* Fix is_onnx_available
Fix: If user install onnxruntime-gpu, is_onnx_available() will return False.
* add more onnxruntime candidates
* Run `make style`
Co-authored-by: anton-l <anton@huggingface.co>
* begin text2img conversion script
* add fn to convert config
* create config if not provided
* update imports and use UNet2DConditionModel
* fix imports, layer names
* fix unet coversion
* add function to convert VAE
* fix vae conversion
* update main
* create text model
* update config creating logic for unet
* fix config creation
* update script to create and save pipeline
* remove unused imports
* fix checkpoint loading
* better name
* save progress
* finish
* up
* up
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* First UNet Flax modeling blocks.
Mimic the structure of the PyTorch files.
The model classes themselves need work, depending on what we do about
configuration and initialization.
* Remove FlaxUNet2DConfig class.
* ignore_for_config non-config args.
* Implement `FlaxModelMixin`
* Use new mixins for Flax UNet.
For some reason the configuration is not correctly applied; the
signature of the `__init__` method does not contain all the parameters
by the time it's inspected in `extract_init_dict`.
* Import `FlaxUNet2DConditionModel` if flax is available.
* Rm unused method `framework`
* Update src/diffusers/modeling_flax_utils.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* Indicate types in flax.struct.dataclass as pointed out by @mishig25
Co-authored-by: Mishig Davaadorj <mishig.davaadorj@coloradocollege.edu>
* Fix typo in transformer block.
* make style
* some more changes
* make style
* Add comment
* Update src/diffusers/modeling_flax_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Rm unneeded comment
* Update docstrings
* correct ignore kwargs
* make style
* Update docstring examples
* Make style
* Style: remove empty line.
* Apply style (after upgrading black from pinned version)
* Remove some commented code and unused imports.
* Add init_weights (not yet in use until #513).
* Trickle down deterministic to blocks.
* Rename q, k, v according to the latest PyTorch version.
Note that weights were exported with the old names, so we need to be
careful.
* Flax UNet docstrings, default props as in PyTorch.
* Fix minor typos in PyTorch docstrings.
* Use FlaxUNet2DConditionOutput as output from UNet.
* make style
Co-authored-by: Mishig Davaadorj <dmishig@gmail.com>
Co-authored-by: Mishig Davaadorj <mishig.davaadorj@coloradocollege.edu>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* beta never changes removed from state
* fix typos in docs
* removed unused var
* initial ddim flax scheduler
* import
* added dummy objects
* fix style
* fix typo
* docs
* fix typo in comment
* set return type
* added flax ddom
* fix style
* remake
* pass PRNG key as argument and split before use
* fix doc string
* use config
* added flax Karras VE scheduler
* make style
* fix dummy
* fix ndarray type annotation
* replace returns a new state
* added lms_discrete scheduler
* use self.config
* add_noise needs state
* use config
* use config
* docstring
* added flax score sde ve
* fix imports
* fix typos
* add different method for sliced attention
* Update src/diffusers/models/attention.py
* Apply suggestions from code review
* Update src/diffusers/models/attention.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* initial attempt at solving
* fix pndm power of 3 inference_step
* add power of 3 test
* fix index in pndm test, remove ddim test
* add comments, change to round()
* update expected results of slow tests
* relax sum and mean tests
* Print shapes when reporting exception
* formatting
* fix sentence
* relax test_stable_diffusion_fast_ddim for gpu fp16
* relax flakey tests on GPU
* added comment on large tolerences
* black
* format
* set scheduler seed
* added generator
* use np.isclose
* set num_inference_steps to 50
* fix dep. warning
* update expected_slice
* preprocess if image
* updated expected results
* updated expected from CI
* pass generator to VAE
* undo change back to orig
* use orignal
* revert back the expected on cpu
* revert back values for CPU
* more undo
* update result after using gen
* update mean
* set generator for mps
* update expected on CI server
* undo
* use new seed every time
* cpu manual seed
* reduce num_inference_steps
* style
* use generator for randn
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* renamed variable names
q -> query
k -> key
v -> value
b -> batch
c -> channel
h -> height
w -> weight
* rename variable names
missed some in the initial commit
* renamed more variable names
As per code review suggestions, renamed x -> hidden_states and x_in -> residual
* fixed minor typo
* 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>
* 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>
