* base template file - train_instruct_pix2pix.py
* additional import and parser argument requried for lora
* finetune only instructpix2pix model -- no need to include these layers
* inject lora layers
* freeze unet model -- only lora layers are trained
* training modifications to train only lora parameters
* store only lora parameters
* move train script to research project
* run quality and style code checks
* move train script to a new folder
* add README
* update README
* update references in README
---------
Co-authored-by: Rahul Raman <rahulraman@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* enable stable-xl textual inversion
* check if optimizer_2 exists
* check text_encoder_2 before using
* add textual inversion for sdxl in a single file
* fix style
* fix example style
* reset for error changes
* add readme for sdxl
* fix style
* disable autocast as it will cause cast error when weight_dtype=bf16
* fix spelling error
* fix style and readme and 8bit optimizer
* add README_sdxl.md link
* add tracker key on log_validation
* run style
* rm the second center crop
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* add: experimental script for diffusion dpo training.
* random_crop cli.
* fix: caption tokenization.
* fix: pixel_values index.
* fix: grad?
* debug
* fix: reduction.
* fixes in the loss calculation.
* style
* fix: unwrap call.
* fix: validation inference.
* add: initial sdxl script
* debug
* make sure images in the tuple are of same res
* fix model_max_length
* report print
* boom
* fix: numerical issues.
* fix: resolution
* comment about resize.
* change the order of the training transformation.
* save call.
* debug
* remove print
* manually detaching necessary?
* use the same vae for validation.
* add: readme.
* unwrap text encoder when saving hook only for full text encoder tuning
* unwrap text encoder when saving hook only for full text encoder tuning
* save embeddings in each checkpoint as well
* save embeddings in each checkpoint as well
* save embeddings in each checkpoint as well
* Update examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Make WDS pipeline interpolation type configurable.
* Make the VAE encoding batch size configurable.
* Make lora_alpha and lora_dropout configurable for LCM LoRA scripts.
* Generalize scalings_for_boundary_conditions function and make the timestep scaling configurable.
* Make LoRA target modules configurable for LCM-LoRA scripts.
* Move resolve_interpolation_mode to src/diffusers/training_utils.py and make interpolation type configurable in non-WDS script.
* apply suggestions from review
* change timesteps used to calculate snr when --with_prior_preservation is enabled
* change timesteps used to calculate snr when --with_prior_preservation is enabled (canonical script)
* style
* revert canonical script to before snr gamma change
---------
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)
---------
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
* [Community Pipeline] Add Marigold Monocular Depth Estimation
- add single-file pipeline
- update README
* fix format - add one blank line
* format script with ruff
* use direct image link in example code
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* 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>
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Typo: The script for LoRA training is `train_text_to_image_lora_prior.py` not `train_text_to_image_prior_lora.py`.
Alternatively you could rename the file and keep the README.md unchanged.
