Instructions to use simota1987/Sana_Sprint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Sana
How to use simota1987/Sana_Sprint 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://simota1987/Sana_Sprint") 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
- Xet hash:
- c1cde18103c1a6bb987fae748994039d58f9b361b6e4c3284e2c7fab9ababea9
- Size of remote file:
- 320 MB
- SHA256:
- 07c01b46af6a1a7e519b76738fe0a2ea962f1dde0dea12cf050c57f05b764779
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