vae_flax.py

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# JAX implementation of VQGAN from taming-transformers https://github.com/CompVis/taming-transformers

import math
from functools import partial
from typing import Tuple

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..modeling_flax_utils import FlaxModelMixin
from ..utils import BaseOutput


@flax.struct.dataclass
class FlaxDecoderOutput(BaseOutput):
    """
    Output of decoding method.

    Args:
        sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
            Decoded output sample of the model. Output of the last layer of the model.
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    sample: jnp.ndarray


@flax.struct.dataclass
class FlaxAutoencoderKLOutput(BaseOutput):
    """
    Output of AutoencoderKL encoding method.

    Args:
        latent_dist (`FlaxDiagonalGaussianDistribution`):
            Encoded outputs of `Encoder` represented as the mean and logvar of `FlaxDiagonalGaussianDistribution`.
            `FlaxDiagonalGaussianDistribution` allows for sampling latents from the distribution.
    """

    latent_dist: "FlaxDiagonalGaussianDistribution"


class FlaxUpsample2D(nn.Module):
    """
    Flax implementation of 2D Upsample layer

    Args:
        in_channels (`int`):
            Input channels
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    in_channels: int
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.conv = nn.Conv(
            self.in_channels,
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

    def __call__(self, hidden_states):
        batch, height, width, channels = hidden_states.shape
        hidden_states = jax.image.resize(
            hidden_states,
            shape=(batch, height * 2, width * 2, channels),
            method="nearest",
        )
        hidden_states = self.conv(hidden_states)
        return hidden_states


class FlaxDownsample2D(nn.Module):
    """
    Flax implementation of 2D Downsample layer

    Args:
        in_channels (`int`):
            Input channels
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    in_channels: int
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.conv = nn.Conv(
            self.in_channels,
            kernel_size=(3, 3),
            strides=(2, 2),
            padding="VALID",
            dtype=self.dtype,
        )

    def __call__(self, hidden_states):
        pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
        hidden_states = jnp.pad(hidden_states, pad_width=pad)
        hidden_states = self.conv(hidden_states)
        return hidden_states


class FlaxResnetBlock2D(nn.Module):
    """
    Flax implementation of 2D Resnet Block.

    Args:
        in_channels (`int`):
            Input channels
        out_channels (`int`):
            Output channels
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        groups (:obj:`int`, *optional*, defaults to `32`):
            The number of groups to use for group norm.
        use_nin_shortcut (:obj:`bool`, *optional*, defaults to `None`):
            Whether to use `nin_shortcut`. This activates a new layer inside ResNet block
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """

    in_channels: int
    out_channels: int = None
    dropout: float = 0.0
    groups: int = 32
    use_nin_shortcut: bool = None
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        out_channels = self.in_channels if self.out_channels is None else self.out_channels

        self.norm1 = nn.GroupNorm(num_groups=self.groups, epsilon=1e-6)
        self.conv1 = nn.Conv(
            out_channels,
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

        self.norm2 = nn.GroupNorm(num_groups=self.groups, epsilon=1e-6)
        self.dropout_layer = nn.Dropout(self.dropout)
        self.conv2 = nn.Conv(
            out_channels,
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

        use_nin_shortcut = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

        self.conv_shortcut = None
        if use_nin_shortcut:
            self.conv_shortcut = nn.Conv(
                out_channels,
                kernel_size=(1, 1),
                strides=(1, 1),
                padding="VALID",
                dtype=self.dtype,
            )

    def __call__(self, hidden_states, deterministic=True):
        residual = hidden_states
        hidden_states = self.norm1(hidden_states)
        hidden_states = nn.swish(hidden_states)
        hidden_states = self.conv1(hidden_states)

        hidden_states = self.norm2(hidden_states)
        hidden_states = nn.swish(hidden_states)
        hidden_states = self.dropout_layer(hidden_states, deterministic)
        hidden_states = self.conv2(hidden_states)

        if self.conv_shortcut is not None:
            residual = self.conv_shortcut(residual)

        return hidden_states + residual


class FlaxAttentionBlock(nn.Module):
    r"""
    Flax Convolutional based multi-head attention block for diffusion-based VAE.

