Here are a few key takeaways about NVIDIA’s Sana: High-Resolution Image Generation: Sana can generate images up to 4096 × 4096 resolution, making it capable of producing ultra-high-quality visuals. Efficiency: It uses a deep compression autoencoder that compresses images 32 times, significantly reducing the number of latent tokens and improving efficiency. Linear Diffusion Transformer (DiT): Sana replaces traditional attention mechanisms with linear attention, which is more efficient at high resolutions without sacrificing quality. Text-Image Alignment: The model employs a decoder-only small language model (LLM) as the text encoder, enhancing the understanding and alignment of text prompts with generated images. Fast and Accessible: Sana can generate a 1024 × 1024 resolution image in less than a second on a 16GB laptop GPU, making high-quality image generation accessible even on consumer-grade hardware.
I'll believe it, when I see it. That's just too good, an open model that generates higher quality images than Flux at a higher resolution and with better prompt understanding while using a lot less resources and hence a lot faster too. That sounds absurd to me. Also it's one thing to generate big image but another to create an image that actually uses it's resolution to the fullest and doesn't just look upscaled.
Resources:
nvlabs.github.io/Sana/
sana-gen.mit.edu/
arxiv.org/abs/2410.10629
hanlab.mit.edu/projects/sana
Here are a few key takeaways about NVIDIA’s Sana:
High-Resolution Image Generation: Sana can generate images up to 4096 × 4096 resolution, making it capable of producing ultra-high-quality visuals.
Efficiency: It uses a deep compression autoencoder that compresses images 32 times, significantly reducing the number of latent tokens and improving efficiency.
Linear Diffusion Transformer (DiT): Sana replaces traditional attention mechanisms with linear attention, which is more efficient at high resolutions without sacrificing quality.
Text-Image Alignment: The model employs a decoder-only small language model (LLM) as the text encoder, enhancing the understanding and alignment of text prompts with generated images.
Fast and Accessible: Sana can generate a 1024 × 1024 resolution image in less than a second on a 16GB laptop GPU, making high-quality image generation accessible even on consumer-grade hardware.
I'll believe it, when I see it. That's just too good, an open model that generates higher quality images than Flux at a higher resolution and with better prompt understanding while using a lot less resources and hence a lot faster too. That sounds absurd to me. Also it's one thing to generate big image but another to create an image that actually uses it's resolution to the fullest and doesn't just look upscaled.
About bloody time. No prizes for guessing this wont run on Apple and amd hardware
WOW, I cant finish the projects. You always come up with such great news, thank you 👌
It looks more like an upscaler and enhancer than a Diffusion model.
STILL flux is better
That is pretty amazing!
Wow. That model is mindblowing and with low Vram? Thanks for the update.
Low parameters, low VRAM, able to do something similar to Flux, I take it.
@@TheFutureThinker Word
@insurancecasino5790 yes and word on image , this is awesome , it allow media agency to create banners.
Nice 😍
Yup😉
10:28 "skin looks natural without shiny platic style"....... Man, please check that image, you can be more wrong
I said compare with Flux , listen the whole thing not some part.
By the way, run that in Flux, "please check that image, you can be more wrong"
Text in Dutch is much worse then Flux.
No one cares about 4k (or 8k or jibbitebillion k) and no one ever Will.
not impressed at all.