Summary
Face restoration in AI-generated video is tackled head-on in this practical ComfyUI workflow guide from Veteran AI. The tutorial addresses a persistent problem in text-to-video generation: characters whose faces turn blurry or lose detail when positioned in medium or wide shots. The solution combines three technologies — the Minimax-h3_Singularity base model (a fine-tuned MiniMax H3 hybrid supporting text-to-video, first-and-last-frame, and reference-based generation), the ComfyUI-H3-FaceRefine extension for per-frame face tracking and latent injection, and VOSR2, a 1.4-billion-parameter super-resolution model — into a two-stage restoration pipeline tested across four categories: small-face compositions, complex lighting, fast motion, and already-clear faces.
Key technical specifics include LightX2V’s four-step Turbo LoRA (version 0.1) for acceleration, YOLOv8 Face for bounding-box tracking at 768×768, eight Euler sampling steps with a Beta scheduler, and VOSR2’s approximately 7 GB model package pulled automatically from Hugging Face. The guide clearly explains the four universal face-restoration steps — detect and crop, enlarge, redraw, paste back — and provides two complete workflow variants covering first-and-last-frame versus reference-based base-video generation.
RunningHub is recommended as an online ComfyUI workspace for those without local GPU resources, given its fast support for newly released models and extensions. Viewers come away with a reproducible, end-to-end workflow that meaningfully improves face quality in AI video without requiring a full re-generation of the source clip.
📺 Source: Veteran AI · Published September 11, 2026
🏷️ Format: Tutorial Demo
