* make sure fp16 runs well
* add fp16 test for superes
* Update src/diffusers/models/unet_2d.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* gen on cuda
* always run fast inferecne test on cpu
* run on cpu
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* fix non square images with UNet2DModel and DDIM/DDPM pipelines
* fix unet_2d `sample_size` docstring
* update pipeline tests for unet uncond
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Handle batches and Tensors in `prepare_mask_and_masked_image`
* `blackfy`
upgrade `black`
* handle mask as `np.array`
* add docstring
* revert `black` changes with smaller line length
* missing ValueError in docstring
* raise `TypeError` for image as tensor but not mask
* typo in mask shape selection
* check for batch dim
* fix: wrong indentation
* add tests
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add legacy inpainting pipeline compatibility for onnx
* remove commented out line
* Add onnx legacy inpainting test
* Fix slow decorators
* pep8 styling
* isort styling
* dummy object
* ordering consistency
* style
* docstring styles
* Refactor common prompt encoding pattern
* Update tests to permanent repository home
* support all available schedulers until ONNX IO binding is available
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* updated styling from PR suggested feedback
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* being tests
* fix model ids
* don't use safety checker in tests
* add im2img2 tests
* fix integration tests
* integration tests
* style
* add sentencepiece in test dep
* quality
* 4 decimalk points
* fix im2img test
* increase the tok slightly
* add conversion script for vae
* uP
* uP
* more changes
* push
* up
* finish again
* up
* up
* up
* up
* finish
* up
* uP
* up
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* up
* up
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* re-add RL model code
* match model forward api
* add register_to_config, pass training tests
* fix tests, update forward outputs
* remove unused code, some comments
* add to docs
* remove extra embedding code
* unify time embedding
* remove conv1d output sequential
* remove sequential from conv1dblock
* style and deleting duplicated code
* clean files
* remove unused variables
* clean variables
* add 1d resnet block structure for downsample
* rename as unet1d
* fix renaming
* rename files
* add get_block(...) api
* unify args for model1d like model2d
* minor cleaning
* fix docs
* improve 1d resnet blocks
* fix tests, remove permuts
* fix style
* add output activation
* rename flax blocks file
* Add Value Function and corresponding example script to Diffuser implementation (#884)
* valuefunction code
* start example scripts
* missing imports
* bug fixes and placeholder example script
* add value function scheduler
* load value function from hub and get best actions in example
* very close to working example
* larger batch size for planning
* more tests
* merge unet1d changes
* wandb for debugging, use newer models
* success!
* turns out we just need more diffusion steps
* run on modal
* merge and code cleanup
* use same api for rl model
* fix variance type
* wrong normalization function
* add tests
* style
* style and quality
* edits based on comments
* style and quality
* remove unused var
* hack unet1d into a value function
* add pipeline
* fix arg order
* add pipeline to core library
* community pipeline
* fix couple shape bugs
* style
* Apply suggestions from code review
Co-authored-by: Nathan Lambert <nathan@huggingface.co>
* update post merge of scripts
* add mdiblock / outblock architecture
* Pipeline cleanup (#947)
* valuefunction code
* start example scripts
* missing imports
* bug fixes and placeholder example script
* add value function scheduler
* load value function from hub and get best actions in example
* very close to working example
* larger batch size for planning
* more tests
* merge unet1d changes
* wandb for debugging, use newer models
* success!
* turns out we just need more diffusion steps
* run on modal
* merge and code cleanup
* use same api for rl model
* fix variance type
* wrong normalization function
* add tests
* style
* style and quality
* edits based on comments
* style and quality
* remove unused var
* hack unet1d into a value function
* add pipeline
* fix arg order
* add pipeline to core library
* community pipeline
* fix couple shape bugs
* style
* Apply suggestions from code review
* clean up comments
* convert older script to using pipeline and add readme
* rename scripts
* style, update tests
* delete unet rl model file
* remove imports in src
Co-authored-by: Nathan Lambert <nathan@huggingface.co>
* Update src/diffusers/models/unet_1d_blocks.py
* Update tests/test_models_unet.py
* RL Cleanup v2 (#965)
* valuefunction code
* start example scripts
* missing imports
* bug fixes and placeholder example script
* add value function scheduler
* load value function from hub and get best actions in example
* very close to working example
* larger batch size for planning
* more tests
* merge unet1d changes
* wandb for debugging, use newer models
* success!
