feat: sync latest local version as authoritative codebase
Complete rewrite/sync of comfyui_o1key custom nodes. Treat this commit as the current canonical version. Co-Authored-By: Claude Sonnet 4.5 <[email protected]>
This commit is contained in:
o1key
@@ -0,0 +1,986 @@
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"""
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Gemini API 客户端
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处理与 api.o1key.com 的通信，用于图像生成
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"""
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import re
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import time
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from io import BytesIO
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from typing import Any, Callable, Dict, List, Optional
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import aiohttp
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from PIL import Image
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from ..utils.image_utils import encode_image_to_base64, decode_base64_to_pil
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from ..utils.config import get_api_key_or_raise, get_api_base_url
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from .base_client import BaseAPIClient
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class GeminiAPIClient(BaseAPIClient):
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"""
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Gemini API 客户端
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用于调用 Gemini 3 Pro 模型进行图像生成
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"""
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def __init__(self, api_key: Optional[str] = None):
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"""
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初始化客户端
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Args:
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api_key: API 密钥，如果为 None 则从配置文件或环境变量读取
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"""
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if api_key is None:
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api_key = get_api_key_or_raise("O1KEY_API_KEY")
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super().__init__(
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base_url=get_api_base_url(),
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api_key=api_key,
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max_request_size=100 * 1024 * 1024
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)
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def get_endpoint(self, model: str = "", resolution: str = "2K", **kwargs) -> str:
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"""
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根据模型和分辨率获取 API 端点
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Args:
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model: 模型名称
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resolution: 分辨率（1K, 2K, 4K）
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Returns:
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API 端点路径
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"""
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from ..models_config import get_model_endpoint
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# 特殊处理：动态端点模型（根据分辨率选择）
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if model == "nano-banana-pro-限时特价":
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if resolution == "1K":
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return "/v1beta/models/nano-banana-pro:generateContent"
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elif resolution == "2K":
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return "/v1beta/models/nano-banana-pro-2k:generateContent"
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elif resolution == "4K":
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return "/v1beta/models/nano-banana-pro-4k:generateContent"
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else:
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return "/v1beta/models/nano-banana-pro-2k:generateContent"
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elif model == "nano-banana-2-官方计费":
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if resolution == "512":
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return "/v1beta/models/nano-banana-2-0.5k-official:generateContent"
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elif resolution == "1K":
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return "/v1beta/models/nano-banana-2-1k-official:generateContent"
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elif resolution == "2K":
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return "/v1beta/models/nano-banana-2-2k-official:generateContent"
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elif resolution == "4K":
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return "/v1beta/models/nano-banana-2-4k-official:generateContent"
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else:
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return "/v1beta/models/nano-banana-2-2k-official:generateContent"
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elif model == "nano-banana-pro-官方计费":
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if resolution == "1K":
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return "/v1beta/models/nano-banana-pro-1k-official:generateContent"
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elif resolution == "2K":
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return "/v1beta/models/nano-banana-pro-2k-official:generateContent"
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elif resolution == "4K":
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return "/v1beta/models/nano-banana-pro-4k-official:generateContent"
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else:
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return "/v1beta/models/nano-banana-pro-2k-official:generateContent"
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elif model == "gemini-3-pro-image-preview-url":
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if resolution == "1K":
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return "/v1beta/models/gemini-3-pro-image-preview-url:generateContent"
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elif resolution == "2K":
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return "/v1beta/models/gemini-3-pro-image-preview-2k-url:generateContent"
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elif resolution == "4K":
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return "/v1beta/models/gemini-3-pro-image-preview-4k-url:generateContent"
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else:
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return "/v1beta/models/gemini-3-pro-image-preview-2k-url:generateContent"
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# 其他模型：从配置文件读取端点
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endpoint = get_model_endpoint(model)
