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 40264050 of 6689 papers

TitleStatusHype
Sora as an AGI World Model? A Complete Survey on Text-to-Video Generation0
DiffChat: Learning to Chat with Text-to-Image Synthesis Models for Interactive Image Creation0
A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN0
Synthetic Privileged Information Enhances Medical Image Representation Learning0
Beyond Finite Data: Towards Data-free Out-of-distribution Generalization via Extrapolation0
Improving Diffusion-Based Generative Models via Approximated Optimal TransportCode0
Towards Effective Usage of Human-Centric Priors in Diffusion Models for Text-based Human Image Generation0
A spatiotemporal style transfer algorithm for dynamic visual stimulus generation0
Discriminative Probing and Tuning for Text-to-Image Generation0
Unifying Generation and Compression: Ultra-low bitrate Image Coding Via Multi-stage Transformer0
ENOT: Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport0
Investigation of the Impact of Synthetic Training Data in the Industrial Application of Terminal Strip Object Detection0
Measuring Diversity in Co-creative Image Generation0
Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing0
(Un)paired signal-to-signal translation with 1D conditional GANs0
HanDiffuser: Text-to-Image Generation With Realistic Hand Appearances0
AtomoVideo: High Fidelity Image-to-Video Generation0
NiNformer: A Network in Network Transformer with Token Mixing Generated Gating FunctionCode0
Transformer for Times Series: an Application to the S&P5000
SCott: Accelerating Diffusion Models with Stochastic Consistency Distillation0
Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models0
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
An Ordinal Diffusion Model for Generating Medical Images with Different Severity Levels0
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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