mirror of
https://github.com/wassname/denoising-diffusion-pytorch.git
synced 2026-09-09 11:21:11 +08:00
793 lines
27 KiB
Python
793 lines
27 KiB
Python
import math
|
|
import copy
|
|
from pathlib import Path
|
|
from random import random
|
|
from functools import partial
|
|
from collections import namedtuple
|
|
from multiprocessing import cpu_count
|
|
|
|
import torch
|
|
from torch import nn, einsum
|
|
import torch.nn.functional as F
|
|
|
|
from einops import rearrange, reduce, repeat
|
|
from einops.layers.torch import Rearrange
|
|
|
|
from tqdm.auto import tqdm
|
|
|
|
# constants
|
|
|
|
ModelPrediction = namedtuple('ModelPrediction', ['pred_noise', 'pred_x_start'])
|
|
|
|
# helpers functions
|
|
|
|
def exists(x):
|
|
return x is not None
|
|
|
|
def default(val, d):
|
|
if exists(val):
|
|
return val
|
|
return d() if callable(d) else d
|
|
|
|
def identity(t, *args, **kwargs):
|
|
return t
|
|
|
|
def cycle(dl):
|
|
while True:
|
|
for data in dl:
|
|
yield data
|
|
|
|
def has_int_squareroot(num):
|
|
return (math.sqrt(num) ** 2) == num
|
|
|
|
def num_to_groups(num, divisor):
|
|
groups = num // divisor
|
|
remainder = num % divisor
|
|
arr = [divisor] * groups
|
|
if remainder > 0:
|
|
arr.append(remainder)
|
|
return arr
|
|
|
|
def convert_image_to_fn(img_type, image):
|
|
if image.mode != img_type:
|
|
return image.convert(img_type)
|
|
return image
|
|
|
|
# normalization functions
|
|
|
|
def normalize_to_neg_one_to_one(img):
|
|
return img * 2 - 1
|
|
|
|
def unnormalize_to_zero_to_one(t):
|
|
return (t + 1) * 0.5
|
|
|
|
# classifier free guidance functions
|
|
|
|
def uniform(shape, device):
|
|
return torch.zeros(shape, device = device).float().uniform_(0, 1)
|
|
|
|
def prob_mask_like(shape, prob, device):
|
|
if prob == 1:
|
|
return torch.ones(shape, device = device, dtype = torch.bool)
|
|
elif prob == 0:
|
|
return torch.zeros(shape, device = device, dtype = torch.bool)
|
|
else:
|
|
return torch.zeros(shape, device = device).float().uniform_(0, 1) < prob
|
|
|
|
# small helper modules
|
|
|
|
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
|
|
|
|
def Upsample(dim, dim_out = None):
|
|
return nn.Sequential(
|
|
nn.Upsample(scale_factor = 2, mode = 'nearest'),
|
|
nn.Conv2d(dim, default(dim_out, dim), 3, padding = 1)
|
|
)
|
|
|
|
def Downsample(dim, dim_out = None):
|
|
return nn.Conv2d(dim, default(dim_out, dim), 4, 2, 1)
|
|
|
|
class WeightStandardizedConv2d(nn.Conv2d):
|
|
"""
|
|
https://arxiv.org/abs/1903.10520
|
|
weight standardization purportedly works synergistically with group normalization
|
|
"""
|
|
def forward(self, x):
|
|
eps = 1e-5 if x.dtype == torch.float32 else 1e-3
|
|
|
|
weight = self.weight
|
|
mean = reduce(weight, 'o ... -> o 1 1 1', 'mean')
|
|
var = reduce(weight, 'o ... -> o 1 1 1', partial(torch.var, unbiased = False))
|
|
normalized_weight = (weight - mean) * (var + eps).rsqrt()
|
|
|
|
return F.conv2d(x, normalized_weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
|
|
|
|
class LayerNorm(nn.Module):
|
|
def __init__(self, dim):
|
|
super().__init__()
|
|
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
|
|
|
|
def forward(self, x):
|
|
eps = 1e-5 if x.dtype == torch.float32 else 1e-3
|
|
var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
|
|
mean = torch.mean(x, dim = 1, keepdim = True)
|
