['http'] = proxy_config['http']
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if proxy_config.get('https'):
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proxies['https'] = proxy_config['https']
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return proxies if proxies else None
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except Exception:
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return None
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@classmethod
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def define_schema(cls) -> io.Schema:
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model_options = cls._load_models_from_config()
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default_model = model_options[0]
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return io.Schema(
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node_id="YCYY_OpenAI_Image_API",
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display_name="OpenAI Image API",
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category="YCYY/API/image",
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inputs=[
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io.String.Input(
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id="prompt",
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multiline=True,
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tooltip="Text prompt used to generate or edit the image."
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),
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io.Combo.Input(
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id="model",
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options=model_options,
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default=default_model,
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tooltip="OpenAI GPT image model."
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),
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io.Combo.Input(
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id="size",
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options=[
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"auto",
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"1024x1024",
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"1024x1536",
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"1536x1024",
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"1152x2048",
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"2048x1152",
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"2048x2048",
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"2160x3840",
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"3840x2160",
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"Custom"
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],
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default="auto",
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tooltip="Output image size. Select 'Custom' to specify custom width and height."
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),
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io.Int.Input(
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id="custom_width",
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default=1024,
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min=256,
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max=3840,
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step=16,
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tooltip="Used only when size is 'Custom'. Must be a multiple of 16."
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),
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io.Int.Input(
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id="custom_height",
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default=1024,
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min=256,
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max=3840,
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step=16,
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tooltip="Used only when size is 'Custom'. Must be a multiple of 16."
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),
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io.Combo.Input(
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id="quality",
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options=[
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"auto",
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"low",
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"medium",
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"high"
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],
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default="auto",
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tooltip="Image quality level for GPT image models."
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),
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io.Combo.Input(
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id="background",
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options=[
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"auto",
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"opaque",
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"transparent"
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],
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default="auto",
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tooltip="Return image with or without background. 'transparent' outputs PNG with alpha transparency."
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),
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io.Int.Input(
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id="n",
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default=1,
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min=1,
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max=10,
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step=1,
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tooltip="Number of images to generate (1 to 10)."
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),
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io.Int.Input(
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id="seed",
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min=0,
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max=0xFFFFFFFFFFFFFFFF,
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default=0,
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control_after_generate=True,
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tooltip="Random seed for generation."
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),
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io.Image.Input(
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id="images",
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optional=True,
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tooltip="Optional reference image(s) for image editing. GPT image models support up to 16 images."
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),
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io.Mask.Input(
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id="mask",
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optional=True,
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tooltip="Optional mask for inpainting (white areas will be replaced). Requires exactly one reference image."
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),
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io.AnyType.Input(
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id="config_options",
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optional=True,
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tooltip="Optional configuration override from YCYY API Config Options"
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),
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io.AnyType.Input(
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id="proxy_options",
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optional=True,
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tooltip="Optional proxy configuration override from YCYY API Proxy Options"
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),
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],
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outputs=[
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io.Image.Output(),
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io.String.Output()
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],
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description="This node uses the OpenAI Image API to generate or edit images using the gpt-image model series."
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)
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@classmethod
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def _resolve_size(cls, size: str, custom_width: int, custom_height: int) -> Optional[str]:
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"""
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Validates and resolves the output image size string.
