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 20512075 of 6689 papers

TitleStatusHype
GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual GenerationCode3
Improving Visual Commonsense in Language Models via Multiple Image GenerationCode1
What's Next? Exploring Utilization, Challenges, and Future Directions of AI-Generated Image Tools in Graphic Design0
Training Diffusion Models with Federated Learning0
Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PETCode0
AITTI: Learning Adaptive Inclusive Token for Text-to-Image GenerationCode1
ARTIST: Improving the Generation of Text-rich Images with Disentangled Diffusion Models and Large Language Models0
Not All Prompts Are Made Equal: Prompt-based Pruning of Text-to-Image Diffusion ModelsCode1
Decomposed evaluations of geographic disparities in text-to-image models0
GeoGPT4V: Towards Geometric Multi-modal Large Language Models with Geometric Image GenerationCode0
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes0
PhyBench: A Physical Commonsense Benchmark for Evaluating Text-to-Image Models0
Latent Denoising Diffusion GAN: Faster sampling, Higher image qualityCode1
Generative Visual Instruction TuningCode0
Autoregressive Image Generation without Vector QuantizationCode5
Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%Code2
Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models0
Mixture-of-Subspaces in Low-Rank AdaptationCode0
STAR: Scale-wise Text-to-image generation via Auto-Regressive representationsCode2
An Analysis on Quantizing Diffusion Transformers0
Can Generative AI Replace Immunofluorescent Staining Processes? A Comparison Study of Synthetically Generated CellPainting Images from Brightfield0
Poetry2Image: An Iterative Correction Framework for Images Generated from Chinese Classical Poetry0
MINT: a Multi-modal Image and Narrative Text Dubbing Dataset for Foley Audio Content Planning and GenerationCode0
Make It Count: Text-to-Image Generation with an Accurate Number of ObjectsCode2
Crafting Parts for Expressive Object Composition0
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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