diffusion quantization 썸네일형 리스트형 [ICLR 2025] DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models https://arxiv.org/abs/2501.04304 AbstractText-to-Image 생성을 위한 Diffusion모델은 높은 계산량과 메모리사용으로 인해 실제 적용에 제약을 줌.이를 하기 위해 Quantization 기법이 이용되는데, 기존 Diffusion모델의 quantization은 낮은 비트에서 이미지 품질과 텍스트-이미지 alignment를 유지하는데 한계를 지님.본 논문에서는 activation에서 outlier가 있으며, 이는 이미지 품질을 결정하는데 중요한 역할을 한다는 것을 분석함. 또한, 텍스트-이미지 alignment에 cross-attention이 중요한 역할을 한다는 것을 분석함.논문에서 제안하는 DGQ(Distribution-aware Group Quantizat.. 더보기 [CVPR 2025] Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers https://arxiv.org/abs/2406.17343 , CVPR 2025 (Accepted) Q-DiT: Accurate Post-Training Quantization for Diffusion TransformersRecent advancements in diffusion models, particularly the architectural transformation from UNet-based models to Diffusion Transformers (DiTs), significantly improve the quality and scalability of image and video generation. However, despite their impressiarxiv.org Abstrac.. 더보기 [ECCV2024] Post-training Quantization for Text-to-Image Diffusion Models with Progressive Calibration and Activation Relaxing https://arxiv.org/abs/2311.06322 , ECCV 2024 Post-training Quantization for Text-to-Image Diffusion Models with Progressive Calibration and Activation RelaxingHigh computational overhead is a troublesome problem for diffusion models. Recent studies have leveraged post-training quantization (PTQ) to compress diffusion models. However, most of them only focus on unconditional models, leaving the q.. 더보기 이전 1 다음