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"""
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if size == "Custom":
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if custom_width % 16 != 0 or custom_height % 16 != 0:
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raise ValueError(
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f"Custom width and height must be multiples of 16, got {custom_width}x{custom_height}"
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)
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if max(custom_width, custom_height) > 3840:
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raise ValueError(
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f"Custom resolution max edge must be <= 3840, got {custom_width}x{custom_height}"
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)
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min_edge = min(custom_width, custom_height)
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if min_edge <= 0:
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raise ValueError(f"Custom dimensions must be positive, got {custom_width}x{custom_height}")
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ratio = max(custom_width, custom_height) / min_edge
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if ratio > 3.0:
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raise ValueError(
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f"Custom resolution aspect ratio must not exceed 3:1, got {custom_width}x{custom_height}"
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)
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total_pixels = custom_width * custom_height
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if not (655_360 <= total_pixels <= 8_294_400):
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raise ValueError(
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f"Custom resolution total pixels must be between 655,360 and 8,294,400, got {total_pixels}"
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)
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return f"{custom_width}x{custom_height}"
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return size
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@classmethod
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def execute(
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cls,
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prompt: str,
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model: str,
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size: str,
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custom_width: int,
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custom_height: int,
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quality: str,
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background: str,
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n: int,
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seed: int,
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images: Optional[torch.Tensor] = None,
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mask: Optional[torch.Tensor] = None,
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config_options: Optional[dict] = None,
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proxy_options: Optional[dict] = None,
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) -> io.NodeOutput:
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if not prompt or not prompt.strip():
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raise ValueError("prompt cannot be empty")
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# Load credentials & proxies
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base_url, api_key, timeout = cls._load_config_credentials(config_options)
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proxies = cls._get_proxy_config(proxy_options)
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# Normalize endpoints
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clean_base_url = base_url.rstrip("/")
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for suffix in ("/images/generations", "/images/edits", "/images"):
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if clean_base_url.endswith(suffix):
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clean_base_url = clean_base_url[:-len(suffix)]
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break
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gen_url = f"{clean_base_url}/images/generations"
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edit_url = f"{clean_base_url}/images/edits"
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# Resolve size
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resolved_size = cls._resolve_size(size, custom_width, custom_height)
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if images is not None:
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return cls._edit_images(
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api_url=edit_url,
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api_key=api_key,
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prompt=prompt,
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model=model,
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size=resolved_size,
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quality=quality,
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background=background,
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n=n,
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seed=seed,
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images=images,
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mask=mask,
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timeout=timeout,
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proxies=proxies
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)
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else:
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return cls._generate_images(
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api_url=gen_url,
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api_key=api_key,
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prompt=prompt,
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model=model,
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size=resolved_size,
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quality=quality,
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background=background,
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n=n,
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seed=seed,
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timeout=timeout,
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proxies=proxies
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)
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@classmethod
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def _generate_images(
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cls,
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api_url: str,
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api_key: str,
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prompt: str,
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model: str,
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size: str,
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quality: str,
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background: str,
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n: int,
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seed: int,
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timeout: int,
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proxies: Optional[dict] = None
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) -> io.NodeOutput:
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": model,
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"prompt": prompt,
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"n": n,
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}
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if size:
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payload["size"] = size
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if quality and quality != "auto":
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payload["quality"] = quality
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if background and background != "auto":
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payload["background"] = background
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try:
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resp = requests.post(api_url, headers=headers, json=payload, timeout=timeout, proxies=proxies)
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return cls._parse_response(resp, model=model, background=background, timeout=timeout, proxies=proxies)
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except Exception as e:
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empty_image = cls._create_empty_image()
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err_info = {
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"success": False,
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"message": f"API request failed. Please check endpoint address and key: {str(e)}"
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}
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return io.NodeOutput(empty_image, json.dumps(err_info, ensure_ascii=False))
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@classmethod
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def _tensor_to_png_bytes(cls, tensor: torch.Tensor) -> bytes:
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"""
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Convert torch.Tensor [1, H, W, C] or [H, W, C] to PNG encoded bytes.
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"""
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if tensor.ndim == 4:
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tensor = tensor[0]
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tensor_cpu = tensor.cpu()
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channels = tensor_cpu.shape[-1]
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arr = (tensor_cpu.numpy() * 255.0).clip(0, 255).astype(np.uint8)
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if channels == 4:
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mode = "RGBA"
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elif channels == 3:
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mode = "RGB"
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elif channels == 1:
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mode = "L"
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arr = arr.squeeze(-1)
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else:
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mode = "RGB"
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pil_img = Image.fromarray(arr, mode=mode)
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buf = BytesIO()
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pil_img.save(buf, format="PNG")
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return buf.getvalue()
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|
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@classmethod
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def _edit_images(
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cls,
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api_url: str,
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api_key: str,
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prompt: str,
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model: str,
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size: str,
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quality: str,
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background: str,
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n: int,
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seed: int,
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images: torch.Tensor,
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mask: Optional[torch.Tensor] = None,
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timeout: int = 120,
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proxies: Optional[dict] = None
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) -> io.NodeOutput:
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# Split image batch: gpt-image models support up to 16 images
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if len(images.shape) == 4:
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flat_images = [images[i : i + 1] for i in range(images.shape[0])]
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else:
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flat_images = [images.unsqueeze(0)]
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flat_images = flat_images[:16]
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if mask is not None and len(flat_images) != 1:
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raise ValueError("Mask inpainting is only supported when exactly one reference image is provided.")
