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some stylistic changes for the sampler code

This commit is contained in:
AUTOMATIC1111 2023-07-29 08:38:00 +03:00
parent aefe1325df
commit 79d6e9cd32
1 changed files with 24 additions and 20 deletions
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44
modules/sd_samplers_extra.py
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@ -1,17 +1,20 @@
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'''
from tqdm.auto import trange
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):
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)
@ -30,6 +33,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
x = x + d_prime * dt
step_id += 1
return x
steps = sigmas.shape[0] - 1
if restart_list is None:
if steps >= 20:
@ -41,11 +45,10 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
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 = 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
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]))
@ -58,13 +61,14 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
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 i in trange(len(step_list), disable=disable):
if last_sigma is None:
last_sigma = step_list[i][0]
elif last_sigma < step_list[i][0]:
x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (step_list[i][0] ** 2 - last_sigma ** 2) ** 0.5
x = heun_step(x, step_list[i][0], step_list[i][1])
last_sigma = step_list[i][1]
return x
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