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 21012125 of 6689 papers

TitleStatusHype
Beware of Aliases -- Signal Preservation is Crucial for Robust Image Restoration0
Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation ModelsCode2
Image and Video Tokenization with Binary Spherical QuantizationCode3
Image Textualization: An Automatic Framework for Creating Accurate and Detailed Image DescriptionsCode2
Ctrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance0
Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?0
The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems0
ProcessPainter: Learn Painting Process from Sequence DataCode2
Autoregressive Model Beats Diffusion: Llama for Scalable Image GenerationCode5
TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 StepsCode1
OmniControlNet: Dual-stage Integration for Conditional Image Generation0
Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models0
TIGeR: Unifying Text-to-Image Generation and Retrieval with Large Multimodal Models0
Rapid Review of Generative AI in Smart Medical Applications0
Revisiting Non-Autoregressive Transformers for Efficient Image SynthesisCode1
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce RegimesCode1
Medical Vision Generalist: Unifying Medical Imaging Tasks in ContextCode2
Regularized Training with Generated Datasets for Name-Only Transfer of Vision-Language ModelsCode0
Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at InitializationCode0
CLoG: Benchmarking Continual Learning of Image Generation ModelsCode1
GANetic Loss for Generative Adversarial Networks with a Focus on Medical ApplicationsCode0
Streaming Diffusion Policy: Fast Policy Synthesis with Variable Noise Diffusion ModelsCode2
AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation0
PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance PredictionCode0
GenAI Arena: An Open Evaluation Platform for Generative ModelsCode2
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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