* support and example launch for sdxl turbo
* White space fixes
* Trailing whitespace character
* ruff format
* fix guidance_scale and steps for turbo mode
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Radames Ajna <radamajna@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
* move model helper function in pipeline to EfficiencyMixin
---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* fix minsnr implementation for v-prediction case
* format code
* always compute snr when snr_gamma is specified
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* initial commit for unconditional/class-conditional consistency training script
* make style
* Add entry for consistency training script in community README.
* Move consistency training script from community to research_projects/consistency_training
* Add requirements.txt and README to research_projects/consistency_training directory.
* Manually revert community README changes for consistency training.
* Fix path to script after moving script to research projects.
* Add option to load U-Net weights from pretrained model.
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* move unets to module 🦋
* parameterize unet-level import.
* fix flax unet2dcondition model import
* models __init__
* mildly depcrecating models.unet_2d_blocks in favor of models.unets.unet_2d_blocks.
* noqa
* correct depcrecation behaviour
* inherit from the actual classes.
* Empty-Commit
* backwards compatibility for unet_2d.py
* backward compatibility for unet_2d_condition
* bc for unet_1d
* bc for unet_1d_blocks
* 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
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Co-authored-by: Rahul Raman <rahulraman@gmail.com>
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.
Min-SNR Gamma: correct the fix for SNR weighted loss in v-prediction by adding 1 to SNR rather than the resulting loss weights
Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* - Added validation parameters
- Changed some parameter descriptions to better explain their use.
- Fixed a few typos.
- Added concept_list parameter for better management of multiple subjects
- changed logic for image validation
* - Fixed bad logic for class data root directories
* Defaulting validation_steps to None for an easier logic
* Fixed multiple validation prompts
* Fixed bug on validation negative prompt
* Changed validation logic for tracker.
* Added uuid for validation image labeling
* Fix error when comparing validation prompts and validation negative prompts
* Improved error message when negative prompts for validation are more than the number of prompts
* - Changed image tracking number from epoch to global_step
- Added Typing for functions
* Added some validations more when using concept_list parameter and the regular ones.
* Fixed error message
* Added more validations for validation parameters
* Improved messaging for errors
* Fixed validation error for parameters with default values
* - Added train step to image name for validation
- reformatted code
* - Added train step to image's name for validation
- reformatted code
* Updated README.md file.
* reverted back original script of train_dreambooth.py
* reverted back original script of train_dreambooth.py
* left one blank line at the eof
* reverted back setup.py
* reverted back setup.py
* added same logic for when parameters for prior preservation are used without enabling the flag while using concept_list parameter.
* Ran black formatter.
* fixed a few strings
* fixed import sort with isort and removed fstrings without placeholder
* fixed import order with ruff (since with isort wasn't ok)
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* update code to reflect latest changes as of May 30th
* update text to image example
* reflect changes to textual inversion
* make style
* fix typo
* Revert unnecessary readme changes
---------
Co-authored-by: root <root@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
Co-authored-by: Prathik Rao <prathikrao@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
