init

README.md

@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 title: ObjCtrl 2.5D
 colorFrom: purple
 colorTo: red
@@ -7,3 +8,18 @@ app_file: app.py
 pinned: false
 license: apache-2.0
 short_description: Training-free object control with camera poses
+=======
+---
+title: ObjCtrl 2.5D
+emoji: ⚡
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 5.7.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+>>>>>>> 8085650ca5418a3401650b762a3b63d7c2176d32

app.py

@@ -1,7 +1,350 @@
+import os
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+import torch
+from gradio_image_prompter import ImagePrompter
+from sam2.sam2_image_predictor import SAM2ImagePredictor
+from omegaconf import OmegaConf
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+from objctrl_2_5d.utils.ui_utils import process_image, get_camera_pose, run_segment, run_depth, get_points, undo_points
+
+
+from cameractrl.inference import get_pipeline
+from objctrl_2_5d.objctrl_2_5d import run
+from objctrl_2_5d.utils.examples import examples, sync_points
+
+
+### Title and Description ###
+#### Description ####
+title = r"""<h1 align="center">ObjCtrl-2.5D: Training-free Object Control with Camera Poses</h1>"""
+# subtitle = r"""<h2 align="center">Deployed on SVD Generation</h2>"""
+important_link = r"""
+<div align='center'>
+ <a href='https://wzhouxiff.github.io/projects/MotionCtrl/assets/paper/MotionCtrl.pdf'>[Paper]</a>
+&ensp; <a href='https://wzhouxiff.github.io/projects/MotionCtrl/'>[Project Page]</a>
+&ensp; <a href='https://github.com/TencentARC/MotionCtrl'>[Code]</a>
+</div>
+"""
+
+authors = r"""
+<div align='center'>
+ <a href='https://wzhouxiff.github.io/'>Zhouxia Wang</a>
+&ensp; <a href='https://nirvanalan.github.io/'>Yushi Lan</a>
+&ensp; <a href='https://shangchenzhou.com/'>Shanchen Zhou</a>
+&ensp; <a href='https://www.mmlab-ntu.com/person/ccloy/index.html'>Chen Change Loy</a>
+</div>
+"""
+
+affiliation = r"""
+<div align='center'>
+ <a href='https://www.mmlab-ntu.com/'>S-Lab, NTU Singapore</a>
+</div>
+"""
+
+description = r"""
+<b>Official Gradio demo</b> for <a href='https://github.com/TencentARC/MotionCtrl' target='_blank'><b>ObjCtrl-2.5D: Training-free Object Control with Camera Poses</b></a>.<br>
+🔥 ObjCtrl2.5D enables object motion control in a I2V generated video via transforming 2D trajectories to 3D using depth, subsequently converting them into camera poses, 
+thereby leveraging the exisitng camera motion control module for object motion control without requiring additional training.<br>
+"""
+
+article = r"""
+If ObjCtrl2.5D is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/MotionCtrl' target='_blank'>Github Repo</a>. Thanks! 
+[![GitHub Stars](https://img.shields.io/github/stars/TencentARC%2FMotionCtrl
+)](https://github.com/TencentARC/MotionCtrl)
+
+---
+
+📝 **Citation**
+<br>
+If our work is useful for your research, please consider citing:
+```bibtex
+@inproceedings{wang2024motionctrl,
+  title={Motionctrl: A unified and flexible motion controller for video generation},
+  author={Wang, Zhouxia and Yuan, Ziyang and Wang, Xintao and Li, Yaowei and Chen, Tianshui and Xia, Menghan and Luo, Ping and Shan, Ying},
+  booktitle={ACM SIGGRAPH 2024 Conference Papers},
+  pages={1--11},
+  year={2024}
+}
+```
+
+📧 **Contact**
+<br>
+If you have any questions, please feel free to reach me out at <b>zhouzi1212@gmail.com</b>.
