custom_nodes/ComfyUI_WanVideoWrapper/readme.md

ComfyUI wrapper nodes for WanVideo

WORK IN PROGRESS

Installation

1. Clone this repo into custom_nodes folder.
2. Install dependencies: pip install -r requirements.txt or if you use the portable install, run this in ComfyUI_windows_portable -folder:

python_embeded\python.exe -m pip install -r ComfyUI\custom_nodes\ComfyUI-WanVideoWrapper\requirements.txt

Models

https://huggingface.co/Kijai/WanVideo_comfy/tree/main

Text encoders to ComfyUI/models/text_encoders

Clip vision to ComfyUI/models/clip_vision

Transformer to ComfyUI/models/diffusion_models

Vae to ComfyUI/models/vae

You can also use the native ComfyUI text encoding and clip vision loader with the wrapper instead of the original models:

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Examples:

ReCamMaster:

https://github.com/user-attachments/assets/c58a12c2-13ba-4af8-8041-e283dbef197e

TeaCache (with the old temporary WIP naive version, I2V):

Note that with the new version the threshold values should be 10x higher

Range of 0.25-0.30 seems good when using the coefficients, start step can be 0, with more aggressive threshold values it may make sense to start later to avoid any potential step skips early on, that generally ruin the motion.

https://github.com/user-attachments/assets/504a9a50-3337-43d2-97b8-8e1661f29f46

Context window test:

1025 frames using window size of 81 frames, with 16 overlap. With the 1.3B T2V model this used under 5GB VRAM and took 10 minutes to gen on a 5090:

https://github.com/user-attachments/assets/89b393af-cf1b-49ae-aa29-23e57f65911e

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This very first test was 512x512x81

~16GB used with 20/40 blocks offloaded

https://github.com/user-attachments/assets/fa6d0a4f-4a4d-4de5-84a4-877cc37b715f

Vid2vid example:

with 14B T2V model:

https://github.com/user-attachments/assets/ef228b8a-a13a-4327-8a1b-1eb343cf00d8

with 1.3B T2V model

https://github.com/user-attachments/assets/4f35ba84-da7a-4d5b-97ee-9641296f391e