[training docs] smol update to README files (#11616)

add comment to install prodigy

examples/advanced_diffusion_training/README.md

@@ -128,6 +128,7 @@ You can also load a dataset straight from by specifying it's name in `dataset_na
 Look [here](https://huggingface.co/blog/sdxl_lora_advanced_script#custom-captioning) for more info on creating/loading your own caption dataset.
 
 - **optimizer**: for this example, we'll use [prodigy](https://huggingface.co/blog/sdxl_lora_advanced_script#adaptive-optimizers) - an adaptive optimizer
+  - To use Prodigy, please make sure to install the prodigyopt library: `pip install prodigyopt`
 - **pivotal tuning**
 - **min SNR gamma**
 

examples/advanced_diffusion_training/README_flux.md

@@ -144,6 +144,7 @@ You can also load a dataset straight from by specifying it's name in `dataset_na
 Look [here](https://huggingface.co/blog/sdxl_lora_advanced_script#custom-captioning) for more info on creating/loading your own caption dataset.
 
 - **optimizer**: for this example, we'll use [prodigy](https://huggingface.co/blog/sdxl_lora_advanced_script#adaptive-optimizers) - an adaptive optimizer 
+    - To use Prodigy, please make sure to install the prodigyopt library: `pip install prodigyopt`
 - **pivotal tuning**
 
 ### Example #1: Pivotal tuning

examples/dreambooth/README_flux.md

@@ -134,7 +134,7 @@ Note also that we use PEFT library as backend for LoRA training, make sure to ha
 Prodigy is an adaptive optimizer that dynamically adjusts the learning rate learned parameters based on past gradients, allowing for more efficient convergence. 
 By using prodigy we can "eliminate" the need for manual learning rate tuning. read more [here](https://huggingface.co/blog/sdxl_lora_advanced_script#adaptive-optimizers).
 
-to use prodigy, specify
+to use prodigy, first make sure to install the prodigyopt library: `pip install prodigyopt`, and then specify -
 ```bash
 --optimizer="prodigy"
 ```