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