Update app.py

app.py

@@ -1,39 +1,29 @@
 import gradio as gr
-import numpy as np
-from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
+from diffusers import StableDiffusionInpaintPipeline
+import torch
 
-model_id = "google/ddpm-cat-256"
-ddpm = DDPMPipeline.from_pretrained(model_id)
+pipeline = StableDiffusionInpaintPipeline.from_pretrained(
+    "runwayml/stable-diffusion-inpainting",
+    torch_dtype=torch.float16,
+)
+ 
+pipeline = pipeline.to("cuda")
 
-def flip_text(text):
-    return text[::-1]
+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
 
-def flip_img(img):
-    return np.flipud(img)
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(),
+    outputs=gr.Image(),
+    title="Stable Diffusion Inpainting"
+)
 
-def show_cat():
-    return ddpm(num_inference_steps=10).images[0]
-
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("flip the text or image using this demo")
-    with gr.Tab("Flip Text"):
-        text_input = gr.Textbox();
-        text_output = gr.Textbox();
-        text_btn = gr.Button("Flip");
-    with gr.Tab("Flip Img"):
-        with gr.Row():
-            image_input = gr.Image(source="webcam");
-            image_outpt = gr.Image();
-        image_btn = gr.Button("Flip");
-    with gr.Tab("Goolg Cat"):
-        img_cat = gr.Image()
-        cat_btn = gr.Button("Show Cat")
-    with gr.Accordion("Open for more"):
-        gr.Markdown("Look at me");
-    
-    text_btn.click(flip_text, inputs=text_input, outputs=text_output)
-    image_btn.click(flip_img, inputs=image_input, outputs=image_outpt)
-    cat_btn.click(show_cat, outputs=img_cat)
-
-
-demo.launch()
+demo.launch()