init

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# main/evaluation
+.vscode/
+
+*.pyc
+gradio_temp
+*.pth

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -0,0 +1,83 @@
+
+<div align="center">
+<!-- <h1>AnimateZero</h1> -->
+<h3><b>MotionCtrl</b>: A Unified and Flexible
+                Motion Controller
+                for Video Generation</h3>
+
+<!-- [![ Paper](https://img.shields.io/badge/Paper-MotionCtrl-red
+)](https://wzhouxiff.github.io/projects/MotionCtrl/assets/paper/MotionCtrl.pdf) &ensp; [![ arXiv](https://img.shields.io/badge/arXiv-2312.03641-red
+)](https://arxiv.org/pdf/2312.03641.pdf) &ensp; [![Porject Page](https://img.shields.io/badge/Project%20%20Page-MotionCtrl-red)
+](https://wzhouxiff.github.io/projects/MotionCtrl/) &ensp; [![Demo](https://img.shields.io/badge/Demo-MotionCtrl-orange
+)]() -->
+
+[![ Paper](https://img.shields.io/badge/Paper-gray
+)](https://wzhouxiff.github.io/projects/MotionCtrl/assets/paper/MotionCtrl.pdf) &ensp; [![ arXiv](https://img.shields.io/badge/arXiv-red
+)](https://arxiv.org/pdf/2312.03641.pdf) &ensp; [![Porject Page](https://img.shields.io/badge/Project%20Page-green
+)
+](https://wzhouxiff.github.io/projects/MotionCtrl/) &ensp; [![Demo](https://img.shields.io/badge/Gradio%20Demo-orange
+)]()
+
+[Zhouxia Wang](https://vvictoryuki.github.io/website/)<sup>1,2</sup>, [Ziyang Yuan](https://github.com/jiangyzy)<sup>1,4</sup>, [Xintao Wang](https://xinntao.github.io/)<sup>1,3</sup>, [Tianshui Chen](http://tianshuichen.com/)<sup>6</sup>, [Menghan Xia](https://menghanxia.github.io/)<sup>3</sup>, [Ping Luo](http://luoping.me/)<sup>2,5</sup>, [Ying Shan](https://scholar.google.com/citations?hl=zh-CN&user=4oXBp9UAAAAJ)<sup>1,3</sup>
+
+<sup>1</sup> ARC Lab, Tencent PCG, <sup>2</sup> The University of Hong Kong, <sup>3</sup> Tencent AI Lab, <sup>4</sup> Tsinghua University, <sup>5</sup> Shanghai AI Laboratory, <sup>6</sup> Guangdong University of Technology
+
+
+</div>
+
+<!-- ## Results of MotionCtrl -->
+
+Our proposed <b>MotionCtrl</b> is capable of independently controlling the complex camera motion and object motion of the generated videos, with <b>only a unified</b> model. 
+There are some results attained with <b>MotionCtrl</b> and more results are showcased in our [Project Page](https://wzhouxiff.github.io/projects/MotionCtrl/).
+
+<!-- </br>
+<video poster="" id="steve" autoplay controls muted loop playsinline height="100%" width="100%">
+<source src="https://wzhouxiff.github.io/projects/MotionCtrl/assets/videos/teasers/camera_d971457c81bca597.mp4" type="video/mp4">
+</video>
+<video poster="" id="steve" autoplay controls muted loop playsinline height="100%" width="100%">
+<source src="https://wzhouxiff.github.io/projects/MotionCtrl/assets/videos/teasers/camera_Round-R_ZoomIn.mp4" type="video/mp4">
+</video>
+<video poster="" id="steve" autoplay controls muted loop playsinline height="100%" width="100%">
+<source src="https://wzhouxiff.github.io/projects/MotionCtrl/assets/videos/teasers/shake_1.mp4" type="video/mp4">
+</video>
+<video poster="" id="steve" autoplay controls muted loop playsinline height="100%" width="100%">
+<source src="https://wzhouxiff.github.io/projects/MotionCtrl/assets/videos/teasers/s_curve_3_v1.mp4" type="video/mp4">
+</video> -->
+
+<div align="center">
+    <img src="assets/hpxvu-3d8ym.gif", width="600">
+    <img src="assets/w3nb7-9vz5t.gif", width="600">  
+    <img src="assets/62n2a-wuvsw.gif", width="600">
+    <img src="assets/ilw96-ak827.gif", width="600">
+</div>
+
+
+## Updating
+- [x] Release MotionCtrl depolyed on *LVDM/VideoCrafter*
+- [ ] Gradio Demo Available
+ 
+<!-- ## training
+    sh configs/training/train_cmcm.sh
+    sh configs/training/train_omcm_dense.sh
+    sh configs/training/train_omcm_sparse.sh -->
+
+## Inference
+
+1. Download the weights of MotionCtrl from [weipan](https://drive.weixin.qq.com/s?k=AJEAIQdfAAogLtIAPh) to `./checkpoints`.
+2. Go into `configs/inference/run.sh` and set `condtype` as 'camera_motion', 'object_motion', or 'both'.
+- `condtype=camera_motion` means only control the **camera motion** in the generated video.
+- `condtype=object_motion` means only control the **object motion** in the generated video.
+- `condtype=both` means control the camera motion and object motion in the generated video **simultaneously**.
+3. sh configs/inference/run.sh
+
+## Citation
+If you make use of our work, please cite our paper.
+```bibtex
+@inproceedings{wang2023motionctrl,
+  title={MotionCtrl: A Unified and Flexible Motion Controller for Video Generation},
+  author={Wang, Zhouxia and Yuan, Ziyang and Wang, Xintao and Chen, Tianshui and Xia, Menghan and Luo, Ping and Shan, Yin},
+  booktitle={arXiv preprint arXiv:2312.03641},
+  year={2023}
+}
+```
+

