|
import torch |
|
import tqdm |
|
import k_diffusion.sampling |
|
from modules import sd_samplers_common, sd_samplers_kdiffusion, sd_samplers |
|
from tqdm.auto import trange, tqdm |
|
from k_diffusion import utils |
|
import math |
|
|
|
|
|
NAME = 'Euler_Max' |
|
ALIAS = 'euler_max' |
|
|
|
|
|
@torch.no_grad() |
|
def sample_euler_max(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., |
|
s_tmax=float('inf'), s_noise=1.): |
|
extra_args = {} if extra_args is None else extra_args |
|
s_in = x.new_ones([x.shape[0]]) |
|
for i in trange(len(sigmas) - 1, disable=disable): |
|
gamma = max(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. |
|
eps = torch.randn_like(x) * s_noise |
|
sigma_hat = sigmas[i] * (gamma + 1) |
|
if gamma > 0: |
|
x = x - eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 |
|
denoised = model(x, sigma_hat * s_in, **extra_args) |
|
d = k_diffusion.sampling.to_d(x, sigma_hat, denoised) |
|
if callback is not None: |
|
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) |
|
dt = sigmas[i + 1] - sigma_hat |
|
|
|
x = x + (math.cos(i + 1)/(i + 1) + 1) * d * dt |
|
return x |
|
|
|
|
|
if not NAME in [x.name for x in sd_samplers.all_samplers]: |
|
euler_max_samplers = [(NAME, sample_euler_max, [ALIAS], {})] |
|
samplers_data_euler_max_samplers = [ |
|
sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: sd_samplers_kdiffusion.KDiffusionSampler(funcname, model), aliases, options) |
|
for label, funcname, aliases, options in euler_max_samplers |
|
if callable(funcname) or hasattr(k_diffusion.sampling, funcname) |
|
] |
|
sd_samplers.all_samplers += samplers_data_euler_max_samplers |
|
sd_samplers.all_samplers_map = {x.name: x for x in sd_samplers.all_samplers} |
|
sd_samplers.set_samplers() |
|
|
