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| from fastapi import FastAPI, HTTPException, Request | |
| from fastapi.responses import HTMLResponse, JSONResponse, FileResponse | |
| from fastapi.middleware.cors import CORSMiddleware | |
| import threading | |
| import gradio as gr | |
| import torch | |
| import numpy as np | |
| from diffusers import DiffusionPipeline | |
| from transformers import pipeline | |
| from PIL import Image | |
| import os | |
| app = FastAPI() | |
| # تنظیمات CORS | |
| origins = [ | |
| "http://localhost", | |
| "http://localhost:8000", | |
| "http://localhost:7860", | |
| "https://nikajoon-test1.hf.space", # URL فضای شما در Hugging Face | |
| "http://tomko.ir", # دامنه سایت شما | |
| ] | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=origins, | |
| allow_credentials=True, | |
| allow_methods=["*"], | |
| allow_headers=["*"], | |
| ) | |
| pipe = pipeline('text-generation', model='daspartho/prompt-extend') | |
| def extend_prompt(prompt): | |
| return pipe(prompt+',', num_return_sequences=1)[0]["generated_text"] | |
| def text_it(inputs): | |
| return extend_prompt(inputs) | |
| def load_pipeline(use_cuda): | |
| device = "cuda" if use_cuda and torch.cuda.is_available() else "cpu" | |
| if device == "cuda": | |
| torch.cuda.max_memory_allocated(device=device) | |
| torch.cuda.empty_cache() | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True) | |
| pipe.enable_xformers_memory_efficient_attention() | |
| pipe = pipe.to(device) | |
| torch.cuda.empty_cache() | |
| else: | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True) | |
| pipe = pipe.to(device) | |
| return pipe | |
| def genie(prompt="sexy woman", use_details=False, steps=2, seed=398231747038484200, use_cuda=False): | |
| pipe = load_pipeline(use_cuda) | |
| generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed) | |
| if use_details: | |
| extended_prompt = extend_prompt(prompt) | |
| else: | |
| extended_prompt = prompt | |
| int_image = pipe(prompt=extended_prompt, generator=generator, num_inference_steps=steps, guidance_scale=0.0).images[0] | |
| return int_image, extended_prompt | |
| async def generate_image(request: Request): | |
| data = await request.json() | |
| prompt = data.get("prompt") | |
| if not prompt: | |
| raise HTTPException(status_code=400, detail="Prompt is required") | |
| image, _ = genie(prompt) | |
| image_path = "images/output.png" | |
| os.makedirs(os.path.dirname(image_path), exist_ok=True) | |
| image.save(image_path) | |
| return JSONResponse(content={"image_url": f"/images/output.png"}) | |
| async def get_image(image_name: str): | |
| image_path = os.path.join("images", image_name) | |
| if not os.path.exists(image_path): | |
| raise HTTPException(status_code=404, detail="Image not found") | |
| return FileResponse(image_path) | |
| async def read_root(): | |
| html_content = """ | |
| <html> | |
| <head> | |
| <title>My FastAPI App</title> | |
| </head> | |
| <body> | |
| <h1>Hello from FastAPI!</h1> | |
| <p>This is a sample application running on Hugging Face Spaces.</p> | |
| </body> | |
| </html> | |
| """ | |
| return html_content | |
| if __name__ == "__main__": | |
| import uvicorn | |
| uvicorn.run(app, host="0.0.0.0", port=7860) | |