* remove 2 shapes from SDFunctionTesterMixin::test_vae_tiling
* combine freeu enable/disable test to reduce many inference runs
* remove low signal unet test for signature
* remove low signal embeddings test
* remove low signal progress bar test from PipelineTesterMixin
* combine ip-adapter single and multi tests to save many inferences
* fix broken tests
* Update tests/pipelines/test_pipelines_common.py
* Update tests/pipelines/test_pipelines_common.py
* add progress bar tests
* speed up test_vae_slicing in animatediff
* speed up test_karras_schedulers_shape for attend and excite.
* style.
* get the static slices out.
* specify torch print options.
* modify
* test run with controlnet
* specify kwarg
* fix: things
* not None
* flatten
* controlnet img2img
* complete controlet sd
* finish more
* finish more
* finish more
* finish more
* finish the final batch
* add cpu check for expected_pipe_slice.
* finish the rest
* remove print
* style
* fix ssd1b controlnet test
* checking ssd1b
* disable the test.
* make the test_ip_adapter_single controlnet test more robust
* fix: simple inpaint
* multi
* disable panorama
* enable again
* panorama is shaky so leave it for now
* remove print
* raise tolerance.
* Add properties and `IPAdapterTesterMixin` tests for `StableDiffusionPanoramaPipeline`
* Fix variable name typo and update comments
* Update deprecated `output_type="numpy"` to "np" in test files
* Discard changes to src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py
* Update test_stable_diffusion_panorama.py
* Update numbers in README.md
* Update get_guidance_scale_embedding method to use timesteps instead of w
* Update number of checkpoints in README.md
* Add type hints and fix var name
* Fix PyTorch's convention for inplace functions
* Fix a typo
* Revert "Fix PyTorch's convention for inplace functions"
This reverts commit 74350cf65b.
* Fix typos
* Indent
* Refactor get_guidance_scale_embedding method in LEditsPPPipelineStableDiffusionXL class
* Add custom timesteps support to LCMScheduler.
* Add custom timesteps support to StableDiffusionPipeline.
* Add custom timesteps support to StableDiffusionXLPipeline.
* Add custom timesteps support to remaining Stable Diffusion pipelines which support LCMScheduler (img2img, inpaint).
* Add custom timesteps support to remaining Stable Diffusion XL pipelines which support LCMScheduler (img2img, inpaint).
* Add custom timesteps support to StableDiffusionControlNetPipeline.
* Add custom timesteps support to T21 Stable Diffusion (XL) Adapters.
* Clean up Stable Diffusion inpaint tests.
* Manually add support for custom timesteps to AltDiffusion pipelines since make fix-copies doesn't appear to work correctly (it deletes the whole pipeline).
* make style
* Refactor pipeline timestep handling into the retrieve_timesteps function.
* Change StableDiffusionInpaintPipelineFastTests.get_dummy_inputs to produce a random image and a white mask_image.
* Add dummy expected slices for the test_stable_diffusion_inpaint tests.
* Remove print statement
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* update expected slice so img2img compile tests pass
* use default attn processor
* use default attn processor and update expected slice value to pass test
* use default attn processor
* set default attn processor and update expected slice
* set default attn processor and change precision for check
* set unet to use default attn processor
* Correct controlnet out of list error
* Apply suggestions from code review
* correct tests
* correct tests
* fix
* test all
* Apply suggestions from code review
* test all
* test all
* Apply suggestions from code review
* Apply suggestions from code review
* fix more tests
* Fix more
* Apply suggestions from code review
* finish
* Apply suggestions from code review
* Update src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py
* finish
VaeImageProcessor.preprocess refactor
* refactored VaeImageProcessor
- allow passing optional height and width argument to resize()
- add convert_to_rgb
* refactored prepare_latents method for img2img pipelines so that if we pass latents directly as image input, it will not encode it again
* added a test in test_pipelines_common.py to test latents as image inputs
* refactored img2img pipelines that accept latents as image:
- controlnet img2img, stable diffusion img2img , instruct_pix2pix
---------
Co-authored-by: yiyixuxu <yixu310@gmail,com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Throw error if strength adjusted num_inference_steps < 1
* Added new fast test to check ValueError raised when num_inference_steps < 1
when strength adjusts the num_inference_steps then the inpainting pipeline should fail
* fix#3487 initial latents are now only scaled by init_noise_sigma when pure noise
updated this commit w.r.t the latest merge here: https://github.com/huggingface/diffusers/pull/3533
* fix
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add default to inpaint
* Make sure controlnet also works with normal sd for inpaint
* Add tests
* improve
* Correct encode images function
* Correct inpaint controlnet
* Improve text2img inpanit
* make style
* up
* up
* up
* up
* fix more
* Run ControlNet compile test in a separate subprocess
`torch.compile()` spawns several subprocesses and the GPU memory used
was not reclaimed after the test ran. This approach was taken from
`transformers`.
* Style
* Prepare a couple more compile tests to run in subprocess.
* Use require_torch_2 decorator.
* Test inpaint_compile in subprocess.
* Run img2img compile test in subprocess.
* Run stable diffusion compile test in subprocess.
* style
* Temporarily trigger on pr to test.
* Revert "Temporarily trigger on pr to test."
This reverts commit 82d76868dd.
* 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
* 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>
* 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 🚲
* 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
* StableDiffusionInpaintingPipeline now resizes input images and masks w.r.t to passed input height and width. Default is already set to 512. This addresses the common tensor mismatch error. Also moved type check into relevant funciton to keep main pipeline body tidy.
* Fixed StableDiffusionInpaintingPrepareMaskAndMaskedImageTests
Due to previous commit these tests were failing as height and width need to be passed into the prepare_mask_and_masked_image function, I have updated the code and added a height/width variable per unit test as it seemed more appropriate than the current hard coded solution
* Added a resolution test to StableDiffusionInpaintPipelineSlowTests
this unit test simply gets the input and resizes it into some that would fail (e.g. would throw a tensor mismatch error/not a mult of 8). Then passes it through the pipeline and verifies it produces output with correct dims w.r.t the passed height and width
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
