李明朔

扩散模型学习（二）

文章目录

一、扩散模型微调
二、引导
三、其他引导方法
- 1.CLIIP引导
- 2. 类别条件扩散模型

一、扩散模型微调

import numpy as np
import torch
import torch.nn.functional as F
import torchvision
from datasets import load_dataset
from diffusers import DDIMScheduler, DDPMPipeline
from matplotlib import pyplot as plt
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm

device = (
    "mps"
    if torch.backends.mps.is_available()
    else "cuda"
    if torch.cuda.is_available()
    else "cpu"
)

加载预训练管线

image_pipe = DDPMPipeline.from_pretrained("google/ddpm-celebahq-256")
image_pipe.to(device);

加载数据集

# @markdown load and prepare a dataset:
# Not on Colab? Comments with #@ enable UI tweaks like headings or user inputs
# but can safely be ignored if you're working on a different platform.

dataset_name = "huggan/smithsonian_butterflies_subset"  # @param
dataset = load_dataset(dataset_name, split="train")
image_size = 256  # @param
batch_size = 4  # @param
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
)

print("Previewing batch:")
batch = next(iter(train_dataloader))
grid = torchvision.utils.make_grid(batch["images"], nrow=4)
plt.imshow(grid.permute(1, 2, 0).cpu().clip(-1, 1) * 0.5 + 0.5);

开始训练

num_epochs = 2  # @param
lr = 1e-5  # 2param
grad_accumulation_steps = 2  # @param

optimizer = torch.optim.AdamW(image_pipe.unet.parameters(), lr=lr)

losses = []

for epoch in range(num_epochs):
    for step, batch in tqdm(enumerate(train_dataloader), total=len(train_dataloader)):
        clean_images = batch["images"].to(device)
        # 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,
            image_pipe.scheduler.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 = image_pipe.scheduler.add_noise(clean_images, noise, timesteps)

        # Get the model prediction for the noise
        noise_pred = image_pipe.unet(noisy_images, timesteps, return_dict=False)[0]

        # Compare the prediction with the actual noise:
        loss = F.mse_loss(
            noise_pred, noise
        )  # NB - trying to predict noise (eps) not (noisy_ims-clean_ims) or just (clean_ims)

        # Store for later plotting
        losses.append(loss.item())

        # Update the model parameters with the optimizer based on this loss
        loss.backward(loss)

        # Gradient accumulation:
        if (step + 1) % grad_accumulation_steps == 0:
            optimizer.step()
            optimizer.zero_grad()

    print(
        f"Epoch {epoch} average loss: {sum(losses[-len(train_dataloader):])/len(train_dataloader)}"
    )

# Plot the loss curve:
plt.plot(losses)

二、引导

如果我们想对生成的样本施加点控制，那需要怎么做呢？例如，我们想让生成的图片偏向于靠近某种颜色。该怎么做呢？这里我们要介绍引导（guidance），它可以用来在采样的过程中施加额外控制。

第一步，我们先创建一个函数，定义我们希望优化的一个指标（损失值）。这里是一个让生成的图片趋向于某种颜色的例子，它将图片像素值和目标颜色（这里用的是一种浅蓝绿色）对比，返回平均的误差：

def color_loss(images, target_color=(0.1, 0.9, 0.5)):
    """Given a target color (R, G, B) return a loss for how far away on average
    the images' pixels are from that color. Defaults to a light teal: (0.1, 0.9, 0.5)"""
    target = (
        torch.tensor(target_color).to(images.device) * 2 - 1
    )  # Map target color to (-1, 1)
    target = target[
        None, :, None, None
    ]  # Get shape right to work with the images (b, c, h, w)
    error = torch.abs(
        images - target
    ).mean()  # Mean absolute difference between the image pixels and the target color
    return error

接下来，我们要修改采样循环，在每一步，我们要做这些事情：

创建一个新版的 x，并且 requires_grad = True
算出去噪后的版本（x0）
将预测出的x0送入我们的损失函数中
找到这个损失函数对于 x 的梯度
在使用调度器前，用这个梯度去修改 x ，希望 x 朝着能减低损失值的方向改进

