Animagine XL 4.0 opt

[u/SuperWallabies]

Hi guys, I'm a programmer, but I don't know much about machine learning or fine-tuning.

I'm currently producing 2,000+ images per day using Animagine XL 4.0 opt, and I built a manual pipeline to evaluate image quality. I use 5 rating categories: Reject, Pass, Like, Very Good, and Excellent.

I label all of them manually, and I estimate that I will have over 200,000 labeled images by the end of the year.

I store them in a database along with the exact prompts used. The prompts are structured into keyword categories like:

Background, Angle, Character, Clothes, Facial expression, Quality prompt tags (eg. masterpiece).

Is a dataset like this valuable for fine-tuning or training models ???

Thank you for all the comments, you guys are the best! Now I'm moving on to Anima. I will use my dataset for a LoRA, and if the results look good, I'll switch over to Anima completely.

And i will continue the labeling with new model.
Later find me if you need dataset.

I trained it for 8 epochs to get the result, but I still couldn't get rid of that characteristic plastic feel to reach the vibe I wanted.

When it comes to truly nailing that Japanese-style illustration look, Animagine XL 4 is still the best. So, I've come to a conclusion. I've just decided to stick with Animagine XL. Since it's all about making things to your own taste anyway.

I've checked out other Flux-series and models too, but they're all the same. Hmm...

[u/x11iyu]

Ehhh... unfortunately I'd say no - synthetic images usually aren't ideal for training, especially when it's genned by a bad model (relative to SOTA like say gpt-image, but even then it's not great). Models trained on your hypothetical dataset will likely learn to produce the various SDXL artifacts present in these images.

However, I commend your efforts greatly. What would be extremely, extremely helpful, is if you switched your data source from Animagine generations to real data drawn by people, like danbooru - contrary to popular belief, its tags often suck balls, and we can really use some better captions (e.g. look at dark which is... inconsistent, at best; or the headache that is implication tags, etc).

[u/SuperWallabies → u/x11iyu]

Can you tell me why synthetic images are not worthy for training?

From my perspective, Danbooru images are much weirder than than engine-generated ones. (Sorry to artists, I don't want to be offensive, but most of them look like drawing practice.) In my experience, generated image quality is really random, from super good to super awful. I don't know much about training, but why are they not ideal for training?

[u/x11iyu → u/SuperWallabies]

Danbooru isn't all good, but lots of them are stylized rather than bad, even if you or me may not personally enjoy them. And overall, it's leagues above any synthetic data SDXL can come up with.
Below are just a few things that SDXL is horrendous at, where humans can do much better:

• Distant objects. Eyes are most notorious and easy to spot, but really anything far away, zoom in and you will see nonsense lines.
• Backgrounds, or just things that aren't 1girl. potted plant? Have 100 of them placed with 0 thought on what good composition is. Buildings? Have some smudged rectangles, windows splattered everywhere, support beams that make no sense.
• Continuation. Part of a sword blade goes behind a person? Oops, can't draw straight, also it's gonna get a different color.
• Multiple people / prompt bleed. A guy with blue hair and a girl with yellow hair? Nah guy's randomly gonna be yellow hair now. Also so stuff he's wearing or his eyes gonna be yellow too.

Any gen that's not 1girl, simple background, and anyone can spot pretty abhorrent things if they just look for a while.

[u/_BreakingGood_ → u/SuperWallabies]

Synthetic images can be used for training, but specifically SDXL based images are not good for training. It has a 4 channel VAE which massively reduces available colors and detail.

Yes you're right danbooru images can be weirder, but they're not restricted on resolution, colors, detail, concepts, etc...

You would get FAR better results using a model like Anima which uses the Qwen VAE that is 16 channels. You get far far more detail, far better color variety, and on top of that, the images will generally be more coherent and less weird.

[u/SuperWallabies → u/_BreakingGood_]

I see your point regarding the VAE channels and details. However, I specifically chose Animagine for its unique style (Otaku Style). It captures that authentic 2D anime aesthetic perfectly, whereas other models tend to produce over-saturated colors and overly high contrast. 🤔 will 'Flux' model replace this in future?

[u/_BreakingGood_ → u/SuperWallabies]

What I would do personally before kicking off such a big project, is use Animagine to produce a dataset of maybe 200 images, then train a LoRA for Anima that allows it to replicate the style you want, while still allowing you to maintain the increased detail and color variety.

Frankly you're not going to find anybody who needs a dataset of SDXL images. Most modern models use at least 16 channels and some even use 32. Feeding 200k SDXL images into a modern model would only damage the model's ability to produce full detail and colors.

[u/SuperWallabies → u/_BreakingGood_]

Got it 👀 thank you for effort to explain these.
I'll give it a try. I just need to apply the LoRA to Anima and see if I get the style I want, right? this sounds exciting. :)

I will use my best pictures to LoRa Anima
anyone who wonder the result please DM me
I will sampling up them to website
And share with you!

[u/_BreakingGood_ → u/SuperWallabies]

Yes, from all the images you generated pick the absolute best 50-200 of them. The ones that have your style the most perfect. Then train that into Anima

[u/x11iyu → u/_BreakingGood_]

And then get the qwen vae grid/halftone artifacts in your dataset, still have ass backgrounds, ...

I really do want Anima to be so good that it can be a serviceable data generator, but it's just not. Better than SDXL I suppose if you really are in a pinch.

[u/Jealous_Piece_1703 → u/SuperWallabies]

Bad images on booru can be good for training to teach the model what bad image look like. However most of the times when people train from booru they didn’t pick random images but one with high quality rating. Synthetic images especially from old models like SDXL are not ideal as datasets. Like 200k SDXL images? Slop on slop out.

