README.md

---
license: mit
base_model: Wan-AI/Wan2.2-TI2V-5B-Diffusers
library_name: diffusers
tags:
- diffusers
- autoencoder
- wan2.2
- taehv
---

# taew2_2 — Tiny Video AutoEncoder for Wan 2.2 (Diffusers)

[`taew2_2`](https://github.com/madebyollin/taehv) by Ollin Boer Bohan, converted to `AutoencoderTinyVideo`
(`scripts/convert_taehv_to_diffusers.py`). A tiny causal video autoencoder (11.4M parameters) for the latent space
of the Wan 2.2 VAE (`AutoencoderKLWan`, 48 channels, 16× spatial / 4× temporal): previews and real-time decoding,
roughly 50× faster than the full VAE. It works on the *normalized* (roughly unit Gaussian) Wan 2.2 latents.

```python
import torch
from diffusers import AutoencoderTinyVideo
from diffusers.models.autoencoders.autoencoder_tiny_video import TinyVideoDecodeCache

vae = AutoencoderTinyVideo.from_pretrained("YiYiXu/taew2_2-diffusers", dtype=torch.bfloat16).to("cuda")

# one shot: [B, 48, T, h, w] normalized latents -> [B, 3, 4 * T - 3, 16 * h, 16 * w] frames in [-1, 1]
frames = vae.decode(latents).sample

# chunk by chunk (streaming / world models): identical result, only the new latents are decoded per call
cache = TinyVideoDecodeCache()
for chunk in latent_chunks:
    frames = vae.decode(chunk, cache=cache).sample
```

Weights: the `taew2_2.pth` release of [madebyollin/taehv](https://github.com/madebyollin/taehv) (MIT).