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# Build multipart files
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files = []
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for i, single_img in enumerate(flat_images):
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# Scale reference image down to <= 2048x2048 if needed
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scaled_img = downscale_image_tensor(single_img, total_pixels=2048 * 2048)
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img_bytes = cls._tensor_to_png_bytes(scaled_img)
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field_name = "image" if len(flat_images) == 1 else "image[]"
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files.append((field_name, (f"image_{i}.png", img_bytes, "image/png")))
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# Process mask if provided
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if mask is not None:
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ref_img = flat_images[0]
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ref_h, ref_w = ref_img.shape[1], ref_img.shape[2]
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cur_mask = mask.squeeze()
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if cur_mask.ndim == 2:
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if cur_mask.shape != (ref_h, ref_w):
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m_tensor = cur_mask.unsqueeze(0).unsqueeze(0).float()
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m_tensor = torch.nn.functional.interpolate(
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m_tensor, size=(ref_h, ref_w), mode="bilinear", align_corners=False
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)
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cur_mask = m_tensor.squeeze()
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# OpenAI inpainting specification: transparent alpha areas indicate the region to be modified.
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# ComfyUI masks: white (1.0) is the inpaint region, black (0.0) is the preserved region.
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# Therefore alpha = 1.0 - mask (white area -> alpha 0.0, black area -> alpha 1.0)
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rgba_mask = torch.zeros((ref_h, ref_w, 4), dtype=torch.float32, device="cpu")
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rgba_mask[:, :, 3] = (1.0 - cur_mask.cpu()).clamp(0.0, 1.0)
|
|
scaled_mask = downscale_image_tensor(rgba_mask.unsqueeze(0), total_pixels=2048 * 2048)
|
|
mask_bytes = cls._tensor_to_png_bytes(scaled_mask)
|
|
files.append(("mask", ("mask.png", mask_bytes, "image/png")))
|
|
|
|
# Form fields for multipart request
|
|
form_data = {
|
|
"model": model,
|
|
"prompt": prompt,
|
|
"n": str(n),
|
|
}
|
|
if size:
|
|
form_data["size"] = size
|
|
if quality and quality != "auto":
|
|
form_data["quality"] = quality
|
|
if background and background != "auto":
|
|
form_data["background"] = background
|
|
|
|
headers = {
|
|
"Authorization": f"Bearer {api_key}"
|
|
}
|
|
|
|
try:
|
|
resp = requests.post(api_url, headers=headers, files=files, data=form_data, timeout=timeout, proxies=proxies)
|
|
|
|
# If the endpoint rejected multipart/form-data with 415 or indicates JSON is expected,
|
|
# fallback to JSON request with base64 data URLs
|
|
if resp.status_code == 415 or (resp.status_code in (400, 422) and "json" in resp.text.lower()):
|
|
return cls._edit_images_json_fallback(
|
|
api_url=api_url,
|
|
api_key=api_key,
|
|
prompt=prompt,
|
|
model=model,
|
|
size=size,
|
|
quality=quality,
|
|
background=background,
|
|
n=n,
|
|
flat_images=flat_images,
|
|
mask=mask,
|
|
timeout=timeout,
|
|
proxies=proxies
|
|
)
|
|
|
|
return cls._parse_response(resp, model=model, background=background, timeout=timeout, proxies=proxies)
|
|
except Exception as e:
|
|
empty_image = cls._create_empty_image()
|
|
err_info = {
|
|
"success": False,
|
|
"message": f"API request failed. Please check endpoint address and key: {str(e)}"
|
|
}
|
|
return io.NodeOutput(empty_image, json.dumps(err_info, ensure_ascii=False))
|
|
|
|
@classmethod
|
|
def _edit_images_json_fallback(
|
|
cls,
|
|
api_url: str,
|
|
api_key: str,
|
|
prompt: str,
|
|
model: str,
|
|
size: str,
|
|
quality: str,
|
|
background: str,
|
|
n: int,
|
|
flat_images: List[torch.Tensor],
|
|
mask: Optional[torch.Tensor] = None,
|
|
timeout: int = 120,
|
|
proxies: Optional[dict] = None
|
|
) -> io.NodeOutput:
|
|
"""
|
|
Fallback for proxies or gateways that only accept JSON with base64 data URLs.
