Wan2.1: Generating High Quality Video on Consumer GPUs

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General Introduction

Wan2.1 is a suite of video generation tools developed by the Wan-Video team and open sourced on GitHub, focusing on pushing the boundaries of video creation through artificial intelligence technology. Based on an advanced diffusion converter architecture, it integrates a unique time-varying auto-encoder (Wan-VAE) that supports text-to-video, image-to-video, etc. The highlight of Wan2.1 is its excellent performance and support for consumer-grade hardware, for example, the T2V-1.3B model requires only 8.19GB of video RAM to run, and is capable of generating 5-second 480P videos on RTX 4090. video on the RTX 4090. The project not only provides efficient video generation capabilities, but also supports 1080P encoding and decoding without length limitation, making it widely applicable to content creators, developers, and academic research teams.

Wan2.1: Generating High Quality Video on Consumer GPUs-1

Function List

Text-to-Video: Generate dynamic video content based on input text descriptions, supporting multi-language text input.
Image-to-Video: Convert still images into motion video, maintaining the original proportions and natural motion of the image.
Video Editing: Modify or optimize existing videos with AI technology.
Supports high resolution output: 480P and 720P videos can be generated, and some models support 1080P with no length limit.
Wan-VAE technology:: Provides efficient temporal compression, supports long video generation and retains temporal information.
Consumer GPU Optimization:: Runs on common hardware, lowering the barrier to use.
multitasking support: Includes text-to-image, video-to-audio, and other extensions.
Chinese and English text generation: Generate clear Chinese and English text in your videos.

Using Help

Wan2.1 is a powerful open source video generation tool for users who want to quickly generate high quality video content. Below is a detailed installation and usage guide to help you get started quickly.

Installation process

The installation of Wan2.1 requires a certain technical foundation, mainly through the GitHub repository to obtain the code and model weights. Here are the steps:

1. Environmental preparation

operating system: Support for Windows, Linux or macOS.
hardware requirement: GPUs with at least 8GB of video memory (such as RTX 3060 Ti or 4090), Nvidia GPUs are recommended.
software dependency: Python 3.10+, Git, graphics drivers and CUDA (if using a GPU).
Installing Python: Download Python 3.10 or later from the official website, and check the "Add Python to PATH" box when installing.

2. Downloading code and models

Open a terminal or command line and enter the following command to clone the repository:

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

Install dependent libraries:

pip install -r requirements.txt

Download model weights from Hugging Face (T2V-1.3B as an example):

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

Optional models: T2V-14B (higher performance, requires more video memory), I2V-480P/720P.

3. Configuration environment

If the video memory is low, enable the optimization parameters (e.g. --offload_model True cap (a poem) ---t5_cpu).
Ensure that the GPU driver and CUDA are installed correctly by using the nvidia-smi Check.

4. Verification of installation

Run the following command to test the environment:

python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir . /Wan2.1-T2V-1.3B --prompt "test video generation"

If the video file is output, the installation is successful.

Functional operation flow

Text-to-Video

Prepared text:: Write descriptive prompts, such as "A cat walks gracefully on the grass as the camera follows."
Run command:

python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir . /Wan2.1-T2V-1.3B --prompt "A cute cat walking gracefully on a lush green field"

parameterization:

--size: Set the resolution (e.g., 832)480 or 1280720).
--offload_model True: Low video memory optimization.
--sample_shift 8 --sample_guide_scale 6:: Improving the quality of generation.

exports: The generated video is saved in the current directory and is about 5 seconds long.

Image-to-Video

Prepare the image: Upload a JPG/PNG image (e.g. input.jpg).
Run command:

python generate.py --task i2v-14B --size 1280*720 --ckpt_dir . /Wan2.1-I2V-14B-720P --image input.jpg --prompt "Summer beach vacation style"

in the end: The model generates dynamic video based on the image, maintaining original proportions and natural motion.

Video Editing

Input Video: Prepare an existing video file.
Editorial operations: Use a tool such as DiffSynth-Studio (Wan 2.1 supports extensions) to invoke the relevant module from the command line.
sample command (computing): See the GitHub documentation for specific parameters, and basic editing is currently supported.

High Resolution Output

Using a T2V-14B or I2V-720P model, set the --size 1280*720The video memory is higher (about 17GB).
Wan-VAE supports 1080P without length limitation, suitable for long video generation.

Generate Chinese and English text

Include a textual description in the prompt, such as "A sign saying 'Welcome' in English and Chinese".
Run the Text to Video command and the model will automatically embed clear text in the video.

Tips for use

optimize performance: For low-end hardware, a 1.3B model and 480P resolution are recommended; for high-end hardware try 14B and 720P.
Cue word suggestions: Improve the quality of generation using detailed descriptions (e.g., action, scene, lighting).
Community Support: Join the GitHub Issues or Discord discussion groups for help.

With these steps, you can easily use Wan2.1 to generate professional-grade video content for both creative presentations and academic research.