| --- |
| 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] |
| ``` |
| |