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if endpoint:
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return endpoint
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# 兜底：使用标准模式端点
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return "/v1beta/models/gemini-3-pro-image-preview:generateContent"
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def get_http_error_message(self, status_code: int, error_message: str) -> Optional[str]:
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"""Gemini 请求 429/503 时返回图中约定的多行错误框文案。"""
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if status_code == 429:
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return (
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"莫慌!该模型暂时超出速率限制啦\n"
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"解决方案如下(任意一种):\n"
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"1.切换当前模型\n"
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"2.前往后台,修改令牌分组"
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)
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if status_code == 503:
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return (
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"警报!谷歌服务器当前过载!\n"
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"解决方案如下:\n"
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"1.摸会儿鱼吧,我也没办法,谷歌会尽快恢复,嘿嘿~\n"
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"2.切换其他模型\n"
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"3.前往后台,修改令牌分组"
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)
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return None
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def build_request_body(
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self,
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prompt: str = "",
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images: Optional[List[Image.Image]] = None,
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aspect_ratio: str = "1:1",
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resolution: str = "2K",
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enable_grounding: bool = False,
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enable_image_search: bool = False,
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candidate_count: int = 1,
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**kwargs
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) -> Dict[str, Any]:
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"""
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构建 API 请求体
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Args:
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prompt: 提示词
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images: 输入图像列表（可选）
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aspect_ratio: 宽高比
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resolution: 分辨率
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enable_grounding: 是否启用 Google Search Grounding
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enable_image_search: 是否同时启用 Google Image Search（仅 Gemini 3.1 Flash 支持）
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candidate_count: 单次请求返回的候选图数量，默认 1
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Returns:
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请求体字典
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"""
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parts = []
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# 添加文本部分
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parts.append({"text": prompt})
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# 添加图像部分（如果有）
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if images:
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for img in images:
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img_base64 = encode_image_to_base64(img)
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parts.append({
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"inline_data": {
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"mime_type": "image/png",
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"data": img_base64
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}
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})
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# 构建请求体
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request_body = {
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"contents": [
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{
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"role": "user",
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"parts": parts
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}
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],
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"generationConfig": {
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"candidateCount": candidate_count,
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"responseModalities": ["TEXT", "IMAGE"],
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"imageConfig": {
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"aspectRatio": aspect_ratio,
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"imageSize": resolution
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}
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}
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}
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# 添加 Google Search Grounding 工具（如果启用）
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# 注意：enable_image_search=True 时会自动隐含 enable_grounding
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if enable_grounding or enable_image_search:
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if enable_image_search:
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# 同时启用网页搜索和图片搜索（仅 nano-banana-2 / gemini-3.1-flash-image-preview 支持）
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request_body["tools"] = [
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{
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"google_search": {
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"searchTypes": {
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"webSearch": {},
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"imageSearch": {}
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}
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}
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}
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]
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else:
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# 仅启用网页搜索（通用）
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request_body["tools"] = [{"google_search": {}}]
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return request_body
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def parse_response(self, response: Dict[str, Any]) -> List[Image.Image]:
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"""
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同步解析 API 响应（保留以满足抽象基类要求）
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注意：此方法仅用于兼容基类接口，实际使用请调用 parse_response_async()
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Args:
