# FlexAM: Flexible Appearance-Motion Decomposition for Versatile Video Generation Control arXiv Hugging Face ComfyUI

Mingzhi Sheng1*, Zekai Gu2*, Peng Li2, Cheng Lin3, Hao-Xiang Guo4, Ying-Cong Chen1,2†, Yuan Liu2†
1HKUST(GZ), 2HKUST, 3MUST, 4Tsinghua University
*Equal Contribution, †Corresponding Authors

![teaser](assets/teaser.gif) ## 📰 News - **[2026.02.13]** 🚀 We have released the inference code and **ComfyUI** support! - **[2026.02.14]** 📄 The paper is available on arXiv. ## 🛠️ Installation > 📢 **System Requirements**: Both the official Python inference code and the ComfyUI workflow were tested on **Ubuntu 20.04** with **Python 3.10**, **PyTorch 2.5.1**, and **CUDA 12.1** on an **NVIDIA A800** GPU. Before running any inference (Python or ComfyUI), please setup the environment and download the checkpoints. ### 1. Create environment Clone the repository and create conda environment: ``` git clone https://github.com/IGL-HKUST/FlexAM conda create -n flexam python=3.10 conda activate flexam ``` Install pytorch, we recommend `Pytorch 2.5.1` with `CUDA 12.1`: ``` pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu121 ``` ``` pip install -r requirements.txt ``` ### 2. Download Submodules We rely on several external modules (MoGe, Pi3, etc.). ``` mkdir -p submodules git submodule update --init --recursive pip install -r requirements.txt ```
(Optional) Manual clone if submodule update fails ``` # DELTA git clone https://github.com/snap-research/DELTA_densetrack3d.git submodules/MoGe # Pi3 git clone https://github.com/yyfz/Pi3.git submodules/Pi3 # MoGe git clone https://github.com/microsoft/MoGe.git submodules/MoGe # VGGT git clone https://github.com/facebookresearch/vggt.git submodules/vggt ```
### 3. Download checkpoints Download the FlexAM checkpoint and place it in the`checkpoints/` directory. - HuggingFace Link: [Wan2.2-Fun-5B-FLEXAM](https://huggingface.co/SandwichZ/Wan2.2-Fun-5B-FLEXAM) ## 🚀 Inference We provide two ways to use FlexAM: Python Script and ComfyUI. ### Option A: ComfyUI Integration We provide a native node for seamless integration into ComfyUI workflows. > ⚠️ **Note**: Currently, the ComfyUI node supports **Motion Transfer**, **Foreground Edit**, and **Background Edit**. For *Camera Control* and *Object Manipulation*, please use the Python script. #### 1. Install Node Since we are not yet in the Manager, please install manually: ``` cd ComfyUI/custom_nodes/ git clone https://github.com/IGL-HKUST/FlexAM cd FlexAM pip install -r requirements.txt ``` #### 2. Run Workflow - Step 1: Download the workflow JSON: [workflow.json](assets/flexam_workflow.json) - Step 2: Drag and drop it into ComfyUI. - Step 3: Ensure checkpoints are in `ComfyUI/models/checkpoints`. ### Option B: Python Script We provide a inference script for our tasks. Please refer to `run_demo.sh` to run the `demo.py` script. Or you can run these tasks one by one as follows. #### 1. Motion Transfer ![motion_transfer](assets/motion_transfer.gif) --- ```python python demo.py \ --prompt <"prompt text"> \ # prompt text --checkpoint_path \ # FlexAM checkpoint path (e.g checkpoints/Diffusion_Transformer/Wan2.2-Fun-5B-FLEXAM) --output_dir \ # output directory --input_path \ # the reference video path --repaint \ # the repaint first frame image path of input source video or use FLUX to repaint the first frame \ --video_length=97 \ --sample_size 512 896 \ --generate_type='full_edit' \ --density 10 \ # Control the sparsity of tracking points --gpu \ # the gpu id ``` #### 2. foreground edit ![fg_edit](assets/fg_edit.gif) ```python python demo.py \ --prompt <"prompt text"> \ # prompt text --checkpoint_path \ # FlexAM checkpoint path (e.g checkpoints/Diffusion_Transformer/Wan2.2-Fun-5B-FLEXAM) --output_dir \ # output directory --input_path \ # the reference video path --repaint \ # the repaint first frame image path of input source video or use FLUX to repaint the first frame \ --mask_path \ # White (255) represents the foreground to be edited, and black (0) remains unchanged --video_length=97 \ --sample_size 512 896 \ --generate_type='foreground_edit' \ --dilation_pixels=30 \ # Dilation pixels for mask processing in foreground_edit mode --density 10 \ # Control the sparsity of tracking points --gpu \ # the gpu id ``` #### 3. background edit ![bg_edit](assets/bg_edit.gif) ```python python demo.py \ --prompt <"prompt text"> \ # prompt text --checkpoint_path \ # FlexAM checkpoint path (e.g checkpoints/Diffusion_Transformer/Wan2.2-Fun-5B-FLEXAM) --output_dir \ # output directory --input_path \ # the reference video path --repaint \ # the repaint first frame image path of input source video or use FLUX to repaint the first frame \ --mask_path \ # White (255) represents the unchanged foreground, while the background indicates the area to be edited --video_length=97 \ --sample_size 512 896 \ --generate_type='background_edit' \ --density 10 \ # Control the sparsity of tracking points --gpu \ # the gpu id ``` #### 4. Camera Control ![camera_ctrl](assets/camera_ctrl.gif) We provide three camera control methods: 1. Use predefined templates; 2. Use a pose text file (pose txt); 3. Input another video, where the "Pi3" automatically estimates the camera pose from it and applies it to the video to be generated. ##### 1. Use predefined templates We provide several template camera motion types, you can choose one of them. In