workflow-templates-broken-zap-2026-06-08/site/knowledge/models/humo.md

HuMo

HuMo is a human-centric video generation model by ByteDance that produces videos from collaborative multi-modal conditioning using text, image, and audio inputs.

Model Variants

HuMo (Wan2.1-T2V-1.3B based)

• Built on the Wan2.1-T2V-1.3B video foundation model
• Supports Text+Image (TI), Text+Audio (TA), and Text+Image+Audio (TIA) modes
• Two-stage training: subject preservation then audio-visual sync

Key Features

• Multi-modal conditioning: text, reference images, and audio simultaneously
• Subject identity preservation from reference images across frames
• Audio-driven lip synchronization with facial expression alignment
• Focus-by-predicting strategy for facial region attention during audio sync
• Time-adaptive guidance dynamically adjusts input weights across denoising steps
• Minimal-invasive image injection maintains base model prompt understanding
• Progressive two-stage training separates identity learning from audio sync
• Supports text-controlled appearance editing while preserving identity

Hardware Requirements

• Minimum: 24GB VRAM (RTX 3090/4090 or similar)
• Multi-GPU inference supported via FSDP and sequence parallelism
• Whisper-large-v3 audio encoder required for audio modes
• Optional audio separator for cleaner speech input

Common Use Cases

• Digital avatar and virtual presenter creation
• Audio-driven talking head generation
• Character-consistent video clips from reference photos
• Lip-synced dialogue video from audio tracks
• Prompted reenactment with identity preservation
• Text-controlled outfit and style changes on consistent subjects

Key Parameters

• mode: Generation mode (TI, TA, or TIA)
• scale_t: Text guidance strength (default: 7.5)
• scale_a: Audio guidance strength (default: 2.0)
• frames: Number of output frames (97 at 25 FPS = ~4 seconds)
• height/width: Output resolution (480p or 720p supported)
• steps: Denoising steps (30-50 recommended)