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 41–50 of 58 papers

TitleStatusHype
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
CatVersion: Concatenating Embeddings for Diffusion-Based Text-to-Image Personalization—0
Beyond Inserting: Learning Identity Embedding for Semantic-Fidelity Personalized Diffusion 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
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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