Name: Wan2.1 T2V 1.3B
Author: Wan-AI

Overview

Wan2.1 T2V 1.3B is a compact, high-efficiency text-to-video model designed for developers who need a balance between generation quality and computational overhead. Unlike massive proprietary models, this 1.3B parameter version is optimized for faster inference and lower VRAM requirements, making it viable for local deployment or integration into agile pipelines. It excels at translating descriptive prompts into coherent motion, serving as a strong foundation for short-form content generation, prototyping visual assets, or building custom video-gen wrappers. Licensed under Apache-2.0, it offers the flexibility needed for commercial scaling without restrictive licensing hurdles. For devs, this means a streamlined path from prompt to render with a model that is lightweight enough to iterate on rapidly.

Highlights

Compact 1.3B parameter size for efficient local deployment
Apache-2.0 license enables flexible commercial integration
Optimized for fast text-to-video inference and iteration
Low VRAM overhead compared to larger video models

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("Wan-AI/Wan2.1-T2V-1.3B")
tokenizer = AutoTokenizer.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B")

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

huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B

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

huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Wan-AI/Wan2.1-T2V-1.3B')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B

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

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('Wan-AI/Wan2.1-T2V-1.3B')
tokenizer = AutoTokenizer.from_pretrained('Wan-AI/Wan2.1-T2V-1.3B')

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

modelscope download --model Wan-AI/Wan2.1-T2V-1.3B

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

modelscope download --model Wan-AI/Wan2.1-T2V-1.3B README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('Wan-AI/Wan2.1-T2V-1.3B')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/Wan-AI/Wan2.1-T2V-1.3B.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/Wan-AI/Wan2.1-T2V-1.3B.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

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

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'Wan-AI/Wan2.1-T2V-1.3B')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
language:

en

zh

pipeline_tag: text-to-video
library_name: diffusers
tags:

video

video-generation

---

Wan2.1

<p align="center">
💜 <a href=""><b>Wan</b></a> &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ <a href="https://github.com/Wan-Video/Wan2.1">GitHub</a> &nbsp&nbsp | &nbsp&nbsp🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="">Paper (Coming soon)</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://wanxai.com">Blog</a> &nbsp&nbsp | &nbsp&nbsp💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat Group</a>&nbsp&nbsp | &nbsp&nbsp 📖 <a href="https://discord.gg/p5XbdQV7">Discord</a>&nbsp&nbsp
<br>

-----

Wan: Open and Advanced Large-Scale Video Generative Models <be>

In this repository, we present Wan2.1, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. Wan2.1 offers these key features:

👍 SOTA Performance: Wan2.1 consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks.

👍 Supports Consumer-grade GPUs: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models.

👍 Multiple Tasks: Wan2.1 excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.

👍 Visual Text Generation: Wan2.1 is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.

👍 Powerful Video VAE: Wan-VAE delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation.

This repository hosts our T2V-1.3B model, a versatile solution for video generation that is compatible with nearly all consumer-grade GPUs. In this way, we hope that Wan2.1 can serve as an easy-to-use tool for more creative teams in video creation, providing a high-quality foundational model for academic teams with limited computing resources. This will facilitate both the rapid development of the video creation community and the swift advancement of video technology.

Video Demos

<div align="center">
<video width="80%" controls>
<source src="https://cloud.video.taobao.com/vod/Jth64Y7wNoPcJki_Bo1ZJTDBvNjsgjlVKsNs05Fqfps.mp4" type="video/mp4">
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</video>
</div>

🔥 Latest News!!

Feb 25, 2025: 👋 We've released the inference code and weights of Wan2.1.

📑 Todo List

Wan2.1 Text-to-Video

- [x] Multi-GPU Inference code of the 14B and 1.3B models - [x] Checkpoints of the 14B and 1.3B models - [x] Gradio demo - [ ] Diffusers integration - [ ] ComfyUI integration

Wan2.1 Image-to-Video

- [x] Multi-GPU Inference code of the 14B model - [x] Checkpoints of the 14B model - [x] Gradio demo - [ ] Diffusers integration - [ ] ComfyUI integration

Quickstart

#### Installation
Clone the repo:

code

git clone https://github.com/Wan-Video/Wan2.1.git
cd Wan2.1

Install dependencies:

code

# Ensure torch >= 2.4.0
pip install -r requirements.txt

#### Model Download

| Models | Download Link | Notes |
| --------------|-------------------------------------------------------------------------------|-------------------------------|
| T2V-14B | 🤗 Huggingface 🤖 ModelScope | Supports both 480P and 720P
| I2V-14B-720P | 🤗 Huggingface 🤖 ModelScope | Supports 720P
| I2V-14B-480P | 🤗 Huggingface 🤖 ModelScope | Supports 480P
| T2V-1.3B | 🤗 Huggingface 🤖 ModelScope | Supports 480P

> 💡Note: The 1.3B model is capable of generating videos at 720P resolution. However, due to limited training at this resolution, the results are generally less stable compared to 480P. For optimal performance, we recommend using 480P resolution.

Download models using 🤗 huggingface-cli:

code

pip install "huggingface_hub[cli]"
huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B --local-dir ./Wan2.1-T2V-1.3B

Download models using 🤖 modelscope-cli:

code

pip install modelscope
modelscope download Wan-AI/Wan2.1-T2V-1.3B --local_dir ./Wan2.1-T2V-1.3B

#### Run Text-to-Video Generation

This repository supports two Text-to-Video models (1.3B and 14B) and two resolutions (480P and 720P). The parameters and configurations for these models are as follows:

<table>
<thead>
<tr>
<th rowspan="2">Task</th>
<th colspan="2">Resolution</th>
<th rowspan="2">Model</th>
</tr>
<tr>
<th>480P</th>
<th>720P</th>
</tr>
</thead>
<tbody>
<tr>
<td>t2v-14B</td>
<td style="color: green;">✔️</td>
<td style="color: green;">✔️</td>
<td>Wan2.1-T2V-14B</td>
</tr>
<tr>
<td>t2v-1.3B</td>
<td style="color: green;">✔️</td>
<td style="color: red;">❌</td>
<td>Wan2.1-T2V-1.3B</td>
</tr>
</tbody>
</table>

##### (1) Without Prompt Extention

To facilitate implementation, we will start with a basic version of the inference process that skips the prompt extension step.

Single-GPU inference

code

python generate.py  --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."

If you encounter OOM (Out-of-Memory) issues, you can use the --offload_model True and --t5_cpu options to reduce GPU memory usage. For example, on an RTX 4090 GPU:

code

python generate.py  --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --offload_model True --t5_cpu --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."

> 💡Note: If you are using the T2V-1.3B model, we recommend setting the parameter --sample_guide_scale 6. The --sample_shift parameter can be adjusted within the range of 8 to 12 based on the performance.

Multi-GPU inference using FSDP + xDiT USP

code

pip install "xfuser>=0.4.1"
torchrun --nproc_per_node=8 generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --dit_fsdp --t5_fsdp --ulysses_size 8 --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."

##### (2) Using Prompt Extention

Extending the prompts can effectively enrich the details in the generated videos, further enhancing the video quality. Therefore, we recommend enabling prompt extension. We provide the following two methods for prompt extension:

Use the D