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from __future__ import annotations
from typing import Any, Dict, Optional, Sequence
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import nn
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.modeling_utils import ModelMixin
def swish(x: torch.Tensor) -> torch.Tensor:
return x * torch.sigmoid(x)
class ResBlock(nn.Module):
def __init__(
self,
in_filters: int,
out_filters: int,
use_conv_shortcut: bool = False,
use_agn: bool = False,
) -> None:
super().__init__()
self.in_filters = in_filters
self.out_filters = out_filters
self.use_conv_shortcut = use_conv_shortcut
self.use_agn = use_agn
if not use_agn:
self.norm1 = nn.GroupNorm(32, in_filters, eps=1e-6)
self.norm2 = nn.GroupNorm(32, out_filters, eps=1e-6)
self.conv1 = nn.Conv2d(in_filters, out_filters, kernel_size=3, padding=1, bias=False)
self.conv2 = nn.Conv2d(out_filters, out_filters, kernel_size=3, padding=1, bias=False)
if in_filters != out_filters:
if use_conv_shortcut:
self.conv_shortcut = nn.Conv2d(in_filters, out_filters, kernel_size=3, padding=1, bias=False)
else:
self.nin_shortcut = nn.Conv2d(in_filters, out_filters, kernel_size=1, padding=0, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
residual = x
if not self.use_agn:
x = self.norm1(x)
x = swish(x)
x = self.conv1(x)
x = self.norm2(x)
x = swish(x)
x = self.conv2(x)
if self.in_filters != self.out_filters:
if self.use_conv_shortcut:
residual = self.conv_shortcut(residual)
else:
residual = self.nin_shortcut(residual)
return x + residual
class Encoder(nn.Module):
def __init__(
self,
*,
ch: int,
out_ch: int,
in_channels: int,
num_res_blocks: int,
z_channels: int,
ch_mult: Sequence[int] = (1, 2, 2, 4),
resolution: Optional[int] = None,
double_z: bool = False,
) -> None:
super().__init__()
del out_ch, double_z
self.in_channels = in_channels
self.z_channels = z_channels
self.resolution = resolution
self.num_res_blocks = num_res_blocks
self.num_blocks = len(ch_mult)
self.conv_in = nn.Conv2d(in_channels, ch, kernel_size=3, padding=1, bias=False)
self.down = nn.ModuleList()
in_ch_mult = (1,) + tuple(ch_mult)
block_out = ch * ch_mult[0]
for i_level in range(self.num_blocks):
block = nn.ModuleList()
block_in = ch * in_ch_mult[i_level]
block_out = ch * ch_mult[i_level]
for _ in range(self.num_res_blocks):
block.append(ResBlock(block_in, block_out))
block_in = block_out
down = nn.Module()
down.block = block
if i_level < self.num_blocks - 1:
down.downsample = nn.Conv2d(block_out, block_out, kernel_size=3, stride=2, padding=1)
self.down.append(down)
self.mid_block = nn.ModuleList([ResBlock(block_out, block_out) for _ in range(self.num_res_blocks)])
self.norm_out = nn.GroupNorm(32, block_out, eps=1e-6)
self.conv_out = nn.Conv2d(block_out, z_channels, kernel_size=1)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.conv_in(x)
for i_level in range(self.num_blocks):
for i_block in range(self.num_res_blocks):
x = self.down[i_level].block[i_block](x)
if i_level < self.num_blocks - 1:
x = self.down[i_level].downsample(x)
for block in self.mid_block:
x = block(x)
x = self.norm_out(x)
x = swish(x)
x = self.conv_out(x)
return x
def depth_to_space(x: torch.Tensor, block_size: int) -> torch.Tensor:
if x.dim() < 3:
raise ValueError("Expected a channels-first (*CHW) tensor of at least 3 dims.")
c, h, w = x.shape[-3:]
s = block_size**2
if c % s != 0:
raise ValueError(f"Expected C divisible by {s}, but got C={c}.")
