Combined and quantized models for WanVideo, originating from here:

https://huggingface.co/Wan-AI/

Can be used with: https://github.com/kijai/ComfyUI-WanVideoWrapper and ComfyUI native WanVideo nodes.

Other model sources:

TinyVAE from https://github.com/madebyollin/taehv

SkyReels: https://huggingface.co/collections/Skywork/skyreels-v2-6801b1b93df627d441d0d0d9

WanVideoFun: https://huggingface.co/collections/alibaba-pai/wan21-fun-v11-680f514c89fe7b4df9d44f17

CausVid 14B: https://huggingface.co/lightx2v/Wan2.1-T2V-14B-CausVid

CausVid 1.3B: https://huggingface.co/tianweiy/CausVid

AccVideo: https://huggingface.co/aejion/AccVideo-WanX-T2V-14B

Phantom: https://huggingface.co/bytedance-research/Phantom

ATI: https://huggingface.co/bytedance-research/ATI

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CausVid LoRAs are experimental extractions from the CausVid finetunes, the aim with them is to benefit from the distillation in CausVid, rather than any actual causal inference.
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v1 = direct extraction, has adverse effects on motion and introduces flashing artifact at full strength.

v1.5 = same as above, but without the first block which fixes the flashing at full strength.

v2 = further pruned version with only attention layers and no first block, fixes flashing and retains motion better, needs more steps and can also benefit from cfg.