This repository provides a PyTorch implementation of Slot Attention, a neural network architecture that uses a differentiable attention mechanism to decompose data into distinct slots representing objects or entities. The implementation includes the core SlotAttention module with configurable iteration counts, and extends to MultiHeadSlotAttention and AdaptiveSlotWrapper for generating soft slot masks. The model learns to segment and represent multiple objects in a scene without explicit supervision on object boundaries.