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 65766600 of 6689 papers

TitleStatusHype
MAGAN: Margin Adaptation for Generative Adversarial NetworksCode0
Hita: Holistic Tokenizer for Autoregressive Image GenerationCode0
StyleT2F: Generating Human Faces from Textual Description Using StyleGAN2Code0
Magic 1-For-1: Generating One Minute Video Clips within One MinuteCode0
Distribution Matching Losses Can Hallucinate Features in Medical Image TranslationCode0
ArtGAN: Artwork Synthesis with Conditional Categorical GANsCode0
Recursive Reasoning in Minimax Games: A Level k Gradient Play MethodCode0
HoloGAN: Unsupervised learning of 3D representations from natural imagesCode0
Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at InitializationCode0
Optimal Linear Subspace Search: Learning to Construct Fast and High-Quality Schedulers for Diffusion ModelsCode0
A New Paradigm for Generative Adversarial Networks based on Randomized Decision RulesCode0
How Control Information Influences Multilingual Text Image Generation and Editing?Code0
MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-Robust ClassifierCode0
Semantic Approach to Quantifying the Consistency of Diffusion Model Image GenerationCode0
Semantic-aware Data Augmentation for Text-to-image SynthesisCode0
Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic ModelsCode0
Decontextualized learning for interpretable hierarchical representations of visual patternsCode0
Towards Segment Anything Model (SAM) for Medical Image Segmentation: A SurveyCode0
Semantic-aware Network for Aerial-to-Ground Image SynthesisCode0
How to Backdoor Consistency Models?Code0
Semantic Bottleneck Scene GenerationCode0
Synthetic Trajectory Generation Through Convolutional Neural NetworksCode0
Unsupervised Holistic Image Generation from Key Local PatchesCode0
Energy-Calibrated VAE with Test Time Free LunchCode0
ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding InteractionCode0
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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