728 lines
27 KiB
Python
728 lines
27 KiB
Python
"""
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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
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from .base_client import BaseAPIClient
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# API 基础配置
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API_BASE_URL = "https://api.o1key.com"
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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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@staticmethod
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def get_timeout_by_resolution(resolution: str) -> int:
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"""
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根据分辨率获取超时时间
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Args:
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resolution: 分辨率（1K, 2K, 4K）
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Returns:
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超时时间（秒）
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"""
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timeout_map = {
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"1K": 180, # 3 分钟
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"2K": 300, # 5 分钟
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"4K": 360 # 6 分钟
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}
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return timeout_map.get(resolution, 300) # 默认 5 分钟
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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=API_BASE_URL,
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api_key=api_key,
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max_request_size=20 * 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":
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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 == "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 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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**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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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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"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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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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) -> List[Image.Image]:
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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
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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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# ========== 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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"内容审核拒绝 - candidatesTokenCount = 0\n\n"
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"原因：提示词或参考图包含不适当内容（色情、暴力、敏感话题等），"
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"在内容审核阶段就被拒绝，连候选内容都未生成。\n\n"
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"建议：\n"
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" - 检查提示词，确保不包含敏感或违规内容\n"
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" - 如使用参考图，确保图片内容健康合规\n"
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" - 避免描述暴力、色情等不当内容\n"
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" - 调整提示词后重试"
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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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# 根据不同的 finishReason 提供具体建议
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reason_messages = {
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"PROHIBITED_CONTENT": (
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"违禁内容拒绝",
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"生成内容触发了违禁内容策略",
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[
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"避免引用未来未发布的产品或概念（知识库截止2025年1月）",
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"使用专业图片编辑软件处理特殊需求",
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"确保请求内容在模型知识范围内"
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]
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),
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"SAFETY": (
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"安全过滤器拒绝",
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"内容触发了安全过滤器",
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[
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"使用健康、正面的描述",
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"避免涉及隐私和伦理问题的内容",
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"调整提示词后重试"
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]
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),
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"RECITATION": (
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"版权问题",
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"可能涉及版权或重复已有内容",
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[
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"避免涉及版权敏感话题",
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"使用更原创的描述方式",
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"调整提示词后重试"
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]
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),
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"MAX_TOKENS": (
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"Token 超限",
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"生成的内容超过了 Token 限制",
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[
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"简化提示词",
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"减少输入图片数量",
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"降低请求复杂度"
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]
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)
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}
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if finish_reason in reason_messages:
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title, reason, suggestions = reason_messages[finish_reason]
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suggestions_text = "\n".join([f" - {s}" for s in suggestions])
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error_msg = (
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f"{title} - finishReason = {finish_reason}\n\n"
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f"原因：{reason}\n\n"
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f"建议：\n{suggestions_text}"
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)
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else:
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# 未知的 finishReason
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error_msg = (
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f"生成异常 - finishReason = {finish_reason}\n\n"
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"原因：生成过程中断，具体原因未知\n\n"
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"建议：\n"
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" - 使用健康、正面的描述\n"
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" - 避免敏感话题\n"
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" - 调整提示词后重试"
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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 in candidates:
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content = candidate.get("content", {})
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parts = content.get("parts", [])
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for part in 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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# 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 in urls:
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try:
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# 使用 aiohttp 异步下载，支持更大的超时
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download_start = time.time()
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timeout = aiohttp.ClientTimeout(total=120)
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async with session.get(url, timeout=timeout) 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_mb = len(img_data) / 1024 / 1024
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speed_mbps = img_size_mb / download_time if download_time > 0 else 0
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# print(f"🔽 图片下载: {img_size_mb:.2f}MB 耗时 {download_time:.2f}s 速度 {speed_mbps:.2f}MB/s")
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img = Image.open(BytesIO(img_data))
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images.append(img)
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else:
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print(f"Nano Banana Pro: 下载图片失败 - HTTP {img_response.status}")
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except Exception as e:
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print(f"Nano Banana Pro: 下载图片失败 - {str(e)}")
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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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timeout = aiohttp.ClientTimeout(total=120)
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async with session.get(url, timeout=timeout) 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_mb = len(img_data) / 1024 / 1024
