Split the SDXL trainer into a shared, architecture-agnostic layer so more model
families can be trained without duplicating the plumbing:
- New core/training/diffusion_train_common.py holds the config + validation, dataset
discovery, event emission, stop protocol, adapter publishing, and a lazy trainer
registry (get_trainer). diffusion_lora_trainer.py keeps the SDXL-specific loop and
re-exports the moved names so existing imports are unchanged.
- The SDXL-only base-model blocklist becomes a positive check: the family is resolved
from the base model (or an explicit model_family) via the diffusion family registry,
and a known-but-not-yet-trainable family is refused with a clear message. Unknown
custom names still default to the SDXL trainer.
- DiffusionFamily gains a trainable flag and train_base_repos; SDXL is marked trainable.
DiT families flip on when their trainers land.
- Trained adapters now write a <name>.json metadata sidecar (family, base model, rank,
trigger prompt, ...) that the LoRA scanner reads to family-gate the adapter in the
picker instead of showing it as unknown for every model.
- The training base-model trust allowlist adds the official FLUX.1-dev, Z-Image-Turbo,
and Qwen-Image repos (safetensors-only, no remote code).
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