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 3376–3400 of 6689 papers

TitleStatusHype
Training Energy-Based Models with Diffusion Contrastive Divergences—0
ARTIST: Improving the Generation of Text-rich Images with Disentangled Diffusion Models and Large Language Models—0
Training face verification models from generated face identity data—0
Training-free Color-Style Disentanglement for Constrained Text-to-Image Synthesis—0
LM-IGTD: a 2D image generator for low-dimensional and mixed-type tabular data to leverage the potential of convolutional neural networks—0
Load Balanced GANs for Multi-view Face Image Synthesis—0
Abnormal Colon Polyp Image Synthesis Using Conditional Adversarial Networks for Improved Detection Performance—0
Local Attention Pyramid for Scene Image Generation—0
Local Conditional Controlling for Text-to-Image Diffusion Models—0
Local Curvature Smoothing with Stein's Identity for Efficient Score Matching—0
Local Differential Privacy Image Generation Using Flow-based Deep Generative Models—0
Locality-Aware Generalizable Implicit Neural Representation—0
Locality-aware Parallel Decoding for Efficient Autoregressive Image Generation—0
Training-free Diffusion Acceleration with Bottleneck Sampling—0
Localized Text-to-Image Generation for Free via Cross Attention Control—0
Localizing and Editing Knowledge in Text-to-Image Generative Models—0
Locally-Focused Face Representation for Sketch-to-Image Generation Using Noise-Induced Refinement—0
WEM-GAN: Wavelet transform based facial expression manipulation—0
Local Statistics for Generative Image Detection—0
LoCo: Locally Constrained Training-Free Layout-to-Image Synthesis—0
Training-free Editioning of Text-to-Image Models—0
LogicRank: Logic Induced Reranking for Generative Text-to-Image Systems—0
ArtiScene: Language-Driven Artistic 3D Scene Generation Through Image Intermediary—0
Training-Free Identity Preservation in Stylized Image Generation Using Diffusion Models—0
LoomNet: Enhancing Multi-View Image Generation via Latent Space Weaving—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