|
return (x - mean) * (var + eps).rsqrt() * self.g
|
|
|
|
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)
|
|
|
|
# sinusoidal positional embeds
|
|
|
|
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 RandomOrLearnedSinusoidalPosEmb(nn.Module):
|
|
""" following @crowsonkb 's lead with random (learned optional) sinusoidal pos emb """
|
|
""" https://github.com/crowsonkb/v-diffusion-jax/blob/master/diffusion/models/danbooru_128.py#L8 """
|
|
|
|
def __init__(self, dim, is_random = False):
|
|
super().__init__()
|
|
assert (dim % 2) == 0
|
|
half_dim = dim // 2
|
|
self.weights = nn.Parameter(torch.randn(half_dim), requires_grad = not is_random)
|
|
|
|
def forward(self, x):
|
|
x = rearrange(x, 'b -> b 1')
|
|
freqs = x * rearrange(self.weights, 'd -> 1 d') * 2 * math.pi
|
|
fouriered = torch.cat((freqs.sin(), freqs.cos()), dim = -1)
|
|
fouriered = torch.cat((x, fouriered), dim = -1)
|
|
return fouriered
|
|
|
|
# building block modules
|
|
|
|
class Block(nn.Module):
|
|
def __init__(self, dim, dim_out, groups = 8):
|
|
super().__init__()
|
|
self.proj = WeightStandardizedConv2d(dim, dim_out, 3, padding = 1)
|
|
self.norm = nn.GroupNorm(groups, dim_out)
|
|
self.act = nn.SiLU()
|
|
|
|
def forward(self, x, scale_shift = None):
|
|
x = self.proj(x)
|
|
x = self.norm(x)
|
|
|
|
if exists(scale_shift):
|
|
scale, shift = scale_shift
|
|
x = x * (scale + 1) + shift
|
|
|
|
x = self.act(x)
|
|
return x
|
|
|
|
class ResnetBlock(nn.Module):
|
|
def __init__(self, dim, dim_out, *, time_emb_dim = None, classes_emb_dim = None, groups = 8):
|
|
super().__init__()
|
|
self.mlp = nn.Sequential(
|
|
nn.SiLU(),
|
|
nn.Linear(int(time_emb_dim) + int(classes_emb_dim), dim_out * 2)
|
|
) if exists(time_emb_dim) or exists(classes_emb_dim) else None
|
|
|
|
self.block1 = Block(dim, dim_out, groups = groups)
|
|
self.block2 = Block(dim_out, dim_out, groups = groups)
|
|
self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
|
|
|
|
def forward(self, x, time_emb = None, class_emb = None):
|
|
|
|
scale_shift = None
|
|
if exists(self.mlp) and (exists(time_emb) or exists(class_emb)):
|
|
cond_emb = tuple(filter(exists, (time_emb, class_emb)))
|
|
cond_emb = torch.cat(cond_emb, dim = -1)
|
|
cond_emb = self.mlp(cond_emb)
|
|
cond_emb = rearrange(cond_emb, 'b c -> b c 1 1')
|
|
scale_shift = cond_emb.chunk(2, dim = 1)
|
|
|
|
h = self.block1(x, scale_shift = scale_shift)
|
|
|
|
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.scale = dim_head ** -0.5
|
|
self.heads = heads
|
|
hidden_dim = dim_head * heads
|
|
self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
|
|
|
|
self.to_out = nn.Sequential(
|
|
nn.Conv2d(hidden_dim, dim, 1),
|
|
LayerNorm(dim)
|
|
)
|
|
|
|
def forward(self, x):
|
|
b, c, h, w = x.shape
|
|
qkv = self.to_qkv(x).chunk(3, dim = 1)
|
|
q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = self.heads), qkv)
|
|
|
|
q = q.softmax(dim = -2)
|
|
k = k.softmax(dim = -1)
|
|
|
|
q = q * self.scale
|
|
v = v / (h * w)
|
|
|
|
context = torch.einsum('b h d n, b h e n -> b h d e', k, v)
|
|
|
|
out = torch.einsum('b h d e, b h d n -> b h e n', context, q)
|
|
out = rearrange(out, 'b h c (x y) -> b (h c) x y', h = self.heads, x = h, y = w)
|
|
return self.to_out(out)
|
|
|
|
class Attention(nn.Module):
|
|
def __init__(self, dim, heads = 4, dim_head = 32):
