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Light-DiT: An Importance-Aware Dynamic Compression Framework for Diffusion Transformers
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AI
DOI：10.1007/978-3-031-99857-7_25.png)
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Abstract
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Diffusion Transformers (DiTs) demonstrate remarkable generative abilities in AI. However, the iterative denoising process inherent in diffusion incurs substantial computational cost and memory overhead, impeding fast and energy-efficient edge inference. To mitigate the overhead of denoising, we propose a post-training framework that jointly utilizes pruning and quantization for hardware-efficient DiT inference, which is based on the observation that not all denoising blocks within a model are equally important during image generation. We introduce metrics to assess the importance of DiTs' blocks and layers. To achieve importance-aware dynamic compression, we unify mixed-sparsity pruning and mixed-precision quantization based on importance metrics. Experiments show that our approach achieves a 1.41.x inference speedup through pruning with a mixed precision of W3.2A4.9 while incurring minimal accuracy loss. Furthermore, the evaluation of bit-flexible DNN accelerators demonstrates up to 2.78.x performance improvement, and 1.99.x better energy efficiency can be achieved compared to W8A8 quantization without pruning.
Keywords:
Diffusion Transformers
Model Compression
Hardware-Efficient Inference
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