mirror of
https://github.com/wassname/denoising-diffusion-pytorch.git
synced 2026-09-21 12:50:17 +08:00
585 lines
20 KiB
Python
585 lines
20 KiB
Python
import math
|
|
import copy
|
|
import torch
|
|
from torch import nn, einsum
|
|
import torch.nn.functional as F
|
|
from inspect import isfunction
|
|
from functools import partial
|
|
|
|
from torch.utils import data
|
|
from pathlib import Path
|
|
from torch.optim import Adam
|
|
from torchvision import transforms, utils
|
|
from PIL import Image
|
|
|
|
import numpy as np
|
|
from tqdm import tqdm
|
|
from einops import rearrange
|
|
|
|
try:
|
|
from apex import amp
|
|
APEX_AVAILABLE = True
|
|
except:
|
|
APEX_AVAILABLE = False
|
|
|
|
# helpers functions
|
|
|
|
def exists(x):
|
|
return x is not None
|
|
|
|
def default(val, d):
|
|
if exists(val):
|
|
return val
|
|
return d() if isfunction(d) else d
|
|
|
|
def cycle(dl):
|
|
while True:
|
|
for data in dl:
|
|
yield data
|
|
|
|
def num_to_groups(num, divisor):
|
|
groups = num // divisor
|
|
remainder = num % divisor
|
|
arr = [divisor] * groups
|
|
if remainder > 0:
|
|
arr.append(remainder)
|
|
return arr
|
|
|
|
def loss_backwards(fp16, loss, optimizer, **kwargs):
|
|
if fp16:
|
|
with amp.scale_loss(loss, optimizer) as scaled_loss:
|
|
scaled_loss.backward(**kwargs)
|
|
else:
|
|
loss.backward(**kwargs)
|
|
|
|
# small helper modules
|
|
|
|
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
|
|
|
|
class Residual(nn.Module):
|
|
def __init__(self, fn):
|
|
super().__init__()
|
|
self.fn = fn
|
|
|
|
def forward(self, x, *args, **kwargs):
|
|
return self.fn(x, *args, **kwargs) + x
|
|
|
|
class SinusoidalPosEmb(nn.Module):
|
|
def __init__(self, dim):
|
|
super().__init__()
|
|
self.dim = dim
|
|
|
|
def forward(self, x):
|
|
device = x.device
|
|
half_dim = self.dim // 2
|
|
emb = math.log(10000) / (half_dim - 1)
|
|
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
|
|
emb = x[:, None] * emb[None, :]
|
|
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
|
|
return emb
|
|
|
|
class Mish(nn.Module):
|
|
def forward(self, x):
|
|
return x * torch.tanh(F.softplus(x))
|
|
|
|
class Upsample(nn.Module):
|
|
def __init__(self, dim):
|
|
super().__init__()
|
|
self.conv = nn.ConvTranspose2d(dim, dim, 4, 2, 1)
|
|
|
|
def forward(self, x):
|
|
return self.conv(x)
|
|
|
|
class Downsample(nn.Module):
|
|
def __init__(self, dim):
|
|
super().__init__()
|
|
self.conv = nn.Conv2d(dim, dim, 3, 2, 1)
|
|
|
|
def forward(self, x):
|
|
return self.conv(x)
|
|
|
|
class LayerNorm(nn.Module):
|
|
def __init__(self, dim, eps = 1e-5):
|
|
super().__init__()
|
|
self.eps = eps
|
|
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
|
|
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
|
|
|
|
def forward(self, x):
|
|
std = torch.var(x, dim = 1, unbiased = False, keepdim = True).sqrt()
|
|
mean = torch.mean(x, dim = 1, keepdim = True)
|
|
return (x - mean) / (std + self.eps) * self.g + self.b
|
|
|
|
class PreNorm(nn.Module):
|
|
def __init__(self, dim, fn):
|
|
super().__init__()
|
|
self.fn = fn
|
|
self.norm = LayerNorm(dim)
|
|
|
|
def forward(self, x):
|
|
x = self.norm(x)
|
|
return self.fn(x)
|
|
|
|
# building block modules
|
|
|
|
class Block(nn.Module):
|
|
def __init__(self, dim, dim_out, groups = 8):
|
|
super().__init__()
|
|
self.block = nn.Sequential(
|
|
nn.Conv2d(dim, dim_out, 3, padding=1),
|
|
nn.GroupNorm(groups, dim_out),
|
|
Mish()
|
|
)
|
|
def forward(self, x):
|
|
return self.block(x)
|
|
|
|
class ResnetBlock(nn.Module):
|
|
def __init__(self, dim, dim_out, *, time_emb_dim = None, groups = 8):
|
|
super().__init__()
|
|
self.mlp = nn.Sequential(
|
|