|
|
super().__init__()
|
|
self.scale = dim_head ** -0.5
|
|
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).chunk(3, dim = 1)
|
|
q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = self.heads), qkv)
|
|
|
|
q = q * self.scale
|
|
|
|
sim = einsum('b h d i, b h d j -> b h i j', q, k)
|
|
attn = sim.softmax(dim = -1)
|
|
out = einsum('b h i j, b h d j -> b h i d', attn, v)
|
|
|
|
out = rearrange(out, 'b h (x y) d -> b (h d) x y', x = h, y = w)
|
|
return self.to_out(out)
|
|
|
|
# model
|
|
|
|
class Unet(nn.Module):
|
|
def __init__(
|
|
self,
|
|
dim,
|
|
num_classes,
|
|
cond_drop_prob = 0.5,
|
|
init_dim = None,
|
|
out_dim = None,
|
|
dim_mults=(1, 2, 4, 8),
|
|
channels = 3,
|
|
resnet_block_groups = 8,
|
|
learned_variance = False,
|
|
learned_sinusoidal_cond = False,
|
|
random_fourier_features = False,
|
|
learned_sinusoidal_dim = 16,
|
|
):
|
|
super().__init__()
|
|
|
|
# classifier free guidance stuff
|
|
|
|
self.cond_drop_prob = cond_drop_prob
|
|
|
|
# determine dimensions
|
|
|
|
self.channels = channels
|
|
input_channels = channels
|
|
|
|
init_dim = default(init_dim, dim)
|
|
self.init_conv = nn.Conv2d(input_channels, init_dim, 7, padding = 3)
|
|
|
|
dims = [init_dim, *map(lambda m: dim * m, dim_mults)]
|
|
in_out = list(zip(dims[:-1], dims[1:]))
|
|
|
|
block_klass = partial(ResnetBlock, groups = resnet_block_groups)
|
|
|
|
# time embeddings
|
|
|
|
time_dim = dim * 4
|
|
|
|
self.random_or_learned_sinusoidal_cond = learned_sinusoidal_cond or random_fourier_features
|
|
|
|
if self.random_or_learned_sinusoidal_cond:
|
|
sinu_pos_emb = RandomOrLearnedSinusoidalPosEmb(learned_sinusoidal_dim, random_fourier_features)
|
|
fourier_dim = learned_sinusoidal_dim + 1
|
|
else:
|
|
sinu_pos_emb = SinusoidalPosEmb(dim)
|
|
fourier_dim = dim
|
|
|
|
self.time_mlp = nn.Sequential(
|
|
sinu_pos_emb,
|
|
nn.Linear(fourier_dim, time_dim),
|
|
nn.GELU(),
|
|
nn.Linear(time_dim, time_dim)
|
|
)
|
|
|
|
# class embeddings
|
|
|
|
self.classes_emb = nn.Embedding(num_classes, dim)
|
|
self.null_classes_emb = nn.Parameter(torch.randn(dim))
|
|
|
|
classes_dim = dim * 4
|
|
|
|
self.classes_mlp = nn.Sequential(
|
|
nn.Linear(dim, classes_dim),
|
|
nn.GELU(),
|
|
nn.Linear(classes_dim, classes_dim)
|
|
)
|
|
|
|
# layers
|
|
|
|
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([
|
|
block_klass(dim_in, dim_in, time_emb_dim = time_dim, classes_emb_dim = classes_dim),
|
|
block_klass(dim_in, dim_in, time_emb_dim = time_dim, classes_emb_dim = classes_dim),
|
|
Residual(PreNorm(dim_in, LinearAttention(dim_in))),
|
|
Downsample(dim_in, dim_out) if not is_last else nn.Conv2d(dim_in, dim_out, 3, padding = 1)
|
|
]))
|
|
|
|
mid_dim = dims[-1]
|
|
self.mid_block1 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim, classes_emb_dim = classes_dim)
|
|
self.mid_attn = Residual(PreNorm(mid_dim, Attention(mid_dim)))
|
|
self.mid_block2 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim, classes_emb_dim = classes_dim)
|
|
|
|
for ind, (dim_in, dim_out) in enumerate(reversed(in_out)):
|
|
is_last = ind == (len(in_out) - 1)
|
|
|
|
self.ups.append(nn.ModuleList([
|
|
block_klass(dim_out + dim_in, dim_out, time_emb_dim = time_dim, classes_emb_dim = classes_dim),
|
|
block_klass(dim_out + dim_in, dim_out, time_emb_dim = time_dim, classes_emb_dim = classes_dim),
|
|
