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:
- 787eb9e0a5c88e1fabf16a5064b3b5e9496525a070a039a3a6e2b8d16cc22e5b
- Size of remote file:
- 320 MB
- SHA256:
- 01652cae83048c0ac993ffccb2c8193b4f10e0be59c0fdbadccec7a9df4c0106
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