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ClothingGAN
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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

"""Minimal script for reproducing the figures of the StyleGAN paper using pre-trained generators."""

import os
import pickle
import numpy as np
import PIL.Image
import dnnlib
import dnnlib.tflib as tflib
import config

#----------------------------------------------------------------------------
# Helpers for loading and using pre-trained generators.

url_ffhq        = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ' # karras2019stylegan-ffhq-1024x1024.pkl
url_celebahq    = 'https://drive.google.com/uc?id=1MGqJl28pN4t7SAtSrPdSRJSQJqahkzUf' # karras2019stylegan-celebahq-1024x1024.pkl
url_bedrooms    = 'https://drive.google.com/uc?id=1MOSKeGF0FJcivpBI7s63V9YHloUTORiF' # karras2019stylegan-bedrooms-256x256.pkl
url_cars        = 'https://drive.google.com/uc?id=1MJ6iCfNtMIRicihwRorsM3b7mmtmK9c3' # karras2019stylegan-cars-512x384.pkl
url_cats        = 'https://drive.google.com/uc?id=1MQywl0FNt6lHu8E_EUqnRbviagS7fbiJ' # karras2019stylegan-cats-256x256.pkl

synthesis_kwargs = dict(output_transform=dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True), minibatch_size=8)

_Gs_cache = dict()

def load_Gs(url):
    if url not in _Gs_cache:
        with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f:
            _G, _D, Gs = pickle.load(f)
        _Gs_cache[url] = Gs
    return _Gs_cache[url]

#----------------------------------------------------------------------------
# Figures 2, 3, 10, 11, 12: Multi-resolution grid of uncurated result images.

def draw_uncurated_result_figure(png, Gs, cx, cy, cw, ch, rows, lods, seed):
    print(png)
    latents = np.random.RandomState(seed).randn(sum(rows * 2**lod for lod in lods), Gs.input_shape[1])
    images = Gs.run(latents, None, **synthesis_kwargs) # [seed, y, x, rgb]

    canvas = PIL.Image.new('RGB', (sum(cw // 2**lod for lod in lods), ch * rows), 'white')
    image_iter = iter(list(images))
    for col, lod in enumerate(lods):
        for row in range(rows * 2**lod):
            image = PIL.Image.fromarray(next(image_iter), 'RGB')
            image = image.crop((cx, cy, cx + cw, cy + ch))
            image = image.resize((cw // 2**lod, ch // 2**lod), PIL.Image.ANTIALIAS)
            canvas.paste(image, (sum(cw // 2**lod for lod in lods[:col]), row * ch // 2**lod))
    canvas.save(png)

#----------------------------------------------------------------------------
# Figure 3: Style mixing.

def draw_style_mixing_figure(png, Gs, w, h, src_seeds, dst_seeds, style_ranges):
    print(png)
    src_latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in src_seeds)
    dst_latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in dst_seeds)
    src_dlatents = Gs.components.mapping.run(src_latents, None) # [seed, layer, component]
    dst_dlatents = Gs.components.mapping.run(dst_latents, None) # [seed, layer, component]
    src_images = Gs.components.synthesis.run(src_dlatents, randomize_noise=False, **synthesis_kwargs)
    dst_images = Gs.components.synthesis.run(dst_dlatents, randomize_noise=False, **synthesis_kwargs)

    canvas = PIL.Image.new('RGB', (w * (len(src_seeds) + 1), h * (len(dst_seeds) + 1)), 'white')
    for col, src_image in enumerate(list(src_images)):
        canvas.paste(PIL.Image.fromarray(src_image, 'RGB'), ((col + 1) * w, 0))
    for row, dst_image in enumerate(list(dst_images)):
        canvas.paste(PIL.Image.fromarray(dst_image, 'RGB'), (0, (row + 1) * h))
        row_dlatents = np.stack([dst_dlatents[row]] * len(src_seeds))
        row_dlatents[:, style_ranges[row]] = src_dlatents[:, style_ranges[row]]
        row_images = Gs.components.synthesis.run(row_dlatents, randomize_noise=False, **synthesis_kwargs)
        for col, image in enumerate(list(row_images)):
            canvas.paste(PIL.Image.fromarray(image, 'RGB'), ((col + 1) * w, (row + 1) * h))
    canvas.save(png)

#----------------------------------------------------------------------------
# Figure 4: Noise detail.

def draw_noise_detail_figure(png, Gs, w, h, num_samples, seeds):
    print(png)
    canvas = PIL.Image.new('RGB', (w * 3, h * len(seeds)), 'white')
    for row, seed in enumerate(seeds):
        latents = np.stack([np.random.RandomState(seed).randn(Gs.input_shape[1])] * num_samples)
        images = Gs.run(latents, None, truncation_psi=1, **synthesis_kwargs)
        canvas.paste(PIL.Image.fromarray(images[0], 'RGB'), (0, row * h))
        for i in range(4):
            crop = PIL.Image.fromarray(images[i + 1], 'RGB')
            crop = crop.crop((650, 180, 906, 436))
            crop = crop.resize((w//2, h//2), PIL.Image.NEAREST)
            canvas.paste(crop, (w + (i%2) * w//2, row * h + (i//2) * h//2))
        diff = np.std(np.mean(images, axis=3), axis=0) * 4
        diff = np.clip(diff + 0.5, 0, 255).astype(np.uint8)
        canvas.paste(PIL.Image.fromarray(diff, 'L'), (w * 2, row * h))
    canvas.save(png)

#----------------------------------------------------------------------------
# Figure 5: Noise components.

def draw_noise_components_figure(png, Gs, w, h, seeds, noise_ranges, flips):
    print(png)
    Gsc = Gs.clone()
    noise_vars = [var for name, var in Gsc.components.synthesis.vars.items() if name.startswith('noise')]
    noise_pairs = list(zip(noise_vars, tflib.run(noise_vars))) # [(var, val), ...]
    latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in seeds)
    all_images = []
    for noise_range in noise_ranges:
        tflib.set_vars({var: val * (1 if i in noise_range else 0) for i, (var, val) in enumerate(noise_pairs)})
        range_images = Gsc.run(latents, None, truncation_psi=1, randomize_noise=False, **synthesis_kwargs)
        range_images[flips, :, :] = range_images[flips, :, ::-1]
        all_images.append(list(range_images))

    canvas = PIL.Image.new('RGB', (w * 2, h * 2), 'white')
    for col, col_images in enumerate(zip(*all_images)):
        canvas.paste(PIL.Image.fromarray(col_images[0], 'RGB').crop((0, 0, w//2, h)), (col * w, 0))
        canvas.paste(PIL.Image.fromarray(col_images[1], 'RGB').crop((w//2, 0, w, h)), (col * w + w//2, 0))
        canvas.paste(PIL.Image.fromarray(col_images[2], 'RGB').crop((0, 0, w//2, h)), (col * w, h))
        canvas.paste(PIL.Image.fromarray(col_images[3], 'RGB').crop((w//2, 0, w, h)), (col * w + w//2, h))
    canvas.save(png)

#----------------------------------------------------------------------------
# Figure 8: Truncation trick.

def draw_truncation_trick_figure(png, Gs, w, h, seeds, psis):
    print(png)
    latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in seeds)
    dlatents = Gs.components.mapping.run(latents, None) # [seed, layer, component]
    dlatent_avg = Gs.get_var('dlatent_avg') # [component]

    canvas = PIL.Image.new('RGB', (w * len(psis), h * len(seeds)), 'white')
    for row, dlatent in enumerate(list(dlatents)):
        row_dlatents = (dlatent[np.newaxis] - dlatent_avg) * np.reshape(psis, [-1, 1, 1]) + dlatent_avg
        row_images = Gs.components.synthesis.run(row_dlatents, randomize_noise=False, **synthesis_kwargs)
        for col, image in enumerate(list(row_images)):
            canvas.paste(PIL.Image.fromarray(image, 'RGB'), (col * w, row * h))
    canvas.save(png)

#----------------------------------------------------------------------------
# Main program.

def main():
    tflib.init_tf()
    os.makedirs(config.result_dir, exist_ok=True)
    draw_uncurated_result_figure(os.path.join(config.result_dir, 'figure02-uncurated-ffhq.png'), load_Gs(url_ffhq), cx=0, cy=0, cw=1024, ch=1024, rows=3, lods=[0,1,2,2,3,3], seed=5)
    draw_style_mixing_figure(os.path.join(config.result_dir, 'figure03-style-mixing.png'), load_Gs(url_ffhq), w=1024, h=1024, src_seeds=[639,701,687,615,2268], dst_seeds=[888,829,1898,1733,1614,845], style_ranges=[range(0,4)]*3+[range(4,8)]*2+[range(8,18)])
    draw_noise_detail_figure(os.path.join(config.result_dir, 'figure04-noise-detail.png'), load_Gs(url_ffhq), w=1024, h=1024, num_samples=100, seeds=[1157,1012])
    draw_noise_components_figure(os.path.join(config.result_dir, 'figure05-noise-components.png'), load_Gs(url_ffhq), w=1024, h=1024, seeds=[1967,1555], noise_ranges=[range(0, 18), range(0, 0), range(8, 18), range(0, 8)], flips=[1])
    draw_truncation_trick_figure(os.path.join(config.result_dir, 'figure08-truncation-trick.png'), load_Gs(url_ffhq), w=1024, h=1024, seeds=[91,388], psis=[1, 0.7, 0.5, 0, -0.5, -1])
    draw_uncurated_result_figure(os.path.join(config.result_dir, 'figure10-uncurated-bedrooms.png'), load_Gs(url_bedrooms), cx=0, cy=0, cw=256, ch=256, rows=5, lods=[0,0,1,1,2,2,2], seed=0)
    draw_uncurated_result_figure(os.path.join(config.result_dir, 'figure11-uncurated-cars.png'), load_Gs(url_cars), cx=0, cy=64, cw=512, ch=384, rows=4, lods=[0,1,2,2,3,3], seed=2)
    draw_uncurated_result_figure(os.path.join(config.result_dir, 'figure12-uncurated-cats.png'), load_Gs(url_cats), cx=0, cy=0, cw=256, ch=256, rows=5, lods=[0,0,1,1,2,2,2], seed=1)

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

if __name__ == "__main__":
    main()

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