---
license: cc-by-4.0
pretty_name: Lifelong Drive
tags:
- video
- lifelong-learning
- continual-learning
- online-learning
- video-diffusion
- video-prediction
- world-models
- autonomous-driving
- latent-diffusion
size_categories:
- 1M<n<10M
viewer: false
---

# Lifelong Drive

One of the five single-video-stream datasets introduced in
**Lifelong Learning of Video Diffusion Models From a Single Video Stream**
([arXiv:2406.04814](https://arxiv.org/abs/2406.04814)).
Code: <https://github.com/plai-group/lifelong-vdm>.

Continuous dashcam footage of a 1,675 km drive from Chongqing to Shanghai. The drive spans
mountains, cities and tunnels, sunny, cloudy and rainy weather, and other vehicles moving at
different speeds: real-world perceptual data from a single correlated and non-stationary stream.
The drive is nearly continuous; rare, subtle discontinuities occur where the driver took breaks
(the video resumes at the same or a nearby highway location).

The repository holds both the 512 x 512 RGB video and the pre-computed Stable Diffusion latents the
paper's models were trained on.

| | Train stream | Test stream |
|---|---|---|
| Frames | 1,000,000 | 100,000 |
| Source video | 512 x 512, 20 FPS (~13.9 h), `train/LifelongDriveTrain.mp4` | `test/LifelongDriveTest.mp4` |
| Latents | 4 x 64 x 64 per frame, 2,000 x 500 frames | 200 x 500 frames |

The test stream is the 100,000 frames that follow the train stream in the same video.

## Files

```
train/LifelongDriveTrain.mp4
train/encoded_video/batch_0000.pt … batch_1999.pt
test/LifelongDriveTest.mp4
test/encoded_video/batch_0000.pt … batch_0199.pt
```

Each `batch_XXXX.pt` is a `torch.save`d dict covering 500 consecutive frames
(`batch_i` = stream frames `i*500 … i*500+499`):

| key | shape | dtype | meaning |
|---|---|---|---|
| `quantized_latents` | (500, 4, 64, 64) | uint8 | per-frame 8-bit quantized VAE latents |
| `min_vals` | (500,) | float16 | per-frame dequantization offset |
| `scales` | (500,) | float16 | per-frame dequantization scale |

Latents were produced by `datasets/stable_diffusion_encoder.py` in the code repository: frames are
mapped to [-1, 1], encoded with the SDXL VAE
[`madebyollin/sdxl-vae-fp16-fix`](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)
(`latent = vae.encode(x).latent_dist.sample() * 0.13025`), then quantized per frame with
`q = round((latent - min) / scale)`, `scale = (max - min) / 255`.

## Loading

```python
import torch
d = torch.load("train/encoded_video/batch_0000.pt", map_location="cpu")
q, lo, sc = d["quantized_latents"], d["min_vals"], d["scales"]
latents = q.float() * sc.float()[:, None, None, None] + lo.float()[:, None, None, None]  # (500, 4, 64, 64)

# optional: decode back to RGB with diffusers
from diffusers import AutoencoderKL
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix")
rgb = (vae.decode(latents[:4] / 0.13025).sample.clamp(-1, 1) + 1) / 2  # (4, 3, 512, 512) in [0, 1]
```

To use the dataset with the paper's code (it expects the directory at `datasets/drive`):

```bash
pip install -U huggingface_hub
hf download jason-yoo-108/lifelong-drive --repo-type dataset --local-dir datasets/drive   # add --exclude "*.mp4" to skip the video
```

## Provenance and license

The footage comes from the YouTube video
["1674.8 kilometers of long-distance travel across half of China - driving from Chongqing to Shanghai"](https://www.youtube.com/watch?v=-iSewsdMxkg)
by 中国街景 ChinaStreetView (2023). It was cropped and downsampled to 512 x 512 at 20 FPS; the first
1,000,000 frames form the train stream and the next 100,000 the test stream (recipe in the
`datasets/mp4_to_npy.py` docstring of the code repository).

The derived video and latents are released under
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) for research use. The underlying footage
belongs to its original creator; please credit the source channel when redistributing.
<!-- TODO(author): review the licensing statement above before publishing. -->

## Citation

```bibtex
@article{yoo2024lifelong,
  title   = {Lifelong Learning of Video Diffusion Models From a Single Video Stream},
  author  = {Yoo, Jason and He, Yingchen and Naderiparizi, Saeid and Green, Dylan and van de Ven, Gido M. and Pleiss, Geoff and Wood, Frank},
  journal = {arXiv preprint arXiv:2406.04814},
  year    = {2024}
}
```