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 56265650 of 6689 papers

TitleStatusHype
Efficient Training with Denoised Neural Weights0
Efficient Transfer Learning in Diffusion Models via Adversarial Noise0
Systematic Analysis of Image Generation using GANs0
Efficient-VQGAN: Towards High-Resolution Image Generation with Efficient Vision Transformers0
Systematic Review of Techniques in Brain Image Synthesis using Deep Learning0
Enhancing CT Image synthesis from multi-modal MRI data based on a multi-task neural network framework0
T2CI-GAN: Text to Compressed Image generation using Generative Adversarial Network0
EIDT-V: Exploiting Intersections in Diffusion Trajectories for Model-Agnostic, Zero-Shot, Training-Free Text-to-Video Generation0
Hierarchy Composition GAN for High-fidelity Image Synthesis0
EINS: Long Short-Term Memory with Extrapolated Input Network Simplification0
T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generation0
ELBO-T2IAlign: A Generic ELBO-Based Method for Calibrating Pixel-level Text-Image Alignment in Diffusion Models0
BlobGAN-3D: A Spatially-Disentangled 3D-Aware Generative Model for Indoor Scenes0
BlobCtrl: A Unified and Flexible Framework for Element-level Image Generation and Editing0
ELODIN: Naming Concepts in Embedding Spaces0
Elucidating Flow Matching ODE Dynamics with Respect to Data Geometries0
Adapting Text-to-Image Generation with Feature Difference Instruction for Generic Image Restoration0
BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing0
T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts0
Visual Madlibs: Fill in the blank Image Generation and Question Answering0
Emage: Non-Autoregressive Text-to-Image Generation0
T2IW: Joint Text to Image & Watermark Generation0
Emergence and Evolution of Interpretable Concepts in Diffusion Models0
Blind Motion Deblurring through SinGAN Architecture0
EM-GAN: Fast Stress Analysis for Multi-Segment Interconnect Using Generative Adversarial Networks0
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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