+
+"""
+
+# -------------- initialization --------------
+
+CAMERA_MODE = ["Traj2Cam", "Rotate", "Clockwise", "Translate"]
+
+# select the device for computation
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+elif torch.backends.mps.is_available():
+    device = torch.device("mps")
+else:
+    device = torch.device("cpu")
+print(f"using device: {device}")
+
+# segmentation model
+segmentor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-tiny", cache_dir="ckpt", device=device)
+
+# depth model
+d_model_NK = torch.hub.load('./ZoeDepth', 'ZoeD_NK', source='local', pretrained=True).to(device)
+
+# cameractrl model
+config = "configs/svd_320_576_cameractrl.yaml"
+model_id = "stabilityai/stable-video-diffusion-img2vid"
+ckpt = "checkpoints/CameraCtrl_svd.ckpt"
+if not os.path.exists(ckpt):
+    os.makedirs("checkpoints", exist_ok=True)
+    os.system("wget -c https://huggingface.co/hehao13/CameraCtrl_SVD_ckpts/resolve/main/CameraCtrl_svd.ckpt?download=true")
+    os.system("mv CameraCtrl_svd.ckpt?download=true checkpoints/CameraCtrl_svd.ckpt")
+model_config = OmegaConf.load(config)
+
+
+pipeline = get_pipeline(model_id, "unet", model_config['down_block_types'], model_config['up_block_types'],
+                        model_config['pose_encoder_kwargs'], model_config['attention_processor_kwargs'],
+                        ckpt, True, device)
+
+# segmentor = None
+# d_model_NK = None
+# pipeline = None
+
+
+# -------------- UI definition --------------
+with gr.Blocks() as demo:
+    # layout definition
+    gr.Markdown(title)
+    gr.Markdown(authors)
+    gr.Markdown(affiliation)
+    gr.Markdown(important_link)
+    gr.Markdown(description)
+    
+    
+    # with gr.Row():
+    #     gr.Markdown("""# <center>Repositioning the Subject within Image </center>""")
+    mask = gr.State(value=None) # store mask
+    removal_mask = gr.State(value=None) # store removal mask
+    selected_points = gr.State([]) # store points
+    selected_points_text = gr.Textbox(label="Selected Points", visible=False)
+    
+    original_image = gr.State(value=None) # store original input image
+    masked_original_image = gr.State(value=None) # store masked input image
+    mask_logits = gr.State(value=None) # store mask logits
+    
+    depth = gr.State(value=None) # store depth
+    org_depth_image = gr.State(value=None) # store original depth image
+    
+    camera_pose = gr.State(value=None) # store camera pose
+    
+    with gr.Column():
+        
+        outlines = """
+        <font size="5"><b>There are total 5 steps to complete the task.</b></font>
+        - Step 1: Input an image and Crop it to a suitable size;
+        - Step 2: Attain the subject mask;
+        - Step 3: Get depth and Draw Trajectory;
+        - Step 4: Get camera pose from trajectory or customize it;
+        - Step 5: Generate the final video.
+        """
+        
+        gr.Markdown(outlines)
+        
+        
+        with gr.Row():
+            with gr.Column():
+                # Step 1: Input Image
+                step1_dec = """
+                    <font size="4"><b>Step 1: Input Image</b></font>
+                    - Select the region using a <mark>bounding box</mark>, aiming for a ratio close to </mark>320:576</mark> (height:width).
+                    - All provided images in `Examples` are in 320 x 576 resolution. Simply press `Process` to proceed.
+                    """
+                step1 = gr.Markdown(step1_dec)
+                raw_input = ImagePrompter(type="pil", label="Raw Image", show_label=True, interactive=True)
+                # left_up_point = gr.Textbox(value = "-1 -1", label="Left Up Point", interactive=True)
+                process_button = gr.Button("Process")
+                
+            with gr.Column():
+                # Step 2: Get Subject Mask
+                step2_dec = """
+                    <font size="4"><b>Step 2: Get Subject Mask</b></font>
+                    - Use the <mark>bounding boxes</mark> or <mark>paints</mark> to select the subject.
+                    - Press `Segment Subject` to get the mask. <mark>Can be refined iteratively by updating points<mark>.
+                    """
+                step2 = gr.Markdown(step2_dec)
+                canvas = ImagePrompter(type="pil", label="Input Image", show_label=True, interactive=True) # for mask painting
+
+                select_button = gr.Button("Segment Subject")
+                
+        with gr.Row():
+            with gr.Column():
+                mask_dec = """
+                    <font size="4"><b>Mask Result</b></font>
+                    - Just for visualization purpose. No need to interact.
+                """
+                mask_vis = gr.Markdown(mask_dec)
+                mask_output = gr.Image(type="pil", label="Mask", show_label=True, interactive=False)
+            with gr.Column():
+                # Step 3: Get Depth and Draw Trajectory
+                step3_dec = """
+                    <font size="4"><b>Step 3: Get Depth and Draw Trajectory</b></font>
+                    - Press `Get Depth` to get the depth image.