Mish(),
|
|
nn.Linear(time_emb_dim, dim_out)
|
|
) if exists(time_emb_dim) else None
|
|
|
|
self.block1 = Block(dim, dim_out)
|
|
self.block2 = Block(dim_out, dim_out)
|
|
self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
|
|
|
|
def forward(self, x, time_emb):
|
|
h = self.block1(x)
|
|
|
|
if exists(self.mlp):
|
|
h += self.mlp(time_emb)[:, :, None, None]
|
|
|
|
h = self.block2(h)
|
|
return h + self.res_conv(x)
|
|
|
|
class LinearAttention(nn.Module):
|
|
def __init__(self, dim, heads = 4, dim_head = 32):
|
|
super().__init__()
|
|
self.heads = heads
|
|
hidden_dim = dim_head * heads
|
|
self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
|
|
self.to_out = nn.Conv2d(hidden_dim, dim, 1)
|
|
|
|
def forward(self, x):
|
|
b, c, h, w = x.shape
|
|
qkv = self.to_qkv(x)
|
|
q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
|
|
k = k.softmax(dim=-1)
|
|
context = torch.einsum('bhdn,bhen->bhde', k, v)
|
|
out = torch.einsum('bhde,bhdn->bhen', context, q)
|
|
out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
|
|
return self.to_out(out)
|
|
|
|
# model
|
|
|
|
class Unet(nn.Module):
|
|
def __init__(
|
|
self,
|
|
dim,
|
|
out_dim = None,
|
|
dim_mults=(1, 2, 4, 8),
|
|
groups = 8,
|
|
channels = 3,
|
|
with_time_emb = True
|
|
):
|
|
super().__init__()
|
|
self.channels = channels
|
|
|
|
dims = [channels, *map(lambda m: dim * m, dim_mults)]
|
|
in_out = list(zip(dims[:-1], dims[1:]))
|
|
|
|
if with_time_emb:
|
|
time_dim = dim
|
|
self.time_mlp = nn.Sequential(
|
|
SinusoidalPosEmb(dim),
|
|
nn.Linear(dim, dim * 4),
|
|
Mish(),
|
|
nn.Linear(dim * 4, dim)
|
|
)
|
|
else:
|
|
time_dim = None
|
|
self.time_mlp = None
|
|
|
|
self.downs = nn.ModuleList([])
|
|
self.ups = nn.ModuleList([])
|
|
num_resolutions = len(in_out)
|
|
|
|
for ind, (dim_in, dim_out) in enumerate(in_out):
|
|
is_last = ind >= (num_resolutions - 1)
|
|
|
|
self.downs.append(nn.ModuleList([
|
|
ResnetBlock(dim_in, dim_out, time_emb_dim = time_dim),
|
|
ResnetBlock(dim_out, dim_out, time_emb_dim = time_dim),
|
|
Residual(PreNorm(dim_out, LinearAttention(dim_out))),
|
|
Downsample(dim_out) if not is_last else nn.Identity()
|
|
]))
|
|
|
|
mid_dim = dims[-1]
|
|
self.mid_block1 = ResnetBlock(mid_dim, mid_dim, time_emb_dim = time_dim)
|
|
self.mid_attn = Residual(PreNorm(mid_dim, LinearAttention(mid_dim)))
|
|
self.mid_block2 = ResnetBlock(mid_dim, mid_dim, time_emb_dim = time_dim)
|
|
|
|
for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
|
|
is_last = ind >= (num_resolutions - 1)
|
|
|
|
self.ups.append(nn.ModuleList([
|
|
ResnetBlock(dim_out * 2, dim_in, time_emb_dim = time_dim),
|
|
ResnetBlock(dim_in, dim_in, time_emb_dim = time_dim),
|
|
Residual(PreNorm(dim_in, LinearAttention(dim_in))),
|
|
Upsample(dim_in) if not is_last else nn.Identity()
|
|
]))
|
|
|
|
out_dim = default(out_dim, channels)
|
|
self.final_conv = nn.Sequential(
|
|
Block(dim, dim),
|
|
nn.Conv2d(dim, out_dim, 1)
|
|
)
|
|
|
|
def forward(self, x, time):
|
|
t = self.time_mlp(time) if exists(self.time_mlp) else None
|
|
|
|
h = []
|
|
|
|
for resnet, resnet2, attn, downsample in self.downs:
|
|
x = resnet(x, t)
|
|
x = resnet2(x, t)
|
|
x = attn(x)
|
|
h.append(x)
|
|
x = downsample(x)
|
|
|
|
x = self.mid_block1(x, t)
|
|
x = self.mid_attn(x)
|
|
x = self.mid_block2(x, t)
|
|
|
|
for resnet, resnet2, attn, upsample in self.ups:
|
|
x = torch.cat((x, h.pop()), dim=1)
|
|
x = resnet(x, t)
|
|
x = resnet2(x, t)
|
|
x = attn(x)
|
|
x = upsample(x)
|
|
|
|
return self.final_conv(x)
|
|
|
|
# gaussian diffusion trainer class
|
|
|
|
def extract(a, t, x_shape):
|
|
b, *_ = t.shape
|
|
out = a.gather(-1, t)
|
|
