* Modularized the train_lora_sdxl file
* Modularized the train_lora_sdxl file
* Modularized the train_lora_sdxl file
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Modularized the train_lora file
* Modularized the train_lora file
* Modularized the train_lora file
* Modularized the train_lora file
* Modularized the train_lora file
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Update requirements.txt
If the datasets library is old, it will not read the metadata.jsonl and the label will default to an integer of type int.
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* 7879 - adjust documentation to use naruto dataset, since pokemon is now gated
* replace references to pokemon in docs
* more references to pokemon replaced
* Japanese translation update
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Co-authored-by: bghira <bghira@users.github.com>
* Add Ascend NPU support for SDXL fine-tuning and fix the model saving bug when using DeepSpeed.
* fix check code quality
* Decouple the NPU flash attention and make it an independent module.
* add doc and unit tests for npu flash attention.
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Co-authored-by: mhh001 <mahonghao1@huawei.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
A new function compute_dream_and_update_latents has been added to the
training utilities that allows you to do DREAM rectified training in line
with the paper https://arxiv.org/abs/2312.00210. The method can be used
with an extra argument in the train_text_to_image.py script.
Co-authored-by: Jimmy <39@🇺🇸.com>
* Restore unet params back to normal from EMA when validation call is finished
* empty commit
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* 7529 do not disable autocast for cuda devices
* Remove typecasting error check for non-mps platforms, as a correct autocast implementation makes it a non-issue
* add autocast fix to other training examples
* disable native_amp for dreambooth (sdxl)
* disable native_amp for pix2pix (sdxl)
* remove tests from remaining files
* disable native_amp on huggingface accelerator for every training example that uses it
* convert more usages of autocast to nullcontext, make style fixes
* make style fixes
* style.
* Empty-Commit
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Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* apple mps: training support for SDXL LoRA
* sdxl: support training lora, dreambooth, t2i, pix2pix, and controlnet on apple mps
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Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* log loss per image
* add commandline param for per image loss logging
* style
* debug-loss -> debug_loss
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* add tags for diffusers training
* add tags for diffusers training
* add tags for diffusers training
* add tags for diffusers training
* add tags for diffusers training
* add tags for diffusers training
* add dora tags for drambooth lora scripts
* style
* fix minsnr implementation for v-prediction case
* format code
* always compute snr when snr_gamma is specified
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Fixed the bug related to saving DeepSpeed models.
* Add information about training SD models using DeepSpeed to the README.
* Apply suggestions from code review
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Co-authored-by: mhh001 <mahonghao1@huawei.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Fix gradient-checkpointing option is ignored in SDXL+LoRA training. (#6388)
* Fix gradient-checkpointing option is ignored in SD+LoRA training.
* Fix gradient checkpoint is not applied to text encoders. (SDXL+LoRA)
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* remove validation args from textual onverson tests
* reduce number of train steps in textual inversion tests
* fix: directories.
* debig
* fix: directories.
* remove validation tests from textual onversion
* try reducing the time of test_text_to_image_checkpointing_use_ema
* fix: directories
* speed up test_text_to_image_checkpointing
* speed up test_text_to_image_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints
* fix
* speed up test_instruct_pix2pix_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints
* set checkpoints_total_limit to 2.
* test_text_to_image_lora_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints speed up
* speed up test_unconditional_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints
* debug
* fix: directories.
* speed up test_instruct_pix2pix_checkpointing_checkpoints_total_limit
* speed up: test_controlnet_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints
* speed up test_controlnet_sdxl
* speed up dreambooth tests
* speed up test_dreambooth_lora_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints
* speed up test_custom_diffusion_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints
* speed up test_text_to_image_lora_sdxl_text_encoder_checkpointing_checkpoints_total_limit
* speed up # checkpoint-2 should have been deleted
* speed up examples/text_to_image/test_text_to_image.py::TextToImage::test_text_to_image_checkpointing_checkpoints_total_limit
* additional speed ups
* style
* fix RuntimeError: Input type (float) and bias type (c10::Half) should be the same
* format source code
* format code
* remove the autocast blocks within the pipeline
* add autocast blocks to pipeline caller in train_text_to_image_lora.py
* fix: unscale fp16 gradient problem
* fix for dreambooth lora sdxl
* make the type-casting conditional.
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
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Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Tolga Cangöz