Evaluated 14B step-80 settings

The released configuration corresponds to the three-encoder step-80 non-EMA model used in the human and VLM evaluations. The earlier two-encoder / 8,852-reference configuration described a different experiment.

Setting	Value
Generator	Wan2.1-T2V-14B, causal softmax attention
Initialization	Causal ODE; SHA-256 `01a21da77adf0e1e803d74ae73ac9c2bc397b1a05a74cbf89709a653282f7ab6`
Frozen encoders	DINOv3 + V-JEPA 2 + VideoMAEv2
Loss weights / divisor	10 : 1 : 1 / 3, after detached per-encoder normalization
Reference videos	8,000
Distribution / differentiation / reference minibatch	256 / 128 / 128
GPUs / per-rank generated / per-rank differentiated	8 / 32 / 16
Physical generation / logical feature / physical replay batch	2 / 16 / 1
AdamW	LR `2.5e-6`, betas `(0, 0.999)`, epsilon `1e-8`, weight decay `0`, gradient clipping `10`
Schedule	Cosine to `5e-7` over 200 updates; take checkpoint 80
Nyström / Monte Carlo mixture	2 : 1
Training video / causal chunk	81 RGB frames at 480 × 832; 21 latent frames; 3 latent frames per chunk
Inference weights	`checkpoint_model_000080/model.pt`, regular `generator`, no EMA or CFG

The original training config SHA-256 is fba9c9abd26f9b6b08ed5450c97d01a250da2747c7325db469a2e2f204f694ab. Public asset paths and descriptive identities are relocated; the three reference-pack hashes and numerical training settings are retained. The public recipe stops at update 80 without shortening the original 200-update learning-rate curve. seed: 0 chooses an effective seed at startup; exact historical training replay additionally requires its effective seed and RNG state.

Inference

The pipeline and sample-batch noise order follow the previous step-80 inference run:

Four trained stages [1000, 750, 500, 250], warped to approximately [1000, 937.5, 833.3333, 625], then a terminal t=0 cleanup.
Training-matched context-cache writes, with auxiliary noise seed 12345. Scheduler t=0 has a small nonzero noise level; cache input is noised separately from the saved clean output.
Seed 0, five samples per prompt, continuous random stream. Draw all five samples' initial/transition noises together, then execute one video at a time.
81 output frames, 832 × 480, 16 FPS.

CHECKPOINT=/path/to/checkpoint_model_000080/model.pt \
PROMPTS=/path/to/prompts.txt \
SEED=0 NUM_SAMPLES=5 OUTPUT_DIR=outputs/step80 \
bash scripts/infer.sh

Set NUM_SAMPLES=1 for a smaller demo. This changes the random-number sequence. Exact historical video reproduction requires the same prompts, ordering and shard assignment in addition to the same checkpoint. The original VBench source used four contiguous shards with 237 / 237 / 236 / 236 prompt rows, restarting seed 0 for each shard. This package provides generation, not human/VLM scoring infrastructure.

See asset preparation for encoder geometry and reference bandwidths, and validation for checks and limits.