It’s fast too! I would reckon about 2-3x faster than non-turbo SDXL.

Had a test of it and my option is it's an improvement when it comes to following prompts and I do find the images more visually appealing.

This text was part of the Stability Japan leak (the 20gb VRAM reference was dropped in the release today):

"Stages C and B will be released in two different models. Stage C uses parameters of 1B and 3.6B, and Stage B uses parameters of 700M and 1.5B. However, if you want to minimize your hardware needs, you can also use the 1B parameter version. In Stage B, both give great results, but 1.5 billion is better at reconstructing finer details. Thanks to Stable Cascade's modular approach, the expected amount of VRAM required for inference can be kept at around 20GB, but can be reduced even further by using smaller variations (as mentioned earlier, this (which may reduce the final output quality)."

Maybe running stage C first, unloading it from VRAM, and then do B and A would make it fit in 12 or even 8 GB, but I wonder if the memory transfers would negate any time saving. Might still be worth it if it produces better images though.

For example I pulled a (2GB I think, 4 tops) 6870 out of my desktop because it's a beast (in physical size, and power consumption) and I wasn't using it for gaming or anything, figured I'd be fine just with the Intel integrated graphics. But if I wanted to play around with some models locally, it'd be worth putting it back & figuring out how to use it as a secondary card?

Much faster models will come

From what I understand, Stability AI is currently VC funded. It’s bound to burn through tons of money and it’s not clear whether the business model (if any) is sustainable. Perhaps worthy of government funding.

> The company is spending significant amounts of money to grow its business. At the time of its deal with Intel, Stability was spending roughly $8 million a month on bills and payroll and earning a fraction of that in revenue, two of the people familiar with the matter said.

> It made $1.2 million in revenue in August and was on track to make $3 million this month from software and services, according to a post Mostaque wrote on Monday on X, the platform formerly known as Twitter. The post has since been deleted.

https://fortune.com/2023/11/29/stability-ai-sale-intel-ceo-r...

Given how much VC money is chasing the AI space, this isn't necessarily a bad plan. Give stuff away for free while developing deep expertise, then either figure out something to sell, or pivot to proprietary, or get aquihired by a tech giant.

The main reason is probably Mid journey and OpenAi using their tech without any kind of contribution back. AI desperately needs a GPL equivalent…

I am not sure if that is still the case.

For MJ in particular, knowing that they at least used to use Stable Diffusion under the hood, it would not surprise me if the majority of the secret sauce is actually a middle layer that processes the prompt and converts it to one that is better for working with SD. Prompting SD to get output at the MJ quality level takes significantly more tokens, lots of refinement, heavy tweaking of negative prompting, etc. Also a stack of embeddings and LoRAs, though I would place those more in the category of finetuning like you had mentioned.

I've also used it to generate art for deskmats I got printed at https://specterlabs.co/

For commercial stuff I still pay human artists.

I like them quite a bit, and you can get basically any size cut to fit your needs even if they don't directly offer it on the site.

Why not just the GPL then?

The model weights are shared openly, but the training data used to create these models isn't. This is at least partly because all these models, including OpenAI's, are trained on copyrighted data, so the copyright status of the models themselves is somewhat murky.

In the future we may see models that are 100% trained in the open, but foundational models are currently very expensive to train from scratch. Either prices would need to come down, or enthusiasts will need some way to share radically distributed GPU resources.

There are approaches to get the right type of augmented and generated data to feed these models right, check out our QDAIF paper we worked on for example

https://arxiv.org/pdf/2310.13032.pdf

Just did some pes2o ablations too which were eh

One thing that’s hard to measure is the knowledge contained in books3. If someone asks about certain books, it won’t be able to give an answer unless the knowledge is there in some form. I’ve often wondered whether scraping the internet is enough rather than training on books directly.

But be careful about relying too much on evals. Ultimately the only benchmark that matters is whether users find the model useful. The clearest test of this would be to train two models side by side, with and without books3, and then ask some people which they prefer.

It’s really tricky to get all of this right. But if there’s more details on the pes2o ablations I’d be curious to see.

HN search doesn't seem to agree with me today though and I cannot find the specific comment/s I have in mind, maybe someone else has any luck? This is their user https://news.ycombinator.com/user?id=emadm

We now have top models of every type, sites like www.stableaudio.com, memberships, custom model deals etc so lots of demand

We're the only AI company that can make a model of any type for anyone from scratch & are the most liked / one of the most downloaded on HuggingFace (https://x.com/Jarvis_Data/status/1730394474285572148?s=20, https://x.com/EMostaque/status/1727055672057962634?s=20)

Its going ok, team working hard and shipping good models, the team are accelerating their work on building ComfyUI to bring it all together.

My favourite recent model was CheXagent, I think medical models should be open & will really save lives: https://x.com/Kseniase_/status/1754575702824038717?s=20

Instead we've given 10m+ supercomputer hours in grants to all sorts of projects, now we have our grant team in place & there is a huge increase in available funding for folk that can actually build stuff we can tap into.

All the original Stable Diffusion researchers (Robin Rombach, Patrick Esser, Dominik Lorenz, Andreas Blattman) also work for Stability AI.

Is it legal to use an older snapshot before the license was changed in accordance with the previous MIT license?

