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Efficient-Large-Model / Sana_600M_1024px_ControlNet_HED
Model's Last Updated: July 31 2026
text-to-image
Introduction of Sana_600M_1024px_ControlNet_HED
Model Details of Sana_600M_1024px_ControlNet_HED
Model card
We incorporate a ControlNet-like( https://github.com/lllyasviel/ControlNet ) module enables fine-grained control over text-to-image diffusion models. We implement a ControlNet-Transformer architecture, specifically tailored for Transformers, achieving explicit controllability alongside high-quality image generation.
Source code is available at https://github.com/NVlabs/Sana .
How to Use
refer to Sana-ControlNet Guidance for more details.
import torch
from PIL import Image
from app.sana_controlnet_pipeline import SanaControlNetPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = SanaControlNetPipeline("configs/sana_controlnet_config/Sana_600M_img1024_controlnet.yaml")
pipe.from_pretrained("hf://Efficient-Large-Model/Sana_600M_1024px_ControlNet_HED/checkpoints/Sana_600M_1024px_ControlNet_HED.pth")
ref_image = Image.open("asset/controlnet/ref_images/A transparent sculpture of a duck made out of glass. The sculpture is in front of a painting of a la.jpg")
prompt = "A transparent sculpture of a duck made out of glass. The sculpture is in front of a painting of a landscape."
images = pipe(
prompt=prompt,
ref_image=ref_image,
guidance_scale=4.5,
num_inference_steps=10,
sketch_thickness=2,
generator=torch.Generator(device=device).manual_seed(0),
)
Model Description
- Developed by: NVIDIA, Sana
- Model type: Linear-Diffusion-Transformer-based text-to-image generative model, ControlNet
- Model size: 900M parameters
- Model resolution: This model is developed to generate 1024px based images with multi-scale heigh and width.
- License: 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 .
- 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_600M_1024px and it supports HED ControlNet.
- 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: ttps://github.com/NVlabs/Sana
- Demo: https://nv-sana.mit.edu/
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.
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