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 38513875 of 6689 papers

TitleStatusHype
Pyramid Diffusion Models For Low-light Image EnhancementCode1
FastComposer: Tuning-Free Multi-Subject Image Generation with Localized AttentionCode2
Controllable Mind Visual Diffusion ModelCode1
Fusion-S2iGan: An Efficient and Effective Single-Stage Framework for Speech-to-Image Generation0
AR-Diffusion: Auto-Regressive Diffusion Model for Text GenerationCode1
Generative Adversarial Networks for Brain Images Synthesis: A Review0
Towards Pragmatic Semantic Image Synthesis for Urban ScenesCode0
A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image ReconstructionCode0
Wavelet-based Unsupervised Label-to-Image TranslationCode0
Interactive Fashion Content Generation Using LLMs and Latent Diffusion Models0
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)0
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity0
Beware of diffusion models for synthesizing medical images -- A comparison with GANs in terms of memorizing brain MRI and chest x-ray images0
Better speech synthesis through scalingCode6
Learning the Visualness of Text Using Large Vision-Language Models0
SparseGNV: Generating Novel Views of Indoor Scenes with Sparse Input ViewsCode1
WeditGAN: Few-Shot Image Generation via Latent Space RelocationCode0
Generative Steganographic Flow0
Relightify: Relightable 3D Faces from a Single Image via Diffusion Models0
MMoT: Mixture-of-Modality-Tokens Transformer for Composed Multimodal Conditional Image Synthesis0
Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentationCode1
Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic SegmentationCode0
SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language ModelsCode1
Vision-Language Models in Remote Sensing: Current Progress and Future TrendsCode1
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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