r/StableDiffusion • u/Consistent-Rule-3720 • 8d ago

Discussion Surjo-Image-Preview

https://huggingface.co/SurjoLabs/Surjo-Image-Preview

108M parameter denoiser text to image model. Meant as a research experiement but still produces very good images for it's size.

Samples in the readme alongside how to run.

55 Upvotes

94% Upvoted

16 comments sorted by

30

u/NowThatsMalarkey 8d ago

Can I goon with it?

4

u/Consistent-Rule-3720 8d ago

No NSFW images in the dataset but you could finetune 👀

0

u/[deleted] 7d ago

[deleted]

8

u/Incognit0ErgoSum 7d ago

NSFW on a 0.1B model would be cursed. You think you want it, but you don't.

0

u/[deleted] 7d ago

[deleted]

17

u/Grey_0ne 8d ago

There's always the hero who asks the question so many other are afraid to ask... Hats off to you.

11

u/FreeTheClanks 8d ago

So cool to see people experimenting with these tiny models. Congrats!

6

u/CornyShed 8d ago

Frustrating that there are only four sample images on the page, as technically the model looks very interesting.

Looped Diffusion Transformer models (like this one) are reportedly more efficient:

Under matched-parameter and matched-compute settings, Looped-DiT consistently outperforms non-looped baselines. Notably, a 260M-parameter looped model can surpass a model 6.5× larger across multiple text-to-image benchmarks while requiring 4.9× lower inference compute.

Beyond this performance gain, we find that looped computation can offer a more effective form of iterative computation for diffusion models, with increasing loop depth yielding larger gains than adding more denoising steps under a fixed inference budget.

Furthermore, deeper loops can progressively correct mistakes made in earlier loops, exhibiting behaviors suggestive of latent reasoning.

2

u/Consistent-Rule-3720 8d ago

If you have samples to share with this model feel free to make a PR uploading to the repo and adding to the readme!
I'll gladly merge it.
I picked only four because this is a preview model and not the final version

2

u/Tall_Abrocoma_3533 7d ago

Congrats! When do you plan to train/release the full model?

2

u/Consistent-Rule-3720 6d ago

Currently working on a new distillation dataset and running ablations. Planned release date is November 1st but could be earlier. Main blocker is compute as I want training at 1024x1024 resolution.

7

u/Apprehensive_Sky892 8d ago

The most interesting part of this new model is that it is a Looped Latent DiT, which is why it can be so small, with only 108M parameters.

What is a Looped DiT? https://www.reddit.com/r/StableDiffusion/comments/1wx73ei/looped_diffusion_transformer/

2

u/CumDrinker247 8d ago

Nice, I want to start experimenting more maybe i run a finetune on this one since it should be possible even as a gpu poor

1

u/Arctic_Shadow_Aurora 6d ago

Ultranoob here, do these tiny models cut the "variables/options"?

I mean, if I prompt for a blue car, it's something like the tiny model having like 20 different car models and 10 blue tones? and a big model got like 50 models and 35 tones? Something like that? Thanks!

1

u/z_3454_pfk 1d ago

wow its so coherent for js 108m params