app.py

@@ -0,0 +1,1079 @@
+import argparse
+import os
+import tempfile
+from functools import partial
+
+import cv2
+import gradio as gr
+import imageio
+import numpy as np
+import torch
+import torchvision
+from omegaconf import OmegaConf
+from PIL import Image
+from pytorch_lightning import seed_everything
+
+from gradio_utils.camera_utils import CAMERA_MOTION_MODE, process_camera
+from gradio_utils.traj_utils import (OBJECT_MOTION_MODE, get_provided_traj,
+                                     process_points, process_traj)
+from gradio_utils.utils import vis_camera
+from lvdm.models.samplers.ddim import DDIMSampler
+from main.evaluation.motionctrl_inference import (DEFAULT_NEGATIVE_PROMPT,
+                                                  load_model_checkpoint,
+                                                  post_prompt)
+from utils.utils import instantiate_from_config
+
+os.environ['KMP_DUPLICATE_LIB_OK']='True'
+
+
+#### Description ####
+title = r"""<h1 align="center">MotionCtrl: A Unified and Flexible Motion Controller for Video Generation</h1>"""
+
+description = r"""
+<b>Official Gradio demo</b> for <a href='https://github.com/TencentARC/MotionCtrl' target='_blank'><b>MotionCtrl: A Unified and Flexible Motion Controller for Video Generation</b></a>.<br>
+🔥 MotionCtrl is capable of independently and flexibly controling the camera motion and object motion of a generated video, with only a unified model.<br>
+🤗 Try to control the motion of the generated videos yourself!<br>
+❗❗❗ Please note that current version of **MotionCtrl** is deployed on **LVDM/VideoCrafter**. The versions that depolyed on **AnimateDiff** and **SVD** will be released soon.<br>
+"""
+article = r"""
+If MotionCtrl 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{wang2023motionctrl,
+  title={MotionCtrl: A Unified and Flexible Motion Controller for Video Generation},
+  author={Wang, Zhouxia and Yuan, Ziyang and Wang, Xintao and Chen, Tianshui and Xia, Menghan and Luo, Ping and Shan, Yin},
+  booktitle={arXiv preprint arXiv:2312.03641},
+  year={2023}
+}
+```
+
+📧 **Contact**
+<br>
+If you have any questions, please feel free to reach me out at <b>wzhoux@connect.hku.hk</b>.
+
+"""
+css = """
+.gradio-container {width: 85% !important}
+.gr-monochrome-group {border-radius: 5px !important; border: revert-layer !important; border-width: 2px !important; color: black !important;}
+span.svelte-s1r2yt {font-size: 17px !important; font-weight: bold !important; color: #d30f2f !important;}
+button {border-radius: 8px !important;}
+.add_button {background-color: #4CAF50 !important;}
+.remove_button {background-color: #f44336 !important;}
+.clear_button {background-color: gray !important;}
+.mask_button_group {gap: 10px !important;}
+.video {height: 300px !important;}
+.image {height: 300px !important;}
+.video .wrap.svelte-lcpz3o {display: flex !important; align-items: center !important; justify-content: center !important;}
+.video .wrap.svelte-lcpz3o > :first-child {height: 100% !important;}
+.margin_center {width: 50% !important; margin: auto !important;}
+.jc_center {justify-content: center !important;}
+"""
+
+
+T_base = [
+            [1.,0.,0.],             ## W2C  x 的正方向： 相机朝左  left
+            [-1.,0.,0.],            ## W2C  x 的负方向： 相机朝右  right
+            [0., 1., 0.],           ## W2C  y 的正方向： 相机朝上  up     
+            [0.,-1.,0.],            ## W2C  y 的负方向： 相机朝下  down
+            [0.,0.,1.],             ## W2C  z 的正方向： 相机往前  zoom out
+            [0.,0.,-1.],            ## W2C  z 的负方向： 相机往前  zoom in
+        ]   
+radius = 1
+n = 16
+# step = 
+look_at = np.array([0, 0, 0.8]).reshape(3,1)
+# look_at = np.array([0, 0, 0.2]).reshape(3,1)
+
+T_list = []
+base_R = np.array([[1., 0., 0.],
+                [0., 1., 0.],
+                [0., 0., 1.]])
+res = [] 
+res_forsave = []
+T_range = 1.8
+
+
+
+for i in range(0, 16):
+    # theta = (1)*np.pi*i/n
+
+    R = base_R[:,:3]
+    T = np.array([0.,0.,1.]).reshape(3,1) * (i/n)*2
+    RT = np.concatenate([R,T], axis=1)
+    res.append(RT)
+    
+fig = vis_camera(res)
+    
+# MODE = ["camera motion control", "object motion control", "camera + object motion control"]
+MODE = ["control camera poses", "control object trajectory", "control both camera and object motion"]
+BASE_MODEL = ['LVDM/VideoCrafter', 'AnimateDiff', 'SVD']
+
+
+traj_list = [] 
+camera_dict = {
+                "motion":[],
+                "mode": "Customized Mode 1: First A then B",  # "First A then B", "Both A and B", "Custom"
+                "speed": 1.0,
+                "complex": None
+                }   
+
+def fn_vis_camera(info_mode):
+    global camera_dict
+    RT = process_camera(camera_dict) # [t, 3, 4]
+    if camera_dict['complex'] is not None:
+        # rescale T to [-2,2]
+        for i in range(3):
+            min_T = np.min(RT[:,i,-1])
+            max_T = np.max(RT[:,i,-1])
+            if min_T < -2 or max_T > 2:
+                RT[:,i,-1] = RT[:,i,-1] - min_T
+                RT[:,i,-1] = RT[:,i,-1] / (np.max(RT[:,:,-1]) + 1e-6)
+                RT[:,i,-1] = RT[:,i,-1] * 4
+                RT[:,i,-1] = RT[:,i,-1] - 2
+
+    fig = vis_camera(RT)
+
+    if info_mode == MODE[0]:
+        vis_step3_prompt_generate = True
+        vis_prompt = True
+        vis_num_samples = True
+        vis_seed = True
+        vis_start = True
+        vis_gen_video = True
+
+        vis_object_mode = False
+        vis_object_info = False
+
+    else:
+        vis_step3_prompt_generate = False
+        vis_prompt = False
+        vis_num_samples = False
+        vis_seed = False
+        vis_start = False
+        vis_gen_video = False
+
+        vis_object_mode = True
+        vis_object_info = True
+
+    return fig, \
+            gr.update(visible=vis_object_mode), \
+            gr.update(visible=vis_object_info), \
+            gr.update(visible=vis_step3_prompt_generate), \
+            gr.update(visible=vis_prompt), \
+            gr.update(visible=vis_num_samples), \
+            gr.update(visible=vis_seed), \
+            gr.update(visible=vis_start), \
+            gr.update(visible=vis_gen_video, value=None)
+
+def fn_vis_traj():
+    global traj_list
+    xy_range = 1024
+    points = process_points(traj_list)
+    imgs = []
+    for idx in range(16):
+        bg_img = np.ones((1024, 1024, 3), dtype=np.uint8) * 255
+        for i in range(15):
+            p = points[i]
+            p1 = points[i+1]
+            cv2.line(bg_img, p, p1, (255, 0, 0), 2)
+
+            if i == idx:
+                cv2.circle(bg_img, p, 2, (0, 255, 0), 20)
+
+        if idx==(15):
+            cv2.circle(bg_img, points[-1], 2, (0, 255, 0), 20)
+        
+        imgs.append(bg_img.astype(np.uint8))
+
+    # size = (512, 512)
+    fps = 10
+    path = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False).name
+    writer = imageio.get_writer(path, format='mp4', mode='I', fps=fps)
+    for img in imgs:
+        writer.append_data(img)
+
+    writer.close()
+
+    vis_step3_prompt_generate = True
+    vis_prompt = True
+    vis_num_samples = True
+    vis_seed = True
+    vis_start = True
+    vis_gen_video = True
+    return path, gr.update(visible=vis_step3_prompt_generate), \
+                gr.update(visible=vis_prompt), \
+                gr.update(visible=vis_num_samples), \
+                gr.update(visible=vis_seed), \
+                gr.update(visible=vis_start), \
+                gr.update(visible=vis_gen_video, value=None)
+
+def display_camera_info(camera_dict, camera_mode=None):
+    if camera_dict['complex'] is not None:
+        res = f"complex : {camera_dict['complex']}. "
+    else:
+        res = ""
+        res += f"motion : {[_ for _ in camera_dict['motion']]}. "
+        res += f"speed : {camera_dict['speed']}. "
+        if camera_mode == 'Custom Camera Poses':
+            res += f"mode : {camera_dict['mode']}. "
+    return res
+
+def add_traj_point(evt: gr.SelectData, ):
+    global traj_list
+    traj_list.append(evt.index)
+    traj_str = [f"{traj}" for traj in traj_list]
+    return ", ".join(traj_str)
+
+def add_provided_traj(traj_name):
+    global traj_list
+    traj_list = get_provided_traj(traj_name)
+    traj_str = [f"{traj}" for traj in traj_list]
+    return ", ".join(traj_str)
+
+def add_camera_motion(camera_motion, camera_mode):  
+    global camera_dict
+    if camera_dict['complex'] is not None:
+        camera_dict['complex'] = None
+    if camera_mode == 'Custom Camera Poses' and len(camera_dict['motion']) <2:
+        camera_dict['motion'].append(camera_motion)
+    else:
+        camera_dict['motion']=[camera_motion]
+    
+    return display_camera_info(camera_dict, camera_mode)
+
+def add_complex_camera_motion(camera_motion):
+    global camera_dict
+    camera_dict['complex']=camera_motion
+    return display_camera_info(camera_dict)
+
+def change_camera_mode(combine_type, camera_mode):
+    global camera_dict
+    camera_dict['mode'] = combine_type
+
+    return display_camera_info(camera_dict, camera_mode)
+
+def change_camera_speed(camera_speed):
+    global camera_dict
+    camera_dict['speed'] = camera_speed
+    return display_camera_info(camera_dict)
+
+def reset_camera():
+    global camera_dict
+    camera_dict = {
+                    "motion":[],
+                    "mode": "Customized Mode 1: First A then B",
+                    "speed": 1.0,
+                    "complex": None
+                    }   
+    return display_camera_info(camera_dict)
+
+
+def fn_traj_droplast():
+    global traj_list
+
+    if traj_list:
+        traj_list.pop()
+
+    if traj_list:
+        traj_str = [f"{traj}" for traj in traj_list]
+        return ", ".join(traj_str)
+    else:   
+        return "Click to specify trajectory"
+
+def fn_traj_reset():
+    global traj_list
+    traj_list = []
+    return "Click to specify trajectory"
+
+###########################################
+model_path='./checkpoints/motionctrl.pth'
+config_path='./configs/inference/config_both.yaml'
+
+config = OmegaConf.load(config_path)
+model_config = config.pop("model", OmegaConf.create())
+model = instantiate_from_config(model_config)
+model = model.cuda()
+
+model = load_model_checkpoint(model, model_path)
+model.eval()
+
+
+def model_run(prompts, infer_mode, seed, n_samples):
+    global traj_list
+    global camera_dict
+
+    RT = process_camera(camera_dict).reshape(-1,12)
+    traj_flow = process_traj(traj_list).transpose(3,0,1,2)
+    print(prompts)
+    print(RT.shape)
+    print(traj_flow.shape)
+
+    noise_shape = [1, 4, 16, 32, 32]
+    unconditional_guidance_scale = 7.5
+    unconditional_guidance_scale_temporal = None
+    # n_samples = 1
+    ddim_steps= 50
+    ddim_eta=1.0
+    cond_T=800
+
+    if n_samples < 1:
+        n_samples = 1
+    if n_samples > 4:
+        n_samples = 4
+
+    seed_everything(seed)
+
+    if infer_mode == MODE[0]:
+        camera_poses = RT
+        camera_poses = torch.tensor(camera_poses).float().cuda()
+        camera_poses = camera_poses.unsqueeze(0)
+        trajs = None
+    elif infer_mode == MODE[1]:
+        trajs = traj_flow
+        trajs = torch.tensor(trajs).float().cuda()
+        trajs = trajs.unsqueeze(0)
+        camera_poses = None
+    else:
+        camera_poses = RT
+        trajs = traj_flow
+        camera_poses = torch.tensor(camera_poses).float().cuda()
+        trajs = torch.tensor(trajs).float().cuda()
+        camera_poses = camera_poses.unsqueeze(0)
+        trajs = trajs.unsqueeze(0)
+
+    ddim_sampler = DDIMSampler(model)
+    batch_size = noise_shape[0]
+    ## get condition embeddings (support single prompt only)
+    if isinstance(prompts, str):
+        prompts = [prompts]
+
+    for i in range(len(prompts)):
+        prompts[i] = f'{prompts[i]}, {post_prompt}'
+
+    cond = model.get_learned_conditioning(prompts)
+    if camera_poses is not None:
+        RT = camera_poses[..., None]
+    else:
+        RT = None
+
+    if trajs is not None:
+        traj_features = model.get_traj_features(trajs)
+    else:
+        traj_features = None
+
+    if unconditional_guidance_scale != 1.0:
+        # prompts = batch_size * [""]
+        prompts = batch_size * [DEFAULT_NEGATIVE_PROMPT]
+        uc = model.get_learned_conditioning(prompts)
+        if traj_features is not None:
+            un_motion = model.get_traj_features(torch.zeros_like(trajs))
+        else:
+            un_motion = None
+        uc = {"features_adapter": un_motion, "uc": uc}
+    else:
+        uc = None
+
+    batch_variants = []
+    for _ in range(n_samples):
+        if ddim_sampler is not None:
+            samples, _ = ddim_sampler.sample(S=ddim_steps,
+                                            conditioning=cond,
+                                            batch_size=noise_shape[0],
+                                            shape=noise_shape[1:],
+                                            verbose=False,
+                                            unconditional_guidance_scale=unconditional_guidance_scale,
+                                            unconditional_conditioning=uc,
+                                            eta=ddim_eta,
+                                            temporal_length=noise_shape[2],
+                                            conditional_guidance_scale_temporal=unconditional_guidance_scale_temporal,
+                                            features_adapter=traj_features,
+                                            pose_emb=RT,
+                                            cond_T=cond_T
+                                            )        
+        ## reconstruct from latent to pixel space
+        batch_images = model.decode_first_stage(samples)
+        batch_variants.append(batch_images)
+    ## variants, batch, c, t, h, w
+    batch_variants = torch.stack(batch_variants, dim=1)
+    batch_variants = batch_variants[0]
+    
+    # file_path = save_results(batch_variants, "MotionCtrl", "gradio_temp", fps=10)
+    file_path = save_results(batch_variants, fps=10)
+    print(file_path)
+
+    return gr.update(value=file_path, width=256*n_samples, height=256)
+
+    # return file_path
+
+def save_results(video, fps=10):
+    
+    # b,c,t,h,w
+    video = video.detach().cpu()
+    video = torch.clamp(video.float(), -1., 1.)
+    n = video.shape[0]
+    video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w
+    frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n)) for framesheet in video] #[3, 1*h, n*w]
+    grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w]
+    grid = (grid + 1.0) / 2.0
+    grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1) # [t, h, w*n, 3]
+    
+    path = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False).name
+
+    writer = imageio.get_writer(path, format='mp4', mode='I', fps=fps)
+    for i in range(grid.shape[0]):
+        img = grid[i].numpy()
+        writer.append_data(img)
+
+    writer.close()
+
+    return path
+
+def visualized_step2(infer_mode):
+
+    # reset
+    reset_camera()
+    fn_traj_reset()
+
+    # camera motion control
+    vis_basic_camera_motion = False
+    vis_basic_camera_motion_des = False
+    vis_custom_camera_motion = False
+    vis_custom_run_status = False
+    vis_complex_camera_motion = False
+    vis_complex_camera_motion_des = False
+    vis_U = False
+    vis_D = False
+    vis_L = False
+    vis_R = False
+    vis_I = False
+    vis_O = False
+    vis_ACW = False
+    vis_CW = False
+    vis_combine1 = False
+    vis_combine2 = False
+    vis_speed = False
+
+    vis_Pose_1, vis_Pose_2, vis_Pose_3, vis_Pose_4 = False, False, False, False
+    vis_Pose_5, vis_Pose_6, vis_Pose_7, vis_Pose_8 = False, False, False, False
+
+    vis_camera_args = False
+    vis_camera_reset = False
+    vis_camera_vis = False
+    vis_vis_camera = False
+
+    # object motion control
+    vis_provided_traj = False
+    vis_provided_traj_des = False
+    vis_draw_yourself = False
