On this tutorial, we are going to do an in-depth, interactive exploration of NVIDIA’s StyleGAN2‑ADA PyTorch mannequin, showcasing its highly effective capabilities for producing photorealistic pictures. Leveraging a pretrained FFHQ mannequin, customers can generate high-quality artificial face pictures from a single latent seed or visualize clean transitions by way of latent house interpolation between completely different seeds. With an intuitive interface powered by interactive widgets, this tutorial is a precious useful resource for researchers, artists, and lovers trying to perceive and experiment with superior generative adversarial networks.
First, we clone the NVIDIA StyleGAN2‑ADA PyTorch repository from GitHub into your present Colab workspace.
!wget -O stylegan2-ada-pytorch/pretrained/ffhq.pkl
On this code half, the primary command creates the mandatory listing (if it doesn’t exist already) for storing pretrained fashions. The second command downloads the FFHQ pretrained mannequin and saves it in that listing to be used with the StyleGAN2‑ADA mannequin.
sys.path.append(‘stylegan2-ada-pytorch’)
On this code, we add the “stylegan2-ada-pytorch” listing to Python’s module search path, making certain that modules from the repository may be simply imported and used.
import numpy as np
import PIL.Picture
import matplotlib.pyplot as plt
import ipywidgets as widgets
from IPython.show import show
Right here, we import statements and cargo important libraries for deep studying, numerical operations, picture processing, visualization, and interactive controls into your code. These libraries guarantee you might have the instruments to construct, manipulate, and show generated pictures interactively.
import dnnlib
def generate_image(seed=42, truncation=1.0, network_pkl=”stylegan2-ada-pytorch/pretrained/ffhq.pkl”):
print(f’Producing picture with seed {seed} and truncation {truncation}’)
system = torch.system(‘cuda’ if torch.cuda.is_available() else ‘cpu’)
with dnnlib.util.open_url(network_pkl) as f: # Load the pretrained generator community
G = legacy.load_network_pkl(f)[‘G_ema’].to(system)
z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(system) # Create a latent vector utilizing the supplied seed
label = None # FFHQ is unconditional
with torch.no_grad(): # Generate picture
img = G(z, label, truncation_psi=truncation, noise_mode=”const”)
# Convert picture tensor to uint8 and format for show
img = (img + 1) * (255/2)
img = img.clamp(0,255).to(torch.uint8)
img = img[0].permute(1,2,0).cpu().numpy()
plt.determine(figsize=(4,4))
plt.imshow(img)
plt.axis(‘off’)
plt.present()
On this half, we outline a operate referred to as generate_image that Hundreds the pretrained StyleGAN2‑ADA generator community from a given URL. Creates a latent vector primarily based on a seed, generates a picture with a specified truncation parameter, after which processes and shows the ensuing picture utilizing matplotlib.
print(f’Interpolating between seeds {seed1} and {seed2} with {steps} steps and truncation {truncation}’)
system = torch.system(‘cuda’ if torch.cuda.is_available() else ‘cpu’)
with dnnlib.util.open_url(network_pkl) as f: # Load the pretrained generator community
G = legacy.load_network_pkl(f)[‘G_ema’].to(system)
# Generate latent vectors for the 2 seeds
z1 = torch.from_numpy(np.random.RandomState(seed1).randn(1, G.z_dim)).to(system)
z2 = torch.from_numpy(np.random.RandomState(seed2).randn(1, G.z_dim)).to(system)
# 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 pictures for every 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.determine(figsize=(steps * 2, 2)) # Plot pictures in a row to visualise the interpolation
for i in vary(steps):
plt.subplot(1, steps, i+1)
img = np.transpose(imgs[i], (1,2,0))
plt.imshow(img)
plt.axis(‘off’)
plt.present()
Right here, we outline interpolate_images, which generates pictures by interpolating between latent vectors derived from two seeds. It hundreds the pretrained StyleGAN2‑ADA generator, computes a clean transition between the latent codes of the 2 seeds over a specified variety of steps, after which shows the ensuing pictures in a row to visualise the interpolation.
In conclusion, we demonstrated a flexible and hands-on strategy utilizing NVIDIA’s StyleGAN2‑ADA mannequin for static picture technology and dynamic latent house interpolation. By permitting customers to regulate parameters comparable to seed values and truncation ranges interactively, this pocket book supplies perception into the intricacies of GAN-based picture synthesis and fosters creativity and innovation.
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