Residual(PreNorm(dim_out, LinearAttention(dim_out))),
|
|
Upsample(dim_out, dim_in) if not is_last else nn.Conv2d(dim_out, dim_in, 3, padding = 1)
|
|
]))
|
|
|
|
default_out_dim = channels * (1 if not learned_variance else 2)
|
|
self.out_dim = default(out_dim, default_out_dim)
|
|
|
|
self.final_res_block = block_klass(dim * 2, dim, time_emb_dim = time_dim, classes_emb_dim = classes_dim)
|
|
self.final_conv = nn.Conv2d(dim, self.out_dim, 1)
|
|
|
|
def forward_with_cond_scale(
|
|
self,
|
|
*args,
|
|
cond_scale = 1.,
|
|
**kwargs
|
|
):
|
|
logits = self.forward(*args, **kwargs)
|
|
|
|
if cond_scale == 1:
|
|
return logits
|
|
|
|
null_logits = self.forward(*args, cond_drop_prob = 1., **kwargs)
|
|
return null_logits + (logits - null_logits) * cond_scale
|
|
|
|
def forward(
|
|
self,
|
|
x,
|
|
time,
|
|
classes,
|
|
cond_drop_prob = None
|
|
):
|
|
batch, device = x.shape[0], x.device
|
|
|
|
cond_drop_prob = default(cond_drop_prob, self.cond_drop_prob)
|
|
|
|
# derive condition, with condition dropout for classifier free guidance
|
|
|
|
classes_emb = self.classes_emb(classes)
|
|
|
|
if cond_drop_prob > 0:
|
|
keep_mask = prob_mask_like((batch,), 1 - cond_drop_prob, device = device)
|
|
null_classes_emb = repeat(self.null_classes_emb, 'd -> b d', b = batch)
|
|
|
|
classes_emb = torch.where(
|
|
rearrange(keep_mask, 'b -> b 1'),
|
|
classes_emb,
|
|
null_classes_emb
|
|
)
|
|
|
|
c = self.classes_mlp(classes_emb)
|
|
|
|
# unet
|
|
|
|
x = self.init_conv(x)
|
|
r = x.clone()
|
|
|
|
t = self.time_mlp(time)
|
|
|
|
h = []
|
|
|
|
for block1, block2, attn, downsample in self.downs:
|
|
x = block1(x, t, c)
|
|
h.append(x)
|
|
|
|
x = block2(x, t, c)
|
|
x = attn(x)
|
|
h.append(x)
|
|
|
|
x = downsample(x)
|
|
|
|
x = self.mid_block1(x, t, c)
|
|
x = self.mid_attn(x)
|
|
x = self.mid_block2(x, t, c)
|
|
|
|
for block1, block2, attn, upsample in self.ups:
|
|
x = torch.cat((x, h.pop()), dim = 1)
|
|
x = block1(x, t, c)
|
|
|
|
x = torch.cat((x, h.pop()), dim = 1)
|
|
x = block2(x, t, c)
|
|
x = attn(x)
|
|
|
|
x = upsample(x)
|
|
|
|
x = torch.cat((x, r), dim = 1)
|
|
|
|
x = self.final_res_block(x, t, c)
|
|
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 linear_beta_schedule(timesteps):
|
|
scale = 1000 / timesteps
|
|
beta_start = scale * 0.0001
|
|
beta_end = scale * 0.02
|
|
return torch.linspace(beta_start, beta_end, timesteps, dtype = torch.float64)
|
|
|
|
def cosine_beta_schedule(timesteps, s = 0.008):
|
|
"""
|
|
cosine schedule
|
|
as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
|
|
"""
|
|
steps = timesteps + 1
|
|
x = torch.linspace(0, timesteps, steps, dtype = torch.float64)
|
|
alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * math.pi * 0.5) ** 2
|
|
alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
|
|
betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
|
|
return torch.clip(betas, 0, 0.999)
|
|
|
|
class GaussianDiffusion(nn.Module):
|
|
def __init__(
|
|
self,
|
|
model,
|
|
*,
|
|
image_size,
|
|
timesteps = 1000,
|
|
sampling_timesteps = None,
|
|
loss_type = 'l1',
|
|
objective = 'pred_noise',
|
|
beta_schedule = 'cosine',
|
|
p2_loss_weight_gamma = 0., # p2 loss weight, from https://arxiv.org/abs/2204.00227 - 0 is equivalent to weight of 1 across time - 1. is recommended
|
|
p2_loss_weight_k = 1,
|
|
ddim_sampling_eta = 1.
