arXiv:2606.15615v1 Announce Type: new Abstract: Diffusion Transformers with Mixture-of-Experts (DiT-MoE) improve model capacity under sparse activation, but diffusion inference is still bottlenecked by redundant computation across timesteps. Existing caching methods mainly operate at the token level, which becomes suboptimal in DiT-MoE because each token update is internally decomposed into multiple routed expert branches. Our analysis shows that cross-timestep redundancy in DiT-MoE is better characterized at the expert-branch level than at the whole-token level. Based on this observation, we
The paper addresses a critical current challenge in optimizing Diffusion Transformers, a leading architecture for generative AI, particularly relevant as MoE models gain traction for efficiency.
This research could significantly improve the efficiency of large-scale generative AI models by reducing computational redundancy, making them faster and less resource-intensive to train and deploy.
The focus of optimization shifts from whole-token caching to expert-branch level caching within Mixture-of-Experts Diffusion Transformers, potentially unlocking new performance gains.
- · AI model developers
- · Cloud computing providers (through efficiency)
- · AI research institutions
- · Inefficient AI architectures
More efficient and faster development of generative AI models, particularly Diffusion Transformers.
Reduced operational costs for deploying large AI models, fostering broader adoption and accessibility.
Acceleration in the development of more complex and capable AI systems due to improved computational foundations.
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Read at arXiv cs.LG