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speed_mbps = img_size_mb / download_time if download_time > 0 else 0
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# print(f"🔽 图片下载: {img_size_mb:.2f}MB 耗时 {download_time:.2f}s 速度 {speed_mbps:.2f}MB/s")
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img = Image.open(BytesIO(img_data))
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images.append(img)
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else:
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print(f"Nano Banana Pro: 下载图片失败 - HTTP {img_response.status}")
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except Exception as e:
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print(f"Nano Banana Pro: 下载图片失败 - {str(e)}")
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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
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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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) -> List[Image.Image]:
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"""
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单次异步生成请求
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Args:
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prompt: 提示词
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model: 模型名称
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resolution: 分辨率
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aspect_ratio: 宽高比
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images: 输入图像列表
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session: aiohttp 会话
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Returns:
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生成的图像列表
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"""
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endpoint = self.get_endpoint(model=model, resolution=resolution)
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request_body = self.build_request_body(
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prompt=prompt,
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images=images,
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aspect_ratio=aspect_ratio,
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resolution=resolution
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)
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# 根据分辨率获取超时时间
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timeout = self.get_timeout_by_resolution(resolution)
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response = await self.request_async(endpoint, request_body, session, timeout=timeout)
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# 使用异步解析方法，传入 session 以实现并发图片下载
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return await self.parse_response_async(response, session)
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async def generate_batch_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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batch_size: int,
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images: Optional[List[Image.Image]] = None,
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progress_callback: Optional[Callable[[int, int, bool, Optional[str]], None]] = None
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) -> List[Image.Image]:
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"""
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批量全并发生成
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Args:
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prompt: 提示词
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model: 模型名称
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resolution: 分辨率
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aspect_ratio: 宽高比
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batch_size: 批次大小
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images: 输入图像列表
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progress_callback: 进度回调，签名为 (completed, total, success, error_msg)
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Returns:
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生成的图像列表
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"""
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import aiohttp
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import asyncio
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all_images = []
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completed = 0
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success_count = 0
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fail_count = 0
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connector = aiohttp.TCPConnector(limit=0, limit_per_host=0)
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async with aiohttp.ClientSession(connector=connector) as session:
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tasks = []
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for i in range(batch_size):
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task = asyncio.create_task(
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self.generate_single_async(
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prompt=prompt,
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model=model,
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resolution=resolution,
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aspect_ratio=aspect_ratio,
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images=images,
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session=session
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),
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name=f"task_{i}"
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)
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tasks.append(task)
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# 使用 as_completed 实时获取完成的任务
|
||
for coro in asyncio.as_completed(tasks):
|
||
completed += 1
|
||
try:
|
||
result = await coro
|
||
if result:
|
||
all_images.append(result[0])
|
||
success_count += 1
|
||
if progress_callback:
|
||
progress_callback(completed, batch_size, True, None)
|
||
except Exception as e:
|
||
fail_count += 1
|
||
error_msg = str(e)
|
||
# 截取错误信息的第一行
|
||
if '\n' in error_msg:
|
||
error_msg = error_msg.split('\n')[0]
|
||
if progress_callback:
|
||
progress_callback(completed, batch_size, False, error_msg)
|
||
|
||
if not all_images:
|
||
raise RuntimeError(f"批量生成失败，{fail_count} 个请求全部失败")
|
||
|
||
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
|
||
) -> 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
|
||
|
||
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
|
||
)
|
||
|
||
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
|
||
) -> List[Image.Image]:
|
||
"""
|
||
多提示词批量生成
|
||
|
||
为每个提示词生成指定数量的图像，所有请求并发执行。
|
||
|
||
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)
|
||
|
||
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
|
||
total_tasks = len(prompts) * images_per_prompt
|
||
|
||
connector = aiohttp.TCPConnector(limit=0, limit_per_host=0)
|
||
|
||
async with aiohttp.ClientSession(connector=connector) as session:
|
||
tasks = []
|
||
|
||
# 为每个提示词创建 images_per_prompt 个任务
|
||
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
|
||
),
|
||
name=f"task_{task_idx}"
|
||
)
|
||
tasks.append(task)
|
||
task_idx += 1
|
||
|
||
# 使用 as_completed 实时获取完成的任务
|
||
for coro in asyncio.as_completed(tasks):
|
||
completed += 1
|
||
try:
|
||
result = await coro
|
||
if result:
|
||
all_images.append(result[0])
|
||
success_count += 1
|
||
if progress_callback:
|
||
progress_callback(completed, total_tasks, True, None)
|
||
except Exception as e:
|
||
fail_count += 1
|
||
error_msg = str(e)
|
||
# 截取错误信息的第一行
|
||
if '\n' in error_msg:
|
||
error_msg = error_msg.split('\n')[0]
|
||
if progress_callback:
|
||
progress_callback(completed, total_tasks, False, error_msg)
|
||
|
||
if not all_images:
|
||
raise RuntimeError(f"批量生成失败，{fail_count} 个请求全部失败")
|
||
|
||
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
|
||
) -> 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
|
||
|
||
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
|
||
)
|
||
|
||
return self.run_async_in_thread(coro)
|
||
|
||
async def query_balance_async(self) -> Dict[str, Any]:
|
||
"""
|
||
异步查询余额信息
|
||
|
||
Returns:
|
||
余额信息字典，包含：
|
||
- name: API 名称
|
||
- total_available: 可用余额（原始值）
|
||
- 其他字段...
|
||
|
||
Raises:
|
||
RuntimeError: 查询失败时
|
||
"""
|
||
endpoint = "/api/usage/token"
|
||
response = await self.request_get_async(endpoint, use_bearer_token=True)
|
||
|
||
if not response.get("code"):
|
||
raise RuntimeError("余额查询响应格式错误")
|
||
|
||
data = response.get("data", {})
|
||
return data
|
||
|
||
def query_balance_sync(self) -> Dict[str, Any]:
|
||
"""
|
||
同步查询余额信息（用于 ComfyUI 节点）
|
||
|
||
Returns:
|
||
余额信息字典
|
||
|
||
Raises:
|
||
RuntimeError: 查询失败时
|
||
"""
|
||
coro = self.query_balance_async()
|
||
return self.run_async_in_thread(coro)
|
||
|
||
def format_balance_info(self, balance_data: Dict[str, Any]) -> str:
|
||
"""
|
||
格式化余额信息为展示文本
|
||
|
||
Args:
|
||
balance_data: 余额信息字典
|
||
|
||
Returns:
|
||
格式化的文本，格式为 "当前余额：$XX.XX | API：xxx"
|
||
|
||
Example:
|
||
>>> data = {"name": "test-api", "total_available": 50000000}
|
||
>>> client.format_balance_info(data)
|
||
'当前余额：$100.00 | API：test-api'
|
||
"""
|
||
api_name = balance_data.get("name", "未知")
|
||
total_available = balance_data.get("total_available", 0)
|
||
|
||
# 转换公式：实际显示 = total_available / 500000
|
||
balance_in_dollars = total_available / 500000
|
||
|
||
return f"当前余额：${balance_in_dollars:.2f} | API：{api_name}" |