add example images

app.py

@@ -1,12 +1,14 @@
 import gradio as gr
 import os
+import spaces
+import torch
+import tempfile
+from gradio_imageslider import ImageSlider
 
 import torch
 from PIL import Image
 from diffusers import (
     AutoencoderKL,
-    DiffusionPipeline,
-    # UNet2DConditionModel,
 )
 
 from transformers import CLIPTextModel, CLIPTokenizer
@@ -21,16 +23,8 @@ def load_example(example_image):
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model_repo_id = "zysong212/DepthMaster"  # Replace to the model you would like to use
 
-# if torch.cuda.is_available():
-#     torch_dtype = torch.float16
-# else:
 torch_dtype = torch.float32
 
-# pipe = DepthMasterPipeline.from_pretrained('eval', torch_dtype=torch_dtype)
-# unet = UNet2DConditionModel.from_pretrained(os.path.join('eval', f'unet'))
-# pipe = DepthMasterPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-# unet = UNet2DConditionModel.from_pretrained(model_repo_id, subfolder="unet", torch_dtype=torch_dtype)
-# pipe.unet = unet
 vae = AutoencoderKL.from_pretrained(model_repo_id, subfolder="vae", torch_dtype=torch_dtype, allow_pickle=False)
 unet = UNet2DConditionModel.from_pretrained(model_repo_id, subfolder="unet", torch_dtype=torch_dtype, allow_pickle=False)
 text_encoder = CLIPTextModel.from_pretrained(model_repo_id, subfolder="text_encoder", torch_dtype=torch_dtype)
@@ -45,29 +39,13 @@ except ImportError:
 
 pipe = pipe.to(device)
 
-# MAX_SEED = np.iinfo(np.int32).max
-# MAX_IMAGE_SIZE = 1024
-
 
 # @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
     input_image,
     progress=gr.Progress(track_tqdm=True),
 ):
-    # if randomize_seed:
-    #     seed = random.randint(0, MAX_SEED)
-
-    # generator = torch.Generator().manual_seed(seed)
-
-    # image = pipe(
-    #     prompt=prompt,
-    #     negative_prompt=negative_prompt,
-    #     guidance_scale=guidance_scale,
-    #     num_inference_steps=num_inference_steps,
-    #     width=width,
-    #     height=height,
-    #     generator=generator,
-    # ).images[0]
+
     pipe_out = pipe(
             input_image,
             processing_res=768,
@@ -96,155 +74,67 @@ example_images = [
     "wild_example/sg-11134201-7rd5x-lvlh48byidbqca.jpg"
 ]
 
+# css = """
+# #col-container {
+#     margin: 0 auto;
+#     max-width: 640px;
+# }
+# #example-gallery {
+#     height: 80px; /* 设置缩略图高度 */
+#     width: auto;  /* 保持宽高比 */
+#     margin: 0 auto;  /* 图片间距 */
+#     cursor: pointer; /* 鼠标指针变为手型 */
+# }
+# """
+
 css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
+#img-display-container {
+    max-height: 100vh;
+}
+#img-display-input {
+    max-height: 80vh;
+}
+#img-display-output {
+    max-height: 80vh;
 }
-#example-gallery {
-    height: 80px; /* 设置缩略图高度 */
-    width: auto;  /* 保持宽高比 */
-    margin: 0 auto;  /* 图片间距 */
-    cursor: pointer; /* 鼠标指针变为手型 */
+#download {
+    height: 62px;
 }
 """
 
