File size: 11,605 Bytes
cef1afc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 |
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 vae.layers.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)
|