6 papers
Towards Secure Retrieval-Augmented Generation: A Comprehensive Review of Threats, Defenses and Benchmarks
Yanming Mu, Hao Hu, Feiyang Li +7
Retrieval-Augmented Generation (RAG) significantly mitigates the hallucinations and domain knowledge deficiency in large language models by incorporating external knowledge bases.…
ThinkRL-Edit: Thinking in Reinforcement Learning for Reasoning-Centric Image Editing
Hengjia Li, Liming Jiang, Qing Yan +6
Instruction-driven image editing with unified multimodal generative models has advanced rapidly, yet their underlying visual reasoning remains limited, leading to suboptimal perfor…
InstructMoLE: Instruction-Guided Mixture of Low-rank Experts for Multi-Conditional Image Generation
Jinqi Xiao, Qing Yan, Liming Jiang +8
Parameter-Efficient Fine-Tuning of Diffusion Transformers (DiTs) for diverse, multi-conditional tasks often suffers from task interference when using monolithic adapters like LoRA.…
Learning Joint ID-Textual Representation for ID-Preserving Image Synthesis
Zichuan Liu, Liming Jiang, Qing Yan +3
We propose a novel framework for ID-preserving generation using a multi-modal encoding strategy rather than injecting identity features via adapters into pre-trained models. Our me…
Flux Already Knows -- Activating Subject-Driven Image Generation without Training
Hao Kang, Stathi Fotiadis, Liming Jiang +5
We propose a simple yet effective zero-shot framework for subject-driven image generation using a vanilla Flux model. By framing the task as grid-based image completion and simply…
InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity
Liming Jiang, Qing Yan, Yumin Jia +3
Achieving flexible and high-fidelity identity-preserved image generation remains formidable, particularly with advanced Diffusion Transformers (DiTs) like FLUX. We introduce Infini…