Dreambooth: No checkpoints created and loop

Hi,

I tried to train a model and despite having selected the checkpoints option none has been created at my Google Drive root folder. Also it didn´t created the final CKPT, and the notebook started again creating automatically a second one: https://imgur.com/a/0b8aWVG

My config was like this:

Start DreamBooth
Resume_Training:

If you're not satisfied with the result, check this box, run again the cell and it will continue training the current model.
Training_Steps:
7500
Total Steps = Number of Instance images * 200, if you use 30 images, use 6000 steps, if you're not satisfied with the result, resume training for another 500 steps, and so on ...
Seed:
Insert text here
Leave empty for a random seed.
Resolution:

512
Higher resolution = Higher quality, make sure the instance images are cropped to this selected size (or larger), if you're getting memory issues, check the box below (slower speed but memory effecient) :
Reduce_memory_usage:

fp16:

Enable/disable half-precision, disabling it will double the training time and produce 4.7Gb checkpoints.
Enable_text_encoder_training:

At least 10% of the total training steps are needed, it doesn't matter if they are at the beginning or in the middle or the end, in case you're training the model multiple times.
For example you can devide 5%, 5%, 5% on 3 training runs on the model, or 0%, 0%, 15%, given that 15% will cover the total training steps count (15% of 200 steps is not enough).
Enter the % of the total steps for which to train the text_encoder
Train_text_encoder_for:
100
Keep the % low for better style transfer, more training steps will be necessary for good results.
Higher % will give more weight to the instance, it gives stronger results at lower steps count, but harder to stylize,
Save_Checkpoint_Every_n_Steps:

Save_Checkpoint_Every:
500
Minimum 200 steps between each save.
Start_saving_from_the_step:
1500
Start saving intermediary checkpoints from this step.

Thanks!

[TheLastBen]

@Xtreamer if you set it to 100, it will train 15800s the textenc and 15800s the unet, set it to something like 30%.

don’t use the “contains_faces” option, it’s experimental and doesn’t give much results

[TheLastBen]

I’ll check that out