Text-To-Video-Finetuning: A Comprehensive Overview
Introduction
The Text-To-Video-Finetuning project is an initiative aimed at refining ModelScope's Text-To-Video capabilities using Diffusers. The project allows users to create video outputs from textual descriptions by training and fine-tuning models to enhance video generation quality. The project has a variety of models and updates that broaden its utility and performance.
Key Updates and Developments
As of December 14, 2023, the repository has been archived, but its resources remain available for researchers and developers. The project recommends using the repository released by @damo-vilab for newer implementations. Significant project milestones include the introduction of LoRA (Low Rank Adaptation) training and compatibility with web-based UI extensions, improvements in model conversion, and support for gradient checkpointing and advanced attention mechanisms.
Getting Started with Text-To-Video-Finetuning
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Installation and Setup:
To start using the Text-To-Video-Finetuning project, clone the GitHub repository and set up the environment. The instructions recommend installing Anaconda for managing dependencies and Python versions.
git clone https://github.com/ExponentialML/Text-To-Video-Finetuning.git cd Text-To-Video-Finetuning git lfs install git clone https://huggingface.co/damo-vilab/text-to-video-ms-1.7b ./models/model_scope_diffusers/ -
Conda Environment (Optional): For those preferring a virtual environment, a Conda environment can be created and activated:
conda create -n text2video-finetune python=3.10 conda activate text2video-finetune -
Python Requirements: Ensure all necessary Python packages are installed:
pip install -r requirements.txt
Hardware Recommendations
For optimal performance, a modern GPU, such as an RTX 3090 or equivalent, is recommended, though the project supports GPUs with at least 16GB of RAM. Users are advised to enable features like Xformers, gradient checkpointing, and manage training resolutions to accommodate different hardware capabilities.
Data Preprocessing
The project supports image and video captioning for training datasets. Automatic captioning of videos can be done using the Video-BLIP2-Preprocessor Script.
Configuration and Training
Configuration files, structured in YAML, allow detailed setup of training parameters. Users can copy and modify existing configuration files for tailored implementations. The project offers guidance on training models with LoRA, providing options for specific adaptation strategies compatible with web-based UIs.
Running Inference
Users can generate videos from trained model checkpoints using the provided inference.py script. The script is flexible, allowing customization in terms of video attributes such as resolution and frame count. Example command for inference:
python inference.py \
--model camenduru/potat1 \
--prompt "a fast moving fancy sports car" \
--num-frames 60 \
--window-size 12 \
--width 1024 \
--height 576 \
--sdp
Contributions and Development
The project encourages contributions and has made its code modular to facilitate experimentation and feature enhancement. Collaborative development and community feedback are highly valued.
Conclusion
The Text-To-Video-Finetuning project offers a robust framework for generating videos from text descriptions. It supports various models and training techniques, ensuring adaptability and ease for beginners and advanced users alike. Despite being archived, its resources continue to serve as a platform for innovation in video generation.
Citation
Researchers and developers who utilize the project are encouraged to cite the work as indicated in the ModelScope Text-to-Video Technical Report:
@article{ModelScopeT2V,
title={ModelScope Text-to-Video Technical Report},
author={Wang, Jiuniu and Yuan, Hangjie and Chen, Dayou and Zhang, Yingya and Wang, Xiang and Zhang, Shiwei},
journal={arXiv preprint arXiv:2308.06571},
year={2023}
}