#Diffusion Transformers

Lumina-T2X utilizes flow-based diffusion transformers to effectively convert text into various modalities, including images, videos, and music. It supports high-quality outputs with resolutions up to 2K, and accommodates multilingual prompts and emojis. Recent enhancements improve visual quality, offering new demos that highlight its versatility in vision-language tasks, targeting developers and researchers engaged in generative AI.

Learn how the Geometry-Aware Attention mechanism enhances the functionality of multi-view transformers, facilitating applications such as image generation. Presented at ICLR2024, this method offers a straightforward way to improve multi-view transformers and demonstrates its effectiveness in 2D tasks. Review our experiment results and code examples across datasets like CLEVR-TR, MSN-Hard, and ImageNet with Diffusion Transformers (DiT), showcasing GTA's capabilities for both multi-view and image Vision Transformers (ViT).

DiT-MoE offers a scalable and efficient solution with its PyTorch implementation of Sparse Diffusion Transformers, designed to handle up to 16 billion parameters. Featuring advanced techniques like rectified flow-based training and expert routing, it aids in reducing computational load while enhancing model accuracy and convergence, with practical support from DeepSpeed. This project provides valuable assets such as pre-trained models and detailed scripts, catering to researchers requiring flexible and high-performing AI frameworks.

This extensive compilation delves into diffusion transformers used in various fields such as text, speech, and video production. It highlights groundbreaking research, including text-driven motion generation and scalable image synthesis models, illustrating the latest technological applications. With emphasis on methodologies like transformer-based denoising and high-resolution image synthesis, this collection provides valuable insights into efficient training techniques. Featuring works like MotionDiffuse and scalable diffusion models, it is designed for researchers and practitioners, offering a comprehensive overview of innovations in diffusion transformers, paired with accessible resources and recent research data.