Generally courts are more holistic and look at intent, and understand that clerical errors happen. One exception to this is if a business claims it relied on the previous license and invested a bunch of resources as a result.

I believe the timing of commits is pretty important— it would be hard to claim your business made a substantial investment on a pre-announcement repo that was only MIT’ed for a few hours.

Licenses are important. If you are going to expose your code to the world, make sure it has the right license. If you publish your code with the wrong license, you shouldn't be allowed to take it back. Not for an organization of this size that is going to see a new repo cloned thousands of times upon release.

For the same reason you cannot publish a private corporate repo with an MIT license and then have other people claim in “good faith” to be using it.

All they need is to assert that the license was published in error, or that the person publishing it did not have the authority to publish it.

You can’t “magically” make a license stick by putting it in a repo, any more than putting a “name here” sticker on someone’s car and then claiming to own it.

The license file in the repo is simply the notice of the license.

It does not indicate a binding legal agreement.

You of course, can challenge it in court, and ianal, but I assure you, there is president in incorrectly labelled repos removing and changing their licenses.

Stability's initial commit had an MIT license, so you can fork that commit and do whatever you want with it. It's MIT licensed.

Now, the tricky part here is that they committed a change to the license that changes it from MIT to proprietary, but they didn't change any code with it. That is definitely invalid, because they cannot license the exact same codebase with two different contradictory licenses. They can only license the changes made to the codebase after the license change. I wouldn't call it "illegal", but it wouldn't stand up in court if they tried to claim that the software is proprietary, because they already distributed it verbatim with an open license.

Why couldn't they? Of course they can. If you are the copyright owner, you can publish/sell your stuff under as many licenses as you like.

That's a marketing opportunity being missed, especially given how crowded the space is now. The HN crowd is more likely to run it themselves when presented with signing up just to test out a single generation.

I guess my zero attempts per minute was too much. You really shouldn't post your product on HN if you aren't prepared for it to work. Reputations are hard to earn, and you're losing people's interest by directing them to a broken product.

[1] https://fal.ai/models/stable-cascade?share=13d35b76-d32f-45c...

That's an odd comment to place in a thread about an image generation model that is bigger than SDXL. Yes, it works in a smaller latent space, yes its faster in the hardware configuration they've used, but its not smaller.

[1] https://zero123.cs.columbia.edu/

Better coming

ref.: "The model can also understand image embeddings, which makes it possible to generate variations of a given image (left). There was no prompt given here."

There is a model that is trained to compress (very lossy) and decompress the latent, but it's not the main generative model, of course the model doesn't store images in it, you just give the encoder an image and it will encode it and then you can decode it with the decoder and get a very similar image, this encoder and decoder is used during training so that the stage C can work on a compressed latent instead of directly at the pixel level because it's expensive, but the main generative model (stage C) should be able to generate any of the images that were present in the dataset or it fails to do its job. Stages C, B, and A do not store any images.

The B and A stages work like an advanced image decoder, so unless you have something wrong with image decoders in general, I don't see how this could be a problem (a JPEG decoder doesn't store images either, of course).

This principle is the idea behind all Stable Diffusion models, this one "just" achieved a much better compression ratio

But yeah, the inference speed improvement is mediocre (until I take a look at exactly what computation performed to have more informed opinion on whether it is implementation issue or model issue).

The prompt alignment should be better though. It looks like the model have more parameters to work with text conditioning.

https://medium.com/@furkangozukara/stable-cascade-prompt-fol...

My Gradio APP even works amazing on 8 GB gpu with CPU offloading

I remember setting up the python env for stable diffusion, but then shortly after there were a host of nice GUIs. Are there some popular GUIs that can be used to try out newer models? Similarly, what's the best GUI for some of the older models? Preferably for macos.

ComfyUI is cool but very DIY. You don't get good results unless you wrap your head around all the augmentations and defaults.

No idea if it will support cascade.

There are also a large amount of resources available for it on YouTube, GitHub (https://github.com/comfyanonymous/ComfyUI_examples), reddit (https://old.reddit.com/r/comfyui), CivitAI, Comfy Workflows (https://comfyworkflows.com/), and OpenArt Flow (https://openart.ai/workflows/).

I still use AUTO1111 (https://github.com/AUTOMATIC1111/stable-diffusion-webui) and the recently released and heavily modified fork of AUTO1111 called Forge (https://github.com/lllyasviel/stable-diffusion-webui-forge).

Not entirely sure we'll be in the Stable Cascade race quite yet. Since Auto/Comfy aren't really built for businesses, they'll get it incorporated sooner vs later.

Invoke's main focus is building open-source tools for the pros using this for work that are getting disrupted, and non-commercial licenses don't really help the ones that are trying to follow the letter of the license.

Theoretically, since we're just a deployment solution, it might come up with our larger customers who want us to run something they license from Stability, but we've had zero interest on any of the closed-license stuff so far.

4*16*24*24*4 = 147,456

vs (removing the alpha channel as it's unused here)

3*3*1024*1024 = 9,437,184

Or 1/64 raw size, assuming I haven't fucked up the math/understanding somewhere (very possible at the moment).

This model used to be known as Würstchen v3.