app.py

import gradio as gr
from diffusers import StableDiffusionInpaintPipeline
import torch

pipeline = StableDiffusionInpaintPipeline.from_pretrained(
    "runwayml/stable-diffusion-inpainting",
    torch_dtype=torch.float16,
)
 
#pipeline = pipeline.to("cuda")

def predict(mask_img):
    prompt = "a green frog, highly detailed, natural lighting"
    image = pipeline(prompt=prompt,
                     num_inference_steps=35,
                     image=mask_img["image"], 
                     mask_image=mask_img["mask"], 
                     guidance_scale=9).images[0]
     
    return image

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(),
    outputs=gr.Image(),
    title="Stable Diffusion Inpainting"
)

demo.launch()