Asset Preparation

The source release includes training and evaluation code, the training configuration, and example prompts. Model weights, training reference data, and generated videos are stored separately.

Required assets

Use	Assets
Inference	Wan2.1-T2V-1.3B base weights, text encoder, tokenizer, VAE, and a trained Anonymous Video Generation checkpoint
Training	Inference base assets, the Self-Forcing ODE initialization, three frozen feature encoders, reference features, and Nyström summaries

The trained Anonymous Video Generation checkpoint and reference collection are not distributed in this repository. Encoder weights alone are insufficient to reproduce training with the provided configuration.

Model families (local files required)

Wan2.1-T2V-1.3B
Self-Forcing initialization
DINOv3
V-JEPA 2 ViT-L
VideoMAEv2-Large

For the supplied configuration, DINOv3 uses ViT-L/16 features from its final layer; V-JEPA 2 and VideoMAEv2 use layer 15. VideoMAEv2 is pinned to revision 9981a9c8f77118c421e5228e1b219468a4b0238d, and the DINOv3 source is pinned to commit 6876159a11b4df116f30f667f8c9888617df0751.

Directory layout

Place assets under a common root. The following filenames are retained for compatibility with the supplied configuration:

ASSET_ROOT/
├── checkpoints/ode_init.pt
├── models/
│   ├── Wan2.1-T2V-1.3B/
│   ├── dinov3-src/
│   ├── dinov3_vitl16_lvd1689m.pth
│   ├── dinov3_vitl16_lvd1689m.pth.provenance.json
│   ├── vjepa2-vitl-fpc64-256/
│   └── VideoMAEv2-Large/
├── teacher_banks/triple_video_new8682_824/
│   ├── manifest.json
│   └── ...                         # All feature shards referenced by the manifest
└── experiments/triple_video_new8682_824/
    ├── source/new8682_824_matched.jsonl
    └── references/
        ├── dinov3_vitl16_f20mean5_k1024_m256_matched5k.pt
        ├── vjepa2_l15_last64_k1024_m1024_matched5k.pt
        └── videomae_v2_l15_fixed5x16_k256_m256_matched5k.pt

The reference collection contains 8,682 videos stored in 272 feature shards. The matched5k suffix is a legacy filename; the manifest determines the actual collection size.

Keep the feature shards, manifests, and summaries together without changing their contents or sample ordering. Preserve the DINOv3 provenance file and the VideoMAEv2 download_manifest.json alongside their weights.

Configure asset paths

From the 1.3B/ directory:

export ASSET_ROOT=/path/to/video-generation-assets
python scripts/prepare_config.py --asset-root "$ASSET_ROOT"

The setup script generates a local configuration with relocated asset paths. Generated configurations are excluded from version control. Asset and source-hash preflight checks are not run by the launchers.

Reference feature preparation

Preprocessing is performed once, before generator training. It requires the raw captioned reference videos, the frozen encoders, and a CUDA device. Training subsequently reads the saved features instead of decoding those reference videos again.

Prepare the prompt/video manifest. Each entry binds a caption to an MP4 basename in a fixed sample order. The loader in matched_video_manifest.py expects a JSONL manifest and its .meta.json sidecar, including the source-data identity. A plain prompt text file is not a training manifest.
Extract all three representations. extract_triple_matched_teacher_bank.py reads the manifest and raw videos and writes feature shards plus a manifest.json. Supply the source manifest, video root, encoder paths, and model identities. For the released collection, set --expected-count 8682 and --shard-rows 32; the default sample count in this lower-level tool is different.
Build the fixed summaries. build_triple_nystrom_references.py reads the extracted feature bank and writes one Nyström reference per encoder. Supply the bank and model identities and set --expected-sample-count 8682. Keep the reference files together with the feature bank from which they were built.

Run the preprocessing tools from the 1.3B/ directory with PYTHONPATH="$PWD/code". Their --help output lists the required manifests, encoder identities, and output paths.

The feature protocol used by the training configuration is:

Encoder	Video sampling	Representation	Nyström landmarks
DINOv3 ViT-L/16	20 frames across the 81-frame video, pooled in groups of 5	Final-layer features, 1,024 tokens per video	256
V-JEPA 2 ViT-L	Last 64 contiguous frames	Layer 15, 1,024 tokens per video	1,024
VideoMAEv2-Large	Skip the first frame, then use five 16-frame clips	Layer 15, 256 tokens per clip	256

Encoder revisions, feature sampling, source ordering, and preprocessing metadata must agree between the reference assets and training configuration. Re-extraction can change serialized hashes; it should not be assumed to recreate the supplied assets byte for byte.

Using another reference collection

A new collection requires a new manifest, feature bank, Nyström summaries, and a training configuration with matching paths and identities.

Update the local configuration for a new experiment. Changing the reference collection should not be expected to reproduce the reported VBench score.

Local snapshot preparation

All model loaders require existing local directories. Hosted model repository URLs, repository IDs and automatic downloads are omitted from this source release. The legacy-named download_videomae_v2_large.py stages an existing local snapshot using --source-dir and an empty --output-dir; it does not download files. If the source includes a valid pinned download_manifest.json, that manifest is preserved for compatibility with frozen reference banks. A snapshot without a manifest gets a new local manifest; rebuild matching reference assets and update their configuration identities before training. prepare_config.py reads model identities from an available local feature bank into private local configuration files.