update README

Author: kohya-ss

README.md

@@ -12,7 +12,7 @@ The command to install PyTorch is as follows:
 ### Recent Updates
 
 Sep 10, 2024:
-In FLUX.1 LoRA training, individual learning rates can be specified for CLIP-L and T5XXL. By specifying multiple numbers in `--text_encoder_lr`, you can set the learning rates for CLIP-L and T5XXL separately. Specify like `--text_encoder_lr 1e-4 1e-5`. If you specify only one, the learning rates for CLIP-L and T5XXL will be the same.
+In FLUX.1 LoRA training, individual learning rates can be specified for CLIP-L and T5XXL. By specifying multiple numbers in `--text_encoder_lr`, you can set the learning rates for CLIP-L and T5XXL separately. Specify like `--text_encoder_lr 1e-4 1e-5`. The first value is the learning rate for CLIP-L, and the second value is for T5XXL. If you specify only one, the learning rates for CLIP-L and T5XXL will be the same.
 
 Sep 9, 2024:
 Added `--negative_prompt` and `--cfg_scale` to `flux_minimal_inference.py`. Negative prompts can be used. 
@@ -145,7 +145,7 @@ The effect of `--timestep_sampling sigmoid` and `--sigmoid_scale` (when `--times
    - Remove `--network_train_unet_only` from your command.
    - Add `train_t5xxl=True` to `--network_args` to train T5XXL LoRA. CLIP-L is also trained at the same time.
    - T5XXL output can be cached for CLIP-L LoRA training. So, `--cache_text_encoder_outputs` or `--cache_text_encoder_outputs_to_disk` is also available.
-   - The learning rates for CLIP-L and T5XXL can be specified separately. Multiple numbers can be specified in `--text_encoder_lr`. For example, `--text_encoder_lr 1e-4 1e-5`. If you specify only one, the learning rates for CLIP-L and T5XXL will be the same. If `--text_encoder_lr` is not specified, the default learning rate `--learning_rate` is used for both CLIP-L and T5XXL.
+   - The learning rates for CLIP-L and T5XXL can be specified separately. Multiple numbers can be specified in `--text_encoder_lr`. For example, `--text_encoder_lr 1e-4 1e-5`. The first value is the learning rate for CLIP-L, and the second value is for T5XXL. If you specify only one, the learning rates for CLIP-L and T5XXL will be the same. If `--text_encoder_lr` is not specified, the default learning rate `--learning_rate` is used for both CLIP-L and T5XXL.
    - The trained LoRA can be used with ComfyUI.
    - Note: `flux_extract_lora.py`, `convert_flux_lora.py`and `merge_flux_lora.py` do not support CLIP-L and T5XXL LoRA yet.