Update app.py

app.py

@@ -3,47 +3,40 @@ import torch
 from diffusers import StableDiffusionImg2ImgPipeline
 from PIL import Image
 
-# Device and dtype setup
-device = "cuda" if torch.cuda.is_available() else "cpu"
-dtype = torch.float16 if device == "cuda" else torch.float32
+# Use CPU and optimize precision
+device = "cpu"
+dtype = torch.float32  # float16 is only for GPUs
 
-# Load optimized model
+# Load model with reduced precision for CPU
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    "stabilityai/sdxl-turbo",  # Faster model
+    "nitrosocke/Ghibli-Diffusion",
     torch_dtype=dtype
 ).to(device)
 
-# Enable optimizations
-pipe.enable_xformers_memory_efficient_attention()
+# Disable xformers (only for GPU)
+print("⚠️ Running on CPU: xformers disabled, inference will be slow.")
 
-if torch.__version__ >= "2.0":
-    pipe.unet = torch.compile(pipe.unet)  # Speed up inference
-
-if device == "cuda":
-    torch.backends.cudnn.benchmark = True  # Optimize CUDA performance
-
-# Process image
 def process_image(input_img):
     if input_img is None:
         return None
-
-    input_img = input_img.convert("RGB").resize((384, 384))  # Optimize input size
+    
+    input_img = input_img.convert("RGB").resize((512, 512))
     
     result = pipe(
         prompt="ghibli style, studio ghibli, anime art",
         image=input_img,
-        strength=0.6,
-        guidance_scale=7
+        strength=0.5,  # Reduce strength to speed up processing
+        guidance_scale=7.5  # Lower guidance for faster inference
     ).images[0]
     
     return result
 
-# Create Gradio interface
+# Gradio UI
 demo = gr.Interface(
     fn=process_image,
     inputs=gr.Image(type="pil"),
     outputs=gr.Image(type="pil"),
-    title="🎨 Ghibli Style Transfer",
+    title="🎨 Ghibli Style Transfer (CPU Optimized)",
     description="Upload an image to transform it into Studio Ghibli style artwork"
 )