stable-diffusion VS invisible-watermark

No, sorry, but there's a whole bunch of one-click things now, I think?

I'm running it on Windows 10 using (a modified version of) https://github.com/bfirsh/stable-diffusion.git and Anaconda to create the environment from their `environment.yaml` (all of which was done using the normal `cmd` shell). Then to use it, I activate that env from `cmd` and switch into cygwin `bash` to run the `txt2img.py` script (because it's easier to script, etc.)

I am using the bfirsh version. And yes, I run "pyhthon scripts/txt2imp.py" to generate an image.

Specifically regarding the first option above, I see that the procedure clones the repository from: https://github.com/bfirsh/stable-diffusion.git

Just done a run on my 3080 under Windows using https://github.com/bfirsh/stable-diffusion.git and it's about 8 iterations/sec when nothing else is using CPU or GPU.

I've cloned this repository on my M1 Mac: https://github.com/bfirsh/stable-diffusion/tree/apple-silicon-mps-support

Boom - nice. Here's a fork with that: https://github.com/bfirsh/stable-diffusion/tree/lstein

Requirements are "requirements-mac.txt" which'll need subbing in the guide.

We're testing this out with a few people in Discord before shipping to the blog post.

I'm not sure your online tool is working. I tried it with the watermarked example image from https://github.com/ShieldMnt/invisible-watermark, and your tool returned that it did not detect a watermark:

The public availability and quality of LLMs and stable diffusion have been an unprecedented disaster for spam mitigation largely because there is no effective way to determine if this content was created and posted by a human being. Particularly with text content, the amount of information present is so small that I don't believe there is a way to definitively analyze it and concretely say whether or not it was generated by an LLM. The only potential way to do so that I can think of would be to check every comment against the output of each LLM service provider, but that's a futile endeavor because you can go back to inserting typos and substitutions, reorder the text or omit some of it, mash multiple outputs together, or even self-host an LLM and skip all the bullshit from the start. At least the images and videos being created by stable diffusion can be watermarked reasonably well.

i found an article but i don't understand it... is there any video tutorial of anything?

Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...

Tools to add this kind of watermarking are publicly available or could be reimplemented by in house developers if they don't like FOSS licenses.

Most major AI generators embed invisible watermarks into the images so that they can detect them and avoid training on generated imagery later. I know Stable Diffusion uses this python library to do it: https://github.com/ShieldMnt/invisible-watermark I haven't bothered to look up others but they have similar steps.

It uses the same library as Stable Diffusion (https://github.com/ShieldMnt/invisible-watermark) without giving credit in its github repository which does not even contain the sources of its 3 lines of code. This watermark doesn't protect anything, it would be necessary that the robots that retrieve the images from the internet make the effort to read the watermark to not add them in their dataset (best case scenario, totally utopian). The repository is suspicious and could be a way to install malware.

- Stable diffusion uses https://github.com/ShieldMnt/invisible-watermark by default unless you check "Do not add watermark to images" in settings

"This script incorporates an invisible watermarking of the outputs, to help viewers identify the images as machine-generated."