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unet_sde_score_estimation.py
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1040 lines (868 loc) · 36.7 KB
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# 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.
# helpers functions
import functools
import string
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
from .embeddings import GaussianFourierProjection, get_timestep_embedding
def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
_, channel, in_h, in_w = input.shape
input = input.reshape(-1, in_h, in_w, 1)
_, in_h, in_w, minor = input.shape
kernel_h, kernel_w = kernel.shape
out = input.view(-1, in_h, 1, in_w, 1, minor)
out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
out = out.view(-1, in_h * up_y, in_w * up_x, minor)
out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
out = out[
:,
max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
:,
]
out = out.permute(0, 3, 1, 2)
out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
out = F.conv2d(out, w)
out = out.reshape(
-1,
minor,
in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
)
out = out.permute(0, 2, 3, 1)
out = out[:, ::down_y, ::down_x, :]
out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
return out.view(-1, channel, out_h, out_w)
# Function ported from StyleGAN2
def get_weight(module, shape, weight_var="weight", kernel_init=None):
"""Get/create weight tensor for a convolution or fully-connected layer."""
return module.param(weight_var, kernel_init, shape)
class Conv2d(nn.Module):
"""Conv2d layer with optimal upsampling and downsampling (StyleGAN2)."""
def __init__(
self,
in_ch,
out_ch,
kernel,
up=False,
down=False,
resample_kernel=(1, 3, 3, 1),
use_bias=True,
kernel_init=None,
):
super().__init__()
assert not (up and down)
assert kernel >= 1 and kernel % 2 == 1
self.weight = nn.Parameter(torch.zeros(out_ch, in_ch, kernel, kernel))
if kernel_init is not None:
self.weight.data = kernel_init(self.weight.data.shape)
if use_bias:
self.bias = nn.Parameter(torch.zeros(out_ch))
self.up = up
self.down = down
self.resample_kernel = resample_kernel
self.kernel = kernel
self.use_bias = use_bias
def forward(self, x):
if self.up:
x = upsample_conv_2d(x, self.weight, k=self.resample_kernel)
elif self.down:
x = conv_downsample_2d(x, self.weight, k=self.resample_kernel)
else:
x = F.conv2d(x, self.weight, stride=1, padding=self.kernel // 2)
if self.use_bias:
x = x + self.bias.reshape(1, -1, 1, 1)
return x
def naive_upsample_2d(x, factor=2):
_N, C, H, W = x.shape
x = torch.reshape(x, (-1, C, H, 1, W, 1))
x = x.repeat(1, 1, 1, factor, 1, factor)
return torch.reshape(x, (-1, C, H * factor, W * factor))
def naive_downsample_2d(x, factor=2):
_N, C, H, W = x.shape
x = torch.reshape(x, (-1, C, H // factor, factor, W // factor, factor))
return torch.mean(x, dim=(3, 5))
def upsample_conv_2d(x, w, k=None, factor=2, gain=1):
"""Fused `upsample_2d()` followed by `tf.nn.conv2d()`.
Args:
Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary
order.
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
C]`.
w: Weight tensor of the shape `[filterH, filterW, inChannels,
outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
k: FIR filter of the shape `[firH, firW]` or `[firN]`
(separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same datatype as
`x`.
"""
assert isinstance(factor, int) and factor >= 1
# Check weight shape.
assert len(w.shape) == 4
convH = w.shape[2]
convW = w.shape[3]
inC = w.shape[1]
assert convW == convH
# Setup filter kernel.
if k is None:
k = [1] * factor
k = _setup_kernel(k) * (gain * (factor**2))
p = (k.shape[0] - factor) - (convW - 1)
stride = (factor, factor)
# Determine data dimensions.
stride = [1, 1, factor, factor]
output_shape = ((_shape(x, 2) - 1) * factor + convH, (_shape(x, 3) - 1) * factor + convW)
output_padding = (
output_shape[0] - (_shape(x, 2) - 1) * stride[0] - convH,
output_shape[1] - (_shape(x, 3) - 1) * stride[1] - convW,
)
assert output_padding[0] >= 0 and output_padding[1] >= 0
num_groups = _shape(x, 1) // inC
# Transpose weights.
w = torch.reshape(w, (num_groups, -1, inC, convH, convW))
w = w[..., ::-1, ::-1].permute(0, 2, 1, 3, 4)
w = torch.reshape(w, (num_groups * inC, -1, convH, convW))
x = F.conv_transpose2d(x, w, stride=stride, output_padding=output_padding, padding=0)
