CollectionLoRA: Collecting 50 Effects in 1 LoRA for Deployment

Customized image editing equips diffusion models with spe-cific visual transformations using limited paired data, typically via Low-Rank Adaptation (LoRA). However, combining acceleration and effectLoRAs during inference often introduces interference, causing conceptbleeding, semantic drift, and degraded stylistic fidelity. In this paper, wepropose a unified paradigm that distills numerous customized conceptsand fast-inference capabilities into a single LoRA, effectively resolvingcompositional conflicts while reducing deployment overhead. To addressthe challenges of multi-concept distillation, particularly gradient van-ishing and training instability, we introduce CollectionLoRA, a multi-teacher hybrid distillation framework. Our framework incorporates: (1)Probabilistic Dual-Stream Routing (PDSR) that leverages general-domaindata as regularization to enhance robustness. (2) Automated Asymmet-ric Conditioning (AAC) utilizing Vision-Language Models (VLMs) andorthogonal trigger words to ensure concept isolation, and (3) the De-coupled Hybrid Objective (DHO) that synergizes Trajectory-AnchoredFlow Matching (TA-FM) priors with target simulation and distribu-tion matching, ensuring robust optimization across heterogeneous multi-task distributions. Extensive experiments demonstrate that our unifiedarchitecture mitigates multi-LoRA interference and achieves enhancedconcept fidelity, effective feature isolation, and high-quality rapid syn-thesis. Code is available at https://github.com/Qwen-Applications/CollectionLoRA.