add two sections

app.py

@@ -6,22 +6,74 @@ print(f"Is CUDA available: {torch.cuda.is_available()}")
 # True
 print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
 
-from diffusers import StableDiffusionImg2ImgPipeline
+from diffusers import StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipeline
 
 os.environ['GRADIO_THEME'] = 'default'
 # load the pipeline
 device = "cuda"
-model_id_or_path = "runwayml/stable-diffusion-v1-5"
-img2img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
+model_id_img2img = "runwayml/stable-diffusion-v1-5"
+img2img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_img2img, torch_dtype=torch.float16)
 img2img_pipe = img2img_pipe.to(device)
 
+model_id_inpaint = "runwayml/stable-diffusion-inpainting"
+inpaint_pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id_inpaint, torch_dtype=torch.float16)
+inpaint_pipe = inpaint_pipe.to(device)
 
 def img2img_diff(prompt, pil_img):
     img = pil_img.resize((768, 512))
     return img2img_pipe(prompt=prompt, image=img, strength=0.75, guidance_scale=7.5).images[0]
 
+def imginpaint_diff(prompt, pil_img, mask_pil_img):
+    return inpaint_pipe(prompt=prompt, image=pil_img, mask_image=mask_pil_img).images[0]
 
-app = gr.Interface(fn=img2img_diff, inputs=[gr.Text(label="prompt text"), gr.Image(type='pil', label='draft image')],
-                   outputs=gr.Image(type='pil'))
+def header_html(title):
+    return f"""
+    <div style="text-align: center; max-width: 650px; margin: 0 auto; padding-top: 7px;">
+        <div
+        style="
+            display: inline-flex;
+            align-items: center;
+            gap: 0.8rem;
+            font-size: 1.75rem;
+            "
+        >
+            <h1 style="font-weight: 900; margin-bottom: 7px;">
+            {title}
+            </h1>
+        </div>
+    </div>
+"""
 
-app.launch(debug=True)
+
+with gr.Blocks() as block:
+    with gr.Group():
+        with gr.Box():
+            gr.HTML(header_html("diffusion image to image transform"))
+            with gr.Row():
+                with gr.Column():
+                    input_img = gr.Image(type='pil', label='draft image')
+                    with gr.Row():
+                        input_prompt = gr.Text(lable="prompt text")
+                        sumit_button = gr.Button("Generate image").style(
+                            margin=False,
+                            rounded=(False, True, True, False),
+                            full_width=False,
+                        )
+                output_img = gr.Image(type="pil")
+            sumit_button.click(img2img_diff, inputs=[input_prompt, input_img], outputs=[output_img])
+        with gr.Box():
+            gr.HTML(header_html("diffusion image inpaint"))
+            with gr.Row():
+                with gr.Column():
+                    input_img = gr.Image(type='pil', label='origin image')
+                    mask_img = gr.Image(type='pil', label='mask image')
+                    with gr.Row():
+                        input_prompt = gr.Text(lable="prompt text")
+                        sumit_button = gr.Button("Generate image").style(
+                            margin=False,
+                            rounded=(False, True, True, False),
+                            full_width=False,
+                        )
+                output_img = gr.Image(type="pil")
+            sumit_button.click(imginpaint_diff, inputs=[input_prompt, input_img, mask_img], outputs=[output_img])
+    block.queue(concurrency_count=40, max_size=20).launch(max_threads=150)