目录
- 写在前面的话
- 下载数据
- 模型配置文件
- 加载数据
- 创建一个UNet2DModel
- 创建一个调度器
- 训练模型
- 完整版代码：
写在前面的话
这是我们研发的用于 消费决策的AI助理 ，我们会持续优化，欢迎体验与反馈。微信扫描二维码，添加即可。
官方链接：https://ailab.smzdm.com/
本教程将讲述 如何在Smithsonian Butterflies数据集的子集上，从头开始训练UNet2DModel，最终训练个【无条件图片生成模型】，就是不能进行文生图的啊，我觉得比较适合垂直领域的数据训练。
下载数据
训练的数据集在这个：https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset。可以使用代码进行下载。
完整的代码在最后，因为网络的原因，调代码花了一些时间(官网默认上传hugging face，我没上传)，所以要运行的话，copy最后的全部代码。我的显卡是3050，8G显存。
from datasets import load_dataset
dataset = load_dataset("huggan/smithsonian_butterflies_subset")
代码运行完成后，它的默认下载路径在：
/Users/用户名/.cache/huggingface/datasets
进入该目录后，可以看见下载的文件夹。
模型配置文件
为了方便起见，训练一个包含超参数的配置文件：
from dataclasses import dataclass
@dataclass
class TrainingConfig:
image_size = 128 # the generated image resolution
train_batch_size = 16
eval_batch_size = 16 # how many images to sample during evaluation
num_epochs = 50
gradient_accumulation_steps = 1
learning_rate = 1e-4
lr_warmup_steps = 500
save_image_epochs = 10
save_model_epochs = 30
mixed_precision = "fp16" # `no` for float32, `fp16` for automatic mixed precision
output_dir = "ddpm-butterflies-128" # the model name locally and on the HF Hub
push_to_hub = True # whether to upload the saved model to the HF Hub
hub_private_repo = False
overwrite_output_dir = True # overwrite the old model when re-running the notebook
seed = 0
config = TrainingConfig()
加载数据
from datasets import load_dataset
config.dataset_name = "huggan/smithsonian_butterflies_subset"
dataset = load_dataset(config.dataset_name, split="train")
大家也可以添加一下，Smithsonian Butterflies 数据集中一些其他数据(创建一个ImageFolder文件夹)，但是在 配置文件中 要进行添加对应的变量 imagefolder。当然，也可以使用自己的数据。
import matplotlib.pyplot as plt
fig, axs = plt.subplots(1, 4, figsize=(16, 4))
for i, image in enumerate(dataset[:4]["image"]):
axs[i].imshow(image)
axs[i].set_axis_off()
fig.show()
不过，这些图像的大小都不一样，所以你需要先对它们进行预处理：
- 统一图像尺寸：缩放到配置文件中的指定尺寸；
- 数据增强：通过裁剪、翻转等方法
- 标准化：将像素值的范围控制在[-1, 1]
from torchvision import transforms
preprocess = transforms.Compose(
[
transforms.Resize((config.image_size, config.image_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
对图像进行预处理，将图像通道转化为RGB
def transform(examples):
images = [preprocess(image.convert("RGB")) for image in examples["image"]]
return {"images": images}
dataset.set_transform(transform)
可以再次可视化图像，以确认它们是否已经被调整。之后就可以将数据集打包到DataLoader中进行训练了！
import torch
train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=config.train_batch_size, shuffle=True)
创建一个UNet2DModel
from diffusers import UNet2DModel
model = UNet2DModel(
sample_size=config.image_size, # the target image resolution
in_channels=3, # the number of input channels, 3 for RGB images
out_channels=3, # the number of output channels
layers_per_block=2, # how many ResNet layers to use per UNet block
block_out_channels=(128, 128, 256, 256, 512, 512), # the number of output channels for each UNet block
down_block_types=(
"DownBlock2D", # a regular ResNet downsampling block
"DownBlock2D",
"DownBlock2D",
"DownBlock2D",
"AttnDownBlock2D", # a ResNet downsampling block with spatial self-attention
"DownBlock2D",
),
up_block_types=(
"UpBlock2D", # a regular ResNet upsampling block
"AttnUpBlock2D", # a ResNet upsampling block with spatial self-attention
"UpBlock2D",
"UpBlock2D",
"UpBlock2D",
"UpBlock2D",
),
)
还有一个方法，快速检查样本图像的形状是否与模型输出形状匹配。
sample_image = dataset[0]["images"].unsqueeze(0)
print("Input shape:", sample_image.shape)
print("Output shape:", model(sample_image, timestep=0).sample.shape)
还需要一个调度器来为图像添加一些噪声。
创建一个调度器
调度器的作用在不同的场景下会生成不同的作用，这取决于您是使用模型进行训练还是推理。
在推理过程中，调度器从噪声中生成图像。
在训练过程中，调度器从图像上生成噪声。
可以看下DDPMScheduler调度器给图像增加噪声的效果：
import torch
from PIL import Image
from diffusers import DDPMScheduler
noise_scheduler = DDPMScheduler(num_train_timesteps=1000)
noise = torch.randn(sample_image.shape)
timesteps = torch.LongTensor([50])
noisy_image = noise_scheduler.add_noise(sample_image, noise, timesteps)
Image.fromarray(((noisy_image.permute(0, 2, 3, 1) + 1.0) * 127.5).type(torch.uint8).numpy()[0])
模型的训练对象，就是去预测这些被覆盖在图像上的噪声。在这个训练过程中，loss可以被计算。
import torch.nn.functional as F
noise_pred = model(noisy_image, timesteps).sample
loss = F.mse_loss(noise_pred, noise)
训练模型
到目前为止，已经完成了开始训练模型的大部分内容，剩下的就是将所有内容组合在一起。
再添加一个优化器和一个学习率调度器：
from diffusers.optimization import get_cosine_schedule_with_warmup
optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate)
lr_scheduler = get_cosine_schedule_with_warmup(
optimizer=optimizer,
num_warmup_steps=config.lr_warmup_steps,
num_training_steps=(len(train_dataloader) * config.num_epochs),
)
你还需要一种方法去评估模型，可以使用 DDPMPipeline 去生成一个batch，然后将他存为一个grid。
from diffusers import DDPMPipeline
import math
import os
def make_grid(images, rows, cols):
w, h = images[0].size
grid = Image.new("RGB", size=(cols * w, rows * h))
for i, image in enumerate(images):
grid.paste(image, box=(i % cols * w, i // cols * h))
return grid
def evaluate(config, epoch, pipeline):
# Sample some images from random noise (this is the backward diffusion process).
# The default pipeline output type is `List[PIL.Image]`
images = pipeline(
batch_size=config.eval_batch_size,
generator=torch.manual_seed(config.seed),
).images
# Make a grid out of the images
image_grid = make_grid(images, rows=4, cols=4)
# Save the images
test_dir = os.path.join(config.output_dir, "samples")
os.makedirs(test_dir, exist_ok=True)
image_grid.save(f"{test_dir}/{epoch:04d}.png")
现在开始梳理 整个模型训练的循环过程：
from accelerate import Accelerator
from huggingface_hub import HfFolder, Repository, whoami
from tqdm.auto import tqdm
from pathlib import Path
import os
def get_full_repo_name(model_id: str, organization: str = None, token: str = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler):
# Initialize accelerator and tensorboard logging
accelerator = Accelerator(
mixed_precision=config.mixed_precision,
gradient_accumulation_steps=config.gradient_accumulation_steps,
log_with="tensorboard",
logging_dir=os.path.join(config.output_dir, "logs"),
)
if accelerator.is_main_process:
if config.push_to_hub:
repo_name = get_full_repo_name(Path(config.output_dir).name)
repo = Repository(config.output_dir, clone_from=repo_name)
elif config.output_dir is not None:
os.makedirs(config.output_dir, exist_ok=True)
accelerator.init_trackers("train_example")
