* Fix DPM single
* add test
* fix one more bug
* Apply suggestions from code review
Co-authored-by: StAlKeR7779 <stalkek7779@yandex.ru>
---------
Co-authored-by: StAlKeR7779 <stalkek7779@yandex.ru>
* 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
* Remove ONNX tests from PR.
They are already a part of push_tests.yml.
* Remove mps tests from PRs.
They are already performed on push.
* Fix workflow name for fast push tests.
* Extract mps tests to a workflow.
For better control/filtering.
* Remove --extra-index-url from mps tests
* Increase tolerance of mps test
This test passes in my Mac (Ventura 13.3) but fails in the CI hardware
(Ventura 13.2). I ran the local tests following the same steps that
exist in the CI workflow.
* Temporarily run mps tests on pr
So we can test.
* Revert "Temporarily run mps tests on pr"
Tests passed, go back to running on push.
Release large tensors in attention (as soon as they're no longer required). Reduces peak VRAM by nearly 2 GB for 1024x1024 (even after slicing), and the savings scale up with image size.
* Added explanation of 'strength' parameter
* Added get_timesteps function which relies on new strength parameter
* Added `strength` parameter which defaults to 1.
* Swapped ordering so `noise_timestep` can be calculated before masking the image
this is required when you aren't applying 100% noise to the masked region, e.g. strength < 1.
* Added strength to check_inputs, throws error if out of range
* Changed `prepare_latents` to initialise latents w.r.t strength
inspired from the stable diffusion img2img pipeline, init latents are initialised by converting the init image into a VAE latent and adding noise (based upon the strength parameter passed in), e.g. random when strength = 1, or the init image at strength = 0.
* WIP: Added a unit test for the new strength parameter in the StableDiffusionInpaintingPipeline
still need to add correct regression values
* Created a is_strength_max to initialise from pure random noise
* Updated unit tests w.r.t new strength parameter + fixed new strength unit test
* renamed parameter to avoid confusion with variable of same name
* Updated regression values for new strength test - now passes
* removed 'copied from' comment as this method is now different and divergent from the cpy
* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Ensure backwards compatibility for prepare_mask_and_masked_image
created a return_image boolean and initialised to false
* Ensure backwards compatibility for prepare_latents
* Fixed copy check typo
* Fixes w.r.t backward compibility changes
* make style
* keep function argument ordering same for backwards compatibility in callees with copied from statements
* make fix-copies
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: William Berman <WLBberman@gmail.com>
* Update pipeline_if_superresolution.py
Allow arbitrary aspect ratio in IFSuperResolutionPipeline by using the input image shape
* IFSuperResolutionPipeline: allow the user to override the height and width through the arguments
* update IFSuperResolutionPipeline width/height doc string to match StableDiffusionInpaintPipeline conventions
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* refactor controlnet and add img2img and inpaint
* First draft to get pipelines to work
* make style
* Fix more
* Fix more
* More tests
* Fix more
* Make inpainting work
* make style and more tests
* Apply suggestions from code review
* up
* make style
* Fix imports
* Fix more
* Fix more
* Improve examples
* add test
* Make sure import is correctly deprecated
* Make sure everything works in compile mode
* make sure authorship is correctly attributed
* 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 π²
* 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
