diffusion 썸네일형 리스트형 [ICLR 2025] ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation https://arxiv.org/abs/2406.02540 ICLR 2025 (Accepted) ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video GenerationDiffusion transformers have demonstrated remarkable performance in visual generation tasks, such as generating realistic images or videos based on textual instructions. However, larger model sizes and multi-frame processing for video generatio.. 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
