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

Make sure your ComfyUI is updated.Workflows in this guide can be found in the Workflow Templates. If you can’t find them in the template, your ComfyUI may be outdated.(Desktop version’s update will delay sometime)If nodes are missing when loading a workflow, possible reasons:
  1. You are not using the latest ComfyUI version(Nightly version)
  2. You are using Stable or Desktop version (Latest changes may not be included)
  3. Some nodes failed to import at startup

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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