* add paint by example
* mkae loading possibel
* up
* Update src/diffusers/models/attention.py
* up
* finalize weight structure
* make example work
* make it work
* up
* up
* fix
* del
* add
* update
* Apply suggestions from code review
* correct transformer 2d
* finish
* up
* up
* up
* up
* fix
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Apply suggestions from code review
* up
* finish
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* 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>
* [CI] Add Apple M1 tests
* setup-python
* python build
* conda install
* remove branch
* only 3.8 is built for osx-arm
* try fetching prebuilt tokenizers
* use user cache
* update shells
* Reports and cleanup
* -> MPS
* Disable parallel tests
* Better naming
* investigate worker crash
* return xdist
* restart
* num_workers=2
* still crashing?
* faulthandler for segfaults
* faulthandler for segfaults
* remove restarts, stop on segfault
* torch version
* change installation order
* Use pre-RC version of PyTorch.
To be updated when it is released.
* Skip crashing test on MPS, add new one that works.
* Skip cuda tests in mps device.
* Actually use generator in test.
I think this was a typo.
* make style
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Patrick von Platen