Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import numpy as np
+from diffusers import StableDiffusionInpaintPipeline
+import torch
+from diffusers.utils import load_image
+from diffusers.pipelines.stable_diffusion import safety_checker
+def sc(self, clip_input, images) :
+    return images, [False for i in images]
+safety_checker.StableDiffusionSafetyChecker.forward = sc
+
+
+pipe = StableDiffusionInpaintPipeline.from_pretrained(
+    "runwayml/stable-diffusion-inpainting",
+    revision="fp16",
+    torch_dtype=torch.float16,
+)
+
+def inpaint_image(image, mask, prompt, negative_prompt):
+    # Convert PIL images to numpy arrays if needed
+    # Process your images as required by your inpainting model
+    n_image = pipe(prompt, image=image, mask_image=mask, guidance_scale=5,height=int(8*round(pil_img.height/8)), width=int(8*round(pil_img.width/8)), num_inference_steps=70,negative_prompt=negative_prompt).images[0]  
+    return n_image
+
+def process_files(image_file, mask_file, prompt, negative_prompt):
+    image = Image.open(image_file)
+    mask = Image.open(mask_file)
+    return inpaint_image(image, mask, prompt, negative_prompt)
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Inpainting App")
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.File(label="Input Image", type="filepath")
+            mask_input = gr.File(label="Mask Image", type="filepath")
+            prompt_input = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...")
+            negative_prompt_input = gr.Textbox(label="Negative Prompt", placeholder="Enter your negative prompt here...")
+
+            submit_button = gr.Button("Inpaint")
+
+        with gr.Column():
+            output_image = gr.Image(type="pil", label="Inpainted Image")
+
+    submit_button.click(
+        fn=process_files,
+        inputs=[image_input, mask_input, prompt_input, negative_prompt_input],
+        outputs=output_image
+    )
+
+# Launch the interface
+demo.launch()