    Parameters:
        channels (:obj:`int`):
            Input channels
        num_head_channels (:obj:`int`, *optional*, defaults to `None`):
            Number of attention heads
        num_groups (:obj:`int`, *optional*, defaults to `32`):
            The number of groups to use for group norm
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`

    """
    channels: int
    num_head_channels: int = None
    num_groups: int = 32
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.num_heads = self.channels // self.num_head_channels if self.num_head_channels is not None else 1

        dense = partial(nn.Dense, self.channels, dtype=self.dtype)

        self.group_norm = nn.GroupNorm(num_groups=self.num_groups, epsilon=1e-6)
        self.query, self.key, self.value = dense(), dense(), dense()
        self.proj_attn = dense()

    def transpose_for_scores(self, projection):
        new_projection_shape = projection.shape[:-1] + (self.num_heads, -1)
        # move heads to 2nd position (B, T, H * D) -> (B, T, H, D)
        new_projection = projection.reshape(new_projection_shape)
        # (B, T, H, D) -> (B, H, T, D)
        new_projection = jnp.transpose(new_projection, (0, 2, 1, 3))
        return new_projection

    def __call__(self, hidden_states):
        residual = hidden_states
        batch, height, width, channels = hidden_states.shape

        hidden_states = self.group_norm(hidden_states)

        hidden_states = hidden_states.reshape((batch, height * width, channels))

        query = self.query(hidden_states)
        key = self.key(hidden_states)
        value = self.value(hidden_states)

        # transpose
        query = self.transpose_for_scores(query)
        key = self.transpose_for_scores(key)
        value = self.transpose_for_scores(value)

        # compute attentions
        scale = 1 / math.sqrt(math.sqrt(self.channels / self.num_heads))
        attn_weights = jnp.einsum("...qc,...kc->...qk", query * scale, key * scale)
        attn_weights = nn.softmax(attn_weights, axis=-1)

        # attend to values
        hidden_states = jnp.einsum("...kc,...qk->...qc", value, attn_weights)

        hidden_states = jnp.transpose(hidden_states, (0, 2, 1, 3))
        new_hidden_states_shape = hidden_states.shape[:-2] + (self.channels,)
        hidden_states = hidden_states.reshape(new_hidden_states_shape)

        hidden_states = self.proj_attn(hidden_states)
        hidden_states = hidden_states.reshape((batch, height, width, channels))
        hidden_states = hidden_states + residual
        return hidden_states


class FlaxDownEncoderBlock2D(nn.Module):
    r"""
    Flax Resnet blocks-based Encoder block for diffusion-based VAE.

    Parameters:
        in_channels (:obj:`int`):
            Input channels
        out_channels (:obj:`int`):
            Output channels
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        num_layers (:obj:`int`, *optional*, defaults to 1):
            Number of Resnet layer block
        resnet_groups (:obj:`int`, *optional*, defaults to `32`):
            The number of groups to use for the Resnet block group norm
        add_downsample (:obj:`bool`, *optional*, defaults to `True`):
            Whether to add downsample layer
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """
    in_channels: int
    out_channels: int
    dropout: float = 0.0
    num_layers: int = 1
    resnet_groups: int = 32
    add_downsample: bool = True
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        resnets = []
        for i in range(self.num_layers):
            in_channels = self.in_channels if i == 0 else self.out_channels

            res_block = FlaxResnetBlock2D(
                in_channels=in_channels,
                out_channels=self.out_channels,
                dropout=self.dropout,
                groups=self.resnet_groups,
                dtype=self.dtype,
            )
            resnets.append(res_block)
        self.resnets = resnets

        if self.add_downsample:
            self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)

    def __call__(self, hidden_states, deterministic=True):
        for resnet in self.resnets:
            hidden_states = resnet(hidden_states, deterministic=deterministic)

        if self.add_downsample:
            hidden_states = self.downsamplers_0(hidden_states)

        return hidden_states


class FlaxUpDecoderBlock2D(nn.Module):
    r"""
    Flax Resnet blocks-based Decoder block for diffusion-based VAE.

