DIET: Deletion-response Expert Trimming for Video Diffusion Transformers
Video diffusion transformers (DiTs) increasingly adopt mixture-of-experts (MoE) architectures to reduce active computation, but their full expert storage remains costly. Existing one-shot pruning criteria mainly rely on static activation or routing statistics and cannot capture layer-level re-routing after expert deletion. We introduce DIET, a training-free expert pruning framework based on deletion responses. A single all-expert calibration pass records expert outputs and router states for matched conditional and unconditional tokens. Candidate deletions are then replayed from cached tensors, requiring no additional model forward passes. The resulting deletion-response signatures charact...
| Authors | Jiachang Zhang, Teng Hu, Bohao Feng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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