+                    - Draw the trajectory by selecting points on the depth image. <mark>No more than 14 points</mark>.
+                    - Press `Undo point` to remove all points.
+                """
+                step3 = gr.Markdown(step3_dec)
+                depth_image = gr.Image(type="pil", label="Depth Image", show_label=True, interactive=False)
+                with gr.Row():
+                    depth_button = gr.Button("Get Depth")
+                    undo_button = gr.Button("Undo point")
+                    
+        with gr.Row():
+            with gr.Column():
+                # Step 4: Trajectory to Camera Pose or Get Camera Pose
+                step4_dec = """
+                    <font size="4"><b>Step 4: Get camera pose from trajectory or customize it</b></font>
+                    - Option 1: Transform the 2D trajectory to camera poses with depth. <mark>`Rescale` is used for depth alignment. Larger value can speed up the object motion.</mark>
+                    - Option 2: Rotate the camera with a specific `Angle`.
+                    - Option 3: Rotate the camera clockwise or counterclockwise with a specific `Angle`.
+                    - Option 4: Translate the camera with `Tx` (<mark>Pan Left/Right</mark>), `Ty` (<mark>Pan Up/Down</mark>), `Tz` (<mark>Zoom In/Out</mark>) and `Speed`.
+                """
+                step4 = gr.Markdown(step4_dec)
+                camera_pose_vis = gr.Plot(None, label='Camera Pose')
+                with gr.Row():
+                    with gr.Column():
+                        speed = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=1.0, label="Speed", interactive=True)
+                        rescale = gr.Slider(minimum=0.0, maximum=10, step=0.1, value=1.0, label="Rescale", interactive=True)
+                        # traj2pose_button = gr.Button("Option1: Trajectory to Camera Pose")
+                        
+                        angle = gr.Slider(minimum=-360, maximum=360, step=1, value=60, label="Angle", interactive=True)
+                        # rotation_button = gr.Button("Option2: Rotate")
+                        # clockwise_button = gr.Button("Option3: Clockwise")
+                    with gr.Column():
+                        
+                        Tx = gr.Slider(minimum=-1, maximum=1, step=1, value=0, label="Tx", interactive=True)
+                        Ty = gr.Slider(minimum=-1, maximum=1, step=1, value=0, label="Ty", interactive=True)
+                        Tz = gr.Slider(minimum=-1, maximum=1, step=1, value=0, label="Tz", interactive=True)
+                        # translation_button = gr.Button("Option4: Translate")
+                with gr.Row():
+                    camera_option = gr.Radio(choices = CAMERA_MODE, label='Camera Options', value=CAMERA_MODE[0], interactive=True)
+                with gr.Row():
+                    get_camera_pose_button = gr.Button("Get Camera Pose")
+                        
+            with gr.Column():
+                # Step 5: Get the final generated video
+                step5_dec = """
+                    <font size="4"><b>Step 5: Get the final generated video</b></font>
+                    - 3 modes for background: <mark>Fixed</mark>, <mark>Reverse</mark>, <mark>Free</mark>.
+                    - Enable <mark>Scale-wise Masks</mark> for better object control.
+                    - Option to enable <mark>Shared Warping Latents</mark> and set <mark>stop frequency</mark> for spatial (`ds`) and temporal (`dt`) dimensions. Larger stop frequency will lead to artifacts.
