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 951975 of 6689 papers

TitleStatusHype
EIDT-V: Exploiting Intersections in Diffusion Trajectories for Model-Agnostic, Zero-Shot, Training-Free Text-to-Video Generation0
TFCustom: Customized Image Generation with Time-Aware Frequency Feature Guidance0
Finding Local Diffusion Schrodinger Bridge using Kolmogorov-Arnold NetworkCode0
Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient TrainingCode0
Black Hole-Driven Identity Absorbing in Diffusion Models0
ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot LearningCode1
MC^2: Multi-concept Guidance for Customized Multi-concept GenerationCode0
One-Way Ticket: Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models0
Devil is in the Detail: Towards Injecting Fine Details of Image Prompt in Image Generation via Conflict-free Guidance and Stratified Attention0
Patch Matters: Training-free Fine-grained Image Caption Enhancement via Local Perception0
DreamRelation: Bridging Customization and Relation Generation0
Unseen Visual Anomaly Generation0
Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization0
FluxSpace: Disentangled Semantic Editing in Rectified Flow Models0
Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation0
A4A: Adapter for Adapter Transfer via All-for-All Mapping for Cross-Architecture Models0
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
Towards Universal Dataset Distillation via Task-Driven Diffusion0
Harnessing Global-Local Collaborative Adversarial Perturbation for Anti-Customization0
Scaling Inference Time Compute for Diffusion Models0
Multi-party Collaborative Attention Control for Image Customization0
SKE-Layout: Spatial Knowledge Enhanced Layout Generation with LLMs0
Generative Zero-Shot Composed Image Retrieval0
Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training0
Beyond Generation: A Diffusion-based Low-level Feature Extractor for Detecting AI-generated Images0
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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