age.py

# 1. Set up StyleGAN

import dnnlib
import dnnlib.tflib as tflib
import pretrained_networks

network_pkl = 'gdrive:networks/stylegan2-ffhq-config-f.pkl'
_G, _D, Gs = pretrained_networks.load_networks(network_pkl)

Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
Gs_syn_kwargs = dnnlib.EasyDict()
Gs_syn_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_syn_kwargs.randomize_noise = False
Gs_syn_kwargs.minibatch_size = 4
noise_vars = [
    var for name, var in Gs.components.synthesis.vars.items()
    if name.startswith('noise')
]
w_avg = Gs.get_var('dlatent_avg')
truncation_psi = 0.75


# 2. Get a vector

# https://github.com/a312863063/generators-with-stylegan2/blob/master/latent_directions/age.npy


# 3. Render results

import numpy as np
import PIL.Image

z = np.random.RandomState(5616).randn(1, 512)
w = Gs.components.mapping.run(z, None)
w = w_avg + (w - w_avg) * truncation_psi
v_age = np.load('age.npy')

n = 5
size = 256
canvas = PIL.Image.new('RGB', (n * size, size))
for i, v in enumerate(np.linspace(-10, 10, n)):
    w_age = w + v * v_age
    image = Gs.components.synthesis.run(w_age, **Gs_syn_kwargs)[0]
    image = PIL.Image.fromarray(image)
    image = image.resize((size, size), PIL.Image.LANCZOS)
    canvas.paste(image, (i * size, 0))
canvas.save('age.png')

As mentioned here, to use your own image instead of a random one:

1. find the latent representation of your image, via projection to latent space,
2. replace:

z = np.random.RandomState(5616).randn(1, 512)
w = Gs.components.mapping.run(z, None)
w = w_avg + (w - w_avg) * truncation_psi

with:

w = np.load('path/to/my/latent_representations/filename.npy')

The code above is for:

• expression transfer (adding a vector and a scaled difference vector),

but could be adapted for:

• morphing (linear interpolation).

For style transfer (crossover), please refer to the code in the official repository.