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SDXL Turbo: A Real-Time Text-to-Image Generation Model

stability.ai

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Re: SDXL Turbo: A Real-Time Text-to-Image Generation Model

#51

Earlier quoted context omitted.

Normal SDXL requires 1024x1024 output or the quality degrades significantly.

Hi Max! Thanks for all the tuts! Small correction - SDXL wants ~1 megapixel resolutions at a variety of aspect ratios. https://github.com/lllyasviel/Fooocus/issues/24

Fair point, although in my experience SDXL still isn't great at non-square ratios. I end up just cropping them to the ratio I want.

Re: SDXL Turbo: A Real-Time Text-to-Image Generation Model

#52

Earlier quoted context omitted.

I've done a bit of fiddling around with it and definitely holding back judgement for now, seems like the 1 and 2 step images are WAY more coherent than LCM, but the images are kinda trash for any kind of prompt complexity so you start to have to use more steps, and since the individual steps take the same amount of time (I think there's a specific sampler for this which may be faster & better?) by the time you start…

Are you finding dpm++ 3M SDE better than dpm++ 2M SDE in sdxl? Afaik the second order (2M) version is the recommended one to use for guided sampling vs the 3rd order one. From here: https://huggingface.co/docs/diffusers/v0.23.1/en/api/schedul... > It is recommended to set solver_order to 2 for guide sampling, and solver_order=3 for unconditional sampling.

It might be placebo, but I find 3M better for upscaling, when I usually set CFG quite low and use a generic prompt that doesn't describe any localised element of the picture.

Which is what it's meant for, I suppose.

Re: SDXL Turbo: A Real-Time Text-to-Image Generation Model

#53

Noncommercial use - aside from being one of my licensing pet peeves - seems to indicate that the money is drying up. My guess is that the investors over at Stability are tired of subsidizing the part of the generative AI market that OpenAI refuses to touch[0]. The thing is, I'm not entirely sure there's a paying portion of the market? Yes, I've heard of people paying for ChatGPT because it answers programming questio…

Open AI has a pretty robust and profitable business without Microsoft. In every enterprise I’ve been involved with over the last few years we have had some incredibly material and important use cases of OpenAI LLMs (as well as Claude). They aren’t spewing slop or whatever, they’re genuinely achieving valuable and foundational business outcomes. I’ve been a bit stunned at how fast we’ve achieved these things and it tells me that the AI hype isn’t hype, and that if we have done these things in a year, it’s hard to estimate how much impact the technologies will have in five but I think it’s substantial. So is our spend with OpenAI. Or rather, with Azure on OpenAI products. The only value from our experiences Microsoft offers is IAM - which is sufficient frankly.

Stability and Midjourney are also making money, but it’s largely with amateurs and people prototyping content for their own creations. A lot of single person Indy game developers are using these tools to generate assets, or at minimum first pass assets. I think a lot of media companies are producing the art for their articles or news letters etc using these tools. Whether this is enough, I don’t know.

Re: SDXL Turbo: A Real-Time Text-to-Image Generation Model

#54

Earlier quoted context omitted.

I've done a bit of fiddling around with it and definitely holding back judgement for now, seems like the 1 and 2 step images are WAY more coherent than LCM, but the images are kinda trash for any kind of prompt complexity so you start to have to use more steps, and since the individual steps take the same amount of time (I think there's a specific sampler for this which may be faster & better?) by the time you start…

Are you finding dpm++ 3M SDE better than dpm++ 2M SDE in sdxl? Afaik the second order (2M) version is the recommended one to use for guided sampling vs the 3rd order one. From here: https://huggingface.co/docs/diffusers/v0.23.1/en/api/schedul... > It is recommended to set solver_order to 2 for guide sampling, and solver_order=3 for unconditional sampling.

In my tests it's basically been 50/50, i probably did ~40 or so comparisons when i was testing samplers and i felt like there were a couple that seemed really good on the 3rd order one, but idfk, it was very very close, I don't know if I saw a single gen where one of the two was bad but the other wasn't.

