nvlabs--sana/docs/installation.md

Installation

Requirements

• Python >= 3.10.0 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 2.5.1+cu12.4

Quick Install

git clone https://github.com/NVlabs/Sana.git
cd Sana

bash ./environment_setup.sh sana
# or you can install each components step by step following environment_setup.sh

Hardware Requirements

Model	VRAM Required
Sana-0.6B	9GB
Sana-1.6B	12GB
4-bit Quantized	< 8GB

!!! Note All the tests are done on A100 GPUs. Different GPU versions may vary.

Diffusers Installation

To use Sana with diffusers, make sure to upgrade to the latest version:

pip install git+https://github.com/huggingface/diffusers

Quick Start with Diffusers

import torch
from diffusers import SanaPipeline

pipe = SanaPipeline.from_pretrained(
    "Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers",
    torch_dtype=torch.bfloat16,
)
pipe.to("cuda")

pipe.vae.to(torch.bfloat16)
pipe.text_encoder.to(torch.bfloat16)

prompt = 'a cyberpunk cat with a neon sign that says "Sana"'
image = pipe(
    prompt=prompt,
    height=1024,
    width=1024,
    guidance_scale=4.5,
    num_inference_steps=20,
    generator=torch.Generator(device="cuda").manual_seed(42),
)[0]

image[0].save("sana.png")

Optional: Docker

# Build Docker image
docker build -t sana .

# Run inference with Docker
docker run --gpus all -it sana python scripts/inference.py

Next Steps

• Model Zoo - Choose your model
• SANA-Sprint - Fast inference mode with 1-4 steps generations
• SANA-Video - Video Gen with Linear Attention and Linear Block KV-Cache