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First released version 1.0 in April this year, Mist is an image preprocessing tool developed to protect style and content of images from being mimicked by common AI-for-Art applications, including LoRA, SDEdit and DreamBooth functions in Stable diffusion,, etc.

How mist works

By adding watermarks to the images, Mist renders them unrecognizable and inimitable for the models employed by AI-for-Art applications. And attempts by AI-for-Art applications to mimic these Misted images will be ineffective, making the output images of such mimicry disrupted and lose its value as a painting, making it impossible to use as a work of art.

Image generation from LoRa without (Image 2) Mist VS with (Image 4) Mist

And at the end of 2023, the team released Mist 2.0, with major optimizations compared to the previous version:

l Become the first image watermarking tool that has been systematically verified to have protective functions against LoRA.

l Optimize the computational resource consumption required by Mist itself that now supports running on GPUs and CPUs with a minimum of 6GB of VRAM.

Image generation from SDEdit with Mist in different Robustness

Mist can provide the most advanced protective watermark for images, which can effectively resist attacks on watermarks such as cropping, scaling, and super-resolution. And after this update, the impact of watermarks on image quality under the same protection intensity has been greatly reduced, effectively achieving a good balance between art sharing and work protection.

Mist user cases by Anonymous

According to the team, Mist is compatible with both Linux and Windows operating systems, and the Mist series watermark tools will permanently be free and open-source. To learn more about Mist, you may visit the official website or get the project in Github.

Mist user cases by 桑德兰的等待

The main creative team of Mist is three developers graduated from Shanghai Jiao Tong University with computer and legal expertise.

As the team said, the project Mist is developed to “draw attention to the challenges that AI-for-Art apps pose to the established copyright system, their impact on the commercial and aesthetic value of the artist community, and in essence, how we perceive and incentivize human creativity.”

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