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sampler.py
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sampler.py
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# Adopted from https://github.com/CompVis/stable-diffusion/tree/main
import torch as th
import torch.nn as nn
from einops import rearrange
from abc import abstractmethod
import math
import functools as ft
import numpy as np
import torch.nn.functional as F
from torch.profiler import profile, record_function, ProfilerActivity
from sampler_util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
def marginal_prob_std(t, x, sigma):
"""Compute the mean and standard deviation of $p_{0t}(x(t) | x(0))$.
Args:
t: A vector of time steps.
sigma: The $\sigma$ in our SDE.
Returns:
The standard deviation.
"""
t = th.tensor(t).to(x.device)
return th.sqrt((sigma**(2 * t) - 1.) / 2. / np.log(sigma))
def diffusion_coeff(t, x, sigma):
"""Compute the diffusion coefficient of our SDE.
Args:
t: A vector of time steps.
sigma: The $\sigma$ in our SDE.
Returns:
The vector of diffusion coefficients.
"""
return th.tensor(sigma**t).to(x.device)
class TimestepBlock(nn.Module):
"""
Any module where forward() takes timestep embeddings as a second argument.
"""
@abstractmethod
def forward(self, x, emb):
"""
Apply the module to `x` given `emb` timestep embeddings.
"""
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
"""
A sequential module that passes timestep embeddings to the children that
support it as an extra input.
"""
def forward(self, x, emb):
for layer in self:
if isinstance(layer, TimestepBlock):
x = layer(x, emb)
else:
x = layer(x)
return x
class Upsample(nn.Module):
"""
An upsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
upsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
if use_conv:
self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
def forward(self, x):
assert x.shape[1] == self.channels
if self.dims == 3:
x = F.interpolate(
x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
)
else:
x = F.interpolate(x, scale_factor=2, mode="nearest")
if self.use_conv:
x = self.conv(x)
return x
class Downsample(nn.Module):
"""
A downsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
stride = 2 if dims != 3 else (1, 2, 2)
if use_conv:
self.op = conv_nd(
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
)
else:
assert self.channels == self.out_channels
self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
def forward(self, x):
assert x.shape[1] == self.channels
return self.op(x)
class ResBlock(TimestepBlock):
"""
A residual block that can optionally change the number of channels.
:param channels: the number of input channels.
:param emb_channels: the number of timestep embedding channels.
:param dropout: the rate of dropout.
:param out_channels: if specified, the number of out channels.
:param use_conv: if True and out_channels is specified, use a spatial
convolution instead of a smaller 1x1 convolution to change the
channels in the skip connection.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param use_checkpoint: if True, use gradient checkpointing on this module.
:param up: if True, use this block for upsampling.
:param down: if True, use this block for downsampling.
"""
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
use_conv=False,
use_scale_shift_norm=False,
dims=2,
use_checkpoint=False,
up=False,
down=False,
use_out_res=True,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_checkpoint = use_checkpoint
self.use_scale_shift_norm = use_scale_shift_norm
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, 3, padding=1),
)
self.updown = up or down
if up:
self.h_upd = Upsample(channels, False, dims)
self.x_upd = Upsample(channels, False, dims)
elif down:
self.h_upd = Downsample(channels, False, dims)
self.x_upd = Downsample(channels, False, dims)
else:
self.h_upd = self.x_upd = nn.Identity()
self.emb_layers = nn.Sequential(
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
)
self.use_out_res=use_out_res
if use_out_res:
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
# zero_module(conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = conv_nd(
dims, channels, self.out_channels, 3, padding=1
)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
def forward(self, x, emb):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features.
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs.
"""
return checkpoint(
self._forward, (x, emb), self.parameters(), self.use_checkpoint
)
def _forward(self, x, emb):
if self.updown:
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
h = in_rest(x)
h = self.h_upd(h)
x = self.x_upd(x)
h = in_conv(h)
else:
h = self.in_layers(x)
emb_out = self.emb_layers(emb)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
if self.use_out_res:
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = th.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
h = h + emb_out
h = self.out_layers(h)
else: h=h+emb_out
return self.skip_connection(x) + h
def padcat(x1,x2):
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2])
return th.cat([x1, x2], dim=1)
class UNetSampler(nn.Module): ##
"""
The full UNet model and timestep embedding.
:param c_in: channels in the input Tensor.
:param dim: base channel count for the model.
:param out_channels: channels in the output Tensor.
:param num_res_blocks: number of residual blocks per downsample.
:param dropout: the dropout probability.