+    vis_draw_run_status = False
+
+    vis_traj_1, vis_traj_2, vis_traj_3, vis_traj_4 = False, False, False, False
+    vis_traj_5, vis_traj_6, vis_traj_7, vis_traj_8 = False, False, False, False
+
+    traj_args = False
+    traj_droplast, traj_reset = False, False
+    traj_vis = False
+    traj_input, vis_traj = False, False
+
+
+    # generate video
+    vis_step3_prompt_generate = False
+    vis_prompt = False
+    vis_num_samples = False
+    vis_seed = False
+    vis_start = False
+    vis_gen_video = False
+
+    if infer_mode == MODE[0]:
+        vis_step2_camera_motion = True
+        vis_step2_camera_motion_des = True
+        vis_camera_mode = True
+        vis_camera_info = True
+
+        vis_step2_object_motion = False
+        vis_step2_object_motion_des = False
+        vis_traj_mode = False
+        vis_traj_info = False
+
+        step2_camera_object_motion = False
+        step2_camera_object_motion_des = False
+
+    elif infer_mode == MODE[1]:
+        vis_step2_camera_motion = False
+        vis_step2_camera_motion_des = False
+        vis_camera_mode = False
+        vis_camera_info = False
+
+        vis_step2_object_motion = True
+        vis_step2_object_motion_des = True
+        vis_traj_mode = True
+        vis_traj_info = True
+
+        step2_camera_object_motion = False
+        step2_camera_object_motion_des = False
+    else: #infer_mode == MODE[2]:
+        vis_step2_camera_motion = False
+        vis_step2_camera_motion_des = False
+        vis_camera_mode = False
+        vis_camera_info = False
+    
+        vis_step2_object_motion = False
+        vis_step2_object_motion_des = False
+        vis_traj_mode = False
+        vis_traj_info = False
+
+        step2_camera_object_motion = True
+        step2_camera_object_motion_des = True
+    
+        vis_basic_camera_motion = True
+        vis_basic_camera_motion_des = True
+        vis_U = True
+        vis_D = True
+        vis_L = True
+        vis_R = True
+        vis_I = True
+        vis_O = True
+        vis_ACW = True
+        vis_CW = True
+        vis_speed = True
+
+        vis_camera_args = True
+        vis_camera_reset = True
+        vis_camera_vis = True
+        vis_vis_camera = True
+        
+    
+    return gr.update(visible=vis_step2_camera_motion), \
+            gr.update(visible=vis_step2_camera_motion_des), \
+            gr.update(visible=vis_camera_mode), \
+            gr.update(visible=vis_camera_info), \
+            gr.update(visible=vis_basic_camera_motion), \
+            gr.update(visible=vis_basic_camera_motion_des), \
+            gr.update(visible=vis_custom_camera_motion), \
+            gr.update(visible=vis_custom_run_status), \
+            gr.update(visible=vis_complex_camera_motion), \
+            gr.update(visible=vis_complex_camera_motion_des), \
+            gr.update(visible=vis_U), gr.update(visible=vis_D), gr.update(visible=vis_L), gr.update(visible=vis_R), \
+            gr.update(visible=vis_I), gr.update(visible=vis_O), gr.update(visible=vis_ACW), gr.update(visible=vis_CW), \
+            gr.update(visible=vis_combine1), gr.update(visible=vis_combine2), \
+            gr.update(visible=vis_speed), \
+            gr.update(visible=vis_Pose_1), gr.update(visible=vis_Pose_2), gr.update(visible=vis_Pose_3), gr.update(visible=vis_Pose_4), \
+            gr.update(visible=vis_Pose_5), gr.update(visible=vis_Pose_6), gr.update(visible=vis_Pose_7), gr.update(visible=vis_Pose_8), \
+            gr.update(visible=vis_camera_args, value=None), \
+            gr.update(visible=vis_camera_reset), gr.update(visible=vis_camera_vis), \
+            gr.update(visible=vis_vis_camera, value=None), \
+            gr.update(visible=vis_step2_object_motion), \
+            gr.update(visible=vis_step2_object_motion_des), \
+            gr.update(visible=vis_traj_mode), \
+            gr.update(visible=vis_traj_info), \
+            gr.update(visible=vis_provided_traj), \
+            gr.update(visible=vis_provided_traj_des), \
+            gr.update(visible=vis_draw_yourself), \
+            gr.update(visible=vis_draw_run_status), \
+            gr.update(visible=vis_traj_1), gr.update(visible=vis_traj_2), gr.update(visible=vis_traj_3), gr.update(visible=vis_traj_4), \
+            gr.update(visible=vis_traj_5), gr.update(visible=vis_traj_6), gr.update(visible=vis_traj_7), gr.update(visible=vis_traj_8), \
+            gr.update(visible=traj_args), \
+            gr.update(visible=traj_droplast), gr.update(visible=traj_reset), \
+            gr.update(visible=traj_vis), \
+            gr.update(visible=traj_input), gr.update(visible=vis_traj, value=None), \
+            gr.update(visible=step2_camera_object_motion), \
+            gr.update(visible=step2_camera_object_motion_des), \
+            gr.update(visible=vis_step3_prompt_generate), \
+            gr.update(visible=vis_prompt), \
+            gr.update(visible=vis_num_samples), \
+            gr.update(visible=vis_seed), \
+            gr.update(visible=vis_start), \
+            gr.update(visible=vis_gen_video)
+
+def visualized_camera_poses(step2_camera_motion):
+    reset_camera()
+
+    # generate video
+    vis_step3_prompt_generate = False
+    vis_prompt = False
+    vis_num_samples = False
+    vis_seed = False
+    vis_start = False
+    vis_gen_video = False
+
+    if step2_camera_motion == CAMERA_MOTION_MODE[0]:
+        vis_basic_camera_motion = True
+        vis_basic_camera_motion_des = True
+        vis_custom_camera_motion = False
+        vis_custom_run_status = False
+        vis_complex_camera_motion = False
+        vis_complex_camera_motion_des = False
+        vis_U = True
+        vis_D = True
+        vis_L = True
+        vis_R = True
+        vis_I = True
+        vis_O = True
+        vis_ACW = True
+        vis_CW = True
+        vis_combine1 = False
+        vis_combine2 = False
+        vis_speed = True
+
+        vis_Pose_1, vis_Pose_2, vis_Pose_3, vis_Pose_4 = False, False, False, False
+        vis_Pose_5, vis_Pose_6, vis_Pose_7, vis_Pose_8 = False, False, False, False
+
+    elif step2_camera_motion == CAMERA_MOTION_MODE[1]:
+        vis_basic_camera_motion = False
+        vis_basic_camera_motion_des = False
+        vis_custom_camera_motion = False
+        vis_custom_run_status = False
+        vis_complex_camera_motion = True
+        vis_complex_camera_motion_des = True
+        vis_U = False
+        vis_D = False
+        vis_L = False
+        vis_R = False
+        vis_I = False
+        vis_O = False
+        vis_ACW = False
+        vis_CW = False
+        vis_combine1 = False
+        vis_combine2 = False
+        vis_speed = False
+
+        vis_Pose_1, vis_Pose_2, vis_Pose_3, vis_Pose_4 = True, True, True, True
+        vis_Pose_5, vis_Pose_6, vis_Pose_7, vis_Pose_8 = True, True, True, True
+
+    else: # step2_camera_motion = CAMERA_MOTION_MODE[2]:
+        vis_basic_camera_motion = False
+        vis_basic_camera_motion_des = False
+        vis_custom_camera_motion = True
+        vis_custom_run_status = True
+        vis_complex_camera_motion = False
+        vis_complex_camera_motion_des = False
+        vis_U = True
+        vis_D = True
+        vis_L = True
+        vis_R = True
+        vis_I = True
+        vis_O = True
+        vis_ACW = True
+        vis_CW = True
+        vis_combine1 = True
+        vis_combine2 = True
+        vis_speed = True
+
+        vis_Pose_1, vis_Pose_2, vis_Pose_3, vis_Pose_4 = False, False, False, False
+        vis_Pose_5, vis_Pose_6, vis_Pose_7, vis_Pose_8 = False, False, False, False
+
+    vis_camera_args = True
+    vis_camera_reset = True
+    vis_camera_vis = True
+    vis_vis_camera = True
+
+    return gr.update(visible=vis_basic_camera_motion), \
+            gr.update(visible=vis_basic_camera_motion_des), \
+            gr.update(visible=vis_custom_camera_motion), \
+            gr.update(visible=vis_custom_run_status), \
+            gr.update(visible=vis_complex_camera_motion), \
+            gr.update(visible=vis_complex_camera_motion_des), \
+            gr.update(visible=vis_U), gr.update(visible=vis_D), gr.update(visible=vis_L), gr.update(visible=vis_R), \
+            gr.update(visible=vis_I), gr.update(visible=vis_O), gr.update(visible=vis_ACW), gr.update(visible=vis_CW), \
+            gr.update(visible=vis_combine1), gr.update(visible=vis_combine2), \
+            gr.update(visible=vis_speed), \
+            gr.update(visible=vis_Pose_1), gr.update(visible=vis_Pose_2), gr.update(visible=vis_Pose_3), gr.update(visible=vis_Pose_4), \
+            gr.update(visible=vis_Pose_5), gr.update(visible=vis_Pose_6), gr.update(visible=vis_Pose_7), gr.update(visible=vis_Pose_8), \
+            gr.update(visible=vis_camera_args, value=None), \
+            gr.update(visible=vis_camera_reset), gr.update(visible=vis_camera_vis), \
+            gr.update(visible=vis_vis_camera, value=None), \
+            gr.update(visible=vis_step3_prompt_generate), \
+            gr.update(visible=vis_prompt), \
+            gr.update(visible=vis_num_samples), \
+            gr.update(visible=vis_seed), \
+            gr.update(visible=vis_start), \
+            gr.update(visible=vis_gen_video)
+
+def visualized_traj_poses(step2_object_motion):
+    
+    fn_traj_reset()
+
+    # generate video
+    vis_step3_prompt_generate = False
+    vis_prompt = False
+    vis_num_samples = False
+    vis_seed = False
+    vis_start = False
+    vis_gen_video = False
+
+    if step2_object_motion == "Provided Trajectory":
+        vis_provided_traj = True
+        vis_provided_traj_des = True
+        vis_draw_yourself = False
+        vis_draw_run_status = False
+
+        vis_traj_1, vis_traj_2, vis_traj_3, vis_traj_4 = True, True, True, True
+        vis_traj_5, vis_traj_6, vis_traj_7, vis_traj_8 = True, True, True, True
+
+        traj_args = True
+        traj_droplast, traj_reset = False, True
+        traj_vis = True
+        traj_input, vis_traj = False, True
+
+
+    elif step2_object_motion == "Custom Trajectory":
+        vis_provided_traj = False
+        vis_provided_traj_des = False
+        vis_draw_yourself = True
+        vis_draw_run_status = True
+
+        vis_traj_1, vis_traj_2, vis_traj_3, vis_traj_4 = False, False, False, False
+        vis_traj_5, vis_traj_6, vis_traj_7, vis_traj_8 = False, False, False, False
+
+        traj_args = True
+        traj_droplast, traj_reset = True, True
+        traj_vis = True
+        traj_input, vis_traj = True, True
+
+    return gr.update(visible=vis_provided_traj), \
+            gr.update(visible=vis_provided_traj_des), \
+            gr.update(visible=vis_draw_yourself), \
+            gr.update(visible=vis_draw_run_status), \
+            gr.update(visible=vis_traj_1), gr.update(visible=vis_traj_2), gr.update(visible=vis_traj_3), gr.update(visible=vis_traj_4), \
+            gr.update(visible=vis_traj_5), gr.update(visible=vis_traj_6), gr.update(visible=vis_traj_7), gr.update(visible=vis_traj_8), \
+            gr.update(visible=traj_args), \
+            gr.update(visible=traj_droplast), gr.update(visible=traj_reset), \
+            gr.update(visible=traj_vis), \
+            gr.update(visible=traj_input), gr.update(visible=vis_traj, value=None), \
+            gr.update(visible=vis_step3_prompt_generate), \
+            gr.update(visible=vis_prompt), \
+            gr.update(visible=vis_num_samples), \
+            gr.update(visible=vis_seed), \
+            gr.update(visible=vis_start), \
+            gr.update(visible=vis_gen_video)
+
+def main(args):
+    demo = gr.Blocks()
+    with demo:
+
+        gr.Markdown(title)
+        gr.Markdown(description)
+
+        # state = gr.State({
+        #     "mode": "camera_only",
+        #     "camera_input": [],
+        #     "traj_input": [],
+        # })
+
+        with gr.Column():
+            '''
+            # step 0: select based model.
+            gr.Markdown("## Step0: Selecting the model", show_label=False)
+            gr.Markdown( f'- {BASE_MODEL[0]}: **MotionCtrl** deployed on {BASE_MODEL[0]}', show_label=False)
+            gr.Markdown( f'- {BASE_MODEL[1]}: **MotionCtrl** deployed on {BASE_MODEL[1]}', show_label=False)
+            gr.Markdown( f'- {BASE_MODEL[2]}: **MotionCtrl** deployed on {BASE_MODEL[2]}', show_label=False)
+            gr.Markdown( f'- **Only the model that deployed on {BASE_MODEL[0]} is avalible now. MotionCtrl models deployed on {BASE_MODEL[1]} and {BASE_MODEL[2]} are coming soon.**', show_label=False)
+            gr.Radio(choices=BASE_MODEL, value=BASE_MODEL[0], label="Based Model", interactive=False)
+            '''
+
+            # step 1: select motion control mode
+            gr.Markdown("## Step 1/3: Selecting the motion control mode", show_label=False)
+            gr.Markdown( f'- {MODE[0]}: Control the camera motion only', show_label=False)
+            gr.Markdown( f'- {MODE[1]}: Control the object motion only', show_label=False)
+            gr.Markdown( f'- {MODE[2]}: Control both the camera and object motion', show_label=False)
+            gr.Markdown( f'- Click `Proceed` to go into next step', show_label=False)
+            infer_mode = gr.Radio(choices=MODE, value=MODE[0], label="Motion Control Mode", interactive=True)
+            mode_info = gr.Button(value="Proceed")
+
+            # step2 - camera + object motion control
+            step2_camera_object_motion  = gr.Markdown("---\n## Step 2/3: Select the camera poses and trajectory", show_label=False, visible=False)
+            step2_camera_object_motion_des = gr.Markdown(f"\n 1. Select a basic camera pose. \
+                                                            \n 2. Select a provided trajectory or draw the trajectory yourself.",
+                                                        show_label=False, visible=False)
+        
+            # step2 - camera motion control
+            step2_camera_motion = gr.Markdown("---\n## Step 2/3: Select the camera poses", show_label=False, visible=False)
+            step2_camera_motion_des = gr.Markdown(f"\n - {CAMERA_MOTION_MODE[0]}: Including 8 basic camera poses, such as pan up, pan down, zoom in, and zoom out. \
+                                                    \n - {CAMERA_MOTION_MODE[1]}: Complex camera poses extracted from the real videos. \
+                                                    \n - {CAMERA_MOTION_MODE[2]}: You can customize complex camera poses yourself by combining or fusing two of the eight basic camera poses. \
+                                                    \n - Click `Proceed` to go into next step", 
+                                                  show_label=False, visible=False)
+            camera_mode = gr.Radio(choices=CAMERA_MOTION_MODE, value=CAMERA_MOTION_MODE[0], label="Camera Motion Control Mode", interactive=True, visible=False)
+            camera_info = gr.Button(value="Proceed", visible=False)
+
+            with gr.Row():
+                with gr.Column():
+                    # step2.1 - camera motion control - basic
+                    basic_camera_motion = gr.Markdown("---\n### Basic Camera Poses", show_label=False, visible=False)
+                    basic_camera_motion_des = gr.Markdown(f"\n 1. Click one of the basic camera poses, such as `Pan Up`; \
+                                                            \n 2. Slide the `Motion speed` to get a speed value. The large the value, the fast the camera motion; \
+                                                            \n 3. Click `Visualize Camera and Proceed` to visualize the camera poses and go proceed; \
+                                                            \n 4. Click `Reset Camera` to reset the camera poses (If needed). ",
+                                                        show_label=False, visible=False)
+                    
+                    
+                    # step2.2 - camera motion control - provided complex
