from typing import Optional, Union
import torch
import math
import torch.nn as nn
from diffusers import ConfigMixin, ModelMixin
from diffusers.models.autoencoders.vae import (
DecoderOutput,
DiagonalGaussianDistribution,
)
from diffusers.models.modeling_outputs import AutoencoderKLOutput
from xora.models.autoencoders.conv_nd_factory import make_conv_nd
class AutoencoderKLWrapper(ModelMixin, ConfigMixin):
"""Variational Autoencoder (VAE) model with KL loss.
VAE from the paper Auto-Encoding Variational Bayes by Diederik P. Kingma and Max Welling.
This model is a wrapper around an encoder and a decoder, and it adds a KL loss term to the reconstruction loss.
Args:
encoder (`nn.Module`):
Encoder module.
decoder (`nn.Module`):
Decoder module.
latent_channels (`int`, *optional*, defaults to 4):
Number of latent channels.
"""
def __init__(
self,
encoder: nn.Module,
decoder: nn.Module,
latent_channels: int = 4,
dims: int = 2,
sample_size=512,
use_quant_conv: bool = True,
):
super().__init__()
# pass init params to Encoder
self.encoder = encoder
self.use_quant_conv = use_quant_conv
# pass init params to Decoder
quant_dims = 2 if dims == 2 else 3
self.decoder = decoder
if use_quant_conv:
self.quant_conv = make_conv_nd(
quant_dims, 2 * latent_channels, 2 * latent_channels, 1
)
self.post_quant_conv = make_conv_nd(
quant_dims, latent_channels, latent_channels, 1
)
else:
self.quant_conv = nn.Identity()
self.post_quant_conv = nn.Identity()
self.use_z_tiling = False
self.use_hw_tiling = False
self.dims = dims
self.z_sample_size = 1
# only relevant if vae tiling is enabled
self.set_tiling_params(sample_size=sample_size, overlap_factor=0.25)
def set_tiling_params(self, sample_size: int = 512, overlap_factor: float = 0.25):
self.tile_sample_min_size = sample_size
num_blocks = len(self.encoder.down_blocks)
self.tile_latent_min_size = int(sample_size / (2 ** (num_blocks - 1)))
self.tile_overlap_factor = overlap_factor
def enable_z_tiling(self, z_sample_size: int = 8):
r"""
Enable tiling during VAE decoding.
When this option is enabled, the VAE will split the input tensor in tiles to compute decoding in several
steps. This is useful to save some memory and allow larger batch sizes.
"""
self.use_z_tiling = z_sample_size > 1
self.z_sample_size = z_sample_size
assert (
z_sample_size % 8 == 0 or z_sample_size == 1
), f"z_sample_size must be a multiple of 8 or 1. Got {z_sample_size}."
def disable_z_tiling(self):
r"""
Disable tiling during VAE decoding. If `use_tiling` was previously invoked, this method will go back to computing
decoding in one step.
"""
self.use_z_tiling = False
def enable_hw_tiling(self):
r"""
Enable tiling during VAE decoding along the height and width dimension.
"""
self.use_hw_tiling = True
def disable_hw_tiling(self):
r"""
Disable tiling during VAE decoding along the height and width dimension.
"""
self.use_hw_tiling = False
def _hw_tiled_encode(self, x: torch.FloatTensor, return_dict: bool = True):
overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)
row_limit = self.tile_latent_min_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
rows = []
for i in range(0, x.shape[3], overlap_size):
row = []
for j in range(0, x.shape[4], overlap_size):
tile = x[
:,
:,
:,
i : i + self.tile_sample_min_size,
j : j + self.tile_sample_min_size,
]
tile = self.encoder(tile)
tile = self.quant_conv(tile)
row.append(tile)
rows.append(row)
result_rows = []
for i, row in enumerate(rows):
result_row = []
for j, tile in enumerate(row):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
if j > 0:
tile = self.blend_h(row[j - 1], tile, blend_extent)
result_row.append(tile[:, :, :, :row_limit, :row_limit])
result_rows.append(torch.cat(result_row, dim=4))
moments = torch.cat(result_rows, dim=3)
return moments
def blend_z(
self, a: torch.Tensor, b: torch.Tensor, blend_extent: int
) -> torch.Tensor:
blend_extent = min(a.shape[2], b.shape[2], blend_extent)
for z in range(blend_extent):
b[:, :, z, :, :] = a[:, :, -blend_extent + z, :, :] * (
1 - z / blend_extent
) + b[:, :, z, :, :] * (z / blend_extent)
return b
def blend_v(
self, a: torch.Tensor, b: torch.Tensor, blend_extent: int
) -> torch.Tensor:
blend_extent = min(a.shape[3], b.shape[3], blend_extent)
for y in range(blend_extent):
b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * (
1 - y / blend_extent
) + b[:, :, :, y, :] * (y / blend_extent)
