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 1501–1550 of 6689 papers

TitleStatusHype
ChatHouseDiffusion: Prompt-Guided Generation and Editing of Floor PlansCode1
A Simple Approach to Unifying Diffusion-based Conditional Generation—0
KITTEN: A Knowledge-Intensive Evaluation of Image Generation on Visual Entities—0
On the Effectiveness of Dataset Alignment for Fake Image DetectionCode1
Ctrl-U: Robust Conditional Image Generation via Uncertainty-aware Reward Modeling—0
Evolutionary Retrofitting—0
InvSeg: Test-Time Prompt Inversion for Semantic Segmentation—0
Efficient Diffusion Models: A Comprehensive Survey from Principles to PracticesCode1
How to Backdoor Consistency Models?Code0
TextCtrl: Diffusion-based Scene Text Editing with Prior Guidance ControlCode2
FasterDiT: Towards Faster Diffusion Transformers Training without Architecture ModificationCode5
Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinement—0
Saliency Guided Optimization of Diffusion Latents—0
SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion TransformersCode9
MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling—0
HART: Efficient Visual Generation with Hybrid Autoregressive TransformerCode9
Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models—0
Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations—0
Anatomical feature-prioritized loss for enhanced MR to CT translationCode1
Vision-guided and Mask-enhanced Adaptive Denoising for Prompt-based Image EditingCode0
When Does Perceptual Alignment Benefit Vision Representations?Code4
Customize Your Visual Autoregressive Recipe with Set Autoregressive ModelingCode1
Evaluating Semantic Variation in Text-to-Image Synthesis: A Causal PerspectiveCode0
High-Precision Dichotomous Image Segmentation via Probing Diffusion CapacityCode2
First Creating Backgrounds Then Rendering Texts: A New Paradigm for Visual Text BlendingCode1
EBDM: Exemplar-guided Image Translation with Brownian-bridge Diffusion Models—0
Generating Intermediate Representations for Compositional Text-To-Image GenerationCode0
Multi class activity classification in videos using Motion History Image generationCode0
TULIP: Token-length Upgraded CLIPCode1
DuoDiff: Accelerating Diffusion Models with a Dual-Backbone ApproachCode0
CtrLoRA: An Extensible and Efficient Framework for Controllable Image GenerationCode3
RealEra: Semantic-level Concept Erasure via Neighbor-Concept Mining—0
One-shot Generative Domain Adaptation in 3D GANsCode0
Diffusion Models Need Visual Priors for Image Generation—0
Synth-SONAR: Sonar Image Synthesis with Enhanced Diversity and Realism via Dual Diffusion Models and GPT PromptingCode1
Score Neural Operator: A Generative Model for Learning and Generalizing Across Multiple Probability Distributions—0
SceneCraft: Layout-Guided 3D Scene GenerationCode3
Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image SynthesisCode3
Scaling Laws For Diffusion Transformers—0
DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models—0
Relational Diffusion Distillation for Efficient Image GenerationCode0
DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation—0
Minority-Focused Text-to-Image Generation via Prompt OptimizationCode1
Data Augmentation for Surgical Scene Segmentation with Anatomy-Aware Diffusion ModelsCode0
A Unified Debiasing Approach for Vision-Language Models across Modalities and TasksCode0
FLIER: Few-shot Language Image Models Embedded with Latent Representations—0
G2D2: Gradient-guided Discrete Diffusion for image inverse problem solving—0
TextLap: Customizing Language Models for Text-to-Layout PlanningCode1
On the Solution of Linearized Inverse Scattering Problems in Near-Field Microwave Imaging by Operator Inversion and Matched Filtering—0
EvolveDirector: Approaching Advanced Text-to-Image Generation with Large Vision-Language ModelsCode2
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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