mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-14 06:28:12 +03:00
75 lines
3.2 KiB
Python
75 lines
3.2 KiB
Python
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([(old_sigma, new_sigma) for (old_sigma, new_sigma) in 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
|