All mine are with Bing, it's free but you only get 15 full speed image generations a day. After that they take a few minutes per prompt.
I dislike that it has restrictions so I can't make the fully unhinged pictures I would like 😂
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All mine are with Bing, it's free but you only get 15 full speed image generations a day. After that they take a few minutes per prompt.
I dislike that it has restrictions so I can't make the fully unhinged pictures I would like 😂
Yeah, that is what I use too, but the limitations kind of suck.
I need a Nic Cage Wolf man picture in my life, but Bing blocks celebrity prompts.
I use stable diffusion with automatic1111's webui ran locally with an AMD GPU. I use the card for gaming and encoding too, so the cost for just AI is basically free. The webui is excellent, and I learn about new things it can do every time I use it. Setting it up took some time, but nothing beyond what I am familiar with. I do loathe that so much data science/AI stuff is python based, because python's dependency management is an unruly beast, but oh well.
You use it on Linux? I have used it in Windows (6700xt) and it is slow af (2 it/s or even in s/it range), apparently it should be a lot faster in Linux but haven't tested it.
I run it in a container on a NixOS host yes, eventually I'll learn how to do it in a flake but my nix skills aren't quite there yet. EDIT: I use a 6900xt, and some quick runs I did give me roughly 10 it/s. Which feels reasonably fast, only a couple seconds per image.
I recognize that those are words and numbers…
No worries, I'll link to some Arch Wiki stuff to help explain. Containers are a very cool system for isolating environments. Similar to how python uses VENV to contain all the dependencies for a python program, containers let you have a full environment beyond just the python stuff. I use podman to actually run the container on my computer. You use a Containerfile, to define what you want this environment to look like, and docker/podman does all the hard work for you, by making an image file that holds the whole thing in one place separate from our real OS.
This is my start script.
#!/usr/bin/env bash
podman run -it --rm --name stablediff2 -p 7860:7860 \
-e COMMANDLINE_ARGS="--api --listen --port 7860 --enable-insecure-extension-access --medvram-sdxl --cors-allow-origins *" \
--device /dev/dri:dev/dri \
--device /dev/kfd:/dev/kfd \
-v ./models:/dockerx/stable-diffusion-webui/models:z \
-v ./repos:/dockerx/stable-diffusion-webui/repositories:z \
-v ./extensions:/dockerx/stable-diffusion-webui/extensions:z \
-v ./embeddings:/dockerx/stable-diffusion-webui/embeddings:z \
-v ./outputs:/dockerx/stable-diffusion-webui/outputs:z \
-v ./inputfiles:/dockerx/stable-diffusion-webui/inputfiles:z
localhost:stablediffusion:latest
This is just telling podman to start the container, give it an actual terminal to connect to, remove the container if it stops running, give it a name, and tell it what ports it can run on.
podman run -it --rm --name stablediff2 -p 7860:7860
These are the arguments passed to the webui start script itself, mostly for my own convenience. The medvram-sdxl is not required, since my card has enough vram, but then I can't be doing anything else with it. So I sacrifice a bit of generation speed for more free memory for the rest of my computer. I'm running this locally, so insecure extension access also doesn't matter since I'm the only one using this, just makes installing extensions from the webui directly.
-e COMMANDLINE_ARGS="--api --listen --port 7860 --enable-insecure-extension-access --medvram-sdxl --cors-allow-origins *" \
These are just the device files that correspond to my GPU, so that the container has access to it. Without this, the container would only have access to CPU based generation. Everything else is just the folders that holds my models, extensions etc. You have to give the container exactly what you want it to, because its isolated away from your normal files unless you tell it otherwise.
--device /dev/dri:dev/dri \
--device /dev/kfd:/dev/kfd \
This is iterations per second, I believe. It's basically a measure of how fast stablediffusion is is running a particular generation of an image. It lets people compare performance across different software and hardware configurations.
