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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 copy
import math
from pathlib import Path
import torch
from torch import nn
from torch.cuda.amp import GradScaler, autocast
from torch.optim import Adam
from torch.utils import data
from torchvision import transforms, utils
from PIL import Image
from tqdm import tqdm
from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
"""
assert len(timesteps.shape) == 1
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
emb = emb.to(device=timesteps.device)
emb = timesteps.float()[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
return emb
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 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
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)
self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
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)
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 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 UNetModel(ModelMixin, ConfigMixin):
def __init__(
self,
ch=128,
out_ch=3,
ch_mult=(1, 1, 2, 2, 4, 4),
num_res_blocks=2,
attn_resolutions=(16,),
dropout=0.0,
resamp_with_conv=True,
in_channels=3,
resolution=256,
):
super().__init__()
self.register(
ch=ch,
out_ch=out_ch,
ch_mult=ch_mult,
num_res_blocks=num_res_blocks,
attn_resolutions=attn_resolutions,
dropout=dropout,
resamp_with_conv=resamp_with_conv,
in_channels=in_channels,
resolution=resolution,
)
ch_mult = tuple(ch_mult)
self.ch = ch
self.temb_ch = self.ch * 4
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
# timestep embedding
self.temb = nn.Module()
self.temb.dense = nn.ModuleList(
[
torch.nn.Linear(self.ch, self.temb_ch),
torch.nn.Linear(self.temb_ch, self.temb_ch),
]
)
# downsampling
self.conv_in = torch.nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1)
curr_res = resolution
in_ch_mult = (1,) + ch_mult
self.down = nn.ModuleList()
for i_level in range(self.num_resolutions):
block = nn.ModuleList()
attn = nn.ModuleList()
block_in = ch * in_ch_mult[i_level]
block_out = ch * ch_mult[i_level]
for i_block in range(self.num_res_blocks):
block.append(
ResnetBlock(
in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout
)
)
block_in = block_out
if curr_res in attn_resolutions:
attn.append(AttnBlock(block_in))
down = nn.Module()
down.block = block
down.attn = attn
if i_level != self.num_resolutions - 1:
down.downsample = Downsample(block_in, resamp_with_conv)
curr_res = curr_res // 2
self.down.append(down)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(
in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout
)
self.mid.attn_1 = AttnBlock(block_in)
self.mid.block_2 = ResnetBlock(
in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout
)
# upsampling
self.up = nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = nn.ModuleList()
attn = nn.ModuleList()
block_out = ch * ch_mult[i_level]
skip_in = ch * ch_mult[i_level]
for i_block in range(self.num_res_blocks + 1):
if i_block == self.num_res_blocks:
skip_in = ch * in_ch_mult[i_level]
block.append(
ResnetBlock(
in_channels=block_in + skip_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout,
)
)
block_in = block_out
if curr_res in attn_resolutions:
attn.append(AttnBlock(block_in))
up = nn.Module()
up.block = block
up.attn = attn
if i_level != 0:
up.upsample = Upsample(block_in, resamp_with_conv)
curr_res = curr_res * 2
self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
def forward(self, x, t):
assert x.shape[2] == x.shape[3] == self.resolution
if not torch.is_tensor(t):
t = torch.tensor([t], dtype=torch.long, device=x.device)
# timestep embedding
temb = get_timestep_embedding(t, self.ch)
temb = self.temb.dense[0](temb)
temb = nonlinearity(temb)
temb = self.temb.dense[1](temb)
# downsampling
hs = [self.conv_in(x)]
for i_level in range(self.num_resolutions):
for i_block in range(self.num_res_blocks):
h = self.down[i_level].block[i_block](hs[-1], temb)
if len(self.down[i_level].attn) > 0:
h = self.down[i_level].attn[i_block](h)
hs.append(h)
if i_level != self.num_resolutions - 1:
hs.append(self.down[i_level].downsample(hs[-1]))
# middle
h = hs[-1]
h = self.mid.block_1(h, temb)
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# upsampling
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks + 1):
