af638f98c3
Addresses review findings on the SDXL LoRA trainer: - Gate the base model with the same trust check as inference (unsloth/*, allowlisted official bases, or a local path) before from_pretrained, so an untrusted remote repo is never fetched or deserialised. - Check the stop signal before the (slow) model load, not only between steps, so a cancel during download is honoured; a stop may carry save=False to cancel without leaving a partial adapter. - Per-sample SDXL add_time_ids from the actual crop (original size + crop offset, with the offset mirrored on horizontal flip) instead of a fixed uncropped-square tensor. - Apply EXIF orientation before resize/crop so rotated photos train upright. - Skip gradient clipping when max_grad_norm <= 0 (the Studio 'disable' value) instead of scaling every gradient to zero. - Coerce Studio config strings/blanks: learning_rate string to float, blank hf_token to anonymous, gradient_checkpointing 'none'/'true'/'unsloth' to bool; reject a zero/negative lora_alpha or learning_rate. - Alias the generic Studio training payload keys (model_name/max_steps/batch_size/lora_r/ lr_scheduler_type/random_seed) onto the diffusion field names. - Mirror the trained adapter into loras/diffusion so the Images LoRA picker discovers it. - Report worker exceptions in both message and error keys so the failure is not lost. Adds regression tests for the config coercion/validation and aliasing. |
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| .. | ||
| 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 | ||