GitLab

* start removing flax stuff.

* add deprecation warning.

* add warning messages.

* more warnings.

* remove dockerfiles.

* remove more.

* Update src/diffusers/models/attention_flax.py
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

* up

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Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

update

* Use HF Papers

* Apply style fixes

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Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

Co-authored-by: Juan Acevedo <jfacevedo@google.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Aryan <aryan@huggingface.co>

change to 2024

* ruff format

* not need to use doc-builder's black styling as the doc is styled in ruff

* make fix-copies

* comment

* use run_ruff

* split_head_dim flax attn

* Make split_head_dim non default

* make style and make quality

* add description for split_head_dim flag

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

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

* feat: add Dropout to Flax UNet

* feat: add @compact decorator

* fix: drop nn.compact

* add use_memory_efficient params placeholder

* test

* add memory efficient attention jax

* add memory efficient attention jax

* newline

* forgot dot

* Rename use_memory_efficient

* Keep dtype last.

* Actually use key_chunk_size

* Rename symbol

* Apply style

* Rename use_memory_efficient

* Keep dtype last

* Pass `use_memory_efficient_attention` in `from_pretrained`

* Move JAX memory efficient attention to attention_flax.

* Simple test.

* style

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Co-authored-by: muhammad_hanif <muhammad_hanif@sofcograha.co.id>
Co-authored-by: MuhHanif <48muhhanif@gmail.com>

* rename file

* rename attention

* fix more

* rename more

* up

* more deprecation imports

* fixes

Bring flax attention naming in sync with PyTorch.

* Flax: start adapting to Stable Diffusion 2

* More changes.

* attention_head_dim can be a tuple.

* Fix typos

* Add simple SD 2 integration test.

Slice values taken from my Ampere GPU.

* Add simple UNet integration tests for Flax.

Note that the expected values are taken from the PyTorch results. This
ensures the Flax and PyTorch versions are not too far off.

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Typos and style

* Tests: verify jax is available.

* Style

* Make flake happy

* Remove typo.

* Simple Flax SD 2 pipeline tests.

* Import order

* Remove unused import.
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: @camenduru 

* 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>

* documenting `attention_flax.py` file

* documenting `embeddings_flax.py`

* documenting `unet_blocks_flax.py`

* Add new objs to doc page

* document `vae_flax.py`

* Apply suggestions from code review

* modify `unet_2d_condition_flax.py`

* make style

* Apply suggestions from code review

* make style

* Apply suggestions from code review

* fix indent

* fix typo

* fix indent unet

* Update src/diffusers/models/vae_flax.py

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

* WIP: flax FlaxDiffusionPipeline & FlaxStableDiffusionPipeline

* todo comment

* Fix imports

* Fix imports

* add dummies

* Fix empty init

* make pipeline work

* up

* Use Flax schedulers (typing, docstring)

* Wrap model imports inside availability checks.

* more updates

* make sure flax is not broken

* make style

* more fixes

* up
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@latenitesoft.com>

* first commit:

- add `from_pt` argument in `from_pretrained` function
- add `modeling_flax_pytorch_utils.py` file

* small nit

- fix a small nit - to not enter in the second if condition

* major changes

- modify FlaxUnet modules
- first conversion script
- more keys to be matched

* keys match

- now all keys match
- change module names for correct matching
- upsample module name changed

* working v1

- test pass with atol and rtol= `4e-02`

* replace unsued arg

* make quality

* add small docstring

* add more comments

- add TODO for embedding layers

* small change

- use `jnp.expand_dims` for converting `timesteps` in case it is a 0-dimensional array

* add more conditions on conversion

- add better test to check for keys conversion

* make shapes consistent

- output `img_w x img_h x n_channels` from the VAE

* Revert "make shapes consistent"

This reverts commit 4cad1aeb4aeb224402dad13c018a5d42e96267f6.

* fix unet shape

- channels first!

* 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>