ComfyUI Native Using Wan 2.1 VACE & GGUF for Less Memory Processing!
Added 2025-05-21 14:51:00 +0000 UTC
Video : https://youtu.be/UUCmCyABmSc
Additional Contents for Patreon Supporters: https://www.patreon.com/posts/129526216
In this video, we explore the latest updates for WAN 2.1 Vace in ComfyUI, including native node support and GGUF quantized models for low-VRAM setups. Learn how to run the 14B FP8 and BF16 models efficiently, even on mid-range hardware, and discover optimized workflows for text-to-video generation with ControlNet pose and style transfer. We also cover LoRA integration (COS V14B) for faster sampling and higher-quality outputs, along with practical tips for video resolution, frame settings, and VRAM management. Whether you're an AI developer, video creator, or tech enthusiast, this guide unlocks the full potential of WAN 2.1 Vace for professional-grade AI video production.
Who is this for?
AI researchers and developers using WAN 2.1
Video creators leveraging ComfyUI for dynamic workflows
Users with mid-to-low VRAM GPUs exploring quantized models
Tech enthusiasts interested in AI-powered video synthesis
Why it matters:
The 14B Vace model delivers cinematic-quality video with enhanced coherence and detail, while GGUF quantization makes it accessible for lower-end hardware. Integration with ControlNet and LoRA ensures precise control over motion and style, revolutionizing AI-assisted video editing.
GGUF Repo
https://huggingface.co/QuantStack/Wan2.1-VACE-14B-GGUF
ComfyUI Repackaged Repo
https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main
ComfyUI Blog Post
https://blog.comfy.org/p/wan21-vace-native-support-and-ace
Attached VACE v2v workflow example:
