app.py

import gradio as gr
import numpy as np
import torch
from diffusers import StableDiffusionInpaintPipeline
from PIL import Image
from segment_anything import SamPredictor, sam_model_registry


device="cpu"
sam_checkpoint = "Weight/sam_vit_h_4b8939.pth"
model_type = "vit_h"
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
sam.to(device)
predictor = SamPredictor(sam)

pipe = StableDiffusionInpaintPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-inpainting",
    torch_dtype=torch.float32
)

pipe = pipe.to(device)
selected_pixels = []

with gr.Blocks() as demo:
    with gr.Row():
        input_img = gr.Image(label="Input")
        mask_img = gr.Image(label="Mas")
        output_img = gr.Image(label="Output")
    with gr.Blocks():
        prompt_text = gr.Textbox(lines=1, label="Prompt")
    with gr.Blocks():
        submit = gr.Button("Submit")

    def generate_mask(image, evt:gr.SelectData):
      
      input_labels = np.ones(len(selected_pixels))
      selected_pixels.append(evt.index)

      predictor.set_image(image)
      input_points = np.array(selected_pixels)

      input_labels = np.ones(input_labels.shape[0])
  
      mask, _, _ = predictor.predict(
          point_coords= input_points,
          point_labels= input_labels,
          multimask_output=False
      )
      # (n, sz, sz)
      mask = Image.fromarray(mask[0, : , :])
      mask = mask.resize((512, 512)) # Resize the mask to (512, 512)
      mask = np.expand_dims(mask, axis=2)
      return mask

    def inpaint(image, mask, prompt):
        image = Image.fromarray(image)
        mask = Image.fromarray(mask)

        image = image.resize((512,512))
        mask = mask.resize((512,512))

        output = pipe(
            prompt=prompt, 
            image=image, 
            mask_image=mask,
            ).images[0]
            
        return output
    
    input_img.select(generate_mask, [input_img], [mask_img])
    submit.click(
        inpaint,
        inputs=[input_img, mask_img, prompt_text],
        outputs=[output_img],
    )
if __name__ == "__main__":
    demo.launch()