Co-occurrence Patterns of LoRA Adapters in Production Diffusion Model Inference Services

arXiv:2609.23321 · cs.DC, cs.AI, cs.LG · Submitted 2026-09-20 · Read on arXiv

cs.DC, cs.AI, cs.LG

Submitted: 2026-09-20

Updated: 2026-09-20

Comments: 18 pages, 7 figures, 5 tables. Code and data: https://gitee.com/liaobin665/lora-cooccurrence-reproduce

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Trace — 40× in this paper · explained in 2 episodes
• Core — 25× in this paper · explained in 2 episodes
• Diffusion Model — 18× in this paper · explained in 8 episodes
• LoRA Adapters — 16× in this paper · explained in 3 episodes
• Sparsity — 10× in this paper · explained in 4 episodes
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• Clustering — 5× in this paper · explained in 2 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Modularity — 4× in this paper · explained in 9 episodes
• Verification — 3× in this paper · explained in 2 episodes
• ACM Symposium on Cloud Computing — 2× in this paper
• ACM Symposium on Operating Systems Principles — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• International Conference on Learning Representations — 2× in this paper
• KV cache — 2× in this paper · explained in 3 episodes
• Low-Rank Adaptation (LoRA) — 2× in this paper · explained in 9 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Symposium on Operating Systems Design and Implementation — 1× in this paper

Sources

• SwiftDiffusion: Efficient Diffusion Model Serving with Add-on Modules

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