GitLab

* post-release

* style

* use make_image_grid in diffusers.utils

* use checkpoint on the Hub

* fix vae dtype when accelerate config using --mixed_precision="fp16"

* Add param for upcast vae

* fix: retain memory utility.

* fix

* quality

* free_memory.

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>

* add controlnet sd3 example

* add controlnet sd3 example

* update controlnet sd3 example

* add controlnet sd3 example test

* fix quality and style

* update test

* update test

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

* post release

* fix quality

post release

* post release v0.28.0

* style

* 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>

* post-release

* quality

switch to logger.warning

* 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

* log_validation unification for controlnet.

* additional fixes.

* remove print.

* better reuse and loading

* make final inference run conditional.

* Update examples/controlnet/README_sdxl.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* resize the control image in the snippet.

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Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* post release

* style

* Empty-Commit

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

* controlnet

* style

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

change to 2024

* update: make controlnet script torch compile compatible
Signed-off-by: Suvaditya Mukherjee <suvadityamuk@gmail.com>

* update: correct earlier mistakes for compilation
Signed-off-by: Suvaditya Mukherjee <suvadityamuk@gmail.com>

* update: fix code style issues
Signed-off-by: Suvaditya Mukherjee <suvadityamuk@gmail.com>

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Signed-off-by: Suvaditya Mukherjee <suvadityamuk@gmail.com>

* post release

* style

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

* Post Release: v0.24.0

* post pone deprecation

* post pone deprecation

* Add model_index.json

* add variant

* add variant

* Apply suggestions from code review

* reformat

* fix: textual_inversion.py

* fix: variant in model_info

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

* post release

* fix: variant test

* up

* fix: test

post release

* [Release 0.21] Bump version

* fix & remove

* fix more

* fix all, upload

* 0.20.0dev0

* make style

* Multiply lr scheduler steps by `num_processes`.

* Stop multiplying steps by gradient accumulation.

* manual check for checkpoints_total_limit instead of using accelerate

* remove controlnet_conditioning_embedding_out_channels

* Post release

* Post release

Apply deprecations

do not scale the initial global step by gradient accumulation steps when loading from checkpoint (#3506)

Remove required from tracker_project_name.

As observed by https://github.com/off99555 in https://github.com/huggingface/diffusers/issues/2695#issuecomment-1470755050, it already has a default value.

Write model card in controlnet training script.

* Post release

* fix more

controlnet training center crop input images to multiple of 8

The pipeline code resizes inputs to multiples of 8.
Not doing this resizing in the training script is causing
the encoded image to have different height/width dimensions
than the encoded conditioning image (which uses a separate
encoder that's part of the controlnet model).

We resize and center crop the inputs to make sure they're the
same size (as well as all other images in the batch). We also
check that the initial resolution is a multiple of 8.