[train_lcm_distill_lora_sdxl.py] Fix the LR schedulers when num_train_epochs is passed in a distributed training env

[rootonchair]

What does this PR do?

Part of #8384

Test script

export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
export OUTPUT_DIR="sdxl_lcm"

accelerate launch train_lcm_distill_lora_sdxl.py \
    --pretrained_teacher_model=$MODEL_NAME \
    --output_dir=$OUTPUT_DIR \
    --mixed_precision=fp16 \
    --resolution=512 \
    --lora_rank=64 \
    --learning_rate=1e-4 --loss_type="huber" --adam_weight_decay=0.0 \
    --num_train_epochs=100 \
    --dataloader_num_workers=8 \
    --dataset_name="lambdalabs/naruto-blip-captions" \
    --validation_steps=200 \
    --checkpointing_steps=200 --checkpoints_total_limit=10 \
    --train_batch_size=2 \
    --gradient_checkpointing --enable_xformers_memory_efficient_attention \
    --gradient_accumulation_steps=1 \
    --use_8bit_adam \
    --resume_from_checkpoint=latest \
    --report_to=tensorboard \
    --seed=453645634

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[sayakpaul]

Thank you!

[HuggingFaceDocBuilderDev]

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[Bhavay-2001]

Hi @rootonchair, did you try to run this test script on google colab first?

[rootonchair]

Hi @Bhavay-2001 , I haven't ran with Cobal yet but I did run on my machine's GPU