HQ-DiT: Efficient Diffusion Transformer with FP4 Hybrid Quantization

• URL: http://arxiv.org/abs/2405.19751v2
• Date: Fri, 31 May 2024 15:48:05 GMT
• Title: HQ-DiT: Efficient Diffusion Transformer with FP4 Hybrid Quantization
• Authors: Wenxuan Liu, Sai Qian Zhang,
• Abstract summary: Diffusion Transformers (DiTs) have recently gained substantial attention for their superior visual generation capabilities. DiTs also come with high parameter counts and implementation costs, seriously restricting their use on resource-limited devices such as mobile phones. We introduce the Hybrid Floating-point Quantization for DiT(HQ-DiT), an efficient post-training quantization method that utilizes 4-bit floating-point (FP) precision on both weights and activations for DiT inference.
• Score: 10.307268005739202
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion Transformers (DiTs) have recently gained substantial attention in both industrial and academic fields for their superior visual generation capabilities, outperforming traditional diffusion models that use U-Net. However,the enhanced performance of DiTs also comes with high parameter counts and implementation costs, seriously restricting their use on resource-limited devices such as mobile phones. To address these challenges, we introduce the Hybrid Floating-point Quantization for DiT(HQ-DiT), an efficient post-training quantization method that utilizes 4-bit floating-point (FP) precision on both weights and activations for DiT inference. Compared to fixed-point quantization (e.g., INT8), FP quantization, complemented by our proposed clipping range selection mechanism, naturally aligns with the data distribution within DiT, resulting in a minimal quantization error. Furthermore, HQ-DiT also implements a universal identity mathematical transform to mitigate the serious quantization error caused by the outliers. The experimental results demonstrate that DiT can achieve extremely low-precision quantization (i.e., 4 bits) with negligible impact on performance. Our approach marks the first instance where both weights and activations in DiTs are quantized to just 4 bits, with only a 0.12 increase in sFID on ImageNet.

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