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v_diffusion.py
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v_diffusion.py
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import math
import torch
import torch.nn.functional as F
def make_diffusion(model, n_timestep, time_scale, device):
betas = make_beta_schedule("cosine", cosine_s=8e-3, n_timestep=n_timestep).to(device)
return GaussianDiffusion(model, betas, time_scale=time_scale)
def make_beta_schedule(
schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3
):
if schedule == "cosine":
timesteps = (
torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
)
alphas = timesteps / (1 + cosine_s) * math.pi / 2
alphas = torch.cos(alphas).pow(2)
alphas = alphas / alphas[0]
betas = 1 - alphas[1:] / alphas[:-1]
betas = betas.clamp(max=0.999)
else:
raise Exception()
return betas
def E_(input, t, shape):
out = torch.gather(input, 0, t)
reshape = [shape[0]] + [1] * (len(shape) - 1)
out = out.reshape(*reshape)
return out
def noise_like(shape, noise_fn, device, repeat=False):
if repeat:
resid = [1] * (len(shape) - 1)
shape_one = (1, *shape[1:])
return noise_fn(*shape_one, device=device).repeat(shape[0], *resid)
else:
return noise_fn(*shape, device=device)
class GaussianDiffusion:
def __init__(self, net, betas, time_scale=1, sampler="ddpm"):
super().__init__()
self.net_ = net
self.time_scale = time_scale
betas = betas.type(torch.float64)
self.num_timesteps = int(betas.shape[0])
alphas = 1 - betas
alphas_cumprod = torch.cumprod(alphas, 0)
alphas_cumprod_prev = torch.cat(
(torch.tensor([1], dtype=torch.float64, device=betas.device), alphas_cumprod[:-1]), 0
)
posterior_variance = betas * (1 - alphas_cumprod_prev) / (1 - alphas_cumprod)
self.betas = betas
self.alphas_cumprod = alphas_cumprod
self.posterior_variance = posterior_variance
self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1 - alphas_cumprod)
self.posterior_log_variance_clipped = torch.log(posterior_variance.clamp(min=1e-20))
self.posterior_mean_coef1 = (betas * torch.sqrt(alphas_cumprod_prev) / (1 - alphas_cumprod))
self.posterior_mean_coef2 = (1 - alphas_cumprod_prev) * torch.sqrt(alphas) / (1 - alphas_cumprod)
if sampler == "ddpm":
self.p_sample = self.p_sample_ddpm
else:
self.p_sample = self.p_sample_clipped
def inference(self, x, t, extra_args):
return self.net_(x, t * self.time_scale, **extra_args)
def p_loss(self, x_0, t, extra_args, noise=None):
if noise is None:
noise = torch.randn_like(x_0)
alpha_t, sigma_t = self.get_alpha_sigma(x_0, t)
z = alpha_t * x_0 + sigma_t * noise
v_recon = self.inference(z.float(), t.float(), extra_args)
v = alpha_t * noise - sigma_t * x_0
return F.mse_loss(v_recon, v.float())
def q_posterior(self, x_0, x_t, t):
mean = E_(self.posterior_mean_coef1, t, x_t.shape) * x_0 \
+ E_(self.posterior_mean_coef2, t, x_t.shape) * x_t
var = E_(self.posterior_variance, t, x_t.shape)
log_var_clipped = E_(self.posterior_log_variance_clipped, t, x_t.shape)
return mean, var, log_var_clipped
def p_mean_variance(self, x, t, extra_args, clip_denoised):
v = self.inference(x.float(), t.float(), extra_args).double()
alpha_t, sigma_t = self.get_alpha_sigma(x, t)
x_recon = alpha_t * x - sigma_t * v
if clip_denoised:
x_recon = x_recon.clamp(min=-1, max=1)
mean, var, log_var = self.q_posterior(x_recon, x, t)
return mean, var, log_var
def p_sample_ddpm(self, x, t, extra_args, clip_denoised=True, **kwargs):
mean, _, log_var = self.p_mean_variance(x, t, extra_args, clip_denoised)
noise = torch.randn_like(x)
shape = [x.shape[0]] + [1] * (x.ndim - 1)
nonzero_mask = (1 - (t == 0).type(torch.float32)).view(*shape)
return mean + nonzero_mask * torch.exp(0.5 * log_var) * noise
def p_sample_clipped(self, x, t, extra_args, eta=0, clip_denoised=True, clip_value=3):
v = self.inference(x.float(), t, extra_args)
alpha, sigma = self.get_alpha_sigma(x, t)
# if clip_denoised:
# x = x.clip(-1, 1)
pred = (x * alpha - v * sigma)
if clip_denoised:
pred = pred.clip(-clip_value, clip_value)
eps = (x - alpha * pred) / sigma
if clip_denoised:
eps = eps.clip(-clip_value, clip_value)
t_mask = (t > 0)
if t_mask.any().item():
if not t_mask.all().item():
raise Exception()
alpha_, sigma_ = self.get_alpha_sigma(x, (t - 1).clip(min=0))
ddim_sigma = eta * (sigma_ ** 2 / sigma ** 2).sqrt() * \
(1 - alpha ** 2 / alpha_ ** 2).sqrt()
adjusted_sigma = (sigma_ ** 2 - ddim_sigma ** 2).sqrt()
pred = pred * alpha_ + eps * adjusted_sigma
if eta:
pred += torch.randn_like(pred) * ddim_sigma
return pred
@torch.no_grad()
def p_sample_loop(self, x, extra_args, eta=0):
mode = self.net_.training
self.net_.eval()
for i in reversed(range(self.num_timesteps)):
x = self.p_sample(
x,
torch.full((x.shape[0],), i, dtype=torch.int64).to(x.device),
extra_args,
eta=eta,
)
self.net_.train(mode)
return x
def get_alpha_sigma(self, x, t):
alpha = E_(self.sqrt_alphas_cumprod, t, x.shape)
sigma = E_(self.sqrt_one_minus_alphas_cumprod, t, x.shape)
return alpha, sigma
class GaussianDiffusionDefault(GaussianDiffusion):
def __init__(self, net, betas, time_scale=1, gamma=0.3):
super.__init__(net, betas, time_scale)
self.gamma = gamma
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def distill_loss(self, student_diffusion, x, t, extra_args, eps=None, student_device=None):
if eps is None:
eps = torch.randn_like(x)
with torch.no_grad():
alpha, sigma = self.get_alpha_sigma(x, t + 1)
z = alpha * x + sigma * eps
alpha_s, sigma_s = student_diffusion.get_alpha_sigma(x, t // 2)
alpha_1, sigma_1 = self.get_alpha_sigma(x, t)
v = self.inference(z.float(), t.float() + 1, extra_args).double()
rec = (alpha * z - sigma * v).clip(-1, 1)
z_1 = alpha_1 * rec + (sigma_1 / sigma) * (z - alpha * rec)
v_1 = self.inference(z_1.float(), t.float(), extra_args).double()
x_2 = (alpha_1 * z_1 - sigma_1 * v_1).clip(-1, 1)
eps_2 = (z - alpha_s * x_2) / sigma_s
v_2 = alpha_s * eps_2 - sigma_s * x_2
if self.gamma == 0:
w = 1
else:
w = torch.pow(1 + alpha_s / sigma_s, self.gamma)
v = student_diffusion.net_(z.float(), t.float() * self.time_scale, **extra_args)
my_rec = (alpha_s * z - sigma_s * v).clip(-1, 1)
return F.mse_loss(w * v.float(), w * v_2.float())