526 lines
19 KiB
Python
526 lines
19 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 ComfyExtension, io
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from ..utils.image_utils import tensor_to_base64_string
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from ..utils.config_utils import get_config_section
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DEFAULT_MODELS = [
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"grok-imagine-image-2.0",
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"grok-imagine-image-quality",
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"grok-imagine-image-pro",
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"grok-imagine-image"
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]
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class GrokImage(io.ComfyNode):
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"""
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这个节点使用 xAI Grok Image API 生成或者修改图片
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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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从 config.json 中加载模型列表
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如果获取不到，返回默认模型列表
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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 'grok-image' in config and 'models' in config['grok-image']:
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models = config['grok-image']['models']
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if isinstance(models, list) and len(models) > 0:
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return 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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从 config.json 中加载并验证 API 凭据，如果提供了 config_options 则优先使用
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返回 (base_url, api_key, timeout) 元组
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"""
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# 如果提供了配置覆盖，则使用覆盖配置
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if config_options is not None:
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base_url = config_options.get('base_url', '').strip()
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api_key = config_options.get('api_key', '').strip()
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timeout = config_options.get('timeout', 120)
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# 如果覆盖配置中有有效的 base_url 和 api_key，则直接返回
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if base_url and api_key:
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return base_url, api_key, timeout
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# 否则从配置文件加载
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config_path = os.path.join(os.path.dirname(__file__), '..', "config.json")
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# 检查配置文件是否存在
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if not os.path.exists(config_path):
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raise FileNotFoundError(f"Config file not found: {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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# 检查是否存在 grok-image 配置段
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if 'grok-image' not in config:
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raise ValueError("Missing 'grok-image' section in config file")
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grok_config = config['grok-image']
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# 获取并验证 base_url (默认 https://api.x.ai/v1)
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base_url = grok_config.get('base_url', 'https://api.x.ai/v1')
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base_url = base_url.strip() if isinstance(base_url, str) else str(base_url).strip()
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if not base_url:
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base_url = "https://api.x.ai/v1"
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# 获取并验证 api_key
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if 'api_key' not in grok_config:
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raise ValueError("Missing 'api_key' in grok-image section")
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api_key = grok_config['api_key'].strip() if isinstance(grok_config['api_key'], str) else str(grok_config['api_key']).strip()
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if not api_key:
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raise ValueError("api_key cannot be empty")
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# 获取 timeout 参数，默认值为 120 秒
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timeout = grok_config.get('timeout', 120)
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if isinstance(timeout, str):
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try:
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timeout = int(timeout)
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except ValueError:
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timeout = 120
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# 如果有配置覆盖，则使用覆盖的值（如果提供了）
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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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timeout = config_options['timeout']
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return base_url, api_key, timeout
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except Exception as e:
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raise ValueError(f"Config loading error: {str(e)}")
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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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从 config.json 中获取代理配置，如果提供了 proxy_options 则优先使用
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返回 proxies 字典或 None
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"""
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# 如果提供了代理覆盖配置
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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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# 否则从配置文件加载
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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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"""
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返回 GrokImage 节点 schema
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"""
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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_Grok_Image_API",
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display_name="Grok Image API",
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category="YCYY/API/image",
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inputs=[
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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 image(s) for image-to-image editing. Grok supports up to 3 reference images (1 for pro model)."
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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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io.String.Input(
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id="prompt",
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multiline=True,
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tooltip="The 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="Grok image model"
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),
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io.Combo.Input(
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id="aspect_ratio",
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options=[
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"auto",
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"1:1",
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"2:3",
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"3:2",
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"3:4",
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"4:3",
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"9:16",
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"16:9",
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"9:19.5",
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"19.5:9",
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"9:20",
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"20:9",
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"1:2",
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"2:1"
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],
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default="auto",
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tooltip="Aspect ratio of the output image. 'auto' matches input image in edit mode or generates 1:1."
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),
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io.Combo.Input(
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id="resolution",
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options=[
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"1K",
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"2K"
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],
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default="1K",
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tooltip="Resolution of the output image (1K or 2K)."
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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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"default",
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"medium",
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"low"
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],
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default="medium",
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tooltip="Quality level, supported only by the grok-imagine-image-2.0 model."
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),
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io.Int.Input(
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id="number_of_images",
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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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],
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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 xAI Grok Image API to generate or edit images."
