HunyuanCustom: A Multimodal-Driven Architecture for Customized Video Generation

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.

We propose HunyuanCustom, a multi-modal, conditional, and controllable generation model centered on subject consistency, built upon the Hunyuan Video generation framework. It enables the generation of subject-consistent videos conditioned on text, images, audio, and video inputs. Specifically, HunyuanCustom introduces an image-text fusion module based on LLaVA to facilitate interaction between images and text, allowing identity information from images to be effectively integrated into textual descriptions. Additionally, an image ID enhancement module is proposed, which concatenates image information along the temporal axis and leverages the video model's efficient temporal modeling ability to enhance subject identity throughout the video. To support conditional injection of audio and video, HunyuanCustom designs distinct injection mechanisms for each modality, which are effectively disentangled with the identity condition module. HunyuanCustom ultimately achieves decoupled control over image, audio, and video conditions, demonstrating great potential in subject-centric multi-modal video generation.