Open-OmniVCus is a research monorepo reproducing the NeurIPS 2025 OmniVCus method for subject-driven video customization under multimodal control conditions (reference image, depth, mask, motion/camera). It is hybrid by nature: the dominant character is an application/framework layer provided by a large vendored/forked copy of DiffSynth-Studio, wrapped around research-oriented data-construction and training code (VideoCus-Factory, Video-Depth-Anything). The repo does not expose a hosted service or API; it is a Python library plus executable training/inference scripts for diffusion-based image and video generation.
This repository is the official implementation of the SANA family of diffusion models for high-resolution image and video generation, developed by NVIDIA Research. It is primarily a research codebase, containing training and inference pipelines for multiple model variants: SANA (text-to-image), SANA-1.5, SANA-Sprint (few-step distillation), SANA-Video, SANA-Video 2.0, SANA-Streaming (long video), LongSANA, and SANA-WM (world model for camera-controlled video). The codebase also includes post-training with reinforcement learning (Sol-RL), ControlNet support, and various schedulers (flow matching, DPM-Solver, LCM, self-forcing). While the core purpose is research, the repository also provides deployable Gradio applications, conversion scripts to Hugging Face diffusers format, and integration with ComfyUI and SGLang, giving it a hybrid application character. The code is heavily optimized with custom Triton kernels for efficient attention (LiteMLA, Gated Delta Net) and includes distributed training utilities (FSDP, context parallelism). The repository is actively maintained with frequent commits and supports a wide range of model sizes (600M to 14B parameters).