VGen is a comprehensive open-source codebase for video generation, offering implementations of state-of-the-art diffusion models for various synthesis tasks. It caters to researchers and developers in AI video generation, providing tools for training, inference, and customization with a focus on high-quality output and controllability.

How It Works

VGen leverages cascaded diffusion models and hierarchical spatio-temporal decoupling to achieve high-quality video synthesis. It supports text-to-video, image-to-video, and controllable generation based on motion and subject customization. The ecosystem is designed for expandability and includes components for managing experiments and integrating various diffusion model architectures.

Quick Start & Requirements

• Install: conda create -n vgen python=3.8, conda activate vgen, pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113, pip install -r requirements.txt.
• Prerequisites: Python 3.8, PyTorch 1.12.0 with CUDA 11.3, ffmpeg, libsm6, libxext6.
• Setup: Requires cloning the repository and installing dependencies.
• Docs: Modelscope T2V Technical Report, I2VGen-XL Paper.

Highlighted Details

• Implements multiple advanced models: I2VGen-xl, VideoComposer, HiGen, InstructVideo, DreamVideo, VideoLCM, TF-T2V.
• Supports customization via LoRA fine-tuning, subject learning, and motion learning.
• Includes tools for metric calculation (CLIP-T, CLIP-I, DINO-I, Temporal Consistency).
• Offers Gradio demos for local testing and HuggingFace/ModelScope integration.

Maintenance & Community

• Developed by Tongyi Lab of Alibaba Group.
• Active development with frequent releases of new models and features (e.g., InstructVideo, DreamVideo, VideoLCM).
• Links to relevant papers and technical reports are provided.

Licensing & Compatibility

• License: Trained with WebVid-10M and LAION-400M datasets. Intended for RESEARCH/NON-COMMERCIAL USE ONLY.

Limitations & Caveats

• The current models perform inadequately on anime images and images with black backgrounds due to limited training data.
• Super-resolution models for TF-T2V only support 32-frame input, requiring frame duplication for 16-frame videos.