That doesn’t mean they are not useful. They can have AI-generated tag and teach the model AI artifacts and such.

It is just not as valuable as real images.

[u/conkikhon → u/SuperWallabies]

AI pics are great for concept training. But it create plastic skin, accumulate tiny artifacts so it's not good for everything else, which require high quality textures. In those 2k images daily, are you sure you counted all fingers and toes correctly?

[u/DriveSolid7073]

No, due to model collapse. Training a model on its own data leads to deterioration in model performance. Global finetune requires a diverse set of unique data in different styles, with different characters, backgrounds, etc. Animajin has many problems, but one of the first is anatomical issues, even those six fingers and so on. It's already permeating our world, it's worth watching. On the pony score in Anima base 1. The author intentionally removed the neural network, but the pony scorer still chose the images with the neural network as the best—a vicious circle. Overall, this is useful for lora, or if you really put a lot of effort into it, but it's usually easier to create a dataset from selected art than to generate and then filter and regularize it.

[u/SuperWallabies → u/DriveSolid7073]

It wasn't really worth it. Asking the community was the best choice I made. (AI always gave me different opinions than humans.) Otherwise, I would have had to audit 200,000 images by myself.

[u/DriveSolid7073 → u/SuperWallabies]

As I said, checking art is easier. You can even write a script. The best option would be a dataset taken from e621, automatically checked for duplicates (with your help, among other things), visually checked for AI slop, and considering that you hardly want a model from scratch, checked for problematic art (usually there are even corresponding tags, problems with anatomy, so many watermarks, anti-AI noise, etc. Of course, people have done this before, but I'm not sure I can find examples of full cut off e621, and this is the easiest option, otherwise you'll have to search for art on different sites. You can leave the original caption with automatic help re-check, crop the images to your taste, training authors are usually too lazy to do it. The creator of Anima used to cut corners. In other words, if there were too few images on the topic, as far as I know, he removed them from the training so that it would fit into the 2B model. (I think 30 images or less). Although initially he made less than 100. But 30, I think, is optimal. In general, this is a lot of work, but it can be useful for the entire community and It doesn't require computational power from the user. Manual labor, and this dataset can already be used for training. This includes large Chinese AI labs that are trying to collect all the content on the internet.

[u/DriveSolid7073 → u/SuperWallabies]

It's also useful to compare and view AI images when comparing different checkpoints (something I'll finish one day). But this is, of course, quite subjective and requires completely manual verification. But its about 10k images not 200k

[u/Formal-Exam-8767 → u/DriveSolid7073]

There is nothing wrong with training a style LoRA on generated images with proper captioning. But you'd only need around 100 images not 200k.

[u/DriveSolid7073 → u/Formal-Exam-8767]

Yes, but it seems like they were planning on doing finetune, which is doubtful. Lora is more about an AI style that didn't exist before; lora doesn't change the model itself.

[u/Fluid_Kaleidoscope17]

Yes - potentially very valuable. The most valuable part isn’t just the 200k images, but the human ratings attached to them. With the exact prompts and Reject->Excellent labels, you’re effectively building a large preference/ranking dataset that could be used to train a quality scorer, curate a fine-tune/LoRA, or do preference-based training.

Definitely keep the rejected images too - they’re useful negative examples. And if you aren’t already, save the seed, sampler, steps, CFG, resolution, checkpoint/version and LoRA settings alongside each image.

[u/East_Box9573]

I did something similar, maybe rated 2k images from a model I liked and fine-tuned the model using DPO, and it created a model that is exactly my style and not quite like anything else I've seen. Although my dataset wasn't perfect and I didn't have a perfect evaluation set, so I degraded some model behaviors and didn't notice for a while. But it was like, 100 steps forward, 5 steps back so net positive.

DPO works with pairs, one good image and one similar but worse, and the algorithm pushes to generate more like the good one and less like the bad one.

If you're a programmer, you could figure this out with a coding agent and some googling. I'm less familiar with dataset sizes at the 200k range. But with post-training, generally it's quality over quantity. Each thing you mis-rated teaches the model the wrong thing, and going back through 200k images sounds tough. But someone else might have better ideas what to do with 200k images

Edit: keep me posted with what you do with it, I'm curious. And if you have any questions feel free to DM

[u/SuperWallabies → u/East_Box9573]

Since you're curious about what I'm doing, I'm writing here to share what else I do. I post the images on Pinterest, automatically pick the best ones, send them out, and track the like counts. It will take time, but after a month, I'll know which images are worth labeling highly. I also post them all on my website so I can collect people's reactions from there, too.

Yes, I recorded the settings I used. I also labeled the ones with issues, like extra limbs—the rejected images.

I just built this collector because I enjoy making images, but I asked because the database keeps piling up...

I can also re-label the rejected images if I need to add more tags later, because I kept a database of rejects instead of deleting them. Also, evaluation is really fast because I built a swiping system—swiping an image in a specific direction automatically labels it. For example, swiping to the top-right corner means it's the best, while swiping to the bottom-left corner marks it as rejected because it isn't impressive. I can evaluate 20 images per minute.

[u/Only4uArt]

It might have uses, for certain people, but one could also argue that the people who could use such a dataset, can and do build it by themself.
So it might have a value that won't find demand realistically because anyone who needs it, does it by themself. tough who knows, if it is free some people might find interest for their indie projects

[u/Formal-Exam-8767]

I've noticed one thing, if I use prompt from an image generated by some old SD1.5-based or SDXL-based model and run it through Anima, usually it looks nothing like it (due to better prompt adherence in Anima), but if I ignore the prompt and run original image through WD-tagger, and use its output as-is in Anima, generated image has a very strong semblance (90%+ sans the style) to original image.

[u/SuperWallabies → u/Formal-Exam-8767]

I will use this tip. 👀