Guide-and-Rescale: Self-Guidance Mechanism for Effective Tuning-Free Real Image Editing
Vadim Titov, Madina Khalmatova, Alexandra Ivanova, Dmitry Vetrov, Aibek Alanov
2024-09-06
Summary
This paper talks about Guide-and-Rescale, a new method for editing real images using a self-guidance mechanism that allows for effective changes without needing complex adjustments.
What's the problem?
Editing real images with existing models can be difficult because they either produce inconsistent quality across different edits or require a lot of time to adjust settings (called hyperparameters) to maintain the original look of the image. This makes the editing process slow and complicated.
What's the solution?
The authors propose a new approach that simplifies the editing process by using a self-guidance technique. This method helps preserve the important details and structure of the original image while allowing for edits. They introduce special energy functions that focus on keeping both the overall layout and local details intact. Additionally, they implement a noise rescaling mechanism to balance different aspects of the image during editing, which means they don’t need to fine-tune the model or go through complex steps. This results in faster and higher-quality image edits.
Why it matters?
This research is important because it makes it easier and quicker to edit real images while maintaining their quality. By simplifying the editing process, it can benefit artists, designers, and anyone who works with images, allowing them to make changes more efficiently without losing important details.
Abstract
Despite recent advances in large-scale text-to-image generative models, manipulating real images with these models remains a challenging problem. The main limitations of existing editing methods are that they either fail to perform with consistent quality on a wide range of image edits or require time-consuming hyperparameter tuning or fine-tuning of the diffusion model to preserve the image-specific appearance of the input image. We propose a novel approach that is built upon a modified diffusion sampling process via the guidance mechanism. In this work, we explore the self-guidance technique to preserve the overall structure of the input image and its local regions appearance that should not be edited. In particular, we explicitly introduce layout-preserving energy functions that are aimed to save local and global structures of the source image. Additionally, we propose a noise rescaling mechanism that allows to preserve noise distribution by balancing the norms of classifier-free guidance and our proposed guiders during generation. Such a guiding approach does not require fine-tuning the diffusion model and exact inversion process. As a result, the proposed method provides a fast and high-quality editing mechanism. In our experiments, we show through human evaluation and quantitative analysis that the proposed method allows to produce desired editing which is more preferable by humans and also achieves a better trade-off between editing quality and preservation of the original image. Our code is available at https://github.com/FusionBrainLab/Guide-and-Rescale.