+                """
+                step5 = gr.Markdown(step5_dec)
+                generated_video = gr.Video(None, label='Generated Video')
+                
+                with gr.Row():
+                    seed = gr.Textbox(value = "42", label="Seed", interactive=True)
+                    # num_inference_steps = gr.Slider(minimum=1, maximum=100, step=1, value=25, label="Number of Inference Steps", interactive=True)
+                    bg_mode = gr.Radio(choices = ["Fixed", "Reverse", "Free"], label="Background Mode", value="Fixed", interactive=True)
+                # swl_mode = gr.Radio(choices = ["Enable SWL", "Disable SWL"], label="Shared Warping Latent", value="Disable SWL", interactive=True)
+                scale_wise_masks = gr.Checkbox(label="Enable Scale-wise Masks", interactive=True, value=True)
+                with gr.Row():
+                    with gr.Column():
+                        shared_wapring_latents = gr.Checkbox(label="Enable Shared Warping Latents", interactive=True)
+                    with gr.Column():
+                        ds = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.5, label="ds", interactive=True)
+                        dt = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.5, label="dt", interactive=True)
+                
+                generated_button = gr.Button("Generate")
+
+                
+
+    # # event definition
+    process_button.click(
+        fn = process_image,
+        inputs = [raw_input],
+        outputs = [original_image, canvas]
+    )
+    
+    select_button.click(
+        run_segment(segmentor),
+        [canvas, original_image, mask_logits],
+        [mask, mask_output, masked_original_image, mask_logits]
+    )
+    
+    depth_button.click(
+        run_depth(d_model_NK),
+        [original_image, selected_points],
+        [depth, depth_image, org_depth_image]
+    )
+    
+    depth_image.select(
+        get_points,
+        [depth_image, selected_points],
+        [depth_image, selected_points],
+    )
+    undo_button.click(
+        undo_points,
+        [org_depth_image],
+        [depth_image, selected_points]
+    )
+    
+    get_camera_pose_button.click(
+        get_camera_pose(CAMERA_MODE),
+        [camera_option, selected_points, depth, mask, rescale, angle, Tx, Ty, Tz, speed],
+        [camera_pose, camera_pose_vis]
+    )
+    
+    generated_button.click(
+        run(pipeline, device),
+        [
+         original_image,
+         mask,
+         depth,
+         camera_pose,
+         bg_mode,
+         shared_wapring_latents,
+         scale_wise_masks,
+         rescale,
+         seed,
+         ds,
+         dt,
+        #  num_inference_steps
+         ],
+        [generated_video],
+    )
+
+    gr.Examples(
+        examples=examples,
+        inputs=[
+            raw_input,
+            rescale,
+            speed,
+            angle,
+            Tx,
+            Ty,
+            Tz,
+            camera_option,
+            bg_mode,
+            shared_wapring_latents,
+            scale_wise_masks,
+            ds,
+            dt,
+            seed,
+            selected_points_text  # selected_points
+        ],
+        outputs=[generated_video], 
+        examples_per_page=10
+    )
+    
+    selected_points_text.change(
+        sync_points,
+        inputs=[selected_points_text],
+        outputs=[selected_points]
+    )
+
+
+    gr.Markdown(article)
+
+
+demo.queue().launch(share=True)

objctrl-2.5d_gradio.py

@@ -1,350 +0,0 @@
-import os
-import gradio as gr
-
-import torch
-from gradio_image_prompter import ImagePrompter
-from sam2.sam2_image_predictor import SAM2ImagePredictor
-from omegaconf import OmegaConf
-
-from objctrl_2_5d.utils.ui_utils import process_image, get_camera_pose, run_segment, run_depth, get_points, undo_points
-
-
-from cameractrl.inference import get_pipeline
-from objctrl_2_5d.objctrl_2_5d import run
-from objctrl_2_5d.utils.examples import examples, sync_points
-
-
-### Title and Description ###
-#### Description ####
-title = r"""<h1 align="center">ObjCtrl-2.5D: Training-free Object Control with Camera Poses</h1>"""
-# subtitle = r"""<h2 align="center">Deployed on SVD Generation</h2>"""
-important_link = r"""
-<div align='center'>
- <a href='https://wzhouxiff.github.io/projects/MotionCtrl/assets/paper/MotionCtrl.pdf'>[Paper]</a>
-&ensp; <a href='https://wzhouxiff.github.io/projects/MotionCtrl/'>[Project Page]</a>
-&ensp; <a href='https://github.com/TencentARC/MotionCtrl'>[Code]</a>
-</div>
-"""
-
-authors = r"""
-<div align='center'>
- <a href='https://wzhouxiff.github.io/'>Zhouxia Wang</a>
-&ensp; <a href='https://nirvanalan.github.io/'>Yushi Lan</a>
-&ensp; <a href='https://shangchenzhou.com/'>Shanchen Zhou</a>
-&ensp; <a href='https://www.mmlab-ntu.com/person/ccloy/index.html'>Chen Change Loy</a>
-</div>
-"""
-
-affiliation = r"""
-<div align='center'>
- <a href='https://www.mmlab-ntu.com/'>S-Lab, NTU Singapore</a>
-</div>
-"""
-
-description = r"""
-<b>Official Gradio demo</b> for <a href='https://github.com/TencentARC/MotionCtrl' target='_blank'><b>ObjCtrl-2.5D: Training-free Object Control with Camera Poses</b></a>.<br>
-🔥 ObjCtrl2.5D enables object motion control in a I2V generated video via transforming 2D trajectories to 3D using depth, subsequently converting them into camera poses, 
-thereby leveraging the exisitng camera motion control module for object motion control without requiring additional training.<br>
-"""
-
-article = r"""
-If ObjCtrl2.5D is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/MotionCtrl' target='_blank'>Github Repo</a>. Thanks! 