|
|
):
|
|
super().__init__()
|
|
assert not (type(self) == GaussianDiffusion and model.channels != model.out_dim)
|
|
assert not model.random_or_learned_sinusoidal_cond
|
|
|
|
self.model = model
|
|
self.channels = self.model.channels
|
|
|
|
self.image_size = image_size
|
|
|
|
self.objective = objective
|
|
|
|
assert objective in {'pred_noise', 'pred_x0', 'pred_v'}, 'objective must be either pred_noise (predict noise) or pred_x0 (predict image start) or pred_v (predict v [v-parameterization as defined in appendix D of progressive distillation paper, used in imagen-video successfully])'
|
|
|
|
if beta_schedule == 'linear':
|
|
betas = linear_beta_schedule(timesteps)
|
|
elif beta_schedule == 'cosine':
|
|
betas = cosine_beta_schedule(timesteps)
|
|
else:
|
|
raise ValueError(f'unknown beta schedule {beta_schedule}')
|
|
|
|
alphas = 1. - betas
|
|
alphas_cumprod = torch.cumprod(alphas, dim=0)
|
|
alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value = 1.)
|
|
|
|
timesteps, = betas.shape
|
|
self.num_timesteps = int(timesteps)
|
|
self.loss_type = loss_type
|
|
|
|
# sampling related parameters
|
|
|
|
self.sampling_timesteps = default(sampling_timesteps, timesteps) # default num sampling timesteps to number of timesteps at training
|
|
|
|
assert self.sampling_timesteps <= timesteps
|
|
self.is_ddim_sampling = self.sampling_timesteps < timesteps
|
|
self.ddim_sampling_eta = ddim_sampling_eta
|
|
|
|
# helper function to register buffer from float64 to float32
|
|
|
|
register_buffer = lambda name, val: self.register_buffer(name, val.to(torch.float32))
|
|
|
|
register_buffer('betas', betas)
|
|
register_buffer('alphas_cumprod', alphas_cumprod)
|
|
register_buffer('alphas_cumprod_prev', alphas_cumprod_prev)
|
|
|
|
# calculations for diffusion q(x_t | x_{t-1}) and others
|
|
|
|
register_buffer('sqrt_alphas_cumprod', torch.sqrt(alphas_cumprod))
|
|
register_buffer('sqrt_one_minus_alphas_cumprod', torch.sqrt(1. - alphas_cumprod))
|
|
register_buffer('log_one_minus_alphas_cumprod', torch.log(1. - alphas_cumprod))
|
|
register_buffer('sqrt_recip_alphas_cumprod', torch.sqrt(1. / alphas_cumprod))
|
|
register_buffer('sqrt_recipm1_alphas_cumprod', torch.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)
|
|
|
|
register_buffer('posterior_variance', posterior_variance)
|
|
|
|
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
|
|
|
|
register_buffer('posterior_log_variance_clipped', torch.log(posterior_variance.clamp(min =1e-20)))
|
|
register_buffer('posterior_mean_coef1', betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
|
|
register_buffer('posterior_mean_coef2', (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod))
|
|
|
|
# calculate p2 reweighting
|
|
|
|
register_buffer('p2_loss_weight', (p2_loss_weight_k + alphas_cumprod / (1 - alphas_cumprod)) ** -p2_loss_weight_gamma)
|
|
|
|
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 predict_noise_from_start(self, x_t, t, x0):
|
|
return (
|
|
(extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - x0) / \
|
|
extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
|
|
)
|
|
|
|
def predict_v(self, x_start, t, noise):
|
|
return (
|
|
extract(self.sqrt_alphas_cumprod, t, x_start.shape) * noise -
|
|
extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * x_start