# Original TF code.
# x = tf.nn.conv2d_transpose(
# x,
# w,
# output_shape=output_shape,
# strides=stride,
# padding='VALID',
# data_format=data_format)
# JAX equivalent
return upfirdn2d(x, torch.tensor(k, device=x.device), pad=((p + 1) // 2 + factor - 1, p // 2 + 1))
def conv_downsample_2d(x, w, k=None, factor=2, gain=1):
"""Fused `tf.nn.conv2d()` followed by `downsample_2d()`.
Args:
Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary
order.
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
C]`.
w: Weight tensor of the shape `[filterH, filterW, inChannels,
outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
k: FIR filter of the shape `[firH, firW]` or `[firN]`
(separable). The default is `[1] * factor`, which corresponds to average pooling.
factor: Integer downsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and same datatype
as `x`.
"""
assert isinstance(factor, int) and factor >= 1
_outC, _inC, convH, convW = w.shape
assert convW == convH
if k is None:
k = [1] * factor
k = _setup_kernel(k) * gain
p = (k.shape[0] - factor) + (convW - 1)
s = [factor, factor]
x = upfirdn2d(x, torch.tensor(k, device=x.device), pad=((p + 1) // 2, p // 2))
return F.conv2d(x, w, stride=s, padding=0)
def _setup_kernel(k):
k = np.asarray(k, dtype=np.float32)
if k.ndim == 1:
k = np.outer(k, k)
k /= np.sum(k)
assert k.ndim == 2
assert k.shape[0] == k.shape[1]
return k
def _shape(x, dim):
return x.shape[dim]
def upsample_2d(x, k=None, factor=2, gain=1):
r"""Upsample a batch of 2D images with the given filter.
Args:
Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given
filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified
`gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is a:
multiple of the upsampling factor.
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
C]`.
k: FIR filter of the shape `[firH, firW]` or `[firN]`
(separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H * factor, W * factor]`
"""
assert isinstance(factor, int) and factor >= 1
if k is None:
k = [1] * factor
k = _setup_kernel(k) * (gain * (factor**2))
p = k.shape[0] - factor
return upfirdn2d(x, torch.tensor(k, device=x.device), up=factor, pad=((p + 1) // 2 + factor - 1, p // 2))
def downsample_2d(x, k=None, factor=2, gain=1):
r"""Downsample a batch of 2D images with the given filter.
Args:
Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the
given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the
specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its
shape is a multiple of the downsampling factor.
x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
C]`.
k: FIR filter of the shape `[firH, firW]` or `[firN]`
(separable). The default is `[1] * factor`, which corresponds to average pooling.
factor: Integer downsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
Returns:
Tensor of the shape `[N, C, H // factor, W // factor]`
"""
assert isinstance(factor, int) and factor >= 1
if k is None:
k = [1] * factor
k = _setup_kernel(k) * gain
p = k.shape[0] - factor
return upfirdn2d(x, torch.tensor(k, device=x.device), down=factor, pad=((p + 1) // 2, p // 2))
def ddpm_conv1x1(in_planes, out_planes, stride=1, bias=True, init_scale=1.0, padding=0):
"""1x1 convolution with DDPM initialization."""
conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=padding, bias=bias)
conv.weight.data = default_init(init_scale)(conv.weight.data.shape)
nn.init.zeros_(conv.bias)
return conv
def ddpm_conv3x3(in_planes, out_planes, stride=1, bias=True, dilation=1, init_scale=1.0, padding=1):
"""3x3 convolution with DDPM initialization."""
conv = nn.Conv2d(
in_planes, out_planes, kernel_size=3, stride=stride, padding=padding, dilation=dilation, bias=bias
)
conv.weight.data = default_init(init_scale)(conv.weight.data.shape)
nn.init.zeros_(conv.bias)
return conv
conv1x1 = ddpm_conv1x1
conv3x3 = ddpm_conv3x3
def _einsum(a, b, c, x, y):
einsum_str = "{},{}->{}".format("".join(a), "".join(b), "".join(c))
return torch.einsum(einsum_str, x, y)
def contract_inner(x, y):
"""tensordot(x, y, 1)."""
