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 31–40 of 58 papers

TitleStatusHype
CAT: Contrastive Adapter Training for Personalized Image GenerationCode0
LoRACLR: Contrastive Adaptation for Customization of Diffusion Models—0
MM-Diff: High-Fidelity Image Personalization via Multi-Modal Condition Integration—0
MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation—0
DreamCache: Finetuning-Free Lightweight Personalized Image Generation via Feature Caching—0
PersonaCraft: Personalized Full-Body Image Synthesis for Multiple Identities from Single References Using 3D-Model-Conditioned Diffusion—0
Personalize Anything for Free with Diffusion Transformer—0
DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image Diffusion Models—0
ViPer: Visual Personalization of Generative Models via Individual Preference Learning—0
A4A: Adapter for Adapter Transfer via All-for-All Mapping for Cross-Architecture Models—0
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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