    Parameters:
        in_channels (:obj:`int`):
            Input channels
        out_channels (:obj:`int`):
            Output channels
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        num_layers (:obj:`int`, *optional*, defaults to 1):
            Number of Resnet layer block
        resnet_groups (:obj:`int`, *optional*, defaults to `32`):
            The number of groups to use for the Resnet block group norm
        add_upsample (:obj:`bool`, *optional*, defaults to `True`):
            Whether to add upsample layer
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """
    in_channels: int
    out_channels: int
    dropout: float = 0.0
    num_layers: int = 1
    resnet_groups: int = 32
    add_upsample: bool = True
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        resnets = []
        for i in range(self.num_layers):
            in_channels = self.in_channels if i == 0 else self.out_channels
            res_block = FlaxResnetBlock2D(
                in_channels=in_channels,
                out_channels=self.out_channels,
                dropout=self.dropout,
                groups=self.resnet_groups,
                dtype=self.dtype,
            )
            resnets.append(res_block)

        self.resnets = resnets

        if self.add_upsample:
            self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)

    def __call__(self, hidden_states, deterministic=True):
        for resnet in self.resnets:
            hidden_states = resnet(hidden_states, deterministic=deterministic)

        if self.add_upsample:
            hidden_states = self.upsamplers_0(hidden_states)

        return hidden_states


class FlaxUNetMidBlock2D(nn.Module):
    r"""
    Flax Unet Mid-Block module.

    Parameters:
        in_channels (:obj:`int`):
            Input channels
        dropout (:obj:`float`, *optional*, defaults to 0.0):
            Dropout rate
        num_layers (:obj:`int`, *optional*, defaults to 1):
            Number of Resnet layer block
        resnet_groups (:obj:`int`, *optional*, defaults to `32`):
            The number of groups to use for the Resnet and Attention block group norm
        attn_num_head_channels (:obj:`int`, *optional*, defaults to `1`):
            Number of attention heads for each attention block
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """
    in_channels: int
    dropout: float = 0.0
    num_layers: int = 1
    resnet_groups: int = 32
    attn_num_head_channels: int = 1
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        resnet_groups = self.resnet_groups if self.resnet_groups is not None else min(self.in_channels // 4, 32)

        # there is always at least one resnet
        resnets = [
            FlaxResnetBlock2D(
                in_channels=self.in_channels,
                out_channels=self.in_channels,
                dropout=self.dropout,
                groups=resnet_groups,
                dtype=self.dtype,
            )
        ]

        attentions = []

        for _ in range(self.num_layers):
            attn_block = FlaxAttentionBlock(
                channels=self.in_channels,
                num_head_channels=self.attn_num_head_channels,
                num_groups=resnet_groups,
                dtype=self.dtype,
            )
            attentions.append(attn_block)

            res_block = FlaxResnetBlock2D(
                in_channels=self.in_channels,
                out_channels=self.in_channels,
                dropout=self.dropout,
                groups=resnet_groups,
                dtype=self.dtype,
            )
            resnets.append(res_block)

        self.resnets = resnets
        self.attentions = attentions

    def __call__(self, hidden_states, deterministic=True):
        hidden_states = self.resnets[0](hidden_states, deterministic=deterministic)
        for attn, resnet in zip(self.attentions, self.resnets[1:]):
            hidden_states = attn(hidden_states)
            hidden_states = resnet(hidden_states, deterministic=deterministic)

        return hidden_states


class FlaxEncoder(nn.Module):
    r"""
    Flax Implementation of VAE Encoder.

    This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
    general usage and behavior.

