The article explores the integration of Meta AI's Segment Anything Model (SAM) with Stable Diffusion and GroundingDINO to create a sophisticated text-to-image inpainting and outpainting pipeline. SAM is highlighted as a groundbreaking foundational model for computer vision due to its ability to generalize zero-shot to unfamiliar objects without additional training, leveraging a vast dataset of 11 million images and 1.1 billion segmentation masks. The tutorial demonstrates how SAM's segmentation masks can be used in conjunction with GroundingDINO for object detection and Stable Diffusion for generating photo-realistic images from text prompts. By logging each step to Comet, the process allows for detailed tracking and debugging, providing flexibility in image manipulation tasks such as replacing or extending sections of an image. The article emphasizes the potential of these tools in advancing computer vision applications and offers resources for readers to experiment with the technology themselves.