Instructions to use Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Sana
How to use Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers 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_1.6B_1024px_diffusers") 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, ) - Diffusers
How to use Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Verified signatureUpload model_index.json Upload scheduler/scheduler_config.json Upload text_encoder/model.safetensors.index.json Upload text_encoder/model-00001-of-00002.safetensors Upload text_encoder/config.json Upload text_encoder/model-00002-of-00002.safetensors Upload tokenizer/tokenizer_config.json Upload tokenizer/tokenizer.json Upload tokenizer/tokenizer.model Upload tokenizer/special_tokens_map.json Upload transformer/diffusion_pytorch_model.safetensors Upload transformer/config.json Upload vae/diffusion_pytorch_model.safetensors Upload vae/config.jsonedbbb69