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本文介绍了如何使用Diffusers库中的稳定扩散技术进行图像生成,包括预训练模型的加载和管线的应用。通过学习本文,您将了解到如何利用稳定扩散技术生成图像,并掌握使用预训练模型和管线的技巧。
from huggingface_hub import notebook_login
# 登录huggingface
notebook_login()
# 记得设置代理,端口是梯子的端口号
# set HTTPS_PROXY=http://127.0.0.1:自己的
# set HTTP_PROXY=http://127.0.0.1:自己的
VBox(children=(HTML(value='<center> <imgnsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…
import numpy as np
import torch
import torch.nn.functional as F
import torchvision
from matplotlib import pyplot as plt
from PIL import Image
def show_images(x):
"""给定一批图像,创建一个网格并将其转换为PIL"""
x = x * 0.5 + 0.5
grid = torchvision.utils.make_grid(x)
grid_im = grid.detach().cpu().permute(1, 2, 0).clip(0, 1) * 255
grid_im = Image.fromarray(np.array(grid_im).astype(np.uint8))
return grid_im
def make_grid(images, size=64):
"""给定一个PIL图像列表,将它们叠加成一行以便查看"""
output_im = Image.new("RGB", (size * len(images), size))
for i, im in enumerate(images):
output_im.paste(im.resize((size, size)), (i * size, 0))
return output_im
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 加载管线
from diffusers import StableDiffusionPipeline
model_id = "sd-dreambooth-library/mr-potato-head"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(device)
num_inference_steps代表采样步骤的数量
guidance_scale决定模型输出与提示词之间的匹配程度
Diffusers核心API:
- 管线:从高层次设计的多种类函数,便于部署的方式实现,能够快速利用预训练的主流扩散模型来生成样本。
- 模型:在训练新的扩散模型时需要用到的网络结构。
- 调度器:在推理过程中使用多种不同的技巧来从噪声中生成图像,同时可以生成训练过程中所需的“带噪”图像。
from diffusers import DDPMPipeline
# 加载预设好的管线
model = "johnowhitaker/ddpm-butterflies-32px"
butterfly_pipeline = DDPMPipeline.from_pretrained(model).to(device)
# 生成8张图片
images = butterfly_pipeline(batch_size=8).images
make_grid(images)
实战 生成蝴蝶图像
下载蝴蝶图像集
import torchvision
from datasets import load_dataset
from torchvision import transforms
dataset = load_dataset("huggan/smithsonian_butterflies_subset", split="train")
image_size = 32
batch_size = 64
# 定义数据增强过程
preprocess = transforms.Compose(
[
transforms.Resize((image_size, image_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])
])
def transform(examples):
images = [preprocess(image.convert("RGB")) for image in examples["image"]]
return {"images": images}
dataset.set_transform(transform)
train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
xb = next(iter(train_dataloader))["images"].to(device)[:8]
print("X shape:", xb.shape)
show_images(xb).resize((8 * 64, 64), resample=Image.NEAREST)
X shape: torch.Size([8, 3, 32, 32])
调度器
from diffusers import DDPMScheduler
noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_start=0.001, beta_end=0.004)
# 添加不同程度的噪声
timesteps = torch.linspace(0, 999, 8).long().to(device)
noise = torch.rand_like(xb)
noisy_xb = noise_scheduler.add_noise(xb, noise, timesteps)
print("Nosiy X Shape", noisy_xb.shape)
show_images(noisy_xb).resize((8 * 64, 64), resample=Image.NEAREST)
Nosiy X Shape torch.Size([8, 3, 32, 32])
定义扩散模型
from diffusers import UNet2DModel
model = UNet2DModel(
sample_size=image_size, # 目标图像的分辨率
in_channels=3,
out_channels=3,
layers_per_block=2, # 每一个UNet块中的ResNet层数
block_out_channels=(64, 128, 128, 256),
down_block_types=(
"DownBlock2D",
"DownBlock2D",
"AttnDownBlock2D", # 带有空域维度的self-att的ResNet下采样模块
"AttnDownBlock2D",
),
up_block_types=(
"AttnUpBlock2D",
"AttnUpBlock2D", # 带有空域维度的self-att的ResNet上采样模块
"UpBlock2D",
"UpBlock2D"
),
)
model.to(device);
with torch.no_grad():
model_prediction = model(noisy_xb, timesteps).sample
model_prediction.shape
torch.Size([8, 3, 32, 32])
训练扩散模型
# 设定噪声调度器
noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule="squaredcos_cap_v2")
# 训练循环
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)
# 为图片添加采样噪声
noise = torch.randn(clean_images.shape).to(clean_images.device)
bs = clean_images.shape[0]
# 为每张图片随机采样一个时间步
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bs, ),
device=clean_images.device).long()
# 根据每个时间步的噪声幅度,向清晰的图片中添加噪声
noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)
# 获得模型的预测结果
noise_pred = model(noisy_images, timesteps, return_dict=False)[0]
# 计算损失
loss = F.mse_loss(noise_pred, noise)
loss.backward(loss)
losses.append(loss.item())
# 迭代模型参数
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}")
Epoch:5, loss: 0.14714546548202634
Epoch:10, loss: 0.11393213225528598
Epoch:15, loss: 0.09713644068688154
Epoch:20, loss: 0.08644208777695894
Epoch:25, loss: 0.07677834457717836
Epoch:30, loss: 0.0714822830632329
fig, axs = plt.subplots(1, 2, figsize=(12, 4))
axs[0].plot(losses)
axs[1].plot(np.log(losses))
plt.show()
图像的生成
# 方法1:建立一个管线生成图像
from diffusers import DDPMPipeline
image_pipe = DDPMPipeline(unet=model, scheduler=noise_scheduler)
pipeline_output = image_pipe()
print(pipeline_output.images[0])
pipeline_output.images[0].resize((64, 64), resample=Image.NEAREST)
# 方法2:采样循环
sample = torch.randn(8, 3, 32, 32).to(device)
show_images(sample).resize((8 * 64, 64), resample=Image.NEAREST)
# print(noise_scheduler.timesteps) # 0-999
for i, t in enumerate(noise_scheduler.timesteps):
with torch.no_grad():
residual = model(sample, t).sample
sample = noise_scheduler.step(residual, t, sample).prev_sample
show_images(sample).resize((8 * 64, 64), resample=Image.NEAREST)
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