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import torch
import tqdm
import k_diffusion.sampling
@torch.no_grad()
def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list=None):
"""Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)
Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}
If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list
"""
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
step_id = 0
from k_diffusion.sampling import to_d, get_sigmas_karras
def heun_step(x, old_sigma, new_sigma, second_order=True):
nonlocal step_id
denoised = model(x, old_sigma * s_in, **extra_args)
d = to_d(x, old_sigma, denoised)
if callback is not None:
callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
dt = new_sigma - old_sigma
if new_sigma == 0 or not second_order:
# Euler method
x = x + d * dt
else:
# Heun's method
x_2 = x + d * dt
denoised_2 = model(x_2, new_sigma * s_in, **extra_args)
d_2 = to_d(x_2, new_sigma, denoised_2)
d_prime = (d + d_2) / 2
x = x + d_prime * dt
step_id += 1
return x
steps = sigmas.shape[0] - 1
if restart_list is None:
if steps >= 20:
restart_steps = 9
restart_times = 1
if steps >= 36:
restart_steps = steps // 4
restart_times = 2
sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)
restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
else:
restart_list = {}
restart_list = {int(torch.argmin(abs(sigmas - key), dim=0)): value for key, value in restart_list.items()}
step_list = []
for i in range(len(sigmas) - 1):
step_list.append((sigmas[i], sigmas[i + 1]))
if i + 1 in restart_list:
restart_steps, restart_times, restart_max = restart_list[i + 1]
min_idx = i + 1
max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
if max_idx < min_idx:
sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
while restart_times > 0:
restart_times -= 1
step_list.extend(zip(sigma_restart[:-1], sigma_restart[1:]))
last_sigma = None
for old_sigma, new_sigma in tqdm.tqdm(step_list, disable=disable):
if last_sigma is None:
last_sigma = old_sigma
elif last_sigma < old_sigma:
x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (old_sigma ** 2 - last_sigma ** 2) ** 0.5
x = heun_step(x, old_sigma, new_sigma)
last_sigma = new_sigma
return x
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