生成AI(三)—创建自己的MidJorney

背景：MidJorney是面向互联网的图像AIGC产品，在政企内部，存在大量需求训练内部的知识作为自己的AIGC工具。基本需求是信息安全考虑，合规考虑。

目标：通过自准备的数据训练MidJorney同类模型，成为私有化部署的AIGC产品。

Diffusion原理：
在这里插入图片描述

训练：

扩散过程：从X0到XT，采用高斯函数“扩散”，无需学习算法可以直接推导。

反扩散过程：从XT到X0，根据时间t等相关进行无监督学习，得到模型参数。

推理：

通过文本向量转为输入向量，反扩散给定轮次，得到最终图片X0

技术方案：

最新的方式是文本到图像的AIGC生成，但要求的GPU配置笔者手边电脑无法达到。

本例：预训练蝴蝶数据集，预初始随机点生成新的蝴蝶图片，无需任何输入。

模型：本例采用无条件的Diffusion图片生成（有条件的例如，文本到图像、图像到图像等）

数据：Smithsonian Butterflies

时长：GPU3小时，CPU78小时

用法：直接命令生成蝴蝶图像

一、环境准备

1、安装Anaconda

查看Conda环境

conda env list

创建新环境

conda create -n mydiffusion python=3.8
activate mydiffusion

2、安装依赖包

#如果是CPU
pip install torch
#如果是GPU，首先查看本机的GPU版本
#去官网查看pytorch与CUDA对应，https://pytorch.org/get-started/previous-versions/
#以下是11.6cuda安装pytorch例子
nvcc -V
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia

pip install diffusers[training]

#如果有提示其它包安装,看错误提示

pip install xxx

二、训练代码

train.py文件

from dataclasses import dataclass
from datasets import load_dataset
import matplotlib.pyplot as plt
from torchvision import transforms
import torch
from diffusers import UNet2DModel
import torch
from PIL import Image
from diffusers import DDPMScheduler
import torch.nn.functional as F
from diffusers.optimization import get_cosine_schedule_with_warmup
from diffusers import DDPMPipeline
import math
import os
from accelerate import Accelerator
from huggingface_hub import HfFolder, Repository, whoami
from tqdm.auto import tqdm
from pathlib import Path

from accelerate import notebook_launcher
import glob

#训练参数
@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  # 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
#gpu or cpu  
if torch.cuda.is_available():
    torch.cuda.empty_cache()
    device = torch.device("cuda")          
    print("There are %d GPU(s) available." % torch.cuda.device_count())
    print("We will use the GPU:", torch.cuda.get_device_name(0))
else:
    print("No GPU available, using the CPU instead.")
    device = torch.device("cpu")
config = TrainingConfig()

#加载数据集
config.dataset_name = "huggan/smithsonian_butterflies_subset"
dataset = load_dataset(config.dataset_name, split="train")

#查看数据集
# 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()
# plt.pause(3000)

#预处理尺寸归一化
preprocess = transforms.Compose(
    [
        transforms.Resize((config.image_size, config.image_size)),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

#预处理函数
def transform(examples):
    images = [preprocess(image.convert("RGB")) for image in examples["image"]]
    return {"images": images}
dataset.set_transform(transform)
#加载数据
train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=config.train_batch_size, shuffle=True)

#创建模型
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",
    ),
)
#验证形状
sample_image = dataset[0]["images"].unsqueeze(0)
print("Input shape:", sample_image.shape)
print("Output shape:", model(sample_image, timestep=0).sample.shape)
#创建执行计划
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])
#创建损失函数
noise_pred = model(noisy_image, timesteps).sample
loss = F.mse_loss(noise_pred, noise)
#优化器
optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate)
#调度器
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),
)

#保存为网格
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

#DDPMPipeline 
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}"


def train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler):
     # 初始化加速器和张量板日志记录
    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

    # 训练模型
    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"]
            # 添加到图像的样本噪声
            noise = torch.randn(clean_images.shape).to(clean_images.device)
            bs = clean_images.shape[0]

            # 为每个图像采样一个随机时间步长
            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给干净图像添加噪声
            # (这是前向扩散过程)
            noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)

            with accelerator.accumulate(model):
                # 预测噪声残差
                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

        # 在每个 epoch 之后，您可以选择使用 evaluate() 对一些演示图像进行采样并保存模型
        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)

#将训练循环、所有训练参数和进程数（您可以将此值更改为可用的 GPU 数）传递给函数以用于训练
# args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)
train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)
#准备好使用 Accelerate 的notebook_launcher功能启动训练
# notebook_launcher(train_loop, args, num_processes=1)
#查看生成结果
sample_images = sorted(glob.glob(f"{config.output_dir}/samples/*.png"))
Image.open(sample_images[-1])

训练代码中的TrainingConfig，比较关键的几个配置：

名称	解释
图片大小	决定生成图像的大小
batch_size	如果在较小的GPU下训练，请适当调低该参数，比如8、4

在Terminal中运行：

python train.py

查看gpu,CMD输入以下命令

nvidia-smi

会提示请求连接关闭，多尝试几次，笔者尝试20多次才正常下载模型548M

三、推理代码

test.py

from diffusers import DiffusionPipeline

generator = DiffusionPipeline.from_pretrained("ddpm-butterflies-128")
generator.to("cuda")
image = generator().images[0]
image.save("generated_image.png")