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

$ 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)