# Prepare everything
# There is no specific order to remember, you just need to unpack the
# objects in the same order you gave them to the prepare method.
model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, lr_scheduler
)
global_step = 0
# Now you train the model
for epoch in range(config.num_epochs):
progress_bar = tqdm(total=len(train_dataloader), disable=not accelerator.is_local_main_process)
progress_bar.set_description(f"Epoch {epoch}")
for step, batch in enumerate(train_dataloader):
clean_images = batch["images"]
# Sample noise to add to the images
noise = torch.randn(clean_images.shape).to(clean_images.device)
bs = clean_images.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bs,), device=clean_images.device
).long()
# Add noise to the clean images according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)
with accelerator.accumulate(model):
# Predict the noise residual
noise_pred = model(noisy_images, timesteps, return_dict=False)[0]
loss = F.mse_loss(noise_pred, noise)
accelerator.backward(loss)
accelerator.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
global_step += 1
# After each epoch you optionally sample some demo images with evaluate() and save the model
if accelerator.is_main_process:
pipeline = DDPMPipeline(unet=accelerator.unwrap_model(model), scheduler=noise_scheduler)
if (epoch + 1) % config.save_image_epochs == 0 or epoch == config.num_epochs - 1:
evaluate(config, epoch, pipeline)
if (epoch + 1) % config.save_model_epochs == 0 or epoch == config.num_epochs - 1:
if config.push_to_hub:
repo.push_to_hub(commit_message=f"Epoch {epoch}", blocking=True)
else:
pipeline.save_pretrained(config.output_dir)
现在你终于可以使用 Accelerate的notebook_launcher函数来启动训练了。将训练循环、所有训练参数以及要用于训练的进程数（你可以将其更改为可用的GPU数量）传递给这个函数：
from accelerate import notebook_launcher
args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)
notebook_launcher(train_loop, args, num_processes=1)
在训练完成后，就可以看最后生成图像的模型了。
import glob
sample_images = sorted(glob.glob(f"{config.output_dir}/samples/*.png"))
Image.open(sample_images[-1])
完整版代码：
# -*- coding:utf-8 _*-
# Author : Robin Chen
# Time : 2024/3/27 20:06
# File : train_diffusion.py
# Purpose: train a unconditional diffusion model
from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
from accelerate import Accelerator
from huggingface_hub import HfFolder, Repository, whoami
from tqdm.auto import tqdm
from pathlib import Path
import os
from accelerate import notebook_launcher
from diffusers.optimization import get_cosine_schedule_with_warmup
from PIL import Image
import torch.nn.functional as F
import torch
from torchvision import transforms
from dataclasses import dataclass
from datasets import load_dataset
# from huggingface_hub import notebook_login
os.environ['HTTP_PROXY'] = 'http://127.0.0.1:7890'
os.environ['HTTPS_PROXY'] = 'http://127.0.0.1:7890'
# notebook_login()
dataset = load_dataset("huggan/smithsonian_butterflies_subset")
@dataclass
class TrainingConfig:
image_size = 128 # the generated image resolution
train_batch_size = 16
eval_batch_size = 16 # how many images to sample during evaluation
num_epochs = 50
gradient_accumulation_steps = 1
learning_rate = 1e-4
lr_warmup_steps = 500
save_image_epochs = 10
save_model_epochs = 30
mixed_precision = "fp16" # `no` for float32, `fp16` for automatic mixed precision
output_dir = "ddpm-butterflies-128" # the model name locally and on the HF Hub
push_to_hub = False # True # whether to upload the saved model to the HF Hub
hub_private_repo = False
overwrite_output_dir = True # overwrite the old model when re-running the notebook
seed = 0
def transform(examples):
images = [preprocess(image.convert("RGB")) for image in examples["image"]]
return {"images": images}
def make_grid(images, rows, cols):
w, h = images[0].size
grid = Image.new("RGB", size=(cols * w, rows * h))
for i, image in enumerate(images):
grid.paste(image, box=(i % cols * w, i // cols * h))
return grid
def evaluate(config, epoch, pipeline):
