GitLab

* update

* update

* remove libsndfile1-dev and libgl1 from workflows and ensure that re present in the respective dockerfiles.

* change to self-hosted runner; let's see 🤞

* add libsndfile1-dev libgl1 for now

* use self-hosted runners for building and push too.

* clean dep installation step in push_tests

* fix: deps

update

* use uv for installing stuff in the workflows.

* fix: from source installation command when using uv.

* fix uv venv issue

* edit editable installation.

* fix quality installation

* checking

* make editable.

* more check

* check

* add: export step

* venv handling.

* checking.

* fix: dependency workflows.

* peft tests.

* proper way to initialize env.

* Empty-Commit

* Empty-Commit

update

make tests conditional and other things.

* disable running peft non-peft lora test in the peft env.

* Empty-Commit

* add peft slow tests workflow

* Update .github/workflows/push_tests.yml

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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* move xformers to dedicated runner

* fix

* remove ptl from test runner images

* pipline fetcher

* update script

* clean up

* clean up

* clean up

* new pipeline runner

* rename tests to match modules

* test actions in pr

* change runner to gpu

* clean up

* clean up

* clean up

* fix report

* fix reporting

* clean up

* show test stats in failure reports

* give names to jobs

* add lora tests

* split torch cuda tests and add compile tests

* clean up

* fix tests

* change push to run only on main

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* test fix

* fix tests

* fix report name

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Revert "Temp Revert "[Core] better support offloading when side loading is enabled… (#4927)"

This reverts commit 2ab17049.

* tests: install accelerate from main

* improve sd xl

* correct more

* finish

* make style

* fix more

[tests] Run slow matrix sequentially.

* 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

* Revert "Disable ONNX tests (#2509)"

This reverts commit a0549fea.

* add external weights

* + pb

* style

* [Tests] Refactor push tests

* correct

add fast tests

* [Dtype] Align automatic dtype

* up

* up

* fix

* re-add accelerate

* use repeat_interleave

* fix repeat

* Trigger Build

* don't install accelerate from main

* install released accelrate for mps test

* Remove additional accelerate installation from main.
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* [WIP] Nightly integration tests

* initial SD tests

* update SD slow tests

* style

* repaint

* ImageVariations

* style

* finish imgvar

* img2img tests

* debug

* inpaint 1.5

* inpaint legacy

* torch isn't happy about deterministic ops

* allclose -> max diff for shorter logs

* add SD2

* debug

* Update tests/pipelines/stable_diffusion_2/test_stable_diffusion.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update tests/pipelines/stable_diffusion/test_stable_diffusion.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix refs

* Update src/diffusers/utils/testing_utils.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* fix refs

* remove debug
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* [CI] Add slow MPS tests

* fix yml

* temporarily resolve caching

* Tests: fix mps crashes.

* Skip test_load_pipeline_from_git on mps.

Not compatible with float16.

* Increase tolerance, use CPU generator, alt. slices.

* Move to nightly

* style
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* add StableDiffusionImageVariationPipeline

* add ini init

* use CLIPVisionModelWithProjection

* fix _encode_image

* add copied from

* fix copies

* add doc

* handle tensor in _encode_image

* add tests

* correct model_id

* remove copied from in enable_sequential_cpu_offload

* fix tests

* make slow tests pass

* update slow tests

* use temp model for now

* fix test_stable_diffusion_img_variation_intermediate_state

* fix test_stable_diffusion_img_variation_intermediate_state

* check for torch.Tensor

* quality

* fix name

* fix slow tests

* install transformers from source

* fix install

* fix install

* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* input_image -> image

* remove deprication warnings

* fix test_stable_diffusion_img_variation_multiple_images

* make flake happy
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* [WIP][CI] Framework and hardware-specific docker images for CI tests

* username

* fix cpu

* try out the image

* push latest

* update workspace

* no root isolation for actions

* add a flax image

* flax and onnx matrix

* fix runners

* add reports

* onnxruntime image

* retry tpu

* fix

* fix

* build onnxruntime

* naming

* onnxruntime-gpu image

* onnxruntime-gpu image, slow tests

* latest jax version

* trigger flax

* run flax tests in one thread

* fast flax tests on cpu

* fast flax tests on cpu

* trigger slow tests

* rebuild torch cuda

* force cuda provider

* fix onnxruntime tests

* trigger slow

* don't specify gpu for tpu

* optimize

* memory limit

* fix flax tests

* disable docker cache

* Fix pipelines user_agent, ignore CI requests

* fix circular import

* N/A versions

* N/A versions

Tests: upgrade PyTorch cuda to 11.7.

Otherwise the cuda versions of torch and torchvision mismatch, and
examples tests fail. We were requesting cuda 11.6 for PyTorch, and the
default torchvision (via setup.py).

Another option would be to include torchvision in the same pip install
line as torch.

* 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

* [CI] Localize the HF cache

* pip cache

* de-env

* refactor matrix

* fix fast cache

* less onnx steps

* revert

* revert pip cache

* revert pip cache

* remove debugging trigger

* Add smoke tests for the training examples

* upd

* use a dummy dataset

* mark as slow

* cleanup

* Update test cases

* naming

* make_reports

* add test utils

* style

* style

Add datasets + transformers to test deps

* Init CI

* clarify cpu

* style

* Check scripts quality too

* Drop smi for cpu tests

* Run PR tests on cpu docker envs

* Update .github/workflows/push_tests.yml
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Try minimal python container

* Print env, install stable GPU torch

* Manual torch install

* remove deprecated platform.dist()
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>