caT text to video 2.3b

Providermotexture
Categorytext-to-video
Licenseapache-2.0
Downloads20
Stars0

Overview

The caT text-to-video 2.3b is a compact generative model designed for developers who need a balance between output quality and computational efficiency. With a 2.3 billion parameter architecture, it provides a lightweight alternative to massive video foundation models, making it suitable for local deployment or integration into resource-constrained pipelines. The model specializes in translating textual prompts into short-form video sequences, offering a viable path for automating social media content, prototyping visual assets, or building interactive AI applications. Because it is released under the Apache-2.0 license, developers have significant flexibility for commercial modification and deployment without restrictive licensing overhead.

Highlights

  • Compact 2.3B parameter size for efficient local hosting
  • Permissive Apache-2.0 license for commercial flexibility
  • Optimized for rapid text-to-video prototype generation
  • Low hardware overhead compared to large-scale models

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("motexture/caT-text-to-video-2.3b")
tokenizer = AutoTokenizer.from_pretrained("motexture/caT-text-to-video-2.3b")

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 motexture/caT-text-to-video-2.3b

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 motexture/caT-text-to-video-2.3b 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('motexture/caT-text-to-video-2.3b')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/motexture/caT-text-to-video-2.3b

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/motexture/caT-text-to-video-2.3b

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('motexture/caT-text-to-video-2.3b')
tokenizer = AutoTokenizer.from_pretrained('motexture/caT-text-to-video-2.3b')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
datasets:

  • TempoFunk/webvid-10M

language:
  • en

tags:
  • text-to-video

base_model:
  • ali-vilab/text-to-video-ms-1.7b

---

caT text to video

Conditionally augmented text-to-video model. Uses pre-trained weights from modelscope text-to-video model, augmented with temporal conditioning transformers to extend generated clips and create a smooth transition between them.
Supports prompt interpolation as well to change scenes during clip extensions.

The model was trained on two RTX 6000 Ada GPUs for 5 million steps using the WebWid 10M dataset, with a batch size of 1 and a learning rate of 1e-6 at a resolution of 320x320. It used 8 frames for conditioning and 8 frames for noisy samples, with a stride of 6.

Installation

Clone the Repository

bash
git clone https://github.com/motexture/caT-text-to-video-2.3b/
cd caT-text-to-video-2.3b
python3 -m venv venv
source venv/bin/activate  # On Windows use venv\Scripts\activate
pip install -r requirements.txt
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
python run.py

Visit the provided URL in your browser to interact with the interface and start generating videos.

Examples:

<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/64a86f7d03835e13f95c3687/OPFi_f4bp2WuCDSYodHJE.mp4"></video>

A guy is riding a bike -> A guy is riding a motorcycle

<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/64a86f7d03835e13f95c3687/U0Jx7U-Oo4lBgFJoB7E0v.mp4"></video>

Will Smith is eating a hamburger -> Will Smith is eating an ice cream

<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/64a86f7d03835e13f95c3687/hZprbX6TTpJxWyMDMJIrl.mp4"></video>

A lion is looking around -> A lion is running

<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/64a86f7d03835e13f95c3687/FrGfwcXRU7FyM9aMAyu3x.mp4"></video>

Darth Vader is surfing on the ocean

<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/64a86f7d03835e13f95c3687/VoUg8tnsZqnn1QsXz93Xh.mp4"></video>

A beautiful anime girl with pink hair -> Anime girl laughing

View official source
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