【Diffusion实战】训练一个diffusion模型生成蝴蝶图像（Pytorch代码详解）

上一篇Diffusion实战是确确实实一步一步走的公式，这回采用一个更方便的库：diffusers，来实现Diffusion模型训练。

Diffusion实战篇：
【Diffusion实战】训练一个diffusion模型生成S曲线（Pytorch代码详解）
Diffusion综述篇：
【Diffusion综述】医学图像分析中的扩散模型（一）
【Diffusion综述】医学图像分析中的扩散模型（二）

0、所需安装

pip install diffusers  # diffusers库
pip install datasets

1、数据集下载

下载地址：蝴蝶数据集
下载好后的文件夹中包括以下文件，放在当前目录下就可以了。

在这里插入图片描述
加载数据集，并对一批数据进行可视化：

import torch
import torchvision
from datasets import load_dataset
from torchvision import transforms
import numpy as np
import torch.nn.functional as F
from matplotlib import pyplot as plt
from PIL import Imagedef show_images(x):"""Given a batch of images x, make a grid and convert to PIL"""x = x * 0.5 + 0.5  # Map from (-1, 1) back to (0, 1)grid = torchvision.utils.make_grid(x)grid_im = grid.detach().cpu().permute(1, 2, 0).clip(0, 1) * 255grid_im = Image.fromarray(np.array(grid_im).astype(np.uint8))return grid_imdef transform(examples):images = [preprocess(image.convert("RGB")) for image in examples["image"]]return {"images": images}device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)# 数据加载
dataset = load_dataset("./smithsonian_butterflies_subset", split='train')image_size = 32
batch_size = 64# 数据增强
preprocess = transforms.Compose([transforms.Resize((image_size, image_size)),  # Resizetransforms.RandomHorizontalFlip(),  # Randomly flip (data augmentation)transforms.ToTensor(),  # Convert to tensor (0, 1)transforms.Normalize([0.5], [0.5]),  # Map to (-1, 1)]
)dataset.set_transform(transform)# 数据装载
train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)# 抽取一批数据可视化
xb = next(iter(train_dataloader))["images"].to(device)[:8]
print("X shape:", xb.shape)
show_images(xb).resize((8 * 64, 64), resample=Image.NEAREST)

输出可视化结果：

在这里插入图片描述

2、加噪调度器

即DDPM论文中需要预定义的 ${\beta_t }$ ，可使用DDPMScheduler类来定义，其中num_train_timesteps参数为时间步 ${t}$ 。

from diffusers import DDPMScheduler# βt值
noise_scheduler = DDPMScheduler(num_train_timesteps=1000)plt.figure(dpi=300)
plt.plot(noise_scheduler.alphas_cumprod.cpu() ** 0.5, label=r"${\sqrt{\bar{\alpha}_t}}$")
plt.plot((1 - noise_scheduler.alphas_cumprod.cpu()) ** 0.5, label=r"$\sqrt{(1 - \bar{\alpha}_t)}$")
plt.legend(fontsize="x-large");

根据定义的 ${\beta_t }$ ，可视化 ${\sqrt {{{\bar \alpha }_t}}}$ 和 ${\sqrt {1 - {{\bar \alpha }_t}}}$ ：

在这里插入图片描述

通过设置beta_start、beta_end和beta_schedule三个参数来控制噪声调度器的超参数 ${\beta_t }$ 。

noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_start=0.001, beta_end=0.004)

在这里插入图片描述

beta_schedule可以通过一个函数映射来为模型推理的每一步生成一个 ${\beta_t }$ 值。

noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule='squaredcos_cap_v2')

在这里插入图片描述

${{x_t} = \sqrt {{{\bar \alpha }_t}} {x_0} + \sqrt {1 - {{\bar \alpha }_t}} \varepsilon }$ 加噪前向过程可视化：

timesteps = torch.linspace(0, 999, 8).long().to(device)  # 随机采样时间步
noise = torch.randn_like(xb)
noisy_xb = noise_scheduler.add_noise(xb, noise, timesteps)  # 加噪
print("Noisy X shape", noisy_xb.shape)
show_images(noisy_xb).resize((8 * 64, 64), resample=Image.NEAREST)

输出为：

在这里插入图片描述

3、扩散模型定义

diffusers库中模型的定义也非常简洁：

# 创建模型
from diffusers import UNet2DModelmodel = UNet2DModel(sample_size=image_size,  # the target image resolutionin_channels=3,  # the number of input channels, 3 for RGB imagesout_channels=3,  # the number of output channelslayers_per_block=2,  # how many ResNet layers to use per UNet blockblock_out_channels=(64, 128, 128, 256),  # More channels -> more parametersdown_block_types=("DownBlock2D",  # a regular ResNet downsampling block"DownBlock2D","AttnDownBlock2D",  # a ResNet downsampling block with spatial self-attention"AttnDownBlock2D",),up_block_types=("AttnUpBlock2D","AttnUpBlock2D",  # a ResNet upsampling block with spatial self-attention"UpBlock2D","UpBlock2D",  # a regular ResNet upsampling block),
)model.to(device)
with torch.no_grad():model_prediction = model(noisy_xb, timesteps).sample
model_prediction.shape  # 验证输出与输出尺寸相同

4、扩散模型训练

定义优化器，和传统模型一样的训练写法：

# 定义噪声调度器
noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule="squaredcos_cap_v2"
)# 优化器
optimizer = torch.optim.AdamW(model.parameters(), lr=4e-4)losses = []for epoch in range(30):for step, batch in enumerate(train_dataloader):clean_images = batch["images"].to(device)# 为图像添加随机噪声noise = torch.randn(clean_images.shape).to(clean_images.device)  # epsbs = clean_images.shape[0]# 为每一张图像随机选择一个时间步timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bs,), device=clean_images.device).long()  # 根据时间步,向清晰的图像中加噪声, 前向过程：根号下αt^ * x0 + 根号下(1-αt^) * epsnoisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)# 获得模型预测结果noise_pred = model(noisy_images, timesteps, return_dict=False)[0]# 计算损失, 损失回传loss = F.mse_loss(noise_pred, noise)  loss.backward(loss)losses.append(loss.item())# 更新模型参数optimizer.step()optimizer.zero_grad()if (epoch + 1) % 5 == 0:loss_last_epoch = sum(losses[-len(train_dataloader) :]) / len(train_dataloader)print(f"Epoch:{epoch+1}, loss: {loss_last_epoch}")

30个epoch训练过程如下所示：

在这里插入图片描述

可用以下代码查看损失曲线：

# 损失曲线可视化
fig, axs = plt.subplots(1, 2, figsize=(12, 4))
axs[0].plot(losses)
axs[1].plot(np.log(losses))  # 对数坐标
plt.show()

损失曲线可视化：

在这里插入图片描述

5、图像生成

（1）通过建立pipeline生成图像：

# 图像生成
# 方法一：建立一个pipeline, 打包模型和噪声调度器
from diffusers import DDPMPipeline
image_pipe = DDPMPipeline(unet=model, scheduler=noise_scheduler)pipeline_output = image_pipe()
plt.figure()
plt.imshow(pipeline_output.images[0])
plt.axis('off')
plt.show()# 保存pipeline
image_pipe.save_pretrained("my_pipeline")  # 在当前目录下保存了一个 my_pipeline 的文件夹

生成的蝴蝶图像如下：

在这里插入图片描述

生成的my_pipeline文件夹如下：

在这里插入图片描述

（2）通过随机采样循环生成图像：

# 方法二：模型调用, 写采样循环 
# 随机初始化8张图像:
sample = torch.randn(8, 3, 32, 32).to(device)for i, t in enumerate(noise_scheduler.timesteps):# 获得模型预测结果with torch.no_grad():residual = model(sample, t).sample# 根据预测结果更新图像sample = noise_scheduler.step(residual, t, sample).prev_sampleshow_images(sample)

