扩散模型生成图像代码实现

### 使用扩散模型生成图像的代码实现 为了展示如何利用扩散模型生成图像，下面提供了一个基于Python和PyTorch框架的基础示例。此实例展示了简单的去噪扩散概率模型（DDPM），这是一种广泛应用于图像合成的技术[^2]。 ```python import torch from torch import nn import torchvision.transforms.functional as TF from PIL import Image import numpy as np class DiffusionModel(nn.Module): def __init__(self, timesteps=1000, img_size=(64, 64), channels=3): super(DiffusionModel, self).__init__() self.timesteps = timesteps self.img_size = img_size self.channels = channels # 定义网络结构 self.model = nn.Sequential( nn.Conv2d(channels, 64, kernel_size=3, padding=1), nn.ReLU(), nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1), nn.ReLU(), nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1), nn.ReLU(), nn.Conv2d(64, channels, kernel_size=3, padding=1), nn.Tanh() ) def forward(self, x, t): return self.model(x) def linear_beta_schedule(timesteps): beta_start = 0.0001 beta_end = 0.02 return torch.linspace(beta_start, beta_end, timesteps) device = "cuda" if torch.cuda.is_available() else "cpu" model = DiffusionModel().to(device) betas = linear_beta_schedule(model.timesteps).to(device) alphas = 1. - betas alphas_cumprod = torch.cumprod(alphas, axis=0) sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod) sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod) @torch.no_grad() def sample_timestep(x, t): """ 对给定的时间步t执行采样操作. 参数: x (Tensor): 输入张量. t (int): 时间步数. 返回: Tensor: 更新后的张量表示. """ betat = extract(betas, t, x.shape) sqrt_one_minus_alphat = extract(sqrt_one_minus_alphas_cumprod, t, x.shape) sqrt_recip_at = extract(torch.rsqrt(alphas_cumprod), t, x.shape) # 预测噪声成分并移除部分噪声 e_theta = model(x, t) mean_theta = sqrt_recip_at * (x - betat * e_theta / sqrt_one_minus_alphat) posterior_variance_t = extract((1 - alphas) * betas / (1 - alphas_cumprod), t, x.shape) noise = torch.randn_like(x) if t > 0 else torch.zeros_like(x) return mean_theta + torch.sqrt(posterior_variance_t) * noise def p_sample_loop(shape): cur_x = torch.randn(shape).to(device) xs = [] for i in reversed(range(0, model.timesteps)): t = torch.full((shape[0], ), i, device=device, dtype=torch.long) cur_x = sample_timestep(cur_x, t) xs.append(cur_x.cpu()) return xs[-1] img_shape = (1, 3, *model.img_size) sample_img = p_sample_loop(img_shape)[0] TF.to_pil_image(sample_img.add(1).div(2)).save('output.png') ``` 上述代码片段定义了一个简化版的扩散模型类`DiffusionModel`以及辅助函数用于前向传播过程中的时间步骤处理。通过调用`sampling loop`方法可以从随机噪声逐步恢复出清晰图片。