SAVRN
Search Contact SAVRN

Open-weight model · Text to video

Wan2.2-T2V-A14B-GGUF

by QuantStack QuantStack/Wan2.2-T2V-A14B-GGUF

This GGUF file is a direct conversion of Wan-AI/Wan2.2-T2V-A14B Since this is a quantized model, all original licensing terms and usage restrictions remain in effect.

Parameters
Context
Weights250.0 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads726.7k

Model Card

By QuantStack, published under apache-2.0, revision 73eafba53a1a.

This GGUF file is a direct conversion of Wan-AI/Wan2.2-T2V-A14B Since this is a quantized model, all original licensing terms and usage restrictions remain in effect. Usage The model can be used with the ComfyUI custom node ComfyUI-GGUF by city96 Place model files in ComfyUI/models/unet see the GitHub readme for further installation instructions.

Read QuantStack's full model card

This GGUF file is a direct conversion of Wan-AI/Wan2.2-T2V-A14B

Since this is a quantized model, all original licensing terms and usage restrictions remain in effect.

Usage

The model can be used with the ComfyUI custom node ComfyUI-GGUF by city96

Place model files in ComfyUI/models/unet see the GitHub readme for further installation instructions.

Identity and Version

Repository
QuantStack/Wan2.2-T2V-A14B-GGUF
Publisher
QuantStack
Task
Text to video
Modality
Video
Library
gguf
Parameters
Not stated by the source
Languages
Not stated by the source
Revision
73eafba53a1a8f29254e4c77f92e74ea27d7cd6f
First published
2025-07-28
Last updated
2025-07-29

Files and Weights

29 files, 250.0 GB in total. The weights are 27 files totalling 250.0 GB in gguf, safetensors.

Weights27 files · 250.0 GB
Documentation1 file · 607 B
Repository1 file · 4.2 KB
Every file
FileTypeSizeSHA-256
HighNoise/Wan2.2-T2V-A14B-HighNoise-Q2_K.ggufWeights5.3 GB 65dd92f0d444
HighNoise/Wan2.2-T2V-A14B-HighNoise-Q3_K_M.ggufWeights7.2 GB 0a1166c94296
HighNoise/Wan2.2-T2V-A14B-HighNoise-Q3_K_S.ggufWeights6.5 GB f10a599aeec8
HighNoise/Wan2.2-T2V-A14B-HighNoise-Q4_0.ggufWeights8.6 GB 3bf215a4b67b
HighNoise/Wan2.2-T2V-A14B-HighNoise-Q4_1.ggufWeights9.3 GB d42d4ee3f64f
HighNoise/Wan2.2-T2V-A14B-HighNoise-Q4_K_M.ggufWeights9.7 GB e0c490c6e316
HighNoise/Wan2.2-T2V-A14B-HighNoise-Q4_K_S.ggufWeights8.7 GB 4943639d985d
HighNoise/Wan2.2-T2V-A14B-HighNoise-Q5_0.ggufWeights10.3 GB 694902f31873
HighNoise/Wan2.2-T2V-A14B-HighNoise-Q5_1.ggufWeights11.0 GB a2daf4e45be0
HighNoise/Wan2.2-T2V-A14B-HighNoise-Q5_K_M.ggufWeights10.8 GB fe704eb3541b
HighNoise/Wan2.2-T2V-A14B-HighNoise-Q5_K_S.ggufWeights10.1 GB 95048e4718af
HighNoise/Wan2.2-T2V-A14B-HighNoise-Q6_K.ggufWeights12.0 GB 504340d1d4eb
HighNoise/Wan2.2-T2V-A14B-HighNoise-Q8_0.ggufWeights15.4 GB e15fecd4ce8f
LowNoise/Wan2.2-T2V-A14B-LowNoise-Q2_K.ggufWeights5.3 GB 9350378f9156
LowNoise/Wan2.2-T2V-A14B-LowNoise-Q3_K_M.ggufWeights7.2 GB 13bf25ca46c9
LowNoise/Wan2.2-T2V-A14B-LowNoise-Q3_K_S.ggufWeights6.5 GB 1d97051aca33
LowNoise/Wan2.2-T2V-A14B-LowNoise-Q4_0.ggufWeights8.6 GB 892a353ea21c
LowNoise/Wan2.2-T2V-A14B-LowNoise-Q4_1.ggufWeights9.3 GB f565d1d217bc
LowNoise/Wan2.2-T2V-A14B-LowNoise-Q4_K_M.ggufWeights9.7 GB 091a5bae02e1
LowNoise/Wan2.2-T2V-A14B-LowNoise-Q4_K_S.ggufWeights8.7 GB 55439a8ee4c5
LowNoise/Wan2.2-T2V-A14B-LowNoise-Q5_0.ggufWeights10.3 GB 9dbceecceb04
LowNoise/Wan2.2-T2V-A14B-LowNoise-Q5_1.ggufWeights11.0 GB 291d22892ec7
LowNoise/Wan2.2-T2V-A14B-LowNoise-Q5_K_M.ggufWeights10.8 GB 67242c61f055
LowNoise/Wan2.2-T2V-A14B-LowNoise-Q5_K_S.ggufWeights10.1 GB 47a8031ef94d
LowNoise/Wan2.2-T2V-A14B-LowNoise-Q6_K.ggufWeights12.0 GB db3506181004
LowNoise/Wan2.2-T2V-A14B-LowNoise-Q8_0.ggufWeights15.4 GB 71574f62260f
VAE/Wan2.1_VAE.safetensorsWeights253.8 MB 2fc39d31359a
README.mdDocumentation607 B
.gitattributesRepository4.2 KB

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
250.0 GB
Download from QuantStack

Released by QuantStack through its official repository on Hugging Face. Read the license.

