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 14011425 of 6689 papers

TitleStatusHype
Focal Frequency Loss for Image Reconstruction and SynthesisCode1
Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion ModelsCode1
Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEsCode1
Improved Techniques for Training Consistency ModelsCode1
Frame Interpolation with Consecutive Brownian Bridge DiffusionCode1
GANs in computer vision ebookCode1
Beyond Surface Statistics: Scene Representations in a Latent Diffusion ModelCode1
Decoupled Textual Embeddings for Customized Image GenerationCode1
An Attentive-based Generative Model for Medical Image SynthesisCode1
Improving GANs for Long-Tailed Data through Group Spectral RegularizationCode1
DeLoRA: Decoupling Angles and Strength in Low-rank AdaptationCode1
Decoupling Global and Local Representations via Invertible Generative FlowsCode1
Generative Neurosymbolic MachinesCode1
Flow Contrastive Estimation of Energy-Based ModelsCode1
Anchor Token Matching: Implicit Structure Locking for Training-free AR Image EditingCode1
Detecting Human Artifacts from Text-to-Image ModelsCode1
FlexiFilm: Long Video Generation with Flexible ConditionsCode1
Deep Unrolled Low-Rank Tensor Completion for High Dynamic Range ImagingCode1
FlexIT: Towards Flexible Semantic Image TranslationCode1
Aligning Text to Image in Diffusion Models is Easier Than You ThinkCode1
BézierSketch: A generative model for scalable vector sketchesCode1
Improving Visual Commonsense in Language Models via Multiple Image GenerationCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
CookGAN: Meal Image Synthesis from IngredientsCode1
Deep Spatial Transformation for Pose-Guided Person Image Generation and AnimationCode1
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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