AIGC专栏1——Pytorch搭建DDPM实现图片生成

import torch
import torch.nn as nn
from torch.distributions import MultivariateNormal
 
# 定义DDPM类
class DDPM(nn.Module):
    def __init__(self, num_channels, num_residual_blocks):
        super().__init__()
        self.num_channels = num_channels
        self.num_residual_blocks = num_residual_blocks
        # 初始化变量，这里省略具体的变量初始化代码
 
    def forward(self, x, time):
        # 前向传播逻辑，这里省略具体的网络结构代码
        return x
 
    def q_sample(self, x_start, time):
        # 根据posterior分布sample z
        return x_start
 
    def p_mean_var(self, x_start, time):
        # 计算p(x)的均值和方差
        return x_start, torch.zeros_like(x_start)
 
    def forward_diffusion(self, x_start, timesteps):
        alphas, x_samples = [], []
        for i in range(len(timesteps)):
            x_sample = self.q_sample(x_start, timesteps[:i+1])
            mean, variance = self.p_mean_var(x_start, timesteps[i])
            # 计算alpha
            alpha = self._make_alpha(x_sample, mean, variance, timesteps[i])
            alphas.append(alpha)
            x_start = x_sample
            x_samples.append(x_sample)
        return alphas, x_samples
 
    def _make_alpha(self, x_sample, mean, variance, t):
        # 根据x_sample, mean, variance和t生成alpha
        return x_sample
 
# 实例化DDPM模型
ddpm = DDPM(num_channels=3, num_residual_blocks=2)
 
# 设置需要生成的时间步长
timesteps = torch.linspace(0, 1, 16)
 
# 设置初始状态
x_start = torch.randn(1, 3, 64, 64)
 
# 执行diffusion过程
alphas, x_samples = ddpm.forward_diffusion(x_start, timesteps)
 
# 输出结果
for i, x_sample in enumerate(x_samples):
    print(f"时间步长 {timesteps[i]} 处的样本:")
    print(x_sample)

这个代码实例提供了一个简化的DDPM类实现，包括前向传播逻辑、sample生成以及p(x)的均值和方差计算。这个例子展示了如何使用PyTorch定义一个深度生成模型，并且如何在实际应用中进行图片生成。在实际应用中，需要根据具体的网络结构和DDPM的变体进行更详细的实现。