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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3826–3850 of 6689 papers

TitleStatusHype
Holistic Evaluation for Interleaved Text-and-Image Generation—0
The State of the Art when using GPUs in Devising Image Generation Methods Using Deep Learning—0
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation—0
How Animals Dance (When You're Not Looking)—0
How do Minimum-Norm Shallow Denoisers Look in Function Space?—0
The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling—0
How Generative Adversarial Networks and Their Variants Work: An Overview—0
How Image Generation Helps Visible-to-Infrared Person Re-Identification?—0
How Real Is Real? A Human Evaluation Framework for Unrestricted Adversarial Examples—0
How Stable is Stable Diffusion under Recursive InPainting (RIP)?—0
How to build a consistency model: Learning flow maps via self-distillation—0
How to Construct Energy for Images? Denoising Autoencoder Can Be Energy Based Model—0
How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity—0
The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models—0
How to make words with vectors: Phrase generation in distributional semantics—0
EvolvED: Evolutionary Embeddings to Understand the Generation Process of Diffusion Models—0
The Value of AI Guidance in Human Examination of Synthetically-Generated Faces—0
Things not Written in Text: Exploring Spatial Commonsense from Visual Signals—0
Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation—0
A Unified Framework for Diffusion Bridge Problems: Flow Matching and Schrödinger Matching into One—0
Human Appearance Transfer—0
HumanDiffusion: a Coarse-to-Fine Alignment Diffusion Framework for Controllable Text-Driven Person Image Generation—0
Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark—0
HERO: Human-Feedback Efficient Reinforcement Learning for Online Diffusion Model Finetuning—0
HumanGAN: A Generative Model of Humans Images—0
Show:102550
← PrevPage 154 of 268Next →
All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3—Unverified
2ADMFID11.84—Unverified
3BigGAN-deepFID8.1—Unverified
4Polarity-BigGANFID6.82—Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59—Unverified
6MaskGITFID6.18—Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2—Unverified
8CDMFID4.88—Unverified
9ADM-GFID4.59—Unverified
10RINFID4.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2—Unverified
2RESFLOWFID48.29—Unverified
3Residual FlowFID46.37—Unverified
4GLF+perceptual loss (ours)FID44.6—Unverified
5ProdPoly no activation functionsFID40.45—Unverified
6ProdPoly no activation functionsFID36.77—Unverified
7ACGANFID35.47—Unverified
8DenseFlow-74-10FID34.9—Unverified
9NVAE w/ flowFID32.53—Unverified
10QSNGANFID31.97—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87—Unverified
2GLIDE + CLIPFID30.46—Unverified
3GLIDE + CLS-FREEFID29.22—Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18—Unverified
5PGMGANFID21.73—Unverified
6CLR-GANFID20.27—Unverified
7FMFID14.45—Unverified
8CT (Direct Generation, NFE=1)FID13—Unverified
9CT (Direct Generation, NFE=2)FID11.1—Unverified
10GLIDE +CLSKID7.95—Unverified