diffusers/main/en/api/models/autoencoder_kl_kvae.md

# 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]]

#### diffusers.AutoencoderKLKVAE[[diffusers.AutoencoderKLKVAE]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/autoencoders/autoencoder_kl_kvae.py#L521)

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).

wrapperdiffusers.AutoencoderKLKVAE.decodehttps://github.com/huggingface/diffusers/blob/main/src/diffusers/utils/accelerate_utils.py#L43[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]

**Parameters:**

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.
#### forward[[diffusers.AutoencoderKLKVAE.forward]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/autoencoders/autoencoder_kl_kvae.py#L776)

**Parameters:**

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.
#### tiled_decode[[diffusers.AutoencoderKLKVAE.tiled_decode]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/autoencoders/autoencoder_kl_kvae.py#L729)

Decode a batch of images using a tiled decoder.

**Parameters:**

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.

**Returns:**

``~models.vae.DecoderOutput` or `tuple``

If return_dict is True, a `~models.vae.DecoderOutput` is returned, otherwise a plain `tuple` is
returned.