SOTAVerified

Image Generation

Image Generation (synthesis) is the task of generating new images from an existing dataset.

  • Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
  • Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.

In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.

( Image credit: StyleGAN )

Papers

Showing 41764200 of 6689 papers

TitleStatusHype
PFGM++: Unlocking the Potential of Physics-Inspired Generative ModelsCode2
Fair Diffusion: Instructing Text-to-Image Generation Models on FairnessCode1
Information-Theoretic DiffusionCode1
Med-NCA: Robust and Lightweight Segmentation with Neural Cellular AutomataCode1
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable DiffusionCode1
Spatial Functa: Scaling Functa to ImageNet Classification and Generation0
Private GANs, RevisitedCode0
Generative Diffusion Models on Graphs: Methods and ApplicationsCode2
Latent Space Bayesian Optimization with Latent Data Augmentation for Enhanced Exploration0
ReDi: Efficient Learning-Free Diffusion Inference via Trajectory RetrievalCode1
KDEformer: Accelerating Transformers via Kernel Density EstimationCode1
ShiftDDPMs: Exploring Conditional Diffusion Models by Shifting Diffusion Trajectories0
Mixture of Diffusers for scene composition and high resolution image generationCode2
Eliminating Contextual Prior Bias for Semantic Image Editing via Dual-Cycle DiffusionCode0
Divide and Compose with Score Based Generative ModelsCode0
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification0
This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generation0
TEXTure: Text-Guided Texturing of 3D ShapesCode2
On Suppressing Range of Adaptive Stepsizes of Adam to Improve Generalisation Performance0
Are Diffusion Models Vulnerable to Membership Inference Attacks?Code1
GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face SynthesisCode1
Towards CGAN-based Satellite Image Synthesis with Partial Pixel-Wise Annotation0
Stable Attribute Group Editing for Reliable Few-shot Image GenerationCode0
Stable Target Field for Reduced Variance Score Estimation in Diffusion ModelsCode1
Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified