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Fast tests on main / ${{ matrix.config.name }} (map[framework:pytorch image:diffusers/diffusers-pytorch-cpu name:Fast PyTorch CPU tests on Ubuntu report:torch_cpu runner:aws-general-8-plus]) (push) Waiting to run
Fast tests on main / ${{ matrix.config.name }} (map[framework:pytorch_examples image:diffusers/diffusers-pytorch-cpu name:PyTorch Example CPU tests on Ubuntu report:torch_example_cpu runner:aws-general-8-plus]) (push) Waiting to run
* 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.
Fast tests on main / ${{ matrix.config.name }} (map[framework:pytorch image:diffusers/diffusers-pytorch-cpu name:Fast PyTorch CPU tests on Ubuntu report:torch_cpu runner:aws-general-8-plus]) (push) Waiting to run
Fast tests on main / ${{ matrix.config.name }} (map[framework:pytorch_examples image:diffusers/diffusers-pytorch-cpu name:PyTorch Example CPU tests on Ubuntu report:torch_example_cpu runner:aws-general-8-plus]) (push) Waiting to run
* 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.
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
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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
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>
* [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
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
