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refactor sigmas and gamma generation

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Phil Wang
2022-06-28 17:26:58 -07:00
parent b87ea27781
commit 5db64fec4b
1 changed files with 29 additions and 26 deletions
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denoising_diffusion_pytorch/elucidated_diffusion.py
+29 -26
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@@ -110,18 +110,14 @@ class ElucidatedDiffusion(nn.Module):
def sample_schedule(self, num_sample_steps = None):
num_sample_steps = default(num_sample_steps, self.num_sample_steps)
rho, sigma_max, sigma_min, S_tmin, S_tmax, S_churn = self.rho, self.sigma_max, self.sigma_min, self.S_tmin, self.S_tmax, self.S_churn
gamma = min(S_churn / num_sample_steps, sqrt(2) - 1)
N = num_sample_steps
inv_rho = 1 / rho
inv_rho = 1 / self.rho
for i in range(num_sample_steps):
sigma_i = (sigma_max ** inv_rho + i / (N - 1) * (sigma_min ** inv_rho - sigma_max ** inv_rho)) ** rho
gamma_i = gamma if S_tmin <= sigma_i <= S_tmax else 0.
yield sigma_i, gamma_i
steps = torch.arange(num_sample_steps, device = self.device, dtype = torch.float32)
sigmas = (self.sigma_max ** inv_rho + steps / (N - 1) * (self.sigma_min ** inv_rho - self.sigma_max ** inv_rho)) ** self.rho
yield 0., 0. # last step return 0.
sigmas = F.pad(sigmas, (0, 1), value = 0.) # last step is sigma value of 0.
return sigmas
# preconditioned network output
# equation (7) in the paper
@@ -130,7 +126,7 @@ class ElucidatedDiffusion(nn.Module):
batch, device = noised_images.shape[0], noised_images.device
if isinstance(sigma, float):
sigma = torch.ones((batch,), device = device) * sigma
sigma = torch.full((batch,), sigma, device = device)
padded_sigma = rearrange(sigma, 'b -> b 1 1 1')
@@ -149,36 +145,43 @@ class ElucidatedDiffusion(nn.Module):
# get the schedule, which is returned as (sigma, gamma) tuple, and pair up with the next sigma and gamma
sigma_schedule = [*self.sample_schedule()]
sigma_schedule = list(zip(sigma_schedule[:-1], sigma_schedule[1:]))
sigmas = self.sample_schedule()
# function to return noise, given a sigma value
gammas = torch.where(
(sigmas >= self.S_tmin) & (sigmas <= self.S_tmax),
min(self.S_churn / self.num_sample_steps, sqrt(2) - 1),
0.
)
get_noise = lambda std_dev: (std_dev * torch.randn(shape, device = self.device))
sigmas_and_gammas = list(zip(sigmas[:-1], sigmas[1:], gammas[:-1]))
# images is None, set on first iteration
# images is noise at the beginning
images = None
init_sigma = sigmas[0]
for (sigma, gamma), (sigma_next, gamma_next) in tqdm(sigma_schedule, desc = 'sampling time step'):
if not exists(images):
# images start off as the noise based off the first sigma
images = get_noise(sigma)
images = init_sigma * torch.randn(shape, device = self.device)
# gradually denoise
for sigma, sigma_next, gamma in tqdm(sigmas_and_gammas, desc = 'sampling time step'):
sigma, sigma_next, gamma = map(lambda t: t.item(), (sigma, sigma_next, gamma))
eps = gamma * torch.randn(shape, device = self.device)
eps = get_noise(gamma)
sigma_hat = sigma + gamma * sigma
images_hat = images + sqrt(sigma_hat ** 2 - sigma ** 2) * eps
model_output = self.preconditioned_network_forward(images_hat, sigma_hat)
denoised = (images_hat - model_output) / sigma_hat
denoised_over_sigma = (images_hat - model_output) / sigma_hat
images_next = images_hat + (sigma_next - sigma_hat) * denoised
images_next = images_hat + (sigma_next - sigma_hat) * denoised_over_sigma
# second order correction, if not the last timestep
if sigma_next != 0:
# second order correction
model_output_next = self.preconditioned_network_forward(images_next, sigma_next)
denoised_prime = (images_next - model_output_next) / sigma_next
images_next = images_hat + (sigma_next - sigma_hat) * (0.5 * denoised + 0.5 * denoised_prime)
denoised_prime_over_sigma = (images_next - model_output_next) / sigma_next
images_next = images_hat + 0.5 * (sigma_next - sigma_hat) * (denoised_over_sigma + denoised_prime_over_sigma)
images = images_next