Artificial Intelligence 6 min read

Hands‑On Tutorial: HuMo‑1.7B Multimodal Video Generation Framework for Unified Text‑Image‑Audio Creation

The article introduces HuMo‑1.7B, a multimodal video generation framework that jointly processes text, reference images, and audio, achieves SOTA performance on several sub‑tasks, and provides a step‑by‑step tutorial for running the model on the HyperAI platform with detailed resource and parameter guidance.

HyperAI Super Neural

Oct 23, 2025

Hands‑On Tutorial: HuMo‑1.7B Multimodal Video Generation Framework for Unified Text‑Image‑Audio Creation

AI‑generated videos are becoming increasingly realistic, yet they often fall into an uncanny‑valley effect because most models rely on a single modality. In creative workflows, vague client briefs lead to unsatisfactory results; detailed specifications across style, characters, and tone are needed, and video generation must coordinate visual and auditory information simultaneously.

The HuMo framework, jointly released by Tsinghua University and ByteDance’s Intelligent Creation Lab, proposes a "collaborative multimodal conditional generation" paradigm. It incorporates text, reference images, and audio as inputs to a single diffusion model, employs a progressive training strategy, and uses a time‑adaptive guidance mechanism that dynamically adjusts guidance weights during denoising steps. This enables better consistency between appearance, sound, and motion, moving video synthesis from multi‑stage stitching toward a one‑stop generation process.

The accompanying paper (https://arxiv.org/abs/2509.08519) reports state‑of‑the‑art results on tasks such as text‑to‑video tracking and image‑video consistency. HuMo is released in two model sizes, 1.7 B and 17 B, catering to both lightweight experimentation and professional research.

Running the tutorial on HyperAI involves four main steps:

Navigate to the HyperAI homepage, select the "Tutorial" section, and choose "HuMo‑1.7B: Multimodal Video Generation Framework", then click "Run this tutorial online".

On the tutorial page, click the top‑right "Clone" button to copy the repository into your own container.

Select the "NVIDIA GeForce RTX 5090" GPU and a "PyTorch" image, then choose a billing option (pay‑as‑you‑go or subscription). New users can use the invitation link https://openbayes.com/console/signup?r=Ada0322_NR0n for 4 h of RTX 5090 and 5 h of CPU free time.

Wait roughly two minutes for the environment to start, then open the workspace to access the demo page.

On the demo page, enter a textual description, upload an image and an audio file, adjust parameters (e.g., set Sampling Steps to 10), and click "Generate Video". Generation takes about 3–5 minutes and produces a video that aligns the three modalities.

The article concludes with links to the HuMo‑1.7B and HuMo‑17B tutorials (https://go.hyper.ai/BGQT1 and https://go.hyper.ai/RSYAi) and encourages readers to try the framework.