Not always true.

[xingp-ng]

We believe that we have reproduced the appropriate results, but there are still some questions that we would like to be answered.

1. The results are generated in such a way that it usually takes one out of 8 to find a suitable result, making it difficult to use them in practice.

2. These results may not be aligned with the content image.

3. Is there any technique to alleviate these problems？

[yardenfren1996]

Regarding your questions, since it is an optimization process. There's a possibility that training different LoRAs on the same image will yield different results due to different initializations. Sometimes, the optimization process struggles to 'learn' the given concept perfectly.
I recommend training with different seeds, or adjusting other training parameters.
And since it is a personalization technique, the resulting image may not align perfectly with the content image. I suggest integrating our approach with content preservation techniques like ControlNet, although I haven't personally tested this.

[liusida]

Ha-ha, interesting workaround. 5 B-LoRAs with different seeds have the comparable size of a LoRA model xD