* use model_class.load_singlefile() instead of converting; works, but performance is poor
* adjust the convert api - not right just yet
* working, needs sql migrator update
* rename migration_11 before conflict merge with main
* Update invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* Update invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* implement lightweight version-by-version config migration
* simplified config schema migration code
* associate sdxl config with sdxl VAEs
* remove use of original_config_file in load_single_file()
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
We can get black outputs when moving tensors from CPU to MPS. It appears MPS to CPU is fine. See:
- https://github.com/pytorch/pytorch/issues/107455
- https://discuss.pytorch.org/t/should-we-set-non-blocking-to-true/38234/28
Changes:
- Add properties for each device on `TorchDevice` as a convenience.
- Add `get_non_blocking` static method on `TorchDevice`. This utility takes a torch device and returns the flag to be used for non_blocking when moving a tensor to the device provided.
- Update model patching and caching APIs to use this new utility.
Fixes: #6545
* add support for probing and loading SDXL VAE checkpoint files
* broaden regexp probe for SDXL VAEs
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
* allow model patcher to optimize away the unpatching step when feasible
* remove lazy_offloading functionality
* allow model patcher to optimize away the unpatching step when feasible
* remove lazy_offloading functionality
* do not save original weights if there is a CPU copy of state dict
* Update invokeai/backend/model_manager/load/load_base.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* documentation fixes requested during penultimate review
* add non-blocking=True parameters to several torch.nn.Module.to() calls, for slight performance increases
* fix ruff errors
* prevent crash on non-cuda-enabled systems
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* allow model patcher to optimize away the unpatching step when feasible
* remove lazy_offloading functionality
* allow model patcher to optimize away the unpatching step when feasible
* remove lazy_offloading functionality
* do not save original weights if there is a CPU copy of state dict
* Update invokeai/backend/model_manager/load/load_base.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* documentation fixes added during penultimate review
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* avoid copying model back from cuda to cpu
* handle models that don't have state dicts
* add assertions that models need a `device()` method
* do not rely on torch.nn.Module having the device() method
* apply all patches after model is on the execution device
* fix model patching in latents too
* log patched tokenizer
* closes#6375
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Pending:
- Move model install calls into model manager and create passthrus in invocation_context.
- Consider splitting load_model_from_url() into a call to get the path and a call to load the path.
* introduce new abstraction layer for GPU devices
* add unit test for device abstraction
* fix ruff
* convert TorchDeviceSelect into a stateless class
* move logic to select context-specific execution device into context API
* add mock hardware environments to pytest
* remove dangling mocker fixture
* fix unit test for running on non-CUDA systems
* remove unimplemented get_execution_device() call
* remove autocast precision
* Multiple changes:
1. Remove TorchDeviceSelect.get_execution_device(), as well as calls to
context.models.get_execution_device().
2. Rename TorchDeviceSelect to TorchDevice
3. Added back the legacy public API defined in `invocation_api`, including
choose_precision().
4. Added a config file migration script to accommodate removal of precision=autocast.
* add deprecation warnings to choose_torch_device() and choose_precision()
* fix test crash
* remove app_config argument from choose_torch_device() and choose_torch_dtype()
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
- Allow user-defined precision on MPS.
- Use more explicit logic to handle all possible cases.
- Add comments.
- Remove the app_config args (they were effectively unused, just get the config using the singleton getter util)
Add `dump_path` arg to the converter function & save the model to disk inside the conversion function. This is the same pattern as in the other conversion functions.
* pass model config to _load_model
* make conversion work again
* do not write diffusers to disk when convert_cache set to 0
* adding same model to cache twice is a no-op, not an assertion error
* fix issues identified by psychedelicious during pr review
* following conversion, avoid redundant read of cached submodels
* fix error introduced while merging
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
"Normal" models have 4 in-channels, while "Depth" models have 5 and "Inpaint" models have 9.
We need to explicitly tell diffusers the channel count when converting models.
Closes #6058
- Move base of t2i and clip_vision config models to DiffusersBase, which contains
a field to record the model variant (e.g. "fp16")
- This restore the ability to load fp16 t2i and clip_vision models
- Also add defensive coding to load the vanilla model when the fp16 model
has been replaced (or more likely, user's preferences changed since installation)
Co-authored-by: Lincoln Stein <lstein@gmail.com>
- No longer install core conversion models. Use the HuggingFace cache to load
them if and when needed.
- Call directly into the diffusers library to perform conversions with only shallow
wrappers around them to massage arguments, etc.
- At root configuration time, do not create all the possible model subdirectories,
but let them be created and populated at model install time.
- Remove checks for missing core conversion files, since they are no
longer installed.