+                    complex_camera_motion = gr.Markdown("---\n### Provided Complex Camera Poses", show_label=False, visible=False)
+                    complex_camera_motion_des = gr.Markdown(f"\n 1. Click one of the complex camera poses, such as `Pose_1`; \
+                                                            \n 2. Click `Visualize Camera and Proceed` to visualize the camera poses and go proceed; \
+                                                            \n 3. Click `Reset Camera` to reset the camera poses (If needed). ",
+                                                        show_label=False, visible=False)
+
+                    # step2.3 - camera motion control - custom
+                    custom_camera_motion = gr.Markdown("---\n### Custom Camera Poses", show_label=False, visible=False)
+                    custom_run_status = gr.Markdown(f"\n 1. Click two of the basic camera poses, such as `Pan Up` and `Pan Left`; \
+                                                    \n 2. Click `Customized Mode 1: First A then B` or `Customized Mode 1: First A then B` \
+                                                    \n - `Customized Mode 1: First A then B`: The camera first `Pan Up` and then `Pan Left`; \
+                                                    \n - `Customized Mode 2: Both A and B`: The camera move towards the upper left corner; \
+                                                    \n 3. Slide the `Motion speed` to get a speed value. The large the value, the fast the camera motion; \
+                                                    \n 4. Click `Visualize Camera and Proceed` to visualize the camera poses and go proceed; \
+                                                    \n 5. Click `Reset Camera` to reset the camera poses (If needed). ",
+                                                        show_label=False, visible=False)
+
+                    gr.HighlightedText(value=[("",""), ("1. Select two of the basic camera poses; 2. Select Customized Mode 1 OR Customized Mode 2. 3. Visualized Camera to show the customized camera poses", "Normal")],
+                                                        color_map={"Normal": "green", "Error": "red", "Clear clicks": "gray", "Add mask": "green", "Remove mask": "red"}, visible=False)
+                    
+                    with gr.Row():
+                        U = gr.Button(value="Pan Up", visible=False)
+                        D = gr.Button(value="Pan Down", visible=False)
+                        L = gr.Button(value="Pan Left", visible=False)
+                        R = gr.Button(value="Pan Right", visible=False)
+                    with gr.Row():
+                        I = gr.Button(value="Zoom In", visible=False)
+                        O = gr.Button(value="Zoom Out", visible=False)
+                        ACW = gr.Button(value="ACW", visible=False)
+                        CW = gr.Button(value="CW", visible=False)
+                    
+                    with gr.Row():
+                        combine1 = gr.Button(value="Customized Mode 1: First A then B", visible=False)
+                        combine2 = gr.Button(value="Customized Mode 2: Both A and B", visible=False)
+
+                    with gr.Row():    
+                        speed = gr.Slider(minimum=0, maximum=2, step=0.2, label="Motion Speed", value=1.0, visible=False)
+
+                    with gr.Row():
+                        Pose_1 = gr.Button(value="Pose_1", visible=False)
+                        Pose_2 = gr.Button(value="Pose_2", visible=False)
+                        Pose_3 = gr.Button(value="Pose_3", visible=False)
+                        Pose_4 = gr.Button(value="Pose_4", visible=False)
+                    with gr.Row():
+                        Pose_5 = gr.Button(value="Pose_5", visible=False)
+                        Pose_6 = gr.Button(value="Pose_6", visible=False)
+                        Pose_7 = gr.Button(value="Pose_7", visible=False)
+                        Pose_8 = gr.Button(value="Pose_8", visible=False)
+                
+                    with gr.Row():
+                        camera_args = gr.Textbox(value="Camera Type", label="Camera Type", visible=False)
+                    with gr.Row():
+                        camera_vis= gr.Button(value="Visualize Camera and Proceed", visible=False)
+                        camera_reset = gr.Button(value="Reset Camera", visible=False)
+                with gr.Column():
+                    vis_camera = gr.Plot(fig, label='Camera Poses', visible=False)
+
+            # step2 - object motion control
+            step2_object_motion = gr.Markdown("---\n## Step 2/3: Select a Provided Trajectory of Draw Yourself", show_label=False, visible=False)
+            step2_object_motion_des = gr.Markdown(f"\n - {OBJECT_MOTION_MODE[0]}: We provide some example trajectories. You can select one of them directly. \
+                                                    \n - {OBJECT_MOTION_MODE[1]}: Draw the trajectory yourself. \
+                                                    \n - Click `Proceed` to go into next step", 
+                                                  show_label=False, visible=False)
+
+            object_mode = gr.Radio(choices=OBJECT_MOTION_MODE, value=OBJECT_MOTION_MODE[0], label="Motion Control Mode", interactive=True, visible=False)
+            object_info = gr.Button(value="Proceed", visible=False)
+
+            with gr.Row():
+                with gr.Column():
+                    # step2.1 - object motion control - provided
+                    provided_traj = gr.Markdown("---\n### Provided Trajectory", show_label=False, visible=False)
+                    provided_traj_des = gr.Markdown(f"\n 1. Click one of the provided trajectories, such as `horizon_1`; \
+                                                      \n 2. Click `Visualize Trajectory and Proceed` to visualize the trajectory and go proceed; \
+                                                        \n 3. Click `Reset Trajectory` to reset the trajectory (If needed). ",
+                                                        show_label=False, visible=False)
+
+                    # step2.2 - object motion control - draw yourself
+                    draw_traj = gr.Markdown("---\n### Draw Yourself", show_label=False, visible=False)
+                    draw_run_status = gr.Markdown(f"\n 1. Click the `Canvas` in the right to draw the trajectory. **Note that You have to click the canva many times. For time saving, \
+                                                  the click point will not appear in the canvas but its coordinates will be written in `Points of Trajectory`**; \
+                                                  \n 2. Click `Visualize Trajectory and Proceed` to visualize the trajectory and go proceed; \
+                                                  \n 3. Click `Reset Trajectory` to reset the trajectory (If needed). ",
+                                                    show_label=False, visible=False)
+                    
+                    with gr.Row():
+                        traj_1 = gr.Button(value="horizon_1", visible=False)
+                        traj_2 = gr.Button(value="swaying_1", visible=False)
+                        traj_3 = gr.Button(value="swaying_2", visible=False)
+                        traj_4 = gr.Button(value="swaying_3", visible=False)
+                    with gr.Row():
+                        traj_5 = gr.Button(value="curve_1", visible=False)
+                        traj_6 = gr.Button(value="curve_2", visible=False)
+                        traj_7 = gr.Button(value="curve_3", visible=False)
+                        traj_8 = gr.Button(value="curve_4", visible=False)
+
+                    traj_args = gr.Textbox(value="", label="Points of Trajectory", visible=False)
+                    with gr.Row():
+                        traj_vis = gr.Button(value="Visualize Trajectory and Proceed", visible=False)
+                        traj_reset = gr.Button(value="Reset Trajectory", visible=False)
+                        traj_droplast = gr.Button(value="Drop Last Point", visible=False)
+                
+                with gr.Column():
+                    traj_input = gr.Image("assets/traj_layout.png", tool='sketch', source="canvas", 
+                                    width=256, height=256,
+                                    label="Canvas for Drawing", visible=False)
+                    vis_traj = gr.Video(value=None, label="Trajectory", visible=False, width=256, height=256)
+
+
+
+            # step3 - Add prompt and Generate videos
+            with gr.Row():
+                with gr.Column():
+                    step3_prompt_generate = gr.Markdown("---\n## Step 3/3: Add prompt and Generate videos", show_label=False, visible=False)
+                    prompt = gr.Textbox(value="a dog sitting on grass", label="Prompt", interactive=True, visible=False)
+                    n_samples = gr.Number(value=3, precision=0, interactive=True, label="n_samples", visible=False)
+                    seed = gr.Number(value=1234, precision=0, interactive=True, label="Seed", visible=False)
+                    start = gr.Button(value="Start generation !", visible=False)
+                with gr.Column():
+                    gen_video = gr.Video(value=None, label="Generate Video", visible=False)
+
+        mode_info.click(
+            fn=visualized_step2,
+            inputs=[infer_mode],
+            outputs=[step2_camera_motion, 
+                     step2_camera_motion_des,
+                     camera_mode, 
+                     camera_info,
+
+                     basic_camera_motion,
+                     basic_camera_motion_des,
+                     custom_camera_motion,
+                     custom_run_status,
+                     complex_camera_motion,
+                     complex_camera_motion_des,
+                     U, D, L, R, 
+                     I, O, ACW, CW, 
+                     combine1, combine2,
+                     speed, 
+                     Pose_1, Pose_2, Pose_3, Pose_4, 
+                     Pose_5, Pose_6, Pose_7, Pose_8,
+                     camera_args, 
+                     camera_reset, camera_vis,
+                     vis_camera,
+
+                     step2_object_motion,
+                     step2_object_motion_des,
+                     object_mode,
+                     object_info,
+
+                    provided_traj,
+                    provided_traj_des,
+                    draw_traj,
+                    draw_run_status,
+                    traj_1, traj_2, traj_3, traj_4,
+                    traj_5, traj_6, traj_7, traj_8,
+                    traj_args,
+                    traj_droplast, traj_reset,
+                    traj_vis,
+                    traj_input, vis_traj,
+
+                    step2_camera_object_motion,
+                    step2_camera_object_motion_des,
+
+                     step3_prompt_generate, prompt, n_samples, seed, start, gen_video,
+                     
+                     ],
+        )
+
+        camera_info.click(
+            fn=visualized_camera_poses,
+            inputs=[camera_mode],
+            outputs=[basic_camera_motion,
+                     basic_camera_motion_des,
+                     custom_camera_motion,
+                     custom_run_status,
+                     complex_camera_motion,
+                     complex_camera_motion_des,
+                     U, D, L, R, 
+                     I, O, ACW, CW, 
+                     combine1, combine2,
+                     speed, 
+                     Pose_1, Pose_2, Pose_3, Pose_4, 
+                     Pose_5, Pose_6, Pose_7, Pose_8,
+                     camera_args, 
+                     camera_reset, camera_vis,
+                     vis_camera,
+                     step3_prompt_generate, prompt, n_samples, seed, start, gen_video],
+        )
+
+        object_info.click(
+            fn=visualized_traj_poses,
+            inputs=[object_mode],
+            outputs=[provided_traj,
+                     provided_traj_des,
+                        draw_traj,
+                        draw_run_status,
+                        traj_1, traj_2, traj_3, traj_4,
+                        traj_5, traj_6, traj_7, traj_8,
+                        traj_args,
+                        traj_droplast, traj_reset,
+                        traj_vis,
+                        traj_input, vis_traj,
+                        step3_prompt_generate, prompt, n_samples, seed, start, gen_video,],
+        )
+
+
+        U.click(fn=add_camera_motion, inputs=[U, camera_mode], outputs=camera_args)
+        D.click(fn=add_camera_motion, inputs=[D, camera_mode], outputs=camera_args)
+        L.click(fn=add_camera_motion, inputs=[L, camera_mode], outputs=camera_args)
+        R.click(fn=add_camera_motion, inputs=[R, camera_mode], outputs=camera_args)
+        I.click(fn=add_camera_motion, inputs=[I, camera_mode], outputs=camera_args)
+        O.click(fn=add_camera_motion, inputs=[O, camera_mode], outputs=camera_args)
+        ACW.click(fn=add_camera_motion, inputs=[ACW, camera_mode], outputs=camera_args)
+        CW.click(fn=add_camera_motion, inputs=[CW, camera_mode], outputs=camera_args)
+        speed.change(fn=change_camera_speed, inputs=speed, outputs=camera_args)
+        camera_reset.click(fn=reset_camera, inputs=None, outputs=[camera_args])
+
+        combine1.click(fn=change_camera_mode, inputs=[combine1, camera_mode], outputs=camera_args)
+        combine2.click(fn=change_camera_mode, inputs=[combine2, camera_mode], outputs=camera_args)
+
+        camera_vis.click(fn=fn_vis_camera, inputs=[infer_mode], outputs=[vis_camera, object_mode, object_info, step3_prompt_generate, prompt, n_samples, seed, start, gen_video])
+
+        Pose_1.click(fn=add_complex_camera_motion, inputs=Pose_1, outputs=camera_args)
+        Pose_2.click(fn=add_complex_camera_motion, inputs=Pose_2, outputs=camera_args)
+        Pose_3.click(fn=add_complex_camera_motion, inputs=Pose_3, outputs=camera_args)
+        Pose_4.click(fn=add_complex_camera_motion, inputs=Pose_4, outputs=camera_args)
+        Pose_5.click(fn=add_complex_camera_motion, inputs=Pose_5, outputs=camera_args)
+        Pose_6.click(fn=add_complex_camera_motion, inputs=Pose_6, outputs=camera_args)
+        Pose_7.click(fn=add_complex_camera_motion, inputs=Pose_7, outputs=camera_args)
+        Pose_8.click(fn=add_complex_camera_motion, inputs=Pose_8, outputs=camera_args)
+
+        traj_1.click(fn=add_provided_traj, inputs=traj_1, outputs=traj_args)
+        traj_2.click(fn=add_provided_traj, inputs=traj_2, outputs=traj_args)
+        traj_3.click(fn=add_provided_traj, inputs=traj_3, outputs=traj_args)
+        traj_4.click(fn=add_provided_traj, inputs=traj_4, outputs=traj_args)
+        traj_5.click(fn=add_provided_traj, inputs=traj_5, outputs=traj_args)
+        traj_6.click(fn=add_provided_traj, inputs=traj_6, outputs=traj_args)
+        traj_7.click(fn=add_provided_traj, inputs=traj_7, outputs=traj_args)
+        traj_8.click(fn=add_provided_traj, inputs=traj_8, outputs=traj_args)
+
+        traj_vis.click(fn=fn_vis_traj, inputs=None, outputs=[vis_traj, step3_prompt_generate, prompt, n_samples, seed, start, gen_video])
+        traj_input.select(fn=add_traj_point, inputs=None, outputs=traj_args)
+        traj_droplast.click(fn=fn_traj_droplast, inputs=None, outputs=traj_args)
+        traj_reset.click(fn=fn_traj_reset, inputs=None, outputs=traj_args)
+
+
+        start.click(fn=model_run, inputs=[prompt, infer_mode, seed, n_samples], outputs=gen_video)
+
+        gr.Markdown(article)
+
+    # demo.launch(server_name='0.0.0.0', share=False, server_port=args.port)
+    demo.queue(concurrency_count=1, max_size=10)
+    demo.launch()
+    
+
+
+if __name__=="__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--port", type=int, default=12345)
+    args = parser.parse_args()
+
+    main(args)

