* Initial support for SDXL refiner
* Cleanup
* Add arg for converting Unet in float32 precision
* Setup scale factor with pipeline in CLI
* Update cli arg and future warning
* Bundle refiner unet if specified
* Update script for bundled refiner
- Also skip loading model if check_output_correctness is missing, since the model does not require inferencing at conversion time
* Flip skip_model_load bool
* Cleanup
* Support bundled UnetRefiner
* Add seperate refiner config value
- Includes unloading base unet when swapping to refiner
* Update readme for SDXL refiner
* Add condition for new SDXL coreml input features
* Revert pipeline interface change, add extra logging on pipe load
* Reset model_version after refiner conversion
* Reset model_version before refiner conversion but after pipe init
* Add refiner chunking
* Ensure unets are unloaded for reduceMemory true
* Handle missing UnetRefiner.mlmodelc on pipeline load
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Prewarm refiner on load, unload on complete
* Force cpu_and_gpu for VAE until it can be fixed
* Include output dtype of np.float32 for all conversions
* Allow a custom VAE to be converted.
* Revert hardcoded reduceMemory
* Fix merge
* Default chunking arg for --merge-chunks-in-pipeline-model when called from torch2coreml
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Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Zach Nagengast