The use of the continuous code of Python is to create the contacts of the partners with NVIRI STYLEGAN2-Ada

nimda February 18, 2025

0 0 3 minutes read

The use of the continuous code of Python is to create the contacts of the partners with NVIRI STYLEGAN2-Ada

In this lesson, we will do the deeper, effective tests of the Nvidi's Stalkangan2-Ado Pytorch model, shows its powerful skills to produce positive photoristic photos. The prescribed FFHQ Model, users can produce high-quality photos from one last seed or psychological changes through the translation of the space of this area between different seeds. With the correct interface powered by active widgets, the lesson is an important source of researchers, artists, and those who love to view and test the advanced networks produced.

!git clone

First, we combine the NVIIDIA Saltylegan2-Ado Pytorch repository from GitHub on your current colob space.

!mkdir -p stylegan2-ada-pytorch/pretrained
!wget  -O stylegan2-ada-pytorch/pretrained/ffhq.pkl

In this code section, the first command created the required directory (if not available) by storing beautiful models. The second commandment is downloading the selected FFHQ model and saves from the direct guide used by styalganganam2-Ada Model.

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

In this code, we include the word “style2-bantro-poytorch” in the form of the Python search, to ensure that the modules from the storage can now be easily imported.

import torch
import numpy as np
import PIL.Image
import matplotlib.pyplot as plt
import ipywidgets as widgets
from IPython.display import display

Here, import statements and download important libraries of deep reading, pricing, photographic processing, visual recognition, and effective control in your code. These libraries ensures that you have tools for building, deception, and expressions.

import legacy
import dnnlib


def 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 section, it describes the work called the agency to formulate the Latent Vector based on seeds, producing a photo in a specified TRNCIONA parameter, and applies and shows the required photo 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, it describes Interpoote_Images, which produces pictures in closing between two seeds based on two seeds. Loading the stytracung stylegangan3-Ada generator, includes a smooth transformation between the latent codes in the two characters above the specified number of steps, showing pictures that appear in a row to see translation.

A picture produced by sample

In conclusion, we showed a variable and hands-up pathways using the Styalgangangan model – Adatic General of Status and translation of the latest space. By allowing users to use parameters such as seed prices and power quality, the brochure provides understanding of the GAN images based on gan and creating.

Here is the Colab Notebook of the above project. Also, don't forget to follow Sane and join ours Telegraph station including LinkedIn Grtopic. Don't forget to join ours 75k + ml subreddit.

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