* port final set of model tests and others
* fix extracter.
* fix cuda tests for models.
* Revert "fix extracter."
This reverts commit 178c4cb99b.
* Revert "port final set of model tests and others"
This reverts commit a92c70c081.
* fix more
* address reviewer feedback
---------
Co-authored-by: dg845 <58458699+dg845@users.noreply.github.com>
* refactor unet2d condition model tests.
* fix tests
* up
* fix
* Revert "fix"
This reverts commit 46d44b73d8.
* up
* recompile limit
* [tests] refactor test_models_unet_1d.py to use modular testing mixins
Refactor UNet1D model tests to follow the modern testing pattern using
BaseModelTesterConfig and focused mixin classes (ModelTesterMixin,
MemoryTesterMixin, TrainingTesterMixin, LoraTesterMixin).
Both UNet1D standard and RL variants now have separate config classes
and dedicated test classes organized by concern (core, memory, training,
LoRA, hub loading).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* [tests] refactor test_models_unet_2d.py to use modular testing mixins
Refactor UNet2D model tests (standard, LDM, NCSN++) to follow the
modern testing pattern. Each variant gets its own config class and
dedicated test classes organized by concern (core, memory, training,
LoRA, hub loading).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* [tests] refactor test_models_unet_3d_condition.py to use modular testing mixins
Refactor UNet3DConditionModel tests to follow the modern testing pattern
with separate classes for core, attention, memory, training, and LoRA.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* [tests] refactor test_models_unet_controlnetxs.py to use modular testing mixins
Refactor UNetControlNetXSModel tests to follow the modern testing
pattern with separate classes for core, memory, training, and LoRA.
Specialized tests (from_unet, freeze_unet, forward_no_control,
time_embedding_mixing) remain in the core test class.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* [tests] refactor test_models_unet_spatiotemporal.py to use modular testing mixins
Refactored the spatiotemporal UNet test file to follow the modern modular testing
pattern with BaseModelTesterConfig and focused test classes:
- UNetSpatioTemporalTesterConfig: Base configuration with model setup
- TestUNetSpatioTemporal: Core model tests (ModelTesterMixin, UNetTesterMixin)
- TestUNetSpatioTemporalAttention: Attention-related tests (AttentionTesterMixin)
- TestUNetSpatioTemporalMemory: Memory/offloading tests (MemoryTesterMixin)
- TestUNetSpatioTemporalTraining: Training tests (TrainingTesterMixin)
- TestUNetSpatioTemporalLoRA: LoRA adapter tests (LoraTesterMixin)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* remove test suites that are passed.
* fix consistencydecodervae tests
* Revert "fix consistencydecodervae tests"
This reverts commit 41b036b989.
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: dg845 <58458699+dg845@users.noreply.github.com>
* add clarity in documentation for device_map
* docs
* fix how compiler tester mixins are used.
* propagate
* more
* typo.
* fix tests
* fix order of decroators.
* clarify more.
* more test cases.
* fix doc
* fix device_map docstring in pipeline_utils.
* more examples
* more
* update
* remove code for stuff that is already supported.
* fix stuff.
* 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>
* Motion Model / Adapter versatility
- allow to use a different number of layers per block
- allow to use a different number of transformer per layers per block
- allow a different number of motion attention head per block
- use dropout argument in get_down/up_block in 3d blocks
* Motion Model added arguments renamed & refactoring
* Add test for asymmetric UNetMotionModel
* Fix sharding when no device_map is passed
* style
* add tests
* align
* add docstring
* format
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* feat: support saving a model in sharded checkpoints.
* feat: make loading of sharded checkpoints work.
* add tests
* cleanse the loading logic a bit more.
* more resilience while loading from the Hub.
* parallelize shard downloads by using snapshot_download()/
* default to a shard size.
* more fix
* Empty-Commit
* debug
* fix
* uality
* more debugging
* fix more
* initial comments from Benjamin
* move certain methods to loading_utils
* add test to check if the correct number of shards are present.
* add a test to check if loading of sharded checkpoints from the Hub is okay
* clarify the unit when passed as an int.
* use hf_hub for sharding.
* remove unnecessary code
* remove unnecessary function
* lucain's comments.
* fixes
* address high-level comments.
* fix test
* subfolder shenanigans./
* Update src/diffusers/utils/hub_utils.py
Co-authored-by: Lucain <lucainp@gmail.com>
* Apply suggestions from code review
Co-authored-by: Lucain <lucainp@gmail.com>
* remove _huggingface_hub_version as not needed.
* address more feedback.
* add a test for local_files_only=True/
* need hf hub to be at least 0.23.2
* style
* final comment.
* clean up subfolder.
* deal with suffixes in code.
* _add_variant default.
* use weights_name_pattern
* remove add_suffix_keyword
* clean up downloading of sharded ckpts.
* don't return something special when using index.json
* fix more
* don't use bare except
* remove comments and catch the errors better
* fix a couple of things when using is_file()
* empty
---------
Co-authored-by: Lucain <lucainp@gmail.com>
