Layer swap in gen_multi_style.py

[crownk1997]

Thank you for your amazing work. I am a little confused about the layer swap part in your implementation. It seems that you first pass the latent code into the base model and then extract the intermediate results for the target model as the following.

img1, swap_res = g_ema1([input_latent], input_is_latent=True, save_for_swap=True, swap_layer=args.swap_layer)

for i in range(args.stylenum):
    sample_z_style = torch.randn(1, 512, device=args.device)
    img_style, _ = g_ema2([input_latent], truncation=0.5, truncation_latent=mean_latent, swap=True, swap_layer=args.swap_layer,  swap_tensor=swap_res, multi_style=True, multi_style_latent=[sample_z_style])
    print(i)
    img_style_name = args.output + "_style_" + str(i) + ".png"
    img_style = make_image(img_style)
    out_style = Image.fromarray(img_style[0])
    out_style.save(img_style_name)```

Is it true that you are trying to keep the low level information such as shape and pose from original model and put the lightening and texture from the target model?