Summary of Schedule on the Fly: Diffusion Time Prediction For Faster and Better Image Generation, by Zilyu Ye et al.
Schedule On the Fly: Diffusion Time Prediction for Faster and Better Image Generation
by Zilyu Ye, Zhiyang Chen, Tiancheng Li, Zemin Huang, Weijian Luo, Guo-Jun Qi
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Time Prediction Diffusion Model (TPDM) is a novel approach for text-to-image generation that adaptively determines the optimal noise schedule for each instance. By employing a plug-and-play Time Prediction Module (TPM), TPDM predicts the next noise level based on current latent features at each denoising step, allowing it to reason and adjust its process accordingly. This adaptive scheduler enables TPDM to generate high-quality images that align with human preferences while minimizing excessive denoising steps. The paper demonstrates the effectiveness of TPDM by achieving an aesthetic score of 5.44 and a human preference score (HPS) of 29.59 using Stable Diffusion 3 Medium architecture, outperforming traditional diffusion models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Time Prediction Diffusion Model is a new way to create images from text that gets better as it goes along. Right now, most image generators use a set schedule for making pictures, but this model predicts the best steps to take based on what it’s already done. This helps it make high-quality images that people like, and it does so more efficiently than other models. The results are impressive, with an aesthetic score of 5.44 and a preference score of 29.59. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Image generation