x_chars = list(string.ascii_lowercase[: len(x.shape)])
y_chars = list(string.ascii_lowercase[len(x.shape) : len(y.shape) + len(x.shape)])
y_chars[0] = x_chars[-1] # first axis of y and last of x get summed
out_chars = x_chars[:-1] + y_chars[1:]
return _einsum(x_chars, y_chars, out_chars, x, y)
class NIN(nn.Module):
def __init__(self, in_dim, num_units, init_scale=0.1):
super().__init__()
self.W = nn.Parameter(default_init(scale=init_scale)((in_dim, num_units)), requires_grad=True)
self.b = nn.Parameter(torch.zeros(num_units), requires_grad=True)
def forward(self, x):
x = x.permute(0, 2, 3, 1)
y = contract_inner(x, self.W) + self.b
return y.permute(0, 3, 1, 2)
def get_act(nonlinearity):
"""Get activation functions from the config file."""
if nonlinearity.lower() == "elu":
return nn.ELU()
elif nonlinearity.lower() == "relu":
return nn.ReLU()
elif nonlinearity.lower() == "lrelu":
return nn.LeakyReLU(negative_slope=0.2)
elif nonlinearity.lower() == "swish":
return nn.SiLU()
else:
raise NotImplementedError("activation function does not exist!")
def default_init(scale=1.0):
"""The same initialization used in DDPM."""
scale = 1e-10 if scale == 0 else scale
return variance_scaling(scale, "fan_avg", "uniform")
def variance_scaling(scale, mode, distribution, in_axis=1, out_axis=0, dtype=torch.float32, device="cpu"):
"""Ported from JAX."""
def _compute_fans(shape, in_axis=1, out_axis=0):
receptive_field_size = np.prod(shape) / shape[in_axis] / shape[out_axis]
fan_in = shape[in_axis] * receptive_field_size
fan_out = shape[out_axis] * receptive_field_size
return fan_in, fan_out
def init(shape, dtype=dtype, device=device):
fan_in, fan_out = _compute_fans(shape, in_axis, out_axis)
if mode == "fan_in":
denominator = fan_in
elif mode == "fan_out":
denominator = fan_out
elif mode == "fan_avg":
denominator = (fan_in + fan_out) / 2
else:
raise ValueError("invalid mode for variance scaling initializer: {}".format(mode))
variance = scale / denominator
if distribution == "normal":
return torch.randn(*shape, dtype=dtype, device=device) * np.sqrt(variance)
elif distribution == "uniform":
return (torch.rand(*shape, dtype=dtype, device=device) * 2.0 - 1.0) * np.sqrt(3 * variance)
else:
raise ValueError("invalid distribution for variance scaling initializer")
return init
class Combine(nn.Module):
"""Combine information from skip connections."""
def __init__(self, dim1, dim2, method="cat"):
super().__init__()
self.Conv_0 = conv1x1(dim1, dim2)
self.method = method
def forward(self, x, y):
h = self.Conv_0(x)
if self.method == "cat":
return torch.cat([h, y], dim=1)
elif self.method == "sum":
return h + y
else:
raise ValueError(f"Method {self.method} not recognized.")
class AttnBlockpp(nn.Module):
"""Channel-wise self-attention block. Modified from DDPM."""
