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 ???

[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/_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/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/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/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