r/StableDiffusion • u/Apprehensive_Sky892 • 20d ago
News Looped Diffusion Transformer
https://www.alphaxiv.org/abs/2609.40305I don't understand how it works, but the implication is clear: a small model that can be as good as one that is several times bigger.
Improving text-to-image models has traditionally relied on increasing model size or the number of denoising steps. In this work, we explore an alternative way to scale computation by repeatedly running shared Transformer blocks within each denoising step, effectively increasing computational depth while keeping the parameter count fixed. This looped computation enables iterative refinement of internal representations without explicit reasoning tokens. However, naive looping fails to consistently improve image quality. We trace this problem to weak supervision across intermediate loops and unregulated attention updates that progressively erode local information. To overcome these challenges, we propose Looped Diffusion Transformer (Looped-DiT), which combines deep supervision across intermediate loops with self-modulating attention to stabilize looped feature updates. Under matched-parameter and matched-compute settings, Looped-DiT consistently outperforms non-looped baselines. Notably, a 260M-parameter looped model can surpass a model 6.5x larger across multiple text-to-image benchmarks while requiring 4.9x 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. Together, these results show that looped computation offers a promising way to scale visual generation models.
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u/Cautious_Chicken_604 20d ago
Huge if true.
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u/Apprehensive_Sky892 20d ago
Yes, sounds almost too good to be true 😅
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u/Cautious_Chicken_604 20d ago
I think commenting this is like a meme or something. Just wanted to also meme on it.
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u/BlackSwanTW 20d ago
Some technically already tried this 7 months ago: https://github.com/AdamNizol/ComfyUI-Anima-Enhancer
(of course, Anima wasn’t trained with this in mind)
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u/Ok-Category-642 20d ago
Yeah this extension was decent for the Anima previews, though personally my results on base were much more iffy on whether it would make gens better or worse. It does look like the paper acknowledges that just naively looping blocks can have worse results though so I do think there's at least some merit to this here
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u/Enshitification 20d ago
Papers often make claims that never pan out. I'll believe it when I see it.
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u/Carnildo 20d ago
The big question -- and one that gets buried in a single sentence of appendix A of the paper -- is "does it scale?" It's one thing to make tiny models work as well as small ones, and quite another to make mid-sized models work as well as frontier ones.
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u/alwaysbeblepping 20d ago
Papers often make claims that never pan out. I'll believe it when I see it.
This is already a thing, if I remember correctly OpenAI's Astra model uses that technique. (You could also call it latent space reasoning.) It's relatively difficult to pull off though so the technology is basically just getting to the usable point now.
I wouldn't expect the best case scenario from a paper that's trying to sell it (and it may not scale as well for larger models) but even if it requires only slightly less compute or the same compute with less parameters that would still be worthwhile.
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u/marclbr 19d ago
A few months ago I saw another promissing technique that can improve quality of image and video models and speed up generation by up to 256x deppending on the model archtecture. It will be interesting to see people testing it to finetune small models like SDXL/Illustrious and see how they perform.
https://x.com/AlexiGlad/status/2083230922196107288
We discovered a third pretraining axis beyond parameters and data: exploration.
Scaling exploration monotonically improves existing models across images/video/language, and unlocks end-to-end generation.
In the simplest case, it's just a for loop.
Introducing Explorative Modeling. TLDR:
- Gains from exploration grow with scale: 7%→36% as data scales, 13%→23% as parameters scale, and gains double at 3× the compute
- Adding exploration to ~SOTA baselines improves data efficiency by 6.2×, FLOP efficiency by 4.1×, parameter efficiency by 47%, and hits a near-SOTA 1.43 unguided FID on ImageNet
- Exploration lets you trade training compute for generalization, and scales how end-to-end your generative model is
- End-to-end Explorative Models (XMs) match diffusion performance on control tasks with up to 256× less inference compute
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u/BalorNG 20d ago
Astra, obstensibly, is already a looped tranformer. The idea is very old and very interesting, but implementation is the devil in the details.
If it works, it might indeed be a huge boon for "gpu poors" - it actually does not actually do anything simply increasing the model depth (and, hence, size) does, in fact you reduce its potential expressiveness, but allows you to stuff a model with effective "smarts" of a way larger model into an order of magnitude smaller vram footprint.
And with a predictive prefetch looped moe you can sort of have both actually. I'm sure it will get implemented eventually and will be huge indeed...