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. |
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| .. | ||
| __init__.py | ||
| diffusion_dit_trainer.py | ||
| diffusion_lora_trainer.py | ||
| diffusion_train_common.py | ||
| diffusion_training_service.py | ||
| resume.py | ||
| s3_dataset.py | ||
| trainer.py | ||
| training.py | ||
| worker.py | ||