We’ve generated text, audio, and images. The final frontier is Video. Now, Hugging Face Text to Video technology is opening up exciting new possibilities for content creation.
While still in its early stages compared to image generation, you can now generate short video clips (gifs/mp4) using Python and open-source models. We will use the ModelScope text-to-video model via diffusers.
⚠️ Hardware Warning
Video generation is computationally heavy. You will need a GPU with at least 12GB-16GB VRAM. If you don’t have one, this is a perfect project to run on Google Colab (free tier).
Step 1: Installation
pip install diffusers transformers accelerate torch opencv-python
Step 2: The Code
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import export_to_video
# 1. Load the Model
# 'damo-vilab/text-to-video-ms-1.7b' is a popular open model
pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
# 2. Enable CPU Offload (Critical for saving VRAM)
pipe.enable_model_cpu_offload()
# 3. Your Prompt
prompt = "An astronaut riding a horse on Mars, 4k, high quality"
# 4. Generate Frames
# 'num_frames' controls length (default is usually 16 frames)
video_frames = pipe(prompt, num_inference_steps=25).frames[0]
# 5. Save to Video
video_path = export_to_video(video_frames, "astronaut_video.mp4", fps=7)
print(f"Video saved at {video_path}")Step 3: The Result
The script will output an MP4 file. It will be short (about 2-4 seconds) and silent, but it will be a fully AI-generated video clip created entirely from your text prompt.
Key Takeaways
- You can now generate video clips using Python and open-source models, specifically with the Hugging Face Text to Video model.
- Video generation requires substantial computational power; a GPU with 12GB-16GB VRAM is necessary.
- For those without a powerful GPU, using Google Colab is an excellent option for this project.
- The resulting output will be a short, silent MP4 file created from a text prompt.
