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Consistency Training
train_cm_ct_unconditional.py trains a consistency model (CM) from scratch following the consistency training (CT) algorithm introduced in Consistency Models and refined in Improved Techniques for Training Consistency Models. Both unconditional and class-conditional training are supported.
A usage example is as follows:
accelerate launch examples/research_projects/consistency_training/train_cm_ct_unconditional.py \
--dataset_name="cifar10" \
--dataset_image_column_name="img" \
--output_dir="/path/to/output/dir" \
--mixed_precision=fp16 \
--resolution=32 \
--max_train_steps=1000 --max_train_samples=10000 \
--dataloader_num_workers=8 \
--noise_precond_type="cm" --input_precond_type="cm" \
--train_batch_size=4 \
--learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \
--use_8bit_adam \
--use_ema \
--validation_steps=100 --eval_batch_size=4 \
--checkpointing_steps=100 --checkpoints_total_limit=10 \
--class_conditional --num_classes=10 \