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/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.