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 5326–5350 of 6689 papers

TitleStatusHype
Subject-driven Text-to-Image Generation via Apprenticeship Learning—0
You Only Submit One Image to Find the Most Suitable Generative Model—0
DiffUMI: Training-Free Universal Model Inversion via Unconditional Diffusion for Face Recognition—0
Bridging CLIP and StyleGAN through Latent Alignment for Image Editing—0
BRIDGING ADVERSARIAL SAMPLES AND ADVERSARIAL NETWORKS—0
Vision-Language Models Represent Darker-Skinned Black Individuals as More Homogeneous than Lighter-Skinned Black Individuals—0
Bridge Diffusion Model: bridge non-English language-native text-to-image diffusion model with English communities—0
SuperNeRF-GAN: A Universal 3D-Consistent Super-Resolution Framework for Efficient and Enhanced 3D-Aware Image Synthesis—0
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images—0
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
Diffusion idea exploration for art generation—0
Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One—0
Diffusion Instruction Tuning—0
Diffusion Lens: Interpreting Text Encoders in Text-to-Image Pipelines—0
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives—0
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient—0
Supervised Adversarial Networks for Image Saliency Detection—0
Diffusion Models as Data Mining Tools—0
PanoFree: Tuning-Free Holistic Multi-view Image Generation with Cross-view Self-Guidance—0
BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity—0
Diffusion Models Enable Zero-Shot Pose Estimation for Lower-Limb Prosthetic Users—0
Vision Reimagined: AI-Powered Breakthroughs in WiFi Indoor Imaging—0
Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors—0
Surf-CDM: Score-Based Surface Cold-Diffusion Model For Medical Image Segmentation—0
Brain Image Synthesis With Unsupervised Multivariate Canonical CSCl4Net—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