Summary of One-step Image Translation with Text-to-image Models, by Gaurav Parmar et al.
One-Step Image Translation with Text-to-Image Models
by Gaurav Parmar, Taesung Park, Srinivasa Narasimhan, Jun-Yan Zhu
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); 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 This paper addresses two limitations of conditional diffusion models: slow inference speed and reliance on paired data for fine-tuning. To overcome these issues, the authors introduce a general method for adapting single-step diffusion models to new tasks and domains using adversarial learning objectives. The approach consolidates various modules of the vanilla latent diffusion model into a single end-to-end generator network with small trainable weights, enhancing its ability to preserve input image structure while reducing overfitting. The authors demonstrate that their model CycleGAN-Turbo outperforms existing GAN-based and diffusion-based methods for scene translation tasks like day-to-night conversion and adding/removing weather effects. They also extend their method to paired settings, where the pix2pix-Turbo model is comparable to recent works like Control-Net for Sketch2Photo and Edge2Image, but with single-step inference. The study suggests that single-step diffusion models can serve as strong backbones for a range of GAN learning objectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fix two problems with existing image-to-image translation models: they take too long to process images and need paired data to work well. To solve these issues, the researchers created a way to adapt a single-step model to new tasks and domains using a technique called adversarial learning. The new approach combines different parts of the original model into one simple network that preserves image details while reducing mistakes. The authors tested their method on various image translation tasks and found it outperformed other methods in many cases, especially when there was no paired data available. This study shows that single-step models can be strong tools for a wide range of image-to-image translation tasks. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model * Fine tuning * Gan * Inference * Overfitting * Translation