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
|
|
|
|
def noise_like(shape, device, repeat=False):
|
|
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
|
|
noise = lambda: torch.randn(shape, device=device)
|
|
return repeat_noise() if repeat else noise()
|
|
|
|
def cosine_beta_schedule(timesteps, s = 0.008):
|
|
"""
|
|
cosine schedule
|
|
as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
|
|
"""
|
|
steps = timesteps + 1
|
|
x = np.linspace(0, steps, steps)
|
|
alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2
|
|
alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
|
|
betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
|
|
return np.clip(betas, a_min = 0, a_max = 0.999)
|
|
|
|
class GaussianDiffusion(nn.Module):
|
|
def __init__(
|
|
self,
|
|
denoise_fn,
|
|
*,
|
|
image_size,
|
|
channels = 3,
|
|
timesteps = 1000,
|
|
loss_type = 'l1',
|
|
betas = None
|
|
):
|
|
super().__init__()
|
|
self.channels = channels
|
|
self.image_size = image_size
|
|
self.denoise_fn = denoise_fn
|
|
|
|
if exists(betas):
|
|
betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas
|
|
else:
|
|
betas = cosine_beta_schedule(timesteps)
|
|
|
|
alphas = 1. - betas
|
|
alphas_cumprod = np.cumprod(alphas, axis=0)
|
|
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
|
|
|
|
timesteps, = betas.shape
|
|
self.num_timesteps = int(timesteps)
|
|
self.loss_type = loss_type
|
|
|
|
to_torch = partial(torch.tensor, dtype=torch.float32)
|
|
|
|
self.register_buffer('betas', to_torch(betas))
|
|
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
|
|
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
|
|
|
|
# calculations for diffusion q(x_t | x_{t-1}) and others
|
|
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
|
|
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
|
|
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
|
|
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
|
|
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
|
|
|
|
# calculations for posterior q(x_{t-1} | x_t, x_0)
|
|
posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
|
|
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
|
|
self.register_buffer('posterior_variance', to_torch(posterior_variance))
|
|
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
|
|
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
|
|
self.register_buffer('posterior_mean_coef1', to_torch(
|
|
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
|
|
self.register_buffer('posterior_mean_coef2', to_torch(
|
|
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
|
|
|
|
def q_mean_variance(self, x_start, t):
|
|
mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
|
|
variance = extract(1. - self.alphas_cumprod, t, x_start.shape)
|
|
log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
|
|
return mean, variance, log_variance
|
|
|
|
def predict_start_from_noise(self, x_t, t, noise):
|
|
return (
|
|
extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
|
|
extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
|
|
)
|
|
|
|
def q_posterior(self, x_start, x_t, t):
|
|
posterior_mean = (
|
|
extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
|
|
extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
|
|
)
|
|
posterior_variance = extract(self.posterior_variance, t, x_t.shape)
|
|
posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
|
|
return posterior_mean, posterior_variance, posterior_log_variance_clipped
|
|
|
|
def p_mean_variance(self, x, t, clip_denoised: bool):
|
|
x_recon = self.predict_start_from_noise(x, t=t, noise=self.denoise_fn(x, t))
|
|
|
|
if clip_denoised:
|
|
x_recon.clamp_(-1., 1.)
