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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 31–40 of 58 papers

TitleStatusHype
FreeTuner: Any Subject in Any Style with Training-free Diffusion—0
RectifID: Personalizing Rectified Flow with Anchored Classifier GuidanceCode2
InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation—0
MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation—0
CAT: Contrastive Adapter Training for Personalized Image GenerationCode0
MoMA: Multimodal LLM Adapter for Fast Personalized Image GenerationCode3
MM-Diff: High-Fidelity Image Personalization via Multi-Modal Condition Integration—0
IDAdapter: Learning Mixed Features for Tuning-Free Personalization of Text-to-Image Models—0
Fast Personalized Text-to-Image Syntheses With Attention Injection—0
Gen4Gen: Generative Data Pipeline for Generative Multi-Concept CompositionCode2
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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