GitLab

* find & replace all FloatTensors to Tensor

* apply formatting

* Update torch.FloatTensor to torch.Tensor in the remaining files

* formatting

* Fix the rest of the places where FloatTensor is used as well as in documentation

* formatting

* Update new file from FloatTensor to Tensor

* support custom sigmas and timesteps, dpm euler

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

Fix imports

`model_output.shape` may only have rank 1.

There are warnings related to use of random keys.

```
tests/schedulers/test_scheduler_flax.py: 13 warnings
  /Users/phillypham/diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py:268: FutureWarning: normal accepts a single key, but was given a key array of shape (1, 2) != (). Use jax.vmap for batching. In a future JAX version, this will be an error.
    noise = jax.random.normal(split_key, shape=model_output.shape, dtype=self.dtype)

tests/schedulers/test_scheduler_flax.py::FlaxDDPMSchedulerTest::test_betas
  /Users/phillypham/virtualenv/diffusers/lib/python3.9/site-packages/jax/_src/random.py:731: FutureWarning: uniform accepts a single key, but was given a key array of shape (1,) != (). Use jax.vmap for batching. In a future JAX version, this will be an error.
    u = uniform(key, shape, dtype, lo, hi)  # type: ignore[arg-type]
```

* Deprecate resume_download

* align docstring with transformers

* style

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

* Added get_velocity function to EulerDiscreteScheduler.

* Fix white space on blank lines

* Added copied from statement

* back to the original.

---------
Co-authored-by: Ruining Li <ruining@robots.ox.ac.uk>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* introduce sigma schedule.
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* address yiyi

* update docstrings.

* implement the schedule for EDMDPMSolverMultistepScheduler

---------
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* UniPC Multistep add `rescale_betas_zero_snr`

Same patch as DPM and Euler with the patched final alpha cumprod

BF16 doesn't seem to break down, I think cause UniPC upcasts during some
phases already? We could still force an upcast since it only
loses ≈ 0.005 it/s for me but the difference in output is very small. A
better endeavor might upcasting in step() and removing all the other
upcasts elsewhere?

* UniPC ZSNR UT

* Re-add `rescale_betas_zsnr` doc oops

* UniPC UTs iterate solvers on FP16

It wasn't catching errs on order==3. Might be excessive?

* UniPC Multistep fix tensor dtype/device on order=3

* UniPC UTs Add v_pred to fp16 test iter

For completions sake. Probably overkill?

* add: utility to format our docs too 📜

* debugging saga

* fix: message

* checking

* should be fixed.

* revert pipeline_fixture

* remove empty line

* make style

* fix: setup.py

* style.

* Add `final_sigma_zero` to UniPCMultistep

Effectively the same trick as DDIM's `set_alpha_to_one` and
DPM's `final_sigma_type='zero'`.
Currently False by default but maybe this should be True?

* `final_sigma_zero: bool` -> `final_sigmas_type: str`

Should 1:1 match DPM Multistep now.

* Set `final_sigmas_type='sigma_min'` in UniPC UTs

* Fix typos

* Fix typo in SVD.md

* fix

* fix

* add a tests

* fix

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: yiyixuxu <yixu310@gmail,com>

* add missing copied from statements in tcd scheduler

* update docstring

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

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

* Fix PyTorch's convention for inplace functions

* Fix import structure in __init__.py and update config loading logic in test_config.py

* Update configuration access

* Fix typos

* Trim trailing white spaces

* Fix typo in logger name

* Revert "Fix PyTorch's convention for inplace functions"

This reverts commit f65dc4afcb57ceb43d5d06389229d47bafb10d2d.

* Fix typo in step_index property description

* Revert "Update configuration access"

This reverts commit 8d44e870b8c1ad08802e3e904c34baeca1b598f8.

* Revert "Fix import structure in __init__.py and update config loading logic in test_config.py"

This reverts commit 2ad5e8bca25aede3b912da22bd57285b598fe171.

* Fix typos

* Fix typos

* Fix typos

* Fix a typo: tranform -> transform

* update get_order_list if statement

* revery

* Change step_offset scheduler docstrings

* Mention it may be needed by some models

* More docstrings

These ones failed literal S&R because I performed it case-sensitive
which is fun.

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

* Setup LEdits++ file structure

* Fix import

* LEditsPP Stable Diffusion pipeline

* Include variable image aspect ratios

* Implement LEDITS++ for SDXL

* clean up LEditsPPPipelineStableDiffusion

* Adjust inversion output

* Added docu, more cleanup for LEditsPPPipelineStableDiffusion

* clean up LEditsPPPipelineStableDiffusionXL

* Update documentation

* Fix documentation import

* Add skeleton IF implementation

* Fix documentation typo

* Add LEDTIS docu to toctree

* Add missing title

* Finalize SD documentation

* Finalize SD-XL documentation

* Fix code style and quality

* Fix typo

* Fix return types

* added LEditsPPPipelineIF; minor changes for LEditsPPPipelineStableDiffusion and LEditsPPPipelineStableDiffusionXL

* Fix copy reference

* add documentation for IF

* Add first tests

* Fix batching for SD-XL

* Fix text encoding and perfect reconstruction for SD-XL

* Add tests for SD-XL, minor changes

* move user_mask to correct device, use cross_attention_kwargs also for inversion

