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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_Smea'
ALIAS = 'euler_smea'
def overall_sampling_step(x, model, dt, sigma_hat, **extra_args):
# 先判断输入的形状类型
original_shape = x.shape
# 计算m和n
batch_size, channels, m, n = original_shape[0], original_shape[1], original_shape[2] // 2, original_shape[3] // 2
extra_row = x.shape[2] % 2 == 1
extra_col = x.shape[3] % 2 == 1
# 提取多余的行和列
if extra_row:
extra_row_content = x[:, :, -1:, :]
x = x[:, :, :-1, :]
# print("成功提取多余行")
# print(x0.shape)
if extra_col:
extra_col_content = x[:, :, :, -1:]
x = x[:, :, :, :-1]
# print("成功提取多余列")
# print(x0.shape)
# 之前的处理逻辑
a_list = x.unfold(2, 2, 2).unfold(3, 2, 2).contiguous().view(batch_size, channels, m * n, 2, 2)
c = a_list[:, :, :, 1, 1].view(batch_size, channels, m, n)
denoised = model(c, sigma_hat * c.new_ones([c.shape[0]]), **extra_args)
d = k_diffusion.sampling.to_d(c, sigma_hat, denoised)
c = c + d * dt
d_list = denoised.view(batch_size, channels, m * n, 1, 1)
a_list[:, :, :, 1, 1] = d_list[:, :, :, 0, 0]
x = a_list.view(batch_size, channels, m, n, 2, 2).permute(0, 1, 2, 4, 3, 5).reshape(batch_size, channels, 2 * m, 2 * n)
# print("成功整体采样")
# print(x1.shape)
# 判断是否需要添加零行或零列
if extra_row or extra_col:
x_expanded = torch.zeros(original_shape, dtype=x.dtype, device=x.device)
x_expanded[:, :, :2 * m, :2 * n] = x
if extra_row:
x_expanded[:, :, -1:, :2 * n + 1] = extra_row_content
if extra_col:
x_expanded[:, :, :2 * m, -1:] = extra_col_content
if extra_row and extra_col:
x_expanded[:, :, -1:, -1:] = extra_col_content[:, :, -1:, :]
x = x_expanded
return x
@torch.no_grad()
def sample_euler_smea(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):
# print(i)
# i第一步为0
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)
# print(sigma_hat)
dt = sigmas[i + 1] - sigma_hat
if i // 2 == 1:
x = overall_sampling_step(x, model, dt, sigma_hat, **extra_args)
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})
# Euler method
x = x + d * dt
return x
if not NAME in [x.name for x in sd_samplers.all_samplers]:
euler_smea_samplers = [(NAME, sample_euler_smea, [ALIAS], {})]
samplers_data_euler_smea_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_smea_samplers
if callable(funcname) or hasattr(k_diffusion.sampling, funcname)
]
sd_samplers.all_samplers += samplers_data_euler_smea_samplers
sd_samplers.all_samplers_map = {x.name: x for x in sd_samplers.all_samplers}
sd_samplers.set_samplers()
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