* docs for attention
* types for embeddings
* unet2d docstrings
* UNet2DConditionModel docstrings
* fix typos
* style and vq-vae docstrings
* docstrings for VAE
* Update src/diffusers/models/unet_2d.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* make style
* added inherits from sentence
* docstring to forward
* make style
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* finish model docs
* up
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* 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>
* 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>
* 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>
* 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.
* 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
Suraj Patil