* Example docstring

* Fix attention resolution for non-square images

* Refactoring for PR review

* Safely remove ledits_utils.py

* Style fixes

* Replace assertions with ValueError

* Remove LEditsPPPipelineIF

* Remove unecessary input checks

* Refactoring of CrossAttnProcessor

* Revert unecessary changes to scheduler

* Remove first progress-bar in inversion

* Refactor scheduler usage and reset

* Use imageprocessor instead of custom logic

* Fix scheduler init warning

* Fix error when running the pipeline in fp16

* Update documentation wrt perfect inversion

* Update tests

* Fix code quality and copy consistency

* Update LEditsPP import

* Remove enable/disable methods that are now in StableDiffusionMixin

* Change import in docs

* Revert import structure change

* Fix ledits imports

---------
Co-authored-by: Katharina Kornmeier <katharina.kornmeier@stud.tu-darmstadt.de>

switch to logger.warning

* I added a new doc string to the class. This is more flexible to understanding other developers what are doing and where it's using.

* Update src/diffusers/models/unet_2d_blocks.py

This changes suggest by maintener.
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update src/diffusers/models/unet_2d_blocks.py

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

* Update unet_2d_blocks.py

I changed the Parameter to Args text.

* Update unet_2d_blocks.py

proper indentation set in this file.

* Update unet_2d_blocks.py

a little bit of change in the act_fun argument line.

* I run the black command to reformat style in the code

* Update unet_2d_blocks.py

similar doc-string add to have in the original diffusion repository.

* Fix bug for mention in this issue section #6901

* Update src/diffusers/schedulers/scheduling_ddim_flax.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Fix linter

* Restore empty line

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* add: support TCD scheduler


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

* Enable FakeTensorMode for EulerDiscreteScheduler scheduler

PyTorch's FakeTensorMode does not support `.numpy()` or `numpy.array()`
calls.

This PR replaces `sigmas` numpy tensor by a PyTorch tensor equivalent

Repro

```python
with torch._subclasses.FakeTensorMode() as fake_mode, ONNXTorchPatcher():
    fake_model = DiffusionPipeline.from_pretrained(model_name, low_cpu_mem_usage=False)
```

that otherwise would fail with
`RuntimeError: .numpy() is not supported for tensor subclasses.`

* Address comments

* DPMMultistep rescale_betas_zero_snr

* DPM upcast samples in step()

* DPM rescale_betas_zero_snr UT

* DPMSolverMulti move sample upcast after model convert

Avoids having to re-use the dtype.

* Add a newline for Ruff

* add DPM scheduler with EDM formulation

* set sigmas in init

* add _compute_sigmas

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

* address some review comments

* up,

* add tests

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

* Add EDMEulerScheduler

* address review comments

* fix import

* fix test

* add tests

* add co-author

Co-authored-by:  @dg845 dgu8957@gmail.com

[DPMSolverSinglestepScheduler] correct `get_order_list` for `solver_order=2`and `lower_order_final=True`  (#6953)

* add

* change default

---------
Co-authored-by: yiyixuxu <yixu310@gmail,com>

change to 2024

add missing param

* load cumprod tensor to device
Signed-off-by: woshiyyya <xiaoyunxuan1998@gmail.com>

* fixing ci
Signed-off-by: woshiyyya <xiaoyunxuan1998@gmail.com>

* make fix-copies
Signed-off-by: woshiyyya <xiaoyunxuan1998@gmail.com>

---------
Signed-off-by: woshiyyya <xiaoyunxuan1998@gmail.com>

* add Sa-Solver



---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: scxue <xueshuchen17@mails.ucas.edu.cn>
Co-authored-by: jschen <chenjunsong4@h-partners.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: yiyixuxu <yixu310@gmail,com>

* fix

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

Fix support for MPS

MPS doesn't support float64

* Remove unused parameters and fixed `FutureWarning`

* Fixed wrong config instance

* update unittest for `DDIMInverseScheduler`

* Add rescale_betas_zero_snr argument to DDPMScheduler.

* Propagate rescale_betas_zero_snr changes to DDPMParallelScheduler.

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

amused

rename

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

AdaLayerNormContinuous default values

custom micro conditioning

micro conditioning docs

put lookup from codebook in constructor

fix conversion script

remove manual fused flash attn kernel

add training script

temp remove training script

add dummy gradient checkpointing func

clarify temperatures is an instance variable by setting it

remove additional SkipFF block args

hardcode norm args

rename tests folder

fix paths and samples

fix tests

add training script

training readme

lora saving and loading

non-lora saving/loading

some readme fixes

guards

Update docs/source/en/api/pipelines/amused.md
Co-authored-by: Suraj Patil <surajp815@gmail.com>

Update examples/amused/README.md
Co-authored-by: Suraj Patil <surajp815@gmail.com>

Update examples/amused/train_amused.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

vae upcasting

add fp16 integration tests

use tuple for micro cond

copyrights

remove casts

delegate to torch.nn.LayerNorm

move temperature to pipeline call

upsampling/downsampling changes

* EulerAncestral add `rescale_betas_zero_snr`

Uses same infinite sigma fix from EulerDiscrete. Interestingly the
ancestral version had the opposite problem: too much contrast instead of
too little.

* UT for EulerAncestral `rescale_betas_zero_snr`

* EulerAncestral upcast samples during step()

It helps this scheduler too, particularly when the model is using bf16.

While the noise dtype is still the model's it's automatically upcasted
for the add so all it affects is determinism.

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

* correct

* Apply suggestions from code review

* make style