Tencent’s HunyuanCustom redefines AI-powered video generation through innovative architecture for consistent subject representation across different input models.
Tencent has released HunyuanCustom, an open-source framework that takes personalized video synthesis to a new level. Based on HunyuanVideo – an AI model with 13 billion parameters – the system integrates text, image, audio and video into a unified architecture. Unlike previous models, which often struggled with identity consistency, HunyuanCustom uses special Temporal ID modules that process reference images along the timeline, guaranteeing remarkable coherence of the depicted subjects across all frames.
The architecture follows a “dual-stream to single-stream” approach, which initially processes visual and textual inputs separately before merging them into a unified latent space. Of particular note is the text-image fusion module, which is based on LLaVA technology and projects image embeddings into the text token space, achieving a detailed alignment between visual subjects and textual descriptions.
For audiovisual synthesis, HunyuanCustom uses a three-stage AudioNet module that converts raw audio signals into mel spectrograms, performs temporal pooling for frame synchronization, and links audio embeddings to visual features through spatial attention maps. This hierarchical approach enables precise audio-video synchronization, which is particularly useful for musical content or speech dialogues.
In the area of video-to-video transformation, the framework relies on a latency compression network that distills input video into low-dimensional codes. An innovative patch disentanglement loss separates content from motion aspects, allowing independent control over subject appearance, motion dynamics and background elements. This separation offers multiple applications such as style transfer between videos while retaining the original subject identities.
Accessibility and integration into existing ecosystems
While the base model requires significant resources (45 GB VRAM for 544×960 resolution), community optimizations have dramatically improved accessibility. FP8 quantization reduces VRAM consumption to 24 GB with minimal loss of quality, while selective layer pruning maintains 95% of output quality with only 18 GB of VRAM. These optimizations enable use on consumer GPUs such as the RTX 4090.
Integration with ComfyUI through pre-built nodes allows visual programming of complex generation pipelines, while integration with LangChain through the TencentHunyuanEmbeddings class enables the use of video semantics in RAG pipelines. A growing ecosystem of community tools, including automatic prompt enhancement and frame interpolation plugins, further lowers the barrier to entry for professional video synthesis.
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Executive Summary
HunyuanCustom is an open source framework for multimodal video generation with a special focus on subject consistency
Based on the 13-billion-parameter HunyuanVideo model, the system outperforms existing methods in ID consistency, realism and text-video alignment
Innovative modules such as text-image fusion and image ID enhancement enable precise control over generated content
Audio-visual synchronization through a three-stage AudioNet module ensures coherent sound-image connections
Quantization techniques and community optimizations reduce hardware requirements from 45GB to up to 18GB VRAM
Integration into ComfyUI and LangChain facilitates integration into existing AI workflows
