Summary of Open-source Acceleration Of Stable-diffusion.cpp Deployable on All Devices, by Jingxu Ng et al.
Open-Source Acceleration of Stable-Diffusion.cpp Deployable on All Devices
by Jingxu Ng, Cheng Lv, Pu Zhao, Wei Niu, Juyi Lin, Minzhou Pan, Yun Liang, Yanzhi Wang
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an optimized version of the Stable Diffusion inference framework (Sdcpp) to accelerate 2D convolution operations, which is a primary bottleneck in image generation. The current implementation of the ggml_conv_2d operator in Sdcpp exhibits high inference latency and massive memory usage. To address this, the authors leverage the Winograd algorithm to optimize 2D convolution operations, achieving substantial performance improvements by exploiting device locality and parallelism. The optimized framework delivers correct end-to-end results across various stable diffusion models, including SDv1.4, v1.5, v2.1, SDXL, and SDXL-Turbo. Evaluation results show a speedup of up to 2.76x for individual convolutional layers and an inference speedup of up to 4.79x compared to the original Sdcpp on M1 pro. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes a stable diffusion framework faster by optimizing how it does a type of math problem called a 2D convolution. This is important because this part of the process takes a long time and uses a lot of memory. The authors use an algorithm called Winograd to make this part of the process go faster, which helps images be generated more quickly. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Inference
