On this tutorial, we’ll 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 latent area interpolation between completely different seeds. With an intuitive interface powered by interactive widgets, this tutorial is a beneficial useful resource for researchers, artists, and fans seeking to perceive and experiment with superior generative adversarial networks.
!git clone https://github.com/NVlabs/stylegan2-ada-pytorch.gitFirst, we clone the NVIDIA StyleGAN2‑ADA PyTorch repository from GitHub into your present 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.pklOn this code half, the primary command creates the required 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.
import sys
sys.path.append('stylegan2-ada-pytorch')On this code, we add the “stylegan2-ada-pytorch” listing to Python’s module search path, guaranteeing that modules from the repository could be simply imported and used.
import torch
import numpy as np
import PIL.Picture
import matplotlib.pyplot as plt
import ipywidgets as widgets
from IPython.show import showRight 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 will have the instruments to construct, manipulate, and show generated pictures interactively.
import legacy
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}')
machine = torch.machine('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(machine)
z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(machine) # Create a latent vector utilizing the offered 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 perform referred to as generate_image that Masses the pretrained StyleGAN2‑ADA generator community from a given URL. Creates a latent vector based mostly on a seed, generates a picture with a specified truncation parameter, after which processes and shows the ensuing picture utilizing 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}')
machine = torch.machine('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(machine)
# Generate latent vectors for the 2 seeds
z1 = torch.from_numpy(np.random.RandomState(seed1).randn(1, G.z_dim)).to(machine)
z2 = torch.from_numpy(np.random.RandomState(seed2).randn(1, G.z_dim)).to(machine)
# 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 method utilizing NVIDIA’s StyleGAN2‑ADA mannequin for static picture era and dynamic latent area interpolation. By permitting customers to regulate parameters equivalent 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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