* utils and test modifications to enable device agnostic testing
* device for manual seed in unet1d
* fix generator condition in vae test
* consistency changes to testing
* make style
* add device agnostic testing changes to source and one model test
* make dtype check fns private, log cuda fp16 case
* remove dtype checks from import utils, move to testing_utils
* adding tests for most model classes and one pipeline
* fix vae import
* add comments to explain the code better
* add comments to explain the code better
* add comments to explain the code better
* add comments to explain the code better
* add comments to explain the code better
* fix more
* fix more
* fix more
* fix more
* fix more
* fix more
* consistency decoder
* rename
* Apply suggestions from code review
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Update src/diffusers/pipelines/consistency_models/pipeline_consistency_models.py
* uP
* Apply suggestions from code review
* uP
* uP
* uP
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Fix AutoencoderTiny encoder scaling convention
* Add [-1, 1] -> [0, 1] rescaling to EncoderTiny
* Move [0, 1] -> [-1, 1] rescaling from AutoencoderTiny.decode to DecoderTiny
(i.e. immediately after the final conv, as early as possible)
* Fix missing [0, 255] -> [0, 1] rescaling in AutoencoderTiny.forward
* Update AutoencoderTinyIntegrationTests to protect against scaling issues.
The new test constructs a simple image, round-trips it through AutoencoderTiny,
and confirms the decoded result is approximately equal to the source image.
This test checks behavior with and without tiling enabled.
This test will fail if new AutoencoderTiny scaling issues are introduced.
* Context: Raw TAESD weights expect images in [0, 1], but diffusers'
convention represents images with zero-centered values in [-1, 1],
so AutoencoderTiny needs to scale / unscale images at the start of
encoding and at the end of decoding in order to work with diffusers.
* Re-add existing AutoencoderTiny test, update golden values
* Add comments to AutoencoderTiny.forward
* Add controlnet from single file
* Updates
* make style
* finish
* Apply suggestions from code review
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* up
* fix more
* Apply suggestions from code review
* fix more
* fix more
* Check it
* Remove 16:8
* fix more
* fix more
* fix more
* up
* up
* Test only stable diffusion
* Test only two files
* up
* Try out spinning up processes that can be killed
* up
* Apply suggestions from code review
* up
* up
* enable deterministic pytorch and cuda operations.
* disable manual seeding.
* make style && make quality for unet_2d tests.
* enable determinism for the unet2dconditional model.
* add CUBLAS_WORKSPACE_CONFIG for better reproducibility.
* relax tolerance (very weird issue, though).
* revert to torch manual_seed() where needed.
* relax more tolerance.
* better placement of the cuda variable and relax more tolerance.
* enable determinism for 3d condition model.
* relax tolerance.
* add: determinism to alt_diffusion.
* relax tolerance for alt diffusion.
* dance diffusion.
* dance diffusion is flaky.
* test_dict_tuple_outputs_equivalent edit.
* fix two more tests.
* fix more ddim tests.
* fix: argument.
* change to diff in place of difference.
* fix: test_save_load call.
* test_save_load_float16 call.
* fix: expected_max_diff
* fix: paint by example.
* relax tolerance.
* add determinism to 1d unet model.
* torch 2.0 regressions seem to be brutal
* determinism to vae.
* add reason to skipping.
* up tolerance.
* determinism to vq.
* determinism to cuda.
* determinism to the generic test pipeline file.
* refactor general pipelines testing a bit.
* determinism to alt diffusion i2i
* up tolerance for alt diff i2i and audio diff
* up tolerance.
* determinism to audioldm
* increase tolerance for audioldm lms.
* increase tolerance for paint by paint.
* increase tolerance for repaint.
* determinism to cycle diffusion and sd 1.
* relax tol for cycle diffusion 🚲
* relax tol for sd 1.0
* relax tol for controlnet.
* determinism to img var.
* relax tol for img variation.
* tolerance to i2i sd
* make style
* determinism to inpaint.
* relax tolerance for inpaiting.
* determinism for inpainting legacy
* relax tolerance.
* determinism to instruct pix2pix
* determinism to model editing.
* model editing tolerance.
* panorama determinism
* determinism to pix2pix zero.
* determinism to sag.
* sd 2. determinism
* sd. tolerance
* disallow tf32 matmul.
* relax tolerance is all you need.
* make style and determinism to sd 2 depth
* relax tolerance for depth.
* tolerance to diffedit.
* tolerance to sd 2 inpaint.
* up tolerance.
* determinism in upscaling.
* tolerance in upscaler.
* more tolerance relaxation.
* determinism to v pred.
* up tol for v_pred
* unclip determinism
* determinism to unclip img2img
* determinism to text to video.
* determinism to last set of tests
* up tol.
* vq cumsum doesn't have a deterministic kernel
* relax tol
* relax tol
* 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>
* make tests deterministic
* run slow tests
* prepare for testing
* finish
* refactor
* add print statements
* finish more
* correct some test failures
* more fixes
* set up to correct tests
* more corrections
* up
* fix more
* more prints
* add
* up
* up
* up
* uP
* uP
* more fixes
* uP
* up
* up
* up
* up
* fix more
* up
* up
* clean tests
* up
* up
* up
* more fixes
* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* make
* correct
* finish
* finish
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* 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>
* 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>
* 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>
