app.py

import gradio as gr
from diffusers import StableDiffusionInpaintPipeline
import torch

pipeline = StableDiffusionInpaintPipeline.from_pretrained(
    "runwayml/stable-diffusion-inpainting",
    torch_dtype=torch.float16,
    use_safetensors=True,
    variant="fp16"
)
 
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"].convert("RGB").resize((512, 512)), 
                     mask_image=mask_img["mask"].convert("RGB").resize((512, 512)) 
                     #guidance_scale=9
                    ).images[0]
     
    return image

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(source = 'upload', tool = 'sketch', type='pil'),
    outputs=gr.Image(),
    title="Stable Diffusion Inpainting"
)

demo.launch()