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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1551–1575 of 6689 papers

TitleStatusHype
Boosting Few-Shot Detection with Large Language Models and Layout-to-Image Synthesis—0
Personalized Visual Instruction TuningCode1
IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image GenerationCode5
Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You ThinkCode5
Decouple-Then-Merge: Towards Better Training for Diffusion Models—0
Story-Adapter: A Training-free Iterative Framework for Long Story VisualizationCode4
AP-LDM: Attentive and Progressive Latent Diffusion Model for Training-Free High-Resolution Image GenerationCode3
Think While You Generate: Discrete Diffusion with Planned DenoisingCode2
Learning AND-OR Templates for Professional Photograph Parsing and Guidance—0
Sparse Repellency for Shielded Generation in Text-to-image Diffusion Models—0
Training-free Diffusion Model Alignment with Sampling DemonsCode1
TextureMeDefect: LLM-based Defect Texture Generation for Railway Components on Mobile Devices—0
Generative Portrait Shadow Removal—0
OmniBooth: Learning Latent Control for Image Synthesis with Multi-modal Instruction—0
HERO: Human-Feedback Efficient Reinforcement Learning for Online Diffusion Model Finetuning—0
Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training—0
Disentangling Regional Primitives for Image Generation—0
Noise Crystallization and Liquid Noise: Zero-shot Video Generation using Image Diffusion Models—0
Accelerating Diffusion Models with One-to-Many Knowledge Distillation—0
Lane Detection System for Driver Assistance in Vehicles—0
Test-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on Predicted-View SynthesisCode0
Chain-of-Jailbreak Attack for Image Generation Models via Editing Step by Step—0
Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization—0
Not All Diffusion Model Activations Have Been Evaluated as Discriminative FeaturesCode1
Dynamic Diffusion TransformerCode2
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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