practice, we find that providing a description of the camera motion in prompt will get better results. ```python python demo.py \ --prompt <"prompt text"> \ # prompt text --checkpoint_path \ # FlexAM checkpoint path (e.g checkpoints/Diffusion_Transformer/Wan2.2-Fun-5B-FLEXAM) --output_dir \ # output directory --input_path \ # the reference image or video path --camera_motion \ # the camera motion type, see examples below --tracking_method \ # the tracking method (moge, DELTA). For image input, 'moge' is necessary. --override_extrinsics \ # how to apply camera motion: "override" to replace original camera, "append" to build upon it --video_length=97 \ --sample_size 512 896 \ --density 5 \ # Control the sparsity of tracking points --gpu \ # the gpu id ``` Here are some tips for camera motion: - trans: translation motion, the camera will move in the direction of the vector (dx, dy, dz) with range [-1, 1] - Positive X: Move left, Negative X: Move right - Positive Y: Move down, Negative Y: Move up - Positive Z: Zoom in, Negative Z: Zoom out - e.g., 'trans -0.1 -0.1 -0.1' moving right, down and zoom in - e.g., 'trans -0.1 0.0 0.0 5 45' moving right 0.1 from frame 5 to 45 - rot: rotation motion, the camera will rotate around the axis (x, y, z) by the angle - X-axis rotation: positive X: pitch down, negative X: pitch up - Y-axis rotation: positive Y: yaw left, negative Y: yaw right - Z-axis rotation: positive Z: roll counter-clockwise, negative Z: roll clockwise - e.g., 'rot y 25' rotating 25 degrees around y-axis (yaw left) - e.g., 'rot x -30 10 40' rotating -30 degrees around x-axis (pitch up) from frame 10 to 40 - spiral: spiral motion, the camera will move in a spiral path with the given radius - e.g., 'spiral 2' spiral motion with radius 2 - e.g., 'spiral 2 15 35' spiral motion with radius 2 from frame 15 to 35 Multiple transformations can be combined using semicolon (;) as separator: - e.g., "trans 0 0 -0.5 0 30; rot x -25 0 30; trans -0.1 0 0 30 48" This will: 1. Zoom in (z-0.5) from frame 0 to 30 2. Pitch up (rotate -25 degrees around x-axis) from frame 0 to 30 3. Move right (x-0.1) from frame 30 to 48 Notes: - If start_frame and end_frame are not specified, the motion will be applied to all frames (0-48) - Frames after end_frame will maintain the final transformation - For combined transformations, they are applied in sequence ##### 2. Use a pose text file (pose txt) ```python python demo.py \ --prompt <"prompt text"> \ # prompt text --checkpoint_path \ # FlexAM checkpoint path (e.g checkpoints/Diffusion_Transformer/Wan2.2-Fun-5B-FLEXAM) --output_dir \ # output directory --input_path \ # the reference image or video path --camera_motion "path" \ # if camera motion type is "path", --pose_file is needed --pose_file \ # txt file of camera pose, Each line corresponds to one frame --tracking_method \ # the tracking method (moge, DELTA). For image input, 'moge' is necessary. --override_extrinsics \ # how to apply camera motion: "override" to replace original camera, "append" to build upon it --video_length=97 \ --sample_size 512 896 \ --density 5 \ # Control the sparsity of tracking points --gpu \ # the gpu id ``` ##### 3. Input another video for extract camera pose ```python python demo.py \ --prompt <"prompt text"> \ # prompt text --checkpoint_path \ # FlexAM checkpoint path (e.g checkpoints/Diffusion_Transformer/Wan2.2-Fun-5B-FLEXAM) --output_dir \ # output directory --input_path \ # the reference image or video path --camera_motion "path" \ # if camera motion type is "path", --pose_file is needed --pose_file \ # "Pi3" automatically estimates the camera pose from this video file --tracking_method \ # the tracking method (moge, DELTA). For image input, 'moge' is necessary. --override_extrinsics \ # how to apply camera motion: "override" to replace original camera, "append" to build upon it --video_length=97 \ --sample_size 512 896 \ --density 5 \ # Control the sparsity of tracking points --gpu \ # the gpu id ``` #### 5. Object Manipulation ![object](assets/object.gif) We provide several template object manipulation types, you can choose one of them. In practice, we find that providing a description of the object motion in prompt will get better results. ```python python demo.py \ --prompt <"prompt text"> \ # prompt text --checkpoint_path \ # FlexAM checkpoint path (e.g checkpoints/Diffusion_Transformer/Wan2.2-Fun-5B-FLEXAM) --input_path \ # the reference image path --object_motion \ # the object motion type (up, down, left, right) --object_mask \ # the object mask path --tracking_method \ # the tracking method (moge, DELTA). For image input, 'moge' is nesserary. --sample_size 512 896 \ --video_length=49 \ --density 30 \ --gpu \ # the gpu id ``` It should be noted that depending on the tracker you choose, you may need to modify the scale of translation. ## 🙏 Acknowledgements This project builds upon several excellent open source projects: * [VideoX-Fun](https://github.com/aigc-apps/VideoX-Fun) * [DELTA](https://github.com/snap-research/DELTA_densetrack3d) * [MoGe](https://github.com/microsoft/MoGe) * [vggt](https://github.com/facebookresearch/vggt) * [Pi3](https://github.com/yyfz/Pi3) We thank the authors and contributors of these projects for their valuable contributions to the open source community! ## 🌟 Citation If you find FlexAM useful for your research, please cite our paper: ``` ```