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 6326–6350 of 6689 papers

TitleStatusHype
Learning Disentangled Representations via Mutual Information EstimationCode0
VIGFace: Virtual Identity Generation for Privacy-Free Face RecognitionCode0
Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large DatasetsCode0
TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial NetworksCode0
Axial Attention in Multidimensional TransformersCode0
A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion ModelsCode0
Towards Understanding Unsafe Video GenerationCode0
PROUD: PaRetO-gUided Diffusion Model for Multi-objective GenerationCode0
Noise Robust Generative Adversarial NetworksCode0
Generative Sliced MMD Flows with Riesz KernelsCode0
AS-IntroVAE: Adversarial Similarity Distance Makes Robust IntroVAECode0
Study of Deep Generative Models for Inorganic Chemical CompositionsCode0
ChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image GenerationCode0
The Bias Amplification Paradox in Text-to-Image GenerationCode0
Generative Visual Instruction TuningCode0
No Modes left behind: Capturing the data distribution effectively using GANsCode0
Multivariate rank via entropic optimal transport: sample efficiency and generative modelingCode0
Learning Generative Models of Tissue Organization with Supervised GANsCode0
Non-Adversarial Image Synthesis with Generative Latent Nearest NeighborsCode0
Nonlinear 3D Face Morphable ModelCode0
Yuan: Yielding Unblemished Aesthetics Through A Unified Network for Visual Imperfections Removal in Generated ImagesCode0
Learning Graph Representation of Agent DiffusersCode0
Pruning then Reweighting: Towards Data-Efficient Training of Diffusion ModelsCode0
Learning Hierarchical Semantic Image Manipulation through Structured RepresentationsCode0
Data Generation Scheme for Thermal Modality with Edge-Guided Adversarial Conditional Diffusion ModelCode0
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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