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Efficient-Large-Model / Sana_1600M_4Kpx_BF16
Model's Last Updated: July 31 2026
text-to-image
Introduction of Sana_1600M_4Kpx_BF16
Model Details of Sana_1600M_4Kpx_BF16
Model card
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 .
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 .
4K samples
| Images |
|
|
|
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|---|---|---|---|---|
| prompt | A hot air balloon in the shape of a heart. Grand Canyon | a melting apple |
A middle-aged woman of Asian descent, her dark hair streaked with silver, appears fractured and splintered, intricately embedded within a sea of broken porcelain. The porcelain glistens with splatter paint patterns in a harmonious blend of glossy and matte blues, greens, oranges, and reds, capturing her dance in a surreal juxtaposition of movement and stillness. Her skin tone, a light hue like the porcelain, adds an almost mystical quality to her form. |
Modern luxury contemporary luxury home interiors house, in the style of mimicking ruined materials, ray tracing, haunting houses, and stone, capture the essence of nature, gray and bronze, dynamic outdoor shots. |
Model Description
- 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 4Kpx based images with multi-scale heigh and width.
- License: CC BY-NC-SA 4.0 License
- 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 ) and one 32x spatial-compressed latent feature encoder ( DC-AE ).
- Special: This model is fine-tuned from the base model 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
provides free Sana inference.
- Repository: https://github.com/NVlabs/Sana
Usage
Refer to original GitHub guidance to use the .pth model in Sana official code repo:
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/4096ms/Sana_1600M_img4096_bf16.yaml")
sana.from_pretrained("hf://Efficient-Large-Model/Sana_1600M_4Kpx_BF16/checkpoints/Sana_1600M_4Kpx_BF16.pth")
prompt = 'a cyberpunk cat with a neon sign that says "Sana"'
image = sana(
prompt=prompt,
height=4096,
width=4096,
guidance_scale=5.0,
pag_guidance_scale=2.0,
num_inference_steps=20,
generator=generator,
)
save_image(image, 'output/sana_4K.png', nrow=1, normalize=True, value_range=(-1, 1))
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.
Excluded uses are described below.
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.
Limitations and Bias
Limitations
- The model does not achieve perfect photorealism
- The model cannot render complex legible text
- fingers, .etc in general may not be generated properly.
- The autoencoding part of the model is lossy.
Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Runs of Efficient-Large-Model Sana_1600M_4Kpx_BF16 on huggingface.co
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