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response: API 响应字典
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Returns:
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图像列表
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Raises:
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RuntimeError: 此方法不应被直接调用
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"""
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raise RuntimeError(
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"parse_response() 不应被直接调用。"
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"请使用 generate_single_async() 或 generate_batch_async() 等高级方法。"
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)
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async def parse_response_async(
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self,
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response: Dict[str, Any],
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session: Optional[aiohttp.ClientSession] = None
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) -> tuple[List[Image.Image], Dict[str, Any]]:
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"""
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异步解析 API 响应，提取生成的图像
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Args:
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response: API 响应字典
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session: aiohttp 会话（用于下载图片）
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Returns:
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(图像列表, 格式信息字典)
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格式信息包含: type (base64/url), size, resolution, download_speed (仅URL)
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Raises:
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RuntimeError: 解析失败或 API 拒绝时
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"""
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# 初始化格式信息
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format_info = {
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"type": None, # "base64" or "url"
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"size": 0,
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"resolution": None,
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"download_speed": None
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}
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candidates = response.get("candidates", [])
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# ========== 错误检测（按优先级顺序）==========
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# 1. 检查 candidatesTokenCount（最高优先级）
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usage_metadata = response.get("usageMetadata", {})
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candidates_token_count = usage_metadata.get("candidatesTokenCount", -1)
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if candidates_token_count == 0:
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error_msg = (
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"Damn!你触发顶级风控啦!还没到生图阶段就被拒了。\n"
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"赶紧调整一下图片或提示词吧!该情况不会返回图片且正常扣费!下次小心哦~"
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)
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raise RuntimeError(error_msg)
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# 2. 检查 finishReason（次优先级）
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candidates = response.get("candidates", [])
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if candidates:
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for candidate in candidates:
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finish_reason = candidate.get("finishReason", "")
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if finish_reason and finish_reason != "STOP":
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error_msg = (
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"Ohh no! 生图过程触发风控,图片被拒绝生成!\n"
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"可能原因如下:\n"
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"1.违禁内容\n"
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"2.触发安全过滤器\n"
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"3.涉及版权问题\n"
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"4. Token超限\n"
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"赶紧调整一下图片或提示词吧!该情况不会返回图片且正常扣费!下次小心哦~"
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)
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raise RuntimeError(error_msg)
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# ========== 图像提取 ==========
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images = []
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text_responses = [] # 收集文本响应
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# 需要关闭 session 的标记
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close_session = False
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if session is None:
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session = aiohttp.ClientSession()
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close_session = True
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try:
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for candidate_idx, candidate in enumerate(candidates):
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content = candidate.get("content", {})
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parts = content.get("parts", [])
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for part_idx, part in enumerate(parts):
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# 方式1: inline_data 或 inlineData (base64)
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# 兼容两种命名方式：蛇形（inline_data）和驼峰（inlineData）
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inline_data_key = None
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if "inline_data" in part:
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inline_data_key = "inline_data"
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elif "inlineData" in part:
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inline_data_key = "inlineData"
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if inline_data_key:
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inline_data = part[inline_data_key]
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# 同样兼容 data/mimeType 的命名
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img_data = inline_data.get("data") or inline_data.get("data", "")
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if img_data:
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img = decode_base64_to_pil(img_data)
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images.append(img)
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# 记录格式信息
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if format_info["type"] is None:
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format_info["type"] = "base64"
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format_info["size"] = len(img_data) * 3 / 4 # Base64 解码后的字节数
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format_info["resolution"] = f"{img.size[0]}x{img.size[1]}"