这里有两种实现方法。第一，我们是在从 UNet 得到噪声预测后才给 x 设置 requires_grad 的，这样对内存来讲更高效一点（因为我们不用穿过扩散模型去追踪梯度），但这样做梯度的精度会低一点。

# Variant 1: shortcut method

# The guidance scale determines the strength of the effect
guidance_loss_scale = 40  # Explore changing this to 5, or 100

x = torch.randn(8, 3, 256, 256).to(device)

for i, t in tqdm(enumerate(scheduler.timesteps)):

    # Prepare the model input
    model_input = scheduler.scale_model_input(x, t)

    # predict the noise residual
    with torch.no_grad():
        noise_pred = image_pipe.unet(model_input, t)["sample"]

    # Set x.requires_grad to True
    x = x.detach().requires_grad_()

    # Get the predicted x0
    x0 = scheduler.step(noise_pred, t, x).pred_original_sample

    # Calculate loss
    loss = color_loss(x0) * guidance_loss_scale
    if i % 10 == 0:
        print(i, "loss:", loss.item())

    # Get gradient
    cond_grad = -torch.autograd.grad(loss, x)[0]

    # Modify x based on this gradient
    x = x.detach() + cond_grad

    # Now step with scheduler
    x = scheduler.step(noise_pred, t, x).prev_sample

# View the output
grid = torchvision.utils.make_grid(x, nrow=4)
im = grid.permute(1, 2, 0).cpu().clip(-1, 1) * 0.5 + 0.5
Image.fromarray(np.array(im * 255).astype(np.uint8))

第二种方法是，我们先给 x 设置 requires_grad，然后再送入 UNet 并计算预测出的 x0。

# Variant 2: setting x.requires_grad before calculating the model predictions

guidance_loss_scale = 40
x = torch.randn(4, 3, 256, 256).to(device)

for i, t in tqdm(enumerate(scheduler.timesteps)):

    # Set requires_grad before the model forward pass
    x = x.detach().requires_grad_()
    model_input = scheduler.scale_model_input(x, t)

    # predict (with grad this time)
    noise_pred = image_pipe.unet(model_input, t)["sample"]

    # Get the predicted x0:
    x0 = scheduler.step(noise_pred, t, x).pred_original_sample

    # Calculate loss
    loss = color_loss(x0) * guidance_loss_scale
    if i % 10 == 0:
        print(i, "loss:", loss.item())

    # Get gradient
    cond_grad = -torch.autograd.grad(loss, x)[0]

    # Modify x based on this gradient
    x = x.detach() + cond_grad

    # Now step with scheduler
    x = scheduler.step(noise_pred, t, x).prev_sample


grid = torchvision.utils.make_grid(x, nrow=4)
im = grid.permute(1, 2, 0).cpu().clip(-1, 1) * 0.5 + 0.5
Image.fromarray(np.array(im * 255).astype(np.uint8))

三、其他引导方法

1.CLIIP引导

CLIP 是一个由 OpenAI 开发的模型，它可以让我们拿图片和文字说明去作比较。这是个非常强大的功能，因为它让我们能量化一张图和一句提示语有多匹配。另外，由于这个过程是可微分的，我们可以使用它作为损失函数去引导我们的扩散模型。

基本的方法是：

给文字提示语做嵌入（embedding），为 CLIP 获取一个 512 维的 embedding
对于扩散模型的生成过程的每一步：
- 做出多个不同版本的预测出来的去噪图片（不同的变种可以提供一个更干净的损失信号）
- 对每一个预测出的去噪图片，用 CLIP 给图片做嵌入（embedding），并将这个嵌入和文字的嵌入做对比（用一种叫 Great Circle Distance Squared 的度量方法）
计算这个损失对于当前带噪的 x 的梯度，并在用调度器（scheduler）更新它之前用这个梯度去修改 x

# @markdown load a CLIP model and define the loss function
import open_clip

clip_model, _, preprocess = open_clip.create_model_and_transforms(
    "ViT-B-32", pretrained="openai"
)
clip_model.to(device)

