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 4601–4625 of 6689 papers

TitleStatusHype
Separable Multi-Concept Erasure from Diffusion Models—0
Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry—0
Sequential Attention GAN for Interactive Image Editing—0
Sequential Posterior Sampling with Diffusion Models—0
Sequential-Scanning Dual-Energy CT Imaging Using High Temporal Resolution Image Reconstruction and Error-Compensated Material Basis Image Generation—0
Sequential Semantic Generative Communication for Progressive Text-to-Image Generation—0
SerialGen: Personalized Image Generation by First Standardization Then Personalization—0
VectorFit : Adaptive Singular & Bias Vector Fine-Tuning of Pre-trained Foundation Models—0
se-Shweshwe Inspired Fashion Generation—0
SETGAN: Scale and Energy Trade-off GANs for Image Applications on Mobile Platforms—0
VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models—0
SG-Adapter: Enhancing Text-to-Image Generation with Scene Graph Guidance—0
Vector Graphics Generation via Mutually Impulsed Dual-domain Diffusion—0
SGM-Net: Semantic Guided Matting Net—0
Unsupervised Image Decomposition in Vector Layers—0
Vector Learning for Cross Domain Representations—0
Shape-conditioned Image Generation by Learning Latent Appearance Representation from Unpaired Data—0
Shape-Preserving Generation of Food Images for Automatic Dietary Assessment—0
Shapes and Context: In-the-Wild Image Synthesis & Manipulation—0
ShapeWords: Guiding Text-to-Image Synthesis with 3D Shape-Aware Prompts—0
Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis—0
ShieldGemma 2: Robust and Tractable Image Content Moderation—0
ShiftDDPMs: Exploring Conditional Diffusion Models by Shifting Diffusion Trajectories—0
AnonymousNet: Natural Face De-Identification with Measurable Privacy—0
ShoeModel: Learning to Wear on the User-specified Shoes via Diffusion Model—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