-[![GitHub Stars](https://img.shields.io/github/stars/TencentARC%2FMotionCtrl
-)](https://github.com/TencentARC/MotionCtrl)
-
----
-
-📝 **Citation**
-<br>
-If our work is useful for your research, please consider citing:
-```bibtex
-@inproceedings{wang2024motionctrl,
-  title={Motionctrl: A unified and flexible motion controller for video generation},
-  author={Wang, Zhouxia and Yuan, Ziyang and Wang, Xintao and Li, Yaowei and Chen, Tianshui and Xia, Menghan and Luo, Ping and Shan, Ying},
-  booktitle={ACM SIGGRAPH 2024 Conference Papers},
-  pages={1--11},
-  year={2024}
-}
-```
-
-📧 **Contact**
-<br>
-If you have any questions, please feel free to reach me out at <b>zhouzi1212@gmail.com</b>.
-
-"""
-
-# -------------- initialization --------------
-
-CAMERA_MODE = ["Traj2Cam", "Rotate", "Clockwise", "Translate"]
-
-# select the device for computation
-if torch.cuda.is_available():
-    device = torch.device("cuda")
-elif torch.backends.mps.is_available():
-    device = torch.device("mps")
-else:
-    device = torch.device("cpu")
-print(f"using device: {device}")
-
-# segmentation model
-segmentor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-tiny", cache_dir="ckpt", device=device)
-
-# depth model
-d_model_NK = torch.hub.load('./ZoeDepth', 'ZoeD_NK', source='local', pretrained=True).to(device)
-
-# cameractrl model
-config = "configs/svd_320_576_cameractrl.yaml"
-model_id = "stabilityai/stable-video-diffusion-img2vid"
-ckpt = "checkpoints/CameraCtrl_svd.ckpt"
-if not os.path.exists(ckpt):
-    os.makedirs("checkpoints", exist_ok=True)
-    os.system("wget -c https://huggingface.co/hehao13/CameraCtrl_SVD_ckpts/resolve/main/CameraCtrl_svd.ckpt?download=true")
-    os.system("mv CameraCtrl_svd.ckpt?download=true checkpoints/CameraCtrl_svd.ckpt")
-model_config = OmegaConf.load(config)
-
-
-pipeline = get_pipeline(model_id, "unet", model_config['down_block_types'], model_config['up_block_types'],
-                        model_config['pose_encoder_kwargs'], model_config['attention_processor_kwargs'],
-                        ckpt, True, device)
-
-# segmentor = None
-# d_model_NK = None
-# pipeline = None
-
-
-# -------------- UI definition --------------
-with gr.Blocks() as demo:
-    # layout definition
-    gr.Markdown(title)
-    gr.Markdown(authors)
-    gr.Markdown(affiliation)
-    gr.Markdown(important_link)
-    gr.Markdown(description)
-    
-    
-    # with gr.Row():
-    #     gr.Markdown("""# <center>Repositioning the Subject within Image </center>""")
-    mask = gr.State(value=None) # store mask
-    removal_mask = gr.State(value=None) # store removal mask
-    selected_points = gr.State([]) # store points
-    selected_points_text = gr.Textbox(label="Selected Points", visible=False)
-    
-    original_image = gr.State(value=None) # store original input image
-    masked_original_image = gr.State(value=None) # store masked input image
-    mask_logits = gr.State(value=None) # store mask logits
-    
-    depth = gr.State(value=None) # store depth
-    org_depth_image = gr.State(value=None) # store original depth image
-    
-    camera_pose = gr.State(value=None) # store camera pose
-    
-    with gr.Column():
-        
-        outlines = """
-        <font size="5"><b>There are total 5 steps to complete the task.</b></font>
-        - Step 1: Input an image and Crop it to a suitable size;
-        - Step 2: Attain the subject mask;
-        - Step 3: Get depth and Draw Trajectory;
-        - Step 4: Get camera pose from trajectory or customize it;
-        - Step 5: Generate the final video.