8张生成图像如下：
在这里插入图片描述

6、代码汇总

import torch
import torchvision
from datasets import load_dataset
from torchvision import transforms
import numpy as np
import torch.nn.functional as F
from matplotlib import pyplot as plt
from PIL import Imagedef show_images(x):"""Given a batch of images x, make a grid and convert to PIL"""x = x * 0.5 + 0.5  # Map from (-1, 1) back to (0, 1)grid = torchvision.utils.make_grid(x)grid_im = grid.detach().cpu().permute(1, 2, 0).clip(0, 1) * 255grid_im = Image.fromarray(np.array(grid_im).astype(np.uint8))return grid_imdef transform(examples):images = [preprocess(image.convert("RGB")) for image in examples["image"]]return {"images": images}# --------------------------------------------------------------------------------
# 1、数据集加载与可视化
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)# 数据加载
dataset = load_dataset("./smithsonian_butterflies_subset", split='train')image_size = 32
batch_size = 64# 数据增强
preprocess = transforms.Compose([transforms.Resize((image_size, image_size)),  # Resizetransforms.RandomHorizontalFlip(),  # Randomly flip (data augmentation)transforms.ToTensor(),  # Convert to tensor (0, 1)transforms.Normalize([0.5], [0.5]),  # Map to (-1, 1)]
)dataset.set_transform(transform)# 数据装载
train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
# --------------------------------------------------------------------------------# --------------------------------------------------------------------------------
# 抽取一批数据可视化
xb = next(iter(train_dataloader))["images"].to(device)[:8]
print("X shape:", xb.shape)
show_images(xb).resize((8 * 64, 64), resample=Image.NEAREST)
# --------------------------------------------------------------------------------# --------------------------------------------------------------------------------
# 2、噪声调度器
from diffusers import DDPMScheduler# 加噪声的系数βt
# noise_scheduler = DDPMScheduler(num_train_timesteps=1000)
# noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_start=0.001, beta_end=0.004)
noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule='squaredcos_cap_v2')plt.figure(dpi=300)
plt.plot(noise_scheduler.alphas_cumprod.cpu() ** 0.5, label=r"${\sqrt{\bar{\alpha}_t}}$")
plt.plot((1 - noise_scheduler.alphas_cumprod.cpu()) ** 0.5, label=r"$\sqrt{(1 - \bar{\alpha}_t)}$")
plt.legend(fontsize="x-large");
# --------------------------------------------------------------------------------# --------------------------------------------------------------------------------
# 加噪声可视化
timesteps = torch.linspace(0, 999, 8).long().to(device)  # 随机采样时间步
noise = torch.randn_like(xb)
noisy_xb = noise_scheduler.add_noise(xb, noise, timesteps)  # 加噪
print("Noisy X shape", noisy_xb.shape)
show_images(noisy_xb).resize((8 * 64, 64), resample=Image.NEAREST)
# --------------------------------------------------------------------------------# --------------------------------------------------------------------------------
# 3、创建模型
from diffusers import UNet2DModelmodel = UNet2DModel(sample_size=image_size,  # the target image resolutionin_channels=3,  # the number of input channels, 3 for RGB imagesout_channels=3,  # the number of output channelslayers_per_block=2,  # how many ResNet layers to use per UNet blockblock_out_channels=(64, 128, 128, 256),  # More channels -> more parametersdown_block_types=("DownBlock2D",  # a regular ResNet downsampling block"DownBlock2D","AttnDownBlock2D",  # a ResNet downsampling block with spatial self-attention"AttnDownBlock2D",),up_block_types=("AttnUpBlock2D","AttnUpBlock2D",  # a ResNet upsampling block with spatial self-attention"UpBlock2D","UpBlock2D",  # a regular ResNet upsampling block),
)model.to(device)
with torch.no_grad():model_prediction = model(noisy_xb, timesteps).sample
model_prediction.shape  # 验证输出与输出尺寸相同
# --------------------------------------------------------------------------------# --------------------------------------------------------------------------------
# 4、扩散模型训练
# 定义噪声调度器
noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule="squaredcos_cap_v2"
)# 优化器
optimizer = torch.optim.AdamW(model.parameters(), lr=4e-4)losses = []for epoch in range(30):for step, batch in enumerate(train_dataloader):clean_images = batch["images"].to(device)# 为图像添加随机噪声noise = torch.randn(clean_images.shape).to(clean_images.device)  # epsbs = clean_images.shape[0]# 为每一张图像随机选择一个时间步timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bs,), device=clean_images.device).long()  # 根据时间步,向清晰的图像中加噪声, 前向过程：根号下αt^ * x0 + 根号下(1-αt^) * epsnoisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)# 获得模型预测结果noise_pred = model(noisy_images, timesteps, return_dict=False)[0]# 计算损失, 损失回传loss = F.mse_loss(noise_pred, noise)  loss.backward(loss)losses.append(loss.item())# 更新模型参数optimizer.step()optimizer.zero_grad()if (epoch + 1) % 5 == 0:loss_last_epoch = sum(losses[-len(train_dataloader) :]) / len(train_dataloader)print(f"Epoch:{epoch+1}, loss: {loss_last_epoch}")
# --------------------------------------------------------------------------------# --------------------------------------------------------------------------------
# 损失曲线可视化
fig, axs = plt.subplots(1, 2, figsize=(12, 4))
axs[0].plot(losses)
axs[1].plot(np.log(losses))  # 对数坐标
plt.show()
# --------------------------------------------------------------------------------# --------------------------------------------------------------------------------
# 5、图像生成
# 方法一：建立一个pipeline, 打包模型和噪声调度器
from diffusers import DDPMPipeline
image_pipe = DDPMPipeline(unet=model, scheduler=noise_scheduler)pipeline_output = image_pipe()plt.figure()
plt.imshow(pipeline_output.images[0])
plt.axis('off')
plt.show()image_pipe.save_pretrained("my_pipeline")  # 在当前目录下保存了一个 my_pipeline 的文件夹# 方法二：模型调用, 写采样循环 
# 随机初始化8张图像:
sample = torch.randn(8, 3, 32, 32).to(device)for i, t in enumerate(noise_scheduler.timesteps):# 获得模型预测结果with torch.no_grad():residual = model(sample, t).sample# 根据预测结果更新图像sample = noise_scheduler.step(residual, t, sample).prev_sampleshow_images(sample)grid_im = show_images(sample).resize((8 * 64, 64), resample=Image.NEAREST)
plt.figure(dpi=300)
plt.imshow(grid_im)
plt.axis('off')
plt.show()
# --------------------------------------------------------------------------------