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Image Generation On Ffhq 256 X 256

Metrics

FID

Results

Performance results of various models on this benchmark

Paper Title
Efficient-VDVAE34.88Efficient-VDVAE: Less is more
Efficient-vdVAE (Exposing)34.88Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
VE (erel=0.01)15.67Gotta Go Fast When Generating Data with Score-Based Models
VE (erel=0.02)15.67Gotta Go Fast When Generating Data with Score-Based Models
BigGAN11.48A U-Net Based Discriminator for Generative Adversarial Networks
VQGAN+Transformer9.6Taming Transformers for High-Resolution Image Synthesis
Unleash-Trans (Exposing)9.02Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
LDM8.11-
LDM (Exposing)8.11Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
GANFormer27.77Compositional Transformers for Scene Generation
U-Net GAN7.48A U-Net Based Discriminator for Generative Adversarial Networks
GANFormer7.42Generative Adversarial Transformers
Unleashing Transformers6.11Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes
UDM (RVE) + ST5.54Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation
StyleGAN2-ada (Exposing)5.30Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
SWAGAN-Bi5.22SWAGAN: A Style-based Wavelet-driven Generative Model
INR-GAN-bil4.95Adversarial Generation of Continuous Images
LFM4.55Flow Matching in Latent Space
CIPS4.38Image Generators with Conditionally-Independent Pixel Synthesis
Projected-GAN (Exposing)4.29Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
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Image Generation On Ffhq 256 X 256 | SOTA | HyperAI