💻 首先，教程演示了如何从GitHub克隆NVIDIA StyleGAN2-ADA PyTorch存储库到Colab工作区，并下载预训练的FFHQ模型，为后续的图像生成奠定基础。

🖼️ 其次，教程定义了一个名为`generate_image`的函数，该函数从给定的URL加载预训练的StyleGAN2-ADA生成器网络，基于种子创建一个潜在向量，使用指定的截断参数生成图像，然后使用matplotlib处理和显示生成的图像。

✨ 此外，教程还定义了`interpolate_images`函数，通过在从两个种子导出的潜在向量之间进行插值来生成图像。它加载预训练的StyleGAN2-ADA生成器，计算两个种子的潜在代码之间在指定数量的步骤上的平滑过渡，然后在一行中显示生成的图像以可视化插值。

🎛️ 通过允许用户交互式地调整种子值和截断级别等参数，本教程深入了解了基于GAN的图像合成的复杂性，并培养了创造力和创新。

In this tutorial, we will do an in-depth, interactive exploration of NVIDIA’s StyleGAN2‑ADA PyTorch model, showcasing its powerful capabilities for generating photorealistic images. Leveraging a pretrained FFHQ model, users can generate high-quality synthetic face images from a single latent seed or visualize smooth transitions through latent space interpolation between different seeds. With an intuitive interface powered by interactive widgets, this tutorial is a valuable resource for researchers, artists, and enthusiasts looking to understand and experiment with advanced generative adversarial networks.

!git clone https://github.com/NVlabs/stylegan2-ada-pytorch.git

First, we clone the NVIDIA StyleGAN2‑ADA PyTorch repository from GitHub into your current Colab workspace.

!mkdir -p stylegan2-ada-pytorch/pretrained!wget https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl -O stylegan2-ada-pytorch/pretrained/ffhq.pkl

In this code part, the first command creates the necessary directory (if it doesn’t already exist) for storing pretrained models. The second command downloads the FFHQ pretrained model and saves it in that directory for use with the StyleGAN2‑ADA model.

import syssys.path.append('stylegan2-ada-pytorch')

In this code, we add the “stylegan2-ada-pytorch” directory to Python’s module search path, ensuring that modules from the repository can be easily imported and used.

import torchimport numpy as npimport PIL.Imageimport matplotlib.pyplot as pltimport ipywidgets as widgetsfrom IPython.display import display

Here, we import statements and load essential libraries for deep learning, numerical operations, image processing, visualization, and interactive controls into your code. These libraries ensure you have the tools to build, manipulate, and display generated images interactively.

import legacyimport dnnlibdef generate_image(seed=42, truncation=1.0, network_pkl='stylegan2-ada-pytorch/pretrained/ffhq.pkl'):    print(f'Generating image with seed {seed} and truncation {truncation}')    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')       with dnnlib.util.open_url(network_pkl) as f:                                # Load the pretrained generator network        G = legacy.load_network_pkl(f)['G_ema'].to(device)       z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device)   # Create a latent vector using the provided seed    label = None  # FFHQ is unconditional    with torch.no_grad():                                                             # Generate image        img = G(z, label, truncation_psi=truncation, noise_mode='const')       # Convert image tensor to uint8 and format for display    img = (img + 1)  (255/2)    img = img.clamp(0,255).to(torch.uint8)    img = img[0].permute(1,2,0).cpu().numpy()       plt.figure(figsize=(4,4))    plt.imshow(img)    plt.axis('off')    plt.show()

In this part, we define a function called generate_image that Loads the pretrained StyleGAN2‑ADA generator network from a given URL. Creates a latent vector based on a seed, generates an image with a specified truncation parameter, and then processes and displays the resulting image using matplotlib.

def interpolate_images(seed1=42, seed2=123, steps=10, truncation=1.0, network_pkl='stylegan2-ada-pytorch/pretrained/ffhq.pkl'):    print(f'Interpolating between seeds {seed1} and {seed2} with {steps} steps and truncation {truncation}')    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')       with dnnlib.util.open_url(network_pkl) as f:                              # Load the pretrained generator network        G = legacy.load_network_pkl(f)['G_ema'].to(device)       # Generate latent vectors for the two seeds    z1 = torch.from_numpy(np.random.RandomState(seed1).randn(1, G.z_dim)).to(device)    z2 = torch.from_numpy(np.random.RandomState(seed2).randn(1, G.z_dim)).to(device)       # Create interpolation latent vectors    alphas = np.linspace(0, 1, steps)    z_interp = []    for a in alphas:        z_interp.append((1 - a)  z1 + a  z2)    z_interp = torch.cat(z_interp, dim=0)    label = None    # Generate images for each interpolated latent vector    with torch.no_grad():        imgs = G(z_interp, label, truncation_psi=truncation, noise_mode='const')       imgs = (imgs + 1)  (255/2)    imgs = imgs.clamp(0,255).to(torch.uint8).cpu().numpy()       plt.figure(figsize=(steps * 2, 2))                                          # Plot images in a row to visualize the interpolation    for i in range(steps):        plt.subplot(1, steps, i+1)        img = np.transpose(imgs[i], (1,2,0))        plt.imshow(img)        plt.axis('off')    plt.show()

Here, we define interpolate_images, which generates images by interpolating between latent vectors derived from two seeds. It loads the pretrained StyleGAN2‑ADA generator, computes a smooth transition between the latent codes of the two seeds over a specified number of steps, and then displays the resulting images in a row to visualize the interpolation.

In conclusion, we demonstrated a versatile and hands-on approach using NVIDIA’s StyleGAN2‑ADA model for static image generation and dynamic latent space interpolation. By allowing users to adjust parameters such as seed values and truncation levels interactively, this notebook provides insight into the intricacies of GAN-based image synthesis and fosters creativity and innovation.

Here is the Colab Notebook for the above project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. Don’t Forget to join our 75k+ ML SubReddit.

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The post A Stepwise Python Code Implementation to Create Interactive Photorealistic Faces with NVIDIA StyleGAN2‑ADA appeared first on MarkTechPost.