--validation_steps=200 \
--checkpointing_steps=200 --checkpoints_total_limit=10 \
--train_batch_size=12 \
--gradient_checkpointing --enable_xformers_memory_efficient_attention \
--gradient_accumulation_steps=1 \
--use_8bit_adam \
--resume_from_checkpoint=latest \
--report_to=wandb \
--seed=453645634 \
--push_to_hub
```  
Once training is complete, you can use your new LCM for inference.  
```py
from diffusers import UNet2DConditionModel, DiffusionPipeline, LCMScheduler
import torch