from flask import Flask, request, jsonify, Response, stream_with_context import requests import json import time import random import logging import sys import re from logging.handlers import TimedRotatingFileHandler app = Flask(__name__) # 配置日志 class RequestFormatter(logging.Formatter): def format(self, record): if request.method in ['POST', 'GET']: # 记录 POST 和 GET 请求 record.url = request.url record.remote_addr = request.remote_addr record.token = request.headers.get('Authorization', 'No Token') return super().format(record) return None formatter = RequestFormatter( '%(remote_addr)s - - [%(asctime)s] - Token: %(token)s - %(message)s', datefmt='%d/%b/%Y %H:%M:%S' ) handler = TimedRotatingFileHandler('app.log', when="midnight", interval=1, backupCount=30) handler.setFormatter(formatter) handler.setLevel(logging.INFO) app.logger.addHandler(handler) app.logger.setLevel(logging.INFO) # 模型映射 MODEL_MAPPING = { "flux.1-schnell": { "provider": "black-forest-labs", "mapping": "Pro/black-forest-labs/FLUX.1-schnell" }, "flux.1-dev": { "provider": "black-forest-labs", "mapping": "black-forest-labs/FLUX.1-dev" }, "stable-diffusion-3-5-large": { "provider": "stabilityai", "mapping": "stabilityai/stable-diffusion-3-5-large" }, "stable-diffusion-2-1": { "provider": "stabilityai", "mapping": "stabilityai/stable-diffusion-2-1" }, "stable-diffusion-3-medium": { "provider": "stabilityai", "mapping": "stabilityai/stable-diffusion-3-medium" }, "stable-diffusion-xl-base-1.0": { "provider": "stabilityai", "mapping": "stabilityai/stable-diffusion-xl-base-1.0" } } # 模拟身份验证函数 def getAuthCookie(req): auth_cookie = req.headers.get('Authorization') if auth_cookie and auth_cookie.startswith('Bearer '): return auth_cookie return None @app.route('/') def index(): usage = """ Text-to-Image API with SiliconFlow

Welcome to the Text-to-Image API with SiliconFlow!

Usage:

  1. Send a POST request to /ai/v1/chat/completions
  2. Include your prompt in the 'content' field of the last message
  3. Optional parameters:

Example Request:


{
    "model": "flux",
    "messages": [
        {
            "role": "user",
            "content": "A beautiful landscape -s 16:9"
        }
    ]
}

For more details, please refer to the API documentation.

