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.
d2e6192eb2