h = self.up[i_level].block[i_block](torch.cat([h, hs.pop()], dim=1), temb)
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h)
if i_level != 0:
h = self.up[i_level].upsample(h)
# end
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
# dataset classes
class Dataset(data.Dataset):
def __init__(self, folder, image_size, exts=['jpg', 'jpeg', 'png']):
super().__init__()
self.folder = folder
self.image_size = image_size
self.paths = [p for ext in exts for p in Path(f'{folder}').glob(f'**/*.{ext}')]
self.transform = transforms.Compose([
transforms.Resize(image_size),
transforms.RandomHorizontalFlip(),
transforms.CenterCrop(image_size),
transforms.ToTensor()
])
def __len__(self):
return len(self.paths)
def __getitem__(self, index):
path = self.paths[index]
img = Image.open(path)
return self.transform(img)
# trainer class
class EMA():
def __init__(self, beta):
super().__init__()
self.beta = beta
def update_model_average(self, ma_model, current_model):
for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()):
old_weight, up_weight = ma_params.data, current_params.data
ma_params.data = self.update_average(old_weight, up_weight)
def update_average(self, old, new):
if old is None:
return new
return old * self.beta + (1 - self.beta) * new
def cycle(dl):
while True:
for data_dl in dl:
yield data_dl
def num_to_groups(num, divisor):
groups = num // divisor
remainder = num % divisor
arr = [divisor] * groups
if remainder > 0:
arr.append(remainder)
return arr
class Trainer(object):
def __init__(
self,
diffusion_model,
folder,
*,
ema_decay=0.995,
image_size=128,
train_batch_size=32,
train_lr=1e-4,
train_num_steps=100000,
gradient_accumulate_every=2,
amp=False,
step_start_ema=2000,
update_ema_every=10,
save_and_sample_every=1000,
results_folder="./results",
):
super().__init__()
self.model = diffusion_model
self.ema = EMA(ema_decay)
self.ema_model = copy.deepcopy(self.model)
self.update_ema_every = update_ema_every
self.step_start_ema = step_start_ema
self.save_and_sample_every = save_and_sample_every
self.batch_size = train_batch_size
self.image_size = diffusion_model.image_size
self.gradient_accumulate_every = gradient_accumulate_every
self.train_num_steps = train_num_steps
self.ds = Dataset(folder, image_size)
self.dl = cycle(data.DataLoader(self.ds, batch_size=train_batch_size, shuffle=True, pin_memory=True))
self.opt = Adam(diffusion_model.parameters(), lr=train_lr)
self.step = 0
self.amp = amp
self.scaler = GradScaler(enabled=amp)
self.results_folder = Path(results_folder)
self.results_folder.mkdir(exist_ok=True)
self.reset_parameters()
def reset_parameters(self):
self.ema_model.load_state_dict(self.model.state_dict())
def step_ema(self):
if self.step < self.step_start_ema:
self.reset_parameters()
return
self.ema.update_model_average(self.ema_model, self.model)
def save(self, milestone):
data = {
"step": self.step,
"model": self.model.state_dict(),
"ema": self.ema_model.state_dict(),
"scaler": self.scaler.state_dict(),
}
torch.save(data, str(self.results_folder / f"model-{milestone}.pt"))
def load(self, milestone):
data = torch.load(str(self.results_folder / f"model-{milestone}.pt"))
self.step = data["step"]
self.model.load_state_dict(data["model"])
self.ema_model.load_state_dict(data["ema"])
self.scaler.load_state_dict(data["scaler"])
def train(self):
with tqdm(initial=self.step, total=self.train_num_steps) as pbar:
while self.step < self.train_num_steps:
for i in range(self.gradient_accumulate_every):
data = next(self.dl).cuda()
with autocast(enabled=self.amp):
loss = self.model(data)
self.scaler.scale(loss / self.gradient_accumulate_every).backward()
pbar.set_description(f"loss: {loss.item():.4f}")
self.scaler.step(self.opt)
self.scaler.update()
self.opt.zero_grad()
if self.step % self.update_ema_every == 0:
self.step_ema()
if self.step != 0 and self.step % self.save_and_sample_every == 0:
self.ema_model.eval()
milestone = self.step // self.save_and_sample_every
batches = num_to_groups(36, self.batch_size)
all_images_list = list(map(lambda n: self.ema_model.sample(batch_size=n), batches))
all_images = torch.cat(all_images_list, dim=0)
utils.save_image(all_images, str(self.results_folder / f"sample-{milestone}.png"), nrow=6)
self.save(milestone)
self.step += 1
pbar.update(1)
print("training complete")