def __init__(self, channels, skip_rescale=False, init_scale=0.0):
super().__init__()
self.GroupNorm_0 = nn.GroupNorm(num_groups=min(channels // 4, 32), num_channels=channels, eps=1e-6)
self.NIN_0 = NIN(channels, channels)
self.NIN_1 = NIN(channels, channels)
self.NIN_2 = NIN(channels, channels)
self.NIN_3 = NIN(channels, channels, init_scale=init_scale)
self.skip_rescale = skip_rescale
def forward(self, x):
B, C, H, W = x.shape
h = self.GroupNorm_0(x)
q = self.NIN_0(h)
k = self.NIN_1(h)
v = self.NIN_2(h)
w = torch.einsum("bchw,bcij->bhwij", q, k) * (int(C) ** (-0.5))
w = torch.reshape(w, (B, H, W, H * W))
w = F.softmax(w, dim=-1)
w = torch.reshape(w, (B, H, W, H, W))
h = torch.einsum("bhwij,bcij->bchw", w, v)
h = self.NIN_3(h)
if not self.skip_rescale:
return x + h
else:
return (x + h) / np.sqrt(2.0)
class Upsample(nn.Module):
def __init__(self, in_ch=None, out_ch=None, with_conv=False, fir=False, fir_kernel=(1, 3, 3, 1)):
super().__init__()
out_ch = out_ch if out_ch else in_ch
if not fir:
if with_conv:
self.Conv_0 = conv3x3(in_ch, out_ch)
else:
if with_conv:
self.Conv2d_0 = Conv2d(
in_ch,
out_ch,
kernel=3,
up=True,
resample_kernel=fir_kernel,
use_bias=True,
kernel_init=default_init(),
)
self.fir = fir
self.with_conv = with_conv
self.fir_kernel = fir_kernel
self.out_ch = out_ch
def forward(self, x):
B, C, H, W = x.shape
if not self.fir:
h = F.interpolate(x, (H * 2, W * 2), "nearest")
if self.with_conv:
h = self.Conv_0(h)
else:
if not self.with_conv:
h = upsample_2d(x, self.fir_kernel, factor=2)
else:
h = self.Conv2d_0(x)
return h
class Downsample(nn.Module):
def __init__(self, in_ch=None, out_ch=None, with_conv=False, fir=False, fir_kernel=(1, 3, 3, 1)):
super().__init__()
out_ch = out_ch if out_ch else in_ch
if not fir:
if with_conv:
self.Conv_0 = conv3x3(in_ch, out_ch, stride=2, padding=0)
else:
if with_conv:
self.Conv2d_0 = Conv2d(
in_ch,
out_ch,
kernel=3,
down=True,
resample_kernel=fir_kernel,
use_bias=True,
kernel_init=default_init(),
)
self.fir = fir
self.fir_kernel = fir_kernel
self.with_conv = with_conv
self.out_ch = out_ch
def forward(self, x):
B, C, H, W = x.shape
if not self.fir:
if self.with_conv:
x = F.pad(x, (0, 1, 0, 1))
x = self.Conv_0(x)
else:
x = F.avg_pool2d(x, 2, stride=2)
else:
if not self.with_conv:
x = downsample_2d(x, self.fir_kernel, factor=2)
else:
x = self.Conv2d_0(x)
return x
class ResnetBlockDDPMpp(nn.Module):
"""ResBlock adapted from DDPM."""
def __init__(
self,
act,
in_ch,
out_ch=None,
temb_dim=None,
conv_shortcut=False,
dropout=0.1,
skip_rescale=False,
init_scale=0.0,
):
super().__init__()
out_ch = out_ch if out_ch else in_ch
self.GroupNorm_0 = nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6)
self.Conv_0 = conv3x3(in_ch, out_ch)
if temb_dim is not None:
self.Dense_0 = nn.Linear(temb_dim, out_ch)
self.Dense_0.weight.data = default_init()(self.Dense_0.weight.data.shape)
nn.init.zeros_(self.Dense_0.bias)
self.GroupNorm_1 = nn.GroupNorm(num_groups=min(out_ch // 4, 32), num_channels=out_ch, eps=1e-6)
self.Dropout_0 = nn.Dropout(dropout)
self.Conv_1 = conv3x3(out_ch, out_ch, init_scale=init_scale)
if in_ch != out_ch:
if conv_shortcut:
self.Conv_2 = conv3x3(in_ch, out_ch)
else:
self.NIN_0 = NIN(in_ch, out_ch)
self.skip_rescale = skip_rescale
self.act = act
self.out_ch = out_ch
self.conv_shortcut = conv_shortcut
def forward(self, x, temb=None):
h = self.act(self.GroupNorm_0(x))
h = self.Conv_0(h)
if temb is not None:
h += self.Dense_0(self.act(temb))[:, :, None, None]
h = self.act(self.GroupNorm_1(h))
h = self.Dropout_0(h)
h = self.Conv_1(h)
if x.shape[1] != self.out_ch:
if self.conv_shortcut:
x = self.Conv_2(x)
else:
x = self.NIN_0(x)
if not self.skip_rescale:
return x + h
else:
return (x + h) / np.sqrt(2.0)
class ResnetBlockBigGANpp(nn.Module):
def __init__(
self,
act,
in_ch,
out_ch=None,
temb_dim=None,
up=False,
down=False,
dropout=0.1,
fir=False,
fir_kernel=(1, 3, 3, 1),
skip_rescale=True,
init_scale=0.0,
):
super().__init__()
out_ch = out_ch if out_ch else in_ch
