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Z-Image Turbo is step-distilled, so the trainer offers two ways around that. It needs no 4-bit path: bf16 trains in about 13GB at 512px and about 15GB at 1024px. It trains on the Z-Image Turbo checkpoint a generate node already downloaded.

Choose a base

Fuses a de-distillation adapter into the base for the run and drops it when the LoRA is saved, which keeps the 8-step speed. Put ostris/zimage_turbo_training_adapter in models/loras/. Any filename containing adapter is detected automatically, or point INLINE_ZIMAGE_TRAIN_ADAPTER at a specific file.Keep these runs short. The adapter slows the distillation breakdown but doesn’t stop it.

VRAM

The two modes peak at the same number, because the adapter is fused into the base before training starts. A 16GB card trains at 512px but not 1024px; a 24GB card does both.

Speed

The 12-step benchmark at 512px takes 85s on an L40S and 285s on a T4. That makes Z-Image quick to iterate on, which suits the advice to keep adapter runs short.