text to video model

ProviderDeepak1206
Categorytext-to-video
Licenseapache-2.0
Downloads20
Stars0

Overview

This text-to-video model offers developers an open-source framework for generating dynamic visual content from natural language prompts. Built under the Apache-2.0 license, it provides the flexibility needed for commercial integration without restrictive proprietary overhead. The model is designed to handle temporal consistency across frames, making it suitable for creating short-form social media clips, prototype animations, and synthetic data for computer vision training. Compared to closed-API alternatives, this model allows for local deployment and fine-tuning on custom datasets, giving engineers greater control over the latent space and output style. Integration is streamlined for pipelines requiring automated asset generation or interactive storytelling interfaces.

Highlights

  • Permissive Apache-2.0 license for commercial deployment
  • Generates temporally consistent video from text prompts
  • Supports local hosting and custom dataset fine-tuning
  • Ideal for synthetic data and rapid prototyping

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("Deepak1206/text-to-video-model")
tokenizer = AutoTokenizer.from_pretrained("Deepak1206/text-to-video-model")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download Deepak1206/text-to-video-model

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download Deepak1206/text-to-video-model config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Deepak1206/text-to-video-model')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/Deepak1206/text-to-video-model

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Deepak1206/text-to-video-model

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('Deepak1206/text-to-video-model')
tokenizer = AutoTokenizer.from_pretrained('Deepak1206/text-to-video-model')

Full Documentation

来源: HuggingFace

---
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

View official source
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