self.GroupNorm_0 = nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6)
self.up = up
self.down = down
self.fir = fir
self.fir_kernel = fir_kernel
self.Conv_0 = conv3x3(in_ch, out_ch)
if temb_dim is not None:
self.Dense_0 = nn.Linear(temb_dim, out_ch)
self.Dense_0.weight.data = default_init()(self.Dense_0.weight.shape)
nn.init.zeros_(self.Dense_0.bias)
self.GroupNorm_1 = nn.GroupNorm(num_groups=min(out_ch // 4, 32), num_channels=out_ch, eps=1e-6)
self.Dropout_0 = nn.Dropout(dropout)
self.Conv_1 = conv3x3(out_ch, out_ch, init_scale=init_scale)
if in_ch != out_ch or up or down:
self.Conv_2 = conv1x1(in_ch, out_ch)
self.skip_rescale = skip_rescale
self.act = act
self.in_ch = in_ch
self.out_ch = out_ch
def forward(self, x, temb=None):
h = self.act(self.GroupNorm_0(x))
if self.up:
if self.fir:
h = upsample_2d(h, self.fir_kernel, factor=2)
x = upsample_2d(x, self.fir_kernel, factor=2)
else:
h = naive_upsample_2d(h, factor=2)
x = naive_upsample_2d(x, factor=2)
elif self.down:
if self.fir:
h = downsample_2d(h, self.fir_kernel, factor=2)
x = downsample_2d(x, self.fir_kernel, factor=2)
else:
h = naive_downsample_2d(h, factor=2)
x = naive_downsample_2d(x, factor=2)
h = self.Conv_0(h)
# Add bias to each feature map conditioned on the time embedding
if temb is not None:
h += self.Dense_0(self.act(temb))[:, :, None, None]
h = self.act(self.GroupNorm_1(h))
h = self.Dropout_0(h)
h = self.Conv_1(h)
if self.in_ch != self.out_ch or self.up or self.down:
x = self.Conv_2(x)
if not self.skip_rescale:
return x + h
else:
return (x + h) / np.sqrt(2.0)
class NCSNpp(ModelMixin, ConfigMixin):
"""NCSN++ model"""
def __init__(
self,
centered=False,
image_size=1024,
num_channels=3,
attention_type="ddpm",
attn_resolutions=(16,),
ch_mult=(1, 2, 4, 8, 16, 32, 32, 32),
conditional=True,
conv_size=3,
dropout=0.0,
embedding_type="fourier",
fir=True,
fir_kernel=(1, 3, 3, 1),
fourier_scale=16,
init_scale=0.0,
nf=16,
nonlinearity="swish",
normalization="GroupNorm",
num_res_blocks=1,
progressive="output_skip",
progressive_combine="sum",
progressive_input="input_skip",
resamp_with_conv=True,
resblock_type="biggan",
scale_by_sigma=True,
skip_rescale=True,
continuous=True,
):
super().__init__()
self.register_to_config(
centered=centered,
image_size=image_size,
num_channels=num_channels,
attention_type=attention_type,
attn_resolutions=attn_resolutions,
ch_mult=ch_mult,
conditional=conditional,
conv_size=conv_size,
dropout=dropout,
embedding_type=embedding_type,
fir=fir,
fir_kernel=fir_kernel,
fourier_scale=fourier_scale,
init_scale=init_scale,
nf=nf,
nonlinearity=nonlinearity,
normalization=normalization,
num_res_blocks=num_res_blocks,
progressive=progressive,
progressive_combine=progressive_combine,
progressive_input=progressive_input,
resamp_with_conv=resamp_with_conv,
resblock_type=resblock_type,
scale_by_sigma=scale_by_sigma,
skip_rescale=skip_rescale,
continuous=continuous,
)
self.act = act = get_act(nonlinearity)
self.nf = nf
self.num_res_blocks = num_res_blocks
self.attn_resolutions = attn_resolutions
self.num_resolutions = len(ch_mult)
self.all_resolutions = all_resolutions = [image_size // (2**i) for i in range(self.num_resolutions)]
self.conditional = conditional
self.skip_rescale = skip_rescale
self.resblock_type = resblock_type
self.progressive = progressive
self.progressive_input = progressive_input
self.embedding_type = embedding_type
assert progressive in ["none", "output_skip", "residual"]
assert progressive_input in ["none", "input_skip", "residual"]
assert embedding_type in ["fourier", "positional"]
combine_method = progressive_combine.lower()
combiner = functools.partial(Combine, method=combine_method)
modules = []
# timestep/noise_level embedding; only for continuous training
if embedding_type == "fourier":
# Gaussian Fourier features embeddings.
modules.append(GaussianFourierProjection(embedding_size=nf, scale=fourier_scale))
embed_dim = 2 * nf
elif embedding_type == "positional":
embed_dim = nf
else:
raise ValueError(f"embedding type {embedding_type} unknown.")
