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SOTA
Image Generation
Image Generation On Celeba 256X256
Image Generation On Celeba 256X256
Metrics
bpd
Results
Performance results of various models on this benchmark
Columns
Model Name
bpd
Paper Title
Repository
SPN Menick and Kalchbrenner (2019)
0.61
Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling
-
LSGM
0.70
Score-based Generative Modeling in Latent Space
-
StyleSwin
-
StyleSwin: Transformer-based GAN for High-resolution Image Generation
-
NCP-VAE
-
A Contrastive Learning Approach for Training Variational Autoencoder Priors
-
MaCow (Unf)
0.95
MaCow: Masked Convolutional Generative Flow
-
Efficient-VDVAE
0.51
Efficient-VDVAE: Less is more
-
HiT-B
-
Improved Transformer for High-Resolution GANs
-
StyleALAE
-
Adversarial Latent Autoencoders
-
Glow (Kingma and Dhariwal, 2018)
1.03
Glow: Generative Flow with Invertible 1x1 Convolutions
-
Residual Flow
0.992
Residual Flows for Invertible Generative Modeling
-
Locally Masked PixelCNN
0.74
Locally Masked Convolution for Autoregressive Models
-
GLF+perceptual loss (ours)
-
Generative Latent Flow
-
MSP
-
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
-
VQGAN
-
Taming Transformers for High-Resolution Image Synthesis
-
ANF Huang et al. (2020)
0.72
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
-
NVAE w/ flow
0.70
NVAE: A Deep Hierarchical Variational Autoencoder
-
MaCow (Var)
0.67
MaCow: Masked Convolutional Generative Flow
-
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