+title = "# DepthMaster"
+description = """**Official demo for DepthMaster**.
+Please refer to our [paper](https://arxiv.org/abs/2501.02576), [project page](https://indu1ge.github.io/DepthMaster_page/), and [github](https://github.com/indu1ge/DepthMaster) for more details."""
+
+
 with gr.Blocks(css=css) as demo:
-    gr.Markdown("# DepthMaster")
-    gr.Markdown("Official demo for DepthMaster. Please refer to our [paper](https://arxiv.org/abs/2501.02576), [project page](https://indu1ge.github.io/DepthMaster_page/), and [github](https://github.com/indu1ge/DepthMaster) for more details.")
+    gr.Markdown(title)
+    gr.Markdown(description)
     gr.Markdown(" ### Depth Estimation with DepthMaster.")
     # with gr.Column(elem_id="col-container"):
     #     gr.Markdown(" # Depth Estimation")
     with gr.Row():
         with gr.Column():
-            input_image = gr.Image(label="Input Image", type="pil", elem_id="input-image", interactive=True)
+            input_image = gr.Image(label="Input Image", type="pil", elem_id="img-display-input")
         with gr.Column():
+            # depth_img_slider = ImageSlider(label="Depth Map with Slider View", elem_id="img-display-output", position=0.5)
             depth_map = gr.Image(label="Depth Map with Slider View", type="pil", interactive=False, elem_id="depth-map")
-        
-    # 计算按钮
-    compute_button = gr.Button("Compute Depth")
-
-    # # 添加示例图片选择器
-    # with gr.Row():
-    #     gr.Markdown("### example images")
-    # with gr.Row(elem_id="example-gallery"):
-    #     example_gallery = gr.Gallery(
-    #         label="",
-    #         value=example_images,
-    #         elem_id="example-gallery",
-    #         show_label=False,
-    #         interactive=True,
-    #         columns=10
-    #     )
-
-    # 设置默认图片点击后的操作
-    # example_gallery.select(
-    #     fn=lambda img_path: img_path,  # 回调函数：返回选择的路径
-    #     inputs=[],
-    #     outputs=input_image  # 输出设置为 Input Image
-    # )
-    # example_gallery.click(
-    #     fn=load_example,  # 选择图片的回调
-    #     inputs=[example_gallery],  # 输入：用户点击的图片
-    #     outputs=[input_image]  # 输出：更新 Input Image
-    # )
 
+    # 计算按钮
+    compute_button = gr.Button(value="Compute Depth")
 
     # 设置计算按钮的回调
     compute_button.click(
         fn=infer,  # 回调函数
-        inputs=input_image,  # 输入
-        outputs=depth_map  # 输出
+        inputs=[input_image],  # 输入
+        outputs=[depth_map]  # 输出
     )
 
+    example_files = os.listdir('wild_example')
+    example_files.sort()
+    example_files = [os.path.join('wild_example', filename) for filename in example_files]
+    examples = gr.Examples(examples=example_files, inputs=[input_image], outputs=[depth_map], fn=infer)
+
+
 # 启动 Gradio 应用
-demo.launch()
-        # with gr.Column(scale=45):
-        #     img_in = gr.Image(type="pil")
-        # with gr.Column(scale=45):
-        #     img_out = 
-
-        # with gr.Row():
-        #     prompt = gr.Text(
-        #         label="Prompt",
-        #         show_label=False,
-        #         max_lines=1,
-        #         placeholder="Enter your prompt",
-        #         container=False,
-        #     )
-
-        #     run_button = gr.Button("Run", scale=0, variant="primary")
-
-        # result = gr.Image(label="Result", show_label=False)
-
-        # with gr.Accordion("Advanced Settings", open=False):
-        #     negative_prompt = gr.Text(
-        #         label="Negative prompt",
-        #         max_lines=1,
-        #         placeholder="Enter a negative prompt",
-        #         visible=False,
-        #     )
-
-        #     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=256,
-            #         maximum=MAX_IMAGE_SIZE,
-            #         step=32,
-            #         value=1024,  # Replace with defaults that work for your model
-            #     )
-
-            #     height = gr.Slider(
-            #         label="Height",
-            #         minimum=256,
-            #         maximum=MAX_IMAGE_SIZE,
-            #         step=32,
-            #         value=1024,  # Replace with defaults that work for your model
-            #     )
-
-            # with gr.Row():
-            #     guidance_scale = gr.Slider(
-            #         label="Guidance scale",
-            #         minimum=0.0,
-            #         maximum=10.0,
-            #         step=0.1,
-            #         value=0.0,  # Replace with defaults that work for your model
-            #     )
-
-            #     num_inference_steps = gr.Slider(
-            #         label="Number of inference steps",
-            #         minimum=1,
-            #         maximum=50,
-            #         step=1,
-            #         value=2,  # Replace with defaults that work for your model
-            #     )
-
-    #     gr.Examples(examples=examples, inputs=[prompt])
-    # gr.on(
-    #     triggers=[run_button.click, prompt.submit],
-    #     fn=infer,
-    #     inputs=[
-    #         prompt,
-    #         negative_prompt,
-    #         seed,
-    #         randomize_seed,
-    #         # width,
-    #         # height,
-    #         # guidance_scale,
-    #         # num_inference_steps,
-    #     ],
-    #     outputs=[result, seed],
-    # )
-
-# if __name__ == "__main__":
-#     demo.launch()
+demo.queue().launch(share=True)