* turns out we just need more diffusion steps
* run on modal
* merge and code cleanup
* use same api for rl model
* fix variance type
* wrong normalization function
* add tests
* style
* style and quality
* edits based on comments
* style and quality
* remove unused var
* hack unet1d into a value function
* add pipeline
* fix arg order
* add pipeline to core library
* community pipeline
* fix couple shape bugs
* style
* Apply suggestions from code review
* clean up comments
* convert older script to using pipeline and add readme
* rename scripts
* style, update tests
* delete unet rl model file
* remove imports in src
* add specific vf block and update tests
* style
* Update tests/test_models_unet.py
Co-authored-by: Nathan Lambert <nathan@huggingface.co>
* fix quality in tests
* fix quality style, split test file
* fix checks / tests
* make timesteps closer to main
* unify block API
* unify forward api
* delete lines in examples
* style
* examples style
* all tests pass
* make style
* make dance_diff test pass
* Refactoring RL PR (#1200)
* init file changes
* add import utils
* finish cleaning files, imports
* remove import flags
* clean examples
* fix imports, tests for merge
* update readmes
* hotfix for tests
* quality
* fix some tests
* change defaults
* more mps test fixes
* unet1d defaults
* do not default import experimental
* defaults for tests
* fix tests
* fix-copies
* fix
* changes per Patrik's comments (#1285)
* changes per Patrik's comments
* update conversion script
* fix renaming
* skip more mps tests
* last test fix
* Update examples/rl/README.md
Co-authored-by: Ben Glickenhaus <benglickenhaus@gmail.com>
* Match the generator device to the pipeline for DDPM and DDIM
* style
* fix
* update values
* fix fast tests
* trigger slow tests
* deprecate
* last value fixes
* mps fixes
Flax tests: don't hardcode number of devices.
This makes it possible to test on CPU/GPU. However, expected slices are
only checked when there are 8 devices.
* [Scheduler] Move predict epsilon to init
* up
* uP
* uP
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* up
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Schedulers: don't use float64 on mps
* Test set_timesteps() on device (float schedulers).
* SD pipeline: use device in set_timesteps.
* SD in-painting pipeline: use device in set_timesteps.
* Tests: fix mps crashes.
* Skip test_load_pipeline_from_git on mps.
Not compatible with float16.
* Use device.type instead of str in Euler schedulers.
* add enable sequential cpu offloading to other stable diffusion pipelines
* trigger ci
* fix styling
* interpolate before converting to device to avoid breking when cpu_offload is enabled with fp16
Co-authored-by: Pedro Gengo <pedro.gabriel.lourenco@hotmail.com>
* style again I need to stop forgething this thing
* fix inpainting bug that could cause device misalignment
Co-authored-by: Pedro Gengo <pedro.gabriel.lourenco@hotmail.com>
* Apply suggestions from code review
Co-authored-by: Pedro Gengo <pedro.gabriel.lourenco@hotmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* increase the precision of slice-based tests and make the default test case easier to single out
* increase precision of unit tests which already rely on float comparisons
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* make accelerate hard dep
* default fast init
* move params to cpu when device map is None
* handle device_map=None
* handle torch < 1.9
* remove device_map="auto"
* style
* add accelerate in torch extra
* remove accelerate from extras["test"]
* raise an error if torch is available but not accelerate
* update installation docs
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* improve defautl loading speed even further, allow disabling fats loading
* address review comments
* adapt the tests
* fix test_stable_diffusion_fast_load
* fix test_read_init
* temp fix for dummy checks
* Trigger Build
* Apply suggestions from code review
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Anton Lozhkov <anton@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>
* feat: add repaint
* fix: fix quality check with `make fix-copies`
* fix: remove old unnecessary arg
* chore: change default to DDPM (looks better in experiments)
* ".to(device)" changed to "device="
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* make generator device-specific
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* make generator device-specific and change shape
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* fix: add preprocessing for image and mask
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* fix: update test
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* Update src/diffusers/pipelines/repaint/pipeline_repaint.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add docs and examples
* Fix toctree
Co-authored-by: fja <fja@zurich.ibm.com>
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
* Fix equality test for ddim and ddpm
* add docs for use_clipped_model_output in DDIM
* fix inline comment
* reorder imports in test_pipelines.py
* Ignore use_clipped_model_output if scheduler doesn't take it
* improve test precision
get tests passing with greater precision using lewington images
* make old numpy load function a wrapper around a more flexible numpy loading function
* adhere to black formatting
* add more black formatting
* adhere to isort
* loosen precision and replace path
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* [Better scheduler docs] Improve usage examples of schedulers
* finish
* fix warnings and add test
* finish
* more replacements
* adapt fast tests hf token
* correct more
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Integrate compatibility with euler
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Add failing test for #940.