    Finally, this model supports inherent JAX features such as:
    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

    Parameters:
        in_channels (:obj:`int`, *optional*, defaults to 3):
            Input channels
        out_channels (:obj:`int`, *optional*, defaults to 3):
            Output channels
        down_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(DownEncoderBlock2D)`):
            DownEncoder block type
        block_out_channels (:obj:`Tuple[str]`, *optional*, defaults to `(64,)`):
            Tuple containing the number of output channels for each block
        layers_per_block (:obj:`int`, *optional*, defaults to `2`):
            Number of Resnet layer for each block
        norm_num_groups (:obj:`int`, *optional*, defaults to `32`):
            norm num group
        act_fn (:obj:`str`, *optional*, defaults to `silu`):
            Activation function
        double_z (:obj:`bool`, *optional*, defaults to `False`):
            Whether to double the last output channels
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            Parameters `dtype`
    """
    in_channels: int = 3
    out_channels: int = 3
    down_block_types: Tuple[str] = ("DownEncoderBlock2D",)
    block_out_channels: Tuple[int] = (64,)
    layers_per_block: int = 2
    norm_num_groups: int = 32
    act_fn: str = "silu"
    double_z: bool = False
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        block_out_channels = self.block_out_channels
        # in
        self.conv_in = nn.Conv(
            block_out_channels[0],
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

        # downsampling
        down_blocks = []
        output_channel = block_out_channels[0]
        for i, _ in enumerate(self.down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1

            down_block = FlaxDownEncoderBlock2D(
                in_channels=input_channel,
                out_channels=output_channel,
                num_layers=self.layers_per_block,
                resnet_groups=self.norm_num_groups,
                add_downsample=not is_final_block,
                dtype=self.dtype,
            )
            down_blocks.append(down_block)
        self.down_blocks = down_blocks

        # middle
        self.mid_block = FlaxUNetMidBlock2D(
            in_channels=block_out_channels[-1],
            resnet_groups=self.norm_num_groups,
            attn_num_head_channels=None,
            dtype=self.dtype,
        )

        # end
        conv_out_channels = 2 * self.out_channels if self.double_z else self.out_channels
        self.conv_norm_out = nn.GroupNorm(num_groups=self.norm_num_groups, epsilon=1e-6)
        self.conv_out = nn.Conv(
            conv_out_channels,
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

    def __call__(self, sample, deterministic: bool = True):
        # in
        sample = self.conv_in(sample)

        # downsampling
        for block in self.down_blocks:
            sample = block(sample, deterministic=deterministic)

        # middle
        sample = self.mid_block(sample, deterministic=deterministic)

        # end
        sample = self.conv_norm_out(sample)
        sample = nn.swish(sample)
        sample = self.conv_out(sample)

        return sample


class FlaxDecoder(nn.Module):
    r"""
    Flax Implementation of VAE Decoder.

    This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
    general usage and behavior.

    Finally, this model supports inherent JAX features such as:
    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

    Parameters:
        in_channels (:obj:`int`, *optional*, defaults to 3):
            Input channels
        out_channels (:obj:`int`, *optional*, defaults to 3):
            Output channels
        up_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(UpDecoderBlock2D)`):
            UpDecoder block type
        block_out_channels (:obj:`Tuple[str]`, *optional*, defaults to `(64,)`):
            Tuple containing the number of output channels for each block
        layers_per_block (:obj:`int`, *optional*, defaults to `2`):
            Number of Resnet layer for each block
        norm_num_groups (:obj:`int`, *optional*, defaults to `32`):
            norm num group
        act_fn (:obj:`str`, *optional*, defaults to `silu`):
            Activation function
        double_z (:obj:`bool`, *optional*, defaults to `False`):
            Whether to double the last output channels
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            parameters `dtype`
    """
    in_channels: int = 3
    out_channels: int = 3
    up_block_types: Tuple[str] = ("UpDecoderBlock2D",)
    block_out_channels: int = (64,)
    layers_per_block: int = 2
    norm_num_groups: int = 32
    act_fn: str = "silu"
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        block_out_channels = self.block_out_channels

        # z to block_in
        self.conv_in = nn.Conv(
            block_out_channels[-1],
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