10 it/s
NixOS is the name of the GNU/Linux operating system I'm using, similar to how MacOS is different than Windows, NixOS is another type of operating system. I've only been using it for a few months, but its extremely cool. Before that I mostly used Debian and Fedora, but the main difference between NixOS and them is that you can define you whole OS as a configuration files, and then the tools it's designed around build your system for you. So instead of say, installing a program, opening it up and going into settings and changing everything to be how you like it. You can instead just make a file that lists everything the way you want it from the start, and Nix installs the program and sets it all up all in one go. It has a pretty big learning curve, and its features are numerous that I have yet to take full advantage of them. Probably not the best to start with if you are new to GNU/Linux systems, but once you see the benefits of why it does things differently, its awesome.
Hopefully that explains most of the words I used. Pardon my formatting, as I don't know markdown very well and I think I separated everything okay. :)
Wow, this is fascinating. Looks like I will need to devote some time to this.
I'm using Bing Image Creator mostly. It's powered by DALL-E 3.
I've used https://www.craiyon.com/ for one because I purposely wanted it to be crappy.
Stable diffusion. 1.5 on my own PC (with an old GTX 970), as it gets too old to run SD XL, I use getimg.ai which is uses XL and a selection XL based models. The reason why I use this one is what I can buy credits without a subscription. I am fine paying 10€ every so and on but less keen to pay 10 a month, just for a fun toy)
Where can I find some well-explained step by step instructions for running an AI directly on my Mac?
I use that package https://easydiffusion.github.io/
If you're geek enough to be here you should find how to use it, might require a couple of evening tweaking
I just use bing cuz im a dweeb and its free and easy
You say dweeb like it’s a bad thing
Stable Diffusion on a 1080TI. Not the fastest and can't handle the some models, but it runs. Cost noting but time and a slight uptick in my already high power bill(and thousands in hardware).
Does anyone know if SDXL can run split tasks with SLI cards? I've been thinking of building a dual A80 tesla rig since they are so cheap but I want to be able to render on all 48gb as one.
For OP -- I run totally on OpenAI using API calls.
I use ChatGPT premium almost every day, mostly for coding, rarely for image generation. $20/month. It can write/refactor decent (not great) code faster than me if I can type out what I want faster than just writing the code myself. Dalle-3 through ChatGPT produces pretty good images and seems to understand the prompts better than SD (ChatGPT actually writes the prompt for you, so that might have something to do with it). It's much better than Dalle-2, but they've put guardrails on it so you can't ask to do things like create images in the style of a modern artist.
I've messed around with Automatic1111 and SD a little bit. ControlNet is very nice for when you need to have control over the output. I would draw shitty outlines with Inkscape then used SD+ControlNet to kind of fill everything else in. Free and open source model and software. Ran it on a RTX 3090 which costed me $800 a year ago.
Messed around with DeepFloyd IF on replicate.ai for a while, which was very nice. It seemed to understand the prompts much better than SD. I think it was $2/hr, with each image generation using something like 30s of GPU time. Cold starts can take minutes though, which is annoying.
I use OpenAI's API in a prototype application; both GPT-4 and Dalle-3. GPT-4 is by far the most well-behaved and "knowledgeable" LLM, but all the guardrails put on it can be annoying. Dalle-3 is pretty good, but not sure if it's the best. The cost isn't significant yet while prototyping.
I get ads, news, and video recommendations served to me which probably uses some kind multi-armed bandit AI algorithm. Costs me my privacy. I don't like it; I rate it 0/10.
I’m actually really curious to see how the 10 core GPU on my M2 Mac Mini performs.
I haven't tried using Macs. I've heard their GPUs are kinda slow (compared to high-end discrete GPU), but have unified memory so you can run very large models.
I bought 3090s because I needed to train a classifier. It took months of training 24/7, so it was cheaper to buy 3090s than pay for cloud compute time. A 3090 is probably overkill for just running SDXL (unless they release an even larger model in the future).
CodeLLaMa. It costs 15GB of vram which my gpu has
I mostly prefer to use Stable Diffusion with ComfyUI. But microsoft image creator/bing image creator are really neat with their flexibility to blend different things together, that Stable Diffusion cant do without a lot of tinkering. But Microsoft's solutions are really annoying to use. Rate limited, censored to heck and back, and at the moment its not even letting me make images because its faking that "theres too many users". Basically softbanning me.