stable-diffusion
invisible-watermark
stable-diffusion | invisible-watermark | |
---|---|---|
382 | 20 | |
65,504 | 1,453 | |
1.1% | 1.7% | |
0.0 | 3.2 | |
21 days ago | 7 months ago | |
Jupyter Notebook | Python | |
GNU General Public License v3.0 or later | MIT License |
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.
stable-diffusion
-
Go is bigger than crab!
Which is a 1-click install of Stable Diffusion with an alternative web interface. You can choose a different approach but this one is pretty simple and I am new to this stuff.
-
Why & How to check Invisible Watermark
an invisible watermarking of the outputs, to help viewers identify the images as machine-generated.
-
How to create an Image generating AI?
It sounds like you just want to set up Stable Diffusion to run locally. I don't think your computer's specs will be able to do it. You need a graphics card with a decent amount of VRAM. Stable diffusion is in Python as is almost every AI open source project I've seen. If you can get your hands on a system with an Nvidia RTX card with as much VRAM as possible, you're in business. I have an RTX 3060 with 12 gigs of VRAM and I can run stable diffusion and a whole variety of open source LLMs as well as other projects like face swap, Roop, tortoise TTS, sadtalker, etc...
-
Two video cards...one dedicated to Stable Diffusion...the other for everything else on my PC?
Use specific GPU on multi GPU systems · Issue #87 · CompVis/stable-diffusion · GitHub
- Automatic1111 - Multiple GPUs
- Ist Google inzwischen einfach unbrauchbar?
-
Why are people so against compensation for artists?
I dealt with this in one of my posts. At least SD 1.1 till 1.5 are all trained on a batch size of 2048. The version pretty much everyone uses (1.5) is first pretrained at a resolution of 256x256 for 237K steps on laion2B-en, at the end of those training steps it will have seen roughly 500M images in laion2B-en. After that it is pre-trained for 194K steps on laion-high-resolution at a resolution of 512x512, which is a subset of 170M images from laion5B. Finally it is trained for 1.110K steps on LAION aesthetic v2 5+. This is easily verified by taking a glance at the model card of SD 1.5. Though that one doesn't specify for part of the training exactly which aesthetic set was used for part of the training, for that you have to look at the CompVis github repo. Thus at the end of it all both the most recent images and the majority of images will have come from LAION aesthetic v2 5+ (seeing every image approx 4 times). Realistically a lot of the weights obtained from pretraining on 2B will have been lost, and only provided a good starting point for the weights.
-
Is SDXL really open-source?
stable diffusion · CompVis/stable-diffusion@2ff270f · GitHub
- I want to ask the AI to draw me as a Pokemon anime character then draw six of Pokemon of my choice next to me. What are my best free, 15$ or under and 30$ or under choices?
-
how can i create my own ai image model
Here for example --> https://github.com/CompVis/stable-diffusion
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."
What are some alternatives?
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM
diffusers-uncensored - Uncensored fork of diffusers
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]
onnx - Open standard for machine learning interoperability
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
stable-diffusion
stable-diffusion