""" return usage, 200 @app.route('/ai/v1/models', methods=['GET']) def get_models(): try: # 验证身份 auth_cookie = getAuthCookie(request) if not auth_cookie: app.logger.info(f'GET /ai/v1/models - 401 Unauthorized') return jsonify({"error": "Unauthorized"}), 401 # 返回模型列表 models_list = [ { "id": model_id, "object": "model", "created": int(time.time()), "owned_by": info["provider"], "permission": [], "root": model_id, "parent": None } for model_id, info in MODEL_MAPPING.items() ] # 记录日志 app.logger.info(f'GET /ai/v1/models - 200 OK') return jsonify({ "object": "list", "data": models_list }) except Exception as error: app.logger.error(f"Error: {str(error)}") return jsonify({"error": "Authentication failed", "details": str(error)}), 401 @app.route('/ai/v1/chat/completions', methods=['POST']) def handle_request(): try: body = request.json model = body.get('model') messages = body.get('messages') stream = body.get('stream', False) if not model or not messages or len(messages) == 0: app.logger.info(f"POST /ai/v1/chat/completions - Status: 400 - Bad Request - Missing required fields") return jsonify({"error": "Bad Request: Missing required fields"}), 400 # 映射 model if model in MODEL_MAPPING: mapped_model = MODEL_MAPPING[model]['mapping'] else: app.logger.info(f"POST /ai/v1/chat/completions - Status: 400 - Bad Request - Model '{model}' not found") return jsonify({"error": f"Model '{model}' not found"}), 400 prompt = messages[-1]['content'] image_size, clean_prompt, use_original, size_param = extract_params_from_prompt(prompt) auth_header = request.headers.get('Authorization') random_token = get_random_token(auth_header) if not random_token: app.logger.info(f"POST /ai/v1/chat/completions - Status: 401 - Unauthorized - Invalid or missing Authorization header") return jsonify({"error": "Unauthorized: Invalid or missing Authorization header"}), 401 if use_original: enhanced_prompt = clean_prompt else: enhanced_prompt = translate_and_enhance_prompt(clean_prompt, random_token) new_url = 'https://api.siliconflow.cn/v1/images/generations' new_request_body = { "model": mapped_model, # 使用映射后的模型名称 "prompt": enhanced_prompt, "negative_prompt": "", # 如果不需要负提示词，可以留空 "image_size": image_size, "batch_size": 1, "seed": random.randint(0, 4999999999), # 随机生成一个种子值 "num_inference_steps": 20, "guidance_scale": 7.5 } headers = { 'Authorization': f'Bearer {random_token}', 'Content-Type': 'application/json' } response = requests.post(new_url, headers=headers, json=new_request_body, timeout=60) response.raise_for_status() response_body = response.json() if 'data' in response_body and response_body['data'] and 'url' in response_body['data'][0]: image_url = response_body['data'][0]['url'] else: raise ValueError("Unexpected response structure from image generation API") unique_id = str(int(time.time() * 1000)) current_timestamp = int(time.time()) system_fingerprint = "fp_" + ''.join(random.choices('abcdefghijklmnopqrstuvwxyz0123456789', k=9)) image_data = {'data': [{'url': image_url}]} # Log the key information params = [] if size_param != "16:9": params.append(f"-s {size_param}") if use_original: params.append("-o") params_str = " ".join(params) if params else "no params" app.logger.info(f'POST /ai/v1/chat/completions - Status: 200 - Token: {random_token} - Model: {mapped_model} - Params: {params_str} - Image URL: {image_url}') if stream: return stream_response(unique_id, image_data, clean_prompt, enhanced_prompt, image_size, current_timestamp, model, system_fingerprint, use_original) else: return non_stream_response(unique_id, image_data, clean_prompt, enhanced_prompt, image_size, current_timestamp, model, system_fingerprint, use_original) except Exception as e: app.logger.error(f"Error: {str(e)}") return jsonify({"error": f"Internal Server Error: {str(e)}"}), 500 def extract_params_from_prompt(prompt): size_match = re.search(r'-s\s+(\S+)', prompt) original_match = re.search(r'-o', prompt) if size_match: size = size_match.group(1) clean_prompt = re.sub(r'-s\s+\S+', '', prompt).strip() else: size = "16:9" clean_prompt = prompt use_original = bool(original_match) if use_original: clean_prompt = re.sub(r'-o', '', clean_prompt).strip() image_size = RATIO_MAP.get(size, RATIO_MAP["16:9"]) return