Training on the full bf16 Tongyi-MAI/Z-Image-Turbo was extremely slow — a ~10-min
download plus a heavy bf16 model that doesn't fit cleanly on 24 GB. Switch _train_dit
to QLoRA: load the transformer in 4-bit (frozen, ~4 GB, no CPU offload) and train the
LoRA on top. This trains directly on the already-cached 4-bit (e.g. unsloth) build —
no redirect to the full model, no download.

- Load transformer with diffusers BitsAndBytesConfig (nf4); an already-4-bit
  checkpoint (embedded quant config, e.g. unsloth) is loaded as-is via fallback.
- Enable gradient checkpointing and force the input-embedding (all_x_embedder)
  output to require grad so QLoRA grads reach the attention LoRA layers; hooks
  removed at job end.
- Drop the quantized-base -> full-model redirect added earlier.

LoRA still applies to the quantized model at inference (identical architecture).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RdMufYvtTbtGDWsiZVoXce