|
|
|
|
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
|
|
return model_mean, posterior_variance, posterior_log_variance
|
|
|
|
@torch.no_grad()
|
|
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
|
|
b, *_, device = *x.shape, x.device
|
|
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
|
|
noise = noise_like(x.shape, device, repeat_noise)
|
|
# no noise when t == 0
|
|
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
|
|
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
|
|
|
|
@torch.no_grad()
|
|
def p_sample_loop(self, shape):
|
|
device = self.betas.device
|
|
|
|
b = shape[0]
|
|
img = torch.randn(shape, device=device)
|
|
|
|
for i in tqdm(reversed(range(0, self.num_timesteps)), desc='sampling loop time step', total=self.num_timesteps):
|
|
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long))
|
|
return img
|
|
|
|
@torch.no_grad()
|
|
def sample(self, batch_size = 16):
|
|
image_size = self.image_size
|
|
channels = self.channels
|
|
return self.p_sample_loop((batch_size, channels, image_size, image_size))
|
|
|
|
@torch.no_grad()
|
|
def interpolate(self, x1, x2, t = None, lam = 0.5):
|
|
b, *_, device = *x1.shape, x1.device
|
|
t = default(t, self.num_timesteps - 1)
|
|
|
|
assert x1.shape == x2.shape
|
|
|
|
t_batched = torch.stack([torch.tensor(t, device=device)] * b)
|
|
xt1, xt2 = map(lambda x: self.q_sample(x, t=t_batched), (x1, x2))
|
|
|
|
img = (1 - lam) * xt1 + lam * xt2
|
|
for i in tqdm(reversed(range(0, t)), desc='interpolation sample time step', total=t):
|
|
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long))
|
|
|
|
return img
|
|
|
|
def q_sample(self, x_start, t, noise=None):
|
|
noise = default(noise, lambda: torch.randn_like(x_start))
|
|
|
|
return (
|
|
extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
|
|
extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
|
|
)
|
|
|
|
def p_losses(self, x_start, t, noise = None):
|
|
b, c, h, w = x_start.shape
|
|
noise = default(noise, lambda: torch.randn_like(x_start))
|
|
|
|
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
|
|
x_recon = self.denoise_fn(x_noisy, t)
|
|
|
|
if self.loss_type == 'l1':
|
|
loss = (noise - x_recon).abs().mean()
|
|
elif self.loss_type == 'l2':
|
|
loss = F.mse_loss(noise, x_recon)
|
|
else:
|
|
raise NotImplementedError()
|
|
|
|
return loss
|
|
|
|
def forward(self, x, *args, **kwargs):
|
|
b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
|
|
assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
|
|
t = torch.randint(0, self.num_timesteps, (b,), device=device).long()
|
|
return self.p_losses(x, t, *args, **kwargs)
|
|
|
|
# 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(),
|
|
transforms.Lambda(lambda t: (t * 2) - 1)
|
|
])
|
|
|
|
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 Trainer(object):
|
|
def __init__(
|
|
self,
|
|
diffusion_model,
|
|
folder,
|
|
*,
|
|
ema_decay = 0.995,
|
|
image_size = 128,
|
|
train_batch_size = 32,
|
|
train_lr = 2e-5,
|
|
train_num_steps = 100000,
|
|
gradient_accumulate_every = 2,
|
|
fp16 = 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
|
|
|
|
assert not fp16 or fp16 and APEX_AVAILABLE, 'Apex must be installed in order for mixed precision training to be turned on'
|
|
|
|
self.fp16 = fp16
|
|
if fp16:
|
|
(self.model, self.ema_model), self.opt = amp.initialize([self.model, self.ema_model], self.opt, opt_level='O1')
|
|
|
|
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()
|
|
}
|
|
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'])
|
|
|
|
def train(self):
|
|
backwards = partial(loss_backwards, self.fp16)
|
|
|
|
while self.step < self.train_num_steps:
|
|
for i in range(self.gradient_accumulate_every):
|
|
data = next(self.dl).cuda()
|
|
loss = self.model(data)
|
|
print(f'{self.step}: {loss.item()}')
|
|
backwards(loss / self.gradient_accumulate_every, self.opt)
|
|
|
|
self.opt.step()
|
|
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:
|
|
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)
|
|
all_images = (all_images + 1) * 0.5
|
|
utils.save_image(all_images, str(self.results_folder / f'sample-{milestone}.png'), nrow = 6)
|
|
self.save(milestone)
|
|
|
|
self.step += 1
|
|
|
|
print('training completed')
|