Wan2.2 I2V A14B Lightning Diffusers

Name: Wan2.2 I2V A14B Lightning Diffusers
Author: magespace

Providermagespace

Categorytext-to-video

Licenseapache-2.0

Downloads49.1K

Stars0

Overview

Wan2.2 I2V A14B Lightning is a high-efficiency image-to-video diffusion model optimized for rapid inference via the Diffusers library. By leveraging a distilled 'Lightning' architecture, it significantly reduces the sampling steps required to generate high-fidelity motion, making it viable for real-time application prototyping. Developers can integrate this model into existing PyTorch pipelines to animate static assets with strong temporal consistency. Compared to full-parameter diffusion models, this version prioritizes throughput and lower VRAM overhead without sacrificing the cinematic quality of the underlying 14B parameter base. It is particularly suited for automated content pipelines, dynamic UI elements, and rapid iterative testing of visual effects.

Highlights

Optimized for fast inference via Diffusers integration
High-fidelity image-to-video animation with temporal stability
Reduced sampling steps for lower latency production
Permissive Apache-2.0 license for commercial deployment
Efficient 14B parameter architecture for professional workflows

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("magespace/Wan2.2-I2V-A14B-Lightning-Diffusers")
tokenizer = AutoTokenizer.from_pretrained("magespace/Wan2.2-I2V-A14B-Lightning-Diffusers")

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 magespace/Wan2.2-I2V-A14B-Lightning-Diffusers

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

huggingface-cli download magespace/Wan2.2-I2V-A14B-Lightning-Diffusers 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('magespace/Wan2.2-I2V-A14B-Lightning-Diffusers')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/magespace/Wan2.2-I2V-A14B-Lightning-Diffusers

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/magespace/Wan2.2-I2V-A14B-Lightning-Diffusers

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('magespace/Wan2.2-I2V-A14B-Lightning-Diffusers')
tokenizer = AutoTokenizer.from_pretrained('magespace/Wan2.2-I2V-A14B-Lightning-Diffusers')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
pipeline_tag: text-to-video
library_name: diffusers
---

Wan2.2 + Lightx2v

<p align="center">
💜 <a href="https://wan.video"><b>Wan</b></a> &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ <a href="https://github.com/Wan-Video/Wan2.2">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="https://arxiv.org/abs/2503.20314">Technical Report</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://wan.video/welcome?spm=a2ty_o02.30011076.0.0.6c9ee41eCcluqg">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/AKNgpMK4Yj">Discord</a>&nbsp&nbsp
<br>

<p align="center">
🔗 <a href="https://huggingface.co/lightx2v/Wan2.2-Lightning"><b>Lightx2v</b></a> — Distilled & optimized Wan2.2 for fast, high-quality 480P / 720P image-to-video generation
</p>
<br>
-----

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

We are excited to introduce Wan2.2, a major upgrade to our foundational video models. With Wan2.2, we have focused on incorporating the following innovations:

👍 Effective MoE Architecture: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.

👍 Cinematic-level Aesthetics: Wan2.2 incorporates meticulously curated aesthetic data, complete with detailed labels for lighting, composition, contrast, color tone, and more. This allows for more precise and controllable cinematic style generation, facilitating the creation of videos with customizable aesthetic preferences.

👍 Complex Motion Generation: Compared to Wan2.1, Wan2.2 is trained on a significantly larger data, with +65.6% more images and +83.2% more videos. This expansion notably enhances the model's generalization across multiple dimensions such as motions, semantics, and aesthetics, achieving TOP performance among all open-sourced and closed-sourced models.

👍 Efficient High-Definition Hybrid TI2V: Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest 720P@24fps models currently available, capable of serving both the industrial and academic sectors simultaneously.

This repository contains our T2V-A14B model, which supports generating 5s videos at both 480P and 720P resolutions. Built with a Mixture-of-Experts (MoE) architecture, it delivers outstanding video generation quality. On our new benchmark Wan-Bench 2.0, the model surpasses leading commercial models across most key evaluation dimensions.

Video Demos

<div align="center">
<video width="80%" controls>
<source src="https://cloud.video.taobao.com/vod/4szTT1B0LqXvJzmuEURfGRA-nllnqN_G2AT0ZWkQXoQ.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
</div>

🔥 Latest News!!

Jul 28, 2025: 👋 We've released the inference code and model weights of Wan2.2.

Community Works

If your research or project builds upon Wan2.1 or Wan2.2, we welcome you to share it with us so we can highlight it for the broader community.

📑 Todo List

Wan2.2 Text-to-Video

- [x] Multi-GPU Inference code of the A14B and 14B models - [x] Checkpoints of the A14B and 14B models - [x] ComfyUI integration - [x] Diffusers integration

Wan2.2 Image-to-Video

- [x] Multi-GPU Inference code of the A14B model - [x] Checkpoints of the A14B model - [x] ComfyUI integration - [x] Diffusers integration

Wan2.2 Text-Image-to-Video

- [x] Multi-GPU Inference code of the 5B model - [x] Checkpoints of the 5B model - [x] ComfyUI integration - [x] Diffusers integration

Run Wan2.2

#### Installation
Clone the repo:

git clone https://github.com/Wan-Video/Wan2.2.git
cd Wan2.2

Install dependencies:

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

#### Model Download

| Models | Download Links | Description |
|--------------------|---------------------------------------------------------------------------------------------------------------------------------------------|-------------|
| T2V-A14B | 🤗 Huggingface 🤖 ModelScope | Text-to-Video MoE model, supports 480P & 720P |
| I2V-A14B | 🤗 Huggingface 🤖 ModelScope | Image-to-Video MoE model, supports 480P & 720P |
| TI2V-5B | 🤗 Huggingface 🤖 ModelScope | High-compression VAE, T2V+I2V, supports 720P |

> 💡Note:
> The TI2V-5B model supports 720P video generation at 24 FPS.

Download models using huggingface-cli:
``sh pip install "huggingface_hub[cli]" huggingface-cli download Wan-AI/Wan2.2-T2V-A14B --local-dir ./Wan2.2-T2V-A14B

code

Download models using modelscope-cli:

 sh
pip install modelscope
modelscope download Wan-AI/Wan2.2-T2V-A14B --local_dir ./Wan2.2-T2V-A14B
code
#### Run Text-to-Video Generation
This repository supports the Wan2.2-T2V-A14B Text-to-Video model and can simultaneously support video generation at 480P and 720P resolutions.

##### (1) Without Prompt Extension
To facilitate implementation, we will start with a basic version of the inference process that skips the prompt extension step.
Single-GPU inference sh
python generate.py  --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --offload_model True --convert_model_dtype --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
code
> 💡 This command can run on a GPU with at least 80GB VRAM.
> 💡If you encounter OOM (Out-of-Memory) issues, you can use the --offload_model True, --convert_model_dtype and --t5_cpu options to reduce GPU memory usage.

Multi-GPU inference using FSDP + DeepSpeed Ulysses
We use PyTorch FSDP and DeepSpeed Ulysses to accelerate inference.
 sh
torchrun --nproc_per_node=8 generate.py --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."


##### (2) Using Prompt Extension
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 Dashscope API for extension.
  - Apply for a

dashscope.api_key

 in advance (EN | CN).
  - Configure the environment variable

DASH_API_KEY` to specify the Dashscope API key. For users of Alibaba Cloud's international site, you also need to set the environment var

View official source