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 3751–3775 of 6689 papers

TitleStatusHype
Nested Diffusion Models Using Hierarchical Latent Priors—0
3D representation in 512-Byte:Variational tokenizer is the key for autoregressive 3D generation—0
Nested Scale Editing for Conditional Image Synthesis—0
Nested Scale-Editing for Conditional Image Synthesis—0
M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers—0
UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis—0
Neural Rendering and Reenactment of Human Actor Videos—0
Neural Cellular Automata Manifold—0
Neural Design Network: Graphic Layout Generation with Constraints—0
Neural Diffusion Models—0
Neural FFTs for Universal Texture Image Synthesis—0
Towards a Neural Graphics Pipeline for Controllable Image Generation—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Neural Knitworks: Patched Neural Implicit Representation Networks—0
UGen: Unified Autoregressive Multimodal Model with Progressive Vocabulary Learning—0
An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks—0
NeurInt-Learning Interpolation by Neural ODEs—0
NeurInt : Learning to Interpolate through Neural ODEs—0
NeuroPrompts: An Adaptive Framework to Optimize Prompts for Text-to-Image Generation—0
3D Neural Field Generation using Triplane Diffusion—0
Exploring Efficient-Tuned Learning Audio Representation Method from BriVL—0
New Ideas and Trends in Deep Multimodal Content Understanding: A Review—0
Ultra-High-Resolution Image Synthesis with Pyramid Diffusion Model—0
Anti-Aesthetics: Protecting Facial Privacy against Customized Text-to-Image Synthesis—0
NFIG: Autoregressive Image Generation with Next-Frequency Prediction—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