Instructions to use Efficient-Large-Model/SANA-Video_2.0_5B_720p with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Sana
How to use Efficient-Large-Model/SANA-Video_2.0_5B_720p with Sana:
# Load the model and infer image from text import torch from app.sana_pipeline import SanaPipeline from torchvision.utils import save_image sana = SanaPipeline("configs/sana_config/1024ms/Sana_1600M_img1024.yaml") sana.from_pretrained("hf://Efficient-Large-Model/SANA-Video_2.0_5B_720p") image = sana( prompt='a cyberpunk cat with a neon sign that says "Sana"', height=1024, width=1024, guidance_scale=5.0, pag_guidance_scale=2.0, num_inference_steps=18, ) - Notebooks
- Google Colab
- Kaggle
Demo for this model on Spaces
Hi @Efficient-Large-Model 🤗
I'm Apolinario, from the open-source team at Hugging Face. Congrats and thanks for open-sourcing Efficient-Large-Model/SANA-Video_2.0_5B_720p on the Hub! We were excited about this work and built with an agent an interactive demo app of it on Hugging Face Spaces, running on a free ZeroGPU infrastructure.
Here's a link to the demo: https://huggingface.co/spaces/hugging-apps/sana-video2-5b-720p-demo
We would love to transfer this demo to you or your organization. Would you like this demo to live under your own account or organization? If so just let me know here which username to transfer to, and we'll transfer the Space over to you, we hope it can give your work more visibility, discoverability and allows folks to try it out.
(If you have any questions or just want to chat more about this, you can find me on Twitter, LinkedIn or apolinario @ huggingface.co)
Cheers,
Poli
Hi Poli,
Long time no see. Thanks so much for your support and we would appreciate if you could set this demo under Efficient-Large-Model space.
Best,
Junsong