要通过代码实现AI图像生成，可以使用深度学习框架如TensorFlow、PyTorch或GANs等技术。下面是一个简单的示例代码，演示如何使用GANs生成手写数字图像：

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
from torchvision.utils import save_image
import os

# 数据预处理
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# 加载MNIST数据集
trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

# 定义生成器和判别器网络
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(100, 256),
            nn.ReLU(),
            nn.Linear(256, 784),
            nn.Tanh()
        )

    def forward(self, x):
        return self.model(x)

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(784, 256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, 1),
            nn.Sigmoid()
        )

    def forward(self, x):
        return self.model(x)

# 初始化网络和优化器
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
gen = Generator().to(device)
disc = Discriminator().to(device)
criterion = nn.BCELoss()
gen_optimizer = optim.Adam(gen.parameters(), lr=0.0002)
disc_optimizer = optim.Adam(disc.parameters(), lr=0.0002)

# 训练GANs模型
num_epochs = 50
for epoch in range(num_epochs):
    for i, data in enumerate(trainloader, 0):
        real_images, _ = data
        real_images = real_images.view(real_images.size(0), -1).to(device)
        real_labels = torch.ones(real_images.size(0), 1).to(device)
        fake_labels = torch.zeros(real_images.size(0), 1).to(device)

        # 训练判别器
        disc.zero_grad()
        real_outputs = disc(real_images)
        real_loss = criterion(real_outputs, real_labels)
        real_score = real_outputs

        z = torch.randn(real_images.size(0), 100).to(device)
        fake_images = gen(z)
        fake_outputs = disc(fake_images)
        fake_loss = criterion(fake_outputs, fake_labels)
        fake_score = fake_outputs

        d_loss = real_loss + fake_loss
        d_loss.backward()
        disc_optimizer.step()

        # 训练生成器
        gen.zero_grad()
        z = torch.randn(real_images.size(0), 100).to(device)
        fake_images = gen(z)
        outputs = disc(fake_images)
        g_loss = criterion(outputs, real_labels)
        g_loss.backward()
        gen_optimizer.step()

        print('Epoch [%d/%d], Step [%d/%d], d_loss: %.4f, g_loss: %.4f, D(x): %.2f, D(G(z)): %.2f'
              % (epoch, num_epochs, i, len(trainloader), d_loss.item(), g_loss.item(), real_score.mean().item(), fake_score.mean().item()))

    if epoch % 10 == 0:
        if not os.path.exists('images'):
            os.mkdir('images')
        save_image(fake_images.view(fake_images.size(0), 1, 28, 28), 'images/{}.png'.format(epoch))

这段代码实现了一个简单的基于GANs的手写数字生成器。在训练过程中，生成器和判别器交替训练，以使生成器生成更逼真的手写数字图像。注意，这只是一个简单的示例，实际应用中可能需要更复杂的网络结构和更多的训练数据。