Text-to-Video
Diffusers
Safetensors
PyTorch
Transformers
English
TextToVideoSDPipeline
video-generation
generative-ai
diffusion
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Deepak1206/text-to-video-model", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]🎬 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:
pip install diffusers transformers accelerate torch imageio imageio-ffmpeg
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