Hello,

It's me again. I have played with OneTrainer for a while since the issue I mentioned has been fixed. During testing it, I feel like it's quiet good and easy to use.

Based on my experience, I'd like to share some feedback to you.

1. Model Output Destination only works when enable Backup Before save. If disable it, I will lose the final trained model because the script won't save the final trained model to a backup (in my case, I backup after 20 epochs and total epochs to 100). To save the final model, I need to set the epoch to 101 so that the script can save model at 100 epochs.
2. An error happens when run "start_ui.py". It said: "A matching Triton is not available, some optimizations will not be enabled. Error caught was: No module named 'triton'"
3. Can add a tab 'Test' to let user load their trained model to play and test with.
4. Once there are "workspace" and "workspace-cache" folders, the script will overwrite the results and cache inside the folder. Instead, I prefer to let the script create a new folder under "workspace" and "workspace-cache" called "run_xxx" (means you will have different running id for each experiment. We can look into the workspace folder to see existing folder id and plus it by 1 to have a new assigned id.
5. After reading the description of latent caching, I still not understood it clearly yet. Just out of curiosity, could you explain a little bit to me about what is this feature actually doing? I feel like it is caching some intermediary data of the training samples by a given latent caching epochs. E.g., if the latent caching epochs set to 10, it will create different 10 versions of training data based on the data augmentation methods I enabled. During training, the script will load those 10 versions of data (after 10 epochs) so that the diversity of training samples is increased. Am I right?

Other features look really nice! It's quiet easy to play with! Thank you for delivering this cool tool!