Principle:Alibaba ROLL LoRA Checkpoint Merging

Knowledge Sources	LoRA Alibaba ROLL
Domains	Model_Management, Diffusion_Models
Last Updated	2026-02-07 20:00 GMT

Overview

A model management principle for merging trained LoRA adapter weights back into the base diffusion model for deployment.

Description

After training, LoRA adapters are stored separately from the base model. For deployment, the adapters must be merged back into the base weights. The merge formula computes: merged_weight = base_weight + alpha * (W_up @ W_down), where W_up and W_down are the LoRA matrices.

This principle also covers shard consolidation when the base model is stored in multiple safetensor shard files.

Usage

Use after reward flow training is complete, before deploying the fine-tuned diffusion model for inference.

Theoretical Basis

LoRA merge: $W_{merged}=W_{base}+\alpha \cdot B\cdot A$

Where $B\in {\mathbb{ℝ}}^{d\times r}$ and $A\in {\mathbb{ℝ}}^{r\times d}$.

Related Pages

Implemented By

• Implementation:Alibaba_ROLL_Merge_LoRA_Into_State_Dict

Related Heuristics

No specific heuristics inform this principle.