|
|
)
|
|
|
|
def predict_start_from_v(self, x_t, t, v):
|
|
return (
|
|
extract(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
|
|
extract(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
|
|
)
|
|
|
|
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 model_predictions(self, x, t, classes, cond_scale = 3., clip_x_start = False):
|
|
model_output = self.model.forward_with_cond_scale(x, t, classes, cond_scale = cond_scale)
|
|
maybe_clip = partial(torch.clamp, min = -1., max = 1.) if clip_x_start else identity
|
|
|
|
if self.objective == 'pred_noise':
|
|
pred_noise = model_output
|
|
x_start = self.predict_start_from_noise(x, t, pred_noise)
|
|
x_start = maybe_clip(x_start)
|
|
|
|
elif self.objective == 'pred_x0':
|
|
x_start = model_output
|
|
x_start = maybe_clip(x_start)
|
|
pred_noise = self.predict_noise_from_start(x, t, x_start)
|
|
|
|
elif self.objective == 'pred_v':
|
|
v = model_output
|
|
x_start = self.predict_start_from_v(x, t, v)
|
|
x_start = maybe_clip(x_start)
|
|
pred_noise = self.predict_noise_from_start(x, t, x_start)
|
|
|
|
return ModelPrediction(pred_noise, x_start)
|
|
|
|
def p_mean_variance(self, x, t, classes, cond_scale, clip_denoised = True):
|
|
preds = self.model_predictions(x, t, classes, cond_scale)
|
|
x_start = preds.pred_x_start
|
|
|
|
if clip_denoised:
|
|
x_start.clamp_(-1., 1.)
|
|
|
|
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start = x_start, x_t = x, t = t)
|
|
return model_mean, posterior_variance, posterior_log_variance, x_start
|
|
|
|
@torch.no_grad()
|
|
def p_sample(self, x, t: int, classes, cond_scale = 3., clip_denoised = True):
|
|
b, *_, device = *x.shape, x.device
|
|
batched_times = torch.full((x.shape[0],), t, device = x.device, dtype = torch.long)
|
|
model_mean, _, model_log_variance, x_start = self.p_mean_variance(x = x, t = batched_times, classes = classes, cond_scale = cond_scale, clip_denoised = clip_denoised)
|
|
noise = torch.randn_like(x) if t > 0 else 0. # no noise if t == 0
|
|
pred_img = model_mean + (0.5 * model_log_variance).exp() * noise
|
|
return pred_img, x_start
|
|
|
|
@torch.no_grad()
|
|
def p_sample_loop(self, classes, shape, cond_scale = 3.):
|
|
batch, device = shape[0], self.betas.device
|
|
|
|
img = torch.randn(shape, device=device)
|
|
|
|
x_start = None
|
|
|
|
for t in tqdm(reversed(range(0, self.num_timesteps)), desc = 'sampling loop time step', total = self.num_timesteps):
|
|
img, x_start = self.p_sample(img, t, classes, cond_scale)
|
|
|
|
img = unnormalize_to_zero_to_one(img)
|
|
return img
|
|
|
|
@torch.no_grad()
|
|
def ddim_sample(self, classes, shape, cond_scale = 3., clip_denoised = True):
|
|
batch, device, total_timesteps, sampling_timesteps, eta, objective = shape[0], self.betas.device, self.num_timesteps, self.sampling_timesteps, self.ddim_sampling_eta, self.objective
|
|
|
|
times = torch.linspace(-1, total_timesteps - 1, steps=sampling_timesteps + 1) # [-1, 0, 1, 2, ..., T-1] when sampling_timesteps == total_timesteps
|
|
times = list(reversed(times.int().tolist()))
|
|
time_pairs = list(zip(times[:-1], times[1:])) # [(T-1, T-2), (T-2, T-3), ..., (1, 0), (0, -1)]
|
|
|
|
img = torch.randn(shape, device = device)
|
|
|
|
x_start = None
|
|
|
|
for time, time_next in tqdm(time_pairs, desc = 'sampling loop time step'):
