tweak docs

doc/OPTIMIZER.md

@@ -34,7 +34,7 @@ Lucidrains' [implementation](https://github.com/lucidrains/lion-pytorch) of the
 
 LR can be set in `optimizer.json` and excluded from the main CLI arg or train.json but if you use the main CLI arg or set it in the main train.json it will override the setting. This was done to make sure existing behavior will not break.  To set LR in the `optimizer.json` make sure to delete `"lr": 1.3e-6` in your main train.json and exclude the CLI arg.
 
-The text encoder LR can run at a different value to the U-net LR. This may help prevent over-fitting, especially if you're training from SD2 checkpoints. To set the text encoder LR, add a value for `text_encoder_lr_scale` to `optimizer.json`. For example, to have the text encoder LR to 50% of the U-net LR, add `"text_encoder_lr_scale": 0.5` to `optimizer.json`. The default value is `1.0`, meaning the text encoder and U-net are trained with the same LR.
+The text encoder LR can run at a different value to the Unet LR. This may help prevent over-fitting, especially if you're training from SD2 checkpoints. To set the text encoder LR, add a value for `text_encoder_lr_scale` to `optimizer.json`. For example, to train the text encoder with an LR that is half that of the Unet, add `"text_encoder_lr_scale": 0.5` to `optimizer.json`. The default value is `1.0`, meaning the text encoder and Unet are trained with the same LR.
 
 Betas, weight decay, and epsilon are documented in the [AdamW paper](https://arxiv.org/abs/1711.05101) and there is a wealth of information on the web, but consider those experimental to tweak.  I cannot provide advice on what might be useful to tweak here.