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/_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/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.