Image Generation On Ffhq 256 X 256

Metrics

FID

Results

Performance results of various models on this benchmark

		Paper Title	Repository
Efficient-VDVAE	34.88	Efficient-VDVAE: Less is more
Efficient-vdVAE (Exposing)	34.88	Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
VE (erel=0.01)	15.67	Gotta Go Fast When Generating Data with Score-Based Models
VE (erel=0.02)	15.67	Gotta Go Fast When Generating Data with Score-Based Models
BigGAN	11.48	A U-Net Based Discriminator for Generative Adversarial Networks
VQGAN+Transformer	9.6	Taming Transformers for High-Resolution Image Synthesis
Unleash-Trans (Exposing)	9.02	Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
LDM	8.11	-	-
LDM (Exposing)	8.11	Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
GANFormer2	7.77	Compositional Transformers for Scene Generation	-
U-Net GAN	7.48	A U-Net Based Discriminator for Generative Adversarial Networks
GANFormer	7.42	Generative Adversarial Transformers
Unleashing Transformers	6.11	Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes
UDM (RVE) + ST	5.54	Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation
StyleGAN2-ada (Exposing)	5.30	Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
SWAGAN-Bi	5.22	SWAGAN: A Style-based Wavelet-driven Generative Model
INR-GAN-bil	4.95	Adversarial Generation of Continuous Images
LFM	4.55	Flow Matching in Latent Space
CIPS	4.38	Image Generators with Conditionally-Independent Pixel Synthesis
Projected-GAN (Exposing)	4.29	Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models

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Image Generation On Ffhq 256 X 256

Metrics

Results