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 51–58 of 58 papers

TitleStatusHype
Beyond Inserting: Learning Identity Embedding for Semantic-Fidelity Personalized Diffusion Generation—0
SerialGen: Personalized Image Generation by First Standardization Then Personalization—0
BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing—0
A Training-Free Style-Personalization via Scale-wise Autoregressive Model—0
FreeTuner: Any Subject in Any Style with Training-free Diffusion—0
Towards More Accurate Personalized Image Generation: Addressing Overfitting and Evaluation BiasCode0
Personalized Image Generation for Color Vision Deficiency PopulationCode0
CAT: Contrastive Adapter Training for Personalized Image GenerationCode0
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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