GitLab

* init taylor_seer cache

* make compatible with any tuple size returned

* use logger for printing, add warmup feature

* still update in warmup steps

* refractor, add docs

* add configurable cache, skip compute module

* allow special cache ids only

* add stop_predicts (cooldown)

* update docs

* apply ruff

* update to handle multple calls per timestep

* refractor to use state manager

* fix format & doc

* chores: naming, remove redundancy

* add docs

* quality & style

* fix taylor precision

* Apply style fixes

* add tests

* Apply style fixes

* Remove TaylorSeerCacheTesterMixin from flux2 tests

* rename identifiers, use more expressive taylor predict loop

* torch compile compatible

* Apply style fixes

* Update src/diffusers/hooks/taylorseer_cache.py
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* update docs

* make fix-copies

* fix example usage.

* remove tests on flux kontext

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* start varlen variants for attn backend kernels.

* maybe unflatten heads.

* updates

* remove unused function.

* doc

* up

* up

* support automatic dispatch.

* disable compile support for now./

* up

* flash too.

* document.

* up

* up

* up

* up

* add aiter attention backend

* Apply style fixes

---------
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* checks

* feedback

---------
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* change syntax

* make style

* slight edits to the attention backends docs.

* Update docs/source/en/optimization/attention_backends.md
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* docs: introduce cache-dit to diffusers

* docs: introduce cache-dit to diffusers

* docs: introduce cache-dit to diffusers

* docs: introduce cache-dit to diffusers

* docs: introduce cache-dit to diffusers

* docs: introduce cache-dit to diffusers

* docs: introduce cache-dit to diffusers

* misc: update examples link

* misc: update examples link

* docs: introduce cache-dit to diffusers

* docs: introduce cache-dit to diffusers

* docs: introduce cache-dit to diffusers

* docs: introduce cache-dit to diffusers

* docs: introduce cache-dit to diffusers

* Refine documentation for CacheDiT features

Updated the wording for clarity and consistency in the documentation. Adjusted sections on cache acceleration, automatic block adapter, patch functor, and hybrid cache configuration.

* init

* feedback

* update

* feedback

* fixes

* feat: support group offloading at the pipeline level.

* add tests

* up

* [docs] Pipeline group offloading (#12286)

init
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[docs] typo: corrected 'compile regions' to 'compile_regions'

* up

* up

* Apply suggestions from code review
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* include bp link.

* Update docs/source/en/optimization/fp16.md
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* resources.

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* add blog post

* feedback

* feedback

* add test for checking compile on different shapes.

* update

* update

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* [rfc][compile] compile method for DiffusionPipeline

* Apply suggestions from code review
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* Apply style fixes

* Update docs/source/en/optimization/fp16.md

* check

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draft
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* 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

* Add Pruna optimization framework documentation

- Introduced a new section for Pruna in the table of contents.
- Added comprehensive documentation for Pruna, detailing its optimization techniques, installation instructions, and examples for optimizing and evaluating models

* Enhance Pruna documentation with image alt text and code block formatting

- Added alt text to images for better accessibility and context.
- Changed code block syntax from diff to python for improved clarity.

* Add installation section to Pruna documentation

- Introduced a new installation section in the Pruna documentation to guide users on how to install the framework.
- Enhanced the overall clarity and usability of the documentation for new users.

* Update pruna.md

* Update pruna.md

* Update Pruna documentation for model optimization and evaluation

- Changed section titles for consistency and clarity, from "Optimizing models" to "Optimize models" and "Evaluating and benchmarking optimized models" to "Evaluate and benchmark models".
- Enhanced descriptions to clarify the use of `diffusers` models and the evaluation process.
- Added a new example for evaluating standalone `diffusers` models.
- Updated references and links for better navigation within the documentation.

* Refactor Pruna documentation for clarity and consistency

- Removed outdated references to FLUX-juiced and streamlined the explanation of benchmarking.
- Enhanced the description of evaluating standalone `diffusers` models.
- Cleaned up code examples by removing unnecessary imports and comments for better readability.

* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Enhance Pruna documentation with new examples and clarifications

- Added an image to illustrate the optimization process.
- Updated the explanation for sharing and loading optimized models on the Hugging Face Hub.
- Clarified the evaluation process for optimized models using the EvaluationAgent.
- Improved descriptions for defining metrics and evaluating standalone diffusers models.

---------
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* cache

* feedback

* combine

* Update docs/source/en/optimization/fp16.md
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* Use HF Papers

* Apply style fixes

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* tip for group offloding + quantization
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* Apply suggestions from code review
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---------
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* add regional compilation docs.

* minor.

* reviwer feedback.

* Update docs/source/en/optimization/torch2.0.md
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* 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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mps

* update

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

* Apply suggestions from code review
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* apply review suggestions

* update

---------
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* 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
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* 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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fix image path in para attention docs

* 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
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* 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
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* 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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* add para_attn_flux.md and para_attn_hunyuan_video.md

* add enable_sequential_cpu_offload in para_attn_hunyuan_video.md

* add comment

* refactor

* fix

* fix

* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* fix

* update links

* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* fix

* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* Update docs/source/en/optimization/para_attn.md
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* start draft

* add doc

* Update docs/source/en/optimization/neuron.md
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* Update docs/source/en/optimization/neuron.md
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* Update docs/source/en/optimization/neuron.md
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* Update docs/source/en/optimization/neuron.md
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* Update docs/source/en/optimization/neuron.md
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* Update docs/source/en/optimization/neuron.md
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* Update docs/source/en/optimization/neuron.md
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* bref intro of ON

* Update docs/source/en/optimization/neuron.md
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* docs: fix xDiT doc image damage

* doc: move xdit images to hf dataset

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* Fix bug

* import imageio

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

* Update inpaint.md

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