I have conducted some simple tests for qiqi, including the following: 
First, I added three sets of lora: 
  1. Same training set, removed regular datasets. 
  2. Same training set, removed regular datasets + modified dim to 32dim. 
  3. Introduced qiqi lora that I trained myself last time as a control group (250 images + 1000 steps * 8bs, definitely enough training).

I first tested the original version, with a weight of 0.8. 
Then, I replaced the weight with 1 for testing on epoch 000018. 
Finally, I added "ofuda" at the beginning of the prompts.

Please refer to the Excel file for details.

In summary, I believe the reasons for this phenomenon consist of multiple components.

1.4dim itself is not easy to overfit. If a weight less than 1 is used simultaneously, it will cause the model show underfit.

2.The number of epochs given in the range of 1000 to 2000 images may be too small.
In the case of no regular set, the best epoch maybe is 18/20, which is already at a critical point, and it is uncertain whether it is a one-time outlier.

3.Under 2, a large number of regular datasets were added, which slows down the fitting speed and requires more epochs. However, the epoch empirical formula is based on training without reg datasets.

4.The "ofuda" label should be a core tag, but it was not deleted, so if the ofuda prompt is not used, the ccip score will decrease.

The results are a result of the combined effects of 1, 2, 3, 4.


To address this issue, I suggest:

1.Change the testing weight to 1

2.Increase the number of epochs for 1000 to 2000 images. Consider increasing it to 24 or even 26.

3.Further increase the number of epochs when introducing regular datasets. The specific amount needs to be tested (I have not extensively used regular training datasets, so I cannot be sure).

4.I don't think 4dim are insufficient, but if necessary, a slight increase to 6dim/8dim can be considered.