configs/inference/config_both.yaml

@@ -0,0 +1,104 @@
+model:
+  base_learning_rate: 0.0001
+  scale_lr: false
+  target: motionctrl.motionctrl.MotionCtrl
+  params:
+    # param for object motion control
+    omcm_config:
+      pretrained: ~
+      target: lvdm.modules.encoders.adapter.Adapter
+      params:
+        channels:
+        - 320
+        - 640
+        - 1280
+        - 1280
+        nums_rb: 2
+        cin: 128
+        sk: true
+        use_conv: false
+
+    linear_start: 0.00085
+    linear_end: 0.012
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: video
+    cond_stage_key: caption
+    cond_stage_trainable: false
+    conditioning_key: crossattn
+    image_size:
+    - 32
+    - 32
+    channels: 4
+    scale_by_std: false
+    scale_factor: 0.18215
+    use_ema: false
+    uncond_prob: 0.1
+    uncond_type: empty_seq
+    empty_params_only: true
+    scheduler_config:
+      target: utils.lr_scheduler.LambdaLRScheduler
+      interval: step
+      frequency: 100
+      params:
+        start_step: 0
+        final_decay_ratio: 0.01
+        decay_steps: 20000
+    unet_config:
+      target: lvdm.modules.networks.openaimodel3d_next.UNetModel
+      params:
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions:
+        - 4
+        - 2
+        - 1
+        num_res_blocks: 2
+        channel_mult:
+        - 1
+        - 2
+        - 4
+        - 4
+        num_head_channels: 64
+        transformer_depth: 1
+        context_dim: 1024
+        use_linear: true
+        use_checkpoint: true
+        temporal_conv: true
+        temporal_attention: true
+        temporal_selfatt_only: true
+        use_relative_position: false
+        use_causal_attention: false
+        temporal_length: 16
+        use_image_dataset: false
+        addition_attention: true
+    first_stage_config:
+      target: lvdm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+    cond_stage_config:
+      target: lvdm.modules.encoders.condition2.FrozenOpenCLIPEmbedder
+      params:
+        freeze: true
+        layer: penultimate
+   

