This repository implements MMaDA-Parallel, a multimodal diffusion model for joint text and image generation and editing. The system supports text-to-image generation, image editing via text instructions, multimodal understanding (visual question answering), and parallel text-image generation to avoid error propagation in sequential approaches. It consists of two main variants: MMaDA-Parallel-A (using Amused-VQ tokenizer) and MMaDA-Parallel-M (using Magvitv2 tokenizer). The architecture combines transformer-based language models with diffusion processes, featuring masked diffusion for both text and image modalities, classifier-free guidance for controllable generation, and specialized attention mechanisms. Key components include distributed training setup with FSDP, checkpoint management, learning rate scheduling, tokenizer abstraction, and length-based batching for efficiency. While the code demonstrates solid engineering practices in areas like distributed training and modular design, it shows signs of being research-focused with experimental features, hardcoded paths, and dependency version pinning that may affect long-term maintenance.