docs: fix typos in docs, comments and docstrings
Prose, comments and docstrings only. No executable line changed.
docs/:
- optimization/memory.md: maxmium -> maximum; heigh -> height (in the
channels_last layout tuple, which read "(batch size, heigh, width,
channels)")
- api/pipelines/bria_fibo.md: proffesional -> professional
- api/pipelines/wan.md: involed -> involved
src/ comments and docstrings:
- guiders/magnitude_aware_guidance.py: supression -> suppression
- models/attention.py: spliting -> splitting
- models/attention_dispatch.py: abritrary -> arbitrary (x2)
- models/transformers/transformer_ltx2.py: timstamps -> timestamps (x2)
- pipelines/ltx2/pipeline_ltx2{,_image2video}.py: corrct -> correct
- pipelines/deprecated/unidiffuser/modeling_uvit.py: tbe -> the
- pipelines/visualcloze/pipeline_visualcloze_{combined,generation}.py:
"whe they are lists" -> "when they are lists" in a user-facing
ValueError message
Deliberately not touched:
- pipeline_kandinsky*.py "promt"/"scren" live inside prompt_template,
which is fed to the text encoder verbatim. Changing them would change
generation output.
- ANE (Apple Neural Engine), MoT (Mixture-of-Transformers): initialisms.
- "racoon": appears in prompt examples whose reference images were
generated with that exact prompt.
- "Shuting Wang": a paper author's name.
* 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>
* 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
