VQModel  
A VQ-VAE model for decoding latent representations.  
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).  
Parameters:
in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
out_channels (int,  *optional*, defaults to 3): Number of channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
Tuple of downsample block types.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
Tuple of upsample block types.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
Tuple of block output channels.
layers_per_block (`int`, *optional*, defaults to `1`): Number of layers per block.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
latent_channels (`int`, *optional*, defaults to `3`): Number of channels in the latent space.
sample_size (`int`, *optional*, defaults to `32`): Sample input size.
num_vq_embeddings (`int`, *optional*, defaults to `256`): Number of codebook vectors in the VQ-VAE.
norm_num_groups (`int`, *optional*, defaults to `32`): Number of groups for normalization layers.
vq_embed_dim (`int`, *optional*): Hidden dim of codebook vectors in the VQ-VAE.
scaling_factor (`float`, *optional*, defaults to `0.18215`):
The component-wise standard deviation of the trained latent space computed using the first batch of the
training set. This is used to scale the latent space to have unit variance when training the diffusion
model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
/ scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
norm_type (`str`, *optional*, defaults to `"group"`):
Type of normalization layer to use. Can be one of `"group"` or `"spatial"`.
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md