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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.

* from audioldm

* unet down + mid

* vae, clap, flan-t5

* start sequence audio mae

* iterate on audioldm encoder

* finish encoder

* finish weight conversion

* text pre-processing

* gpt2 pre-processing

* fix projection model

* working

* unet equivalence

* finish in base

* add unet cond

* finish unet

* finish custom unet

* start clean-up

* revert base unet changes

* refactor pre-processing

* tests: from audioldm

* fix some tests

* more fixes

* iterate on tests

* make fix copies

* harden fast tests

* slow integration tests

* finish tests

* update checkpoint

* update copyright

* docs

* remove outdated method

* add docstring

* make style

* remove decode latents

* enable cpu offload

* (text_encoder_1, tokenizer_1) -> (text_encoder, tokenizer)

* more clean up

* more refactor

* build pr docs

* Update docs/source/en/api/pipelines/audioldm2.md
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* small clean

* tidy conversion

* update for large checkpoint

* generate -> generate_language_model

* full clap model

* shrink clap-audio in tests

* fix large integration test

* fix fast tests

* use generation config

* make style

* update docs

* finish docs

* finish doc

* update tests

* fix last test

* syntax

* finalise tests

* refactor projection model in prep for TTS

* fix fast tests

* style

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

* Update loaders.py

add config_file to from_single_file, 
when the download_from_original_stable_diffusion_ckpt use

* Update loaders.py

add config_file to from_single_file,
when the download_from_original_stable_diffusion_ckpt use

* change config_file to original_config_file

* make style && make quality

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

* Add SDXL long weighted prompt pipeline

* Add SDXL long weighted prompt pipeline usage sample in the readme document

* Add SDXL long weighted prompt pipeline usage sample in the readme document, add result image

* Impl tae slicing and tiling

* add tae tiling test

* add parameterized test

* formatted code

* fix failed test

* style docs

Fix `use_onnx` parameter usage in `from_pretrained` func and update `test_download_no_onnx_by_default` test (#4508)

* add missing use_onnx in from_pretrained func

* fix test_download_no_onnx_by_default test func

* address comments

* split test cases

* make safetensors default

* set default save method as safetensors

* update tests

* update to support saving safetensors

* update test to account for safetensors default

* update example tests to use safetensors

* update example to support safetensors

* update unet tests for safetensors

* fix failing loader tests

* fix qc issues

* fix pipeline tests

* fix example test

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

* Support higher dimension LoRAs

* add: tests

* fix: assertion values.

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

* fixed

* add: tests

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

fix loading custom text encoder when using from_single_file

multicontrolnet docs