d2e6192eb2
The SDXL trainer drew min(train_batch_size, len(pairs)) indices, so a dataset with fewer images than the batch trained at a smaller effective batch than configured while the scheduler and samples-per-second still assumed the full batch. The shared PermutationBatchSampler already refills across permutation cycles to return exactly k indices, and the DiT trainer calls it with the full batch size, so drop the clamp and pass train_batch_size through for parity and to honor the configured batch. |
||
|---|---|---|
| .. | ||
| assets | ||
| auth | ||
| core | ||
| hub | ||
| loggers | ||
| models | ||
| plugins | ||
| requirements | ||
| routes | ||
| state | ||
| storage | ||
| tests | ||
| utils | ||
| __init__.py | ||
| _platform_compat.py | ||
| cloudflare_tunnel.py | ||
| colab.py | ||
| main.py | ||
| run.py | ||
| startup_banner.py | ||