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)
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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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aspect_ratio: str,
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resolution: str,
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quality: str,
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number_of_images: int,
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seed: int,
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images: 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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# 加载配置和凭据，如果提供了 config_options 则使用覆盖配置
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base_url, api_key, timeout = cls._load_config_credentials(config_options)
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# 获取代理配置，如果提供了 proxy_options 则使用覆盖配置
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proxies = cls._get_proxy_config(proxy_options)
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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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clean_base_url = base_url.rstrip("/")
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if clean_base_url.endswith("/images/generations") or clean_base_url.endswith("/images/edits"):
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clean_base_url = clean_base_url.rsplit("/images", 1)[0]
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elif clean_base_url.endswith("/images"):
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clean_base_url = clean_base_url.rsplit("/images", 1)[0]
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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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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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aspect_ratio=aspect_ratio,
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resolution=resolution,
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quality=quality,
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number_of_images=number_of_images,
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seed=seed,
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images=images,
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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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aspect_ratio=aspect_ratio,
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resolution=resolution,
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quality=quality,
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number_of_images=number_of_images,
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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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aspect_ratio: str,
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resolution: str,
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quality: str,
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number_of_images: 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": number_of_images,
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"seed": seed,
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"response_format": "b64_json",
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"resolution": resolution.lower() if resolution else "1k"
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}
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if aspect_ratio != "auto":
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payload["aspect_ratio"] = aspect_ratio
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if quality and quality != "default":
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payload["quality"] = quality
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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, 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"The API request failed. Please check if the interface address and key are correct: {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 _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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aspect_ratio: str,
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resolution: str,
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quality: str,
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number_of_images: int,
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seed: int,
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images: torch.Tensor,
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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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input_images = []
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total_imgs = images.shape[0] if len(images.shape) >= 4 else 1
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max_imgs = 1 if "pro" in model else 3
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num_to_take = min(total_imgs, max_imgs)
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for idx in range(num_to_take):
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img_tensor = images[idx].unsqueeze(0) if len(images.shape) >= 4 else images.unsqueeze(0)
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b64_str = tensor_to_base64_string(img_tensor, mime_type="image/png")
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input_images.append({
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"url": f"data:image/png;base64,{b64_str}"
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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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"images": input_images,
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"n": number_of_images,
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"seed": seed,
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"response_format": "b64_json",
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"resolution": resolution.lower() if resolution else "1k"
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}
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if aspect_ratio != "auto":
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payload["aspect_ratio"] = aspect_ratio
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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, 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"The API request failed. Please check if the interface address and key are correct: {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 _parse_response(
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cls,
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resp: requests.Response,
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model: str = "",
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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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# 检查 HTTP 状态码
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if resp.status_code != 200:
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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 returns an error. status_code: {resp.status_code}, error_reason: {resp.text}"
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}
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return io.NodeOutput(empty_image, json.dumps(err_info, ensure_ascii=False))
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# 检查返回内容是否为空
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if not resp.text.strip():
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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": "The API returns an empty content"
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}
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return io.NodeOutput(empty_image, json.dumps(err_info, ensure_ascii=False))
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try:
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data = resp.json()
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except Exception as json_exception:
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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"The API returned a JSON parsing failure: {str(json_exception)}"
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}
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return io.NodeOutput(empty_image, json.dumps(err_info, ensure_ascii=False))
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# 提取图像数据列表
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items = data.get("data", [])
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if not items:
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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": "Image data not found in response",
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"raw_response": data
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}
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return io.NodeOutput(empty_image, json.dumps(err_info, ensure_ascii=False))
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image_tensors = []
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revised_prompts = []
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for item in items:
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if not isinstance(item, dict):
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continue
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if item.get("revised_prompt"):
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revised_prompts.append(item["revised_prompt"])
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b64_json = item.get("b64_json")
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image_url = item.get("url")
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if b64_json:
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try:
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image_bytes = base64.b64decode(b64_json)
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pil_image = Image.open(BytesIO(image_bytes)).convert("RGB")
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img_np = np.array(pil_image).astype(np.float32) / 255.0
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img_tensor = torch.from_numpy(img_np).unsqueeze(0)
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image_tensors.append(img_tensor)
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except Exception:
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pass
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elif image_url:
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try:
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img_resp = requests.get(image_url, timeout=timeout, proxies=proxies)
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if img_resp.status_code == 200:
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pil_image = Image.open(BytesIO(img_resp.content)).convert("RGB")
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img_np = np.array(pil_image).astype(np.float32) / 255.0
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img_tensor = torch.from_numpy(img_np).unsqueeze(0)
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image_tensors.append(img_tensor)
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except Exception:
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pass
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if not image_tensors:
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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": "Failed to decode or download any image from response",
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"raw_response": data
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}
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return io.NodeOutput(empty_image, json.dumps(err_info, ensure_ascii=False))
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if len(image_tensors) == 1:
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final_tensor = image_tensors[0]
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else:
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final_tensor = torch.cat(image_tensors, dim=0)
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usage = data.get("usage", {})
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info = {
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"success": True,
|
|
"model": model,
|
|
"created": data.get("created"),
|
|
"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:
|
|
"""创建空图像"""
|
|
try:
|
|
return torch.zeros(1, 512, 512, 3, dtype=torch.float32)
|
|
except Exception:
|
|
return None
|