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 26512675 of 6689 papers

TitleStatusHype
Behavior Generation with Latent ActionsCode3
PLACE: Adaptive Layout-Semantic Fusion for Semantic Image SynthesisCode1
HanDiffuser: Text-to-Image Generation With Realistic Hand Appearances0
NiNformer: A Network in Network Transformer with Token Mixing Generated Gating FunctionCode0
OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-onCode9
ViewDiff: 3D-Consistent Image Generation with Text-to-Image ModelsCode3
ResAdapter: Domain Consistent Resolution Adapter for Diffusion ModelsCode4
Transformer for Times Series: an Application to the S&P5000
AtomoVideo: High Fidelity Image-to-Video Generation0
Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models0
SCott: Accelerating Diffusion Models with Stochastic Consistency Distillation0
Critical windows: non-asymptotic theory for feature emergence in diffusion models0
Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models0
TCIG: Two-Stage Controlled Image Generation with Quality Enhancement through Diffusion0
VisionLLaMA: A Unified LLaMA Backbone for Vision TasksCode3
Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks0
An Ordinal Diffusion Model for Generating Medical Images with Different Severity Levels0
Improving Explicit Spatial Relationships in Text-to-Image Generation through an Automatically Derived DatasetCode0
Rethinking cluster-conditioned diffusion models for label-free image synthesisCode0
Learning to Find Missing Video Frames with Synthetic Data Augmentation: A General Framework and Application in Generating Thermal Images Using RGB Cameras0
WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image SynthesisCode2
ViewFusion: Towards Multi-View Consistency via Interpolated DenoisingCode2
Disentangling representations of retinal images with generative modelsCode0
A Quantitative Evaluation of Score Distillation Sampling Based Text-to-3D0
A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online AdaptationCode2
Show:102550
← PrevPage 107 of 268Next →

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