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Image Generation On Lsun Churches 256 X 256

Metrics

FID

Results

Performance results of various models on this benchmark

Paper Title
VE (erel=0.01)26.46Gotta Go Fast When Generating Data with Score-Based Models
VE (erel=0.02)26.46Gotta Go Fast When Generating Data with Score-Based Models
TransGAN8.94TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up
PNDM8.69Pseudo Numerical Methods for Diffusion Models on Manifolds
Denoising Diffusion Probabilistic Model7.89Denoising Diffusion Probabilistic Models
PGGAN6.42Progressive Growing of GANs for Improved Quality, Stability, and Variation
LFM5.54Flow Matching in Latent Space
DDGAN5.25Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
MSG-StyleGAN5.2MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks
WaveDiff5.06Wavelet Diffusion Models are fast and scalable Image Generators
SWAGAN-Bi4.97SWAGAN: A Style-based Wavelet-driven Generative Model
StyleGAN4.21A Style-Based Generator Architecture for Generative Adversarial Networks
Unleashing Transformers4.07Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes
INR-GAN-bil4.04Adversarial Generation of Continuous Images
TDPM+ (TTrunc=99)3.98Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders
Polarity-StyleGAN23.92Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values
StyleGAN23.86Analyzing and Improving the Image Quality of StyleGAN
CLR-GAN3.43CLR-GAN: Improving GANs Stability and Quality via Consistent Latent Representation and Reconstruction
StyleNAT3.4StyleNAT: Giving Each Head a New Perspective
Diffusion StyleGAN23.17Diffusion-GAN: Training GANs with Diffusion
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Image Generation On Lsun Churches 256 X 256 | SOTA | HyperAI