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NVIDIA Labs developed SANA model weights and Gradio demo app published —Check out this amazing new Text to Image model by NVIDIA
NVIDIA Labs developed SANA model weights and Gradio demo app published —Check out this amazing new Text to Image model by NVIDIA
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Official repo : 
Official repo : 
link
https://github.com/NVlabs/Sana
https://github.com/NVlabs/Sana
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1-Click Windows, RunPod, Massed Compute installers and free Kaggle notebook : 
1-Click Windows, RunPod, Massed Compute installers and free Kaggle notebook : 
link
https://www.patreon.com/posts/116474081
https://www.patreon.com/posts/116474081
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You can follow instructions on the repository to install and use locally. I tested on my Windows RTX 3060 and 3090 GPUs.
You can follow instructions on the repository to install and use locally. I tested on my Windows RTX 3060 and 3090 GPUs.
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I have tested some speeds and VRAM usage too
I have tested some speeds and VRAM usage too
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Uses 9.5 GB VRAM but someone reported works good on 8 GB GPUs too
Uses 9.5 GB VRAM but someone reported works good on 8 GB GPUs too
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Default settings per image speeds as below
Default settings per image speeds as below
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Free Kaggle Account Notebook on T4 GPU : 15 second
Free Kaggle Account Notebook on T4 GPU : 15 second
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RTX 3060 (12 GB) : 9.5 second
RTX 3060 (12 GB) : 9.5 second
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RTX 3090 : 4 second
RTX 3090 : 4 second
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RTX 4090 : 2 second
RTX 4090 : 2 second
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More info : 
More info : 
link
https://nvlabs.github.io/Sana/
https://nvlabs.github.io/Sana/
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Works great on RunPod and Massed Compute as well (cloud)
Works great on RunPod and Massed Compute as well (cloud)
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Sana : Efficient High-Resolution Image Synthesis
Sana : Efficient High-Resolution Image Synthesis
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with Linear Diffusion Transformer
with Linear Diffusion Transformer
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About Sana — Taken from official repo
About Sana — Taken from official repo
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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. Core designs include: Deep compression autoencoder: unlike traditional AEs, which compress images only 8×, we trained an AE that can compress images 32×, effectively reducing the number of latent tokens. Linear DiT: we replace all vanilla attention in DiT with linear attention, which is more efficient at high resolutions without sacrificing quality. Decoder-only text encoder: we replaced T5 with modern decoder-only small LLM as the text encoder and designed complex human instruction with in-context learning to enhance the image-text alignment. Efficient training and sampling: we propose Flow-DPM-Solver to reduce sampling steps, with efficient caption labeling and selection to accelerate convergence.
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. Core designs include: Deep compression autoencoder: unlike traditional AEs, which compress images only 8×, we trained an AE that can compress images 32×, effectively reducing the number of latent tokens. Linear DiT: we replace all vanilla attention in DiT with linear attention, which is more efficient at high resolutions without sacrificing quality. Decoder-only text encoder: we replaced T5 with modern decoder-only small LLM as the text encoder and designed complex human instruction with in-context learning to enhance the image-text alignment. Efficient training and sampling: we propose Flow-DPM-Solver to reduce sampling steps, with efficient caption labeling and selection to accelerate convergence.
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NVIDIA Labs developed SANA model weights and Gradio demo app published —Check out this amazing new Text to Image model by NVIDIA

Official repo : https://github.com/NVlabs/Sana

1-Click Windows, RunPod, Massed Compute installers and free Kaggle notebook : https://www.patreon.com/posts/116474081

You can follow instructions on the repository to install and use locally. I tested on my Windows RTX 3060 and 3090 GPUs.

I have tested some speeds and VRAM usage too

Uses 9.5 GB VRAM but someone reported works good on 8 GB GPUs too

Default settings per image speeds as below

Free Kaggle Account Notebook on T4 GPU : 15 second
RTX 3060 (12 GB) : 9.5 second
RTX 3090 : 4 second
RTX 4090 : 2 second
More info : https://nvlabs.github.io/Sana/

Works great on RunPod and Massed Compute as well (cloud)

Sana : Efficient High-Resolution Image Synthesis
with Linear Diffusion Transformer

About Sana — Taken from official repo

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. Core designs include: Deep compression autoencoder: unlike traditional AEs, which compress images only 8×, we trained an AE that can compress images 32×, effectively reducing the number of latent tokens. Linear DiT: we replace all vanilla attention in DiT with linear attention, which is more efficient at high resolutions without sacrificing quality. Decoder-only text encoder: we replaced T5 with modern decoder-only small LLM as the text encoder and designed complex human instruction with in-context learning to enhance the image-text alignment. Efficient training and sampling: we propose Flow-DPM-Solver to reduce sampling steps, with efficient caption labeling and selection to accelerate convergence.






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