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# 方式2: text 中的 URL - 改为异步下载
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elif "text" in part:
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text = part["text"]
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# 收集文本响应（用于后续错误检测）
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text_responses.append(text)
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# 尝试 markdown 格式: 
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url_pattern_md = r'!\[.*?\]\((https?://[^\)]+)\)'
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urls = re.findall(url_pattern_md, text)
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# 如果没找到，尝试纯 URL 格式
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if not urls:
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url_pattern_plain = r'https?://[^\s<>"{}|\\^`\[\]]+'
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urls = re.findall(url_pattern_plain, text)
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if urls:
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for url_idx, url in enumerate(urls):
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try:
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# 使用 aiohttp 异步下载
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download_start = time.time()
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async with session.get(url) as img_response:
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if img_response.status == 200:
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img_data = await img_response.read()
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download_time = time.time() - download_start
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img_size = len(img_data)
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speed = img_size / download_time if download_time > 0 else 0
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img = Image.open(BytesIO(img_data))
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images.append(img)
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# 记录格式信息（只记录第一张）
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if format_info["type"] is None:
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format_info["type"] = "url"
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format_info["size"] = img_size
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format_info["resolution"] = f"{img.size[0]}x{img.size[1]}"
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format_info["download_speed"] = speed
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except Exception as e:
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pass # 静默失败，继续尝试其他URL
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# 方式3: 直接的 URL 字段 - 也改为异步
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elif "imageUrl" in part or "url" in part:
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url = part.get("imageUrl") or part.get("url")
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try:
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download_start = time.time()
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async with session.get(url) as img_response:
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if img_response.status == 200:
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img_data = await img_response.read()
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download_time = time.time() - download_start
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img_size = len(img_data)
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speed = img_size / download_time if download_time > 0 else 0
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img = Image.open(BytesIO(img_data))
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images.append(img)
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# 记录格式信息
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if format_info["type"] is None:
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format_info["type"] = "url"
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format_info["size"] = img_size
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format_info["resolution"] = f"{img.size[0]}x{img.size[1]}"
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format_info["download_speed"] = speed
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except Exception as e:
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pass # 静默失败
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except Exception as e:
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raise RuntimeError(f"解析 API 响应失败: {str(e)}")
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finally:
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if close_session:
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await session.close()
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# 3. 检查 API 文本响应拒绝说明
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if not images and text_responses:
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# API 返回了文本但没有图片，说明请求被拒绝
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combined_text = "\n".join(text_responses)
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error_msg = (
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f"API 拒绝响应\n\n"
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f"API 返回说明：\n{combined_text}\n\n"
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f"建议：\n"
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f" - 根据上述说明调整请求内容\n"
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f" - 确保提示词和参考图符合使用规范"
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)
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raise RuntimeError(error_msg)
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if not images:
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raise RuntimeError("API 响应中未找到生成的图像")
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return images, format_info
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async def generate_single_async(
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self,
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prompt: str,
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model: str,
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||||
resolution: str,
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||||
aspect_ratio: str,
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||||
images: Optional[List[Image.Image]] = None,
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session=None,
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task_index: Optional[int] = None,
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total_tasks: Optional[int] = None,
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debug: bool = False,
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debug_request: bool = False,
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enable_grounding: bool = False,
|
||||
enable_image_search: bool = False,
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||||
candidate_count: int = 1
|
||||
) -> tuple[List[Image.Image], Dict[str, Any]]:
|
||||
"""
|
||||
单次异步生成请求（极简单行日志）
|
||||
|
||||
Args:
|
||||
prompt: 提示词
|
||||
model: 模型名称
|
||||
resolution: 分辨率
|
||||
aspect_ratio: 宽高比
|
||||
images: 输入图像列表
|
||||
session: aiohttp 会话
|
||||
task_index: 任务索引（用于批量任务）
|
||||
total_tasks: 总任务数（用于批量任务）
|
||||
debug: 是否打印完整 API 响应
|
||||
debug_request: 是否打印发送的请求体（base64 图片数据将被截断）
|
||||
enable_grounding: 是否启用 Google Search Grounding
|
||||
enable_image_search: 是否同时启用 Google Image Search
|
||||
|
||||
Returns:
|
||||
(生成的图像列表, 计时信息字典)
|
||||
"""
|
||||
import json
|
||||
|
||||
total_start = time.time()
|
||||
|
||||
# 任务前缀
|
||||
task_prefix = f"[{task_index}/{total_tasks}]" if task_index is not None and total_tasks else ""
|
||||
|
||||
# ========== 1. 构建请求 ==========
|
||||
build_start = time.time()
|
||||
endpoint = self.get_endpoint(model=model, resolution=resolution)
|
||||
request_body = self.build_request_body(
|
||||
prompt=prompt,
|
||||
images=images,
|
||||
aspect_ratio=aspect_ratio,
|
||||
resolution=resolution,
|
||||
enable_grounding=enable_grounding,
|
||||
enable_image_search=enable_image_search,
|
||||
candidate_count=candidate_count
|
||||
)
|
||||
build_time = time.time() - build_start
|
||||
|
||||
# ========== 调试日志：打印请求体 ==========
|
||||
if debug_request:
|
||||
import json as _json
|
||||
|
||||
def _truncate_base64_req(obj, max_len=200):
|
||||
if isinstance(obj, dict):
|
||||
return {k: _truncate_base64_req(v, max_len) for k, v in obj.items()}
|
||||
elif isinstance(obj, list):
|
||||
return [_truncate_base64_req(item, max_len) for item in obj]
|
||||
elif isinstance(obj, str) and len(obj) > max_len:
|
||||
if all(c in 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=' for c in obj[:50]):
|
||||
return f"<base64 data, {len(obj)} chars>"
|
||||
return obj
|
||||
return obj
|
||||
|
||||
safe_request = _truncate_base64_req(request_body)
|
||||
print(
|
||||
f"\n{'='*60}\n"
|
||||
f"[请求体日志] 任务 {task_prefix or '?'} 发送请求体：\n"
|
||||
f"端点: {endpoint}\n"
|
||||
f"{_json.dumps(safe_request, ensure_ascii=False, indent=2)}\n"
|
||||
f"{'='*60}\n"
|
||||
)
|
||||
|
||||
# 计算请求体大小
|
||||
request_size = len(json.dumps(request_body).encode('utf-8'))
|
||||
if request_size < 1024 * 1024:
|
||||
size_str = f"{request_size / 1024:.2f}KB"
|
||||
else:
|
||||
size_str = f"{request_size / (1024 * 1024):.2f}MB"
|
||||
|
||||
# ========== 2. 发送网络请求 ==========
|
||||
request_start = time.time()
|
||||
|
||||
try:
|
||||
response = await self.request_async(endpoint, request_body, session)
|
||||
except Exception as e:
|
||||
request_time = time.time() - request_start
|
||||
error_first_line = str(e).split('\n')[0]
|
||||
print(f"{task_prefix} 请求 {size_str} → API {request_time:.1f}s → 失败: {error_first_line} ✗")
|
||||
raise
|
||||
|
||||
request_time = time.time() - request_start
|
||||
|
||||
# ========== 调试日志：打印完整响应 ==========
|
||||
if debug:
|
||||
import json as _json
|
||||
# 构建可安全序列化的响应副本（截断 base64 图片数据避免输出过长）
|
||||
def _truncate_base64(obj, max_len=200):
|
||||
if isinstance(obj, dict):
|
||||
return {k: _truncate_base64(v, max_len) for k, v in obj.items()}
|
||||
elif isinstance(obj, list):
|
||||
return [_truncate_base64(item, max_len) for item in obj]
|
||||
elif isinstance(obj, str) and len(obj) > max_len:
|
||||
# 判断是否为 base64 图片数据（不含空格/换行的长字符串）
|
||||
if all(c in 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=' for c in obj[:50]):
|
||||
return f"<base64 data, {len(obj)} chars>"
|
||||
return obj
|
||||
return obj
|
||||
|
||||
safe_response = _truncate_base64(response)
|
||||
print(
|
||||
f"\n{'='*60}\n"
|
||||
f"[调试日志] 任务 {task_prefix or '?'} 完整 API 响应：\n"
|
||||
f"{_json.dumps(safe_response, ensure_ascii=False, indent=2)}\n"
|
||||
f"{'='*60}\n"
|
||||
)
|
||||
|
||||
# ========== 3. 解析响应 ==========
|
||||
parse_start = time.time()
|
||||
|
||||
try:
|
||||
result_images, format_info = await self.parse_response_async(response, session)
|
||||
except Exception as e:
|
||||
parse_time = time.time() - parse_start
|
||||
error_first_line = str(e).split('\n')[0]
|
||||
print(f"{task_prefix} 请求 {size_str} → API {request_time:.1f}s → 解析失败: {error_first_line} ✗")
|
||||
raise
|
||||
|
||||
parse_time = time.time() - parse_start
|
||||
|
||||
# ========== 4. 格式化输出（单行） ==========
|
||||
# 格式化图像大小
|
||||
img_size = format_info.get("size", 0)
|
||||
if img_size < 1024 * 1024:
|
||||
img_size_str = f"{img_size / 1024:.2f}KB"
|
||||
else:
|
||||
img_size_str = f"{img_size / (1024 * 1024):.2f}MB"
|
||||
|
||||
# 根据类型构建下载信息
|
||||
if format_info.get("type") == "base64":
|
||||
download_info = f"Base64 {img_size_str} ({parse_time:.1f}s)"
|
||||
elif format_info.get("type") == "url":
|
||||
speed = format_info.get("download_speed", 0)
|
||||
speed_str = f"{speed / (1024 * 1024):.1f}MB/s"
|
||||
download_info = f"URL {img_size_str} ({parse_time:.1f}s, {speed_str})"
|
||||
else:
|
||||
download_info = f"{img_size_str}"
|
||||
|
||||
# 单行输出
|
||||
print(f"{task_prefix} 请求 {size_str} → API {request_time:.1f}s → {download_info} ✓")
|
||||
|
||||
# 返回结果和计时信息
|
||||
total_time = time.time() - total_start
|
||||
timing_info = {
|
||||
"build_time": build_time,
|
||||
"request_time": request_time,
|
||||
"parse_time": parse_time,
|
||||
"total_time": total_time,
|
||||
"format_type": format_info.get("type", "unknown")
|
||||
}
|
||||
|
||||
return result_images, timing_info
|
||||
|
||||
async def generate_batch_async(
|
||||
self,
|
||||
prompt: str,
|
||||
model: str,
|
||||
resolution: str,
|
||||
aspect_ratio: str,
|
||||
batch_size: int,
|
||||
images: Optional[List[Image.Image]] = None,
|
||||
progress_callback: Optional[Callable[[int, int, bool, Optional[str]], None]] = None,
|
||||
debug: bool = False,
|
||||
debug_request: bool = False,
|
||||
enable_grounding: bool = False,
|
||||
enable_image_search: bool = False,
|
||||
candidate_count: int = 1
|
||||
) -> List[Image.Image]:
|
||||
"""
|
||||
批量全并发生成 - 改进版：支持分批处理和内存管理
|
||||
|
||||
Args:
|
||||
prompt: 提示词
|
||||
model: 模型名称
|
||||
resolution: 分辨率
|
||||
aspect_ratio: 宽高比
|
||||
batch_size: 批次大小
|
||||
images: 输入图像列表
|
||||
progress_callback: 进度回调，签名为 (completed, total, success, error_msg)
|
||||
debug: 是否打印完整 API 响应
|
||||
debug_request: 是否打印发送的请求体
|
||||
enable_grounding: 是否启用 Google Search Grounding
|
||||
enable_image_search: 是否同时启用 Google Image Search
|
||||
candidate_count: 单次请求返回的候选图数量
|
||||
|
||||
Returns:
|
||||
生成的图像列表
|
||||
"""
|
||||
import aiohttp
|
||||
import asyncio
|
||||
|
||||
all_images = []
|
||||
completed = 0
|
||||
success_count = 0
|
||||
fail_count = 0
|
||||
first_error = None # 保存第一个错误
|
||||
|
||||
# 分批处理配置
|
||||
max_concurrent = 10 # 最大并发数
|
||||
save_batch_size = 10 # 分批保存大小
|
||||
|
||||
# 计算需要多少批次
|
||||
num_batches = (batch_size + max_concurrent - 1) // max_concurrent
|
||||
|
||||
print(f"GeminiClient: 批量生成 {batch_size} 张图片，并发数: {max_concurrent}，分 {num_batches} 批执行")
|
||||
|
||||
connector = aiohttp.TCPConnector(limit=0, limit_per_host=0)
|
||||
|
||||
async with aiohttp.ClientSession(connector=connector) as session:
|
||||
# 分批执行
|
||||
for batch_idx in range(num_batches):
|
||||
batch_start = batch_idx * max_concurrent
|
||||
batch_end = min(batch_start + max_concurrent, batch_size)
|
||||
batch_size_current = batch_end - batch_start
|
||||
|
||||
if num_batches > 1:
|
||||
print(f"GeminiClient: 执行第 {batch_idx + 1}/{num_batches} 批 ({batch_start + 1}-{batch_end})...")
