app.py

import gradio as gr
import torch
from diffusers import StableDiffusionImg2ImgPipeline
from PIL import Image

# Use CPU and optimize precision
device = "cpu"
dtype = torch.float32  # float16 is only for GPUs

# Load model with reduced precision for CPU
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
    "nitrosocke/Ghibli-Diffusion",
    torch_dtype=dtype
).to(device)

# Disable xformers (only for GPU)
print("⚠️ Running on CPU: xformers disabled, inference will be slow.")

def process_image(input_img):
    if input_img is None:
        return None
    
    input_img = input_img.convert("RGB").resize((512, 512))
    
    result = pipe(
        prompt="ghibli style, studio ghibli, anime art",
        image=input_img,
        strength=0.5,  # Reduce strength to speed up processing
        guidance_scale=7.5  # Lower guidance for faster inference
    ).images[0]
    
    return result

# Gradio UI
demo = gr.Interface(
    fn=process_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Image(type="pil"),
    title="🎨 Ghibli Style Transfer (CPU Optimized)",
    description="Upload an image to transform it into Studio Ghibli style artwork"
)

demo.launch()