The lineart model often outputs a lot of almost-black noise. SD1.5 ControlNets seem to be OK with this, but SDXL ControlNets are not - they need a cleaner map. 12 was experimentally determined to be a good threshold, eliminating all the noise while keeping the actual edges. Other approaches to thresholding may be better, for example stretching the contrast or removing noise.
I tried:
- Simple thresholding (as implemented here) - works fine.
- Adaptive thresholding - doesn't work, because the thresholding is done in the context of small blocks, while we want thresholding in the context of the whole image.
- Gamma adjustment - alters the white values too much. Hard to tuen.
- Contrast stretching, with and without pre-simple-thresholding - this allows us to treshold out the noise, then stretch everything above the threshold down to almost-zero. So you have a smoother gradient of lightness near zero. It works but it also stretches contrast near white down a bit, which is probably undesired.
In the end, simple thresholding works fine and is very simple.
Similar to the existing node, but without any resizing. The backend logic was consolidated and modified so that it the model loading can be managed by the model manager.
The ONNX Runtime `InferenceSession` class was added to the `AnyModel` union to satisfy the type checker.
Similar to the existing node, but without any resizing and with a revised model loading API that uses the model manager.
All code related to the invocation now lives in the Invoke repo.
Similar to the existing node, but without any resizing and with a revised model loading API that uses the model manager.
All code related to the invocation now lives in the Invoke repo. Unfortunately, this includes a whole git repo for EfficientNet. I believe we could use the package `timm` instead of this, but it's beyond me.
Similar to the existing node, but without any resizing and with a revised model loading API that uses the model manager.
All code related to the invocation now lives in the Invoke repo.
Similar to the existing node, but without any resizing and with a revised model loading API that uses the model manager.
All code related to the invocation now lives in the Invoke repo.
* 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()
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Co-authored-by: Lincoln Stein <lstein@gmail.com>
