Context

Refer to #8312 for the full context. The changes introduced in the PR should be propagated to the following scripts, too:

• advanced_diffusion_training

  • train_dreambooth_lora_sd15_advanced.py
  • train_dreambooth_lora_sdxl_advanced.py

• consistency_distillation

  • train_lcm_distill_lora_sdxl.py

• controlnet

  • train_controlnet.py
  • train_controlnet_sdxl.py

• custom_diffusion

  • train_custom_diffusion.py

• dreambooth

  • train_dreambooth.py
  • train_dreambooth_lora.py
  • train_dreambooth_lora_sdxl.py

• instruct_pix2pix

  • 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

  • train_text_to_image.py
  • train_text_to_image_sdxl.py
  • 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!