A-SDM: Accelerating Stable Diffusion through Redundancy Removal and Performance Optimization

• URL: http://arxiv.org/abs/2312.15516v3
• Date: Tue, 5 Mar 2024 03:20:12 GMT
• Title: A-SDM: Accelerating Stable Diffusion through Redundancy Removal and Performance Optimization
• Authors: Jinchao Zhu, Yuxuan Wang, Xiaobing Tu, Siyuan Pan, Pengfei Wan, Gao Huang
• Abstract summary: In this work, we first explore the computational redundancy part of the network. We then prune the redundancy blocks of the model and maintain the network performance. Thirdly, we propose a global-regional interactive (GRI) attention to speed up the computationally intensive attention part.
• Score: 54.113083217869516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Stable Diffusion Model (SDM) is a popular and efficient text-to-image (t2i) generation and image-to-image (i2i) generation model. Although there have been some attempts to reduce sampling steps, model distillation, and network quantization, these previous methods generally retain the original network architecture. Billion scale parameters and high computing requirements make the research of model architecture adjustment scarce. In this work, we first explore the computational redundancy part of the network, and then prune the redundancy blocks of the model and maintain the network performance through a progressive incubation strategy. Secondly, in order to maintaining the model performance, we add cross-layer multi-expert conditional convolution (CLME-Condconv) to the block pruning part to inherit the original convolution parameters. Thirdly, we propose a global-regional interactive (GRI) attention to speed up the computationally intensive attention part. Finally, we use semantic-aware supervision (SAS) to align the outputs of the teacher model and student model at the semantic level. Experiments show that this method can effectively train a lightweight model close to the performance of the original SD model, and effectively improve the model speed under limited resources. Experiments show that the proposed method can effectively train a light-weight model close to the performance of the original SD model, and effectively improve the model speed under limited resources. After acceleration, the UNet part of the model is 22% faster and the overall speed is 19% faster.

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