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 4151–4175 of 6689 papers

TitleStatusHype
Score Distillation Sampling with Learned Manifold Corrective—0
AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollocks—0
Content-Conditioned Generation of Stylized Free hand Sketches—0
EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models—0
Vision Reimagined: AI-Powered Breakthroughs in WiFi Indoor Imaging—0
Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding—0
3D-SSGAN: Lifting 2D Semantics for 3D-Aware Compositional Portrait Synthesis—0
Detecting Face Synthesis Using a Concealed Fusion Model—0
Data-Agnostic Face Image Synthesis Detection Using Bayesian CNNs—0
Controllable Image Synthesis of Industrial Data Using Stable Diffusion—0
A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion ModelsCode0
Improving Diffusion-Based Image Synthesis with Context Prediction—0
A Vision Check-up for Language Models—0
DDPM based X-ray Image Synthesizer—0
Instruct-Imagen: Image Generation with Multi-modal Instruction—0
Few-shot Image Generation via Information Transfer from the Built Geodesic Surface—0
GeoPos: A Minimal Positional Encoding for Enhanced Fine-Grained Details in Image Synthesis Using Convolutional Neural Networks—0
SSP: A Simple and Safe automatic Prompt engineering method towards realistic image synthesis on LVM—0
Joint Generative Modeling of Scene Graphs and Images via Diffusion Models—0
Adversarial Text to Continuous Image Generation—0
Unmixing Before Fusion: A Generalized Paradigm for Multi-Source-based Hyperspectral Image Synthesis—0
PaReNeRF: Toward Fast Large-scale Dynamic NeRF with Patch-based Reference—0
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action—0
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution—0
Video Prediction by Modeling Videos as Continuous Multi-Dimensional Processes—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