GitLab

* feat: support group offloading at the pipeline level.

* add tests

* up

* [docs] Pipeline group offloading (#12286)

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

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Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

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

* draft

* feedback

* update

* feedback

* fix

* feedback

* feedback

* fix

* feedback

* start implementing disk offloading in group.

* delete diff file.

* updates.patch

* offload_to_disk_path

* check if safetensors already exist.

* add test and clarify.

* updates

* update todos.

* update more docs.

* update docs

update

* tip for group offloding + quantization
Co-authored-by: Aryan VS <contact.aryanvs@gmail.com>

* Apply suggestions from code review
Co-authored-by: Aryan <aryan@huggingface.co>

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Co-authored-by: Aryan VS <contact.aryanvs@gmail.com>
Co-authored-by: Aryan <aryan@huggingface.co>

* reformat

* initial

* fin

* review

* inference

* feedback

* feedback

* feedback

* implement record_stream for better performance.

* fix

* style.

* merge #11097

* Update src/diffusers/hooks/group_offloading.py
Co-authored-by: Aryan <aryan@huggingface.co>

* fixes

* docstring.

* remaining todos in low_cpu_mem_usage

* tests

* updates to docs.

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Co-authored-by: Aryan <aryan@huggingface.co>

* update

* Update docs/source/en/optimization/memory.md

* Apply suggestions from code review
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

* apply review suggestions

* update

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Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

* 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()

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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

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Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* [docs] Replace runwayml/stable-diffusion-v1-5 with Lykon/dreamshaper-8

Updated documentation as runwayml/stable-diffusion-v1-5 has been removed from Huggingface.

* Update docs/source/en/using-diffusers/inpaint.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Replace with stable-diffusion-v1-5/stable-diffusion-v1-5

* Update inpaint.md

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Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.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

change to 2024

Update and make improvements

* Fix typos, update, trim trailing whitespace

* Trim trailing whitespaces

* Update docs/source/en/optimization/memory.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/en/optimization/memory.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update _toctree.yml

* Update adapt_a_model.md

* Reverse

* Reverse

* Reverse

* Update dreambooth.md

* Update instructpix2pix.md

* Update lora.md

* Update overview.md

* Update t2i_adapters.md

* Update text2image.md

* Update text_inversion.md

* Update create_dataset.md

* Update create_dataset.md

* Update create_dataset.md

* Update create_dataset.md

* Update coreml.md

* Delete docs/source/en/training/create_dataset.md

* Original create_dataset.md

* Update create_dataset.md

* Delete docs/source/en/training/create_dataset.md

* Add original file

* Delete docs/source/en/training/create_dataset.md

* Add original one

* Delete docs/source/en/training/text2image.md

* Delete docs/source/en/training/instructpix2pix.md

* Delete docs/source/en/training/dreambooth.md

* Add original files

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Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

[Docs] Improve

* refactor

* update general optim sections

* update more sections

* few more updates

* benchmark code