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Image Generation On Celeba Hq 256X256

Metrics

FID

Results

Performance results of various models on this benchmark

Model Name
FID
Paper TitleRepository
LFM5.26Flow Matching in Latent Space-
DC-VAE15.81Dual Contradistinctive Generative Autoencoder-
UNCSN++ (RVE) + ST7.16Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation-
VAEBM20.38VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models-
RDUOT5.6A High-Quality Robust Diffusion Framework for Corrupted Dataset-
DDGAN7.64Tackling the Generative Learning Trilemma with Denoising Diffusion GANs-
WaveDiff5.94Wavelet Diffusion Models are fast and scalable Image Generators-
LDM-45.11High-Resolution Image Synthesis with Latent Diffusion Models-
DDMI8.73DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations-
VQGAN+Transformer10.2Taming Transformers for High-Resolution Image Synthesis-
BOSS-Bellman Optimal Stepsize Straightening of Flow-Matching Models-
RDM3.15Relay Diffusion: Unifying diffusion process across resolutions for image synthesis-
Dual-MCMC EBM15.89Learning Energy-based Model via Dual-MCMC Teaching-
StyleSwin3.25StyleSwin: Transformer-based GAN for High-resolution Image Generation-
LSGM7.22Score-based Generative Modeling in Latent Space-
Joint-EBM9.89Learning Joint Latent Space EBM Prior Model for Multi-layer Generator-
RNODE-How to train your neural ODE: the world of Jacobian and kinetic regularization-
Diffusion-JEBM8.78Learning Latent Space Hierarchical EBM Diffusion Models-
GLOW68.93Glow: Generative Flow with Invertible 1x1 Convolutions-
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