| language: | |
| - en | |
| license: apache-2.0 | |
| library_name: diffusers | |
| pipeline_tag: text-to-video | |
| tags: | |
| - text-to-video | |
| - video-generation | |
| - generative-ai | |
| - diffusion | |
| - pytorch | |
| - transformers | |
| - diffusers | |
| # 🎬 Text-to-Video Generation Model | |
| A text-to-video generation project that converts natural language | |
| prompts into short AI-generated videos using a diffusion-based | |
| text-to-video model. | |
| ## 📌 Overview | |
| This project demonstrates text-to-video generation using a | |
| pretrained diffusion model from the Hugging Face ecosystem. | |
| The system takes a textual description as input and generates | |
| a sequence of video frames, which are combined into an MP4 video. | |
| ### Pipeline | |
| Text Prompt | |
| ↓ | |
| Text Encoder | |
| ↓ | |
| Diffusion Model | |
| ↓ | |
| Video Frames | |
| ↓ | |
| MP4 Video | |
| --- | |
| ## ✨ Features | |
| - Text-to-video generation | |
| - Natural language prompts | |
| - Diffusion-based video generation | |
| - GPU acceleration with CUDA | |
| - MP4 video export | |
| - Compatible with Hugging Face Diffusers | |
| - Can be executed using Google Colab | |
| --- | |
| ## 🤖 Model Information | |
| ### Base Model | |
| `damo-vilab/text-to-video-ms-1.7b` | |
| ### Model Architecture | |
| Diffusion-based text-to-video generation model. | |
| ### Framework | |
| - PyTorch | |
| - Hugging Face Diffusers | |
| - Hugging Face Transformers | |
| - Accelerate | |
| --- | |
| ## 🚀 Usage | |
| Install the required libraries: | |
| ```bash | |
| pip install diffusers transformers accelerate torch imageio imageio-ffmpeg |