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

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Showing 31–40 of 58 papers

TitleStatusHype
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
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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