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restart-sampler with correct steps

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lambertae 2023-07-20 02:24:18 -04:00
parent 7bb0fbed13
commit ddbf4a73f5
1 changed files with 14 additions and 8 deletions
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22
modules/sd_samplers_kdiffusion.py
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@ -38,20 +38,19 @@ samplers_k_diffusion = [
def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.): 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 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, second_order = True):
nonlocal step_id nonlocal step_id
denoised = model(x, old_sigma * s_in, **extra_args) denoised = model(x, old_sigma * s_in, **extra_args)
d = to_d(x, old_sigma, denoised) d = to_d(x, old_sigma, denoised)
if callback is not None: if callback is not None:
callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised}) callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
dt = new_sigma - old_sigma dt = new_sigma - old_sigma
if new_sigma == 0: if new_sigma == 0 or not second_order:
# Euler method # Euler method
x = x + d * dt x = x + d * dt
else: else:
@ -63,11 +62,6 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
x = x + d_prime * dt x = x + d_prime * dt
step_id += 1 step_id += 1
return x return x
# print(sigmas)
temp_list = dict()
for key, value in restart_list.items():
temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
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))
@ -78,6 +72,18 @@ 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)
steps = sigmas.shape[0] - 1
if steps >= 20:
restart_steps = 9
restart_times = 2 if steps >= 36 else 1
sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2], sigmas[0], device=sigmas.device)
restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
else:
restart_list = dict()
temp_list = dict()
for key, value in restart_list.items():
temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
restart_list = temp_list
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: