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Hosting Time-to-Move on Hugging Face

This guide explains how to mirror the Time-to-Move (TTM) codebase on the 🤗 Hub and how to expose an interactive demo through a Space. It assumes you already read the main README.md, understand how to run run_wan.py, and have access to Wan 2.2 weights through your Hugging Face account.

1. Prerequisites

  • Hugging Face account with access to the Wan 2.2 Image-to-Video model (Wan-AI/Wan2.2-I2V-A14B-Diffusers at the time of writing).
  • Local environment with Git, Git LFS, Python 3.10+, and the huggingface_hub CLI.
  • GPU-backed hardware both locally (for testing) and on Spaces (A100 or A10 is strongly recommended; CPU-only tiers are too slow for Wan 2.2).
  • Optional: organization namespace on the Hugging Face Hub (recommended if you want to publish under a team/org).

Authenticate once locally (this stores a token in ~/.huggingface):

huggingface-cli login
git lfs install

2. Publish the code as a model repository

  1. Create an empty repo on the Hub. Example:

    huggingface-cli repo create time-to-move/wan-ttm --type=model --yes
git clone https://huggingface.co/time-to-move/wan-ttm
cd wan-ttm

  2. Copy the TTM sources. From the project root, copy the files that users need to reproduce inference:

    rsync -av \
  --exclude ".git/" \
  --exclude "outputs/" \
  /path/to/TTM/ \
  /path/to/wan-ttm/

    Make sure pipelines/, run_wan.py, run_cog.py, run_svd.py, examples/, and the new huggingface_space/ folder are included. Track large binary assets:

    git lfs track "*.mp4" "*.png" "*.gif"
git add .gitattributes

  3. Add a model card. Reuse the main README.md or create a shorter version describing:

    • What Time-to-Move does.
    • How to run run_wan.py with the motion_signal + mask.
    • Which base model checkpoint the repo expects (Wan 2.2 I2V A14B).

  4. Push to the Hub.

    git add .
git commit -m "Initial commit of Time-to-Move Wan implementation"
git push

Users can now do:

from huggingface_hub import snapshot_download
snapshot_download("time-to-move/wan-ttm")

3. Prepare a Hugging Face Space (Gradio)

This repository now contains huggingface_space/, a ready-to-use Space template:

huggingface_space/
├── README.md                # Quickstart instructions
├── app.py                   # Gradio UI (loads Wan + Time-to-Move logic)
└── requirements.txt         # Runtime dependencies

3.1 Create the Space 

huggingface-cli repo create time-to-move/wan-ttm-demo --type=space --sdk=gradio --yes
git clone https://huggingface.co/spaces/time-to-move/wan-ttm-demo
cd wan-ttm-demo

Copy everything from huggingface_space/ into the Space repository (or keep the whole repo and set the Space’s working directory accordingly). Commit and push.

3.2 Configure hardware and secrets

  • Hardware: Select an A100 (preferred) or A10 GPU runtime in the Space settings. Wan 2.2 is too heavy for CPUs.
  • WAN_MODEL_ID: If you mirrored Wan 2.2 into your organization, set the environment variable to point to it. Otherwise leave the default (Wan-AI/Wan2.2-I2V-A14B-Diffusers).
  • HF_TOKEN / WAN_ACCESS_TOKEN: Add a Space secret only if the Wan checkpoint is private. The Gradio app reads from HF_TOKEN automatically when calling from_pretrained.
  • PYTORCH_CUDA_ALLOC_CONF: Recommended value expandable_segments:True to reduce CUDA fragmentation.

3.3 How the app works

huggingface_space/app.py exposes:

  • A dropdown of the pre-packaged examples/cutdrag_wan_* prompts.
  • Optional custom uploads (first_frame, mask.mp4, motion_signal.mp4) following the README workflow.
  • Sliders for tweak-index, tstrong-index, guidance scale, seed, etc.
  • Live status messages and a generated MP4 preview using diffusers.utils.export_to_video.

The UI lazily loads the WanImageToVideoTTMPipeline with tiling/slicing enabled to reduce VRAM usage. All preprocessing matches the logic in run_wan.py (the same compute_hw_from_area helper is reused).

If you need to customize the experience (e.g., restrict to certain prompts, enforce shorter sequences), edit huggingface_space/app.py before pushing.

4. Testing checklist

  1. Local dry-run.

    pip install -r huggingface_space/requirements.txt
WAN_MODEL_ID=Wan-AI/Wan2.2-I2V-A14B-Diffusers \
python huggingface_space/app.py

    Ensure you can generate at least one of the bundled examples.

  2. Space smoke test.

    • Open the deployed Space.
    • Run the default example (cutdrag_wan_Monkey) and confirm you receive a video in ~2–3 minutes on A100 hardware.
    • Optionally upload a small custom mask/video pair and verify that tweak-index/tstrong-index are honored.

  3. Monitor logs. Use the Space “Logs” tab to confirm:

    • The pipeline downloads from the expected WAN_MODEL_ID.
    • VRAM usage stays within the selected hardware tier.

  4. Freeze dependencies. When satisfied, tag the Space (v1, demo) so users know which TTM commit it matches.

You now have both a model repository (for anyone to clone/run) and a public Space for live demos. Feel free to adapt the instructions for the CogVideoX or Stable Video Diffusion pipelines if you plan to expose them as well; start by duplicating the provided Space template and swapping out run_wan.py for the relevant runner.