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lambertae 2023-07-18 01:02:04 -04:00
parent 37e048a7e2
commit 7bb0fbed13
1 changed files with 5 additions and 9 deletions
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14
modules/sd_samplers_kdiffusion.py
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@ -35,17 +35,15 @@ samplers_k_diffusion = [
@torch.no_grad() @torch.no_grad()
def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = {0.1: [10, 2, 2]}): def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.):
"""Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)""" """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""
'''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}''' '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''
restart_list = {0.1: [10, 2, 2]}
from tqdm.auto import trange, tqdm from tqdm.auto import trange
extra_args = {} if extra_args is None else extra_args extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]]) s_in = x.new_ones([x.shape[0]])
step_id = 0 step_id = 0
from k_diffusion.sampling import to_d, append_zero from k_diffusion.sampling import to_d, append_zero
def heun_step(x, old_sigma, new_sigma): def heun_step(x, old_sigma, new_sigma):
nonlocal step_id nonlocal step_id
denoised = model(x, old_sigma * s_in, **extra_args) denoised = model(x, old_sigma * s_in, **extra_args)
@ -70,8 +68,6 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
for key, value in restart_list.items(): for key, value in restart_list.items():
temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
restart_list = temp_list restart_list = temp_list
def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
ramp = torch.linspace(0, 1, n).to(device) ramp = torch.linspace(0, 1, n).to(device)
min_inv_rho = (sigma_min ** (1 / rho)) min_inv_rho = (sigma_min ** (1 / rho))
@ -82,7 +78,6 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
max_inv_rho = max_inv_rho.to(device) max_inv_rho = max_inv_rho.to(device)
sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return append_zero(sigmas).to(device) return append_zero(sigmas).to(device)
for i in trange(len(sigmas) - 1, disable=disable): for i in trange(len(sigmas) - 1, disable=disable):
x = heun_step(x, sigmas[i], sigmas[i+1]) x = heun_step(x, sigmas[i], sigmas[i+1])
if i + 1 in restart_list: if i + 1 in restart_list:
@ -91,7 +86,8 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0)) max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
if max_idx < min_idx: if max_idx < min_idx:
sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end
for times in range(restart_times): while restart_times > 0:
restart_times -= 1
x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5 x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5
for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]): for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]):
x = heun_step(x, old_sigma, new_sigma) x = heun_step(x, old_sigma, new_sigma)