The text discusses the development and testing of a new background removal feature for images, utilizing dichotomous image segmentation models on Workers AI, which builds on previous work in face cropping. Initially, deploying AI workloads was complex and required costly hardware, but advancements like Workers AI have simplified this process. The feature isolates subjects in images from their backgrounds using models such as U^2-Net, IS-Net, BiRefNet, and SAM, each with unique strengths in segmentation accuracy and efficiency. The testing involved evaluating models on datasets with varying complexity and measuring accuracy using metrics like Intersection over Union, Dice coefficient, and pixel accuracy. U^2-Net and IS-Net showed faster inference times on smaller GPUs, while BiRefNet excelled in accuracy and complexity handling, proving suitable for the Images API. The background removal capability is now available in open beta through the Images API, supporting automatic isolation of subjects for enhanced image optimization and creative applications.