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This resulted once again from a bout of curiosity for this new "LORA" type. The result? It seems an in-between IA3 and a LORA in quality. You can compare against the IA3 version , the LORA version or the LOCON...
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This resulted once again from a bout of curiosity for this new "LORA" type. The result? It seems an in-between IA3 and a LORA in quality. You can compare against the IA3 version , the LORA version or the LOCON version . All of them use the same dataset only with changes in repetitions to keep them at 8 epochs total.
Image wise it looks serviceable reflecting the dataset while not getting much "creativity" from the training model. Pretty much an in between AI3 and a LORA.
Here's my analysis of pros and cons
Pro:
As it refrains from overwriting previous training, it is a bit harder to overcook.
Small-ish as it is trained with an equivalent of dim 2
Sources
1 sourceModel artifacts
1 artifactSource excerpts
1 excerptSource context: 325 downloads · Type LoCon · Base model SD 1.5
relatively quick to train with prodigy it requires 150~200 steps per epoch per concept. I got good results in the 5th epoch(using 300 steps per epoch) which remained stable until the 8th epoch. each iteration takes about 1.58 times a LORA iteration step so that lowers the gains somewhat.
Cons:
It is more sensitive to shortcomings of the dataset that normally get filled by the model(missing angles, low hair detail, fabric textures, low color, blurriness)
The quality/training-time ratio is not very favorable as IA3 is faster and LORA/LOCON look better.
Conclusion:
At the moment Diag-OFT seems to just be another addition to the Lycoris zoo. Nothing exotic nor particularly exciting other than it being harder to overcook.
I will add the dataset, previous epochs, training TOML file and an XYZ file comparing the results of 6 different models and all 8 epochs.
Btw got good results using: yoshizuki_iori,
The only tagged outfit is:
school_uniform_purple_shirt_blue_skirt_white_neckerchief_black_thighhighs
