# ComfyUI wrapper nodes for [WanVideo](https://github.com/Wan-Video/Wan2.1) and related models. # WORK IN PROGRESS (perpetually) # Why should I use custom nodes when WanVideo works natively? Short answer: Unless it's a model/feature not available yet on native, you shouldn't. Long answer: Due to the complexity of ComfyUI core code, and my lack of coding experience, in many cases it's far easier and faster to implement new models and features to a standalone wrapper, so this is a way to test things relatively quickly. I consider this my personal sandbox (which is obviously open for everyone) to play with without having to worry about compability issues etc, but as such this code is always work in progress and prone to have issues. Also not all new models end up being worth the trouble to implement in core Comfy, though I've also made some patcher nodes to allow using them in native workflows, such as the [ATI](https://huggingface.co/bytedance-research/ATI) node available in this wrapper. This is also the end goal, idea isn't to compete or even offer alternatives to everything available in native workflows. All that said (this is clearly not a sales pitch) I do appreciate everyone using these nodes to explore new releases and possibilities with WanVideo. # 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 fp8 scaled models (personal recommendation): https://huggingface.co/Kijai/WanVideo_comfy_fp8_scaled Text encoders to `ComfyUI/models/text_encoders` Clip vision to `ComfyUI/models/clip_vision` Transformer (main video model) 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: ![image](https://github.com/user-attachments/assets/6a2fd9a5-8163-4c93-b362-92ef34dbd3a4) GGUF models can now be loaded in the main model loader as well. --- Supported extra models: SkyReels: https://huggingface.co/collections/Skywork/skyreels-v2-6801b1b93df627d441d0d0d9 WanVideoFun: https://huggingface.co/collections/alibaba-pai/wan21-fun-v11-680f514c89fe7b4df9d44f17 ReCamMaster: https://github.com/KwaiVGI/ReCamMaster VACE: https://github.com/ali-vilab/VACE Phantom: https://huggingface.co/bytedance-research/Phantom ATI: https://huggingface.co/bytedance-research/ATI Uni3C: https://github.com/alibaba-damo-academy/Uni3C MiniMaxRemover: https://huggingface.co/zibojia/minimax-remover MAGREF: https://huggingface.co/MAGREF-Video/MAGREF FantasyTalking: https://github.com/Fantasy-AMAP/fantasy-talking FantasyPortrait: https://github.com/Fantasy-AMAP/fantasy-portrait MultiTalk: https://github.com/MeiGen-AI/MultiTalk EchoShot: https://github.com/D2I-ai/EchoShot Stand-In: https://github.com/WeChatCV/Stand-In HuMo: https://github.com/Phantom-video/HuMo WanAnimate: https://github.com/Wan-Video/Wan2.2/tree/main/wan/modules/animate Examples: --- WanAnimate: https://github.com/user-attachments/assets/f370b001-0f98-4c4c-bcb5-cfad0b330697 [ReCamMaster](https://github.com/KwaiVGI/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 --- 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