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 21–30 of 58 papers

TitleStatusHype
Conceptrol: Concept Control of Zero-shot Personalized Image GenerationCode1
PatchDPO: Patch-level DPO for Finetuning-free Personalized Image GenerationCode1
Personalized Image Generation with Large Multimodal ModelsCode1
PersonaMagic: Stage-Regulated High-Fidelity Face Customization with Tandem EquilibriumCode1
When StyleGAN Meets Stable Diffusion: a W_+ Adapter for Personalized Image GenerationCode1
BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and EditingCode0
FaceChain: A Playground for Human-centric Artificial Intelligence Generated ContentCode0
Personalized Image Generation for Color Vision Deficiency PopulationCode0
Towards More Accurate Personalized Image Generation: Addressing Overfitting and Evaluation BiasCode0
FaceChain-FACT: Face Adapter with Decoupled Training for Identity-preserved PersonalizationCode0
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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