wan 1.3b gguf

Providercalcuis
Categorytext-to-video
Licenseapache-2.0
Downloads2.5K
Stars7

Overview

The Wan 1.3B GGUF release brings a lightweight, quantized text-to-video model to the local developer community. By utilizing the GGUF format, this model significantly lowers the VRAM barrier, enabling high-quality video generation on consumer-grade hardware without requiring massive GPU clusters. It is designed for developers building generative media pipelines who need a balance between inference speed and visual coherence. Compared to larger diffusion models, the 1.3B parameter count allows for faster iteration cycles and easier integration into edge-computing environments or local apps via llama.cpp and compatible backends. It is particularly suited for short-form content generation, prototyping motion assets, and integrating dynamic visuals into interactive software under the permissive Apache-2.0 license.

Highlights

  • Quantized GGUF format for low VRAM consumer hardware.
  • Efficient text-to-video generation with fast inference speeds.
  • Permissive Apache-2.0 license for commercial flexibility.
  • Seamless integration with llama.cpp and local AI toolchains.

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("calcuis/wan-1.3b-gguf")
tokenizer = AutoTokenizer.from_pretrained("calcuis/wan-1.3b-gguf")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download calcuis/wan-1.3b-gguf

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download calcuis/wan-1.3b-gguf config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('calcuis/wan-1.3b-gguf')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/calcuis/wan-1.3b-gguf

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/calcuis/wan-1.3b-gguf

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('calcuis/wan-1.3b-gguf')
tokenizer = AutoTokenizer.from_pretrained('calcuis/wan-1.3b-gguf')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model calcuis/wan-1.3b-gguf

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model calcuis/wan-1.3b-gguf README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('calcuis/wan-1.3b-gguf')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/calcuis/wan-1.3b-gguf.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/calcuis/wan-1.3b-gguf.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'calcuis/wan-1.3b-gguf')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
language:

  • en

base_model:
  • Wan-AI/Wan2.1-VACE-1.3B

  • Wan-AI/Wan2.1-T2V-1.3B

pipeline_tag: text-to-video
tags:
  • gguf-node

  • gguf-connector

widget:
  • text: >-

a pig moving quickly in a beautiful winter scenery nature trees sunset
tracking camera
parameters:
negative_prompt: blurry ugly bad
output:
url: samples\ComfyUI_00003_.webp
  • text: >-

a pig moving quickly in a beautiful winter scenery nature trees sunset
tracking camera
parameters:
negative_prompt: blurry ugly bad
output:
url: samples\ComfyUI_00002_.webp
  • text: >-

a pig moving quickly in a beautiful winter scenery nature trees sunset
tracking camera
parameters:
negative_prompt: blurry ugly bad
output:
url: samples\ComfyUI_00001_.webp
  • text: >-

a pig moving quickly in a beautiful winter scenery nature trees sunset
tracking camera
parameters:
negative_prompt: blurry ugly bad
output:
url: samples\ComfyUI_00004_.webp
  • text: >-

a pig moving quickly in a beautiful winter scenery nature trees sunset
tracking camera
parameters:
negative_prompt: blurry ugly bad
output:
url: samples\ComfyUI_00005_.webp
  • text: >-

a pig moving quickly in a beautiful winter scenery nature trees sunset
tracking camera
parameters:
negative_prompt: blurry ugly bad
output:
url: samples\ComfyUI_00006_.webp
---

gguf quantized version of wan 1.3b models


  • run it straight with gguf-connector

  • opt a gguf file in the current directory to interact with by:

code
ggc w2

>
>GGUF file(s) available. Select which one to use:
>
>1. wan2.1-t2v-1.3b-q4_0.gguf
>2. wan2.1-t2v-1.3b-q8_0.gguf
>3. wan2.1-vace-1.3b-q4_0.gguf
>4. wan2.1-vace-1.3b-q8_0.gguf
>
>Enter your choice (1 to 4): _
>>>

run it with gguf-node via comfyui


  • drag wan to > ./ComfyUI/models/diffusion_models

  • drag umt5 to > ./ComfyUI/models/text_encoders

  • drag pig to > ./ComfyUI/models/vae

<Gallery />

review


  • wan architecture; should work on both comfyui-gguf and gguf nodes

  • full set gguf works right away (model + encoder + vae); gguf node is recommended for full gguf

  • vace model is recommended, since it doesn't need vision clip to work (for i2v and v2v) and run faster than fun model a lot, according to the initial test results

  • upgrade your node for umt5 gguf encoder support

  • note: for umt5 gguf, you might encounter oom after rebuilding your tokenizer in the first prompt (once built the tokenizer alive during the session; before you kill it); don't panic, prompt again it should work

!screenshot

reference




  • comfyui-gguf city96 (special thanks; fully compatible)



  • gguf-connector (pypi)

View official source
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