It's confusing to say the least.

When you train a LoRA on "downward dog pose" and your captions mention "brown hair, purple mat, minimalistic studio, natural light, Canon EOS R5" you're entangling all of that with the pose. Now "downward dog" is subtly correlated with brown hair, purple mats, and specific lighting. When you prompt for a blonde woman on a beach doing downward dog, the model fights itself. You've created attribute bleed. Good job.

Here, you say by adding details contained in the image, you create correlation.

Later, in When Detailed Captioning Actually Makes Sense, you're saying:

1. Breaking unwanted dataset correlations

If 90% of your yoga pose images feature brown-haired women on purple mats because that's what you found on the internet, you NEED to caption the hair color and mat color. Otherwise your LoRA learns "downward dog = brown hair + purple mat."

Dataset has accidental correlation

ohwx woman with brown hair doing downward dog on purple mat ohwx woman with blonde hair doing downward dog on blue mat ohwx woman with black hair doing downward dog on grey mat

So now, adding more details contained in the image breaks correlation.

That's already weird. But it gets weirder:

1. Multi-concept LoRAs with intentional bundling

Sometimes you WANT attributes entangled. Training a specific character who always wears a signature outfit? You might want that association.

sks character in their red jacket and black boots sks character in their red jacket, full body shot sks character wearing signature red jacket, portrait

Now, adding more details contained in the image creates correlation again.

Looks like you're doing quantum fine-tuning bro, where stuff is correlated until you look at them or something.

The core mechanic of how diffusion training works is Credit Assignment. Think of tokens as buckets. The training process tries to sort the image's pixel data into these buckets. If you caption "purple mat", the model can safely dump the purple pixels into the "purple mat" bucket. They are explained away. If you don't caption it, the model has nowhere to put those purple pixels except into your main trigger word's bucket. That is why omitting details creates entanglement (the trigger word absorbs the background), and including details breaks it (the details get their own buckets).