stable-diffusion-webui
74 строки · 3.1 Кб
1import torch2import tqdm3import k_diffusion.sampling4
5
6@torch.no_grad()7def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list=None):8"""Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)9Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}
10If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list
11"""
12extra_args = {} if extra_args is None else extra_args13s_in = x.new_ones([x.shape[0]])14step_id = 015from k_diffusion.sampling import to_d, get_sigmas_karras16
17def heun_step(x, old_sigma, new_sigma, second_order=True):18nonlocal step_id19denoised = model(x, old_sigma * s_in, **extra_args)20d = to_d(x, old_sigma, denoised)21if callback is not None:22callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})23dt = new_sigma - old_sigma24if new_sigma == 0 or not second_order:25# Euler method26x = x + d * dt27else:28# Heun's method29x_2 = x + d * dt30denoised_2 = model(x_2, new_sigma * s_in, **extra_args)31d_2 = to_d(x_2, new_sigma, denoised_2)32d_prime = (d + d_2) / 233x = x + d_prime * dt34step_id += 135return x36
37steps = sigmas.shape[0] - 138if restart_list is None:39if steps >= 20:40restart_steps = 941restart_times = 142if steps >= 36:43restart_steps = steps // 444restart_times = 245sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)46restart_list = {0.1: [restart_steps + 1, restart_times, 2]}47else:48restart_list = {}49
50restart_list = {int(torch.argmin(abs(sigmas - key), dim=0)): value for key, value in restart_list.items()}51
52step_list = []53for i in range(len(sigmas) - 1):54step_list.append((sigmas[i], sigmas[i + 1]))55if i + 1 in restart_list:56restart_steps, restart_times, restart_max = restart_list[i + 1]57min_idx = i + 158max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))59if max_idx < min_idx:60sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]61while restart_times > 0:62restart_times -= 163step_list.extend(zip(sigma_restart[:-1], sigma_restart[1:]))64
65last_sigma = None66for old_sigma, new_sigma in tqdm.tqdm(step_list, disable=disable):67if last_sigma is None:68last_sigma = old_sigma69elif last_sigma < old_sigma:70x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (old_sigma ** 2 - last_sigma ** 2) ** 0.571x = heun_step(x, old_sigma, new_sigma)72last_sigma = new_sigma73
74return x75