Post-Training Quantization for Diffusion Transformer via Hierarchical Timestep Grouping

• URL: http://arxiv.org/abs/2503.06930v2
• Date: Sat, 29 Mar 2025 06:37:07 GMT
• Title: Post-Training Quantization for Diffusion Transformer via Hierarchical Timestep Grouping
• Authors: Ning Ding, Jing Han, Yuchuan Tian, Chao Xu, Kai Han, Yehui Tang,
• Abstract summary: Diffusion Transformer (DiT) has now become the preferred choice for building image generation models.<n>DiT is purely composed of a stack of transformer blocks, which renders DiT excellent in scalability like large language models.<n>We propose a post-training quantization framework tailored for Diffusion Transforms to tackle these challenges.
• Score: 36.012101195384744
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diffusion Transformer (DiT) has now become the preferred choice for building image generation models due to its great generation capability. Unlike previous convolution-based UNet models, DiT is purely composed of a stack of transformer blocks, which renders DiT excellent in scalability like large language models. However, the growing model size and multi-step sampling paradigm bring about considerable pressure on deployment and inference. In this work, we propose a post-training quantization framework tailored for Diffusion Transforms to tackle these challenges. We firstly locate that the quantization difficulty of DiT mainly originates from the time-dependent channel-specific outliers. We propose a timestep-aware shift-and-scale strategy to smooth the activation distribution to reduce the quantization error. Secondly, based on the observation that activations of adjacent timesteps have similar distributions, we utilize a hierarchical clustering scheme to divide the denoising timesteps into multiple groups. We further design a re-parameterization scheme which absorbs the quantization parameters into nearby module to avoid redundant computations. Comprehensive experiments demonstrate that out PTQ method successfully quantize the Diffusion Transformer into 8-bit weight and 8-bit activation (W8A8) with state-of-the-art FiD score. And our method can further quantize DiT model into 4-bit weight and 8-bit activation (W4A8) without sacrificing generation quality.

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