|
||||
|
||||
# 创建当前批次的任务
|
||||
tasks = []
|
||||
for i in range(batch_size_current):
|
||||
task_index = batch_start + i
|
||||
task = asyncio.create_task(
|
||||
self.generate_single_async(
|
||||
prompt=prompt,
|
||||
model=model,
|
||||
resolution=resolution,
|
||||
aspect_ratio=aspect_ratio,
|
||||
images=images,
|
||||
session=session,
|
||||
task_index=task_index + 1,
|
||||
total_tasks=batch_size,
|
||||
debug=debug,
|
||||
debug_request=debug_request,
|
||||
enable_grounding=enable_grounding,
|
||||
enable_image_search=enable_image_search,
|
||||
candidate_count=candidate_count
|
||||
),
|
||||
name=f"task_{task_index}"
|
||||
)
|
||||
tasks.append(task)
|
||||
|
||||
# 收集当前批次的结果
|
||||
batch_images = []
|
||||
batch_completed = 0
|
||||
|
||||
for coro in asyncio.as_completed(tasks):
|
||||
batch_completed += 1
|
||||
completed += 1
|
||||
|
||||
try:
|
||||
result_images, timing_info = await coro
|
||||
if result_images:
|
||||
# 立即处理生成的图片
|
||||
for img in result_images:
|
||||
batch_images.append(img)
|
||||
all_images.append(img)
|
||||
|
||||
success_count += 1
|
||||
|
||||
# 通知进度
|
||||
if progress_callback:
|
||||
progress_callback(completed, batch_size, True, None)
|
||||
|
||||
# 每成功生成一张图片就打印日志
|
||||
print(f"GeminiClient: 任务 {completed}/{batch_size} 成功生成图片 ✓")
|
||||
|
||||
except Exception as e:
|
||||
fail_count += 1
|
||||
# 保存第一个错误（用于后续抛出）
|
||||
if first_error is None:
|
||||
first_error = e
|
||||
error_msg = str(e)
|
||||
|
||||
# 传递完整的错误信息（用于排查问题）
|
||||
if progress_callback:
|
||||
progress_callback(completed, batch_size, False, error_msg)
|
||||
|
||||
print(f"GeminiClient: 任务 {completed}/{batch_size} 失败 ✗")
|
||||
|
||||
# 当前批次完成后，立即清理内存
|
||||
if batch_images:
|
||||
print(f"GeminiClient: 第 {batch_idx + 1} 批完成，生成 {len(batch_images)} 张图片")
|
||||
|
||||
# 强制垃圾回收，释放内存
|
||||
import gc
|
||||
gc.collect()
|
||||
|
||||
# 短暂暂停，让系统处理内存
|
||||
await asyncio.sleep(0.1)
|
||||
|
||||
# 清空当前批次图片引用，帮助垃圾回收
|
||||
batch_images = []
|
||||
|
||||
# 最终结果检查
|
||||
if not all_images:
|
||||
# 如果有保存的原始错误，直接抛出原始错误
|
||||
if first_error:
|
||||
raise first_error
|
||||
raise RuntimeError(f"批量生成失败，{fail_count} 个请求全部失败")
|
||||
|
||||
print(f"GeminiClient: 批量生成完成，成功 {success_count}/{batch_size}，失败 {fail_count}")
|
||||
return all_images
|
||||
|
||||
def generate_sync(
|
||||
self,
|
||||
prompt: str,
|
||||
model: str,
|
||||
resolution: str,
|
||||
aspect_ratio: str,
|
||||
batch_size: int,
|
||||
images: Optional[List[Image.Image]] = None,
|
||||
progress_callback: Optional[Callable[[int, int], None]] = None,
|
||||
debug: bool = False,
|
||||
debug_request: bool = False,
|
||||
enable_grounding: bool = False,
|
||||
enable_image_search: bool = False,
|
||||
candidate_count: int = 1
|
||||
) -> List[Image.Image]:
|
||||
"""
|
||||
同步生成接口（用于 ComfyUI）
|
||||
|
||||
Args:
|
||||
prompt: 提示词
|
||||
model: 模型名称
|
||||
resolution: 分辨率
|
||||
aspect_ratio: 宽高比
|
||||
batch_size: 批次大小
|
||||
images: 输入图像列表
|
||||
progress_callback: 进度回调
|
||||
debug: 是否打印完整 API 响应
|
||||
debug_request: 是否打印发送的请求体
|
||||
enable_grounding: 是否启用 Google Search Grounding
|
||||
enable_image_search: 是否同时启用 Google Image Search
|
||||
candidate_count: 单次请求返回的候选图数量
|
||||
|
||||
Returns:
|
||||
生成的图像列表
|
||||
"""
|
||||
