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"""
Modified from https://github.com/CompVis/taming-transformers/blob/master/taming/modules/diffusionmodules/model.py#L34
"""
import math
from typing import Tuple, Union
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
def nonlinearity(x):
# swish
return x * torch.sigmoid(x)
def Normalize(in_channels):
return torch.nn.GroupNorm(
num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
)
class Upsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(
in_channels, in_channels, kernel_size=3, stride=1, padding=1
)
def forward(self, x):
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
if self.with_conv:
x = self.conv(x)
return x
class DepthToSpaceUpsample(nn.Module):
def __init__(
self,
in_channels,
):
super().__init__()
conv = nn.Conv2d(in_channels, in_channels * 4, 1)
self.net = nn.Sequential(
conv,
nn.SiLU(),
Rearrange("b (c p1 p2) h w -> b c (h p1) (w p2)", p1=2, p2=2),
)
self.init_conv_(conv)
def init_conv_(self, conv):
o, i, h, w = conv.weight.shape
conv_weight = torch.empty(o // 4, i, h, w)
nn.init.kaiming_uniform_(conv_weight)
conv_weight = repeat(conv_weight, "o ... -> (o 4) ...")
conv.weight.data.copy_(conv_weight)
nn.init.zeros_(conv.bias.data)
def forward(self, x):
out = self.net(x)
return out
class Downsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
# no asymmetric padding in torch conv, must do it ourselves
self.conv = torch.nn.Conv2d(
in_channels, in_channels, kernel_size=3, stride=2, padding=0
)
def forward(self, x):
if self.with_conv:
pad = (0, 1, 0, 1)
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
x = self.conv(x)
else:
x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
return x
def unpack_time(t, batch):
_, c, w, h = t.size()
out = torch.reshape(t, [batch, -1, c, w, h])
out = rearrange(out, "b t c h w -> b c t h w")
return out
def pack_time(t):
out = rearrange(t, "b c t h w -> b t c h w")
_, _, c, w, h = out.size()
return torch.reshape(out, [-1, c, w, h])
class TimeDownsample2x(nn.Module):
def __init__(
self,
dim,
dim_out=None,
kernel_size=3,
):
super().__init__()
if dim_out is None:
dim_out = dim
self.time_causal_padding = (kernel_size - 1, 0)
self.conv = nn.Conv1d(dim, dim_out, kernel_size, stride=2)
def forward(self, x):
x = rearrange(x, "b c t h w -> b h w c t")
b, h, w, c, t = x.size()
x = torch.reshape(x, [-1, c, t])
x = F.pad(x, self.time_causal_padding)
out = self.conv(x)
out = torch.reshape(out, [b, h, w, c, t])
out = rearrange(out, "b h w c t -> b c t h w")
out = rearrange(out, "b h w c t -> b c t h w")
return out
class TimeUpsample2x(nn.Module):
def __init__(self, dim, dim_out=None):
super().__init__()
if dim_out is None:
dim_out = dim
conv = nn.Conv1d(dim, dim_out * 2, 1)
self.net = nn.Sequential(
nn.SiLU(), conv, Rearrange("b (c p) t -> b c (t p)", p=2)
)
self.init_conv_(conv)
def init_conv_(self, conv):
o, i, t = conv.weight.shape
conv_weight = torch.empty(o // 2, i, t)
nn.init.kaiming_uniform_(conv_weight)
conv_weight = repeat(conv_weight, "o ... -> (o 2) ...")