configs/inference/run.sh

@@ -0,0 +1,52 @@
+
+config="configs/inference/config_both.yaml"
+ckpt='./checkpoints/motionctrl.pth'
+
+condtype='both'
+condtype='object_motion'
+condtype='camera_motion'
+
+cond_dir="examples/"
+
+res_dir="./outputs/"
+if [ ! -d $res_dir ]; then
+    mkdir -p $res_dir
+fi
+
+save_dir=$res_dir/$condtype'_seed'$seed
+
+use_ddp=0
+
+if [ $use_ddp == 0 ]; then
+CUDA_VISIBLE_DEVICES=7 python 'main/evaluation/motionctrl_inference.py' \
+--seed 1234 \
+--ckpt_path $ckpt \
+--base $config \
+--savedir $save_dir \
+--n_samples 5 \
+--bs 1 --height 256 --width 256 \
+--unconditional_guidance_scale 7.5 \
+--ddim_steps 50 \
+--ddim_eta 1.0 \
+--condtype $condtype \
+--cond_dir $cond_dir \
+# --save_imgs
+fi
+
+if [ $use_ddp == 1 ]; then
+python3 -m torch.distributed.launch \
+--nproc_per_node=3 --nnodes=1 --master_port=23466 \
+main/evaluation/ddp_wrapper.py \
+--module 'inference' \
+--seed 2000 \
+--ckpt_path $ckpt \
+--base $config \
+--savedir $res_dir/$name \
+--n_samples 3 \
+--bs 1 --height 256 --width 256 \
+--unconditional_guidance_scale 7.5 \
+--ddim_steps 50 \
+--ddim_eta 1.0 \
+--condtype $condtype \
+--cond_dir $cond_dir
+fi

examples/camera_poses/test_camera_018f7907401f2fef.json

@@ -0,0 +1 @@
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examples/camera_poses/test_camera_088b93f15ca8745d.json

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examples/camera_poses/test_camera_1424acd0007d40b5.json

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examples/camera_poses/test_camera_D.json

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examples/camera_poses/test_camera_I.json

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examples/camera_poses/test_camera_I_0.2x.json

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examples/camera_poses/test_camera_I_0.4x.json

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examples/camera_poses/test_camera_I_1.0x.json

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examples/camera_poses/test_camera_I_2.0x.json

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examples/camera_poses/test_camera_L.json

@@ -0,0 +1,226 @@
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examples/camera_poses/test_camera_O.json

@@ -0,0 +1,226 @@
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examples/camera_poses/test_camera_O_0.2x.json

@@ -0,0 +1,226 @@
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examples/camera_poses/test_camera_O_0.4x.json

@@ -0,0 +1,226 @@
+[
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examples/camera_poses/test_camera_O_1.0x.json

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examples/camera_poses/test_camera_O_2.0x.json

@@ -0,0 +1,226 @@
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examples/camera_poses/test_camera_R.json

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examples/camera_poses/test_camera_Round-RI-120.json

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examples/camera_poses/test_camera_Round-RI.json

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examples/camera_poses/test_camera_Round-RI_90.json

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examples/camera_poses/test_camera_Round-ZoomIn.json

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examples/camera_poses/test_camera_SPIN-ACW-60.json

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examples/camera_poses/test_camera_SPIN-CW-60.json

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examples/camera_poses/test_camera_U.json

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examples/camera_poses/test_camera_b133a504fc90a2d1.json

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examples/camera_poses/test_camera_d9642c8efc01481d.json

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examples/camera_poses/test_camera_d971457c81bca597.json

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examples/trajectories/curve_1.txt

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examples/trajectories/curve_2.txt

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examples/trajectories/curve_3.txt

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examples/trajectories/curve_4.txt

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examples/trajectories/horizon_2.txt

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examples/trajectories/shake_1.txt

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examples/trajectories/shake_2.txt

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examples/trajectories/shaking_10.txt

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gradio_utils/camera_utils.py

@@ -0,0 +1,146 @@
+import copy
+# import plotly.express as px
+# import plotly.graph_objects as go
+import json
+
+import numpy as np
+
+CAMERA_MOTION_MODE = ["Basic Camera Poses", "Provided Complex Camera Poses", "Customize Camera Poses"]
+
+CAMERA = {
+    # T
+    "base_T_norm": 1.5,
+    "base_angle": np.pi/3,
+
+    "Pan Up": {     "angle":[0., 0., 0.],   "T":[0., 1., 0.]},
+    "Pan Down": {   "angle":[0., 0., 0.],   "T":[0.,-1.,0.]},
+    "Pan Left": {   "angle":[0., 0., 0.],   "T":[1.,0.,0.]},
+    "Pan Right": {  "angle":[0., 0., 0.],   "T": [-1.,0.,0.]},
+    "Zoom In": {    "angle":[0., 0., 0.],   "T": [0.,0.,-2.]},
+    "Zoom Out": {   "angle":[0., 0., 0.],   "T": [0.,0.,2.]},
+    "ACW": {        "angle": [0., 0., 1.],  "T":[0., 0., 0.]},
+    "CW": {         "angle": [0., 0., -1.], "T":[0., 0., 0.]},
+}
+
+COMPLEX_CAMERA = {
+    "Pose_1": "examples/camera_poses/test_camera_1424acd0007d40b5.json",
+    "Pose_2": "examples/camera_poses/test_camera_d971457c81bca597.json",
+    "Pose_3": "examples/camera_poses/test_camera_Round-ZoomIn.json",
+    "Pose_4": "examples/camera_poses/test_camera_Round-RI_90.json",
+    "Pose_5": "examples/camera_poses/test_camera_Round-RI_120.json",
+    "Pose_6": "examples/camera_poses/test_camera_018f7907401f2fef.json",
+    "Pose_7": "examples/camera_poses/test_camera_088b93f15ca8745d.json",
+    "Pose_8": "examples/camera_poses/test_camera_b133a504fc90a2d1.json",
+}
+
+
+
+def compute_R_form_rad_angle(angles):
+    theta_x, theta_y, theta_z = angles
+    Rx = np.array([[1, 0, 0],
+                   [0, np.cos(theta_x), -np.sin(theta_x)],
+                   [0, np.sin(theta_x), np.cos(theta_x)]])
+    
+    Ry = np.array([[np.cos(theta_y), 0, np.sin(theta_y)],
+                   [0, 1, 0],
+                   [-np.sin(theta_y), 0, np.cos(theta_y)]])
+    
+    Rz = np.array([[np.cos(theta_z), -np.sin(theta_z), 0],
+                   [np.sin(theta_z), np.cos(theta_z), 0],
+                   [0, 0, 1]])
+    
+    # 计算相机外参的旋转矩阵
+    R = np.dot(Rz, np.dot(Ry, Rx))
+    return R
+
+def get_camera_motion(angle, T, speed, n=16):
+    RT = []
+    for i in range(n):
+        _angle = (i/n)*speed*(CAMERA["base_angle"])*angle
+        R = compute_R_form_rad_angle(_angle) 
+        # _T = (i/n)*speed*(T.reshape(3,1))
+        _T=(i/n)*speed*(CAMERA["base_T_norm"])*(T.reshape(3,1))
+        _RT = np.concatenate([R,_T], axis=1)
+        RT.append(_RT)
+    RT = np.stack(RT)
+    return RT
+    
+def create_relative(RT_list, K_1=4.7, dataset="syn"):
+    RT = copy.deepcopy(RT_list[0])
+    R_inv = RT[:,:3].T
+    T =  RT[:,-1]
+
+    temp = []
+    for _RT in RT_list:
+        _RT[:,:3] = np.dot(_RT[:,:3], R_inv)
+        _RT[:,-1] =  _RT[:,-1] - np.dot(_RT[:,:3], T)
+        temp.append(_RT)
+    RT_list = temp
+
+    return RT_list
+    
+def combine_camera_motion(RT_0, RT_1):
+    RT = copy.deepcopy(RT_0[-1])
+    R = RT[:,:3]
+    R_inv = RT[:,:3].T
+    T =  RT[:,-1]
+
+    temp = []
+    for _RT in RT_1:
+        _RT[:,:3] = np.dot(_RT[:,:3], R)
+        _RT[:,-1] =  _RT[:,-1] + np.dot(np.dot(_RT[:,:3], R_inv), T) 
+        temp.append(_RT)
+
+    RT_1 = np.stack(temp)
+
+    return np.concatenate([RT_0, RT_1], axis=0)
+
+def process_camera(camera_dict):
+    # "First A then B", "Both A and B", "Custom"
+    if camera_dict['complex'] is not None:
+        with open(COMPLEX_CAMERA[camera_dict['complex']]) as f:
+            RT = json.load(f) # [16, 12]
+        RT = np.array(RT).reshape(-1, 3, 4)
+        print(RT.shape)
+        return RT
+
+
+    motion_list = camera_dict['motion']
+    mode = camera_dict['mode']
+    speed = camera_dict['speed']
+    print(len(motion_list))
+    if len(motion_list) == 0:
+        angle = np.array([0,0,0])
+        T = np.array([0,0,0])
+        RT = get_camera_motion(angle, T, speed, 16)
+
+
+    elif len(motion_list) == 1:
+        angle = np.array(CAMERA[motion_list[0]]["angle"])
+        T = np.array(CAMERA[motion_list[0]]["T"])
+        print(angle, T)
+        RT = get_camera_motion(angle, T, speed, 16)
+        
+        
+    
+    elif len(motion_list) == 2:
+        if mode == "Customized Mode 1: First A then B":
+            angle = np.array(CAMERA[motion_list[0]]["angle"]) 
+            T = np.array(CAMERA[motion_list[0]]["T"]) 
+            RT_0 = get_camera_motion(angle, T, speed, 8)
+
+            angle = np.array(CAMERA[motion_list[1]]["angle"]) 
+            T = np.array(CAMERA[motion_list[1]]["T"]) 
+            RT_1 = get_camera_motion(angle, T, speed, 8)
+
+            RT = combine_camera_motion(RT_0, RT_1)
+
+        elif mode == "Customized Mode 2: Both A and B":
+            angle = np.array(CAMERA[motion_list[0]]["angle"]) + np.array(CAMERA[motion_list[1]]["angle"])
+            T = np.array(CAMERA[motion_list[0]]["T"]) + np.array(CAMERA[motion_list[1]]["T"])
+            RT = get_camera_motion(angle, T, speed, 16)
+
+
+    # return RT.reshape(-1, 12)
+    return RT
+

