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61 lines
2.2 KiB
Markdown
61 lines
2.2 KiB
Markdown
# Wan2.1 Text-to-Video Model
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This repository contains the Wan2.1 text-to-video model, adapted for macOS with M1 Pro chip. This adaptation allows macOS users to run the model efficiently, overcoming CUDA-specific limitations.
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## Introduction
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The Wan2.1 model is an open-source text-to-video generation model. It transforms textual descriptions into video sequences, leveraging advanced machine learning techniques.
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## Changes for macOS
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This version includes modifications to make the model compatible with macOS, specifically for systems using the M1 Pro chip. Key changes include:
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- Adaptation of CUDA-specific code to work with MPS (Metal Performance Shaders) on macOS.
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- Environment variable settings for MPS fallback to CPU for unsupported operations.
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- Adjustments to command-line arguments for better compatibility with macOS.
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## Installation Instructions
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Follow these steps to set up the environment on macOS:
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1. **Install Homebrew**: If not already installed, use Homebrew to manage packages.
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```bash
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/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
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```
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2. **Install Python 3.10+**:
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```bash
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brew install python@3.10
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```
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3. **Create and Activate a Virtual Environment**:
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```bash
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python3.10 -m venv venv_wan
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source venv_wan/bin/activate
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```
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4. **Install Dependencies**:
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```bash
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pip install -r requirements.txt
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pip install einops
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```
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## Usage
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To generate a video, use the following command:
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```bash
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export PYTORCH_ENABLE_MPS_FALLBACK=1
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python generate.py --task t2v-1.3B --size "480*832" --frame_num 16 --sample_steps 25 --ckpt_dir ./Wan2.1-T2V-1.3B --offload_model True --t5_cpu --device mps --prompt "Lion running under snow in Samarkand" --save_file output_video.mp4
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```
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## Optimization Tips
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- **Use CPU for Large Models**: If you encounter memory issues, use `--device cpu`.
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- **Reduce Resolution and Frame Count**: Use smaller resolutions and fewer frames to reduce memory usage.
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- **Monitor System Resources**: Keep an eye on memory usage and adjust parameters as needed.
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## Acknowledgments
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This project is based on the original Wan2.1 model. Special thanks to the original authors and contributors for their work.
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