---
license: apache-2.0
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--- 

> [!IMPORTANT]
> ⚠️ **Notice:**  
> This project is intended for experimental use only. 

---

This is a GGUF conversion of an addon experiment of [lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill](https://huggingface.co/lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill) and [Wan-AI/Wan2.1-VACE-14B](https://huggingface.co/Wan-AI/Wan2.1-VACE-14B) scopes. 

The process involved extracting VACE scopes and injecting into the target models, using scripts provided by [wsbagnsv1](https://huggingface.co/wsbagnsv1). 

All quantized versions were created from the FP16 model using the conversion scripts provided by city96, available at the [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF/tree/main/tools) GitHub repository. 


## Usage

The model files can be used in [ComfyUI](https://github.com/comfyanonymous/ComfyUI/) with the [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) custom node. Place the required model(s) in the following folders:

| Type         | Name                                               | Location                       | Download         |
| ------------ | -------------------------------------------------- | ------------------------------ | ---------------- |
| Main Model   | Wan2.1_T2V_14B_LightX2V_Step_Cfg_Distill_VACE-GGUF | `ComfyUI/models/unet`          | GGUF (this repo) |
| Text Encoder | umt5-xxl-encoder                                   | `ComfyUI/models/text_encoders` | [Safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/text_encoders) / [GGUF](https://huggingface.co/city96/umt5-xxl-encoder-gguf/tree/main) |
| VAE          | Wan2_1_VAE_bf16                                    | `ComfyUI/models/vae`           | [Safetensors](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan2_1_VAE_bf16.safetensors) |

[**ComfyUI example workflow**](https://docs.comfy.org/tutorials/video/wan/vace)

### Notes

*All original licenses and restrictions from the base models still apply.*

 ## Reference

- For an overview of quantization types, please see the [GGUF quantization types](https://huggingface.co/docs/hub/gguf#quantization-types).