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WanVideo_comfy-fork – AI Model by chaitnya26 | AlphaNeural AI

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chaitnya26/
WanVideo_comfy-fork

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diffusion-single-file

Wan-AI/Wan2.1-VACE-1.3B

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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.

I've also started to do fp8_scaled versions over here: https://huggingface.co/Kijai/WanVideo_comfy_fp8_scaled

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

Lightx2v:

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

CFG and Step distill 14B: https://huggingface.co/lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill

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

MiniMaxRemover: https://huggingface.co/zibojia/minimax-remover

MAGREF: https://huggingface.co/MAGREF-Video/MAGREF

FantasyTalking: https://github.com/Fantasy-AMAP/fantasy-talking

MultiTalk: https://github.com/MeiGen-AI/MultiTalk

Anisora: https://huggingface.co/IndexTeam/Index-anisora/tree/main/14B

Pusa: https://huggingface.co/RaphaelLiu/PusaV1/tree/main

FastVideo: https://huggingface.co/FastVideo

EchoShot: https://github.com/D2I-ai/EchoShot

Wan22 5B Turbo: https://huggingface.co/quanhaol/Wan2.2-TI2V-5B-Turbo

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.

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.