ColossalAI is a comprehensive distributed training and inference framework for large-scale deep learning models, providing multiple parallelism strategies (data, tensor, pipeline, sequence, mixture-of-experts), ZeRO memory optimization, automatic parallelization via FX tracing, and high-performance custom CUDA/Triton kernels. The architecture is library-based with a plugin-style booster for orchestrating training and inference, a shardformer for model-level parallelism policies, and a legacy subsystem for older APIs. It includes application-level tools: ColossalChat for RLHF (PPO, DPO, GRPO), ColossalQA for retrieval-augmented question answering, ColossalEval for LLM evaluation with GPT judges, and Colossal-LLaMA for domain-specific LLM training. The codebase is actively maintained with recent commits, but has notable technical drift due to outdated dependency pins in some examples and a large legacy namespace that coexists with newer implementations.

Open-Sora is a video generation framework implementing text-to-video, image-to-video, and video-to-video capabilities using diffusion models and variational autoencoders. The system architecture consists of modular components for models (MMDiT, DC-AE, Hunyuan VAE), datasets with bucket-based sampling, acceleration utilities for tensor/sequence parallelism, and training/inference scripts. Key entry points include training scripts in `scripts/diffusion/train/` and `scripts/vae/train.py`, inference scripts in `scripts/diffusion/inference.py` and `scripts/vae/inference.py`, and a Gradio demo at `gradio/app.py`. The system relies on PyTorch, ColossalAI for distributed training, xformers/flash-attention for efficient attention, and safetensors for model checkpointing. Data flows involve loading video datasets, applying spatiotemporal transforms, training diffusion models with classifier-free guidance, and generating videos through denoising loops.