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 21512175 of 6689 papers

TitleStatusHype
PAGER: Progressive Attribute-Guided Extendable Robust Image GenerationCode0
PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative ModelsCode0
CODE: Confident Ordinary Differential EditingCode0
P^2-GAN: Efficient Style Transfer Using Single Style ImageCode0
Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large ScaleCode0
Padding Tone: A Mechanistic Analysis of Padding Tokens in T2I ModelsCode0
Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient TrainingCode0
EAR: Erasing Concepts from Unified Autoregressive ModelsCode0
Adv-KD: Adversarial Knowledge Distillation for Faster Diffusion SamplingCode0
Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian SupervisionCode0
Adversarial Training of Variational Auto-encoders for High Fidelity Image GenerationCode0
COCO-GAN: Generation by Parts via Conditional CoordinatingCode0
CoCoG-2: Controllable generation of visual stimuli for understanding human concept representationCode0
Dynamic Importance in Diffusion U-Net for Enhanced Image SynthesisCode0
Adversarial Out-domain Examples for Generative ModelsCode0
Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at InitializationCode0
Optimal Linear Subspace Search: Learning to Construct Fast and High-Quality Schedulers for Diffusion ModelsCode0
ArtistAuditor: Auditing Artist Style Pirate in Text-to-Image Generation ModelsCode0
Open-Source Acceleration of Stable-Diffusion.cpp Deployable on All DevicesCode0
Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic ModelsCode0
DuoDiff: Accelerating Diffusion Models with a Dual-Backbone ApproachCode0
On the Diversity of Realistic Image SynthesisCode0
On the Cultural Gap in Text-to-Image GenerationCode0
Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-IdentificationCode0
Dual Projection Generative Adversarial Networks for Conditional Image GenerationCode0
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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