if conditional:
modules.append(nn.Linear(embed_dim, nf * 4))
modules[-1].weight.data = default_init()(modules[-1].weight.shape)
nn.init.zeros_(modules[-1].bias)
modules.append(nn.Linear(nf * 4, nf * 4))
modules[-1].weight.data = default_init()(modules[-1].weight.shape)
nn.init.zeros_(modules[-1].bias)
AttnBlock = functools.partial(AttnBlockpp, init_scale=init_scale, skip_rescale=skip_rescale)
Up_sample = functools.partial(Upsample, with_conv=resamp_with_conv, fir=fir, fir_kernel=fir_kernel)
if progressive == "output_skip":
self.pyramid_upsample = Up_sample(fir=fir, fir_kernel=fir_kernel, with_conv=False)
elif progressive == "residual":
pyramid_upsample = functools.partial(Up_sample, fir=fir, fir_kernel=fir_kernel, with_conv=True)
Down_sample = functools.partial(Downsample, with_conv=resamp_with_conv, fir=fir, fir_kernel=fir_kernel)
if progressive_input == "input_skip":
self.pyramid_downsample = Down_sample(fir=fir, fir_kernel=fir_kernel, with_conv=False)
elif progressive_input == "residual":
pyramid_downsample = functools.partial(Down_sample, fir=fir, fir_kernel=fir_kernel, with_conv=True)
if resblock_type == "ddpm":
ResnetBlock = functools.partial(
ResnetBlockDDPMpp,
act=act,
dropout=dropout,
init_scale=init_scale,
skip_rescale=skip_rescale,
temb_dim=nf * 4,
)
elif resblock_type == "biggan":
ResnetBlock = functools.partial(
ResnetBlockBigGANpp,
act=act,
dropout=dropout,
fir=fir,
fir_kernel=fir_kernel,
init_scale=init_scale,
skip_rescale=skip_rescale,
temb_dim=nf * 4,
)
else:
raise ValueError(f"resblock type {resblock_type} unrecognized.")
# Downsampling block
channels = num_channels
if progressive_input != "none":
input_pyramid_ch = channels
modules.append(conv3x3(channels, nf))
hs_c = [nf]
in_ch = nf
for i_level in range(self.num_resolutions):
# Residual blocks for this resolution
for i_block in range(num_res_blocks):
out_ch = nf * ch_mult[i_level]
modules.append(ResnetBlock(in_ch=in_ch, out_ch=out_ch))
in_ch = out_ch
if all_resolutions[i_level] in attn_resolutions:
modules.append(AttnBlock(channels=in_ch))
hs_c.append(in_ch)
if i_level != self.num_resolutions - 1:
if resblock_type == "ddpm":
modules.append(Downsample(in_ch=in_ch))
else:
modules.append(ResnetBlock(down=True, in_ch=in_ch))
if progressive_input == "input_skip":
modules.append(combiner(dim1=input_pyramid_ch, dim2=in_ch))
if combine_method == "cat":
in_ch *= 2
elif progressive_input == "residual":
modules.append(pyramid_downsample(in_ch=input_pyramid_ch, out_ch=in_ch))
input_pyramid_ch = in_ch
hs_c.append(in_ch)
in_ch = hs_c[-1]
modules.append(ResnetBlock(in_ch=in_ch))
modules.append(AttnBlock(channels=in_ch))
modules.append(ResnetBlock(in_ch=in_ch))
pyramid_ch = 0
# Upsampling block
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(num_res_blocks + 1):
out_ch = nf * ch_mult[i_level]
modules.append(ResnetBlock(in_ch=in_ch + hs_c.pop(), out_ch=out_ch))
in_ch = out_ch
if all_resolutions[i_level] in attn_resolutions:
modules.append(AttnBlock(channels=in_ch))
if progressive != "none":
if i_level == self.num_resolutions - 1:
if progressive == "output_skip":
modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6))
modules.append(conv3x3(in_ch, channels, init_scale=init_scale))
pyramid_ch = channels
elif progressive == "residual":
modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6))
modules.append(conv3x3(in_ch, in_ch, bias=True))
pyramid_ch = in_ch
else:
raise ValueError(f"{progressive} is not a valid name.")
