ATI (Any Trajectory Instruction) is a controllable video generation framework proposed by the ByteDance team. ATI is implemented based on Wan2.1 and supports unified control of objects, local regions, and camera motion in videos through arbitrary trajectory instructions. Project URL: https://github.com/bytedance/ATI

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Key Features

  • Unified Motion Control: Supports trajectory control for multiple motion types including objects, local regions, and camera movements.
  • Interactive Trajectory Editor: Visual tool that allows users to freely draw and edit motion trajectories on images.
  • Wan2.1 Compatible: Based on the official Wan2.1 implementation, compatible with environments and model structures.
  • Rich Visualization Tools: Supports visualization of input trajectories, output videos, and trajectory overlays.

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WAN ATI Trajectory Control Workflow Example

If you have not installed ComfyUI, please refer to the ComfyUI System Requirements section to install ComfyUI.If you find missing nodes when loading the workflow file below, it may be due to the following situations:
  1. You are not using the latest Development (Nightly) version of ComfyUI.
  2. You are using the Stable (Release) version or Desktop version of ComfyUI (which does not include the latest feature updates).
  3. You are using the latest Commit version of ComfyUI, but some nodes failed to import during startup.
Please make sure you have successfully updated ComfyUI to the latest Development (Nightly) version. See: How to Update ComfyUI section to learn how to update ComfyUI.

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1. Workflow Download

Download the video below and drag it into ComfyUI to load the corresponding workflow We will use the following image as input: v2v-input

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2. Model Download

If you haven’t successfully downloaded the model files from the workflow, you can try downloading them manually using the links below Diffusion Model VAE Text encoders Chose one of following model clip_vision File save location 
ComfyUI/
├───📂 models/
│   ├───📂 diffusion_models/
│   │   └───Wan2_1-I2V-ATI-14B_fp8_e4m3fn.safetensors
│   ├───📂 text_encoders/
│   │   └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors # or other version
│   ├───📂 clip_vision/
│   │   └─── clip_vision_h.safetensors
│   └───📂 vae/
│       └──  wan_2.1_vae.safetensors

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3. Complete the workflow execution step by step

Workflow step diagram Please follow the numbered steps in the image to ensure smooth execution of the corresponding workflow
  1. Ensure the Load Diffusion Model node has loaded the Wan2_1-I2V-ATI-14B_fp8_e4m3fn.safetensors model
  2. Ensure the Load CLIP node has loaded the umt5_xxl_fp8_e4m3fn_scaled.safetensors model
  3. Ensure the Load VAE node has loaded the wan_2.1_vae.safetensors model
  4. Ensure the Load CLIP Vision node has loaded the clip_vision_h.safetensors model
  5. Upload the provided input image in the Load Image node
  6. Trajectory editing: Currently there is no corresponding trajectory editor in ComfyUI yet, you can use the following link to complete trajectory editing
  7. If you need to modify the prompts (positive and negative), please make changes in the CLIP Text Encoder node numbered 5
  8. Click the Run button, or use the shortcut Ctrl(cmd) + Enter to execute video generation