LongCat-Video is a production-grade video generation foundation model (13.6B parameters) that supports text-to-video, image-to-video, video continuation, and audio-driven avatar animation. The codebase implements a dense DiT-style transformer with 3D patch embedding, block-sparse attention (BSA) via custom Triton kernels, 3D rotary position embeddings (RoPE), and context parallelism for multi-GPU inference. It includes a coarse-to-fine generation pipeline (480p → 720p) using LoRA adapters for distillation and refinement, and an avatar variant that conditions on audio embeddings from Wav2Vec2 or Whisper. The architecture is monolithic, with entry points being demo scripts and a Streamlit UI. External dependencies include PyTorch, HuggingFace Transformers, FlashAttention, Triton, and optional OpenAI API for prompt enhancement. The code demonstrates advanced engineering practices such as INT8 quantization, KV-caching, and tiled VAE decoding, but suffers from some code duplication and tight coupling to specific model dimensions.

LongCat-Flash-Omni is an application providing inference capabilities for a 560B parameter omni-modal model that processes text, audio, image, and video inputs to generate text and/or audio outputs. The system features a modular architecture with separate modality encoders (audio, vision), a unified data processor for multimodal inputs, and a post-processor for converting model outputs to usable formats. The main entry point is longcat_omni_demo.py which orchestrates the inference pipeline, leveraging SGLang for efficient model serving. The repository demonstrates strong engineering practices in its modular design, particularly in the audio feature processing and image transformation components.