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# AutoencoderKLMiniMaxH3

The video variational autoencoder (VAE) model with KL loss used in [MiniMax-H3](https://huggingface.co/MiniMaxAI/MiniMax-H3) by MiniMax. It pairs a causal 3D CNN encoder with a non-causal ViT decoder and compresses 16x spatially and 4x temporally.

Three things set it apart from most autoencoders in the library:

- **Latents are normalized per channel.** There is no `scaling_factor`: a pipeline encodes with `(latent - latents_mean) / latents_std` and decodes with `latent * latents_std + latents_mean`.
- **The pixel convention is ImageNet-normalized RGB over a `[0, 1]` base range**, not the usual `[-1, 1]`. `encode` expects `(pixel - imagenet_mean) / imagenet_std` and `decode` returns values in that same space, so a pipeline applies `sample * imagenet_std + imagenet_mean` and clamps to `[0, 1]` before postprocessing.
- **Spatial tiling is on by default.** MiniMax-H3 was released with tiling enabled for both encoding and decoding and the released frames are the blended-tile ones, so turning it off changes the output. Use `enable_tiling` to change the tile geometry and `disable_tiling` to switch it off.

The temporal geometry is fixed by `clip_length` (17 pixel frames per encoder chunk) and `token_drop` (3 trailing latent frames dropped per encode), so `17 * n + 5` pixel frames map to `5 * n + 2` latent frames.

```python
import torch
from diffusers import AutoencoderKLMiniMaxH3

vae = AutoencoderKLMiniMaxH3.from_pretrained(
    "MiniMaxAI/MiniMax-H3", subfolder="vae", dtype=torch.float32
).to("cuda")
```

## AutoencoderKLMiniMaxH3

[[autodoc]] AutoencoderKLMiniMaxH3
    - encode
    - decode
    - all