GitLab

* Post Release: v0.24.0

* post pone deprecation

* post pone deprecation

* Add model_index.json

* control and custom diffusion

* dreambooth

* instructpix2pix and dreambooth ckpting

* t2i adapters.

* text to image ft

* textual inversion

* unconditional

* workflows

* import fix

* fix import

* post release

* fix: variant test

* up

* fix: test

post release

* update training examples to use HFAPI.

* update training example.

* reflect the changes in the korean version too.

* Empty-Commit

* fix: unconditional generation example

* fix: float in loss.

* apply styling.

fix problem of 'accelerator.is_main_process' to run in mutiple GPUs or NPUs
Co-authored-by: jiaqiw <wangjiaqi50@huawei.com>

* [Release 0.21] Bump version

* fix & remove

* fix more

* fix all, upload

* 0.20.0dev0

* make style

increase the time of timeout when using big dataset or high resolution

* avoid upcasting by assigning dtype to noise tensor

* make style

* Update train_unconditional.py

* Update train_unconditional.py

* make style

* add unit test for pickle

* revert change

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Co-authored-by: root <root@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Prathik Rao <prathikrao@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>

* manual check for checkpoints_total_limit instead of using accelerate

* remove controlnet_conditioning_embedding_out_channels

* Post release

* Post release

Apply deprecations

* Post release

* fix more

* feat: verfication of multi-gpu support for select examples.

* add: multi-gpu training sections to the relvant doc pages.

* Fix tensorboard tracking with `accelerate` @ `main`

* Fix `train_unconditional.py` with accelerate from main.

This reverts commit 024c4376.

* add total number checkpoints to training scripts

* Update examples/dreambooth/train_dreambooth.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Log Unconditional Image Generation Samples to WandB

* Check for wandb installation and parity between onnxruntime script

* Log epoch to wandb

* Check for tensorboard logger early on

* style fixes

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

Resolves ValueError: `num_inference_steps`: 1000 cannot be larger than `self.config.train_timesteps`: 50 as the unet model trained with this scheduler can only handle maximal 50 timesteps.

* [Examples] Test all examples on CPU

* add

* correct

* Apply suggestions from code review