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
CatVersion: Concatenating Embeddings for Diffusion-Based Text-to-Image Personalization—0
Beyond Inserting: Learning Identity Embedding for Semantic-Fidelity Personalized Diffusion Generation—0
FaceChain-FACT: Face Adapter with Decoupled Training for Identity-preserved PersonalizationCode0
Personalized Image Generation for Color Vision Deficiency PopulationCode0
FaceChain: A Playground for Human-centric Artificial Intelligence Generated ContentCode0
Towards More Accurate Personalized Image Generation: Addressing Overfitting and Evaluation BiasCode0
CAT: Contrastive Adapter Training for Personalized Image GenerationCode0
BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and EditingCode0
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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