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image_generator (3).py

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+from diffusers import DiffusionPipeline, LCMScheduler
+import torch
+
+loaded_pipe = None
+loaded_pipe_id = None
+
+def load_model(pipe_id):
+    global loaded_pipe, loaded_pipe_id
+    if loaded_pipe_id != pipe_id:
+        loaded_pipe = DiffusionPipeline.from_pretrained(pipe_id, torch_dtype=torch.float16).to("cuda")
+        loaded_pipe.scheduler = LCMScheduler.from_config(loaded_pipe.scheduler.config)
+        loaded_pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
+        loaded_pipe_id = pipe_id
+    return loaded_pipe
+
+def generate_image(prompt, num_inference_steps, seed, guidance_scale, negative_prompt=None, pipe_id="Linaqruf/animagine-xl"):
+    global loaded_pipe
+    pipe = load_model(pipe_id)
+    generator = torch.manual_seed(seed)
+    image = pipe(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, generator=generator, guidance_scale=guidance_scale).images[0]
+    return image

inference (2).py

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+from fastapi import FastAPI, HTTPException, Body
+from fastapi.middleware.cors import CORSMiddleware
+import uvicorn
+from pyngrok import ngrok
+import os
+from image_generator import generate_image
+import base64
+from io import BytesIO
+import threading
+import queue
+
+app = FastAPI()
+
+# Set up CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Izinkan semua origin
+    allow_credentials=True,
+    allow_methods=["*"],  # Izinkan semua method
+    allow_headers=["*"],  # Izinkan semua header
+)
+
+request_queue = queue.Queue()
+result_queue = {}
+
+def process_request():
+    while True:
+        request_id, data = request_queue.get()
+        try:
+            image = generate_image(
+                data['prompt'],
+                data['num_inference_steps'],
+                data['seed'],
+                data['guidance_scale'],
+                negative_prompt=data.get('negative_prompt')
+            )
+            buffered = BytesIO()
+            image.save(buffered, format="JPEG")
+            img_str = base64.b64encode(buffered.getvalue()).decode()
+            result_queue[request_id] = img_str
+        except Exception as e:
+            result_queue[request_id] = str(e)
+        finally:
+            request_queue.task_done()
+
+@app.post('/generate')
+def generate(
+    prompt: str = Body(...), 
+    num_inference_steps: int = Body(default=30), 
+    seed: int = Body(default=42), 
+    guidance_scale: float = Body(default=1.0), 
+    negative_prompt: str = Body(default=None)
+):
+    if not prompt:
+        raise HTTPException(status_code=400, detail="No prompt provided")
+
+    request_id = os.urandom(8).hex()
+    request_queue.put((request_id, {
+        'prompt': prompt, 
+        'num_inference_steps': num_inference_steps, 
+        'seed': seed, 
+        'guidance_scale': guidance_scale, 
+        'negative_prompt': negative_prompt
+    }))
+
+    return {'request_id': request_id}
+
+@app.get('/result/{request_id}')
+def get_result(request_id: str):
+    if request_id in result_queue:
+        result = result_queue.pop(request_id)
+        return {'image': result}
+    return {'status': 'processing'}, 202
+
+@app.get('/get')
+def get_status():
+    return {'status': 'Server is running'}
+
+def start_ngrok():
+    ngrok_tunnel = ngrok.connect(8000)
+    print(' * Ngrok Tunnel URL:', ngrok_tunnel.public_url)
+
+if __name__ == '__main__':
+    threading.Thread(target=process_request, daemon=True).start()
+    threading.Thread(target=start_ngrok, daemon=True).start()
+    uvicorn.run(app, host="0.0.0.0", port=8000)