In machine learning, the learning rate is a hyperparameter that determines the step size at which the optimizer makes updates to the model parameters during training.
A higher learning rate can result in faster training, but it can also make the generator more prone to overfitting, as it may not fully capture the underlying patterns in the data. A lower learning rate, on the other hand, can help the model better capture the patterns in the data, but it may also result in slower training.
The number of training steps refers to the number of iterations that the model goes through during training, and it is another important factor that can affect the model's performance. A higher number of training steps can result in a more accurate model, but it can also make the model more prone to overfitting, as it may start to memorize the training data rather than generalize to new data.
To get good-quality images, we must find a 'sweet spot' between the number of training steps and the learning rate. We recommend to try using a low learning rate and progressively increasing the number of steps to orient yourself to the behavior of your dataset during the finetune process.