else:
if progressive == "output_skip":
modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6))
modules.append(conv3x3(in_ch, channels, bias=True, init_scale=init_scale))
pyramid_ch = channels
elif progressive == "residual":
modules.append(pyramid_upsample(in_ch=pyramid_ch, out_ch=in_ch))
pyramid_ch = in_ch
else:
raise ValueError(f"{progressive} is not a valid name")
if i_level != 0:
if resblock_type == "ddpm":
modules.append(Upsample(in_ch=in_ch))
else:
modules.append(ResnetBlock(in_ch=in_ch, up=True))
assert not hs_c
if progressive != "output_skip":
modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6))
modules.append(conv3x3(in_ch, channels, init_scale=init_scale))
self.all_modules = nn.ModuleList(modules)
def forward(self, x, timesteps, sigmas=None):
# timestep/noise_level embedding; only for continuous training
modules = self.all_modules
m_idx = 0
if self.embedding_type == "fourier":
# Gaussian Fourier features embeddings.
used_sigmas = timesteps
temb = modules[m_idx](torch.log(used_sigmas))
m_idx += 1
elif self.embedding_type == "positional":
# Sinusoidal positional embeddings.
timesteps = timesteps
used_sigmas = sigmas
temb = get_timestep_embedding(timesteps, self.nf)
else:
raise ValueError(f"embedding type {self.embedding_type} unknown.")
if self.conditional:
temb = modules[m_idx](temb)
m_idx += 1
temb = modules[m_idx](self.act(temb))
m_idx += 1
else:
temb = None
if not self.config.centered:
# If input data is in [0, 1]
x = 2 * x - 1.0
# Downsampling block
input_pyramid = None
if self.progressive_input != "none":
input_pyramid = x
hs = [modules[m_idx](x)]
m_idx += 1
for i_level in range(self.num_resolutions):
# Residual blocks for this resolution
for i_block in range(self.num_res_blocks):
h = modules[m_idx](hs[-1], temb)
m_idx += 1
if h.shape[-1] in self.attn_resolutions:
h = modules[m_idx](h)
m_idx += 1
hs.append(h)
if i_level != self.num_resolutions - 1:
if self.resblock_type == "ddpm":
h = modules[m_idx](hs[-1])
m_idx += 1
else:
h = modules[m_idx](hs[-1], temb)
m_idx += 1
if self.progressive_input == "input_skip":
input_pyramid = self.pyramid_downsample(input_pyramid)
h = modules[m_idx](input_pyramid, h)
m_idx += 1
elif self.progressive_input == "residual":
input_pyramid = modules[m_idx](input_pyramid)
m_idx += 1
if self.skip_rescale:
input_pyramid = (input_pyramid + h) / np.sqrt(2.0)
else:
input_pyramid = input_pyramid + h
h = input_pyramid
hs.append(h)
h = hs[-1]
h = modules[m_idx](h, temb)
m_idx += 1
h = modules[m_idx](h)
m_idx += 1
h = modules[m_idx](h, temb)
m_idx += 1
pyramid = None
# Upsampling block
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks + 1):
h = modules[m_idx](torch.cat([h, hs.pop()], dim=1), temb)
m_idx += 1
if h.shape[-1] in self.attn_resolutions:
h = modules[m_idx](h)
m_idx += 1
if self.progressive != "none":
if i_level == self.num_resolutions - 1:
if self.progressive == "output_skip":
pyramid = self.act(modules[m_idx](h))
m_idx += 1
pyramid = modules[m_idx](pyramid)
m_idx += 1
elif self.progressive == "residual":
pyramid = self.act(modules[m_idx](h))
m_idx += 1
pyramid = modules[m_idx](pyramid)
m_idx += 1
else:
raise ValueError(f"{self.progressive} is not a valid name.")
else:
if self.progressive == "output_skip":
pyramid = self.pyramid_upsample(pyramid)