return b
def blend_h(
self, a: torch.Tensor, b: torch.Tensor, blend_extent: int
) -> torch.Tensor:
blend_extent = min(a.shape[4], b.shape[4], blend_extent)
for x in range(blend_extent):
b[:, :, :, :, x] = a[:, :, :, :, -blend_extent + x] * (
1 - x / blend_extent
) + b[:, :, :, :, x] * (x / blend_extent)
return b
def _hw_tiled_decode(self, z: torch.FloatTensor, target_shape):
overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor)
row_limit = self.tile_sample_min_size - blend_extent
tile_target_shape = (
*target_shape[:3],
self.tile_sample_min_size,
self.tile_sample_min_size,
)
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
rows = []
for i in range(0, z.shape[3], overlap_size):
row = []
for j in range(0, z.shape[4], overlap_size):
tile = z[
:,
:,
:,
i : i + self.tile_latent_min_size,
j : j + self.tile_latent_min_size,
]
tile = self.post_quant_conv(tile)
decoded = self.decoder(tile, target_shape=tile_target_shape)
row.append(decoded)
rows.append(row)
result_rows = []
for i, row in enumerate(rows):
result_row = []
for j, tile in enumerate(row):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
if j > 0:
tile = self.blend_h(row[j - 1], tile, blend_extent)
result_row.append(tile[:, :, :, :row_limit, :row_limit])
result_rows.append(torch.cat(result_row, dim=4))
dec = torch.cat(result_rows, dim=3)
return dec
def encode(
self, z: torch.FloatTensor, return_dict: bool = True
) -> Union[DecoderOutput, torch.FloatTensor]:
if self.use_z_tiling and z.shape[2] > self.z_sample_size > 1:
num_splits = z.shape[2] // self.z_sample_size
sizes = [self.z_sample_size] * num_splits
sizes = (
sizes + [z.shape[2] - sum(sizes)]
if z.shape[2] - sum(sizes) > 0
else sizes
)
tiles = z.split(sizes, dim=2)
moments_tiles = [
(
self._hw_tiled_encode(z_tile, return_dict)
if self.use_hw_tiling
else self._encode(z_tile)
)
for z_tile in tiles
]
moments = torch.cat(moments_tiles, dim=2)
else:
moments = (
self._hw_tiled_encode(z, return_dict)
if self.use_hw_tiling
else self._encode(z)
)
posterior = DiagonalGaussianDistribution(moments)
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=posterior)
def _encode(self, x: torch.FloatTensor) -> AutoencoderKLOutput:
h = self.encoder(x)
moments = self.quant_conv(h)
return moments
def _decode(
self, z: torch.FloatTensor, target_shape=None
) -> Union[DecoderOutput, torch.FloatTensor]:
z = self.post_quant_conv(z)
dec = self.decoder(z, target_shape=target_shape)
return dec
def decode(
self, z: torch.FloatTensor, return_dict: bool = True, target_shape=None
) -> Union[DecoderOutput, torch.FloatTensor]:
assert target_shape is not None, "target_shape must be provided for decoding"
if self.use_z_tiling and z.shape[2] > self.z_sample_size > 1:
reduction_factor = int(
self.encoder.patch_size_t
* 2
** (
len(self.encoder.down_blocks)
- 1
- math.sqrt(self.encoder.patch_size)
)
)
split_size = self.z_sample_size // reduction_factor
num_splits = z.shape[2] // split_size
# copy target shape, and divide frame dimension (=2) by the context size
target_shape_split = list(target_shape)
target_shape_split[2] = target_shape[2] // num_splits
decoded_tiles = [
(
self._hw_tiled_decode(z_tile, target_shape_split)
if self.use_hw_tiling
else self._decode(z_tile, target_shape=target_shape_split)
)
for z_tile in torch.tensor_split(z, num_splits, dim=2)
]
decoded = torch.cat(decoded_tiles, dim=2)
else:
decoded = (
self._hw_tiled_decode(z, target_shape)
if self.use_hw_tiling
else self._decode(z, target_shape=target_shape)
)
if not return_dict:
return (decoded,)
return DecoderOutput(sample=decoded)
def forward(
self,
sample: torch.FloatTensor,
sample_posterior: bool = False,
return_dict: bool = True,
generator: Optional[torch.Generator] = None,
) -> Union[DecoderOutput, torch.FloatTensor]:
r"""
Args:
sample (`torch.FloatTensor`): Input sample.
sample_posterior (`bool`, *optional*, defaults to `False`):
Whether to sample from the posterior.
return_dict (`bool`, *optional*, defaults to `True`):
Whether to return a [`DecoderOutput`] instead of a plain tuple.
generator (`torch.Generator`, *optional*):
Generator used to sample from the posterior.
"""
x = sample
posterior = self.encode(x).latent_dist
if sample_posterior:
z = posterior.sample(generator=generator)
else:
z = posterior.mode()
dec = self.decode(z, target_shape=sample.shape).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)