generate.py

# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.

"""Generate images using pretrained network pickle."""

import os
import re
from typing import List, Optional

import click
import dnnlib
import numpy as np
import PIL.Image
import torch

import legacy

#----------------------------------------------------------------------------

def num_range(s: str) -> List[int]:
    '''Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.'''

    range_re = re.compile(r'^(\d+)-(\d+)$')
    m = range_re.match(s)
    if m:
        return list(range(int(m.group(1)), int(m.group(2))+1))
    vals = s.split(',')
    return [int(x) for x in vals]

#----------------------------------------------------------------------------

@click.command()
@click.pass_context
@click.option('--network', 'network_pkl', help='Network pickle filename', default='https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl')
@click.option('--seeds', type=num_range, help='List of random seeds')
@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=0.8, show_default=True)
@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
@click.option('--projected-w', help='Projection result file', type=str, metavar='FILE')
@click.option('--outdir', help='Where to save the output images', type=str, required=True, metavar='DIR')
@click.option('--resize', help='resize', type=int, default=-1, metavar='INT')
def generate_images(
    ctx: click.Context,
    network_pkl: str,
    seeds: Optional[List[int]],
    truncation_psi: float,
    noise_mode: str,
    outdir: str,
    projected_w: Optional[str],
    resize: int,
):

    print('Loading networks from "%s"...' % network_pkl)
    device = torch.device('cuda')
    with dnnlib.util.open_url(network_pkl) as f:
        G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore

    os.makedirs(outdir, exist_ok=True)

    if len(seeds) == 1:
        seeds = np.arange(0, seeds[0])
    elif len(seeds) == 2:
        seeds = np.arange(seeds[0], seeds[1])
    else:
        raise ValueError("seeds should be given as {num1, num2} or {num1}}")

    # Synthesize the result of a W projection.
    if projected_w is not None:
        if seeds is not None:
            print ('warn: --seeds is ignored when using --projected-w')
        print(f'Generating images from projected W "{projected_w}"')
        ws = np.load(projected_w)['w']
        ws = torch.tensor(ws, device=device) # pylint: disable=not-callable
        assert ws.shape[1:] == (G.num_ws, G.w_dim)
        for idx, w in enumerate(ws):
            img = G.synthesis(w.unsqueeze(0), noise_mode=noise_mode)
            img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
            img = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/proj{idx:02d}.png')
        return

    if seeds is None:
        ctx.fail('--seeds option is required when not using --projected-w')

    # Labels.
    label = torch.zeros([1, G.c_dim], device=device)

    # Generate images.
    for seed_idx, seed in enumerate(seeds):
        print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
        z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device)
        img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
        img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
        if resize > 0:
            PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').resize((resize, resize), PIL.Image.ANTIALIAS).save(f'{outdir}/seed{seed:05d}.png')
        else:
            PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/seed{seed:05d}.png')



#----------------------------------------------------------------------------

if __name__ == "__main__":
    generate_images() # pylint: disable=no-value-for-parameter

#----------------------------------------------------------------------------