#Self-Attention

Delve into basic transformer concepts through a streamlined PyTorch model, ideal for educational use. The project features clear implementations minus the complexity of larger transformers, focusing on a single transformer block stack. Follow simple installation instructions using pip or conda environments. Execute basic experiments such as IMDb data classification with customizable hyperparameters. Perfect for hands-on exploration and customization, for those interested in learning about self-attention mechanisms without added complexity. Best suited for Python 3.6+ users interested in a comprehensive educational experience with transformers.

This project presents a convolutional Transformer named EEG Conformer, which efficiently decodes and visualizes EEG signals by capturing comprehensive local and global features. It integrates temporal-spatial convolution, pooling, and self-attention to evaluate one-dimensional data, using fully-connected layers for signal categorization. A new visualization method allows class activation mapping on brain topography. The model demonstrates effectiveness on datasets such as BCI competition IV and SEED, and is available within the braindecode toolbox. Compatibility requires Python 3.10 and Pytorch 1.12.

MasaCtrl's tuning-free self-attention control revolutionizes non-rigid image synthesis and editing by integrating source image content with synthesized layouts through prompts and additional controls. This method ensures consistent modifications without fine-tuning, and seamlessly integrates with controllable diffusion models such as T2I-Adapter and ControlNet. Versatile in nature, it supports video synthesis and adapts well to various Stable Diffusion models, including Anything-V4. Designed for use with Python 3.8.5 and Pytorch 1.11, MasaCtrl effortlessly incorporates into existing workflows to deliver reliable results.