Re: SDXL Turbo: A Real-Time Text-to-Image Generation Model

#55

Earlier quoted context omitted.

They have a web demo here: https://clipdrop.co/stable-diffusion-turbo

...which requires you to sign in. That nice little text box invites you until you actually click to enter some text and get a registration box thrust at you People that design a UX where the user tricked into a registration 'ambush' need to be punched in the face.

The level of entitlement HN posters have these days is insane. They're giving you free compute resources on the order of an entire $800 GPU. If you don't like it, run the damn thing yourself!

UX designers doing their job and programmers protecting their free product from being abused are not the people deserving abuse here.

Re: SDXL Turbo: A Real-Time Text-to-Image Generation Model

#56

The license is non-commercial, but: > For clarity, Derivative Works do not include the output of any Model. https://huggingface.co/stabilityai/sdxl-turbo/blob/main/LICE... Doesn't that mean that generated images from it should be fine for commercial?

IANAL but it sounds more like SAI is not responsible for any outputs generated using their models and how those are used.

Re: SDXL Turbo: A Real-Time Text-to-Image Generation Model

#57
> A Real-Time Text-to-Image Generation Model

> On an A100, SDXL Turbo generates a 512x512 image in 207ms (prompt encoding + a single denoising step + decoding, fp16), where 67ms are accounted for by a single UNet forward evaluation.

Okay... so what part of this is real time? 207ms is 4.8Hz. 67ms is 14.9Hz. Isn't "real time" in graphics considered to be at least 30Hz (33ms)? And by today's standards at minimum 60Hz (16ms) if not 144Hz (7ms)? I'm lost at what part of this is real time? I'm not sure it even would get there with an H100. Maybe an H100 and everything is TensorRT?

Re: SDXL Turbo: A Real-Time Text-to-Image Generation Model

#58

> A Real-Time Text-to-Image Generation Model > On an A100, SDXL Turbo generates a 512x512 image in 207ms (prompt encoding + a single denoising step + decoding, fp16), where 67ms are accounted for by a single UNet forward evaluation. Okay... so what part of this is real time? 207ms is 4.8Hz. 67ms is 14.9Hz. Isn't "real time" in graphics considered to be at least 30Hz (33ms)? And by today's standards at minimum 60Hz (1…

It's "real-time" as in "I finished typing and the image is already there"

Re: SDXL Turbo: A Real-Time Text-to-Image Generation Model

#59

Earlier quoted context omitted.

Hi Max! Thanks for all the tuts! Small correction - SDXL wants ~1 megapixel resolutions at a variety of aspect ratios. https://github.com/lllyasviel/Fooocus/issues/24

Fair point, although in my experience SDXL still isn't great at non-square ratios. I end up just cropping them to the ratio I want.

What have you seen to be the issue? Composition? Realism? Prompt adherence? I’m just finishing a project having generated tons of images at a mix of ~6 aspect ratios and I haven’t noticed any difference.

Re: SDXL Turbo: A Real-Time Text-to-Image Generation Model

#60

Noncommercial use - aside from being one of my licensing pet peeves - seems to indicate that the money is drying up. My guess is that the investors over at Stability are tired of subsidizing the part of the generative AI market that OpenAI refuses to touch[0]. The thing is, I'm not entirely sure there's a paying portion of the market? Yes, I've heard of people paying for ChatGPT because it answers programming questio…

The EU views Google as a tobacco company. The last thing they want to do is bankrupt them with fees. They want to milk Google - big tech generally - for tax revenue. And besides, it'd take $100 billion per year in fees, which is never going to happen. Meanwhile Google keeps getting bigger year after year (they have nearly doubled in size in four years, up to $300b in sales now) and Bing has made zero headway despite the AI-angled efforts (BingChat etc). Maybe the mainstream adoption of GPT (or similar) would severely damage Google, there's still a lot of time left for Google to take their shot at getting out in front of that outcome.

The US breaking Google up also won't end the monopoly money. More likely one of the children will spin out with an even better margin business.

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