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 15261550 of 6689 papers

TitleStatusHype
DiffEdit: Diffusion-based semantic image editing with mask guidanceCode1
OCR-VQGAN: Taming Text-within-Image GenerationCode1
FedForgery: Generalized Face Forgery Detection with Residual Federated LearningCode1
Differentially Private Diffusion ModelsCode1
UniTune: Text-Driven Image Editing by Fine Tuning a Diffusion Model on a Single ImageCode1
Meta-Learning via Classifier(-free) Diffusion GuidanceCode1
Is synthetic data from generative models ready for image recognition?Code1
TransFusion: Transcribing Speech with Multinomial DiffusionCode1
ImaginaryNet: Learning Object Detectors without Real Images and AnnotationsCode1
Self-Guided Diffusion ModelsCode1
AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video AvatarsCode1
GENIE: Higher-Order Denoising Diffusion SolversCode1
Markup-to-Image Diffusion Models with Scheduled SamplingCode1
CLIP-Diffusion-LM: Apply Diffusion Model on Image CaptioningCode1
SiNeRF: Sinusoidal Neural Radiance Fields for Joint Pose Estimation and Scene ReconstructionCode1
SCAM! Transferring humans between images with Semantic Cross Attention ModulationCode1
Dual Pyramid Generative Adversarial Networks for Semantic Image SynthesisCode1
Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning RulesCode1
Visualize Before You Write: Imagination-Guided Open-Ended Text GenerationCode1
From Face to Natural Image: Learning Real Degradation for Blind Image Super-ResolutionCode1
Visual Prompt Tuning for Generative Transfer LearningCode1
Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2Code1
Denoising MCMC for Accelerating Diffusion-Based Generative ModelsCode1
Make-A-Video: Text-to-Video Generation without Text-Video DataCode1
medigan: a Python library of pretrained generative models for medical image synthesisCode1
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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