🚀 Sana
Sana is a text-to-image framework that can efficiently generate high - resolution images up to 4096 × 4096. It synthesizes high - quality images with strong text - image alignment at high speed and can be deployed on laptop GPUs.
🚀 Quick Start
We introduce Sana, a text - to - image framework that can efficiently generate images up to 4096 × 4096 resolution. Sana can synthesize high - resolution, high - quality images with strong text - image alignment at a remarkably fast speed, deployable on laptop GPU. Source code is available at https://github.com/NVlabs/Sana.
✨ Features
- High - Resolution Image Generation: Capable of generating images up to 4096 × 4096 resolution.
- Fast Synthesis: Synthesizes high - quality images with strong text - image alignment at high speed.
- Multi - language Support: Supports Emoji, Chinese and English and all mixed prompts.
- Laptop GPU Deployment: Can be deployed on laptop GPUs.
💻 Usage Examples
Basic Usage
import torch
from app.sana_pipeline import SanaPipeline
from torchvision.utils import save_image
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
generator = torch.Generator(device=device).manual_seed(42)
sana = SanaPipeline("configs/sana_config/2048ms/Sana_1600M_img2048_bf16.yaml")
sana.from_pretrained("hf://Efficient-Large-Model/Sana_1600M_2Kpx_BF16/checkpoints/Sana_1600M_2Kpx_BF16.pth")
prompt = 'a cyberpunk cat with a neon sign that says "Sana"'
image = sana(
prompt=prompt,
height=2048,
width=2048,
guidance_scale=5.0,
pag_guidance_scale=2.0,
num_inference_steps=20,
generator=generator,
)
save_image(image, 'output/sana.png', nrow=1, normalize=True, value_range=(-1, 1))
📚 Documentation
Model Description
Property |
Details |
Developed by |
NVIDIA, Sana |
Model Type |
Linear - Diffusion - Transformer - based text - to - image generative model |
Model Size |
1648M parameters |
Model Resolution |
This model is developed to generate 2Kpx based images with multi - scale height and width. |
License |
NSCL v2 - custom. Governing Terms: NVIDIA License. Additional Information: [Gemma Terms of Use |
Model Description |
This is a model that can be used to generate and modify images based on text prompts. It is a Linear Diffusion Transformer that uses one fixed, pretrained text encoders ([Gemma2 - 2B - IT](https://huggingface.co/google/gemma - 2 - 2b - it)) and one 32x spatial - compressed latent feature encoder ([DC - AE](https://hanlab.mit.edu/projects/dc - ae)). |
Special |
This model is fine - tuned from the base model [Efficient - Large - Model/Sana_1600M_1024px_BF16](https://huggingface.co/Efficient - Large - Model/Sana_1600M_1024px_BF16) and it supports Emoji, Chinese and English and all mixed prompts. |
Resources for more information |
Check out our GitHub Repository and the Sana report on arXiv. |
Model Sources
For research purposes, we recommend our generative - models
Github repository (https://github.com/NVlabs/Sana), which is more suitable for both training and inference and for which most advanced diffusion sampler like Flow - DPM - Solver is integrated. [MIT Han - Lab](https://nv - sana.mit.edu/) provides free Sana inference.
Uses
Direct Use
The model is intended for research purposes only. Possible research areas and tasks include:
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
Out - of - Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out - of - scope for the abilities of this model.
⚠️ Important Note
- Weakness in Complex Scene Creation: Due to limitation of data, our model has limited capabilities in generating complex scenes, text, and human hands.
- Enhancing Capabilities: The model’s performance can be improved by increasing the complexity and length of prompts. Below are some examples of prompts and samples.
2K samples
Images |
 |
 |
 |
 |
prompt |
A model wearing an orange and blue sweater with a knitted pattern, detailed face, ginger hair color, blue background, shot in the style of Tim Walker |
A studio shot of a figure composed of vivid , realistic flames walking in side profile, no face, with flames trailing naturally behind, against an intense red background, Captured by Helmut Newton, with a Hasselblad H6D and an 80mm f/2.8 lens at f/5.6, 1/125s, ISO 400. The flames are intense and detailed, with a natural, realistic texture that emphasizes the movement. HD quality, natural look |
A surreal photograph of a man with his head covered in large , fluffy cotton clouds, sitting in an armchair facing the camera and using an old computer from the early 20th century. The computer has a green screen monitor. He is wearing a pink suit, and the overall scene has a soft, pastel color palette with a retro futuristic, 1970s vibe |
highend portrait of a cool ginger cat , high fashion Style with crazy glasses and stylish clothes and luxury necklace of jeweleries, hat consisting of lilies, oranges and lemons, color: dark green and gold, background: dark and luxury, hyper detailled, stil: raw, cinematic light, canon, expressive and original, canon, 50 mm lens, editorial, look: expensive, mood: motivated and good vibes |
Images |
 |
 |
 |
prompt |
雪山之巅红日东升 |
一只可爱的 🐼 在吃 🎋, 水墨画风格 |
孤舟蓑笠翁 |
Images |
 |
 |
 |
prompt |
a cat floating in a body of water in the pool, Lean back clear and transparent water, light white, and turquoise, y2k aesthetic, soft and dreamy colors, brightly colored, popular Instagram, highlevel of detail, realistic photo feeling |
A black cat sits on the ground, facing away from us and casting its shadow in front of it. The silhouette of a majestic lion is projected onto the wall by the light source behind the scene. Black and white photography, soft lighting, high contrast, sharp focus, symmetrical composition, simple background, portrait lens, static posture. |
🐶 和 🐱 玩 ⚽ |
📄 License
The model is licensed under NSCL v2 - custom. Governing Terms: NVIDIA License. Additional Information: Gemma Terms of Use | Google AI for Developers for Gemma - 2 - 2B - IT, Gemma Prohibited Use Policy | Google AI for Developers.