# AutoencoderKLKVAEVideo

The 3D variational autoencoder (VAE) model with KL loss.

The model can be loaded with the following code snippet.

```python
import torch
from diffusers import AutoencoderKLKVAEVideo

vae = AutoencoderKLKVAEVideo.from_pretrained("kandinskylab/KVAE-3D-1.0", subfolder="diffusers", torch_dtype=torch.float16)
```

## AutoencoderKLKVAEVideo[[diffusers.AutoencoderKLKVAEVideo]]

- **ch** (`int`, *optional*, defaults to 128) -- Base channel count.
- **ch_mult** (`Tuple[int]`, *optional*, defaults to `(1, 2, 4, 8)`) -- Channel multipliers per level.
- **num_res_blocks** (`int`, *optional*, defaults to 2) -- Number of residual blocks per level.
- **in_channels** (`int`, *optional*, defaults to 3) -- Number of input channels.
- **out_ch** (`int`, *optional*, defaults to 3) -- Number of output channels.
- **z_channels** (`int`, *optional*, defaults to 16) -- Number of latent channels.
- **temporal_compress_times** (`int`, *optional*, defaults to 4) -- Temporal compression factor.

A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. Used in
[KVAE](https://github.com/kandinskylab/kvae-1).

This model inherits from [ModelMixin](/docs/diffusers/main/en/api/models/overview#diffusers.ModelMixin). Check the superclass documentation for its generic methods implemented for
all models (such as downloading or saving).

Disable sliced VAE decoding.

Enable sliced VAE decoding.

- **sample** (`torch.Tensor`) -- Input sample.
- **sample_posterior** (`bool`, *optional*, defaults to `False`) --
  Whether to sample from the posterior.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `DecoderOutput` instead of a plain tuple.
- **generator** (`torch.Generator`, *optional*) --
  A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make sampling
  deterministic.`~models.vae.DecoderOutput` or `tuple`If `return_dict` is True, a `~models.vae.DecoderOutput` is returned, otherwise a plain `tuple` is
returned.