---
tags:
- video
- text-to-video
- video-diffusion
- latents
---

# revt2v-data

Precomputed latent dataset for revT2V (reverse-time video diffusion distillation).
Each `.pt` file is one `(prompt, seed)` clip generated from the frozen
`ali-vilab/text-to-video-ms-1.7b` teacher pipeline. Every record is a dict with:

- `prompt` -- the text prompt
- `latents` -- forward-time latents, shape `(C, F, H, W)`
- `reversed_latents` -- the same latents flipped along the frame axis (the training target)
- `prompt_embeds` / `negative_prompt_embeds` -- cached text-encoder outputs

## Structure

- `dataset1/` -- 154 shards (the original dataset)
- `dataset2/` -- 416 shards + `manifest.jsonl` (prompt, seed, motion_score, kept)

## Loading

```python
from revt2v.data import LatentDataset

ds = LatentDataset("path/to/dataset1")  # or dataset2
record = ds[0]
```