Magic 1-For-1
M
Magic 1 For 1
Overview :
Magic 1-For-1 focuses on efficient video generation, with its core feature being the rapid conversion of text and images into video. The model optimizes memory usage and reduces inference latency by breaking the text-to-video generation task into two sub-tasks: text-to-image and image-to-video. Key advantages include efficiency, low latency, and scalability. Developed by the DA-Group team at Peking University, the model aims to advance the interactive foundational video generation field. The model and related code are open-source and available for free use, subject to compliance with the open-source license agreement.
Target Users :
This model is designed for users who need to quickly generate video content, such as video creators, advertisers, and content developers. It helps users produce high-quality videos in a short time, saving time and effort while enhancing creative efficiency. Additionally, its open-source nature is ideal for researchers and developers looking to further develop and study the technology.
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Use Cases
Video creators can use this model to quickly generate video material, enhancing their creative efficiency.
Advertisers can leverage this model to rapidly produce advertising videos, reducing production costs.
Researchers can build upon this model for further research and development, exploring new video generation technologies.
Features
Efficient image-to-video generation, producing one minute of video in under a minute.
Supports two-stage generation from text to image and from image to video, optimizing memory usage and inference latency.
Offers quantization functionality to further enhance model performance.
Supports both single-GPU and multi-GPU inference, adaptable to various hardware environments.
Provides open-source code and model weights for easy user modification and research.
Includes detailed documentation and scripts for quick user onboarding.
Supports downloads and usage of various pre-trained model components.
How to Use
1. Install git-lfs and create a project environment using conda.
2. Install project dependencies by running the command pip install -r requirements.txt.
3. Create a directory named pretrained_weights and download the model weights and associated components.
4. Run the script python test_ti2v.py or bash scripts/run_flashatt3.sh for inference.
5. Enable quantization or adjust multi-GPU configuration as needed.
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