GitLab

* initial export and design

* update imports

* custom prover, import fixes

* Update src/diffusers/onnx_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/onnx_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* remove push_to_hub

* Update src/diffusers/onnx_utils.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* remove torch_device

* numpify the rest of the pipeline

* torchify the safety checker

* revert tensor

* Code review suggestions + quality

* fix tests

* fix provider, add an end-to-end test

* style
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>

nicer datasets

* up

* remove bogus file

* [Docs] Fix scheduler docs

* up

* Apply suggestions from code review

* Start

* up

* up

* finish

* use gpu and improve

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

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

* improve latent diff example

* use gpu

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

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

* Initial support for mps in Stable Diffusion pipeline.

* Initial "warmup" implementation when using mps.

* Make some deterministic tests pass with mps.

* Disable training tests when using mps.

* SD: generate latents in CPU then move to device.

This is especially important when using the mps device, because
generators are not supported there. See for example
https://github.com/pytorch/pytorch/issues/84288.

In addition, the other pipelines seem to use the same approach: generate
the random samples then move to the appropriate device.

After this change, generating an image in MPS produces the same result
as when using the CPU, if the same seed is used.

* Remove prints.

* Pass AutoencoderKL test_output_pretrained with mps.

Sampling from `posterior` must be done in CPU.

* Style

* Do not use torch.long for log op in mps device.

* Perform incompatible padding ops in CPU.

UNet tests now pass.
See https://github.com/pytorch/pytorch/issues/84535



* Style: fix import order.

* Remove unused symbols.

* Remove MPSWarmupMixin, do not apply automatically.

We do apply warmup in the tests, but not during normal use.
This adopts some PR suggestions by @patrickvonplaten.

* Add comment for mps fallback to CPU step.

* Add README_mps.md for mps installation and use.

* Apply `black` to modified files.

* Restrict README_mps to SD, show measures in table.

* Make PNDM indexing compatible with mps.

Addresses #239.

* Do not use float64 when using LDMScheduler.

Fixes #358.

* Fix typo identified by @patil-suraj
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Adapt example to new output style.

* Restore 1:1 results reproducibility with CompVis.

However, mps latents need to be generated in CPU because generators
don't work in the mps device.

* Move PyTorch nightly to requirements.

* Adapt `test_scheduler_outputs_equivalence` ton MPS.

* mps: skip training tests instead of ignoring silently.

* Make VQModel tests pass on mps.

* mps ddim tests: warmup, increase tolerance.

* ScoreSdeVeScheduler indexing made mps compatible.

* Make ldm pipeline tests pass using warmup.

* Style

* Simplify casting as suggested in PR.

* Add Known Issues to readme.

* `isort` import order.

* Remove _mps_warmup helpers from ModelMixin.

And just make changes to the tests.

* Skip tests using unittest decorator for consistency.

* Remove temporary var.

* Remove spurious blank space.

* Remove unused symbol.

* Remove README_mps.
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> 

* uP

* more

* Update conditional_image_generation.mdx

* Update unconditional_image_generation.mdx

finish training docs

* Initial version of `fp16` page.

* Fix typo in README.

* Change titles of fp16 section in toctree.

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

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

* Clarify attention slicing is useful even for batches of 1

Explained by @patrickvonplaten after a suggestion by @keturn.
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Do not talk about `batches` in `enable_attention_slicing`.

* Use Tip (just for fun), add link to method.

* Comment about fp16 results looking the same as float32 in practice.

* Style: docstring line wrapping.
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

Add typing to scheduling_sde_ve init, set_timesteps, and set_sigmas functions
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix small typos

* capitalize Diffusers

* init schedulers docs

* add some docstrings, fix sidebar formatting

* add docstrings

* [Type hint] PNDM schedulers (#335)

* [Type hint] PNDM Schedulers

* ran make style

* updated timesteps type hint

* apply suggestions from code review

* ran make style

* removed unused import

* [Type hint] scheduling ddim (#343)

* [Type hint] scheduling ddim

* apply suggestions from code review

apply suggestions to also return the return type
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* make style

* update class docstrings

* add docstrings

* missed merge edit

* add general docs page

* modify headings for right sidebar
Co-authored-by: Partho <parthodas6176@gmail.com>
Co-authored-by: Santiago Víquez <santi.viquez@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

fix undefined in toctree

uup

Co-authored-by: Rashmi S <rashmis@Rashmis-MacBook-Pro.local>

* Initial description of Stable Diffusion pipeline.

* Placeholder docstrings to test preview.

* Add docstrings to Stable Diffusion pipeline.

* Style

* Docs for all the SD pipelines + attention slicing.

* Style: wrap long lines.

Small changes to Philosophy.

* karras-ve docs

for issue #293

* make style

* initial score_sde_ve docs

* fixed typo

* fix VE term

* up

* up

* finish

* uP

* better

* up

* finish

* up

* ddim docs

for issue #293

* space

Add mps documentation.

for issue  #293

* Update text_inversion.mdx

Getting in a bit of background info

* fixed typo mode -> model

* Link SD and re-write a few bits for clarity

* Copied in info from the example script

As suggested by surajpatil :)

* removed an unnecessary heading

up

* initial ddpm

for issue #293

* initial ddpm pipeline doc

* added docstrings

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

* make style

* fix docs

* make style

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

* initial description

* add doc strings

Add contribution.mdx by copy/pasting and adapting.

* up

* up

* up

* up

* up

* up

* up

fix saving embeds

* up

* add tests

* correct

* up

* finish

* better naming

* Update README.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

Use `expand` instead of ones to broadcast tensor.

As suggested by @bes-dev. According the documentation this shouldn't
take any memory - it just plays with the strides.

move to fp16, update ddim

* [Type Hint] VAE models

* Update src/diffusers/models/vae.py

* apply suggestions from code review
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>