# Transforms to resize and augment an image + normalize to match CLIP's training data
tfms = torchvision.transforms.Compose(
    [
        torchvision.transforms.RandomResizedCrop(224),  # Random CROP each time
        torchvision.transforms.RandomAffine(
            5
        ),  # One possible random augmentation: skews the image
        torchvision.transforms.RandomHorizontalFlip(),  # You can add additional augmentations if you like
        torchvision.transforms.Normalize(
            mean=(0.48145466, 0.4578275, 0.40821073),
            std=(0.26862954, 0.26130258, 0.27577711),
        ),
    ]
)

# And define a loss function that takes an image, embeds it and compares with
# the text features of the prompt
def clip_loss(image, text_features):
    image_features = clip_model.encode_image(
        tfms(image)
    )  # Note: applies the above transforms
    input_normed = torch.nn.functional.normalize(image_features.unsqueeze(1), dim=2)
    embed_normed = torch.nn.functional.normalize(text_features.unsqueeze(0), dim=2)
    dists = (
        input_normed.sub(embed_normed).norm(dim=2).div(2).arcsin().pow(2).mul(2)
    )  # Squared Great Circle Distance
    return dists.mean()

# @markdown applying guidance using CLIP

prompt = "Red Rose (still life), red flower painting"  # @param

# Explore changing this
guidance_scale = 8  # @param
n_cuts = 4  # @param

# More steps -> more time for the guidance to have an effect
scheduler.set_timesteps(50)

# We embed a prompt with CLIP as our target
text = open_clip.tokenize([prompt]).to(device)
with torch.no_grad(), torch.cuda.amp.autocast():
    text_features = clip_model.encode_text(text)


x = torch.randn(4, 3, 256, 256).to(
    device
)  # RAM usage is high, you may want only 1 image at a time

for i, t in tqdm(enumerate(scheduler.timesteps)):

    model_input = scheduler.scale_model_input(x, t)

    # predict the noise residual
    with torch.no_grad():
        noise_pred = image_pipe.unet(model_input, t)["sample"]

    cond_grad = 0

    for cut in range(n_cuts):

        # Set requires grad on x
        x = x.detach().requires_grad_()

        # Get the predicted x0:
        x0 = scheduler.step(noise_pred, t, x).pred_original_sample

        # Calculate loss
        loss = clip_loss(x0, text_features) * guidance_scale

        # Get gradient (scale by n_cuts since we want the average)
        cond_grad -= torch.autograd.grad(loss, x)[0] / n_cuts

    if i % 25 == 0:
        print("Step:", i, ", Guidance loss:", loss.item())

    # Modify x based on this gradient
    alpha_bar = scheduler.alphas_cumprod[i]
    x = (
        x.detach() + cond_grad * alpha_bar.sqrt()
    )  # Note the additional scaling factor here!

    # Now step with scheduler
    x = scheduler.step(noise_pred, t, x).prev_sample


grid = torchvision.utils.make_grid(x.detach(), nrow=4)
im = grid.permute(1, 2, 0).cpu().clip(-1, 1) * 0.5 + 0.5
Image.fromarray(np.array(im * 255).astype(np.uint8))

2. 类别条件扩散模型

我们输入类别这一条件的方法是：

创建一个标准的 UNet2DModel，加入一些额外的输入通道
通过一个嵌入层，把类别标签映射到一个 (class_emb_size) 形状的学到的向量上
把这个信息作为额外通道和原有的输入向量拼接起来，用这行代码：net_input = torch.cat((x, class_cond), 1)
把这个 net_input (有 class_emb_size+1 个通道)输入到UNet中得到最终预测

import torch
import torchvision
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
from diffusers import DDPMScheduler, UNet2DModel
from matplotlib import pyplot as plt
from tqdm.auto import tqdm

device = 'mps' if torch.backends.mps.is_available() else 'cuda' if torch.cuda.is_available() else 'cpu'
print(f'Using device: {device}')