gradio_utils/flow_utils.py

@@ -0,0 +1,69 @@
+import numpy as np
+
+
+def sigma_matrix2(sig_x, sig_y, theta):
+    """Calculate the rotated sigma matrix (two dimensional matrix).
+    Args:
+        sig_x (float):
+        sig_y (float):
+        theta (float): Radian measurement.
+    Returns:
+        ndarray: Rotated sigma matrix.
+    """
+    d_matrix = np.array([[sig_x**2, 0], [0, sig_y**2]])
+    u_matrix = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])
+    return np.dot(u_matrix, np.dot(d_matrix, u_matrix.T))
+
+
+def mesh_grid(kernel_size):
+    """Generate the mesh grid, centering at zero.
+    Args:
+        kernel_size (int):
+    Returns:
+        xy (ndarray): with the shape (kernel_size, kernel_size, 2)
+        xx (ndarray): with the shape (kernel_size, kernel_size)
+        yy (ndarray): with the shape (kernel_size, kernel_size)
+    """
+    ax = np.arange(-kernel_size // 2 + 1., kernel_size // 2 + 1.)
+    xx, yy = np.meshgrid(ax, ax)
+    xy = np.hstack((xx.reshape((kernel_size * kernel_size, 1)), yy.reshape(kernel_size * kernel_size,
+                                                                           1))).reshape(kernel_size, kernel_size, 2)
+    return xy, xx, yy
+
+
+def pdf2(sigma_matrix, grid):
+    """Calculate PDF of the bivariate Gaussian distribution.
+    Args:
+        sigma_matrix (ndarray): with the shape (2, 2)
+        grid (ndarray): generated by :func:`mesh_grid`,
+            with the shape (K, K, 2), K is the kernel size.
+    Returns:
+        kernel (ndarrray): un-normalized kernel.
+    """
+    inverse_sigma = np.linalg.inv(sigma_matrix)
+    kernel = np.exp(-0.5 * np.sum(np.dot(grid, inverse_sigma) * grid, 2))
+    return kernel
+
+def bivariate_Gaussian(kernel_size, sig_x, sig_y, theta, grid=None, isotropic=True):
+    """Generate a bivariate isotropic or anisotropic Gaussian kernel.
+    In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored.
+    Args:
+        kernel_size (int):
+        sig_x (float):
+        sig_y (float):
+        theta (float): Radian measurement.
+        grid (ndarray, optional): generated by :func:`mesh_grid`,
+            with the shape (K, K, 2), K is the kernel size. Default: None
+        isotropic (bool):
+    Returns:
+        kernel (ndarray): normalized kernel.
+    """
+    if grid is None:
+        grid, _, _ = mesh_grid(kernel_size)
+    if isotropic:
+        sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]])
+    else:
+        sigma_matrix = sigma_matrix2(sig_x, sig_y, theta)
+    kernel = pdf2(sigma_matrix, grid)
+    kernel = kernel / np.sum(kernel)
+    return kernel

gradio_utils/traj_utils.py

@@ -0,0 +1,104 @@
+import cv2
+import numpy as np
+
+from gradio_utils.flow_utils import bivariate_Gaussian
+
+OBJECT_MOTION_MODE = ["Provided Trajectory", "Custom Trajectory"]
+
+PROVIDED_TRAJS = {
+    "horizon_1": "examples/trajectories/horizon_2.txt",
+    "swaying_1": "examples/trajectories/shake_1.txt",
+    "swaying_2": "examples/trajectories/shake_2.txt",
+    "swaying_3": "examples/trajectories/shaking_10.txt",
+    "curve_1": "examples/trajectories/curve_1.txt",
+    "curve_2": "examples/trajectories/curve_2.txt",
+    "curve_3": "examples/trajectories/curve_3.txt",
+    "curve_4": "examples/trajectories/curve_4.txt",
+}
+
+
+def read_points(file, video_len=16, reverse=False):
+    with open(file, 'r') as f:
+        lines = f.readlines()
+    points = []
+    for line in lines:
+        x, y = line.strip().split(',')
+        points.append((int(x), int(y)))
+    if reverse:
+        points = points[::-1]
+
+    if len(points) > video_len:
+        skip = len(points) // video_len
+        points = points[::skip]
+    points = points[:video_len]
+    
+    return points
+
+def get_provided_traj(traj_name):
+    traj = read_points(PROVIDED_TRAJS[traj_name])
+    # xrange from 256 to 1024
+    traj = [[int(1024*x/256), int(1024*y/256)] for x,y in traj]
+    return traj
+
+blur_kernel = bivariate_Gaussian(99, 10, 10, 0, grid=None, isotropic=True)
+
+def process_points(points):
+    frames = 16
+    defualt_points = [[512,512]]*16
+
+    if len(points) < 2:
+        return defualt_points
+    elif len(points) >= frames:
+        skip = len(points)//frames
+        return points[::skip][:15] + points[-1:]
+    else:
+        insert_num = frames - len(points)
+        insert_num_dict = {}
+        interval = len(points) - 1
+        n = insert_num // interval
+        m = insert_num % interval
+        for i in range(interval):
+            insert_num_dict[i] = n
+        for i in range(m):
+            insert_num_dict[i] += 1
+
+        res = []
+        for i in range(interval):
+            insert_points = []
+            x0,y0 = points[i]
+            x1,y1 = points[i+1]
+
+            delta_x = x1 - x0
+            delta_y = y1 - y0
+            for j in range(insert_num_dict[i]):
+                x = x0 + (j+1)/(insert_num_dict[i]+1)*delta_x
+                y = y0 + (j+1)/(insert_num_dict[i]+1)*delta_y
+                insert_points.append([int(x), int(y)])
+
+            res += points[i:i+1] + insert_points
+        res += points[-1:]
+        return res
+
+def get_flow(points, video_len=16):
+    optical_flow = np.zeros((video_len, 256, 256, 2), dtype=np.float32)
+    for i in range(video_len-1):
+        p = points[i]
+        p1 = points[i+1]
+        optical_flow[i+1, p[1], p[0], 0] = p1[0] - p[0]
+        optical_flow[i+1, p[1], p[0], 1] = p1[1] - p[1]
+    for i in range(1, video_len):
+        optical_flow[i] = cv2.filter2D(optical_flow[i], -1, blur_kernel)
+
+
+    return optical_flow
+
+
+def process_traj(points, device='cpu'):
+    xy_range = 1024
+    points = process_points(points)
+    points = [[int(256*x/xy_range), int(256*y/xy_range)] for x,y in points]
+    
+    optical_flow = get_flow(points)
+    # optical_flow = torch.tensor(optical_flow).to(device)
+
+    return optical_flow

gradio_utils/utils.py

@@ -0,0 +1,175 @@
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+
+def vis_camera(RT_list, rescale_T=1):
+    fig = go.Figure()
+    showticklabels = True
+    visible = True
+    scene_bounds = 2
+    base_radius = 2.5
+    zoom_scale = 1.5
+    fov_deg = 50.0
+    
+    edges = [(0, 1), (0, 2), (0, 3), (1, 2), (2, 3), (3, 1), (3, 4)] 
+    
+    colors = px.colors.qualitative.Plotly
+    
+    cone_list = []
+    n = len(RT_list)
+    for i, RT in enumerate(RT_list):
+        R = RT[:,:3]
+        T = RT[:,-1]/rescale_T
+        cone = calc_cam_cone_pts_3d(R, T, fov_deg)
+        cone_list.append((cone, (i*1/n, "green"), f"view_{i}"))
+
+    
+    for (cone, clr, legend) in cone_list:
+        for (i, edge) in enumerate(edges):
+            (x1, x2) = (cone[edge[0], 0], cone[edge[1], 0])
+            (y1, y2) = (cone[edge[0], 1], cone[edge[1], 1])
+            (z1, z2) = (cone[edge[0], 2], cone[edge[1], 2])
+            fig.add_trace(go.Scatter3d(
+                x=[x1, x2], y=[y1, y2], z=[z1, z2], mode='lines',
+                line=dict(color=clr, width=3),
+                name=legend, showlegend=(i == 0))) 
+    fig.update_layout(
+                    height=500,
+                    autosize=True,
+                    # hovermode=False,
+                    margin=go.layout.Margin(l=0, r=0, b=0, t=0),
+                    
+                    showlegend=True,
+                    legend=dict(
+                        yanchor='bottom',
+                        y=0.01,
+                        xanchor='right',
+                        x=0.99,
+                    ),
+                    scene=dict(
+                        aspectmode='manual',
+                        aspectratio=dict(x=1, y=1, z=1.0),
+                        camera=dict(
+                            center=dict(x=0.0, y=0.0, z=0.0),
+                            up=dict(x=0.0, y=-1.0, z=0.0),
+                            eye=dict(x=scene_bounds/2, y=-scene_bounds/2, z=-scene_bounds/2),
+                            ),
+
+                        xaxis=dict(
+                            range=[-scene_bounds, scene_bounds],
+                            showticklabels=showticklabels,
+                            visible=visible,
+                        ),
+                            
+                        
+                        yaxis=dict(
+                            range=[-scene_bounds, scene_bounds],
+                            showticklabels=showticklabels,
+                            visible=visible,
+                        ),
+                            
+                        
+                        zaxis=dict(
+                            range=[-scene_bounds, scene_bounds],
+                            showticklabels=showticklabels,
+                            visible=visible,
+                        )
+                    ))
+    return fig
+
+
+def calc_cam_cone_pts_3d(R_W2C, T_W2C, fov_deg, scale=0.1, set_canonical=False, first_frame_RT=None):
+    fov_rad = np.deg2rad(fov_deg)
+    R_W2C_inv = np.linalg.inv(R_W2C)
+
+    # Camera pose center:
+    T = np.zeros_like(T_W2C) - T_W2C
+    T = np.dot(R_W2C_inv, T)
+    cam_x = T[0]
+    cam_y = T[1]
+    cam_z = T[2]
+    if set_canonical:
+        T = np.zeros_like(T_W2C)
+        T = np.dot(first_frame_RT[:,:3], T) + first_frame_RT[:,-1]
+        T = T - T_W2C 
+        T = np.dot(R_W2C_inv, T)
+        cam_x = T[0]
+        cam_y = T[1]
+        cam_z = T[2]
+
+    # vertex
+    corn1 = np.array([np.tan(fov_rad / 2.0), 0.5*np.tan(fov_rad / 2.0), 1.0]) *scale 
+    corn2 = np.array([-np.tan(fov_rad / 2.0), 0.5*np.tan(fov_rad / 2.0), 1.0]) *scale
+    corn3 = np.array([0, -0.25*np.tan(fov_rad / 2.0), 1.0]) *scale
+    corn4 = np.array([0, -0.5*np.tan(fov_rad / 2.0), 1.0]) *scale
+
+    corn1 = corn1 - T_W2C
+    corn2 = corn2 - T_W2C
+    corn3 = corn3 - T_W2C
+    corn4 = corn4 - T_W2C
+    
+    corn1 = np.dot(R_W2C_inv, corn1)
+    corn2 = np.dot(R_W2C_inv, corn2)
+    corn3 = np.dot(R_W2C_inv, corn3) 
+    corn4 = np.dot(R_W2C_inv, corn4) 
+
+    # Now attach as offset to actual 3D camera position:
+    corn_x1 = corn1[0]
+    corn_y1 = corn1[1]
+    corn_z1 = corn1[2]
+    
+    corn_x2 = corn2[0]
+    corn_y2 = corn2[1]
+    corn_z2 = corn2[2]
+    
+    corn_x3 = corn3[0]
+    corn_y3 = corn3[1]
+    corn_z3 = corn3[2]
+    
+    corn_x4 = corn4[0]
+    corn_y4 = corn4[1]
+    corn_z4 = corn4[2]
+            
+
+    xs = [cam_x, corn_x1, corn_x2, corn_x3, corn_x4, ]
+    ys = [cam_y, corn_y1, corn_y2, corn_y3, corn_y4, ]
+    zs = [cam_z, corn_z1, corn_z2, corn_z3, corn_z4, ]
+
+    return np.array([xs, ys, zs]).T
+
+
+
+    # T_base = [
+    #             [1.,0.,0.],             ## W2C  x 的正方向： 相机朝左  left
+    #             [-1.,0.,0.],            ## W2C  x 的负方向： 相机朝右  right
+    #             [0., 1., 0.],           ## W2C  y 的正方向： 相机朝上  up     
+    #             [0.,-1.,0.],            ## W2C  y 的负方向： 相机朝下  down
+    #             [0.,0.,1.],             ## W2C  z 的正方向： 相机往前  zoom out
+    #             [0.,0.,-1.],            ## W2C  z 的负方向： 相机往前  zoom in
+    #         ]   
+    # radius = 1
+    # n = 16
+    # # step = 
+    # look_at = np.array([0, 0, 0.8]).reshape(3,1)
+    # # look_at = np.array([0, 0, 0.2]).reshape(3,1)
+
+    # T_list = []
+    # base_R = np.array([[1., 0., 0.],
+    #                 [0., 1., 0.],
+    #                 [0., 0., 1.]])
+    # res = [] 
+    # res_forsave = []
+    # T_range = 1.8
+
+
+
+    # for i in range(0, 16):
+    #     # theta = (1)*np.pi*i/n
+
+    #     R = base_R[:,:3]
+    #     T = np.array([0.,0.,1.]).reshape(3,1) * (i/n)*2
+    #     RT = np.concatenate([R,T], axis=1)
+    #     res.append(RT)
+        
+    # fig = vis_camera(res)
+    