-        """
-        
-        gr.Markdown(outlines)
-        
-        
-        with gr.Row():
-            with gr.Column():
-                # Step 1: Input Image
-                step1_dec = """
-                    <font size="4"><b>Step 1: Input Image</b></font>
-                    - Select the region using a <mark>bounding box</mark>, aiming for a ratio close to </mark>320:576</mark> (height:width).
-                    - All provided images in `Examples` are in 320 x 576 resolution. Simply press `Process` to proceed.
-                    """
-                step1 = gr.Markdown(step1_dec)
-                raw_input = ImagePrompter(type="pil", label="Raw Image", show_label=True, interactive=True)
-                # left_up_point = gr.Textbox(value = "-1 -1", label="Left Up Point", interactive=True)
-                process_button = gr.Button("Process")
-                
-            with gr.Column():
-                # Step 2: Get Subject Mask
-                step2_dec = """
-                    <font size="4"><b>Step 2: Get Subject Mask</b></font>
-                    - Use the <mark>bounding boxes</mark> or <mark>paints</mark> to select the subject.
-                    - Press `Segment Subject` to get the mask. <mark>Can be refined iteratively by updating points<mark>.
-                    """
-                step2 = gr.Markdown(step2_dec)
-                canvas = ImagePrompter(type="pil", label="Input Image", show_label=True, interactive=True) # for mask painting
-
-                select_button = gr.Button("Segment Subject")
-                
-        with gr.Row():
-            with gr.Column():
-                mask_dec = """
-                    <font size="4"><b>Mask Result</b></font>
-                    - Just for visualization purpose. No need to interact.
-                """
-                mask_vis = gr.Markdown(mask_dec)
-                mask_output = gr.Image(type="pil", label="Mask", show_label=True, interactive=False)
-            with gr.Column():
-                # Step 3: Get Depth and Draw Trajectory
-                step3_dec = """
-                    <font size="4"><b>Step 3: Get Depth and Draw Trajectory</b></font>
-                    - Press `Get Depth` to get the depth image.
-                    - Draw the trajectory by selecting points on the depth image. <mark>No more than 14 points</mark>.
-                    - Press `Undo point` to remove all points.
-                """
-                step3 = gr.Markdown(step3_dec)
-                depth_image = gr.Image(type="pil", label="Depth Image", show_label=True, interactive=False)
-                with gr.Row():
-                    depth_button = gr.Button("Get Depth")
-                    undo_button = gr.Button("Undo point")
-                    
-        with gr.Row():
-            with gr.Column():
-                # Step 4: Trajectory to Camera Pose or Get Camera Pose
-                step4_dec = """
-                    <font size="4"><b>Step 4: Get camera pose from trajectory or customize it</b></font>
-                    - Option 1: Transform the 2D trajectory to camera poses with depth. <mark>`Rescale` is used for depth alignment. Larger value can speed up the object motion.</mark>
-                    - Option 2: Rotate the camera with a specific `Angle`.
-                    - Option 3: Rotate the camera clockwise or counterclockwise with a specific `Angle`.
-                    - Option 4: Translate the camera with `Tx` (<mark>Pan Left/Right</mark>), `Ty` (<mark>Pan Up/Down</mark>), `Tz` (<mark>Zoom In/Out</mark>) and `Speed`.
-                """
-                step4 = gr.Markdown(step4_dec)
-                camera_pose_vis = gr.Plot(None, label='Camera Pose')
-                with gr.Row():
-                    with gr.Column():
-                        speed = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=1.0, label="Speed", interactive=True)
-                        rescale = gr.Slider(minimum=0.0, maximum=10, step=0.1, value=1.0, label="Rescale", interactive=True)
-                        # traj2pose_button = gr.Button("Option1: Trajectory to Camera Pose")
-                        
-                        angle = gr.Slider(minimum=-360, maximum=360, step=1, value=60, label="Angle", interactive=True)
-                        # rotation_button = gr.Button("Option2: Rotate")
-                        # clockwise_button = gr.Button("Option3: Clockwise")
-                    with gr.Column():
-                        
-                        Tx = gr.Slider(minimum=-1, maximum=1, step=1, value=0, label="Tx", interactive=True)
-                        Ty = gr.Slider(minimum=-1, maximum=1, step=1, value=0, label="Ty", interactive=True)
-                        Tz = gr.Slider(minimum=-1, maximum=1, step=1, value=0, label="Tz", interactive=True)
-                        # translation_button = gr.Button("Option4: Translate")
-                with gr.Row():
-                    camera_option = gr.Radio(choices = CAMERA_MODE, label='Camera Options', value=CAMERA_MODE[0], interactive=True)
-                with gr.Row():
-                    get_camera_pose_button = gr.Button("Get Camera Pose")
-                        
-            with gr.Column():
-                # Step 5: Get the final generated video
-                step5_dec = """
-                    <font size="4"><b>Step 5: Get the final generated video</b></font>
-                    - 3 modes for background: <mark>Fixed</mark>, <mark>Reverse</mark>, <mark>Free</mark>.
