Negative-prompt Inversion: Fast Image Inversion for Editing with Text-guided Diffusion Models

Original Paper: https://arxiv.org/abs/2305.16807

By: Daiki Miyake, Akihiro Iohara, Yu Saito, Toshiyuki Tanaka

Abstract:

In image editing employing diffusion models, it is crucial to preserve the reconstruction quality of the original image while changing its style. Although existing methods ensure reconstruction quality through optimization, a drawback of these is the significant amount of time required for optimization. In this paper, we propose negative-prompt inversion, a method capable of achieving equivalent reconstruction solely through forward propagation without optimization, thereby enabling much faster editing processes. We experimentally demonstrate that the reconstruction quality of our method is comparable to that of existing methods, allowing for inversion at a resolution of 512 pixels and with 50 sampling steps within approximately 5 seconds, which is more than 30 times faster than null-text inversion. Reduction of the computation time by the proposed method further allows us to use a larger number of sampling steps in diffusion models to improve the reconstruction quality with a moderate increase in computation time.

Summary Notes

Simplifying Image Editing with Negative-prompt Inversion

Introduction

In the field of artificial intelligence, a new method called Negative-prompt Inversion is transforming how we edit images using text.

Unlike older methods that were slow and costly, this technique is more than 30 times faster while still keeping the high quality of image editing.

This post explains how Negative-prompt Inversion works and why it's a breakthrough for AI Engineers in enterprises.

Background

Let's briefly overview the current technologies:

• Diffusion models lead the way in image editing, offering various approaches to modify images.
• Prompt-to-prompt and null-text inversion are promising but computationally expensive.
• Plug-and-Play methods skip the heavy computations but sacrifice flexibility and quality.

Negative-prompt Inversion builds on these technologies, providing a faster and efficient solution without compromising on quality.

How It Works

Negative-prompt Inversion simplifies the image generation and editing process:

• It uses DDIM Inversion for a deterministic way to reverse the diffusion process.
• Classifier-Free Guidance (CFG) improves the text conditioning, ensuring images match the prompts accurately.
• It avoids the slow null-text inversion by directly using prompt text embeddings, speeding up the editing process significantly.

Experiments

Our tests reveal:

• Setup: We compared Negative-prompt Inversion with traditional methods using Stable Diffusion v1.5 and the COCO dataset.
• Results: Negative-prompt Inversion matched the quality of older methods but was much faster.

Practical Applications

In real-world use, we found that:

• Image Reconstruction: It achieves high-quality image reconstruction similar to null-text inversion but faster.
• Image Editing: It efficiently edits images based on different prompts.
• Sampling Steps: The method offers a good balance between image quality and speed, adaptable to various needs.

Challenges and Future Directions

While promising, Negative-prompt Inversion struggles with complex images, like human faces. Future work could focus on improving the method for such challenging scenarios.

Conclusion

Negative-prompt Inversion is a game-changer in AI-driven image editing, offering a fast and high-quality solution.

It's particularly valuable for enterprises looking for efficient and scalable image editing tools. As we refine this technique, its potential for advancing AI image editing is immense.

For a Deeper Understanding

For those interested in more details, we provide extensive analyses and comparisons in our figures and tables, alongside supplemental materials that delve into the technical aspects and extended results of our research.

Negative-prompt Inversion represents a significant shift in text-guided image editing, reducing the computational load without losing quality. This opens up new possibilities for efficient and creative image manipulation on a large scale.