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

Showing 2130 of 58 papers

TitleStatusHype
PersonaMagic: Stage-Regulated High-Fidelity Face Customization with Tandem EquilibriumCode1
PatchDPO: Patch-level DPO for Finetuning-free Personalized Image GenerationCode1
Personalized Image Generation with Large Multimodal ModelsCode1
BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion ModelsCode1
When StyleGAN Meets Stable Diffusion: a W_+ Adapter for Personalized Image GenerationCode1
A Training-Free Style-Personalization via Scale-wise Autoregressive Model0
RAGAR: Retrieval Augment Personalized Image Generation Guided by Recommendation0
DRC: Enhancing Personalized Image Generation via Disentangled Representation Composition0
AC-LoRA: Auto Component LoRA for Personalized Artistic Style Image Generation0
Personalize Anything for Free with Diffusion Transformer0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DreamBooth LoRA SDXL v1.0Overall (CP * PF)0.52Unverified
2IP-Adapter ViT-G SDXL v1.0Overall (CP * PF)0.38Unverified
3Emu2 SDXL v1.0Overall (CP * PF)0.36Unverified
4DreamBooth SD v1.5Overall (CP * PF)0.36Unverified
5IP-Adapter-Plus ViT-H SDXL v1.0Overall (CP * PF)0.34Unverified
6BLIP-Diffusion SD v1.5Overall (CP * PF)0.27Unverified
7Textual Inversion SD v1.5Overall (CP * PF)0.24Unverified