The training dataset consisted of artwork where each pixel was equal to 8x8. This is important because if you need "perfect" pixels, I recommend RESIZE at 0.125, and then RESIZE at 8.000, so that the pixels acquire their true nature. Use the Nearest Neighbor method when resizing.
Changelog
Version 2
I expanded the dataset to 281 images (there were about 50 in the first version). The variety of possible generations has increased significantly, but the pixels are still not perfect. And you know what? I intend to review absolutely all the images in the dataset because, in my opinion, the problem might lie in hidden imperfections. If even one of the images has a slight gradient or a broken pixel, the entire training process could be ruined. But what terrifies me even more is that I can't find a similar "pixelate+" tool. For example, in paint.net, there is a "pixelate+" effect, and the pixelation method there is so good that the image is perfectly pixelated almost without changing. And I'm very sorry that I haven't been able to find a similar tool in Comfy yet. If you use the RESIZE method like I do, I have bad news for you—this method is not ideal. At least, that's how it was in my tests. Our conditions might differ, and in reality, everything might work out great for you.
Version 1 (test)
Frankly, it turned out not quite what I want to achieve in the next version.
You can wait for the release of version 2, it will be more worthy than the current one.
If you are not scared by the words above, then know that the test LORA is strange, over-trained and tends to draw much more often what it was trained on than to match text prompts, keep this in mind.
You will encounter many swords, strange books, Easter Island statues during exploitation and, most importantly: GigaChad (even a girl can acquire his facial features, but this is uncontrollable (in any case, I didn't try)).
Contact with me:
https://t.me/Skormino
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Version v3: 1 File
About this version: v3
I corrected the dataset as best as I could; working with 280 images is not that easy (there were enough mistakes). The goal of this endeavor was to expand the potential of the model. The more faces you feed the model, the less likely it is to draw the same thing over and over. Then I decided that it would be a good idea to add some more works; later, I encountered a significant difference. The second version of Lora, it turns out, includes not only pixelation but also a limited palette. Version 3 differs not only in this regard; in some places, it is brighter and richer. In any case, the visual style has changed; decide for yourself which you like more.
I tried to slow down the training, changed the noise offset, increased the dim, but was afraid to touch the alpha. I am considering various possible solutions to the problem of why pixels do not want to be pixelated and get blurred. My biggest fear is the words of my mentor, who believes that the neural network does not remember more than one megapixel of information from a picture. He might be right. If so, we will have to train on works smaller than 1024x1024, which, if multiplied, gives 1,048,576, and 1 megapixel is 1,000,000 pixels. We will have to work hard to adequately train the neural network in such a narrow format. I do not have many sources of high-quality pixel art.
Unlike many other pixel models you can find on CivitAI, mine can boast clear rules for adhering to pixel art, but unfortunately, I have not yet had the strength to tackle testing the control of "Dithering"; for now, it will appear spontaneously. I am sure it can ideally be controlled just like the presence or absence of outlines; for now, the model prefers outlines.
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