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 29763000 of 6689 papers

TitleStatusHype
Breaking Free: How to Hack Safety Guardrails in Black-Box Diffusion Models!Code0
Backdooring Bias into Text-to-Image ModelsCode0
Ink removal from histopathology whole slide images by combining classification, detection and image generation modelsCode0
-Brush: Controllable Large Image Synthesis with Diffusion Models in Infinite DimensionsCode0
EvoGAN: An Evolutionary Computation Assisted GANCode0
Deformable GANs for Pose-based Human Image GenerationCode0
A High-Quality Robust Diffusion Framework for Corrupted DatasetCode0
Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational AutoencodersCode0
High-Resolution Deep Convolutional Generative Adversarial NetworksCode0
Deformation equivariant cross-modality image synthesis with paired non-aligned training dataCode0
Infinite Nature: Perpetual View Generation of Natural Scenes from a Single ImageCode0
Evaluating the Impact of Intensity Normalization on MR Image SynthesisCode0
Increasing diversity of omni-directional images generated from single image using cGAN based on MLPMixerCode0
Conditional Image Generation with PixelCNN DecodersCode0
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning ConventionsCode0
Indonesian Text-to-Image Synthesis with Sentence-BERT and FastGANCode0
Learning from Mistakes: Iterative Prompt Relabeling for Text-to-Image Diffusion Model TrainingCode0
Learn, Imagine and Create: Text-to-Image Generation from Prior KnowledgeCode0
Evaluating Text-to-Image Generative Models: An Empirical Study on Human Image SynthesisCode0
Evaluating Semantic Variation in Text-to-Image Synthesis: A Causal PerspectiveCode0
Improving Fine-Grained Control via Aggregation of Multiple Diffusion ModelsCode0
Improving the Efficiency of Visually Augmented Language ModelsCode0
Improving the Evaluation of Generative Models with Fuzzy LogicCode0
Asymmetric Bias in Text-to-Image Generation with Adversarial AttacksCode0
Improving MMD-GAN Training with Repulsive Loss FunctionCode0
Show:102550
← PrevPage 120 of 268Next →

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