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 15011525 of 6689 papers

TitleStatusHype
Focal Frequency Loss for Image Reconstruction and SynthesisCode1
Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion ModelsCode1
A Unified Agentic Framework for Evaluating Conditional Image GenerationCode1
A U-Net Based Discriminator for Generative Adversarial NetworksCode1
Denoising Diffusion Autoencoders are Unified Self-supervised LearnersCode1
Controllable Person Image Synthesis with Spatially-Adaptive Warped NormalizationCode1
DreamLCM: Towards High-Quality Text-to-3D Generation via Latent Consistency ModelCode1
ForkGAN: Seeing into the Rainy NightCode1
GAN-based Matrix Factorization for Recommender SystemsCode1
Controllable Person Image Synthesis with Attribute-Decomposed GANCode1
Interactive Character Control with Auto-Regressive Motion Diffusion ModelsCode1
Latent Diffusion for Medical Image Segmentation: End to end learning for fast sampling and accuracyCode1
Controllable Mind Visual Diffusion ModelCode1
Denoising Likelihood Score Matching for Conditional Score-based Data GenerationCode1
Flow Contrastive Estimation of Energy-Based ModelsCode1
FlexIT: Towards Flexible Semantic Image TranslationCode1
LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and GenerationCode1
A Content Transformation Block For Image Style TransferCode1
Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image GenerationCode1
Densely connected normalizing flowsCode1
Dense Pixel-to-Pixel Harmonization via Continuous Image RepresentationCode1
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable ModelsCode1
Block Shuffle: A Method for High-resolution Fast Style Transfer with Limited MemoryCode1
Density estimation using Real NVPCode1
FlexiFilm: Long Video Generation with Flexible ConditionsCode1
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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