-                    - Enable <mark>Scale-wise Masks</mark> for better object control.
-                    - Option to enable <mark>Shared Warping Latents</mark> and set <mark>stop frequency</mark> for spatial (`ds`) and temporal (`dt`) dimensions. Larger stop frequency will lead to artifacts.
-                """
-                step5 = gr.Markdown(step5_dec)
-                generated_video = gr.Video(None, label='Generated Video')
-                
-                with gr.Row():
-                    seed = gr.Textbox(value = "42", label="Seed", interactive=True)
-                    # num_inference_steps = gr.Slider(minimum=1, maximum=100, step=1, value=25, label="Number of Inference Steps", interactive=True)
-                    bg_mode = gr.Radio(choices = ["Fixed", "Reverse", "Free"], label="Background Mode", value="Fixed", interactive=True)
-                # swl_mode = gr.Radio(choices = ["Enable SWL", "Disable SWL"], label="Shared Warping Latent", value="Disable SWL", interactive=True)
-                scale_wise_masks = gr.Checkbox(label="Enable Scale-wise Masks", interactive=True, value=True)
-                with gr.Row():
-                    with gr.Column():
-                        shared_wapring_latents = gr.Checkbox(label="Enable Shared Warping Latents", interactive=True)
-                    with gr.Column():
-                        ds = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.5, label="ds", interactive=True)
-                        dt = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.5, label="dt", interactive=True)
-                
-                generated_button = gr.Button("Generate")
-
-                
-
-    # # event definition
-    process_button.click(
-        fn = process_image,
-        inputs = [raw_input],
-        outputs = [original_image, canvas]
-    )
-    
-    select_button.click(
-        run_segment(segmentor),
-        [canvas, original_image, mask_logits],
-        [mask, mask_output, masked_original_image, mask_logits]
-    )
-    
-    depth_button.click(
-        run_depth(d_model_NK),
-        [original_image, selected_points],
-        [depth, depth_image, org_depth_image]
-    )
-    
-    depth_image.select(
-        get_points,
-        [depth_image, selected_points],
-        [depth_image, selected_points],
-    )
-    undo_button.click(
-        undo_points,
-        [org_depth_image],
-        [depth_image, selected_points]
-    )
-    
-    get_camera_pose_button.click(
-        get_camera_pose(CAMERA_MODE),
-        [camera_option, selected_points, depth, mask, rescale, angle, Tx, Ty, Tz, speed],
-        [camera_pose, camera_pose_vis]
-    )
-    
-    generated_button.click(
-        run(pipeline, device),
-        [
-         original_image,
-         mask,
-         depth,
-         camera_pose,
-         bg_mode,
-         shared_wapring_latents,
-         scale_wise_masks,
-         rescale,
-         seed,
-         ds,
-         dt,
-        #  num_inference_steps
-         ],
-        [generated_video],
-    )
-
-    gr.Examples(
-        examples=examples,
-        inputs=[
-            raw_input,
-            rescale,
-            speed,
-            angle,
-            Tx,
-            Ty,
-            Tz,
-            camera_option,
-            bg_mode,
-            shared_wapring_latents,
-            scale_wise_masks,
-            ds,
-            dt,
-            seed,
-            selected_points_text  # selected_points
-        ],
-        outputs=[generated_video], 
-        examples_per_page=10
-    )
-    
-    selected_points_text.change(
-        sync_points,
-        inputs=[selected_points_text],
-        outputs=[selected_points]
-    )
-
-
-    gr.Markdown(article)
-
-
-demo.queue().launch(share=True)