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 14261450 of 6689 papers

TitleStatusHype
InsertDiffusion: Identity Preserving Visualization of Objects through a Training-Free Diffusion ArchitectureCode1
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability VolumesCode1
Aligning Text to Image in Diffusion Models is Easier Than You ThinkCode1
CookGAN: Meal Image Synthesis from IngredientsCode1
Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion ModelsCode1
AutoDiffusion: Training-Free Optimization of Time Steps and Architectures for Automated Diffusion Model AccelerationCode1
An Empirical Study of GPT-4o Image Generation CapabilitiesCode1
BiDM: Pushing the Limit of Quantization for Diffusion ModelsCode1
CooGAN: A Memory-Efficient Framework for High-Resolution Facial Attribute EditingCode1
Flow Contrastive Estimation of Energy-Based ModelsCode1
Integrating Visuospatial, Linguistic, and Commonsense Structure into Story VisualizationCode1
A Dataset and Model for Realistic License Plate DeblurringCode1
ForkGAN: Seeing into the Rainy NightCode1
LeftRefill: Filling Right Canvas based on Left Reference through Generalized Text-to-Image Diffusion ModelCode1
FlexDiT: Dynamic Token Density Control for Diffusion TransformerCode1
Aligning Latent and Image Spaces to Connect the UnconnectableCode1
FlexiFilm: Long Video Generation with Flexible ConditionsCode1
Controlling Style and Semantics in Weakly-Supervised Image GenerationCode1
Controlling Geometric Abstraction and Texture for Artistic ImagesCode1
Rethinking and Defending Protective Perturbation in Personalized Diffusion ModelsCode1
Investigating the Design Space of Diffusion Models for Speech EnhancementCode1
Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual PerceptionCode1
Bi-level Feature Alignment for Versatile Image Translation and ManipulationCode1
Bi-LORA: A Vision-Language Approach for Synthetic Image DetectionCode1
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific DeltaCode1
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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