:param channel_mult: channel multiplier for each level of the UNet.
:param conv_resample: if True, use learned convolutions for upsampling and
downsampling.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param use_checkpoint: use gradient checkpointing to reduce memory usage.
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
:param resblock_updown: use residual blocks for up/downsampling.
"""
def __init__(
self,
marginal_prob_std,
c_in, c_out,
dim=32,
embed_dim=256,
use_t=True,
num_res_blocks=1,
dropout=0.1,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
use_checkpoint=False,
use_scale_shift_norm=False,
resblock_updown=False,
use_out_res=True,
**kwargs
):
super().__init__()
self.use_t=use_t
self.marginal_prob_std = marginal_prob_std
self.c_in = c_in
self.dim = dim
if isinstance(num_res_blocks, int):
self.num_res_blocks = len(channel_mult) * [num_res_blocks]
else:
if len(num_res_blocks) != len(channel_mult):
raise ValueError("provide num_res_blocks either as an int (globally constant) or "
"as a list/tuple (per-level) with the same length as channel_mult")
self.num_res_blocks = num_res_blocks
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.use_checkpoint = use_checkpoint
if self.use_t:
self.time_embed = nn.Sequential(
linear(dim, embed_dim),
nn.SiLU(),
linear(embed_dim, embed_dim),
)
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, c_in, dim, 3, padding=1)
)
]
)
self._feature_size = dim
input_block_chans = [dim]
ch = dim
ds = 1
for level, mult in enumerate(channel_mult):
for nr in range(self.num_res_blocks[level]):
layers = [
ResBlock(
ch,
embed_dim,
dropout,
out_channels=mult * dim,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
use_out_res=use_out_res
)
]
ch = mult * dim
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
ResBlock(
ch,
embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
use_out_res=use_out_res
)
if resblock_updown
else Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch
)
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
use_out_res=use_out_res
),
# ResBlock(
# ch,
# embed_dim,
# dropout,
# dims=dims,
# use_checkpoint=use_checkpoint,
# use_scale_shift_norm=use_scale_shift_norm,
# ),
)
self._feature_size += ch
self.z_dim=ch
self.output_blocks = nn.ModuleList([])
for level, mult in list(enumerate(channel_mult))[::-1]:
for i in range(self.num_res_blocks[level] + 1):
ich = input_block_chans.pop()
layers = [
ResBlock(
ch + ich,
embed_dim,
dropout,
out_channels=dim * mult,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
use_out_res=use_out_res
)
]
ch = dim * mult
if level and i == self.num_res_blocks[level]:
out_ch = ch
layers.append(
ResBlock(
ch,
embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
up=True,
use_out_res=use_out_res,
)
if resblock_updown
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
conv_nd(dims, dim, c_out, 3, padding=1)
# zero_module(conv_nd(dims, dim, c_out, 3, padding=1)),
)
def forward(self, x, emb, t=None, **kwargs): ##
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param context: conditioning plugged in via crossattn
:return: an [N x C x ...] Tensor of outputs.
"""
hs = []
if self.use_t:
assert t is not None
t_emb = timestep_embedding(t, self.dim, repeat_only=False)
emb = emb+self.time_embed(t_emb)
h = x
for module in self.input_blocks:
h = module(h, emb)
hs.append(h)
h = self.middle_block(h, emb)
for module in self.output_blocks:
h = padcat(h, hs.pop())
h = module(h, emb)
h=self.out(h)
if self.use_t: h=h / self.marginal_prob_std(t,h)[:, None, None, None]
return h
if __name__=='__main__':
embed_dim=256
size=32
bs=16
sigma = 25.0#@param {'type':'number'}
marginal_prob_std_fn = ft.partial(marginal_prob_std, sigma=sigma)
model=UNetSampler(
embed_dim=embed_dim,
marginal_prob_std=marginal_prob_std_fn,
c_in=4,
c_out=1,
dim=32,
use_out_res=False
).to('cuda')
print(model)
i=1
inp=th.rand(bs,4,size,size).to('cuda')
t = (th.ones(1)*i).to('cuda')
H=th.rand(bs,embed_dim).to('cuda')
h=model(inp,H,t)
print(h.shape,h.mean(),h.std())
# with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
# profile_memory=True, record_shapes=True) as prof:
# with record_function("model_inference"):
# for i in range(1):
# model(inp,H,t)
# print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=3))
total = sum([param.nelement() for param in model.parameters()])
print("Number of parameter: %.2fM" % (total/1e6))