[Introduction to StyleGAN] That "man who doesn't laugh" smiled unintentionally ♬

This time, I'm aiming for a story. However, I wanted to do it, so I tried it. Please have a look m (_ _) m

Immediately, the result ,. .. ;. By the way, the source is as follows as before. StyleGAN/mayuyu_smile.py / Please take a look at the ingenuity.

Have them laugh anyway

** Somehow, I moved my mouth! ** **

Laugh properly

** I laughed, but I'm a little scared (No Д`) **

Let's have a big laugh

**Yup! You did it! ** **

Look at the smile photo. .. ..

** The real smile was even better! ** **

Summary

・ I tried to make "a man who doesn't laugh" laugh ・ I was laughed at by adjusting the parameters.

・ It seems that a natural smile requires more adjustment and ingenuity in the video.

bonus

In addition, I will paste the source of encode_images.py that was changed to output images during learning.

• You can judge how much learning has progressed from the output during learning.

#python encode_images.py img/ generated_images/ latent/

import os
import argparse
import pickle
from tqdm import tqdm
import PIL.Image
import numpy as np
import dnnlib
import dnnlib.tflib as tflib
import config
from encoder.generator_model import Generator
from encoder.perceptual_model import PerceptualModel

#URL_FFHQ = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ'  # karras2019stylegan-ffhq-1024x1024.pkl

def split_to_batches(l, n):
    for i in range(0, len(l), n):
        yield l[i:i + n]

def main():
    parser = argparse.ArgumentParser(description='Find latent representation of reference images using perceptual loss')
    parser.add_argument('src_dir', help='Directory with images for encoding')
    parser.add_argument('generated_images_dir', help='Directory for storing generated images')
    parser.add_argument('dlatent_dir', help='Directory for storing dlatent representations')

    # for now it's unclear if larger batch leads to better performance/quality
    parser.add_argument('--batch_size', default=1, help='Batch size for generator and perceptual model', type=int)

    # Perceptual model params
    parser.add_argument('--image_size', default=256, help='Size of images for perceptual model', type=int)
    parser.add_argument('--lr', default=1., help='Learning rate for perceptual model', type=float)
    parser.add_argument('--iterations', default=3001, help='Number of optimization steps for each batch', type=int)
    # Generator params
    parser.add_argument('--randomize_noise', default=False, help='Add noise to dlatents during optimization', type=bool)
    args, other_args = parser.parse_known_args()

    ref_images = [os.path.join(args.src_dir, x) for x in os.listdir(args.src_dir)]
    ref_images = list(filter(os.path.isfile, ref_images))
    if len(ref_images) == 0:
        raise Exception('%s is empty' % args.src_dir)

    os.makedirs(args.generated_images_dir, exist_ok=True)
    os.makedirs(args.dlatent_dir, exist_ok=True)

    # Initialize generator and perceptual model
    tflib.init_tf()
    fpath = './weight_files/tensorflow/karras2019stylegan-ffhq-1024x1024.pkl'
    with open(fpath, mode='rb') as f:
        generator_network, discriminator_network, Gs_network = pickle.load(f)

    generator = Generator(Gs_network, args.batch_size, randomize_noise=args.randomize_noise)
    perceptual_model = PerceptualModel(args.image_size, layer=9, batch_size=args.batch_size)
    perceptual_model.build_perceptual_model(generator.generated_image)
    Gs_network.print_layers()
    
    # Optimize (only) dlatents by minimizing perceptual loss between reference and generated images in feature space
    sk = 0
    for sk in range(0,20,1):
        for images_batch in tqdm(split_to_batches(ref_images, args.batch_size), total=len(ref_images)//args.batch_size):
            names = [os.path.splitext(os.path.basename(x))[0] for x in images_batch]
            print(sk)
            perceptual_model.set_reference_images(images_batch)
            op = perceptual_model.optimize(generator.dlatent_variable, iterations=args.iterations, learning_rate=args.lr)
            pbar = tqdm(op, leave=False, total=args.iterations)
            for loss in pbar:
                pbar.set_description(' '.join(names)+' Loss: %.2f' % loss)
            print(' '.join(names), ' loss:', loss)

            # Generate images from found dlatents and save them
            generated_images = generator.generate_images()
            generated_dlatents = generator.get_dlatents()
            for img_array, dlatent, img_name in zip(generated_images, generated_dlatents, names):
                img = PIL.Image.fromarray(img_array, 'RGB')
                img.save(os.path.join(args.generated_images_dir, str(sk)+f'{img_name}.png'), 'PNG')
                np.save(os.path.join(args.dlatent_dir, str(sk)+f'{img_name}.npy'), dlatent)
    generator.reset_dlatents()

if __name__ == "__main__":
    main()