Upload 2 files
Browse files- image_generator (3).py +21 -0
- inference (2).py +86 -0
image_generator (3).py
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from diffusers import DiffusionPipeline, LCMScheduler
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import torch
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loaded_pipe = None
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loaded_pipe_id = None
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def load_model(pipe_id):
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global loaded_pipe, loaded_pipe_id
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if loaded_pipe_id != pipe_id:
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loaded_pipe = DiffusionPipeline.from_pretrained(pipe_id, torch_dtype=torch.float16).to("cuda")
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loaded_pipe.scheduler = LCMScheduler.from_config(loaded_pipe.scheduler.config)
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loaded_pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
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loaded_pipe_id = pipe_id
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return loaded_pipe
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def generate_image(prompt, num_inference_steps, seed, guidance_scale, negative_prompt=None, pipe_id="Linaqruf/animagine-xl"):
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global loaded_pipe
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pipe = load_model(pipe_id)
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generator = torch.manual_seed(seed)
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image = pipe(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, generator=generator, guidance_scale=guidance_scale).images[0]
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return image
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inference (2).py
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from fastapi import FastAPI, HTTPException, Body
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from fastapi.middleware.cors import CORSMiddleware
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import uvicorn
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from pyngrok import ngrok
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import os
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from image_generator import generate_image
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import base64
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from io import BytesIO
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import threading
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import queue
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app = FastAPI()
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# Set up CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"], # Izinkan semua origin
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allow_credentials=True,
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allow_methods=["*"], # Izinkan semua method
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allow_headers=["*"], # Izinkan semua header
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)
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request_queue = queue.Queue()
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result_queue = {}
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def process_request():
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while True:
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request_id, data = request_queue.get()
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try:
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image = generate_image(
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data['prompt'],
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data['num_inference_steps'],
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data['seed'],
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data['guidance_scale'],
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negative_prompt=data.get('negative_prompt')
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)
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buffered = BytesIO()
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image.save(buffered, format="JPEG")
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img_str = base64.b64encode(buffered.getvalue()).decode()
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result_queue[request_id] = img_str
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except Exception as e:
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result_queue[request_id] = str(e)
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finally:
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request_queue.task_done()
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@app.post('/generate')
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def generate(
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prompt: str = Body(...),
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num_inference_steps: int = Body(default=30),
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seed: int = Body(default=42),
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guidance_scale: float = Body(default=1.0),
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negative_prompt: str = Body(default=None)
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):
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if not prompt:
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raise HTTPException(status_code=400, detail="No prompt provided")
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request_id = os.urandom(8).hex()
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request_queue.put((request_id, {
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'prompt': prompt,
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'num_inference_steps': num_inference_steps,
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'seed': seed,
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'guidance_scale': guidance_scale,
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'negative_prompt': negative_prompt
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}))
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return {'request_id': request_id}
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@app.get('/result/{request_id}')
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def get_result(request_id: str):
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if request_id in result_queue:
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result = result_queue.pop(request_id)
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return {'image': result}
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return {'status': 'processing'}, 202
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@app.get('/get')
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def get_status():
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return {'status': 'Server is running'}
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def start_ngrok():
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ngrok_tunnel = ngrok.connect(8000)
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print(' * Ngrok Tunnel URL:', ngrok_tunnel.public_url)
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if __name__ == '__main__':
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threading.Thread(target=process_request, daemon=True).start()
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threading.Thread(target=start_ngrok, daemon=True).start()
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uvicorn.run(app, host="0.0.0.0", port=8000)
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