* 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
* 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>
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
* 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>