conv.weight.data.copy_(conv_weight)
nn.init.zeros_(conv.bias.data)
def forward(self, x):
x = rearrange(x, "b c t h w -> b h w c t")
b, h, w, c, t = x.size()
x = torch.reshape(x, [-1, c, t])
out = self.net(x)
out = out[:, :, 1:].contiguous()
out = torch.reshape(out, [b, h, w, c, t])
out = rearrange(out, "b h w c t -> b c t h w")
return out
class AttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1, padding=0
)
self.k = torch.nn.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1, padding=0
)
self.v = torch.nn.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1, padding=0
)
self.proj_out = torch.nn.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1, padding=0
)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b, c, h, w = q.shape
q = q.reshape(b, c, h * w)
q = q.permute(0, 2, 1) # b,hw,c
k = k.reshape(b, c, h * w) # b,c,hw
w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w_ = w_ * (int(c) ** (-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = v.reshape(b, c, h * w)
w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
h_ = torch.bmm(v, w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
h_ = h_.reshape(b, c, h, w)
h_ = self.proj_out(h_)
return x + h_
class TimeAttention(AttnBlock):
def forward(self, x, *args, **kwargs):
x = rearrange(x, "b c t h w -> b h w t c")
b, h, w, t, c = x.size()
x = torch.reshape(x, (-1, t, c))
x = super().forward(x, *args, **kwargs)
x = torch.reshape(x, [b, h, w, t, c])
return rearrange(x, "b h w t c -> b c t h w")
class Residual(nn.Module):
def __init__(self, fn: nn.Module):
super().__init__()
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(x, **kwargs) + x
def cast_tuple(t, length=1):
return t if isinstance(t, tuple) else ((t,) * length)
class CausalConv3d(nn.Module):
def __init__(
self,
chan_in,
chan_out,
kernel_size: Union[int, Tuple[int, int, int]],
pad_mode="constant",
**kwargs
):
super().__init__()
kernel_size = cast_tuple(kernel_size, 3)
time_kernel_size, height_kernel_size, width_kernel_size = kernel_size
dilation = kwargs.pop("dilation", 1)
stride = kwargs.pop("stride", 1)
self.pad_mode = pad_mode
time_pad = dilation * (time_kernel_size - 1) + (1 - stride)
height_pad = height_kernel_size // 2
width_pad = width_kernel_size // 2
self.time_pad = time_pad
self.time_causal_padding = (
width_pad,
width_pad,
height_pad,
height_pad,
time_pad,
0,
)
stride = (stride, 1, 1)
dilation = (dilation, 1, 1)
self.conv = nn.Conv3d(
chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs
)
def forward(self, x):
pad_mode = self.pad_mode if self.time_pad < x.shape[2] else "constant"
x = F.pad(x, self.time_causal_padding, mode=pad_mode)
return self.conv(x)
def ResnetBlockCausal3D(
dim, kernel_size: Union[int, Tuple[int, int, int]], pad_mode: str = "constant"
):
net = nn.Sequential(
Normalize(dim),
nn.SiLU(),
CausalConv3d(dim, dim, kernel_size, pad_mode),
Normalize(dim),
nn.SiLU(),
CausalConv3d(dim, dim, kernel_size, pad_mode),
)
return Residual(net)
class ResnetBlock(nn.Module):
def __init__(
self,
*,
in_channels,
out_channels=None,
conv_shortcut=False,
dropout,
temb_channels=512
):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.norm1 = Normalize(in_channels)
self.conv1 = torch.nn.Conv2d(
in_channels, out_channels, kernel_size=3, stride=1, padding=1
)
if temb_channels > 0:
self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
else:
self.temb_proj = None
self.norm2 = Normalize(out_channels)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv2d(
out_channels, out_channels, kernel_size=3, stride=1, padding=1
)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
self.conv_shortcut = torch.nn.Conv2d(
in_channels, out_channels, kernel_size=3, stride=1, padding=1
)
else:
self.nin_shortcut = torch.nn.Conv2d(
in_channels, out_channels, kernel_size=1, stride=1, padding=0
)
def forward(self, x, temb):
h = x
h = self.norm1(h)
h = nonlinearity(h)
h = self.conv1(h)
if temb is not None:
h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
h = self.norm2(h)
h = nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
x = self.conv_shortcut(x)
else:
x = self.nin_shortcut(x)
return x + h
class DinoV2Model(nn.Module):
def __init__(
self,
model_name,
local_checkpoint_path="",
renorm_input=False,
old_input_mean=0.5,
old_input_std=0.5,
freeze_model=False,
):
super().__init__()
if local_checkpoint_path != "":
self._model = torch.hub.load(
local_checkpoint_path, model_name, source="local"
)
else:
self._model = torch.hub.load("facebookresearch/dinov2", model_name)
self.register_buffer(
"_dino_input_mean",
torch.tensor([0.485, 0.456, 0.406]).float()[None, :, None, None],
)
self.register_buffer(
"_dino_input_std",
torch.tensor([0.229, 0.224, 0.225]).float()[None, :, None, None],
)
self._old_input_mean = old_input_mean
self._old_input_std = old_input_std
self._renorm_input = renorm_input
if freeze_model:
for param in self._model.parameters():
param.requires_grad = False
def forward(self, inputs):
batch, _, height, width = inputs.size()
if self._renorm_input:
inputs = inputs * self._old_input_mean + self._old_input_std
inputs = (inputs - self._dino_input_mean) / self._dino_input_std
# TODO(yanwan): If we want to remove this resizing, have to modify the decoder to support upscaling by a factor of 14.
# Reduce both height and width to 7/8 of their original values while maintaining aspect ratio to fit dinov2 requirement.
new_height = height // 8 * 7
new_width = width // 8 * 7
inputs = F.interpolate(inputs, (new_height, new_width), mode="bilinear")
features = self._model.forward_features(inputs)["x_norm_patchtokens"]
features = torch.transpose(features, 1, 2).contiguous()
features = torch.reshape(
features, (batch, -1, new_height // 14, new_width // 14)
)
return features
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