app.py

import os
import gradio as gr
from huggingface_hub import InferenceClient
client = InferenceClient()
from gradio_imageslider import ImageSlider


def refine_image(image, prompt, negative_prompt, num_inference_steps, guidance_scale, seed, strength):
    refined_image = client.image_to_image(
        image, 
        prompt=prompt, 
        negative_prompt=negative_prompt, 
        num_inference_steps=num_inference_steps, 
        guidance_scale=guidance_scale, 
        seed=seed, 
        model="stabilityai/stable-diffusion-xl-refiner-1.0",
        strength=strength
    )
    return [image, refined_image]

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            image = gr.Image(type="filepath")
            with gr.Accordion("Advanced Options", open=False):
                prompt = gr.Textbox(lines=3, label="Prompt")
                negative_prompt = gr.Textbox(lines=3, label="Negative Prompt")
                strength = gr.Slider(
                    label="Strength",
                    minimum=0,
                    maximum=300,
                    step=0.01,
                    value=1
                )
                num_inference_steps = gr.Slider(
                    label="Inference steps",
                    minimum=3,
                    maximum=300,
                    step=1,
                    value=25
                )
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=50.0,
                    step=0.1,
                    value=12
                )
                seed = gr.Slider(
                    label="Seed",
                    info="-1 denotes a random seed",
                    minimum=-1,
                    maximum=423538377342,
                    step=1,
                    value=-1
                )
            refine_btn = gr.Button("Refine")
        with gr.Column():
            output = ImageSlider(label="Before / After")

    refine_btn.click(
        refine_image, 
        inputs=[image, prompt, negative_prompt, num_inference_steps, guidance_scale, seed, strength], 
        outputs=output
    )

demo.launch()