|
|
"""
|
|
headers = {
|
|
"Authorization": f"Bearer {api_key}",
|
|
"Content-Type": "application/json"
|
|
}
|
|
input_images = []
|
|
for single_img in flat_images:
|
|
scaled_img = downscale_image_tensor(single_img, total_pixels=2048 * 2048)
|
|
img_b64 = base64.b64encode(cls._tensor_to_png_bytes(scaled_img)).decode("utf-8")
|
|
input_images.append({
|
|
"image_url": f"data:image/png;base64,{img_b64}"
|
|
})
|
|
|
|
payload = {
|
|
"model": model,
|
|
"prompt": prompt,
|
|
"images": input_images,
|
|
"n": n,
|
|
}
|
|
if size:
|
|
payload["size"] = size
|
|
if quality and quality != "auto":
|
|
payload["quality"] = quality
|
|
if background and background != "auto":
|
|
payload["background"] = background
|
|
|
|
if mask is not None:
|
|
ref_img = flat_images[0]
|
|
ref_h, ref_w = ref_img.shape[1], ref_img.shape[2]
|
|
cur_mask = mask.squeeze()
|
|
if cur_mask.ndim == 2 and cur_mask.shape != (ref_h, ref_w):
|
|
m_tensor = cur_mask.unsqueeze(0).unsqueeze(0).float()
|
|
m_tensor = torch.nn.functional.interpolate(
|
|
m_tensor, size=(ref_h, ref_w), mode="bilinear", align_corners=False
|
|
)
|
|
cur_mask = m_tensor.squeeze()
|
|
|
|
rgba_mask = torch.zeros((ref_h, ref_w, 4), dtype=torch.float32, device="cpu")
|
|
rgba_mask[:, :, 3] = (1.0 - cur_mask.cpu()).clamp(0.0, 1.0)
|
|
scaled_mask = downscale_image_tensor(rgba_mask.unsqueeze(0), total_pixels=2048 * 2048)
|
|
mask_b64 = base64.b64encode(cls._tensor_to_png_bytes(scaled_mask)).decode("utf-8")
|
|
payload["mask"] = {
|
|
"image_url": f"data:image/png;base64,{mask_b64}"
|
|
}
|
|
|
|
try:
|
|
resp = requests.post(api_url, headers=headers, json=payload, timeout=timeout, proxies=proxies)
|
|
return cls._parse_response(resp, model=model, background=background, timeout=timeout, proxies=proxies)
|
|
except Exception as e:
|
|
empty_image = cls._create_empty_image()
|
|
err_info = {
|
|
"success": False,
|
|
"message": f"API JSON request failed: {str(e)}"
|
|
}
|
|
return io.NodeOutput(empty_image, json.dumps(err_info, ensure_ascii=False))
|
|
|
|
@classmethod
|
|
def _parse_response(
|
|
cls,
|
|
resp: requests.Response,
|
|
model: str = "",
|
|
background: str = "auto",
|
|
timeout: int = 120,
|
|
proxies: Optional[dict] = None
|
|
) -> io.NodeOutput:
|
|
# Check HTTP status code
|
|
if resp.status_code != 200:
|
|
empty_image = cls._create_empty_image()
|
|
err_info = {
|
|
"success": False,
|
|
"message": f"API request error. HTTP {resp.status_code}: {resp.text}"
|
|
}
|
|
return io.NodeOutput(empty_image, json.dumps(err_info, ensure_ascii=False))
|
|
|
|
if not resp.text.strip():
|
|
empty_image = cls._create_empty_image()
|
|
err_info = {
|
|
"success": False,
|
|
"message": "API returned an empty response"
|
|
}
|
|
return io.NodeOutput(empty_image, json.dumps(err_info, ensure_ascii=False))
|
|
|
|
try:
|
|
data = resp.json()
|
|
except Exception as json_exc:
|
|
empty_image = cls._create_empty_image()
|
|
err_info = {
|
|
"success": False,
|
|
"message": f"Failed to parse API JSON response: {str(json_exc)}"
|
|
}
|
|
return io.NodeOutput(empty_image, json.dumps(err_info, ensure_ascii=False))
|
|
|
|
items = data.get("data", [])
|
|
if not items:
|
|
empty_image = cls._create_empty_image()
|
|
err_info = {
|
|
"success": False,
|
|
"message": "No image data found in API response",
