Segment Anything: Inpainting with Image Segmentation

Image segmentation, the task of identifying object pixels within an image, has been a fundamental challenge in computer vision. However, creating accurate segmentation models for specific tasks has traditionally required extensive expertise, access to AI training infrastructure, and large volumes of meticulously annotated data. But with the Segment Anything Model (SAM), a new AI model from Meta AI, you can "cut out" any object, in any image, with a single click. SAM is a promptable segmentation system with zero-shot generalization to unfamiliar objects and images, without the need for additional training.

Segment Anything Model: SAM

Segment Anything introduces a novel approach to image segmentation, aiming to break down the barriers and make segmentation accessible to a broader audience. The project introduces two key elements: the Segment Anything Model (SAM) and the Segment Anything 1-Billion mask dataset (SA-1B). SAM is a foundational model for image segmentation, trained on diverse data, and capable of adapting to specific tasks with minimal fine-tuning. SA-1B, on the other hand, is the largest ever segmentation dataset, enabling a wide range of applications and fostering further research in computer vision.

The primary goal of Segment Anything is to reduce the need for specialized modeling expertise, extensive training compute, and custom data annotation for image segmentation. SAM, the heart of the project, achieves this by being a promptable model inspired by natural language processing approaches. Unlike traditional segmentation models, SAM can be easily prompted to perform both interactive segmentation and automatic segmentation, thus offering a more versatile and user-friendly experience.

Promptable Segmentation Explained

In natural language processing and computer vision, the concept of foundation models that can perform zero-shot and few-shot learning has gained immense popularity. SAM builds upon this idea by returning a valid segmentation mask for any prompt. A prompt can take the form of foreground/background points, rough boxes or masks, freeform text, or any information indicating what needs to be segmented in an image. This powerful feature allows users to engage with SAM in real time through a web browser and annotate efficiently.

Underneath SAM's hood, an image encoder generates an embedding for the image, while a lightweight encoder converts the prompt into an embedding vector in real time. These two sources of information are then combined in a lightweight decoder to predict segmentation masks. The brilliance of SAM lies in its ability to produce a segment in just 50 milliseconds, making it highly efficient for various applications.

Use Case: Inpaint Anything with SAM

-Using the extension for the latest version of AUTOMATIC1111’s WebUI, you can access SAM in a new tab. Combining its inpainting tools with ControlNet is a new and powerful way to change your image. SAM automatically creates masks that are easily generated and have much more detail than the ones you used to draw yourself. Using this methods also helps to keep the overall style of the image intact and transfer it to the new changes.

-After uploading your input image, you can then click on Run Segment Anything to create the segmentation image. This is the area where you draw a quick selection to create a mask. In the ControlNet Inpaint tab, you can prompt what you want to change!

-It is also possible to create masks that you can send to the Inpaint tab automatically with just one click. There you can inpaint as you are used to but you don’t have to draw any masks. And all that inside the WebUi!

Conclusion: Easy Masking for Inpainting with SAM

The Segment Anything project represents a significant step towards democratizing image segmentation and expanding the possibilities in AI image generation. By introducing the Segment Anything Model (SAM) and the Segment Anything 1-Billion mask dataset (SA-1B), this project opens up new doors for researchers, developers, and content creators alike.

SAM's promptable segmentation and zero-shot transfer capabilities mark a turning point in how segmentation models are designed and utilized. Its ability to generate accurate masks for any prompt in real time makes it a powerful asset across various domains, from content creation and AR/VR applications to scientific research.

As the AI community embraces SAM and explores its potential, we can look forward to a future where segmentation becomes more accessible, creative tasks become more streamlined, and the boundaries of what can be achieved in computer vision are pushed to new frontiers. With Segment Anything, the inpainting possibilities are vast.