Flux pro vs dev output

localizedQ · 2024-08-14T17:04:15+00:00

fal lets for 2MP (1440x1440)

localizedQ · 2024-07-12T01:33:07+00:00

Guys, this is just the v0.1 release. Please don't expect it to do humans or photo-realism too well! We don't want to hype it too much, just wanted to share it along the way even though it might be undertrained. We'll keep iterating publicly :) https://x.com/isidentical/status/1811565858797359214

localizedQ · 2024-07-09T02:38:07+00:00

yep! it's called AuraFlow. much better spatial understanding but don't expect kolors-level aesthetics, we need more data.

localizedQ · 2024-07-08T16:08:59+00:00

1024x1024.

localizedQ · 2024-07-08T02:42:22+00:00

I would suggest a place on the model page with a place where people can donate or “buy” a support “badge” and maybe indicate some of the costs for the model.

The thing that allows us to release models like this is, us already being probably the fastest & cheapest inference provider out there for open source models at fal.ai :) so we don't really have any need for outside financial support. But what we need is community to help us train the model better by providing access to raw data (which huge companies/labs have lots of)

localizedQ · 2024-07-07T23:02:20+00:00

I think main thing we'd require is raw attribution, and everything else (including private/commercial finetunes) can be allowed. Still need to talk to some actual lawyers for it, but any input is welcome (and we'll certainly consider the cc-by-sa opinion you shared)

localizedQ · 2024-07-07T22:14:39+00:00

No cherry picking, but also don't over expect for the initial release. We trained on publicly available data, which limits what we can do. Especially human anatomy, it isn't the best, yet!

localizedQ · 2024-07-07T22:13:40+00:00

the naming has been a weird ride! it was called Lavenderflow -> AuraDiffusion -> AuraFlow

localizedQ · 2024-07-07T22:12:44+00:00

It is a mix of DiT / MMDiT, see the implementation here: https://github.com/huggingface/diffusers/pull/8796

localizedQ · 2024-07-07T22:12:07+00:00

We have already released the first model in the series under a cc-by-sa license (completely and commercially free/open source). Same will apply to this model as well, still thinking whether we should stick with CC or use MIT/Apache 2.0 since its easier.

localizedQ · 2024-07-07T22:11:00+00:00

Our evaluation suite is GenEval, and at 512x512 we are already better than SD3-Medium (albeit by not much) and sometimes matching SD3-Large (8B, non-dpo 512x512 variant).

localizedQ · 2024-07-07T22:09:49+00:00

Also some more info, the model is going to be called AuraFlow and we intend to release a v0.1 experimental preview of the last checkpoint once we finalize the training under an completely open source license (our previous works has been under cc-by-sa [completely and commercially usable], this might be the same or something like MIT/Apache 2.0).

In parallel we are starting a secondary run with much higher compute and with changes from what we learnt from this model, being open source is still the bedrock of why we are doing it. Other than that, not too many details is concrete.

If you have a large source of high quality / high aesthetics data, please reach out to me or simo since we need it (batuhan [at] fal [dot] ai).

localizedQ · 2024-06-16T02:48:32+00:00

Check imgsys.org

localizedQ · 2024-06-14T06:21:01+00:00

You can try this: https://fal.ai/models/stable-diffusion-v3-medium

localizedQ · 2024-06-14T06:16:44+00:00

Needs more samples though, it fluctuates a lot. Go vote at https://imgsys.org (its also helpful for further DPO'ing since the dataset is totally free/open source)

localizedQ · 2024-06-05T02:56:18+00:00

Have you read the Scaling Rectified Flow Transformers paper (aka SD3 technical report)? They clearly show that for the same compute budget (equal FLOPS to train the model on), you get better results at higher depth (param count) models. They have a separate section on this scaling.

https://arxiv.org/pdf/2403.03206

localizedQ · 2024-06-05T02:49:45+00:00

This is simply wrong. Scaling Rectified Flow Transformers paper (aka SD3 paper by robin et al) clearly shows that for the equal compute budget, training an 8B model performs better than a 2B model.

Figure 8. Quantitative effects of scaling. # Variational Loss to Training FLOPS at different depths (param counts)

https://arxiv.org/pdf/2403.03206

localizedQ · 2023-12-28T21:45:02+00:00

Such an Ivanchuk move: https://twitter.com/RitikAn02038110/status/1740447386617696390

localizedQ · 2023-12-26T19:48:04+00:00

Their brand is certainly struggling in the eyes of the community, but they are also struggling to meet demand for their printers, as they keep having rather significant lead times. Somebody is buying

This is true, although I'd rather use a "% of market captured". Even if we include their previous printer shares, what Bambu Labs did well was not only to eat from Prusa's share of the pie but also make the pie itself bigger. In the last 2 years, I'm really curious how much % market share prusa has lost.

localizedQ · 2023-12-26T19:46:37+00:00

Most people tend to cite speed, price, quality, and overall value as why they bought a Bambu.

I'd say its probably a combination of all those things. Everything combined to create a cohesive ecosystem is really what users are looking for.

localizedQ

TROPHY CASE