InstructMoLE: Instruction-Guided Mixture of Low-rank Experts for Multi-Conditional Image Generation

• URL: http://arxiv.org/abs/2512.21788v1
• Date: Thu, 25 Dec 2025 21:37:12 GMT
• Title: InstructMoLE: Instruction-Guided Mixture of Low-rank Experts for Multi-Conditional Image Generation
• Authors: Jinqi Xiao, Qing Yan, Liming Jiang, Zichuan Liu, Hao Kang, Shen Sang, Tiancheng Zhi, Jing Liu, Cheng Yang, Xin Lu, Bo Yuan,
• Abstract summary: InstructMoLE is a novel framework that employs an Instruction-Guided Mixture of Low-Rank Experts.<n>Our work presents a robust and generalizable framework for instruction-driven fine-tuning of generative models.
• Score: 30.210150562465202
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parameter-Efficient Fine-Tuning of Diffusion Transformers (DiTs) for diverse, multi-conditional tasks often suffers from task interference when using monolithic adapters like LoRA. The Mixture of Low-rank Experts (MoLE) architecture offers a modular solution, but its potential is usually limited by routing policies that operate at a token level. Such local routing can conflict with the global nature of user instructions, leading to artifacts like spatial fragmentation and semantic drift in complex image generation tasks. To address these limitations, we introduce InstructMoLE, a novel framework that employs an Instruction-Guided Mixture of Low-Rank Experts. Instead of per-token routing, InstructMoLE utilizes a global routing signal, Instruction-Guided Routing (IGR), derived from the user's comprehensive instruction. This ensures that a single, coherently chosen expert council is applied uniformly across all input tokens, preserving the global semantics and structural integrity of the generation process. To complement this, we introduce an output-space orthogonality loss, which promotes expert functional diversity and mitigates representational collapse. Extensive experiments demonstrate that InstructMoLE significantly outperforms existing LoRA adapters and MoLE variants across challenging multi-conditional generation benchmarks. Our work presents a robust and generalizable framework for instruction-driven fine-tuning of generative models, enabling superior compositional control and fidelity to user intent.

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