yes

app.py

@@ -3,26 +3,38 @@ import torch
 from diffusers import StableDiffusionImg2ImgPipeline
 from PIL import Image
 
-# Load the pre-trained Studio Ghibli style model
-model_id = "nitrosocke/Ghibli-Diffusion"
+# Check if GPU is available
 device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
 
+# Load the model correctly
+model_id = "nitrosocke/Ghibli-Diffusion"
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    model_id, torch_dtype=torch.float16
-).to(device)
+    model_id, torch_dtype=torch.float16 if device == "cuda" else torch.float32
+)
+
+# Enable optimization if using CUDA
+if device == "cuda":
+    pipe.to(device)
+    pipe.enable_model_cpu_offload()  # Helps with memory efficiency
+else:
+    pipe.to("cpu")  # Ensure CPU compatibility
 
 def transform_image(input_image: Image.Image) -> Image.Image:
     input_image = input_image.resize((512, 512))
-    prompt = "ghibli style"
+
+    # Move image to proper format for inference
+    prompt = "ghibli style, cinematic lighting, hand-painted, anime aesthetics"
     output = pipe(
         prompt=prompt,
         image=input_image,
         strength=0.75,
         guidance_scale=7.5,
-        num_inference_steps=50,
+        num_inference_steps=30,  # Reduced steps for faster generation
     )
     return output.images[0]
 
+# Build the Gradio UI
 demo = gr.Interface(
     fn=transform_image,
     inputs=gr.Image(type="pil", label="Upload your portrait/photo"),