This repository provides a PyTorch implementation of Slot Attention and its variants for object-centric learning. The core SlotAttention module refines a set of slot representations via iterative attention from slots to inputs, enabling the model to discover and represent objects in a scene. The repository extends this base with several wrappers: MultiHeadSlotAttention for multi-head attention, AdaptiveSlotWrapper for dynamic slot selection via Gumbel softmax, MetaSlotAttention for deduplication using vector quantization, and OrthoSlotWrapper for adding an orthogonality loss to encourage distinct slot representations. The library is designed to be modular and reusable, with each component having minimal dependencies and clear interfaces. The code leverages einops for readable tensor operations and follows modern PyTorch practices. A training script is included to demonstrate the usage on a toy dataset of colored rectangles, showing how the slot attention can be used to discover and deduplicate objects. The project is actively maintained, with recent commits and a healthy number of stars and forks. Dependencies are well-specified and up-to-date, indicating low technical drift. The codebase exhibits strong craftsmanship in its clean abstractions and reusable components, making it a valuable resource for researchers and practitioners working on object-centric representations.