Some questions about bucketing,，关于分桶的一些不理解。

Some questions about bucketing, in my training, the first training had the bucketing information as:

INFO bucket 0: resolution (448, 448), count: 2 train_util.py:1012
INFO bucket 1: resolution (512, 576), count: 2 train_util.py:1012
INFO bucket 2: resolution (512, 768), count: 2 train_util.py:1012
INFO bucket 3: resolution (512, 896), count: 4 train_util.py:1012
INFO bucket 4: resolution (512, 960), count: 4 train_util.py:1012
INFO bucket 5: resolution (512, 1024), count: 6 train_util.py:1012
INFO bucket 6: resolution (512, 1088), count: 4 train_util.py:1012
INFO bucket 7: resolution (576, 576), count: 2 train_util.py:1012
INFO bucket 8: resolution (576, 832), count: 14 train_util.py:1012
INFO bucket 9: resolution (576, 896), count: 12 train_util.py:1012
INFO bucket 10: resolution (576, 960), count: 18 train_util.py:1012
INFO bucket 11: resolution (576, 1024), count: 2 train_util.py:1012
INFO bucket 12: resolution (640, 640), count: 4 train_util.py:1012
INFO bucket 13: resolution (640, 768), count: 6 train_util.py:1012
INFO bucket 14: resolution (640, 832), count: 16 train_util.py:1012
INFO bucket 15: resolution (640, 896), count: 26 train_util.py:1012
INFO bucket 16: resolution (704, 640), count: 2 train_util.py:1012
INFO bucket 17: resolution (704, 768), count: 4 train_util.py:1012
INFO bucket 18: resolution (704, 832), count: 4 train_util.py:1012
INFO bucket 19: resolution (768, 640), count: 6 train_util.py:1012
INFO bucket 20: resolution (768, 704), count: 2 train_util.py:1012
INFO bucket 21: resolution (768, 768), count: 38 train_util.py:1012
INFO bucket 22: resolution (832, 704), count: 2 train_util.py:1012
INFO mean ar error (without repeats): 0.010474372872520617

From the second training, if ‘$cache_latents = 1’ is turned on, the bucketing results are consistent, but different from the first training:

INFO bucket 0: resolution (448, 448), count: 3 train_util.py:1012
INFO bucket 1: resolution (512, 576), count: 3 train_util.py:1012
INFO bucket 2: resolution (512, 768), count: 2 train_util.py:1012
INFO bucket 3: resolution (512, 896), count: 3 train_util.py:1012
INFO bucket 4: resolution (512, 960), count: 4 train_util.py:1012
INFO bucket 5: resolution (512, 1024), count: 6 train_util.py:1012
INFO bucket 6: resolution (512, 1088), count: 4 train_util.py:1012
INFO bucket 7: resolution (576, 576), count: 2 train_util.py:1012
INFO bucket 8: resolution (576, 832), count: 14 train_util.py:1012
INFO bucket 9: resolution (576, 896), count: 11 train_util.py:1012
INFO bucket 10: resolution (576, 960), count: 18 train_util.py:1012
INFO bucket 11: resolution (576, 1024), count: 2 train_util.py:1012
INFO bucket 12: resolution (640, 640), count: 4 train_util.py:1012
INFO bucket 13: resolution (640, 768), count: 6 train_util.py:1012
INFO bucket 14: resolution (640, 832), count: 16 train_util.py:1012
INFO bucket 15: resolution (640, 896), count: 24 train_util.py:1012
INFO bucket 16: resolution (704, 640), count: 4 train_util.py:1012
INFO bucket 17: resolution (704, 768), count: 3 train_util.py:1012
INFO bucket 18: resolution (704, 832), count: 4 train_util.py:1012
INFO bucket 19: resolution (768, 640), count: 8 train_util.py:1012
INFO bucket 20: resolution (768, 704), count: 1 train_util.py:1012
INFO bucket 21: resolution (768, 768), count: 38 train_util.py:1012
INFO bucket 22: resolution (832, 704), count: 2 train_util.py:1012

Start at train_util.py:1012, where the bucket resolutions and counts are logged, and compare the dataset bucketing path with and without cache_latents enabled. Reproduce both training configurations using the reported settings and trace where the differing counts are produced. Done means explaining the discrepancy from the existing behavior or identifying a reproducible defect and its expected outcome.

Written by the indexing model from the issue text.