from flask import Flask, request, Response import requests import json import time from openai import OpenAI app = Flask(__name__) def get_token(): url = "https://fluxaiweb.com/flux/getToken" response = requests.get(url) if response.status_code == 200: response_json = response.json() return response_json.get("data", {}).get("token") return None def req_flux(token, prompt_value, aspect_ratio="1:1", output_format="webp", num_outputs=1, output_quality=90): url = "https://fluxaiweb.com/flux/generateImage" payload = { "prompt": prompt_value, "aspectRatio": aspect_ratio, "outputFormat": output_format, "numOutputs": num_outputs, "outputQuality": output_quality } headers = { 'Content-Type': 'application/json', 'token': token } try: response = requests.post(url, headers=headers, json=payload) response.raise_for_status() data = response.json() return data.get("data", {}).get("image") except requests.exceptions.RequestException as e: print(f"Error making request: {e}") return None def generate_optimized_prompt(api_key, api_base, system_prompt, user_input): client = OpenAI(api_key=api_key, base_url=api_base) completion = client.chat.completions.create( model="gpt-4o-mini", messages=[ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_input} ] ) return completion.choices[0].message.content def generate_fake_stream(image_url, optimized_prompt): chunks = [ "I'm generating an optimized prompt based on your input...", f"Optimized prompt: {optimized_prompt}", "Now generating an image based on the optimized prompt...", "The image is being processed...", "Almost there...", f"Image generated successfully! Here it is:\n\n![Generated Image]({image_url})" ] for i, chunk in enumerate(chunks): yield f"data: {json.dumps({'id': f'chatcmpl-{int(time.time())}', 'object': 'chat.completion.chunk', 'created': int(time.time()), 'model': 'flux-ai-image-generator', 'choices': [{'index': 0, 'delta': {'role': 'assistant' if i == 0 else None, 'content': chunk}, 'finish_reason': None if i < len(chunks) - 1 else 'stop'}]})}\n\n" time.sleep(1) # Simulate processing time yield "data: [DONE]\n\n" @app.route('/hf/v1/chat/completions', methods=['POST']) def chat_completions(): data = request.json messages = data.get('messages', []) stream = data.get('stream', False) # Extract the prompt from the last user message user_input = next((msg['content'] for msg in reversed(messages) if msg['role'] == 'user'), None) if not user_input: return Response(json.dumps({'error': 'No valid user input provided'}), status=400, mimetype='application/json') # Generate optimized prompt using GPT-4-mini ''' secret_variable： api_key = "" # Replace with your actual API key api_base = "" # Replace with your actual API base URL ''' system_prompt = """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"、"ultra-detailed"、"high resolution"、"photorealistic" 这样的标签可以显著提高图像的细节和整体质量。像"illustration"、"lensflare"、"cinematic lighting" 这样的风格词定义图像的风格和光影效果。像"best lighting"、"volumetric lighting"、"depth of field" 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，同时加入细致的纹理和高光处理。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"lush greenery"、"golden sunlight"、"crystal clear river" 这样的环境词可以丰富场景，并增强其视觉吸引力。考虑添加天气效果，如"soft morning mist"、"sunset glow" 来进一步增强场景的氛围。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：我会发送给您一个图像场景。需要你生成详细的图像描述。图像描述必须是英文，输出为Positive Prompt。确保提示词仅用于描述图像内容，不包含会显示在图像中的文本。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, ultra-detailed, high resolution, photorealistic, illustration style, best lighting, volumetric lighting, depth of field, sharp focus, detailed character, richly textured environment.""" optimized_prompt = generate_optimized_prompt(api_key, api_base, system_prompt, user_input) # Generate image using the optimized prompt token = get_token() if not token: return Response(json.dumps({'error': 'Failed to get token'}), status=500, mimetype='application/json') image_url = req_flux(token, optimized_prompt) if not image_url: return Response(json.dumps({'error': 'Failed to generate image'}), status=500, mimetype='application/json') if stream: return Response(generate_fake_stream(image_url, optimized_prompt), mimetype='text/event-stream') else: response = { "id": f"chatcmpl-{int(time.time())}", "object": "chat.completion", "created": int(time.time()), "model": "flux-ai-image-generator", "choices": [ { "index": 0, "message": { "role": "assistant", "content": f"I've generated an optimized prompt based on your input: '{optimized_prompt}'\n\nUsing this prompt, I've created an image. Here it is:\n\n![Generated Image]({image_url})" }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": len(user_input.split()), "completion_tokens": len(optimized_prompt.split()) + 20, # Approximate "total_tokens": len(user_input.split()) + len(optimized_prompt.split()) + 20 } } return Response(json.dumps(response), mimetype='application/json') if __name__ == '__main__': app.run(host='0.0.0.0', port=7860)