|
|
"raw_response": data
|
|
}
|
|
return io.NodeOutput(empty_image, json.dumps(err_info, ensure_ascii=False))
|
|
|
|
image_tensors = []
|
|
revised_prompts = []
|
|
|
|
for item in items:
|
|
if not isinstance(item, dict):
|
|
continue
|
|
if item.get("revised_prompt"):
|
|
revised_prompts.append(item["revised_prompt"])
|
|
|
|
b64_json = item.get("b64_json")
|
|
image_url = item.get("url")
|
|
pil_image = None
|
|
|
|
if b64_json:
|
|
try:
|
|
img_bytes = base64.b64decode(b64_json)
|
|
pil_image = Image.open(BytesIO(img_bytes))
|
|
except Exception:
|
|
pass
|
|
elif image_url:
|
|
try:
|
|
img_resp = requests.get(image_url, timeout=timeout, proxies=proxies)
|
|
if img_resp.status_code == 200:
|
|
pil_image = Image.open(BytesIO(img_resp.content))
|
|
except Exception:
|
|
pass
|
|
|
|
if pil_image is not None:
|
|
# If transparent background is requested or image has alpha channel
|
|
if background == "transparent" or (pil_image.mode in ("RGBA", "LA") or "transparency" in pil_image.info):
|
|
converted = pil_image.convert("RGBA")
|
|
else:
|
|
converted = pil_image.convert("RGB")
|
|
|
|
img_np = np.asarray(converted).astype(np.float32) / 255.0
|
|
img_tensor = torch.from_numpy(img_np).unsqueeze(0)
|
|
image_tensors.append(img_tensor)
|
|
|
|
if not image_tensors:
|
|
empty_image = cls._create_empty_image()
|
|
err_info = {
|
|
"success": False,
|
|
"message": "Failed to decode or download any image from response",
|
|
"raw_response": data
|
|
}
|
|
return io.NodeOutput(empty_image, json.dumps(err_info, ensure_ascii=False))
|
|
|
|
# Ensure consistent channel count across all batch images
|
|
target_channels = image_tensors[0].shape[-1]
|
|
for idx in range(1, len(image_tensors)):
|
|
cur_t = image_tensors[idx]
|
|
if cur_t.shape[-1] != target_channels:
|
|
if target_channels == 4 and cur_t.shape[-1] == 3:
|
|
# Add opaque alpha channel
|
|
alpha = torch.ones((*cur_t.shape[:-1], 1), dtype=cur_t.dtype)
|
|
image_tensors[idx] = torch.cat([cur_t, alpha], dim=-1)
|
|
elif target_channels == 3 and cur_t.shape[-1] == 4:
|
|
image_tensors[idx] = cur_t[..., :3]
|
|
|
|
# Ensure consistent resolution across all batch images (auto size might have slight pixel differences)
|
|
ref_h, ref_w = image_tensors[0].shape[1], image_tensors[0].shape[2]
|
|
for idx in range(1, len(image_tensors)):
|
|
cur_t = image_tensors[idx]
|
|
if cur_t.shape[1] != ref_h or cur_t.shape[2] != ref_w:
|
|
samples = cur_t.movedim(-1, 1) # [1, C, H, W]
|
|
samples = common_upscale(samples, ref_w, ref_h, "bilinear", "center")
|
|
image_tensors[idx] = samples.movedim(1, -1)
|
|
|
|
final_tensor = image_tensors[0] if len(image_tensors) == 1 else torch.cat(image_tensors, dim=0)
|
|
|
|
usage = data.get("usage", {})
|
|
info = {
|
|
"success": True,
|
|
"model": model,
|
|
"created": data.get("created"),
|
|
"background": data.get("background", background),
|
|
"size": data.get("size"),
|
|
"quality": data.get("quality"),
|
|
"usage": usage,
|
|
"revised_prompts": revised_prompts
|
|
}
|
|
return io.NodeOutput(final_tensor, json.dumps(info, ensure_ascii=False, indent=2))
|
|
|
|
@classmethod
|
|
def _create_empty_image(cls) -> torch.Tensor:
|
|
"""Create empty placeholder image on error [1, 512, 512, 3]."""
|
|
return torch.zeros(1, 512, 512, 3, dtype=torch.float32)
|