update gradio demo with example

Author: River-Zhang

scripts/gradio_demo.py

@@ -35,40 +35,40 @@
 pipe.load_lora_weights(args.lora_path)
 
 if args.enable_model_cpu_offload:
-    pipe.enable_model_cpu_offload() 
+    pipe.enable_model_cpu_offload()
 else:
     pipe = pipe.to("cuda")
 
+
 @spaces.GPU
-def infer(edit_images, 
-          prompt, 
-          seed=666, 
-          randomize_seed=False, 
-          width=1024, 
-          height=1024, 
-          guidance_scale=50, 
-          num_inference_steps=28, 
-          progress=gr.Progress(track_tqdm=True)
-):
-    
+def infer(edit_images,
+          prompt,
+          seed=666,
+          randomize_seed=False,
+          width=1024,
+          height=1024,
+          guidance_scale=50,
+          num_inference_steps=28,
+          progress=gr.Progress(track_tqdm=True)):
+
     image = edit_images
-        
+
     if image.size[0] != 512:
         print("\033[93m[WARNING] We can only deal with the case where the image's width is 512.\033[0m")
         new_width = 512
         scale = new_width / image.size[0]
         new_height = int(image.size[1] * scale)
-        new_height = (new_height // 8) * 8  
+        new_height = (new_height // 8) * 8
         image = image.resize((new_width, new_height))
         print(f"\033[93m[WARNING] Resizing the image to {new_width} x {new_height}\033[0m")
-        
+
     image = image.convert("RGB")
     width, height = image.size
     image = image.resize((512, int(512 * height / width)))
     combined_image = Image.new("RGB", (width * 2, height))
-    combined_image.paste(image, (0, 0)) 
+    combined_image.paste(image, (0, 0))
     mask_array = np.zeros((height, width * 2), dtype=np.uint8)
-    mask_array[:, width:] = 255 
+    mask_array[:, width:] = 255
     mask = Image.fromarray(mask_array)
     instruction = f'A diptych with two side-by-side images of the same scene. On the right, the scene is exactly the same as on the left but {prompt}'
 
@@ -80,41 +80,51 @@ def infer(edit_images,
         image=combined_image,
         mask_image=mask,
         height=height,
-        width=width*2,
+        width=width * 2,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
-        generator=torch.Generator("cpu").manual_seed(seed)
+        generator=torch.Generator().manual_seed(seed),
     ).images[0]
 
-    w,h = image.size
-    image = image.crop((w//2, 0, w, h))
+    w, h = image.size
+    image = image.crop((w // 2, 0, w, h))
 
     os.makedirs(args.output_dir, exist_ok=True)
-        
+
     index = len(os.listdir(args.output_dir))
     image.save(f"{args.output_dir}/result_{index}.png")
-    
+
     return image, seed
-    
-examples = [
+
+
+# 原有的示例
+original_examples = [
     "a tiny astronaut hatching from an egg on the moon",
     "a cat holding a sign that says hello world",
     "an anime illustration of a wiener schnitzel",
 ]
 
-css="""
+# 新增的示例，将元组转换为列表
+new_examples = [
+    ['assets/girl.png', 'Make her hair dark green and her clothes checked.', 42],
+    ['assets/boy.png', 'Change the sunglasses to a Christmas hat.', 27440001],
+    ['assets/kaori.jpg', 'Make it a sketch.', 329918865]
+]
+
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 1000px;
 }
 """
 
 with gr.Blocks(css=css) as demo:
-    
+
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""# IC-Edit
 A demo for [IC-Edit](https://arxiv.org/pdf/2504.20690).
 More **open-source**, with **lower costs**, **faster speed** (it takes about 9 seconds to process one image), and **powerful performance**.
+For more details, check out our [Github Repository](https://github.com/River-Zhang/ICEdit) and [website](https://river-zhang.github.io/ICEdit-gh-pages/). If our project resonates with you or proves useful, we'd be truly grateful if you could spare a moment to give it a star.
 """)
         with gr.Row():
             with gr.Column():
@@ -133,23 +143,23 @@ def infer(edit_images,
                     container=False,
                 )
                 run_button = gr.Button("Run")
-                
+
             result = gr.Image(label="Result", show_label=False)
-        
-        with gr.Accordion("Advanced Settings", open=False):
-            
+
+        with gr.Accordion("Advanced Settings", open=True):
+
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
                 value=0,
             )
-            
+
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
+
             with gr.Row():
-                
+
                 width = gr.Slider(
                     label="Width",
                     minimum=512,
@@ -158,7 +168,7 @@ def infer(edit_images,
                     value=1024,
                     visible=False
                 )
-                
+
                 height = gr.Slider(
                     label="Height",
                     minimum=512,
@@ -167,17 +177,17 @@ def infer(edit_images,
                     value=1024,
                     visible=False
                 )
-            
+
             with gr.Row():
 
                 guidance_scale = gr.Slider(
                     label="Guidance Scale",
                     minimum=1,
-                    maximum=50,
+                    maximum=100,
                     step=0.5,
                     value=50,
                 )
-  
+
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
@@ -186,11 +196,20 @@ def infer(edit_images,
                     value=28,
                 )
 
+        # 添加 Gradio 示例组件
+        gr.Examples(
+            examples=new_examples,
+            inputs=[edit_image, prompt, seed],
+            outputs=[result, seed],
+            fn=infer,
+            cache_examples=False
+        )
+
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [edit_image, prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
+        fn=infer,
+        inputs=[edit_image, prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result, seed]
     )
 
 demo.launch(server_port=args.port)