Importance-Aware Low-Rank Distillation of Diffusion Transformers
Read the original on arXiv Computer Vision →The paper introduces SVDtrunc, a two‑step block‑level compression method for Diffusion Transformers (DiTs) that allocates ranks across blocks, applies truncated SVD to the least important ones, and then fine‑tunes all blocks with modular knowledge distillation and a rectified‑flow objective. Experiments on FLUX.dev show that SVDtrunc achieves near‑full performance at 68% of the original parameters and remains competitive even at 57%, outperforming all competing approaches on GenEval, HPSv2, and DPG benchmarks. The method also works well without fine‑tuning, complementing step distillation and offering a practical path to efficient large‑scale generative models.
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