HunyuanVideo-1.5 is an open-source video generation model by Tencent, implemented as a Python library with CLI scripts for inference (`generate.py`) and training (`train.py`). It generates high-quality videos from text or image prompts using a diffusion transformer (DiT) architecture with 8.3B parameters, supporting resolutions up to 1080p via super-resolution. The codebase provides a modular pipeline (`HunyuanVideo_1_5_Pipeline`) that orchestrates prompt encoding (byT5 text encoder, SigLIP vision encoder), denoising with flow matching, and VAE decoding. It includes advanced features like SSTA (Selective and Sliding Tile Attention) for efficient long-video processing, step distillation, CFG distillation, and various caching mechanisms (DeepCache, TeaCache, TaylorCache). The repository also offers a training script with distributed support (FSDP, context parallelism), LoRA fine-tuning, and the Muon optimizer. While primarily an application for video generation, it contains research-grade model implementations and evaluation benchmarks. The code is well-structured with reusable neural network modules, but relies on many optional hardware-specific dependencies (flash-attn, flex-block-attn, SageAttention) and external services for prompt rewriting, which may introduce operational complexity. The license is a custom Tencent Hunyuan Community License with restrictions.

HY-WU is a machine learning application for text-guided image editing that implements a Functional Neural Memory framework. The system generates instance-conditioned LoRA (Low-Rank Adaptation) weights on-the-fly from hybrid image-instruction representations and injects them into a frozen backbone model (HunyuanImage-3.0-Instruct) during forward pass, enabling test-time adaptation without optimization. The architecture centers around a WUPipeline orchestrator that coordinates a parameter generator, the base model with PG mixin integration, and image processing components. Key entry points include the infer.py CLI script and gradio/app.py web interface. The system processes user prompts and input images through VAE encoding, condition extraction, parameter generation, LoRA injection, and image decoding to produce edited outputs.