Built From

  • Derived from Wan-AI/Wan2.2-T2V-A14B
  • Quantized from Wan-AI/Wan2.2-T2V-A14B

Memory Requirements

PrecisionWeights in memory
As published250.0 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About Wan2.2-T2V-A14B-GGUF

Can I use Wan2.2-T2V-A14B-GGUF commercially?

Yes. Wan2.2-T2V-A14B-GGUF is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

Similar Models

Model · Text to video

MiniMax-H3-Turbo-Lora

Larryvrh

A LoRA for MiniMax-H3 that renders joint video + synchronized stereo audio in as few as 4 sampling steps instead of the usual ~20 — a ~5× sampling speedup — and keeps getting better as you add steps. For most work, use minimaxh3turbov4step600ema.safetensors. It's the markedly better micro-detail (faces, fingers, fine texture), and the over-sharpening / plastic look of the earlier v1 (~850) line is fully resolved. v4 introduced a static-frame enhancement — a big win for static and small-motion content. The one trade-off shows up only at 4 steps with large, fast motion, where v4 can produce motion-smear / trailing ghosting (we're actively fixing this). Two things address it: - Use 6–8 steps.…

Open weights apache-2.0 minimax-h3

Model · Text to video

MiniMax-H3-Turbo-Lora-ComfyUI

DRBAPH

This repository contains MiniMax-H3 Turbo LoRAs converted and optimized for ComfyUI: These LoRAs accelerate MiniMax-H3 video and synchronized-audio generation by reducing the required number of sampling steps. Newly added LoRA, located in the experimental/ folder: Manual recommended sigmas: 3-step 1.0, 0.961165, 0.853333, 0.0 4-step 1.0, 0.970874, 0.907249, 0.640000, 0.0 Three LoRAs extracted from VDN-H3 8 step: The main 8-step LoRA works on both FL2VA and Ref2VA. If you are running a pruned base, choose the pruned version that corresponds to your base — the pruned versions need their own matching pruned base. Three dynamically resized BF16 LoRAs are now included. Their source weights were…

Open weights apache-2.0 minimax-h3

Model · Text to video

Sulphur-2-base

Sulphur

Sulphur 2 An uncensored video generation model based on LTX 2.3 supporting both t2v and i2v natively, as well as all of the other ltx 2.3 formats. Follow us on X Join our Discord Support the next version of the project, even just a few dollars would go a long way: Kofi To get started with the model, I recommend downloading either of the dev versions, (fp8mixed or bf16) and downloading the distill lora provided. By the way, I'm aware the workflows contain sulphurfinal right now, just use the lora or use the full models, don't use both at the same time. This model contains a prompt enhancer. The easiest way to get started with the prompt enhancer is by using it on lmstudio. The way to…

Open weights diffusers

Model · Text to video

Wan2.1-VACE-1.3B-GGUF

Sam

Wan2.1 is an open-source suite of video foundation models, compatible with consumer-grade GPUs, that excels in various video generation tasks like text-to-video, image-to-video, and video editing, even supporting visual text generation. Download models using huggingface-cli: You can also download directly from this page. This model is a derivative work of the original model licensed under the Apache 2.0 License, and is therefore distributed under the terms of the same license. Thanks to Patrick Gillespie for creating the ASCII text art tool used in this project https://patorjk.com/software/taag/ Wan-AI for the Wan model https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B…

Open weights apache-2.0 diffusers

Model · Text to video

Wan2.1-T2V-1.3B-GGUF

Sam

Wan2.1 is an open-source suite of video foundation models, compatible with consumer-grade GPUs, that excels in various video generation tasks like text-to-video, image-to-video, and video editing, even supporting visual text generation. Download models using huggingface-cli: You can also download directly from this page. This model is a derivative work of the original model licensed under the Apache 2.0 License, and is therefore distributed under the terms of the same license. Thanks to Patrick Gillespie for creating the ASCII text art tool used in this project https://patorjk.com/software/taag/ Wan-AI for the Wan model https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B…

Open weights apache-2.0 diffusers

Model · Text to video

Sulphur-2-base-GGUF

Jay

This repository contains GGUF format model files for SulphurAI's Sulphur-2-base. The following quantization tiers are provided to accommodate different hardware capabilities and VRAM constraints.

Open weights gguf