There are as well forks for the GPU in Apple’s M1 chips:

https://github.com/magnusviri/stable-diffusion

Nvidia GTX 1660 Super with 6GB of VRAM.

I do runs at 384px by 384px, with batch size of 1. Sampling method has almost no impact on memory. Using k_euler with 30 steps renders an image in 10 to 20 seconds. The biggest thing that affect rending speed is the steps and the resolution, so 512x512 with C 50 using ddim is much slower than 256x256 with C 25 using k_euler.

The sampling methods run mostly in the same timelines, but the k_euler one can produce viable output at lower C values, meaning it is faster than the rest.

Don't add gfpgan in the same pipeline, as it takes more vram.

I'm running it on Windows 10 with latest drivers. I set the python process to Realtime priority in task manager (makes a slight difference!). Have not tried it on Linux.

I'm impressed that running it on CPU only made it ~20x slower. How did you do it?

Just tried it on Ubuntu 22.04. And it's working! Had to install python-is-python3 and conda, but it's up now. Fun, thanks.

Can you share your script for running on CPU?

What kind of GPU do you have? It takes several minutes to produce an image on my 1070.

What are good and reasonably priced GPUs for this (<$250, possibly less)?

Amazing. Anyone know of a fork where this is hosted on a cloud GPU? Or any existing hosting of this?

Got this working on my 8gb 3070. 7-8s per image with default settings. Thanks for posting this!

If i ran the same input image several times, would it produce the same output?

old.reddit is truly horrible on mobile. Once you click on an image you can't go back. Off topic, but what is the other alternative UI called that people sometimes use?

Just wanted to say thanks. It’s really cool!

You can feed a txt2img output into the img2img pipeline as an init, that's something that I do quite often, eg https://twitter.com/SteWaterman/status/1563872748161613826

Also how slow is your p100? I'm usually getting around 3 it/s. Maybe it's just because I'm used to disco diffusion where a single image took over an hour, but this is ungodly fast to me

FWIW I'm using an old gtx 1080 Ti to play around, it takes about 21 seconds per image. You can make it go even faster by lowering the timesteps taken from the default 50 (--ddim_steps), though both lowering and raising the value can result in quite different first-iteration images (though they tend to be similar) and seems to guarantee totally different further iteration images (as counted by --n_iter)... I'm with you on the feeling that it's hard to control, whether in refinement or in other ways, but I suspect that'll get a lot better in the next couple years (if not weeks or dare I say days).

With a 3090 it's about 10s to generate a 512x512 image from another, maybe less.

HuggingFace is a company that mainly builds open-source libraries and platforms to support open-source ML projects. They started out with their famous Transformers library and have many other libraries including the diffusion model one that is actually what this application here is using. They also have this model/dataset hub and interactive application platform known as "Spaces". Their goal is to be the "GitHub of machine learning".

Their business model is basically supporting enterprise and private use-cases. For example, getting expert support for using these libraries, or hosting models and datasets privately. You can see more information about the pricing here: https://huggingface.co/pricing

They reached a $2 billion valuation after a recent round of funding so overall they're probably pretty flush with cash lol

They are building a model of what everyone else is typing into these open models.

I did see a couple people in the reddit posted here

https://news.ycombinator.com/item?id=32634139

complaining about the price of getting their images done. So part of it may actually exchanging money for a service. I bet a good chunk of it is investor cash though.

Note that it eventually asks for your credit card, it's not indefinitely free like the linked site.

Here's an example of doing img2img: https://replicate.com/stability-ai/stable-diffusion?predicti...

Yes, checkout examples on lexica or use a prompt builder to help, like promptmania.

Also, most of the good ones you see online are cherry picked from hundreds of runs, so set your batch size too 1000 and go to bed! After that, people then tend to run some of the good results through img2img, also with a lot of variations produced from a single image. Finally, some people also run them at higher resolutions if they have enough VRAM, as smaller resolution can distort or generate rubbish. For the messed up faces, they run it through gfpgan a few times to get prettier faces. Other than that, it is pure luck (using random seeds) to figure out what works and what doesn't. Use the 2 sites above to help you improve your prompts.

(meant in the context of stable diffusion)

I managed to get one that was correct with "A little scared girl is building a sandcastle, while a monster is looking at her. Award-winning photograph.", but I couldn't figure out a phrasing where it wouldn't most of the time get confused thinking that the sandcastle is the monster, or that the girl is the monster.

Dalle is bad at being instructed to have an exact count of items in the picture. Ask for 6 kittens and you get 7 & each kitten will be much more "wrong" than a piture of a single kitten.

Dalle is is bad at positional prompts. Ask for somethi g to be in the top rightbhand corner and it will appear bottom centre