ch_size=1 \
  --gradient_accumulation_steps=1 --gradient_checkpointing \
  --learning_rate=5e-6 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --num_class_images=200 \
  --max_train_steps=800 \
  --mixed_precision=fp16

Inference
	
Once you have trained a model, inference can be done using the StableDiffusionPipeline, by simply indicating the path where the model was saved. Make sure that your prompts include the special identifier u