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 501525 of 6689 papers

TitleStatusHype
Model as a Game: On Numerical and Spatial Consistency for Generative Games0
Efficient Multi-Instance Generation with Janus-Pro-Dirven Prompt Parsing0
LeX-Art: Rethinking Text Generation via Scalable High-Quality Data Synthesis0
Lumina-Image 2.0: A Unified and Efficient Image Generative FrameworkCode4
High Quality Diffusion Distillation on a Single GPU with Relative and Absolute Position Matching0
BizGen: Advancing Article-level Visual Text Rendering for Infographics Generation0
Unified Multimodal Discrete DiffusionCode2
MMGen: Unified Multi-modal Image Generation and Understanding in One Go0
Dissecting and Mitigating Diffusion Bias via Mechanistic InterpretabilityCode1
RecTable: Fast Modeling Tabular Data with Rectified FlowCode0
Beyond Words: Advancing Long-Text Image Generation via Multimodal Autoregressive Models0
Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image DehazingCode2
Exploring Disentangled and Controllable Human Image Synthesis: From End-to-End to Stage-by-Stage0
LayerCraft: Enhancing Text-to-Image Generation with CoT Reasoning and Layered Object IntegrationCode0
VectorFit : Adaptive Singular & Bias Vector Fine-Tuning of Pre-trained Foundation Models0
PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion Models0
SITA: Structurally Imperceptible and Transferable Adversarial Attacks for Stylized Image GenerationCode0
Scaling Down Text Encoders of Text-to-Image Diffusion ModelsCode2
Reverse Prompt: Cracking the Recipe Inside Text-to-Image Generation0
PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative ModelsCode0
U-REPA: Aligning Diffusion U-Nets to ViTsCode1
Training-free Diffusion Acceleration with Bottleneck Sampling0
Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion ModelsCode3
Latent Space Super-Resolution for Higher-Resolution Image Generation with Diffusion ModelsCode1
Plug-and-Play Interpretable Responsible Text-to-Image Generation via Dual-Space Multi-facet Concept Control0
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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