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 11–20 of 58 papers

TitleStatusHype
EZIGen: Enhancing zero-shot personalized image generation with precise subject encoding and decoupled guidanceCode2
DreamBench++: A Human-Aligned Benchmark for Personalized Image GenerationCode2
RectifID: Personalizing Rectified Flow with Anchored Classifier GuidanceCode2
Gen4Gen: Generative Data Pipeline for Generative Multi-Concept CompositionCode2
When StyleGAN Meets Stable Diffusion: a W+ Adapter for Personalized Image GenerationCode2
Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuningCode2
FastComposer: Tuning-Free Multi-Subject Image Generation with Localized AttentionCode2
Personalized Text-to-Image Generation with Auto-Regressive ModelsCode1
Conceptrol: Concept Control of Zero-shot Personalized Image GenerationCode1
Beyond Fine-Tuning: A Systematic Study of Sampling Techniques in Personalized Image GenerationCode1
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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