--gradient_accumulation_steps=4 \
  --gradient_checkpointing \
  --mixed_precision="fp16" \
  --max_train_steps=15000 \
  --learning_rate=1e-05 \
  --max_grad_norm=1 \
  --lr_scheduler="constant" --lr_warmup_steps=0 \
  --output_dir=${OUTPUT_DIR}
Once training is finished the model will be saved to the OUTPUT_DIR specified in the command. To load the fine-tuned model for inference, just pass that path to StableDiffusionPipeline:


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