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 426450 of 6689 papers

TitleStatusHype
Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKVCode2
PID: Physics-Informed Diffusion Model for Infrared Image GenerationCode2
Adversarial Attacks and Defenses on Text-to-Image Diffusion Models: A SurveyCode2
MARS: Mixture of Auto-Regressive Models for Fine-grained Text-to-image SynthesisCode2
Generative Image as Action ModelsCode2
ConceptExpress: Harnessing Diffusion Models for Single-image Unsupervised Concept ExtractionCode2
HumanRefiner: Benchmarking Abnormal Human Generation and Refining with Coarse-to-fine Pose-Reversible GuidanceCode2
Powerful and Flexible: Personalized Text-to-Image Generation via Reinforcement LearningCode2
PerLDiff: Controllable Street View Synthesis Using Perspective-Layout Diffusion ModelsCode2
FairDiff: Fair Segmentation with Point-Image DiffusionCode2
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete LatentsCode2
InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image GenerationCode2
AnyControl: Create Your Artwork with Versatile Control on Text-to-Image GenerationCode2
DiffuseHigh: Training-free Progressive High-Resolution Image Synthesis through Structure GuidanceCode2
Q-DiT: Accurate Post-Training Quantization for Diffusion TransformersCode2
DreamBench++: A Human-Aligned Benchmark for Personalized Image GenerationCode2
FaceScore: Benchmarking and Enhancing Face Quality in Human GenerationCode2
Character-Adapter: Prompt-Guided Region Control for High-Fidelity Character CustomizationCode2
Soft Masked Mamba Diffusion Model for CT to MRI ConversionCode2
Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%Code2
STAR: Scale-wise Text-to-image generation via Auto-Regressive representationsCode2
ControlVAR: Exploring Controllable Visual Autoregressive ModelingCode2
Make It Count: Text-to-Image Generation with an Accurate Number of ObjectsCode2
Understanding Hallucinations in Diffusion Models through Mode InterpolationCode2
Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation ModelsCode2
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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