Update app.py

app.py

@@ -1,6 +1,7 @@
 import spaces
 import torch
 from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import HTMLResponse, JSONResponse
 from diffusers import StableDiffusionXLPipeline
 from safetensors.torch import load_file
@@ -14,16 +15,16 @@ from concurrent.futures import ThreadPoolExecutor
 # Model Configuration
 # -----------------------------
 BASE_MODEL = "stabilityai/stable-diffusion-xl-base-1.0"
-
-# Multiple LoRA styles supported
 LORA_MODELS = {
     "Ghibli": "./studioghibli_flux_r32-v2.safetensors",
     "GH1bli": "./gh1bli-style.safetensors"
 }
 
 _cached_pipelines = {}
-executor = ThreadPoolExecutor(max_workers=4)  # parallel generation threads
-semaphore = asyncio.Semaphore(4)  # safely limit concurrent runs
+# Thread pool for parallel inference
+executor = ThreadPoolExecutor(max_workers=6)
+# Semaphore controls how many can run at once (to protect memory)
+semaphore = asyncio.Semaphore(6)
 
 # -----------------------------
 # Pipeline Loader
@@ -32,10 +33,10 @@ def load_pipeline(style="Ghibli"):
     if style in _cached_pipelines:
         return _cached_pipelines[style]
 
+    print(f"🔹 Loading SDXL model for style: {style}")
     device = "cpu"
     dtype = torch.float32
 
-    print(f"🔹 Loading SDXL model ({style}) on CPU...")
     pipe = StableDiffusionXLPipeline.from_pretrained(
         BASE_MODEL,
         dtype=dtype,
@@ -47,7 +48,7 @@ def load_pipeline(style="Ghibli"):
 
     model_path = LORA_MODELS.get(style, LORA_MODELS["Ghibli"])
     try:
-        print(f"🎨 Applying LoRA: {model_path}")
+        print(f"🎨 Applying LoRA weights: {model_path}")
         lora_weights = load_file(model_path)
         pipe.unet.load_state_dict(lora_weights, strict=False)
         print("✅ LoRA loaded successfully.")
@@ -59,19 +60,17 @@ def load_pipeline(style="Ghibli"):
 
 
 # -----------------------------
-# Image Generation Function
+# Synchronous Image Generation
 # -----------------------------
 def generate_image_sync(prompt: str, style: str = "Ghibli", seed: int = 42):
     pipe = load_pipeline(style)
     generator = torch.Generator(device="cpu").manual_seed(int(seed))
 
-    # Enriched prompt to ensure full-frame, high-quality output
     enhanced_prompt = (
         f"{prompt}, Studio Ghibli style, full-frame composition, centered subject, "
-        "no borders, clean background, cinematic tone, highly detailed illustration"
+        "clean background, cinematic tone, detailed illustration, digital painting"
     )
 
-    print(f"🖌️ Generating ({style}): {enhanced_prompt}")
     image = pipe(
         prompt=enhanced_prompt,
         height=512,
@@ -81,7 +80,6 @@ def generate_image_sync(prompt: str, style: str = "Ghibli", seed: int = 42):
         generator=generator,
     ).images[0]
 
-    # Crop to remove faint borders if any
     w, h = image.size
     image = image.crop((5, 5, w - 5, h - 5)).resize((512, 512))
     return image
@@ -99,7 +97,16 @@ async def generate_image_async(prompt, style, seed):
 # -----------------------------
 # FastAPI App Setup
 # -----------------------------
-app = FastAPI(title="Studio Ghibli Generator API")
+app = FastAPI(title="Studio Ghibli Generator API", version="2.0")
+
+# Enable cross-app requests (CORS)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Allow any domain (you can restrict if needed)
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
 
 @app.get("/", response_class=HTMLResponse)
@@ -177,6 +184,7 @@ async def api_generate(request: Request):
         buffer = io.BytesIO()
         image.save(buffer, format="PNG")
         img_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
+
         return JSONResponse({
             "status": "success",
             "prompt": prompt,
@@ -198,6 +206,6 @@ def keep_alive():
 
 if __name__ == "__main__":
     import uvicorn
-    print("🚀 Launching FastAPI (multi-style, CPU/ZeroGPU mode)")
+    print("🚀 Launching FastAPI (Multi-Request / CPU / ZeroGPU Mode)")
     keep_alive()
     uvicorn.run(app, host="0.0.0.0", port=7860)