网站页面配色分析,自己做网站外包,wordpress翻书的主题免费下载,网站制作哈尔滨添加噪声的方式大致分为两种#xff0c;一种是每张图像在任意timestep都加入一样的均匀噪声#xff0c;另一种是按照timestep添加不同程度的噪声 一、在任意timestep都加入一样的noise batch_size 32x_start torch.rand(batch_size,3,256,256) noise torch.randn_like(x_…添加噪声的方式大致分为两种一种是每张图像在任意timestep都加入一样的均匀噪声另一种是按照timestep添加不同程度的噪声 一、在任意timestep都加入一样的noise batch_size 32x_start torch.rand(batch_size,3,256,256) noise torch.randn_like(x_start) x_noisy x_start noiseprint(x_noisy) 二、按照timestep添加不同程度的noise 插入的方式有很多linear、cosine、sqrt_linear、sqrt不论哪种方式插入noise都是按照以下的公式进行的噪声插入只不过是β在每个timestep的值不一样从而造成不同timestep有不同程度的噪声 这里的x0可以是不同的东西加入噪声可以有多种应用例如最常见的输入图像bz, 3, 256, 256【经典的diffusion论文】、输入图像经过pretrain encoder后的表征bz, 512, 1, 1【RCG论文】... 1、最简单的写法linear 通过线性的方式torch.linspace插入noise import torch## ----------------------------- 确定超参数的值 ----------------------------- ## num_steps 100# 制定每一步的beta betas torch.linspace(-6, 6, num_steps) betas torch.sigmoid(betas) * (0.5e-2 - 1e-5) 1e-5 alphas 1 - betas alphas_prod torch.cumprod(alphas, 0)# 定义调整noise的常量 alphas_bar_sqrt torch.sqrt(alphas_prod) one_minus_alphas_bar_sqrt torch.sqrt(1 - alphas_prod) ## ----------------------------- 确定扩散前向过程任意时刻的采样值 x[t] x[0] t -- x[t] ----------------------------- ## def q_x(x_0, t):x[0] t -- x[t]:param x_0:初始数据:param t:任意时刻noise torch.randn_like(x_0)# 取出在某个时刻t所对应的alphas_t、alphas_1_m_t的值alphas_t alphas_bar_sqrt[t]alphas_1_m_t one_minus_alphas_bar_sqrt[t]x_t alphas_t * x_0 alphas_1_m_t * noisereturn x_tbatch_size 32 x_start torch.rand(batch_size,3,256,256) # 给x_start加上在timestep 65 的时候噪声 x_noisy q_x(x_start, 65) 一文弄懂 Diffusion ModelDDPM 代码实现-CSDN博客 2、可选不同的β策略 不同的β策略 import torch from inspect import isfunction from functools import partial import numpy as npto_torch partial(torch.tensor, dtypetorch.float32) def make_beta_schedule(schedule, n_timestep, linear_start1e-4, linear_end2e-2, cosine_s8e-3):设置β的调度值if schedule linear:betas (torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtypetorch.float64) ** 2)elif schedule cosine:timesteps (torch.arange(n_timestep 1, dtypetorch.float64) / n_timestep cosine_s)alphas timesteps / (1 cosine_s) * np.pi / 2alphas torch.cos(alphas).pow(2)alphas alphas / alphas[0]betas 1 - alphas[1:] / alphas[:-1]betas np.clip(betas, a_min0, a_max0.999)elif schedule sqrt_linear:betas torch.linspace(linear_start, linear_end, n_timestep, dtypetorch.float64)elif schedule sqrt:betas torch.linspace(linear_start, linear_end, n_timestep, dtypetorch.float64) ** 0.5else:raise ValueError(fschedule {schedule} unknown.)return betas.numpy() 定义超参数  betas make_beta_schedule(schedulelinear, n_timestep1000, linear_start0.0015, linear_end0.0195, cosine_s0.008)alphas 1. - betas alphas_cumprod np.cumprod(alphas, axis0) alphas_cumprod to_torch(alphas_cumprod) sqrt_alphas_cumprod to_torch(np.sqrt(alphas_cumprod)) sqrt_one_minus_alphas_cumprod to_torch(np.sqrt(1. - alphas_cumprod))batch_size 32 # x_start torch.rand(batch_size,512,1,1) x_start torch.rand(batch_size,3,256,256) noise torch.randn_like(x_start) t torch.randint(0, 1000, (batch_size,)).long()def exists(x):return x is not Nonedef default(val, d):if exists(val):return valreturn d() if isfunction(d) else ddef extract_into_tensor(a, t, x_shape):b, *_ t.shapeout a.gather(-1, t)return out.reshape(b, *((1,) * (len(x_shape) - 1)))def q_sample(x_start, t, noiseNone):noise default(noise, lambda: torch.randn_like(x_start))return (extract_into_tensor(sqrt_alphas_cumprod, t, x_start.shape) * x_start extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)x_noisy q_sample(x_startx_start, tt, noisenoise)