Update app.py

app.py

@@ -3,26 +3,37 @@ import torch
 from diffusers import StableDiffusionImg2ImgPipeline
 from PIL import Image
 
-# Initialize pipeline
-dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+# Device and dtype setup
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16 if device == "cuda" else torch.float32
+
+# Load optimized model
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    "nitrosocke/Ghibli-Diffusion",
+    "stabilityai/sdxl-turbo",  # Faster model
     torch_dtype=dtype
-).to("cuda" if torch.cuda.is_available() else "cpu")
+).to(device)
+
+# Enable optimizations
+pipe.enable_xformers_memory_efficient_attention()
+
+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
     
-    # Convert and resize input
-    input_img = input_img.convert("RGB").resize((512, 512))
-    
-    # Generate output
     result = pipe(
         prompt="ghibli style, studio ghibli, anime art",
         image=input_img,
-        strength=0.7,
-        guidance_scale=10
+        strength=0.6,
+        guidance_scale=7
     ).images[0]
     
     return result
@@ -36,4 +47,4 @@ demo = gr.Interface(
     description="Upload an image to transform it into Studio Ghibli style artwork"
 )
 
-demo.launch()
+demo.launch()