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扩散模型实战#xff08;一#xff09;#xff1a;基本原理介绍
扩散模型实战#xff08;二#xff09;#xff1a;扩散模型的发展
扩散模型实战#xff08;三#xff09;#xff1a;扩散模型的应用
本文以MNIST数据集为例#xff0c;从…推荐阅读列表
扩散模型实战一基本原理介绍
扩散模型实战二扩散模型的发展
扩散模型实战三扩散模型的应用
本文以MNIST数据集为例从零构建扩散模型具体会涉及到如下知识点 退化过程向数据中添加噪声构建一个简单的UNet模型训练扩散模型采样过程分析
下面介绍具体的实现过程
一、环境配置python包的导入
最好有GPU环境比如公司的GPU集群或者Google Colab下面是代码实现
# 安装diffusers库!pip install -q diffusers# 导入所需要的包import torchimport torchvisionfrom torch import nnfrom torch.nn import functional as Ffrom torch.utils.data import DataLoaderfrom diffusers import DDPMScheduler, UNet2DModelfrom matplotlib import pyplot as pltdevice torch.device(cuda if torch.cuda.is_available() else cpu)print(fUsing device: {device})
# 输出Using device: cuda
此时会输出运行环境是GPU还是CPU
二、加载MNIST数据集 MNIST数据集是一个小数据集存储的是0-9手写数字字体每张图片都28X28的灰度图片每个像素的取值范围是[0,1]下面加载该数据集并展示部分数据
dataset torchvision.datasets.MNIST(rootmnist/, trainTrue, downloadTrue, transformtorchvision.transforms.ToTensor())train_dataloader DataLoader(dataset, batch_size8, shuffleTrue)x, y next(iter(train_dataloader))print(Input shape:, x.shape)print(Labels:, y)plt.imshow(torchvision.utils.make_grid(x)[0], cmapGreys);
# 输出Input shape: torch.Size([8, 1, 28, 28])Labels: tensor([7, 8, 4, 2, 3, 6, 0, 2]) 三、扩散模型的退化过程 所谓退化过程其实就是对输入数据加入噪声的过程由于MNIST数据集的像素范围在[0,1]那么我们加入噪声也需要保持在相同的范围这样我们可以很容易的把输入数据与噪声进行混合代码如下
def corrupt(x, amount): Corrupt the input x by mixing it with noise according to amount noise torch.rand_like(x) amount amount.view(-1, 1, 1, 1) # Sort shape so broadcasting works return x*(1-amount) noise*amount
接下来我们看一下逐步加噪的效果代码如下
# Plotting the input datafig, axs plt.subplots(2, 1, figsize(12, 5))axs[0].set_title(Input data)axs[0].imshow(torchvision.utils.make_grid(x)[0], cmapGreys)# Adding noiseamount torch.linspace(0, 1, x.shape[0]) # Left to right - more corruptionnoised_x corrupt(x, amount)# Plottinf the noised versionaxs[1].set_title(Corrupted data (-- amount increases --))axs[1].imshow(torchvision.utils.make_grid(noised_x)[0], cmapGreys); 从上图可以看出从左到右加入的噪声逐步增多当噪声量接近1时数据看起来像纯粹的随机噪声。
四、构建一个简单的UNet模型 UNet模型与自编码器有异曲同工之妙UNet最初是用于完成医学图像中分割任务的网络结构如下所示 代码如下
class BasicUNet(nn.Module): A minimal UNet implementation. def __init__(self, in_channels1, out_channels1): super().__init__() self.down_layers torch.nn.ModuleList([ nn.Conv2d(in_channels, 32, kernel_size5, padding2), nn.Conv2d(32, 64, kernel_size5, padding2), nn.Conv2d(64, 64, kernel_size5, padding2), ]) self.up_layers torch.nn.ModuleList([ nn.Conv2d(64, 64, kernel_size5, padding2), nn.Conv2d(64, 32, kernel_size5, padding2), nn.Conv2d(32, out_channels, kernel_size5, padding2), ]) self.act nn.SiLU() # The activation function self.downscale nn.MaxPool2d(2) self.upscale nn.Upsample(scale_factor2) def forward(self, x): h [] for i, l in enumerate(self.down_layers): x self.act(l(x)) # Through the layer and the activation function if i 2: # For all but the third (final) down layer: h.append(x) # Storing output for skip connection x self.downscale(x) # Downscale ready for the next layer for i, l in enumerate(self.up_layers): if i 0: # For all except the first up layer x self.upscale(x) # Upscale x h.pop() # Fetching stored output (skip connection) x self.act(l(x)) # Through the layer and the activation function return x
