I strongly recommend trying generation with the 8-bit token. In my mind, it seemed like something was off. In reality, it actually helps with generation. However, the question here is more about your goal and whether you need such graphics.
I am pleased with the results at these values:
euler_a
sgm_uniform
step: 36
Model: PlantMilkSuite_walunt OR WAI-NSFW-illustrious-SDXL
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 (If you are repelled by the factor, or by 12.5% if you have percentage manipulations), and then RESIZE at 8.000, so that the pixels acquire their true nature. Use the Nearest Neighbor method when resizing.
To my surprise, the model can generate pixels even without words related to pixels. It works like this: the word "pixel_art" excites the model's desire to draw in the way it knows pixel art, but the thing is, I didn't teach the model pixel art. I only specified the trigger word "pixpix" in the training, and personally, I don't need to write it for the Lora to truly work. It is enough that Lora is enabled.
Words like "Pixel_art" and similar ones can still make the neural network's job easier because contours and limitations that pixel art is prone to appear.
I apologize if I am asking too much of you. Please publish as many of your works as possible under my model. Leave comments, tell me what you don't like, and what you would like to see. I listen to all your words and will be happy with any of your works. I love you for your activity. Thanks to it, I have more opportunities to test.
I really wanted to give you a universal tool, and without your support, it will take too much effort and time to build buzz.
Next on the plan: tests, tests, and more tests. Various parameters can potentially be useful for training. I haven't even tried describing what's happening in the training materials yet. Well, well, well. I don't even know if it's necessary at all.
Changelog
Version 4.0
This version is notable for its very wide range of data, comprising 488 images. Its current state is capable of demonstrating valuable results. However, I was interested in looking at this version from a different angle. Instead of the old policy where I gradually increased the dim and alpha parameters, in this version, I changed them to alpha 32 x dim 48. Is this sufficient for implementing the model? It's difficult for me to answer this question because I don't have specific real tasks required by someone in particular. Therefore, I am not limiting myself or you in this LORA model.
Version 3.1
It was important for me to make sure of some things, hence this version appeared. It is unique in that the dataset is completely different from previous versions. The styles are actually different, which is very important. Although, to be honest, I'm not sure why the pixelation misbehaves in half of the cases and does not reach the level I need (and I thought I would achieve the result I needed, but it seems not this time). Only time will tell how much sense there is in my actions. Please, evaluate with me this essentially completely new (but test) style. By the way, it makes sense to use all the trigger words that I specified. In any case, I have good results.
Why did I decide to name it 3.1? It was created less seriously than the others.
Version 3
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.
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)).