coro = self.generate_batch_async(
|
||||
prompt=prompt,
|
||||
model=model,
|
||||
resolution=resolution,
|
||||
aspect_ratio=aspect_ratio,
|
||||
batch_size=batch_size,
|
||||
images=images,
|
||||
progress_callback=progress_callback,
|
||||
debug=debug,
|
||||
debug_request=debug_request,
|
||||
enable_grounding=enable_grounding,
|
||||
enable_image_search=enable_image_search,
|
||||
candidate_count=candidate_count
|
||||
)
|
||||
|
||||
return self.run_async_in_thread(coro)
|
||||
|
||||
async def generate_multi_prompts_async(
|
||||
self,
|
||||
prompts: List[str],
|
||||
model: str,
|
||||
resolution: str,
|
||||
aspect_ratio: str,
|
||||
images_per_prompt: int,
|
||||
images: Optional[List[Image.Image]] = None,
|
||||
progress_callback: Optional[Callable[[int, int, bool, Optional[str]], None]] = None,
|
||||
debug: bool = False,
|
||||
debug_request: bool = False,
|
||||
enable_grounding: bool = False,
|
||||
enable_image_search: bool = False
|
||||
) -> List[Image.Image]:
|
||||
"""
|
||||
多提示词批量生成 - 改进版：支持分批处理和内存管理
|
||||
|
||||
为每个提示词生成指定数量的图像，分批并发执行。
|
||||
|
||||
Args:
|
||||
prompts: 提示词列表
|
||||
model: 模型名称
|
||||
resolution: 分辨率
|
||||
aspect_ratio: 宽高比
|
||||
images_per_prompt: 每个提示词生成的图像数量
|
||||
images: 输入图像列表（所有提示词共享）
|
||||
progress_callback: 进度回调，签名为 (completed, total, success, error_msg)
|
||||
debug: 是否打印完整 API 响应
|
||||
debug_request: 是否打印发送的请求体
|
||||
enable_grounding: 是否启用 Google Search Grounding
|
||||
enable_image_search: 是否同时启用 Google Image Search
|
||||
|
||||
Returns:
|
||||
生成的图像列表（长度 = len(prompts) * images_per_prompt）
|
||||
"""
|
||||
import aiohttp
|
||||
import asyncio
|
||||
|
||||
all_images = []
|
||||
completed = 0
|
||||
success_count = 0
|
||||
fail_count = 0
|
||||
first_error = None # 保存第一个错误
|
||||
total_tasks = len(prompts) * images_per_prompt
|
||||
|
||||
# 分批处理配置
|
||||
max_concurrent = 10 # 最大并发数
|
||||
|
||||
print(f"GeminiClient: 多提示词批量生成，共 {total_tasks} 个任务，{len(prompts)} 个提示词，每个 {images_per_prompt} 张")
|
||||
|
||||
connector = aiohttp.TCPConnector(limit=0, limit_per_host=0)
|
||||
|
||||
async with aiohttp.ClientSession(connector=connector) as session:
|
||||
# 创建所有任务
|
||||
tasks = []
|
||||
task_idx = 0
|
||||
for prompt in prompts:
|
||||
for _ in range(images_per_prompt):
|
||||
task = asyncio.create_task(
|
||||
self.generate_single_async(
|
||||
prompt=prompt,
|
||||
model=model,
|
||||
resolution=resolution,
|
||||
aspect_ratio=aspect_ratio,
|
||||
images=images,
|
||||
session=session,
|
||||
task_index=task_idx + 1,
|
||||
total_tasks=total_tasks,
|
||||
debug=debug,
|
||||
debug_request=debug_request,
|
||||
enable_grounding=enable_grounding,
|
||||
enable_image_search=enable_image_search
|
||||
),
|
||||
name=f"task_{task_idx}"
|
||||
)
|
||||
tasks.append(task)
|
||||
task_idx += 1
|
||||
|
||||
# 分批处理：每10个任务为一组
|
||||
batch_size = max_concurrent
|
||||
num_batches = (total_tasks + batch_size - 1) // batch_size
|
||||
|
||||
for batch_idx in range(num_batches):
|
||||
batch_start = batch_idx * batch_size
|
||||
batch_end = min(batch_start + batch_size, total_tasks)
|
||||
batch_tasks = tasks[batch_start:batch_end]
|
||||
|
||||
if num_batches > 1:
|
||||
print(f"GeminiClient: 执行第 {batch_idx + 1}/{num_batches} 批 ({batch_start + 1}-{batch_end})...")