我们来检验一下模型输入输出的shape变化是否符合预期代码如下
net BasicUNet()x torch.rand(8, 1, 28, 28)net(x).shape
# 输出torch.Size([8, 1, 28, 28])
再来看一下模型的参数量代码如下
sum([p.numel() for p in net.parameters()])
# 输出309057
至此已经完成数据加载和UNet模型构建当然UNet模型的结构可以有不同的设计。
五、扩散模型训练 扩散模型应该学习什么其实有很多不同的目标比如学习噪声我们先以一个简单的例子开始输入数据为带噪声的MNIST数据扩散模型应该输出对应的最佳数字预测因此学习的目标是预测值与真实值的MSE训练代码如下
# Dataloader (you can mess with batch size)batch_size 128train_dataloader DataLoader(dataset, batch_sizebatch_size, shuffleTrue)# How many runs through the data should we do?n_epochs 3# Create the networknet BasicUNet()net.to(device)# Our loss finctionloss_fn nn.MSELoss()# The optimizeropt torch.optim.Adam(net.parameters(), lr1e-3) # Keeping a record of the losses for later viewinglosses []# The training loopfor epoch in range(n_epochs): for x, y in train_dataloader: # Get some data and prepare the corrupted version x x.to(device) # Data on the GPU noise_amount torch.rand(x.shape[0]).to(device) # Pick random noise amounts noisy_x corrupt(x, noise_amount) # Create our noisy x # Get the model prediction pred net(noisy_x) # Calculate the loss loss loss_fn(pred, x) # How close is the output to the true clean x? # Backprop and update the params: opt.zero_grad() loss.backward() opt.step() # Store the loss for later losses.append(loss.item()) # Print our the average of the loss values for this epoch: avg_loss sum(losses[-len(train_dataloader):])/len(train_dataloader) print(fFinished epoch {epoch}. Average loss for this epoch: {avg_loss:05f})# View the loss curveplt.plot(losses)plt.ylim(0, 0.1);
# 输出Finished epoch 0. Average loss for this epoch: 0.024689Finished epoch 1. Average loss for this epoch: 0.019226Finished epoch 2. Average loss for this epoch: 0.017939
训练过程的loss曲线如下图所示 六、扩散模型效果评估
我们选取一部分数据来评估一下模型的预测效果代码如下
#markdown Visualizing model predictions on noisy inputs:# Fetch some datax, y next(iter(train_dataloader))x x[:8] # Only using the first 8 for easy plotting# Corrupt with a range of amountsamount torch.linspace(0, 1, x.shape[0]) # Left to right - more corruptionnoised_x corrupt(x, amount)# Get the model predictionswith torch.no_grad(): preds net(noised_x.to(device)).detach().cpu()# Plotfig, axs plt.subplots(3, 1, figsize(12, 7))axs[0].set_title(Input data)axs[0].imshow(torchvision.utils.make_grid(x)[0].clip(0, 1), cmapGreys)axs[1].set_title(Corrupted data)axs[1].imshow(torchvision.utils.make_grid(noised_x)[0].clip(0, 1), cmapGreys)axs[2].set_title(Network Predictions)axs[2].imshow(torchvision.utils.make_grid(preds)[0].clip(0, 1), cmapGreys); 从上图可以看出对于噪声量较低的输入模型的预测效果是很不错的当amount1时模型的输出接近整个数据集的均值这正是扩散模型的工作原理。
Note我们的训练并不太充分读者可以尝试不同的超参数来优化模型。