        # middle
        self.mid_block = FlaxUNetMidBlock2D(
            in_channels=block_out_channels[-1],
            resnet_groups=self.norm_num_groups,
            attn_num_head_channels=None,
            dtype=self.dtype,
        )

        # upsampling
        reversed_block_out_channels = list(reversed(block_out_channels))
        output_channel = reversed_block_out_channels[0]
        up_blocks = []
        for i, _ in enumerate(self.up_block_types):
            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]

            is_final_block = i == len(block_out_channels) - 1

            up_block = FlaxUpDecoderBlock2D(
                in_channels=prev_output_channel,
                out_channels=output_channel,
                num_layers=self.layers_per_block + 1,
                resnet_groups=self.norm_num_groups,
                add_upsample=not is_final_block,
                dtype=self.dtype,
            )
            up_blocks.append(up_block)
            prev_output_channel = output_channel

        self.up_blocks = up_blocks

        # end
        self.conv_norm_out = nn.GroupNorm(num_groups=self.norm_num_groups, epsilon=1e-6)
        self.conv_out = nn.Conv(
            self.out_channels,
            kernel_size=(3, 3),
            strides=(1, 1),
            padding=((1, 1), (1, 1)),
            dtype=self.dtype,
        )

    def __call__(self, sample, deterministic: bool = True):
        # z to block_in
        sample = self.conv_in(sample)

        # middle
        sample = self.mid_block(sample, deterministic=deterministic)

        # upsampling
        for block in self.up_blocks:
            sample = block(sample, deterministic=deterministic)

        sample = self.conv_norm_out(sample)
        sample = nn.swish(sample)
        sample = self.conv_out(sample)

        return sample


class FlaxDiagonalGaussianDistribution(object):
    def __init__(self, parameters, deterministic=False):
        # Last axis to account for channels-last
        self.mean, self.logvar = jnp.split(parameters, 2, axis=-1)
        self.logvar = jnp.clip(self.logvar, -30.0, 20.0)
        self.deterministic = deterministic
        self.std = jnp.exp(0.5 * self.logvar)
        self.var = jnp.exp(self.logvar)
        if self.deterministic:
            self.var = self.std = jnp.zeros_like(self.mean)

    def sample(self, key):
        return self.mean + self.std * jax.random.normal(key, self.mean.shape)

    def kl(self, other=None):
        if self.deterministic:
            return jnp.array([0.0])

        if other is None:
            return 0.5 * jnp.sum(self.mean**2 + self.var - 1.0 - self.logvar, axis=[1, 2, 3])

        return 0.5 * jnp.sum(
            jnp.square(self.mean - other.mean) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar,
            axis=[1, 2, 3],
        )

    def nll(self, sample, axis=[1, 2, 3]):
        if self.deterministic:
            return jnp.array([0.0])

        logtwopi = jnp.log(2.0 * jnp.pi)
        return 0.5 * jnp.sum(logtwopi + self.logvar + jnp.square(sample - self.mean) / self.var, axis=axis)

    def mode(self):
        return self.mean


@flax_register_to_config
class FlaxAutoencoderKL(nn.Module, FlaxModelMixin, ConfigMixin):
    r"""
    Flax Implementation of Variational Autoencoder (VAE) model with KL loss from the paper Auto-Encoding Variational
    Bayes by Diederik P. Kingma and Max Welling.

    This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
    general usage and behavior.

    Finally, this model supports inherent JAX features such as:
    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

