FQ4DM: Full Quantization for Diffusion Model

Diffusion models (DMs) have recently gained acclaim for their superior imaging capabilities. However, their extensive computational and memory demands often limit the practical application on portable devices. Post-training quantization (PTQ) offers a solution that enables model compression and reduces runtime without retraining. Nonetheless, traditional PTQ methods struggle to handle the unique time-variant distribution in DMs. Accordingly, we propose a novel timestep-grouping PTQ approach to address the multiple timestep issue. We also identify that non-uniform post-SiLU activations may lead to significant quantization loss. We tackle this issue with a region-specific quantization strategy that better represents extreme values after quantization. Combined with the above methods, we achieve a fully quantized diffusion model feasible for hardware implementation. Our experimental results show that the proposed method successfully maintains the FID score after 8-bit quantization.