SOTAVerified

Personalized Image Generation

Utilizes single or multiple images that contain the same subject or style, along with text prompt, to generate images that contain that subject as well as match the textual description. Includes finetuning-based methods (e.g. DreamBooth, Textual Inversion) as well as encoder-based methods (e.g. E4T, ELITE, and IP-Adapter, etc.).

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–50 of 58 papers

TitleStatusHype
Less-to-More Generalization: Unlocking More Controllability by In-Context GenerationCode5
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion ModelsCode5
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven GenerationCode5
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual InversionCode5
StoryMaker: Towards Holistic Consistent Characters in Text-to-image GenerationCode4
Personalized Image Generation with Deep Generative Models: A Decade SurveyCode3
MoMA: Multimodal LLM Adapter for Fast Personalized Image GenerationCode3
Generative Multimodal Models are In-Context LearnersCode3
Resolving Multi-Condition Confusion for Finetuning-Free Personalized Image GenerationCode2
EZIGen: Enhancing zero-shot personalized image generation with precise subject encoding and decoupled guidanceCode2
TextBoost: Towards One-Shot Personalization of Text-to-Image Models via Fine-tuning Text EncoderCode2
DreamBench++: A Human-Aligned Benchmark for Personalized Image GenerationCode2
RectifID: Personalizing Rectified Flow with Anchored Classifier GuidanceCode2
Gen4Gen: Generative Data Pipeline for Generative Multi-Concept CompositionCode2
When StyleGAN Meets Stable Diffusion: a W+ Adapter for Personalized Image GenerationCode2
Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuningCode2
FastComposer: Tuning-Free Multi-Subject Image Generation with Localized AttentionCode2
Personalized Text-to-Image Generation with Auto-Regressive ModelsCode1
Conceptrol: Concept Control of Zero-shot Personalized Image GenerationCode1
Beyond Fine-Tuning: A Systematic Study of Sampling Techniques in Personalized Image GenerationCode1
PersonaMagic: Stage-Regulated High-Fidelity Face Customization with Tandem EquilibriumCode1
PatchDPO: Patch-level DPO for Finetuning-free Personalized Image GenerationCode1
Personalized Image Generation with Large Multimodal ModelsCode1
BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion ModelsCode1
When StyleGAN Meets Stable Diffusion: a W_+ Adapter for Personalized Image GenerationCode1
A Training-Free Style-Personalization via Scale-wise Autoregressive Model—0
RAGAR: Retrieval Augment Personalized Image Generation Guided by Recommendation—0
DRC: Enhancing Personalized Image Generation via Disentangled Representation Composition—0
AC-LoRA: Auto Component LoRA for Personalized Artistic Style Image Generation—0
Personalize Anything for Free with Diffusion Transformer—0
Towards More Accurate Personalized Image Generation: Addressing Overfitting and Evaluation BiasCode0
A4A: Adapter for Adapter Transfer via All-for-All Mapping for Cross-Architecture Models—0
HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories—0
LoRACLR: Contrastive Adaptation for Customization of Diffusion Models—0
SerialGen: Personalized Image Generation by First Standardization Then Personalization—0
DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image Diffusion Models—0
PersonaCraft: Personalized Full-Body Image Synthesis for Multiple Identities from Single References Using 3D-Model-Conditioned Diffusion—0
DreamCache: Finetuning-Free Lightweight Personalized Image Generation via Feature Caching—0
FaceChain-FACT: Face Adapter with Decoupled Training for Identity-preserved PersonalizationCode0
Imagine yourself: Tuning-Free Personalized Image Generation—0
ViPer: Visual Personalization of Generative Models via Individual Preference Learning—0
Layout-and-Retouch: A Dual-stage Framework for Improving Diversity in Personalized Image Generation—0
FreeTuner: Any Subject in Any Style with Training-free Diffusion—0
InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation—0
MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation—0
CAT: Contrastive Adapter Training for Personalized Image GenerationCode0
MM-Diff: High-Fidelity Image Personalization via Multi-Modal Condition Integration—0
IDAdapter: Learning Mixed Features for Tuning-Free Personalization of Text-to-Image Models—0
Fast Personalized Text-to-Image Syntheses With Attention Injection—0
Beyond Inserting: Learning Identity Embedding for Semantic-Fidelity Personalized Diffusion Generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DreamBooth LoRA SDXL v1.0Overall (CP * PF)0.52—Unverified
2IP-Adapter ViT-G SDXL v1.0Overall (CP * PF)0.38—Unverified
3Emu2 SDXL v1.0Overall (CP * PF)0.36—Unverified
4DreamBooth SD v1.5Overall (CP * PF)0.36—Unverified
5IP-Adapter-Plus ViT-H SDXL v1.0Overall (CP * PF)0.34—Unverified
6BLIP-Diffusion SD v1.5Overall (CP * PF)0.27—Unverified
7Textual Inversion SD v1.5Overall (CP * PF)0.24—Unverified