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* refactor image processor for mask
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Co-authored-by: yiyixuxu <yixu310@gmail,com>

* fix
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Co-authored-by: yiyixuxu <yixu310@gmail,com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* proposal for flaky tests

* clean up

* from audioldm

* fix vae

* move to new pipeline

* copied from audioldm

* remove redundant control flow

* iterate

* fix docstring

* finish pipeline

* tests: from audioldm2

* iterate

* finish fast tests

* finish slow integration tests

* add docs

* remove dtype test

* update toctree

* "copied from" in conversion (where possible)

* Update docs/source/en/api/pipelines/musicldm.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix docstring

* make nightly

* style

* fix dtype test

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

* fix docs

* fix unet docs

* use image output for latents

* fix hub checkpoints

* fix pipeline example

* update example

* return_dict = False

* revert image pipeline output

* revert doc changes

* remove dtype test

* make style

* remove docstring updates

* remove unet docstring update

* Empty commit to re-trigger CI

* fix cpu offload

* fix dtype test

* add offload test

* fix sdxl_lwp empty neg_prompt error issue

* fix sdxl_lwp empty neg_prompt error issue, update code format

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

* fix sdxl dreambooth lora checkpointing.

* style

* relax lora loading logic.

* cater to the other cases too.

* fix: variable name

* bring the chaos down.

* check

* deal with checkpointed files.

* Apply suggestions from code review
Co-authored-by: apolinário <joaopaulo.passos@gmail.com>

* style

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Co-authored-by: apolinário <joaopaulo.passos@gmail.com>

Add madebyollin VAE to LoRA example, along with an explenation

* [fix] multi t2i adapter set total_downscale_factor

* move image checks into check inputs

* remove copied from

* [Tests] Fix paint by example

* Update src/diffusers/pipelines/paint_by_example/image_encoder.py

use max diff to compare model outputs

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

* [AudioLDM2] Doc fixes

* update docstrings

* fix unet docstring

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

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

add self.step_index

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Co-authored-by: yiyixuxu <yixu310@gmail,com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* dreambooth training

* train_dreambooth validation scheduler

* set a particular scheduler via a string

* modify readme after setting a particular scheduler via a string

* modify readme after setting a particular scheduler

* use importlib to set a particular scheduler

* import with correct sort

* fix import

* style

* fix: casting issues.

* fix checkpointing.

* tests

* fix: bugs

* Fix AutoencoderTiny encoder scaling convention

  * Add [-1, 1] -> [0, 1] rescaling to EncoderTiny

  * Move [0, 1] -> [-1, 1] rescaling from AutoencoderTiny.decode to DecoderTiny
    (i.e. immediately after the final conv, as early as possible)

  * Fix missing [0, 255] -> [0, 1] rescaling in AutoencoderTiny.forward

  * Update AutoencoderTinyIntegrationTests to protect against scaling issues.
    The new test constructs a simple image, round-trips it through AutoencoderTiny,
    and confirms the decoded result is approximately equal to the source image.
    This test checks behavior with and without tiling enabled.
    This test will fail if new AutoencoderTiny scaling issues are introduced.

  * Context: Raw TAESD weights expect images in [0, 1], but diffusers'
    convention represents images with zero-centered values in [-1, 1],
    so AutoencoderTiny needs to scale / unscale images at the start of
    encoding and at the end of decoding in order to work with diffusers.

* Re-add existing AutoencoderTiny test, update golden values

* Add comments to AutoencoderTiny.forward

* [Docs] Fix all

* fix

This reverts commit 7b07f981.

* rename test file to run, so that examples tests do not fail

* [Tests] Rename community tests

* ADD SDXL reference & reference adain

* Update README.md

* Update README.md

* format stable_diffusion_xl_reference.py

* format file

* Format file

* format file

* fix format

* fix format with ruff

* fix format

* Update examples/community/README.md
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

* Update examples/community/README.md
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

* Update README.md

* Update README.md & fix typo

* Update README.md

* fix format

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Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

convert diffuser pipeline of XL to original stable diffusion
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

This is a better method than comparing against a list of supported backends as it allows for supporting any number of backends provided they are installed on the user's system.
This should have no effect on the behaviour of tests in Huggingface's CI workers.
See transformers#25506 where this approach has already been added.

default to None when fc alphas are not available.

* debugging starts

* debugging

* debugging

* debugging

* debugging

* debugging

* debugging ends, but does it?

* more robustness.

* enable lora for sdxl controlnets too.

* add: tests

* fix: assertion values.

* Add note regarding UniDiffuser pipeline numerical stability issues on PyTorch 1.X

* Use the doc-builder warning tag.