glaze VS AdverseCleaner

Previously in the Glaze library, you would need to write out your key names along with the member object pointers. However, as of version 1.8.0, these key names are now optional. If the keys are not provided the member variable name will be reflected for serialization/deserialization. I'm even more excited about this reflection than the previously announced pure Clang reflection, because it works well with user customization and supports non-aggregate, non-default constructible, and non-constexpr types.

BEVE fully supports JSON messages. The Glaze C++ JSON library allows users to use the same API to encode/decode to either JSON or EVE binary. Glaze also encodes/decodes directly into your C++ structures and standard library containers, making it easy to use without additional copies.

I would either look at https://github.com/eyalz800/zpp_bits or https://github.com/stephenberry/glaze. FYI, glaze both supports json and binary.

Glaze is a software that uses artificial intelligence to create realistic images from text descriptions. It is based on Stable Diffusion, an open source framework for image synthesis using diffusion models. Glaze claims to offer enhanced model training with minimal style interference by using a technique called ControlNet1.

I ended up adopting this approach in some test code https://godbolt.org/z/GKW8Preva when I was thinking about adding automatic enum serialization/deserialization to stephenberry/glaze. But there are too many limitations.

Alternatively, use a preexisting library for binary serialization and deserialization. If you’re trying to serialize your own stuff, glaze is a good option since it supports both json and binary serialization/deserialization. https://github.com/stephenberry/glaze

This parse file has some of the chunk simd processing: https://github.com/stephenberry/glaze/blob/main/include/glaze/util/parse.hpp

These tools do not work in the real world for several reasons: first, people who train LoRA or models curate their datasets, removing adversarial noise is trivial: https://github.com/lllyasviel/AdverseCleaner; second, if they are trying to defend themselves against training AI models, then it will probably do almost nothing at all, models trained from scratch will learn the distribution, even one where the samples have adversarial noise (which can only attack a frozen model, like a VAE), SDXL has a new VAE, so the VAE will just be more robust if there is adversarial noise on the images, because it will learn to ignore it, many other models do not have a VAE to begin with.

Also, resizing the image (as is almost always done when training the model) will probably destroy most if not all of the adversarial noise.

Glaze's new FAQ says that no one has bypassed Glaze yet and ignores that two papers have already suggested it's simple to bypass. They say that AdverseCleaner doesn't work and show a quote from the creator that suggests it doesn't. Has anyone else tried image restoration techniques like ESRGan or SwinIR? They seem to completely neutralize Glaze.

GitHub - lllyasviel/AdverseCleaner: Remove adversarial noise from images

I appreciate the warning since it is done out of good faith, but I would still like to disagree with you. I tested many of these adversarial noise removers, such as "AdverseCleaner" (https://github.com/lllyasviel/AdverseCleaner), and they simply don't work, they don't remove the noise and just smooth it out to make it less noticeable. As long as you use a high enough strength, these cleaners will not be effective.

Also, aren't most of those techniques beaten by some 16 lines Python scripts, like https://github.com/lllyasviel/AdverseCleaner?

glaze doesn't work (needs millions of glazed images to poison ai models) and also stole code and can also be beaten in 16 lines of code

adverse cleaner (repo owned by the author of controlnet): https://github.com/lllyasviel/AdverseCleaner

This article is about adversarial noise. Check the https://github.com/lllyasviel/AdverseCleaner/blob/main/clean.py. In simple words, it reads an input image, applies the bilateral filter multiple times to remove noise, applies the guided filter multiple times to remove adversarial noise, and writes the resulting denoised image to an output file.