In this tutorial, we will do an in-depth, interactive exploration of NVIDIAs StyleGAN2ADA 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.gitFirst, we clone the NVIDIA StyleGAN2ADA 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.pklIn this code part, the first command creates the necessary directory (if it doesnt already exist) for storing pretrained models. The second command downloads the FFHQ pretrained model and saves it in that directory for use with the StyleGAN2ADA model.import syssys.path.append('stylegan2-ada-pytorch')In this code, we add the stylegan2-ada-pytorch directory to Pythons 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 displayHere, 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 StyleGAN2ADA 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 StyleGAN2ADA 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.Sample Generated ImageIn conclusion, we demonstrated a versatile and hands-on approach using NVIDIAs StyleGAN2ADA 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,dont forget to follow us onTwitterand join ourTelegram ChannelandLinkedIn Group. Dont Forget to join our75k+ ML SubReddit. Asif RazzaqWebsite| + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. 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