Summary of T-stitch: Accelerating Sampling in Pre-trained Diffusion Models with Trajectory Stitching, by Zizheng Pan et al.
T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching
by Zizheng Pan, Bohan Zhuang, De-An Huang, Weili Nie, Zhiding Yu, Chaowei Xiao, Jianfei Cai, Anima Anandkumar
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces T-Stitch, a technique to improve sampling efficiency for high-quality image generation using diffusion probabilistic models (DPMs). The authors propose switching between smaller and larger DPMs during the sampling trajectory, leveraging the ability of smaller models to generate good global structures in early steps. This approach is training-free, applicable to different architectures, and provides flexible speed and quality trade-offs. The authors demonstrate the effectiveness of T-Stitch on DiT-XL, showing that 40% of early timesteps can be replaced with a 10x faster DiT-S without performance drop. Additionally, the method can also accelerate and improve prompt alignment for popular pretrained stable diffusion (SD) models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us generate better images using computers. It’s like having a special tool that makes painting easier and faster! Instead of using a big tool to draw everything, we use a smaller one first and then switch to the bigger one later on. This makes it possible to create great pictures quickly without sacrificing quality. The authors tested this idea and showed that it works really well for making images like those found in books and movies. |
Keywords
* Artificial intelligence * Alignment * Diffusion * Image generation * Prompt
