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 17511775 of 6689 papers

TitleStatusHype
GameIR: A Large-Scale Synthesized Ground-Truth Dataset for Image Restoration over Gaming Content0
Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce ClassificationCode1
Spiking Diffusion ModelsCode2
CSGO: Content-Style Composition in Text-to-Image Generation0
Self-Improving Diffusion Models with Synthetic Data0
GRPose: Learning Graph Relations for Human Image Generation with Pose PriorsCode2
GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative ModelsCode0
ResVG: Enhancing Relation and Semantic Understanding in Multiple Instances for Visual GroundingCode0
Enhancing Conditional Image Generation with Explainable Latent Space ManipulationCode0
Hand1000: Generating Realistic Hands from Text with Only 1,000 Images0
CoRe: Context-Regularized Text Embedding Learning for Text-to-Image Personalization0
Merging and Splitting Diffusion Paths for Semantically Coherent PanoramasCode1
Disentangled Diffusion Autoencoder for Harmonization of Multi-site Neuroimaging Data0
Reflective Human-Machine Co-adaptation for Enhanced Text-to-Image Generation Dialogue System0
Negation Blindness in Large Language Models: Unveiling the NO Syndrome in Image Generation0
Sequential-Scanning Dual-Energy CT Imaging Using High Temporal Resolution Image Reconstruction and Error-Compensated Material Basis Image Generation0
Build-A-Scene: Interactive 3D Layout Control for Diffusion-Based Image Generation0
CrossViewDiff: A Cross-View Diffusion Model for Satellite-to-Street View Synthesis0
Alfie: Democratising RGBA Image Generation With No $Code2
DIAGen: Diverse Image Augmentation with Generative ModelsCode1
GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal-Conditioned PolicyCode2
Foodfusion: A Novel Approach for Food Image Composition via Diffusion Models0
ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty0
Variational autoencoder-based neural network model compression0
HTS-Attack: Heuristic Token Search for Jailbreaking Text-to-Image Models0
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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