Image Generation On Ffhq 1024 X 1024

FID

평가 결과

이 벤치마크에서 각 모델의 성능 결과

비교 표

모델 이름	FID
alias-free-generative-adversarial-networks	2.79
diffusion-gan-training-gans-with-diffusion	2.83
very-deep-vaes-generalize-autoregressive-1	-
efficient-vdvae-less-is-more	-
adversarial-latent-autoencoders	13.09
swagan-a-style-based-wavelet-driven	4.06
msg-gan-multi-scale-gradients-gan-for-more	5.8
polarity-sampling-quality-and-diversity	2.57
a-style-based-generator-architecture-for	4.4
stylenat-giving-each-head-a-new-perspective	4.17
alias-free-generative-adversarial-networks	3.07
analyzing-and-improving-the-image-quality-of	2.84
styleswin-transformer-based-gan-for-high-1	5.07
adversarially-slicing-generative-networks	1.61
image-generators-with-conditionally	10.07
magnet-uniform-sampling-from-deep-generative-1	2.66
stylegan-xl-scaling-stylegan-to-large-diverse	2.02
feature-quantization-improves-gan-training	3.19
improved-transformer-for-high-resolution-gans	6.37
training-generative-adversarial-networks-with-2	3.62