# AutoencoderKLKVAE

The 2D variational autoencoder (VAE) model with KL loss.

The model can be loaded with the following code snippet.

```python
import torch
from diffusers import AutoencoderKLKVAE

vae = AutoencoderKLKVAE.from_pretrained("kandinskylab/KVAE-2D-1.0", subfolder="diffusers", torch_dtype=torch.bfloat16)
```

## AutoencoderKLKVAE[[diffusers.AutoencoderKLKVAE]]

- **in_channels** (int, *optional*, defaults to 3) -- Number of channels in the input image.
- **channels** (int,  *optional*, defaults to 128) -- The base number of channels in multiresolution blocks.
- **num_enc_blocks** (int, *optional*, defaults to 2) --
  The number of Resnet blocks in encoder multiresolution layers.
- **num_dec_blocks** (int, *optional*, defaults to 2) --
  The number of Resnet blocks in decoder multiresolution layers.
- **z_channels** (int, *optional*, defaults to 16) -- Number of channels in the latent space.
- **double_z** (`bool`, *optional*, defaults to `True`) --
  Whether to double the number of output channels of encoder.
- **ch_mult** (`Tuple[int, ...]`, *optional*, default to `(1, 2, 4, 8)`) --
  The channel multipliers in multiresolution blocks.
- **sample_size** (`int`, *optional*, defaults to `1024`) -- Sample input size.

A VAE model with KL loss for encoding images into latents and decoding latent representations into images.

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).

- **z** (`torch.Tensor`) -- Input batch of latent vectors.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether to return a `~models.vae.DecoderOutput` instead of a plain tuple.`~models.vae.DecoderOutput` or `tuple`If return_dict is True, a `~models.vae.DecoderOutput` is returned, otherwise a plain `tuple` is
returned.

Decode a batch of images.

- **x** (`torch.Tensor`) -- Input batch of images.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether to return a `~models.autoencoder_kl.AutoencoderKLOutput` instead of a plain tuple.The latent representations of the encoded images. If `return_dict` is True, a
`~models.autoencoder_kl.AutoencoderKLOutput` is returned, otherwise a plain `tuple` is returned.

Encode a batch of images into latents.

- **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.

- **z** (`torch.Tensor`) -- Input batch of latent vectors.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `~models.vae.DecoderOutput` instead of a plain tuple.`~models.vae.DecoderOutput` or `tuple`If return_dict is True, a `~models.vae.DecoderOutput` is returned, otherwise a plain `tuple` is
returned.

Decode a batch of images using a tiled decoder.