invisible-watermark
stable-diffusion
invisible-watermark | stable-diffusion | |
---|---|---|
20 | 20 | |
1,453 | 338 | |
1.7% | - | |
3.2 | 0.0 | |
8 months ago | over 1 year ago | |
Python | Jupyter Notebook | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
invisible-watermark
-
Why & How to check Invisible Watermark
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 AI bots have arrived at r/programming...
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.
-
SD Watermark checker. How do i check if image is generative?
i found an article but i don't understand it... is there any video tutorial of anything?
-
MidJourney blocked content it generated as sexually explicit...
Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...
-
How would an AI art company like Midjourney know you were selling imagery you created using their platform?
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.
-
New Art Platforms for Artists and the death of old ones?
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.
- [D] Couldn't devs of major GPTs have added an invisible but detectable watermark in the models?
-
Just saw this Post regarding new Anti-AI Software on Linkedin. What are your opinions on this? Can this even work?
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
-
Looks like Stable Diffusion 2.0 was released, with some anticipated features
"This script incorporates an invisible watermarking of the outputs, to help viewers identify the images as machine-generated."
stable-diffusion
- [Machine Learning] [P] Exécutez une diffusion stable sur le GPU de votre M1 Mac
- High-performance image generation using Stable Diffusion in KerasCV
-
Charl-e: “Stable Diffusion on your Mac in 1 click”
SD on an Intel mac with Vega graphics runs pretty well though — I think it ran at something like ~3-5 iterations/s for me, which is decent. I ran either https://github.com/magnusviri/stable-diffusion or https://github.com/lstein/stable-diffusion which have MPS support
-
Stable Diffusion PR optimizes VRAM, generate 576x1280 images with 6 GB VRAM
https://github.com/magnusviri/stable-diffusion/commit/d0b168...
Copying this change fixed seeds on M1 for me.
-
Intel Mac User, How do I start?
You should be able to run it on a CPU. Maybe try this version. If MPS is supported on your Mac you can check this out.
-
[P] Run Stable Diffusion on your M1 Mac’s GPU
A group of open source hackers forked Stable Diffusion on GitHub and optimized the model to run on Apple's M1 chip, enabling images to be generated in ~ 15 seconds (512x512 pixels, 50 diffusion steps).
-
Run Stable Diffusion on Your M1 Mac’s GPU
Magnusviro [0], the original author of the SD M1 repo credited in this article, has merged his fork into the Lstein Stable Diffusion repo [1], and you can now run Lstein fork with M1 as of a few hours ago.
This adds a ton of functionality - GUI, Upscaling & Facial improvements, weighted subprompts etc.
This has been a big undertaking over the last few days, and I highly recommend checking it out.
[0] https://github.com/magnusviri/stable-diffusion
-
How are Mac people using Windows for A.I. stuff?
You can run it on an M1. Using a macbook M1 pro max with 32Gb I get 512x512 in about 50 seconds. use this branch https://github.com/magnusviri/stable-diffusion/tree/apple-mps-support
-
ResolvePackageNotFound
I had this error too, and I tried a ton of things to get cudatoolkit to install, without any luck. This fork has an environment-mac.yml file that actually got it working on my M1 Max: https://github.com/magnusviri/stable-diffusion/tree/apple-silicon-mps-support
-
If I set a seed value and re-run using the exact same settings, should I get the same image back each time?
But when I run it (locally, using the Mac M1 port), every time I run it creates a different image.
What are some alternatives?
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM
stable-diffusion-webui-docker - Easy Docker setup for Stable Diffusion with user-friendly UI
stable-diffusion-ui - Easiest 1-click way to install and use Stable Diffusion on your computer. Provides a browser UI for generating images from text prompts and images. Just enter your text prompt, and see the generated image. [Moved to: https://github.com/easydiffusion/easydiffusion]
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/sd-webui/stable-diffusion-webui]
onnx - Open standard for machine learning interoperability
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
stable-diffusion - A latent text-to-image diffusion model
rocm-build - build scripts for ROCm
stable-diffusion
stable-diffusion - This version of CompVis/stable-diffusion features an interactive command-line script that combines text2img and img2img functionality in a "dream bot" style interface, a WebGUI, and multiple features and other enhancements. [Moved to: https://github.com/invoke-ai/InvokeAI]