Faster Diffusion Through Temporal Attention Decomposition
Authors: Haozhe Liu, Wentian Zhang, Jinheng Xie, Francesco Faccio, Mengmeng Xu, Tao Xiang, Mike Zheng Shou, Juan-Manuel Perez-Rua, Jürgen Schmidhuber
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show when widely applied to various existing textconditional diffusion models, TGATE accelerates these models by 10% 50%. The code of TGATE is available at https://github.com/Haozhe Liu-ST/T-GATE. |
| Researcher Affiliation | Collaboration | 1Center of Excellence for Generative AI, King Abdullah University of Science and Technology (KAUST) 2 Show Lab, National University of Singapore (NUS) 3 Swiss AI Lab, IDSIA, USI & SUPSI, Lugano 4 Meta AI |
| Pseudocode | No | The paper only describes methods in paragraph text and does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of TGATE is available at https://github.com/Haozhe Liu-ST/T-GATE. |
| Open Datasets | Yes | comprehensive experiments are conducted using the MS-COCO (Lin et al., 2014), MJHQ (Li et al., 2023), Open Sora-Sample (Lab & etc., 2024) and DPG-Bench (Hu et al., 2024) datasets. |
| Dataset Splits | Yes | Similar to a previous study (Podell et al., 2023), 10k images from the MS-COCO validation set (Lin et al., 2014) are used to evaluate the zero-shot generation performance. ... The generated images are set at a resolution of 1024 1024, with a total of 10k samples. |
| Hardware Specification | Yes | The latency of generating one image is tested on a 1080 Ti commercial card. ... The computational platform is a single V100 GPU card with pytorch 2.2. |
| Software Dependencies | Yes | computational platform is a single V100 GPU card with pytorch 2.2. |
| Experiment Setup | Yes | The inference configuration, including the number of inference steps and the noise scheduler, follows the default settings for each model. Additionally, the proposed method is compared with other accelerating methods... For Pix Art, parameters are set to m = 15 and k = 3, whereas for SDXL, we utilize m = 10 and k = 5. |