| # LoCon Testing | |
| https://github.com/KohakuBlueleaf/LoCon | |
| ## Dim/Alpha | |
| Testing with | |
| conv: 1dim 1alpha (due to coding error the examples are using 4alpha, has since been fixed in the code base) | |
| linear(network): 4dim 4alpha | |
| We will refer to dim and alpha in the format of dim/alpha, ie 1/1, 4/4 | |
| ## Training Time | |
| Character appears to gain significant accuracy sooner than using Lora (600 steps vs 800). The time per step is a little bit slower but a better analysis of this is required. | |
| We suspect that we can train characters faster in terms of real time. | |
| ## Mixing | |
| Applying style on character appears to work well. The example wlop style applied on Amber is 1/0.25 and 4/1. The character loses some stability but the style LoCon can easily be lowered and still retain great style retention while significantly reducing the impact to character correctness. | |
| Testing with a 3 conv, 6 linear character + 4conv 1 linear style, the style was not able to apply correctly. It appears that the character locon picks up a lot of stylistic information and classes with the desired style. Would need to test with equivalent ranks. | |
| ## Finetune model extractions | |
| To be tested. We suspect the performance should be much better than LoRA extraction since all the model will be extracted from. | |
| https://github.com/KohakuBlueleaf/LoCon/blob/main/extract_locon.py | |