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 5801–5825 of 6689 papers

TitleStatusHype
Extreme Generative Image Compression by Learning Text Embedding from Diffusion Models—0
Eye-for-an-eye: Appearance Transfer with Semantic Correspondence in Diffusion Models—0
Tell Me What You See: Text-Guided Real-World Image Denoising—0
Face0: Instantaneously Conditioning a Text-to-Image Model on a Face—0
FaceChain-FACT: Face Adapter with Decoupled Training for Identity-preserved Personalization—0
FaceChain-SuDe: Building Derived Class to Inherit Category Attributes for One-shot Subject-Driven Generation—0
FaceCrafter: Identity-Conditional Diffusion with Disentangled Control over Facial Pose, Expression, and Emotion—0
Face editing with GAN -- A Review—0
FaceFeat-GAN: a Two-Stage Approach for Identity-Preserving Face Synthesis—0
Template-Free Try-on Image Synthesis via Semantic-guided Optimization—0
Template NeRF: Towards Modeling Dense Shape Correspondences from Category-Specific Object Images—0
Activation Learning by Local Competitions—0
Face sketch to photo translation using generative adversarial networks—0
FaceStudio: Put Your Face Everywhere in Seconds—0
FaceVid-1K: A Large-Scale High-Quality Multiracial Human Face Video Dataset—0
Facing Identity: The Formation and Performance of Identity via Face-Based Artificial Intelligence Technologies—0
Factor Decomposed Generative Adversarial Networks for Text-to-Image Synthesis—0
Factoring Variations in Natural Images with Deep Gaussian Mixture Models—0
Temporally Adjustable Longitudinal Fluid-Attenuated Inversion Recovery MRI Estimation / Synthesis for Multiple Sclerosis—0
Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation—0
Factorized Visual Tokenization and Generation—0
FADE: Adversarial Concept Erasure in Flow Models—0
FA-GAN: Feature-Aware GAN for Text to Image Synthesis—0
Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Generation—0
Temporally Consistent Semantic Video Editing—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