Descriptions:
This video from the Veteran AI channel takes a deep look at the Stand-In character consistency model, now re-tuned on Wan 2.2 after its original release was based on Wan 2.1. The host walks through three distinct ComfyUI workflows: the reference workflow originally shared by developer Kijai, a personally optimized version with a cleaner node layout, and a final refined version that significantly improves both video quality and character fidelity.
A key insight explored here is how the resolution that the character’s face occupies in the reference image directly impacts consistency in the generated output. Close-up facial shots yield near-95% character match, while pulling back to a full-body shot noticeably reduces that resemblance. The video explains the workflow’s two-model structure—one high-noise and one low-noise Stand-In LoRA—and covers critical nodes like “Wan Video Add Standin Latent,” the six-step sampling setup, and the use of the Light X to V acceleration LoRA.
The host also introduces RunningHub as a cloud-based ComfyUI platform where these workflows are hosted and immediately updated as new models drop. Viewers get a practical, reproducible blueprint for generating AI video with stable character consistency using the Wan 2.2 Stand-In model inside ComfyUI, including guidance on image preprocessing, background removal, and latent encoding steps.
📺 Source: Veteran AI · Published December 29, 2025
🏷️ Format: Tutorial Demo
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