Name: text to video ms 1.7b
Author: nachikethmurthy666

Overview

The text-to-video ms 1.7b is a compact generative model designed for efficient video synthesis from textual prompts. At 1.7 billion parameters, it strikes a balance between computational overhead and output quality, making it accessible for developers who cannot deploy massive foundation models. It is particularly suited for rapid prototyping of short-form visual content, dynamic UI backgrounds, and automated social media assets. Integrated under the CreativeML OpenRAIL-M license, it offers significant flexibility for commercial deployment. Compared to larger diffusion models, this version prioritizes faster inference speeds and lower VRAM requirements while maintaining temporal consistency across frames.

Highlights

Efficient 1.7B parameter architecture for faster inference
OpenRAIL-M license enables flexible commercial integration
Optimized for short-form video generation and prototyping
Lower hardware requirements 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("nachikethmurthy666/text-to-video-ms-1.7b")
tokenizer = AutoTokenizer.from_pretrained("nachikethmurthy666/text-to-video-ms-1.7b")

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

huggingface-cli download nachikethmurthy666/text-to-video-ms-1.7b

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

huggingface-cli download nachikethmurthy666/text-to-video-ms-1.7b config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('nachikethmurthy666/text-to-video-ms-1.7b')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/nachikethmurthy666/text-to-video-ms-1.7b

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/nachikethmurthy666/text-to-video-ms-1.7b

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

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('nachikethmurthy666/text-to-video-ms-1.7b')
tokenizer = AutoTokenizer.from_pretrained('nachikethmurthy666/text-to-video-ms-1.7b')

Full Documentation

来源: HuggingFace

---
license: creativeml-openrail-m
tags:
- text-to-video
- generative-ai
- diffusion
- damo
- video-generation
- stable-video-diffusion
- manifold-ai
library_name: diffusers
inference: false
model-index:
- name: DAMO Text-to-Video MS-1.7B (Rehosted by Nachiketh)
results: []
---

🚀 DAMO Text-to-Video MS-1.7B (Rehosted by Nachiketh)

This model is a rehosted and learner-accessible version of the original damo-vilab/text-to-video-ms-1.7b from Alibaba DAMO.

It enables text-to-video generation using diffusion models and is ideal for researchers, learners, and creators building cutting-edge Generative AI experiences.

---

📌 Model Summary

Name: DAMO Text-to-Video MS-1.7B

Architecture: Multi-stage latent diffusion

Inputs: Text prompt

Outputs: 16-frame videos (resolution varies)

Framework: Hugging Face 🤗 Diffusers

Precision: FP16 recommended

License: CreativeML OpenRAIL-M

---

🧪 Example Usage (Python)

Install the Libraries

code

$ pip install diffusers transformers accelerate torch

```python
from diffusers import DiffusionPipeline
import torch
from diffusers.utils import export_to_video

pipe = DiffusionPipeline.from_pretrained(
"nachikethmurthy666/text-to-video-ms-1.7b",
torch_dtype=torch.float16,
variant="fp16"
).to("cuda")

pipe.enable_model_cpu_offload()

prompt = "An astronaut riding a horse on Mars at sunset"
video = pipe(prompt, num_inference_steps=25).frames[0]
video_path = export_to_video(video)