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+ # LoCon Testing
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+ https://github.com/KohakuBlueleaf/LoCon
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+
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+ ## Dim/Alpha
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+ Testing with
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+ conv: 1dim 1alpha (due to coding error the examples are using 4alpha, has since been fixed in the code base)
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+ linear(network): 4dim 4alpha
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+ We will refer to dim and alpha in the format of dim/alpha, ie 1/1, 4/4
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+
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+ ## Training Time
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+ 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.
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+ We suspect that we can train characters faster in terms of real time.
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+
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+ ## Mixing
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+ 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.
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+ Overall it appears that mixing is more stable with LoCon than lora but this could be due to the dim/alpha and training settings between these two locon being closer compared to LoRA land where popular dim sizes range from 8 to 128.
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+
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+ ## Finetune model extractions
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+ To be tested. We suspect the performance should be much better than LoRA extraction since all the model will be extracted from.
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+ https://github.com/KohakuBlueleaf/LoCon/blob/main/extract_locon.py
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+