image_size, clean_prompt, use_original, size def get_random_token(auth_header): if not auth_header: return None if auth_header.startswith('Bearer '): auth_header = auth_header[7:] tokens = [token.strip() for token in auth_header.split(',') if token.strip()] if not tokens: return None return random.choice(tokens) def translate_and_enhance_prompt(prompt, auth_token): translate_url = 'https://api.siliconflow.cn/v1/chat/completions' translate_body = { 'model': 'Qwen/Qwen2-72B-Instruct', 'messages': [ {'role': 'system', 'content': SYSTEM_ASSISTANT}, {'role': 'user', 'content': prompt} ] } headers = { 'Content-Type': 'application/json', 'Authorization': f'Bearer {auth_token}' } response = requests.post(translate_url, headers=headers, json=translate_body, timeout=30) response.raise_for_status() result = response.json() return result['choices'][0]['message']['content'] SYSTEM_ASSISTANT = """作为 Stable Diffusion Prompt 提示词专家，您将从关键词中创建提示，通常来自 Danbooru 等数据库。 提示通常描述图像，使用常见词汇，按重要性排列，并用逗号分隔。避免使用"-"或"."，但可以接受空格和自然语言。避免词汇重复。 为了强调关键词，请将其放在括号中以增加其权重。例如，"(flowers)"将'flowers'的权重增加1.1倍，而"(((flowers)))"将其增加1.331倍。使用"(flowers:1.5)"将'flowers'的权重增加1.5倍。只为重要的标签增加权重。 提示包括三个部分：**前缀**（质量标签+风格词+效果器）+ **主题**（图像的主要焦点）+ **场景**（背景、环境）。 * 前缀影响图像质量。像"masterpiece"、"best quality"、"4k"这样的标签可以提高图像的细节。像"illustration"、"lensflare"这样的风格词定义图像的风格。像"bestlighting"、"lensflare"、"depthoffield"这样的效果器会影响光照和深度。 * 主题是图像的主要焦点，如角色或场景。对主题进行详细描述可以确保图像丰富而详细。增加主题的权重以增强其清晰度。对于角色，描述面部、头发、身体、服装、姿势等特征。 * 场景描述环境。没有场景，图像的背景是平淡的，主题显得过大。某些主题本身包含场景（例如建筑物、风景）。像"花草草地"、"阳光"、"河流"这样的环境词可以丰富场景。你的任务是设计图像生成的提示。请按照以下步骤进行操作： 1. 我会发送给您一个图像场景。需要你生成详细的图像描述 2. 图像描述必须是英文，输出为Positive Prompt。 示例： 我发送：二战时期的护士。 您回复只回复： A WWII-era nurse in a German uniform, holding a wine bottle and stethoscope, sitting at a table in white attire, with a table in the background, masterpiece, best quality, 4k, illustration style, best lighting, depth of field, detailed character, detailed environment. """ RATIO_MAP = { "1:1": "1024x1024", "1:2": "1024x2048", "3:2": "1536x1024", "4:3": "1536x2048", "16:9": "2048x1152", "9:16": "1152x2048" } def stream_response(unique_id, image_data, original_prompt, translated_prompt, size, created, model, system_fingerprint, use_original): return Response(stream_with_context(generate_stream(unique_id, image_data, original_prompt, translated_prompt, size, created, model, system_fingerprint, use_original)), content_type='text/event-stream') def generate_stream(unique_id, image_data, original_prompt, translated_prompt, size, created, model, system_fingerprint, use_original): chunks = [ f"原始提示词：\n{original_prompt}\n", ] if not use_original: chunks.append(f"翻译后的提示词：\n{translated_prompt}\n") chunks.extend([ f"图像规格：{size}\n", "正在根据提示词生成图像...\n", "图像正在处理中...\n", "即将完成...\n", f"生成成功！\n图像生成完毕，以下是结果：\n\n![生成的图像]({image_data['data'][0]['url']})" ]) for i, chunk in enumerate(chunks): json_chunk = json.dumps({ "id": unique_id, "object": "chat.completion.chunk", "created": created, "model": model, "system_fingerprint": system_fingerprint, "choices": [{ "index": 0, "delta": {"content": chunk}, "logprobs": None, "finish_reason": None }] }) yield f"data: {json_chunk}\n\n" time.sleep(0.5) # 模拟生成时间 final_chunk = json.dumps({ "id": unique_id, "object": "chat.completion.chunk", "created": created, "model": model, "system_fingerprint": system_fingerprint, "choices": [{ "index": 0, "delta": {}, "logprobs": None, "finish_reason": "stop" }] }) yield f"data: {final_chunk}\n\n" def non_stream_response(unique_id, image_data, original_prompt, translated_prompt, size, created, model, system_fingerprint, use_original): content = f"原始提示词：{original_prompt}\n" if not use_original: content += f"翻译后的提示词：{translated_prompt}\n" content += ( f"图像规格：{size}\n" f"图像生成成功！\n" f"以下是结果：\n\n" f"![生成的图像]({image_data['data'][0]['url']})" ) response = { 'id': unique_id, 'object': "chat.completion", 'created': created, 'model': model, 'system_fingerprint': system_fingerprint, 'choices': [{ 'index': 0, 'message': { 'role': "assistant", 'content': content }, 'finish_reason': "stop" }], 'usage': { 'prompt_tokens': len(original_prompt), 'completion_tokens': len(content), 'total_tokens': len(original_prompt) + len(content) } } return jsonify(response) if __name__ == '__main__': app.run(host='0.0.0.0', port=8000)