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Add ⚡️Sana: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer Support #5785
Comments
Unfortunately, only the 512-pixel and 1024-pixel versions of the model have been released so far. According to the README, it seems there is a 4096-pixel version as well. |
Pretty sure that's just referring to generating 4096x4096 images, not that there's a separate model. |
is there a way to run this already in comfyui? |
There is an unofficial implementation if you are interested. |
Now use official custom-node ComfyUI_ExtraModels instead. |
Exciting news! |
Full of errors for windows I could not run it |
Waiting for complete, it's in progress. |
I'm currently doing a full rewrite of ExtraModels to try and get better support on both PixArt and Sana. Progress is being tracked on this PR: city96/ComfyUI_ExtraModels#92 |
This is so cool. Thanks for your work. |
Feature Idea
🐱 Sana Model Card
Model
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.
Model Description
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).
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.
🧨 Diffusers
PR developing: Sana and DC-AE
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
Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Existing Solutions
No response
Other
No response
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