b28793418d
SDXL re-encoded every caption with both CLIP text encoders on every step (pure waste, since captions are constant) and kept the encoders resident. Precompute each unique caption's embeddings once, then free the text encoders before the loop: numerically identical (embeddings are deterministic and this consumes no torch RNG, so the noise/timestep stream is unchanged) but faster and ~1.5 GB lighter. Default the optimizer to 8-bit AdamW (bitsandbytes) with an fp32 fallback, halving optimizer state with no meaningful LoRA quality cost. Env toggles (UNSLOTH_DIFFUSION_NO_PRECOMPUTE / _FP32_OPTIM) let the accuracy guard A/B the paths. |
||
|---|---|---|
| .. | ||
| __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 | ||