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

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Showing 1051–1075 of 6689 papers

TitleStatusHype
CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers UpCode3
BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion ModelsCode1
Semi-Supervised Adaptation of Diffusion Models for Handwritten Text Generation—0
Personalized Representation from Personalized GenerationCode2
SemDP: Semantic-level Differential Privacy Protection for Face Datasets—0
PersonaMagic: Stage-Regulated High-Fidelity Face Customization with Tandem EquilibriumCode1
Next Patch Prediction for Autoregressive Visual GenerationCode2
DiffusionTrend: A Minimalist Approach to Virtual Fashion Try-On—0
Qua^2SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models—0
LEDiff: Latent Exposure Diffusion for HDR Generation—0
Comparing noisy neural population dynamics using optimal transport distances—0
LMFusion: Adapting Pretrained Language Models for Multimodal Generation—0
Tiled Diffusion—0
Enhancing Diffusion Models for High-Quality Image Generation—0
FlowAR: Scale-wise Autoregressive Image Generation Meets Flow MatchingCode2
Flowing from Words to Pixels: A Framework for Cross-Modality Evolution—0
GALOT: Generative Active Learning via Optimizable Zero-shot Text-to-image Generation—0
Text2Relight: Creative Portrait Relighting with Text Guidance—0
Self-control: A Better Conditional Mechanism for Masked Autoregressive Model—0
FashionComposer: Compositional Fashion Image Generation—0
Autoregressive Video Generation without Vector QuantizationCode4
MMO-IG: Multi-Class and Multi-Scale Object Image Generation for Remote Sensing—0
Surrealistic-like Image Generation with Vision-Language ModelsCode0
VideoDPO: Omni-Preference Alignment for Video Diffusion Generation—0
E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling—0
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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