* Do not use torch.float64 in mps.
* style
* Temporarily skip add_noise for IPNDMScheduler.
Until #990 is addressed.
* Fix additional float64 error in mps.
* Improve add_noise test
* Slight edit – I think it's clearer this way.
* add method to enable cuda with minimal gpu usage to stable diffusion
* add test to minimal cuda memory usage
* ensure all models but unet are onn torch.float32
* move to cpu_offload along with minor internal changes to make it work
* make it test against accelerate master branch
* coming back, its official: I don't know how to make it test againt the master branch from accelerate
* make it install accelerate from master on tests
* go back to accelerate>=0.11
* undo prettier formatting on yml files
* undo prettier formatting on yml files againn
* 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>
* begin pipe
* add new pipeline
* add tests
* correct fast test
* up
* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
* Update tests/test_pipelines.py
* up
* up
* make style
* add fp16 test
* doc, comments
* up
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Anton Lozhkov <anton@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>
* Bump to 0.6.0.dev0
* Deprecate tensor_format and .samples
* style
* upd
* upd
* style
* sample -> images
* Update src/diffusers/schedulers/scheduling_ddpm.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/schedulers/scheduling_ddim.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/schedulers/scheduling_karras_ve.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/schedulers/scheduling_lms_discrete.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/schedulers/scheduling_pndm.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/schedulers/scheduling_sde_ve.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/diffusers/schedulers/scheduling_sde_vp.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Give more customizable options for safety checker
* Apply suggestions from code review
* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
* Finish
* make style
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* up
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* [Dummy imports] Better error message
* Test: load pipeline with LMS scheduler.
Fails with a cryptic message if scipy is not installed.
* Correct
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* pass norm_num_groups param and add tests
* set resnet_groups for FlaxUNetMidBlock2D
* fixed docstrings
* fixed typo
* using is_flax_available util and created require_flax decorator
* add accelerate to load models with smaller memory footprint
* remove low_cpu_mem_usage as it is reduntant
* move accelerate init weights context to modelling utils
* add test to ensure results are the same when loading with accelerate
* add tests to ensure ram usage gets lower when using accelerate
* move accelerate logic to single snippet under modelling utils and remove it from configuration utils
* format code using to pass quality check
* fix imports with isor
* add accelerate to test extra deps
* only import accelerate if device_map is set to auto
* move accelerate availability check to diffusers import utils
* format code
* add device map to pipeline abstraction
* lint it to pass PR quality check
* fix class check to use accelerate when using diffusers ModelMixin subclasses
* use low_cpu_mem_usage in transformers if device_map is not available
* NoModuleLayer
* comment out tests
* up
* uP
* finish
* Update src/diffusers/pipelines/stable_diffusion/safety_checker.py
* finish
* uP
* make style
Co-authored-by: Pi Esposito <piero.skywalker@gmail.com>
* 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
This is to ensure that the final latent slices stay somewhat consistent as more changes are introduced into the library.
Signed-off-by: James R T <jamestiotio@gmail.com>
Signed-off-by: James R T <jamestiotio@gmail.com>
* Swap fp16 error to warning
Also remove the associated test
* Formatting
* warn -> warning
* Update src/diffusers/pipeline_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* make style
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Suraj Patil