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 26–50 of 58 papers

TitleStatusHype
HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories—0
Identity Encoder for Personalized Diffusion—0
Imagine yourself: Tuning-Free Personalized Image Generation—0
InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning—0
InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation—0
SerialGen: Personalized Image Generation by First Standardization Then Personalization—0
A Training-Free Style-Personalization via Scale-wise Autoregressive Model—0
FreeTuner: Any Subject in Any Style with Training-free Diffusion—0
Fast Personalized Text-to-Image Syntheses With Attention Injection—0
HiFi Tuner: High-Fidelity Subject-Driven Fine-Tuning for Diffusion Models—0
IDAdapter: Learning Mixed Features for Tuning-Free Personalization of Text-to-Image Models—0
Layout-and-Retouch: A Dual-stage Framework for Improving Diversity in Personalized Image Generation—0
LoRACLR: Contrastive Adaptation for Customization of Diffusion Models—0
MM-Diff: High-Fidelity Image Personalization via Multi-Modal Condition Integration—0
MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation—0
DreamCache: Finetuning-Free Lightweight Personalized Image Generation via Feature Caching—0
PersonaCraft: Personalized Full-Body Image Synthesis for Multiple Identities from Single References Using 3D-Model-Conditioned Diffusion—0
Personalize Anything for Free with Diffusion Transformer—0
DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image Diffusion Models—0
ViPer: Visual Personalization of Generative Models via Individual Preference Learning—0
A4A: Adapter for Adapter Transfer via All-for-All Mapping for Cross-Architecture Models—0
AC-LoRA: Auto Component LoRA for Personalized Artistic Style Image Generation—0
PortraitBooth: A Versatile Portrait Model for Fast Identity-preserved Personalization—0
RAGAR: Retrieval Augment Personalized Image Generation Guided by Recommendation—0
DRC: Enhancing Personalized Image Generation via Disentangled Representation Composition—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