app.py

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

# Load the pipeline
pipeline = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell")

def merge_images(model_image, cloth_image):
    # Save uploaded images temporarily
    model_image.save("model_image.png")
    cloth_image.save("cloth_image.png")
    
    # Create prompt
    prompt = f"A model wearing the clothing from the second image model_image.png and cloth_image.png"
    
    # Generate the merged image
    output_image = pipeline(
        prompt,
        guidance_scale=0.8,
        num_inference_steps=4,
        max_sequence_length=256,
        generator=torch.Generator("cpu").manual_seed(0)
    ).images[0]
    
    return output_image

# Create the Gradio interface
iface = gr.Interface(
    fn=merge_images,
    inputs=[
        gr.Image(type="pil", label="Upload model image"),
        gr.Image(type="pil", label="Upload clothing image")
    ],
    outputs=gr.Image(type="pil", label="Result"),
    title="Virtual Clothing Try-On",
    description="Upload a model image and a clothing image to see how the outfit looks on the model."
)

# Launch the interface
iface.launch()