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 2851–2875 of 6689 papers

TitleStatusHype
Detecting Face Synthesis Using a Concealed Fusion Model—0
Data-Agnostic Face Image Synthesis Detection Using Bayesian CNNs—0
3D-SSGAN: Lifting 2D Semantics for 3D-Aware Compositional Portrait Synthesis—0
Controllable Image Synthesis of Industrial Data Using Stable Diffusion—0
Plug-in Diffusion Model for Sequential RecommendationCode1
A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion ModelsCode0
Improving Diffusion-Based Image Synthesis with Context Prediction—0
GeoPos: A Minimal Positional Encoding for Enhanced Fine-Grained Details in Image Synthesis Using Convolutional Neural Networks—0
Instruct-Imagen: Image Generation with Multi-modal Instruction—0
DDPM based X-ray Image Synthesizer—0
aMUSEd: An Open MUSE ReproductionCode2
A Vision Check-up for Language Models—0
Few-shot Image Generation via Information Transfer from the Built Geodesic Surface—0
Joint Generative Modeling of Scene Graphs and Images via Diffusion Models—0
SSP: A Simple and Safe automatic Prompt engineering method towards realistic image synthesis on LVM—0
CoDi-2: In-Context Interleaved and Interactive Any-to-Any Generation—0
Adversarial Text to Continuous Image Generation—0
Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning—0
PaReNeRF: Toward Fast Large-scale Dynamic NeRF with Patch-based Reference—0
AnyScene: Customized Image Synthesis with Composited Foreground—0
Intelligent Grimm - Open-ended Visual Storytelling via Latent Diffusion ModelsCode3
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action—0
Video Prediction by Modeling Videos as Continuous Multi-Dimensional Processes—0
TextNeRF: A Novel Scene-Text Image Synthesis Method based on Neural Radiance FieldsCode0
Generating Handwritten Mathematical Expressions From Symbol Graphs: An End-to-End PipelineCode1
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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