    Parameters:
        in_channels (:obj:`int`, *optional*, defaults to 3):
            Input channels
        out_channels (:obj:`int`, *optional*, defaults to 3):
            Output channels
        down_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(DownEncoderBlock2D)`):
            DownEncoder block type
        up_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(UpDecoderBlock2D)`):
            UpDecoder block type
        block_out_channels (:obj:`Tuple[str]`, *optional*, defaults to `(64,)`):
            Tuple containing the number of output channels for each block
        layers_per_block (:obj:`int`, *optional*, defaults to `2`):
            Number of Resnet layer for each block
        act_fn (:obj:`str`, *optional*, defaults to `silu`):
            Activation function
        latent_channels (:obj:`int`, *optional*, defaults to `4`):
            Latent space channels
        norm_num_groups (:obj:`int`, *optional*, defaults to `32`):
            Norm num group
        sample_size (:obj:`int`, *optional*, defaults to `32`):
            Sample input size
        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
            parameters `dtype`
    """
    in_channels: int = 3
    out_channels: int = 3
    down_block_types: Tuple[str] = ("DownEncoderBlock2D",)
    up_block_types: Tuple[str] = ("UpDecoderBlock2D",)
    block_out_channels: Tuple[int] = (64,)
    layers_per_block: int = 1
    act_fn: str = "silu"
    latent_channels: int = 4
    norm_num_groups: int = 32
    sample_size: int = 32
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.encoder = FlaxEncoder(
            in_channels=self.config.in_channels,
            out_channels=self.config.latent_channels,
            down_block_types=self.config.down_block_types,
            block_out_channels=self.config.block_out_channels,
            layers_per_block=self.config.layers_per_block,
            act_fn=self.config.act_fn,
            norm_num_groups=self.config.norm_num_groups,
            double_z=True,
            dtype=self.dtype,
        )
        self.decoder = FlaxDecoder(
            in_channels=self.config.latent_channels,
            out_channels=self.config.out_channels,
            up_block_types=self.config.up_block_types,
            block_out_channels=self.config.block_out_channels,
            layers_per_block=self.config.layers_per_block,
            norm_num_groups=self.config.norm_num_groups,
            act_fn=self.config.act_fn,
            dtype=self.dtype,
        )
        self.quant_conv = nn.Conv(
            2 * self.config.latent_channels,
            kernel_size=(1, 1),
            strides=(1, 1),
            padding="VALID",
            dtype=self.dtype,
        )
        self.post_quant_conv = nn.Conv(
            self.config.latent_channels,
            kernel_size=(1, 1),
            strides=(1, 1),
            padding="VALID",
            dtype=self.dtype,
        )

    def init_weights(self, rng: jax.random.PRNGKey) -> FrozenDict:
        # init input tensors
        sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
        sample = jnp.zeros(sample_shape, dtype=jnp.float32)

        params_rng, dropout_rng, gaussian_rng = jax.random.split(rng, 3)
        rngs = {"params": params_rng, "dropout": dropout_rng, "gaussian": gaussian_rng}

        return self.init(rngs, sample)["params"]

    def encode(self, sample, deterministic: bool = True, return_dict: bool = True):
        sample = jnp.transpose(sample, (0, 2, 3, 1))

        hidden_states = self.encoder(sample, deterministic=deterministic)
        moments = self.quant_conv(hidden_states)
        posterior = FlaxDiagonalGaussianDistribution(moments)

        if not return_dict:
            return (posterior,)

        return FlaxAutoencoderKLOutput(latent_dist=posterior)

    def decode(self, latents, deterministic: bool = True, return_dict: bool = True):
        if latents.shape[-1] != self.config.latent_channels:
            latents = jnp.transpose(latents, (0, 2, 3, 1))

        hidden_states = self.post_quant_conv(latents)
        hidden_states = self.decoder(hidden_states, deterministic=deterministic)

        hidden_states = jnp.transpose(hidden_states, (0, 3, 1, 2))

        if not return_dict:
            return (hidden_states,)

        return FlaxDecoderOutput(sample=hidden_states)

    def __call__(self, sample, sample_posterior=False, deterministic: bool = True, return_dict: bool = True):
        posterior = self.encode(sample, deterministic=deterministic, return_dict=return_dict)
        if sample_posterior:
            rng = self.make_rng("gaussian")
            hidden_states = posterior.latent_dist.sample(rng)
        else:
            hidden_states = posterior.latent_dist.mode()

        sample = self.decode(hidden_states, return_dict=return_dict).sample

        if not return_dict:
            return (sample,)

        return FlaxDecoderOutput(sample=sample)