Innovate Futures @ Benji
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ComfyUI Wan 2.1 Vace Video2Video With Extended Long Video Length Generation - Tutorial
🕑 Added 2025-07-04 13:00:20 +0000 UTC
Tutorial Video : https://youtu.be/4Y3s1_4FnwE
Another way to apply long video length is img2vid, which I have demonstrate in this tutorial: https://youtu.be/Gj1AwrOmyJ4
For Patreon Supporters: https://www.patreon.com/posts/133334531/?utm_source=youtube&utm_medium=video&utm_campaign=20250704
Learn how to create long-length AI videos using WAN 2.1's video-to-video generation with advanced control net techniques! This tutorial reveals how to stitch seamless 15-30 second clips by mastering overlap frames, multi-character control, and chunk-based sampling. Discover workflows for dance motions, movie scenes, and talking avatars—all while optimizing for GGUF quantization and multi-GPU setups. Whether you're animating robot dances or complex multi-character scenes, this guide unlocks professional-grade AI video production at scale.
Who is this content suitable for?
AI video creators, animators, VFX artists, and developers working with WAN 2.1, ControlNet (DW Pose, Depth Anything V2), and ComfyUI for long-form video generation.
Why it matters:
Traditional AI video tools struggle with long durations and multi-character consistency. This method solves both by dividing videos into optimized chunks (e.g., 129 frames/sampler) and using overlap frames (4-6 frames) for smooth stitching—ideal for music videos, social media content, and cinematic AI projects.
For Tool nodes and Wan
https://github.com/kijai/ComfyUI-WanVideoWrapper
https://github.com/kijai/ComfyUI-KJNodes
https://github.com/Fannovel16/comfyui_controlnet_aux
https://github.com/pythongosssss/ComfyUI-Custom-Scripts
https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite
https://github.com/chflame163/ComfyUI_LayerStyle
For Low VRAM GGUF
https://github.com/city96/ComfyUI-GGUF
https://github.com/neuratech-ai/ComfyUI-MultiGPU
FusionX Wan VACE Model :
Safetensors
https://huggingface.co/QuantStack/Wan2.1_T2V_14B_FusionX_VACE
GGUF
https://huggingface.co/QuantStack/Wan2.1-14B-T2V-FusionX-VACE-GGUF
Attached tutorial workflow here:
