HQ-DM: Single Hadamard Transformation-Based Quantization-Aware Training for Low-Bit Diffusion Models

A new framework called HQ-DM has been introduced, focusing on Single Hadamard Transformation-Based Quantization-Aware Training for low-bit diffusion models. This approach aims to address the high computational and memory costs associated with diffusion models, which are widely used in image generation. HQ-DM effectively reduces activation outliers while maintaining model performance during quantization, a significant advancement in the field.
The development of HQ-DM is crucial as it enhances the efficiency of diffusion models, making them more viable for deployment in real-world applications. By reducing storage overhead and accelerating inference, this framework could lead to broader adoption of diffusion models in various industries, particularly in image generation tasks where performance is paramount.
This innovation reflects a growing trend in artificial intelligence towards optimizing model performance while minimizing resource consumption. As researchers continue to explore methods like frequency-decoupled diffusion and adaptive pruning, the focus remains on improving the efficiency and effectiveness of generative models. The advancements in quantization-aware training underscore the importance of balancing model complexity with practical deployment needs.

HQ-DM: Single Hadamard Transformation-Based Quantization-Aware Training for Low-Bit Diffusion Models