GitLab

* add modelmixin and unets

* remove old model page

* minor fixes

* fix unet2dcondition

* add vqmodel and autoencoderkl

* add rest of models

* fix autoencoderkl path

* fix toctree

* fix toctree again

* apply feedback

* apply feedback

* fix copies

* fix controlnet copy

* fix copies

* relax tolerance slightly

* correct incorrect naming

* correct namingc

* correct more

* Apply suggestions from code review

* Fix more

* Correct more

* correct incorrect naming

* Update src/diffusers/models/controlnet.py

* Correct flax

* Correct renaming

* Correct blocks

* Fix more

* Correct more

* mkae style

* mkae style

* mkae style

* mkae style

* mkae style

* Fix flax

* mkae style

* rename

* rename

* rename attn head dim to attention_head_dim

* correct flax

* make style

* improve

* Correct more

* make style

* fix more

* mkae style

* Update src/diffusers/models/controlnet_flax.py

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

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

add kandinsky2.1

---------
Co-authored-by: yiyixuxu <yixu310@gmail,com>
Co-authored-by: Ayush Mangal <43698245+ayushtues@users.noreply.github.com>
Co-authored-by: ayushmangal <ayushmangal@microsoft.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* gradient checkpointing bug fix

* bug fix; changes for reviews

* reformat

* reformat

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

* Add all files

* update

* Make sure vae is memory efficient for PT 1

* make style

* updated black format

* update black format

* make style format

* updated line endings

* update code formatting

* Update examples/research_projects/onnxruntime/text_to_image/train_text_to_image.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

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

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

* added vae gradient checkpointing test

* make style

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Will Berman <wlbberman@gmail.com>

* [Repro] Correct reproducability

* up

* up

* uP

* up

* need better image

* allow conversion from no state dict checkpoints

* up

* up

* up

* up

* check tensors

* check tensors

* check tensors

* check tensors

* next try

* up

* up

* better name

* up

* up

* Apply suggestions from code review

* correct more

* up

* replace all torch randn

* fix

* correct

* correct

* finish

* fix more

* up

* move files a bit

* more refactors

* fix more

* more fixes

* fix more onnx

* make style

* upload

* fix

* up

* fix more

* up again

* up

* small fix

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

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

small but mighty

* StableDiffusion: Decode latents separately to run larger batches

* Move VAE sliced decode under enable_vae_sliced_decode and vae.enable_sliced_decode

* Rename sliced_decode to slicing

* fix whitespace

* fix quality check and repository consistency

* VAE slicing tests and documentation

* API doc hooks for VAE slicing

* reformat vae slicing tests

* Skip VAE slicing for one-image batches

* Documentation tweaks for VAE slicing
Co-authored-by: Ilmari Heikkinen <ilmari@fhtr.org>

* Changes for VQ-diffusion VQVAE

Add specify dimension of embeddings to VQModel:
`VQModel` will by default set the dimension of embeddings to the number
of latent channels. The VQ-diffusion VQVAE has a smaller
embedding dimension, 128, than number of latent channels, 256.

Add AttnDownEncoderBlock2D and AttnUpDecoderBlock2D to the up and down
unet block helpers. VQ-diffusion's VQVAE uses those two block types.

* Changes for VQ-diffusion transformer

Modify attention.py so SpatialTransformer can be used for
VQ-diffusion's transformer.

SpatialTransformer:
- Can now operate over discrete inputs (classes of vector embeddings) as well as continuous.
- `in_channels` was made optional in the constructor so two locations where it was passed as a positional arg were moved to kwargs
- modified forward pass to take optional timestep embeddings

ImagePositionalEmbeddings:
- added to provide positional embeddings to discrete inputs for latent pixels

BasicTransformerBlock:
- norm layers were made configurable so that the VQ-diffusion could use AdaLayerNorm with timestep embeddings
- modified forward pass to take optional timestep embeddings

CrossAttention:
- now may optionally take a bias parameter for its query, key, and value linear layers

FeedForward:
- Internal layers are now configurable

ApproximateGELU:
- Activation function in VQ-diffusion's feedforward layer

AdaLayerNorm:
- Norm layer modified to incorporate timestep embeddings

* Add VQ-diffusion scheduler

* Add VQ-diffusion pipeline

* Add VQ-diffusion convert script to diffusers

* Add VQ-diffusion dummy objects

* Add VQ-diffusion markdown docs

* Add VQ-diffusion tests

* some renaming

* some fixes

* more renaming

* correct

* fix typo

* correct weights

* finalize

* fix tests

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

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

* finish

* finish

* up
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* start

* add more logic

* Update src/diffusers/models/unet_2d_condition_flax.py

* match weights

* up

* make model work

* making class more general, fixing missed file rename

* small fix

* make new conversion work

* up

* finalize conversion

* up

* first batch of variable renamings

* remove c and c_prev var names

* add mid and out block structure

* add pipeline

* up

* finish conversion

* finish

* upload

* more fixes

* Apply suggestions from code review

* add attr

* up

* uP

* up

* finish tests

* finish

* uP

* finish

* fix test

* up

* naming consistency in tests

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Nathan Lambert <nathan@huggingface.co>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>

* remove hardcoded 16

* Remove bogus

* fix some stuff

* finish

* improve logging

* docs

* upload
Co-authored-by: Nathan Lambert <nol@berkeley.edu>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Nathan Lambert <nathan@huggingface.co>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>

* add or fix license formatting

* fix quality

* handle dtype in vae and image2image pipeline

* fix inpaint in fp16

* dtype should be handled in add_noise

* style

* address review comments

* add simple fast tests to check fp16

* fix test name

* put mask in fp16

* pass norm_num_groups to unet blocs and attention

* fix UNet2DConditionModel

* add norm_num_groups arg in vae

* add tests

* remove comment

* Apply suggestions from code review

* 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>

* 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> 

* [Type Hint] VAE models

* Update src/diffusers/models/vae.py

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

* type hints: models/vae.py

* modify typings in vae.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>

* add outputs for models

* add for pipelines

* finish schedulers

* better naming

* adapt tests as well

* replace dict access with . access

* make schedulers works

* finish

* correct readme

* make  bcp compatible

* up

* small fix

* finish

* more fixes

* more fixes

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

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

* Adapt model outputs

* Apply more suggestions

* finish examples

* correct
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* [Type Hints] VAE models

* apply suggestions from code review

apply suggestions to also return the return type

* [Examples readme]

* Improve

* more

* save

* save

* save more

* up

* up

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

* up

* make deterministic

* up

* better

* up

* add generator to img2img pipe

* save

* make pipelines deterministic

* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
Co-authored-by: Anton Lozhkov <anton@huggingface.co>

* apply all changes

* more correctnios

* finish

* improve table

* more fixes

* up

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>

* Update src/diffusers/pipelines/README.md
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* add better links

* fix more

* finish
Co-authored-by: Nathan Lambert <nathan@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* pass downsample_padding in encoder

* update tests

* [Vae and AutoencoderKL clean]

* save intermediate finished work

* more progress

* more progress

* finish modeling code

* save intermediate

* finish

* Correct tests

* Add a decorator for register_to_config

* All models and test

* uP

* finish downsampling layers

* finish major refactor

* remove bugus file

* make unet rl work

* uploaad files / code

* upload files

* make style correct

* finish