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 22012225 of 6689 papers

TitleStatusHype
RefDrop: Controllable Consistency in Image or Video Generation via Reference Feature Guidance0
Glauber Generative Model: Discrete Diffusion Models via Binary Classification0
PromptFix: You Prompt and We Fix the PhotoCode4
Anonymization Prompt Learning for Facial Privacy-Preserving Text-to-Image Generation0
TIE: Revolutionizing Text-based Image Editing for Complex-Prompt Following and High-Fidelity Editing0
The Poisson Midpoint Method for Langevin Dynamics: Provably Efficient Discretization for Diffusion Models0
Training-free Editioning of Text-to-Image Models0
Unified Editing of Panorama, 3D Scenes, and Videos Through Disentangled Self-Attention Injection0
User-Friendly Customized Generation with Multi-Modal PromptsCode1
Automatic Jailbreaking of the Text-to-Image Generative AI SystemsCode1
Method and Software Tool for Generating Artificial Databases of Biomedical Images Based on Deep Neural Networks0
Underwater Image Enhancement by Diffusion Model with Customized CLIP-ClassifierCode2
Lateralization MLP: A Simple Brain-inspired Architecture for DiffusionCode0
PTQ4DiT: Post-training Quantization for Diffusion TransformersCode1
Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based DiscriminationCode0
Scaling Diffusion Mamba with Bidirectional SSMs for Efficient Image and Video Generation0
Learning to Discretize Denoising Diffusion ODEsCode1
SG-Adapter: Enhancing Text-to-Image Generation with Scene Graph Guidance0
Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion ModelsCode2
Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving GradientCode1
ArtWeaver: Advanced Dynamic Style Integration via Diffusion Model0
Time Cell Inspired Temporal Codebook in Spiking Neural Networks for Enhanced Image Generation0
Learning Multi-dimensional Human Preference for Text-to-Image GenerationCode7
Improved Distribution Matching Distillation for Fast Image SynthesisCode5
PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion TeacherCode1
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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