outer_dims = x.shape[:-3]
x = x.view(-1, block_size, block_size, c // s, h, w)
x = x.permute(0, 3, 4, 1, 5, 2)
x = x.contiguous().view(*outer_dims, c // s, h * block_size, w * block_size)
return x
class Upsampler(nn.Module):
def __init__(self, dim: int) -> None:
super().__init__()
self.conv1 = nn.Conv2d(dim, dim * 4, kernel_size=3, padding=1)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return depth_to_space(self.conv1(x), block_size=2)
class AdaptiveGroupNorm(nn.Module):
def __init__(self, z_channel: int, in_filters: int, num_groups: int = 32, eps: float = 1e-6) -> None:
super().__init__()
self.gn = nn.GroupNorm(num_groups=num_groups, num_channels=in_filters, eps=eps, affine=False)
self.gamma = nn.Linear(z_channel, in_filters)
self.beta = nn.Linear(z_channel, in_filters)
self.eps = eps
def forward(self, x: torch.Tensor, quantizer: torch.Tensor) -> torch.Tensor:
bsz, channels, _, _ = x.shape
scale = rearrange(quantizer, "b c h w -> b c (h w)")
scale = scale.var(dim=-1) + self.eps
scale = scale.sqrt()
scale = self.gamma(scale).view(bsz, channels, 1, 1)
bias = rearrange(quantizer, "b c h w -> b c (h w)")
bias = bias.mean(dim=-1)
bias = self.beta(bias).view(bsz, channels, 1, 1)
x = self.gn(x)
return scale * x + bias
class Decoder(nn.Module):
def __init__(
self,
*,
ch: int,
out_ch: int,
in_channels: int,
num_res_blocks: int,
z_channels: int,
ch_mult: Sequence[int] = (1, 2, 2, 4),
resolution: Optional[int] = None,
double_z: bool = False,
) -> None:
super().__init__()
del in_channels, resolution, double_z
self.num_blocks = len(ch_mult)
self.num_res_blocks = num_res_blocks
block_in = ch * ch_mult[self.num_blocks - 1]
self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, padding=1, bias=True)
self.mid_block = nn.ModuleList([ResBlock(block_in, block_in) for _ in range(self.num_res_blocks)])
self.up = nn.ModuleList()
self.adaptive = nn.ModuleList()
for i_level in reversed(range(self.num_blocks)):
block = nn.ModuleList()
block_out = ch * ch_mult[i_level]
self.adaptive.insert(0, AdaptiveGroupNorm(z_channels, block_in))
for _ in range(self.num_res_blocks):
block.append(ResBlock(block_in, block_out))
block_in = block_out
up = nn.Module()
up.block = block
if i_level > 0:
up.upsample = Upsampler(block_in)
self.up.insert(0, up)
self.norm_out = nn.GroupNorm(32, block_in, eps=1e-6)
self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, padding=1)
def forward(self, z: torch.Tensor) -> torch.Tensor:
style = z.clone()
z = self.conv_in(z)
for block in self.mid_block:
z = block(z)
for i_level in reversed(range(self.num_blocks)):
z = self.adaptive[i_level](z, style)
for i_block in range(self.num_res_blocks):
z = self.up[i_level].block[i_block](z)
if i_level > 0:
z = self.up[i_level].upsample(z)
z = self.norm_out(z)
z = swish(z)
z = self.conv_out(z)
return z
class GANDecoder(nn.Module):
def __init__(
self,
*,
ch: int,
out_ch: int,
in_channels: int,
num_res_blocks: int,
z_channels: int,
ch_mult: Sequence[int] = (1, 2, 2, 4),
resolution: Optional[int] = None,
double_z: bool = False,
) -> None:
super().__init__()
del in_channels, resolution, double_z
self.num_blocks = len(ch_mult)
self.num_res_blocks = num_res_blocks
block_in = ch * ch_mult[self.num_blocks - 1]
self.conv_in = nn.Conv2d(z_channels * 2, block_in, kernel_size=3, padding=1, bias=True)
self.mid_block = nn.ModuleList([ResBlock(block_in, block_in) for _ in range(self.num_res_blocks)])
self.up = nn.ModuleList()
self.adaptive = nn.ModuleList()
for i_level in reversed(range(self.num_blocks)):
block = nn.ModuleList()
block_out = ch * ch_mult[i_level]
self.adaptive.insert(0, AdaptiveGroupNorm(z_channels, block_in))
for _ in range(self.num_res_blocks):
block.append(ResBlock(block_in, block_out))
block_in = block_out
up = nn.Module()
up.block = block
if i_level > 0:
up.upsample = Upsampler(block_in)
self.up.insert(0, up)
self.norm_out = nn.GroupNorm(32, block_in, eps=1e-6)
self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, padding=1)
def forward(self, z: torch.Tensor) -> torch.Tensor:
style = z.clone()
noise = torch.randn_like(z, device=z.device)
z = torch.cat([z, noise], dim=1)
z = self.conv_in(z)
for block in self.mid_block:
z = block(z)
for i_level in reversed(range(self.num_blocks)):
z = self.adaptive[i_level](z, style)
for i_block in range(self.num_res_blocks):
z = self.up[i_level].block[i_block](z)
if i_level > 0:
z = self.up[i_level].upsample(z)
z = self.norm_out(z)
z = swish(z)
z = self.conv_out(z)
return z
class BitDanceAutoencoder(ModelMixin, ConfigMixin):
@register_to_config
def __init__(self, ddconfig: Dict[str, Any], gan_decoder: bool = False) -> None:
super().__init__()
self.encoder = Encoder(**ddconfig)
self.decoder = GANDecoder(**ddconfig) if gan_decoder else Decoder(**ddconfig)
@property
def z_channels(self) -> int:
return int(self.config.ddconfig["z_channels"])
@property
def patch_size(self) -> int:
ch_mult = self.config.ddconfig["ch_mult"]
return 2 ** (len(ch_mult) - 1)
def encode(self, x: torch.Tensor) -> torch.Tensor:
h = self.encoder(x)
codebook_value = torch.tensor([1.0], device=h.device, dtype=h.dtype)
quant_h = torch.where(h > 0, codebook_value, -codebook_value)
return quant_h
def decode(self, quant: torch.Tensor) -> torch.Tensor:
return self.decoder(quant)
def forward(self, x: torch.Tensor) -> torch.Tensor:
quant = self.encode(x)
return self.decode(quant)
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