CollectionLoRA: Collecting 50 Effects in 1 LoRA via Multi-Teacher On-Policy Distillation
Summary
CollectionLoRA proposes a multi-teacher on-policy distillation framework that distills up to 50 different effect LoRAs into a single LoRA, reducing deployment overhead while maintaining concept fidelity.
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Paper page - CollectionLoRA: Collecting 50 Effects in 1 LoRA via Multi-Teacher On-Policy Distillation
Source: https://huggingface.co/papers/2605.25378
Abstract
CollectionLoRA enables efficient deployment of multiple customized image editing effects by distilling numerous LoRAs into a single model through multi-teacher distillation and specialized mechanisms for concept isolation and generation.
Customized image editing aims to equip pre-traineddiffusion modelswith specific visual effects using limited paired data, typically viaLow-Rank Adaptation(LoRA). As the number of desired effects grows, storing and dynamically loading numerous these effectLoRAs significantly increases deployment overhead. Furthermore, current pipelines typically cascade these effectLoRAs with acceleration modules for fast generation, which triggers severeparameter interferenceand results inconcept bleedingandstyle degradation. We propose CollectionLoRA, amulti-teacher on-policy distillationframework capable of distilling the concepts of up to 50 different effectLoRAs along with few-step generation capabilities into a singleLoRA. This fundamentally resolves the feature interference issue and significantly reduces deployment costs. Specifically, the method introduces (i) aProbabilistic Dual-Stream Routingmechanism that enables the model to randomly switch between data sources during training, effectively enhancing its generalization in unseen scenarios; (ii) anAsymmetric Orthogonal Promptingstrategy to achieveconcept isolationwithin the prompt space; (iii) aCoarse-to-Fine Distillation Objectiveto mitigate the distribution gap between the teacher and student models. Extensive evaluations show that CollectionLoRAdistills all customized effects and few-step generation into a singleLoRA, reducing deployment overhead while achieving concept fidelity comparable to or better than independently trained teacher models.
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