* update
* fix
* non_blocking; handle parameters and buffers
* update
* Group offloading with cuda stream prefetching (#10516)
* cuda stream prefetch
* remove breakpoints
* update
* copy model hook implementation from pab
* update; ~very workaround based implementation but it seems to work as expected; needs cleanup and rewrite
* more workarounds to make it actually work
* cleanup
* rewrite
* update
* make sure to sync current stream before overwriting with pinned params
not doing so will lead to erroneous computations on the GPU and cause bad results
* better check
* update
* remove hook implementation to not deal with merge conflict
* re-add hook changes
* why use more memory when less memory do trick
* why still use slightly more memory when less memory do trick
* optimise
* add model tests
* add pipeline tests
* update docs
* add layernorm and groupnorm
* address review comments
* improve tests; add docs
* improve docs
* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* apply suggestions from code review
* update tests
* apply suggestions from review
* enable_group_offloading -> enable_group_offload for naming consistency
* raise errors if multiple offloading strategies used; add relevant tests
* handle .to() when group offload applied
* refactor some repeated code
* remove unintentional change from merge conflict
* handle .cuda()
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* update
* update
* make style
* remove dynamo disable
* add coauthor
Co-Authored-By: Dhruv Nair <dhruv.nair@gmail.com>
* update
* update
* update
* update mixin
* add some basic tests
* update
* update
* non_blocking
* improvements
* update
* norm.* -> norm
* apply suggestions from review
* add example
* update hook implementation to the latest changes from pyramid attention broadcast
* deinitialize should raise an error
* update doc page
* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* update docs
* update
* refactor
* fix _always_upcast_modules for asym ae and vq_model
* fix lumina embedding forward to not depend on weight dtype
* refactor tests
* add simple lora inference tests
* _always_upcast_modules -> _precision_sensitive_module_patterns
* remove todo comments about review; revert changes to self.dtype in unets because .dtype on ModelMixin should be able to handle fp8 weight case
* check layer dtypes in lora test
* fix UNet1DModelTests::test_layerwise_upcasting_inference
* _precision_sensitive_module_patterns -> _skip_layerwise_casting_patterns based on feedback
* skip test in NCSNppModelTests
* skip tests for AutoencoderTinyTests
* skip tests for AutoencoderOobleckTests
* skip tests for UNet1DModelTests - unsupported pytorch operations
* layerwise_upcasting -> layerwise_casting
* skip tests for UNetRLModelTests; needs next pytorch release for currently unimplemented operation support
* add layerwise fp8 pipeline test
* use xfail
* Apply suggestions from code review
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
* add assertion with fp32 comparison; add tolerance to fp8-fp32 vs fp32-fp32 comparison (required for a few models' test to pass)
* add note about memory consumption on tesla CI runner for failing test
---------
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* 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
* Discourage using `revision`
* `make style && make quality`
* Refactor code to use 'variant' instead of 'revision'
* `revision="bf16"` -> `variant="bf16"`
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* find & replace all FloatTensors to Tensor
* apply formatting
* Update torch.FloatTensor to torch.Tensor in the remaining files
* formatting
* Fix the rest of the places where FloatTensor is used as well as in documentation
* formatting
* Update new file from FloatTensor to Tensor
* give it a shot.
* print.
* correct assertion.
* gather results from the rest of the tests.
* change the assertion values where needed.
* remove print statements.
* get device <-> component mapping when using multiple gpus.
* condition the device_map bits.
* relax condition
* device_map progress.
* device_map enhancement
* some cleaning up and debugging
* Apply suggestions from code review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* incorporate suggestions from PR.
* remove multi-gpu condition for now.
* guard check the component -> device mapping
* fix: device_memory variable
* dispatching transformers model to have force_hooks=True
* better guarding for transformers device_map
* introduce support balanced_low_memory and balanced_ultra_low_memory.
* remove device_map patch.
* fix: intermediate variable scoping.
* fix: condition in cpu offload.
* fix: flax class restrictions.
* remove modifications from cpu_offload and model_offload
* incorporate changes.
* add a simple forward pass test
* add: torch_device in get_inputs()
* add: tests
* remove print
* safe-guard to(), model offloading and cpu offloading when balanced is used as a device_map.
* style
* remove .
* safeguard device_map with more checks and remove invalid device_mapping strategues.
* make a class attribute and adjust tests accordingly.
* fix device_map check
* fix test
* adjust comment
* fix: device_map attribute
* fix: dispatching.
* max_memory test for pipeline
* version guard the tests
* fix guard.
* address review feedback.
* reset_device_map method.
* add: test for reset_hf_device_map
* fix a couple things.
* add reset_device_map() in the error message.
* add tests for checking reset_device_map doesn't have unintended consequences.
* fix reset_device_map and offloading tests.
* create _get_final_device_map utility.
* hf_device_map -> _hf_device_map
* add documentation
* add notes suggested by Marc.
* styling.
* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* move updates within gpu condition.
* other docs related things
* note on ignore a device not specified in .
* provide a suggestion if device mapping errors out.
* fix: typo.
* _hf_device_map -> hf_device_map
* Empty-Commit
* add: example hf_device_map.
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Skip `test_freeu_enabled ` on MPS
* Small fixes
- import skip_mps correctly
- disable all instances of test_freeu_enabled
* Empty commit to trigger tests
* Empty commit to trigger CI
* 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
* support ip-adapter in src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
* support ip-adapter in src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py
* support ip-adapter in src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py
* update tests
* support ip-adapter in src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py
* support ip-adapter in src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py
* support ip-adapter in src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py
* support ip-adapter in src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py
* support ip-adapter in src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py
* support ip-adapter in src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py
* revert changes to sd_attend_and_excite and sd_upscale
* make style
* fix broken tests
* update ip-adapter implementation to latest
* apply suggestions from review
---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* 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.
