[Community] Help us fix the LR schedulers when `num_train_epochs` is passed in a distributed training env

## Context

Refer to https://github.com/huggingface/diffusers/pull/8312 for the full context. The changes introduced in the PR should be propagated to the following scripts, too:

- advanced_diffusion_training
- [x] train_dreambooth_lora_sd15_advanced.py
- [x] train_dreambooth_lora_sdxl_advanced.py
- consistency_distillation
- [x] train_lcm_distill_lora_sdxl.py
- controlnet
- [x] train_controlnet.py
- [x] train_controlnet_sdxl.py
- custom_diffusion
- [x] train_custom_diffusion.py
- dreambooth
- [x] train_dreambooth.py
- [x] train_dreambooth_lora.py
- [x] train_dreambooth_lora_sdxl.py
- instruct_pix2pix
- [x] train_instruct_pix2pix.py
- [ ] rain_instruct_pix2pix_sdxl.py
- kandinsky2_2/text_to_image
- [ ] train_text_to_image_decoder.py
- [ ] train_text_to_image_prior.py
- [ ] train_text_to_image_lora_decoder.py
- [ ] train_text_to_image_lora_prior.py

- t2i_adapter
- [ ] train_t2i_adapter_sdxl.py
- text_to_image
- [x] train_text_to_image.py
- [ ] train_text_to_image_sdxl.py
- [x] train_text_to_image_lora.py
- [ ] train_text_to_image_lora_sdxl.py
- textual_inversion
- [ ] textual_inversion.py
- [ ] textual_inversion_sdxl.py
- unconditional_image_generation
- [ ] train_unconditional.py
- wuerstchen
- [ ] text_to_image/train_text_to_image_prior.py
- [ ] text_to_image/train_text_to_image_lora_prior.py
- research_projects (low-priority)
- [ ] consistency_training/train_cm_ct_unconditional.py
- [ ] diffusion_dpo/train_diffusion_dpo.py
- [ ] diffusion_dpo/train_diffusion_dpo_sdxl.py
- [ ] diffusion_orpo/train_diffusion_orpo_sdxl_lora.py
- [ ] dreambooth_inpaint/train_dreambooth_inpaint.py
- [ ] dreambooth_inpaint/train_dreambooth_inpaint_lora.py
- [ ] instructpix2pix_lora/train_instruct_pix2pix_lora.py
- [ ] intel_opts/textual_inversion/textual_inversion_bf16.py
- [ ] intel_opts/textual_inversion_dfq/textual_inversion.py
- [ ] lora/train_text_to_image_lora.py
- [ ] multi_subject_dreambooth/train_multi_subject_dreambooth.py
- [ ] multi_token_textual_inversion/textual_inversion.py
- [ ] onnxruntime/text_to_image/train_text_to_image.py
- [ ] onnxruntime/textual_inversion/textual_inversion.py
- [ ] onnxruntime/unconditional_image_generation/train_unconditional.py
- [ ] realfill/train_realfill.py
- [ ] scheduled_huber_loss_training/dreambooth/train_dreambooth.py
- [ ] scheduled_huber_loss_training/dreambooth/train_dreambooth_lora.py
- [ ] scheduled_huber_loss_training/dreambooth/train_dreambooth_lora_sdxl.py
- [ ] scheduled_huber_loss_training/text_to_image/train_text_to_image.py
- [ ] scheduled_huber_loss_training/text_to_image/train_text_to_image_sdxl.py
- [ ] scheduled_huber_loss_training/text_to_image/train_text_to_image_lora.py
- [ ] scheduled_huber_loss_training/text_to_image/train_text_to_image_lora_sdxl.py

The following scripts do not have the argument `--num_train_epochs`:

- amused
- train_amused.py
- research_projects
- multi_subject_dreambooth_inpainting/train_multi_subject_dreambooth_inpainting.py

So, they don't need to be updated.

Then we have the following scripts that don't use `accelerator` to prepare the datasets:

Distributed dataset sharding is done by WebDataset, not `accelerator`. So, we can skip them for now.

- consistency_distillation
- train_lcm_distill_sd_wds.py
- train_lcm_distill_sdxl_wds.py
- train_lcm_distill_lora_sd_wds.py
- train_lcm_distill_lora_sdxl_wds.py
- research_projects
- controlnet/train_controlnet_webdataset.py
- diffusion_orpo/train_diffusion_orpo_sdxl_lora_wds.py

## Steps to follow when opening PRs

* Target one AND only one training script in a single PR.
* When you open a PR, please mention this issue.
* Mention @sayakpaul and @geniuspatrick for a review.
* Accompany your PR with a minimal training command using the `num_train_epochs` CLI arg.
* Enjoy!

Choisissez un script d’entraînement non vérifié, tel que examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py, et comparez-le avec PR 8312 et les scripts vérifiés correspondants. Exécutez une commande minimale d’entraînement distribué en utilisant l’argument CLI num_train_epochs ; le travail est terminé lorsque le script sélectionné gère correctement le scheduler et que la commande réussit.

Rédigé par le modèle d'indexation à partir du texte de l'issue.