GitLab

Change the one-step dummy pipeline for testing

* Dreambooth: save / restore training state.

* make style

* Rename vars for clarity.
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Remove unused import
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Added Community pipeline for comparing Stable Diffusion v1.1-4
Signed-off-by: Suvaditya Mukherjee <suvadityamuk@gmail.com>

* Made changes to provide support for current iteration of from_pretrained and added example
Signed-off-by: Suvaditya Mukherjee <suvadityamuk@gmail.com>

* updated a small spelling error
Signed-off-by: Suvaditya Mukherjee <suvadityamuk@gmail.com>

* added pipeline entry to table
Signed-off-by: Suvaditya Mukherjee <suvadityamuk@gmail.com>
Signed-off-by: Suvaditya Mukherjee <suvadityamuk@gmail.com>

bug fix
Co-authored-by: Prathik Rao <prathikrao@microsoft.com>
Co-authored-by: anton- <anton@huggingface.co>

dreambooth: fix #1566: maintain fp32 wrapper when saving a checkpoint to avoid crash when running fp16 (#1618)

* dreambooth: fix #1566: maintain fp32 wrapper when saving a checkpoint to avoid crash when running fp16

* dreambooth: guard against passing keep_fp32_wrapper arg to older versions of accelerate. part of fix for #1566

* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update examples/dreambooth/train_dreambooth.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

Remove spurious arg in training scripts.

* do not automatically enable xformers

* uP

* Update requirements.txt

* Update requirements_flax.txt

* Update requirements.txt

* Update requirements_flax.txt

* Update requirements.txt

* Update requirements_flax.txt

* uP

* uP

easy fix for undefined name in train_dreambooth.py

import_model_class_from_model_name_or_path loads a pretrained model
and refers to args.revision in a context where args is undefined. I modified
the function to take revision as an argument and modified the invocation
of the function to pass in the revision from args. Seems like this was caused
by a cut and paste.

* init docs update

* style

* fix bad colab formatting, add pipeline comment

* update todo

* fix lpw_stable_diffusion

* rollback preprocess_mask resample

make collate_fn global

* add check_min_version for examples

* move __version__ to the top

* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* fix comment

* fix error_message

* adapt the install message
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Mega community pipeline

* fix

* Research folder

* Update examples/research_projects/README.md

* up

The mask and instance image were being cropped in different ways without --center_crop, causing the model to learn to ignore the mask in some cases. This PR fixes that and generate more consistent results.

us from_pretrained to load scheduler

[textual_inversion] Add an option to only save embeddings

Add an command line option --only_save_embeds to the example script, for
not saving the full model. Then only the learned embeddings are saved,
which can be added to the original model at runtime in a similar way as
they are created in the training script.
Saving the full model is forced when --push_to_hub is used. (Implements #759)

* Add checkpoint_merger pipeline

* Added missing docs for a parameter.

* Fomratting fixes.

* Fixed code quality issues.

* Bug fix: Off by 1 index

* Added docs for pipeline

* Create train_dreambooth_inpaint.py

train_dreambooth.py adapted to work with the inpaint model, generating random masks during the training

* Update train_dreambooth_inpaint.py

refactored train_dreambooth_inpaint with black

* Update train_dreambooth_inpaint.py

* Update train_dreambooth_inpaint.py

* Update train_dreambooth_inpaint.py

Fix prior preservation

* add instructions to readme, fix SD2 compatibility

* up

* up

* finish

* finish

* up

* up

* finish

Fixed doc to install from training packages

fix typo, remove incorrect arguments from .train()

* Moving the mem efficiient attention activation to the top + recursive

* black, too bad there's no pre-commit ?
Co-authored-by: Benjamin Lefaudeux <benjamin@photoroom.com>

* feat: switch core pipelines to use image arg

* test: update tests for core pipelines

* feat: switch examples to use image arg

* docs: update docs to use image arg

* style: format code using black and doc-builder

* fix: deprecate use of init_image in all pipelines

* Replace deprecated hub utils in `train_unconditional_ort`

* typo

* [Dreambooth] Make compatible with alt diffusion

* make style

* add example

* updates img2img_inpainting README

* Adds example image to community pipeline README

* add get_velocity

* add v prediction for training

* fix saving

* add revision arg

* fix saving

* save checkpoints dreambooth

* fix saving embeds

* add instruction in readme

* quality

* noise_pred -> model_pred

* Adapt ddpm, ddpmsolver to prediction_type.

* Deprecate predict_epsilon in __init__.

* Bring FlaxDDIMScheduler up to date with DDIMScheduler.

* Set prediction_type as an ivar for consistency.

* Convert pipeline_ddpm

* Adapt tests.

* Adapt unconditional training script.

* Adapt BitDiffusion example.

* Add missing kwargs in dpmsolver_multistep

* Ugly workaround to accept deprecated predict_epsilon when loading
schedulers using from_pretrained.

* make style

* Remove import no longer in use.

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Use config.prediction_type everywhere

* Add a couple of Flax prediction type tests.

* make style

* fix register deprecated arg
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* up

* uP