挖掘AI人工智能领域AI作画的文化价值

    [文本输入]
    ↓
    [语言理解模型(如CLIP)]
    ↓
    [图像生成模型(如Stable Diffusion)]
    ↓
    [后处理与风格化]
    ↓
    [艺术输出]
    
    

    import torch
    import torch.nn as nn
    
    # 生成器网络
    class Generator(nn.Module):
    def __init__(self, latent_dim, img_channels):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(latent_dim, 256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, 512),
            nn.LeakyReLU(0.2),
            nn.Linear(512, 1024),
            nn.LeakyReLU(0.2),
            nn.Linear(1024, 28*28*img_channels),
            nn.Tanh()
        )
    
    def forward(self, z):
        return self.net(z).view(-1, 1, 28, 28)
    
    # 判别器网络
    class Discriminator(nn.Module):
    def __init__(self, img_channels):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(28*28*img_channels, 1024),
            nn.LeakyReLU(0.2),
            nn.Linear(1024, 512),
            nn.LeakyReLU(0.2),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, 1),
            nn.Sigmoid()
        )
    
    def forward(self, img):
        img_flat = img.view(img.size(0), -1)
        return self.net(img_flat)
    
    # 训练循环
    def train_gan(generator, discriminator, dataloader, epochs=100):
    criterion = nn.BCELoss()
    optim_G = torch.optim.Adam(generator.parameters())
    optim_D = torch.optim.Adam(discriminator.parameters())
    
    for epoch in range(epochs):
        for real_imgs, _ in dataloader:
            # 训练判别器
            z = torch.randn(real_imgs.size(0), latent_dim)
            fake_imgs = generator(z)
    
            real_labels = torch.ones(real_imgs.size(0), 1)
            fake_labels = torch.zeros(real_imgs.size(0), 1)
    
            # 计算判别器对真实和生成图像的损失
            real_loss = criterion(discriminator(real_imgs), real_labels)
            fake_loss = criterion(discriminator(fake_imgs.detach()), fake_labels)
            d_loss = real_loss + fake_loss
    
            optim_D.zero_grad()
            d_loss.backward()
            optim_D.step()
    
            # 训练生成器
            z = torch.randn(real_imgs.size(0), latent_dim)
            fake_imgs = generator(z)
            g_loss = criterion(discriminator(fake_imgs), real_labels)
    
            optim_G.zero_grad()
            g_loss.backward()
            optim_G.step()
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/sM7aNePqYwO92KmXzU0n45EVpFDt.png)

    import torch
    import torch.nn as nn
    import numpy as np
    
    class DiffusionModel(nn.Module):
    def __init__(self, model, timesteps=1000):
        super().__init__()
        self.model = model  # UNet等噪声预测模型
        self.timesteps = timesteps
    
        # 定义噪声调度
        self.betas = self._linear_beta_schedule(timesteps)
        self.alphas = 1. - self.betas
        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
    
    def _linear_beta_schedule(self, timesteps, beta_start=0.0001, beta_end=0.02):
        return torch.linspace(beta_start, beta_end, timesteps)
    
    def forward(self, x, t):
        # 前向扩散过程：逐步添加噪声
        sqrt_alphas_cumprod_t = self.alphas_cumprod[t].sqrt()
        sqrt_one_minus_alphas_cumprod_t = (1 - self.alphas_cumprod[t]).sqrt()
        noise = torch.randn_like(x)
        return sqrt_alphas_cumprod_t * x + sqrt_one_minus_alphas_cumprod_t * noise
    
    def sample(self, shape, device):
        # 反向生成过程：从噪声逐步生成图像
        x = torch.randn(shape, device=device)
    
        for t in reversed(range(self.timesteps)):
            # 预测噪声
            with torch.no_grad():
                predicted_noise = self.model(x, t)
    
            # 计算去噪步骤
            alpha_t = self.alphas[t]
            alpha_cumprod_t = self.alphas_cumprod[t]
            beta_t = self.betas[t]
    
            if t > 0:
                noise = torch.randn_like(x)
            else:
                noise = torch.zeros_like(x)
    
            x = (1 / alpha_t.sqrt()) * (
                x - ((1 - alpha_t) / (1 - alpha_cumprod_t).sqrt()) * predicted_noise
            ) + beta_t * noise
    
        return x
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/bkPo9hfN7WRHdn4sY3arMtIG0yuU.png)

    # 创建conda环境
    conda create -n ai_art python=3.8
    conda activate ai_art
    
    # 安装核心依赖
    pip install torch torchvision torchaudio
    pip install diffusers transformers scipy ftfy
    pip install opencv-python matplotlib
    
    
    bash

    import torch
    from diffusers import StableDiffusionPipeline
    from PIL import Image
    
    # 加载预训练模型
    model_id = "CompVis/stable-diffusion-v1-4"
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    pipe = StableDiffusionPipeline.from_pretrained(
    model_id,
    torch_dtype=torch.float16 if device == "cuda" else torch.float32
    ).to(device)
    
    # 生成图像
    prompt = "A beautiful oil painting of a futuristic city, sunset, highly detailed, artstation trending"
    negative_prompt = "blurry, low quality, distorted"
    
    image = pipe(
    prompt,
    negative_prompt=negative_prompt,
    height=512,
    width=512,
    num_inference_steps=50,
    guidance_scale=7.5
    ).images[0]
    
    # 保存结果
    image.save("ai_artwork.png")
    Image.open("ai_artwork.png").show()
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-17/F32ysc9Yngb6JGQE5zd4K8piHlhv.png)

 * 使用Hugging Face的`StableDiffusionPipeline`加载预训练模型
 * 自动检测并使用GPU加速(CUDA)如果可用

 * `prompt`描述期望的图像内容和风格
 * `negative_prompt`指定需要避免的特征

 * `height`和`width`控制输出分辨率
 * `num_inference_steps`决定去噪步骤数(更多步骤通常质量更高但更慢)
 * `guidance_scale`控制文本提示对生成结果的影响强度

 * 生成的图像可以直接保存为文件或显示
 * 输出是PIL.Image对象，便于后续处理

 * arXiv上的最新AI艺术论文
 * ACM数字图书馆中的计算机艺术研究
 * IEEE计算机图形学相关期刊

 * Gartner关于生成式AI的预测分析
 * McKinsey数字创意经济报告
 * Adobe创意未来趋势研究

 * AI Art subreddit
 * Discord上的AI艺术创作群组
 * 本地AI艺术Meetup小组

 * SIGGRAPH (计算机图形学顶级会议)
 * NeurIPS (机器学习前沿研究)
 * ISEA (国际电子艺术研讨会)

 * 伦敦V&A博物馆数字艺术收藏
 * 纽约MoMA的"AI: More Than Human"展览
 * 巴黎艺术与科技节相关展出