|
|
time_cond = torch.full((batch,), time, device=device, dtype=torch.long)
|
|
pred_noise, x_start, *_ = self.model_predictions(img, time_cond, classes, cond_scale = cond_scale, clip_x_start = clip_denoised)
|
|
|
|
if time_next < 0:
|
|
img = x_start
|
|
continue
|
|
|
|
alpha = self.alphas_cumprod[time]
|
|
alpha_next = self.alphas_cumprod[time_next]
|
|
|
|
sigma = eta * ((1 - alpha / alpha_next) * (1 - alpha_next) / (1 - alpha)).sqrt()
|
|
c = (1 - alpha_next - sigma ** 2).sqrt()
|
|
|
|
noise = torch.randn_like(img)
|
|
|
|
img = x_start * alpha_next.sqrt() + \
|
|
c * pred_noise + \
|
|
sigma * noise
|
|
|
|
img = unnormalize_to_zero_to_one(img)
|
|
return img
|
|
|
|
@torch.no_grad()
|
|
def sample(self, classes, cond_scale = 3.):
|
|
batch_size, image_size, channels = classes.shape[0], self.image_size, self.channels
|
|
sample_fn = self.p_sample_loop if not self.is_ddim_sampling else self.ddim_sample
|
|
return sample_fn(classes, (batch_size, channels, image_size, image_size), cond_scale)
|
|
|
|
@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
|
|
)
|
|
|
|
@property
|
|
def loss_fn(self):
|
|
if self.loss_type == 'l1':
|
|
return F.l1_loss
|
|
elif self.loss_type == 'l2':
|
|
return F.mse_loss
|
|
else:
|
|
raise ValueError(f'invalid loss type {self.loss_type}')
|
|
|
|
def p_losses(self, x_start, t, *, classes, noise = None):
|
|
b, c, h, w = x_start.shape
|
|
noise = default(noise, lambda: torch.randn_like(x_start))
|
|
|
|
# noise sample
|
|
|
|
x = self.q_sample(x_start = x_start, t = t, noise = noise)
|
|
|
|
# predict and take gradient step
|
|
|
|
model_out = self.model(x, t, classes)
|
|
|
|
if self.objective == 'pred_noise':
|
|
target = noise
|
|
elif self.objective == 'pred_x0':
|
|
target = x_start
|
|
elif self.objective == 'pred_v':
|
|
v = self.predict_v(x_start, t, noise)
|
|
target = v
|
|
else:
|
|
raise ValueError(f'unknown objective {self.objective}')
|
|
|
|
loss = self.loss_fn(model_out, target, reduction = 'none')
|
|
loss = reduce(loss, 'b ... -> b (...)', 'mean')
|
|
|
|
loss = loss * extract(self.p2_loss_weight, t, loss.shape)
|
|
return loss.mean()
|
|
|
|
def forward(self, img, *args, **kwargs):
|
|
b, c, h, w, device, img_size, = *img.shape, img.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()
|
|
|
|
img = normalize_to_neg_one_to_one(img)
|
|
return self.p_losses(img, t, *args, **kwargs)
|
|
|
|
# example
|
|
|
|
if __name__ == '__main__':
|
|
num_classes = 10
|
|
|
|
model = Unet(
|
|
dim = 64,
|
|
dim_mults = (1, 2, 4, 8),
|
|
num_classes = num_classes,
|
|
cond_drop_prob = 0.5
|
|
)
|
|
|
|
diffusion = GaussianDiffusion(
|
|
model,
|
|
image_size = 128,
|
|
timesteps = 1000
|
|
).cuda()
|
|
|
|
training_images = torch.randn(8, 3, 128, 128).cuda() # images are normalized from 0 to 1
|
|
image_classes = torch.randint(0, num_classes, (8,)).cuda() # say 10 classes
|
|
|
|
loss = diffusion(training_images, classes = image_classes)
|
|
loss.backward()
|
|
|
|
# do above for many steps
|
|
|
|
sampled_images = diffusion.sample(
|
|
classes = image_classes,
|
|
cond_scale = 3. # condition scaling, anything greater than 1 strengthens the classifier free guidance. reportedly 3-8 is good empirically
|
|
)
|
|
|
|
sampled_images.shape # (8, 3, 128, 128)
|