CMuon: Accelerating and Stabilizing Diffusion Transformer Training via Chunked Momentum Orthogonalization
The paper presents CMuon, a chunked momentum orthogonalization method that adapts the Muon optimizer for Diffusion Transformers, cutting training cost and improving convergence.
- CMuon partitions fused DiT weights, enabling effective momentum orthogonalization.
- Training time for Diffusion Transformers drops by roughly 30% with CMuon.
- Loss convergence becomes more stable, outperforming AdamW and standard Muon.
- The technique scales across model sizes, offering cost savings for large deployments.
Diffusion Transformers (DiTs) have set new performance records in visual generative modeling, but their training demands are extremely high, limiting wider adoption. The recently introduced Muon optimizer, based on momentum orthogonalization, showed promise as an alternative to AdamW, yet when applied directly to DiTs it struggled to converge in later training stages.
The authors identify that DiT architectures merge distinct weight groups, such as those in AdaLN and QKV layers, into single tensors for efficiency, which hampers the effectiveness of the Muon optimizer. To address this, they propose CMuon, which partitions these fused tensors into chunks and applies momentum orthogonalization separately, preserving the optimizer's benefits while respecting the model's internal structure.
Experimental results on standard diffusion benchmarks demonstrate that CMuon reduces training time by up to 30% and achieves more stable loss curves compared to both AdamW and the unmodified Muon optimizer. The method also scales well across different model sizes, suggesting broader applicability for large‑scale generative models.
By tackling a concrete training inefficiency, CMuon opens the door for faster research cycles and potentially lower compute costs for organizations deploying diffusion‑based generative AI.
Provides a more efficient optimizer for training diffusion models, reducing compute expense.
Lowers operational costs for AI services that rely on diffusion‑based image generation.
Improved training efficiency can accelerate product timelines and enhance ROI for diffusion‑focused startups.
Offers a concrete research example of optimizer design for advanced generative models.
Makes high‑quality AI image generation more accessible by cutting training resources.
- Diffusion Transformer (DiT)
- A neural architecture that combines diffusion processes with transformer layers for image generation.
- Momentum Orthogonalization
- An optimization technique that maintains orthogonal momentum vectors to improve convergence stability.
- AdaLN
- Adaptive Layer Normalization, a variant of layer norm that adjusts scaling parameters per instance.
- QKV
- Query, Key, and Value matrices in transformer attention mechanisms.
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