# WaveForcing-T2V-1.3B-5step-Preview

Preview release of the **WaveForcing** 1.3B text-to-video model (5 denoising steps).
Trained from Wan2.1-T2V-1.3B with Rolling-Forcing + DMD distillation. This is the
raw generator_ema checkpoint (FP32, FSDP layout) converted to a flat
`model.safetensors` (BF16, 825 tensors) for preview purposes.

## VBench scores

| Metric | Rolling Forcing | Rolling Forcing (causal inference) | Wave Forcing |
|---|---|---|---|
| Temporal Flickering | 0.976686 | 0.970226 | 0.976579 |
| Subject Consistency | 0.971557 | 0.966446 | 0.980122 |
| Background Consistency | 0.961879 | 0.958950 | 0.966289 |
| Motion Smoothness | 0.987435 | 0.983091 | 0.984803 |
| Aesthetic Quality | 0.532904 | 0.528120 | 0.530068 |
| Imaging Quality | 0.707718 | 0.673502 | 0.711773 |
| Quality Drift | 0.01525 | 0.02915 | 0.00016 |

Wave Forcing improves subject/background consistency and quality drift over the
Rolling-Forcing baselines.

## Generation throughput (8 GPUs, 399 latent frames / 100 s video)

| Topology | Single-GPU baseline | Fastest config | Steady fps (p50) | E2E fps | Speedup |
|---|---|---|---|---|---|
| 4+3 (4-step) | 15.0 fps (106.5 s) | 1.3B sage causal paged | 125.7 | 117.7 | 7.9× |
| 5+2 (5-step) | 11.9 fps (134.4 s) | 1.3B bf16 causal paged | 86.5 | 84.2 | 7.1× |

Notes:

- E2E fps = 1596 pixel frames / wall time (diffusion + VAE decode, video saved).
  Steady fps = 12×1000 / p50 tick, steady-state window.
- Single-GPU baseline: full Rolling-Forcing generation (diffusion + VAE) on one GPU.
- On 5+2, sage/sagefp8 attention reaches ~127 fps steady state, but the 2-stage VAE
  is a structural bottleneck, capping E2E at ~54 fps; bf16 causal paged is the best
  E2E config there.
- Real generation runs (BF16 causal overlap, 399 frames): 83.0 fps E2E on 4+3,
  81.9 fps E2E on 5+2.