神经网络StyleGAN风格的混合故障

Question

神经网络是从GitHub加载的，带有预先训练的文件，并成功生成随机照片。这似乎是随机的。但当您运行脚本时，generate_figures.py会显示另外两个混合森林的照片，也是随机的。有个问题。如何制作一个神经网络来混合两张用户定义的照片而不是生成的照片？我修改了代码，指定了你的照片的路径，但最后，它生成了与我的照片脸无关的所有相同的随机结果。

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

synthesis_kwargs = dict(output_transform=dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True), minibatch_size=4)

_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]

图3:风格混合。

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.open(r"C:\Users\Kurmyavochka\Desktop\NN\REALISM\stylegan-master\results\1.png"), ((col + 1) * w, 0))
   for row, dst_image in enumerate(list(dst_images)):
       canvas.paste(PIL.Image.open(r"C:\Users\Kurmyavochka\Desktop\NN\REALISM\stylegan-master\results\2.png"), (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)

def main():

   tflib.init_tf()
   os.makedirs(config.result_dir, exist_ok=True)

   issa = 5067
   for iter in range(1):
       draw_style_mixing_figure(
           os.path.join(config.result_dir,
                        str(issa) + 'figure03-style-mixing.png'),
           load_Gs(url_ffhq),
           w=1024,
           h=1024,
           src_seeds=[0],
           dst_seeds=[0],
           style_ranges=[range(0, 4)] * 3 + [range(4, 8)] * 2 +
           [range(8, 18)])
       issa = issa + 1


if __name__ == "__main__":
   main()

Answer 1

你需要生成你想要融合的照片的潜在表示。然后从它们创建平均向量。最后，用你的脚本生成图像。很好的实现，你可以在这里找到https://github.com/Puzer/stylegan-encoder