app.py

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

# Load the model and pipeline
model_id = "ares1123/virtual-dress-try-on"
pipeline = StableDiffusionPipeline.from_pretrained(model_id)
pipeline.to("cuda" if torch.cuda.is_available() else "cpu")

def virtual_try_on(image, clothing_image):
    # Convert images to proper format and get dimensions
    width, height = image.size
    
    # Ensure dimensions are multiples of 8
    width = (width // 8) * 8
    height = (height // 8) * 8

    # Resize images to fit the model's expected input
    image = image.resize((width, height))
    clothing_image = clothing_image.resize((width, height))

    # Define a prompt describing what you want the model to do
    prompt = "A person wearing new clothes"

    # Process the images using the model
    result = pipeline(prompt=prompt, image=image, conditioning_image=clothing_image)
    try_on_image = result.images[0]
    
    return try_on_image

# Set up a simple Gradio interface for testing
interface = gr.Interface(
    fn=virtual_try_on,
    inputs=[gr.Image(type="pil", label="User Image"), 
            gr.Image(type="pil", label="Clothing Image")],
    outputs=gr.Image(type="pil"),
    title="Virtual Dress Try-On",
    description="Upload an image of yourself and a clothing image to try it on virtually!"
)

# Launch the interface
interface.launch(share=True)