# Sample some images from random noise (this is the backward diffusion process).
# The default pipeline output type is `List[PIL.Image]`
images = pipeline(
batch_size=config.eval_batch_size,
generator=torch.manual_seed(config.seed),
).images
# Make a grid out of the images
image_grid = make_grid(images, rows=4, cols=4)
# Save the images
test_dir = os.path.join(config.output_dir, "samples")
os.makedirs(test_dir, exist_ok=True)
image_grid.save(f"{test_dir}/{epoch:04d}.png")
def get_full_repo_name(model_id: str, organization: str = None, token: str = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
config = TrainingConfig()
config.dataset_name = "huggan/smithsonian_butterflies_subset"
dataset = load_dataset(config.dataset_name, split="train")
preprocess = transforms.Compose(
[
transforms.Resize((config.image_size, config.image_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
dataset.set_transform(transform)
train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=config.train_batch_size, shuffle=True)
model = UNet2DModel(
sample_size=config.image_size, # the target image resolution
in_channels=3, # the number of input channels, 3 for RGB images
out_channels=3, # the number of output channels
layers_per_block=2, # how many ResNet layers to use per UNet block
block_out_channels=(128, 128, 256, 256, 512, 512), # the number of output channels for each UNet block
down_block_types=(
"DownBlock2D", # a regular ResNet downsampling block
"DownBlock2D",
"DownBlock2D",
"DownBlock2D",
"AttnDownBlock2D", # a ResNet downsampling block with spatial self-attention
"DownBlock2D",
),
up_block_types=(
"UpBlock2D", # a regular ResNet upsampling block
"AttnUpBlock2D", # a ResNet upsampling block with spatial self-attention
"UpBlock2D",
"UpBlock2D",
"UpBlock2D",
"UpBlock2D",
),
)
sample_image = dataset[0]["images"].unsqueeze(0)
print("Input shape:", sample_image.shape)
print("Output shape:", model(sample_image, timestep=0).sample.shape)
noise_scheduler = DDPMScheduler(num_train_timesteps=1000)
noise = torch.randn(sample_image.shape)
timesteps = torch.LongTensor([50])
noisy_image = noise_scheduler.add_noise(sample_image, noise, timesteps)
Image.fromarray(((noisy_image.permute(0, 2, 3, 1) + 1.0) * 127.5).type(torch.uint8).numpy()[0])
noise_pred = model(noisy_image, timesteps).sample
loss = F.mse_loss(noise_pred, noise)
optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate)
lr_scheduler = get_cosine_schedule_with_warmup(
optimizer=optimizer,
num_warmup_steps=config.lr_warmup_steps,
num_training_steps=(len(train_dataloader) * config.num_epochs),
)
def train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler):
# Initialize accelerator and tensorboard logging
accelerator = Accelerator(
mixed_precision=config.mixed_precision,
gradient_accumulation_steps=config.gradient_accumulation_steps,
log_with="tensorboard",
) # logging_dir=os.path.join(config.output_dir, "logs"),
if accelerator.is_main_process:
if config.push_to_hub:
repo_name = get_full_repo_name(Path(config.output_dir).name)
# repo = Repository(config.output_dir, clone_from=repo_name)
elif config.output_dir is not None:
os.makedirs(config.output_dir, exist_ok=True)
accelerator.init_trackers("train_example")
# Prepare everything
# There is no specific order to remember, you just need to unpack the
# objects in the same order you gave them to the prepare method.
model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, lr_scheduler
)
global_step = 0
# Now you train the model
for epoch in range(config.num_epochs):
progress_bar = tqdm(total=len(train_dataloader), disable=not accelerator.is_local_main_process)
progress_bar.set_description(f"Epoch {epoch}")
for step, batch in enumerate(train_dataloader):
clean_images = batch["images"]
# Sample noise to add to the images
noise = torch.randn(clean_images.shape).to(clean_images.device)
bs = clean_images.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bs,), device=clean_images.device
).long()
# Add noise to the clean images according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)
with accelerator.accumulate(model):
# Predict the noise residual
noise_pred = model(noisy_images, timesteps, return_dict=False)[0]
loss = F.mse_loss(noise_pred, noise)
accelerator.backward(loss)
accelerator.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
global_step += 1
# After each epoch you optionally sample some demo images with evaluate() and save the model
if accelerator.is_main_process:
pipeline = DDPMPipeline(unet=accelerator.unwrap_model(model), scheduler=noise_scheduler)
if (epoch + 1) % config.save_image_epochs == 0 or epoch == config.num_epochs - 1:
evaluate(config, epoch, pipeline)
if (epoch + 1) % config.save_model_epochs == 0 or epoch == config.num_epochs - 1:
if config.push_to_hub:
repo.push_to_hub(commit_message=f"Epoch {epoch}", blocking=True)
else:
pipeline.save_pretrained(config.output_dir)
if __name__ == "__main__":
args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)
notebook_launcher(train_loop, args, num_processes=1)
