HunyuanVideo gets a generation acceleration method with near-lossless quality at 5–7x speedup

Researchers presented LinCa, a method for accelerating diffusion models through learnable feature caching with component decomposition. Instead of a single prediction strategy for all features, LinCa decomposes cached features into subcomponents with varying continuity using a lightweight invertible network and applies a separate prediction order to each. Strict invertibility guarantees lossless reconstruction back into the original feature space. The method was validated on FLUX, Qwen-Image, and HunyuanVideo: with less than 0.2% additional parameters, it achieves 5–7x speedup with near-lossless quality. Details in the arXiv paper.

What it means

For HunyuanVideo, this is a direct hit: the model is named among the three on which LinCa was tested. HunyuanVideo is an open-source text-to-video generator from Tencent that, as described by the authors, requires 45–60 GB of VRAM and runs only on CUDA. A 5–7x speedup with near-lossless quality is a strong argument for those already working with this model or considering it but hitting generation time limits. Important: the HunyuanVideo fact sheet does not indicate whether LinCa is integrated into the main model repository or exists only as a standalone method. The developer does not specify which generation scenarios benefit the most. LinCa's code is released as open source, so you can verify the method on your own tasks. For other tools in the AI video section, the method is not yet confirmed: only FLUX, Qwen-Image, and HunyuanVideo were tested in the paper. If you work with another model, it is worth waiting for independent validation or adaptations for your tool.