|
||||
|
||||
# 收集当前批次的结果
|
||||
batch_images = []
|
||||
|
||||
for coro in asyncio.as_completed(batch_tasks):
|
||||
completed += 1
|
||||
|
||||
try:
|
||||
result_images, timing_info = await coro
|
||||
if result_images:
|
||||
# 立即处理生成的图片
|
||||
for img in result_images:
|
||||
batch_images.append(img)
|
||||
all_images.append(img)
|
||||
|
||||
success_count += 1
|
||||
|
||||
# 通知进度
|
||||
if progress_callback:
|
||||
progress_callback(completed, total_tasks, True, None)
|
||||
|
||||
# 每成功生成一张图片就打印日志
|
||||
print(f"GeminiClient: 任务 {completed}/{total_tasks} 成功生成图片 ✓")
|
||||
|
||||
except Exception as e:
|
||||
fail_count += 1
|
||||
# 保存第一个错误（用于后续抛出）
|
||||
if first_error is None:
|
||||
first_error = e
|
||||
error_msg = str(e)
|
||||
|
||||
# 传递完整的错误信息（用于排查问题）
|
||||
if progress_callback:
|
||||
progress_callback(completed, total_tasks, False, error_msg)
|
||||
|
||||
print(f"GeminiClient: 任务 {completed}/{total_tasks} 失败 ✗")
|
||||
|
||||
# 当前批次完成后，立即清理内存
|
||||
if batch_images:
|
||||
print(f"GeminiClient: 第 {batch_idx + 1} 批完成，生成 {len(batch_images)} 张图片")
|
||||
|
||||
# 强制垃圾回收，释放内存
|
||||
import gc
|
||||
gc.collect()
|
||||
|
||||
# 短暂暂停，让系统处理内存
|
||||
await asyncio.sleep(0.1)
|
||||
|
||||
# 清空当前批次图片引用，帮助垃圾回收
|
||||
batch_images = []
|
||||
|
||||
if not all_images:
|
||||
# 如果有保存的原始错误，直接抛出原始错误
|
||||
if first_error:
|
||||
raise first_error
|
||||
raise RuntimeError(f"批量生成失败，{fail_count} 个请求全部失败")
|
||||
|
||||
print(f"GeminiClient: 多提示词批量生成完成，成功 {success_count}/{total_tasks}，失败 {fail_count}")
|
||||
return all_images
|
||||
|
||||
def generate_multi_prompts_sync(
|
||||
self,
|
||||
prompts: List[str],
|
||||
model: str,
|
||||
resolution: str,
|
||||
aspect_ratio: str,
|
||||
images_per_prompt: int,
|
||||
images: Optional[List[Image.Image]] = None,
|
||||
progress_callback: Optional[Callable[[int, int], None]] = None,
|
||||
debug: bool = False,
|
||||
debug_request: bool = False,
|
||||
enable_grounding: bool = False,
|
||||
enable_image_search: bool = False
|
||||
) -> List[Image.Image]:
|
||||
"""
|
||||
多提示词批量生成（同步接口，用于 ComfyUI）
|
||||
|
||||
Args:
|
||||
prompts: 提示词列表
|
||||
model: 模型名称
|
||||
resolution: 分辨率
|
||||
aspect_ratio: 宽高比
|
||||
images_per_prompt: 每个提示词生成的图像数量
|
||||
images: 输入图像列表
|
||||
progress_callback: 进度回调
|
||||
debug: 是否打印完整 API 响应
|
||||
debug_request: 是否打印发送的请求体
|
||||
enable_grounding: 是否启用 Google Search Grounding
|
||||
enable_image_search: 是否同时启用 Google Image Search
|
||||
|
||||
Returns:
|
||||
生成的图像列表
|
||||
"""
|
||||
coro = self.generate_multi_prompts_async(
|
||||
prompts=prompts,
|
||||
model=model,
|
||||
resolution=resolution,
|
||||
aspect_ratio=aspect_ratio,
|
||||
images_per_prompt=images_per_prompt,
|
||||
images=images,
|
||||
progress_callback=progress_callback,
|
||||
debug=debug,
|
||||
debug_request=debug_request,
|
||||
enable_grounding=enable_grounding,
|
||||
enable_image_search=enable_image_search
|
||||
)
|
||||
|
||||
return self.run_async_in_thread(coro)
|
||||
|
||||
Reference in New Issue
Block a user