lvdm/basics.py

@@ -0,0 +1,106 @@
+# adopted from
+# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+# and
+# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+# and
+# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
+#
+# thanks!
+
+import numpy as np
+from einops import repeat
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from utils.utils import instantiate_from_config
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+def scale_module(module, scale):
+    """
+    Scale the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().mul_(scale)
+    return module
+
+
+def conv_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D convolution module.
+    """
+    if dims == 1:
+        return nn.Conv1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.Conv2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.Conv3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+    """
+    Create a linear module.
+    """
+    return nn.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D average pooling module.
+    """
+    if dims == 1:
+        return nn.AvgPool1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.AvgPool2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.AvgPool3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def nonlinearity(type='silu'):
+    if type == 'silu':
+        return nn.SiLU()
+    elif type == 'leaky_relu':
+        return nn.LeakyReLU()
+
+
+class GroupNormSpecific(nn.GroupNorm):
+    def forward(self, x):
+        return super().forward(x.float()).type(x.dtype)
+
+
+def normalization(channels, num_groups=32):
+    """
+    Make a standard normalization layer.
+    :param channels: number of input channels.
+    :return: an nn.Module for normalization.
+    """
+    return GroupNormSpecific(num_groups, channels)
+
+
+class HybridConditioner(nn.Module):
+
+    def __init__(self, c_concat_config, c_crossattn_config):
+        super().__init__()
+        self.concat_conditioner = instantiate_from_config(c_concat_config)
+        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
+
+    def forward(self, c_concat, c_crossattn):
+        c_concat = self.concat_conditioner(c_concat)
+        c_crossattn = self.crossattn_conditioner(c_crossattn)
+        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}

lvdm/common.py

@@ -0,0 +1,142 @@
+import os, math
+import numpy as np
+from inspect import isfunction
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torch.distributed as dist
+
+
+def gather_data(data, return_np=True):
+    ''' gather data from multiple processes to one list '''
+    data_list = [torch.zeros_like(data) for _ in range(dist.get_world_size())]
+    dist.all_gather(data_list, data)  # gather not supported with NCCL
+    if return_np:
+        data_list = [data.cpu().numpy() for data in data_list]
+    return data_list
+
+def autocast(f):
+    def do_autocast(*args, **kwargs):
+        with torch.cuda.amp.autocast(enabled=True,
+                                     dtype=torch.get_autocast_gpu_dtype(),
+                                     cache_enabled=torch.is_autocast_cache_enabled()):
+            return f(*args, **kwargs)
+    return do_autocast
+
+
+def extract_into_tensor(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+def noise_like(shape, device, repeat=False):
+    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
+    noise = lambda: torch.randn(shape, device=device)
+    return repeat_noise() if repeat else noise()
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+def exists(val):
+    return val is not None
+
+def identity(*args, **kwargs):
+    return nn.Identity()
+
+def uniq(arr):
+    return{el: True for el in arr}.keys()
+
+def mean_flat(tensor):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+def ismap(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] > 3)
+
+def isimage(x):
+    if not isinstance(x,torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
+
+def max_neg_value(t):
+    return -torch.finfo(t.dtype).max
+
+def shape_to_str(x):
+    shape_str = "x".join([str(x) for x in x.shape])
+    return shape_str
+
+def init_(tensor):
+    dim = tensor.shape[-1]
+    std = 1 / math.sqrt(dim)
+    tensor.uniform_(-std, std)
+    return tensor
+
+#import deepspeed
+#ckpt = deepspeed.checkpointing.checkpoint
+ckpt = torch.utils.checkpoint.checkpoint
+def checkpoint(func, inputs, params, flag):
+    """
+    Evaluate a function without caching intermediate activations, allowing for
+    reduced memory at the expense of extra compute in the backward pass.
+    :param func: the function to evaluate.
+    :param inputs: the argument sequence to pass to `func`.
+    :param params: a sequence of parameters `func` depends on but does not
+                   explicitly take as arguments.
+    :param flag: if False, disable gradient checkpointing.
+    """
+    if flag:
+        try:
+            return ckpt(func, *inputs)
+        except:
+            args = tuple(inputs) + tuple(params)
+            return CheckpointFunction.apply(func, len(inputs), *args)
+    else:
+        return func(*inputs)
+
+
+class CheckpointFunction(torch.autograd.Function):
+    @staticmethod
+    @torch.cuda.amp.custom_fwd
+    def forward(ctx, run_function, length, *args):
+        ctx.run_function = run_function
+        ctx.input_tensors = list(args[:length])
+        ctx.input_params = list(args[length:])
+
+        with torch.no_grad():
+            output_tensors = ctx.run_function(*ctx.input_tensors)
+        return output_tensors
+
+    @staticmethod
+    @torch.cuda.amp.custom_bwd # add this
+    def backward(ctx, *output_grads):
+        '''
+        for x in ctx.input_tensors:
+            if isinstance(x, int):
+                print('-----------------', ctx.run_function)
+        '''
+        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
+        with torch.enable_grad():
+            # Fixes a bug where the first op in run_function modifies the
+            # Tensor storage in place, which is not allowed for detach()'d
+            # Tensors.
+            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
+            output_tensors = ctx.run_function(*shallow_copies)
+        input_grads = torch.autograd.grad(
+            output_tensors,
+            ctx.input_tensors + ctx.input_params,
+            output_grads,
+            allow_unused=True,
+        )
+        del ctx.input_tensors
+        del ctx.input_params
+        del output_tensors
+        return (None, None) + input_grads

lvdm/distributions.py

@@ -0,0 +1,95 @@
+import torch
+import numpy as np
+
+
+class AbstractDistribution:
+    def sample(self):
+        raise NotImplementedError()
+
+    def mode(self):
+        raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+    def __init__(self, value):
+        self.value = value
+
+    def sample(self):
+        return self.value
+
+    def mode(self):
+        return self.value
+
+
+class DiagonalGaussianDistribution(object):
+    def __init__(self, parameters, deterministic=False):
+        self.parameters = parameters
+        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+        self.deterministic = deterministic
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+        if self.deterministic:
+            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
+
+    def sample(self, noise=None):
+        if noise is None:
+            noise = torch.randn(self.mean.shape)
+        
+        x = self.mean + self.std * noise.to(device=self.parameters.device)
+        return x
+
+    def kl(self, other=None):
+        if self.deterministic:
+            return torch.Tensor([0.])
+        else:
+            if other is None:
+                return 0.5 * torch.sum(torch.pow(self.mean, 2)
+                                       + self.var - 1.0 - self.logvar,
+                                       dim=[1, 2, 3])
+            else:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean - other.mean, 2) / other.var
+                    + self.var / other.var - 1.0 - self.logvar + other.logvar,
+                    dim=[1, 2, 3])
+
+    def nll(self, sample, dims=[1,2,3]):
+        if self.deterministic:
+            return torch.Tensor([0.])
+        logtwopi = np.log(2.0 * np.pi)
+        return 0.5 * torch.sum(
+            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+            dim=dims)
+
+    def mode(self):
+        return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+    Compute the KL divergence between two gaussians.
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, torch.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for torch.exp().
+    logvar1, logvar2 = [
+        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+        for x in (logvar1, logvar2)
+    ]
+
+    return 0.5 * (
+        -1.0
+        + logvar2
+        - logvar1
+        + torch.exp(logvar1 - logvar2)
+        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+    )

lvdm/ema.py

@@ -0,0 +1,76 @@
+import torch
+from torch import nn
+
+
+class LitEma(nn.Module):
+    def __init__(self, model, decay=0.9999, use_num_upates=True):
+        super().__init__()
+        if decay < 0.0 or decay > 1.0:
+            raise ValueError('Decay must be between 0 and 1')
+
+        self.m_name2s_name = {}
+        self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
+        self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
+                             else torch.tensor(-1,dtype=torch.int))
+
+        for name, p in model.named_parameters():
+            if p.requires_grad:
+                #remove as '.'-character is not allowed in buffers
+                s_name = name.replace('.','')
+                self.m_name2s_name.update({name:s_name})
+                self.register_buffer(s_name,p.clone().detach().data)
+
+        self.collected_params = []
+
+    def forward(self,model):
+        decay = self.decay
+
+        if self.num_updates >= 0:
+            self.num_updates += 1
+            decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
+
+        one_minus_decay = 1.0 - decay
+
+        with torch.no_grad():
+            m_param = dict(model.named_parameters())
+            shadow_params = dict(self.named_buffers())
+
+            for key in m_param:
+                if m_param[key].requires_grad:
+                    sname = self.m_name2s_name[key]
+                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
+                    shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
+                else:
+                    assert not key in self.m_name2s_name
+
+    def copy_to(self, model):
+        m_param = dict(model.named_parameters())
+        shadow_params = dict(self.named_buffers())
+        for key in m_param:
+            if m_param[key].requires_grad:
+                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
+            else:
+                assert not key in self.m_name2s_name
+
+    def store(self, parameters):
+        """
+        Save the current parameters for restoring later.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            temporarily stored.
+        """
+        self.collected_params = [param.clone() for param in parameters]
+
+    def restore(self, parameters):
+        """
+        Restore the parameters stored with the `store` method.
+        Useful to validate the model with EMA parameters without affecting the
+        original optimization process. Store the parameters before the
+        `copy_to` method. After validation (or model saving), use this to
+        restore the former parameters.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            updated with the stored parameters.
+        """
+        for c_param, param in zip(self.collected_params, parameters):
+            param.data.copy_(c_param.data)