HunyuanCustom: A Multimodal-Driven Architecture for Customized Video
  Generation
Authors: name: Teng Hu, name: Zhentao Yu, name: Zhengguang Zhou, name: Sen Liang, name: Yuan Zhou, name: Qin Lin, name: Qinglin Lu

Customized video generation aims to produce videos featuring specific
subjects under flexible user-defined conditions, yet existing methods often
struggle with identity consistency and limited input modalities. In this paper,
we propose HunyuanCustom, a multi-modal customized video generation framework
that emphasizes subject consistency while supporting image, audio, video, and
text conditions. Built upon HunyuanVideo, our model first addresses the
image-text conditioned generation task by introducing a text-image fusion
module based on LLaVA for enhanced multi-modal understanding, along with an
image ID enhancement module that leverages temporal concatenation to reinforce
identity features across frames. To enable audio- and video-conditioned
generation, we further propose modality-specific condition injection
mechanisms: an AudioNet module that achieves hierarchical alignment via spatial
cross-attention, and a video-driven injection module that integrates
latent-compressed conditional video through a patchify-based feature-alignment
network. Extensive experiments on single- and multi-subject scenarios
demonstrate that HunyuanCustom significantly outperforms state-of-the-art open-
and closed-source methods in terms of ID consistency, realism, and text-video
alignment. Moreover, we validate its robustness across downstream tasks,
including audio and video-driven customized video generation. Our results
highlight the effectiveness of multi-modal conditioning and identity-preserving
strategies in advancing controllable video generation. All the code and models
are available at https://hunyuancustom.github.io.