Instructions to use Efficient-Large-Model/SANA1.5_4.8B_1024px with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Sana
How to use Efficient-Large-Model/SANA1.5_4.8B_1024px 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/SANA1.5_4.8B_1024px") 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, ) - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec81685990f71bf5c7d54f697483810ce4a5f270cf791ed56e66ae0f19ec3d40
- Size of remote file:
- 18.9 GB
- SHA256:
- 9434d169b6d95acc6e67960ec841d0b007e63038db2434e036eaf3daf25696c9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.