104 lines
3.1 KiB
Python
104 lines
3.1 KiB
Python
from coremltools import ComputeUnit
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from python_coreml_stable_diffusion.coreml_model import CoreMLModel
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import folder_paths
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from comfy import supported_models_base, model_management
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from comfy.latent_formats import SD15
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from comfy.model_patcher import ModelPatcher
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from .logger import logger
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from .model import CoreMLModelWrapper
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class CoreMLLoader:
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PACKAGE_DIRNAME = ""
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"coreml_name": (list(s.coreml_filenames().keys()),),
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"compute_unit": ([
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ComputeUnit.CPU_AND_NE.name,
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ComputeUnit.CPU_AND_GPU.name,
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ComputeUnit.ALL.name,
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ComputeUnit.CPU_ONLY.name,
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],)
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}
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}
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RETURN_TYPES = ("COREML_MODEL",)
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FUNCTION = "load"
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CATEGORY = "CoreML Suite"
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@classmethod
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def coreml_filenames(cls):
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return {
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p.split('/')[-1]:
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p for p in
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folder_paths.get_filename_list_(cls.PACKAGE_DIRNAME)[1]
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if p.endswith((".mlpackage", ".mlmodelc"))
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}
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def load(self, coreml_name, compute_unit):
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logger.info(f"Loading {coreml_name}")
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coreml_path = self.coreml_filenames()[coreml_name]
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sources = "compiled" if coreml_name.endswith(
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".mlmodelc") else "packages"
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return self._load(coreml_path, compute_unit, sources)
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def _load(self, coreml_path, compute_unit, sources):
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return (CoreMLModel(coreml_path, compute_unit, sources),)
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class CoreMLLoaderCkpt(CoreMLLoader):
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PACKAGE_DIRNAME = "checkpoints"
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RETURN_TYPES = ("MODEL", "CLIP", "VAE")
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def load(self, coreml_name, compute_unit):
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# TODO: Implement this
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pass
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class CoreMLLoaderTextEncoder(CoreMLLoader):
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PACKAGE_DIRNAME = "clip"
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RETURN_TYPES = ("CLIP",)
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def load(self, coreml_name, compute_unit):
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# TODO: Implement this
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pass
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class CoreMLLoaderUNet(CoreMLLoader):
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PACKAGE_DIRNAME = "unet"
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RETURN_TYPES = ("MODEL",)
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def _load(self, coreml_path, compute_unit, sources):
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# TODO: This is a dummy model config, but it should be enough to
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# get the model to load - implement a proper model config
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model_config = supported_models_base.BASE({})
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model_config.latent_format = SD15()
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model_config.unet_config = {
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"disable_unet_model_creation": True,
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"num_res_blocks": 2,
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"attention_resolutions": [1, 2, 4],
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"channel_mult": [1, 2, 4, 4],
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"transformer_depth": [1, 1, 1, 0],
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}
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coreml_model = CoreMLModelWrapper(model_config, coreml_path,
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compute_unit, sources)
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return (ModelPatcher(coreml_model, model_management.get_torch_device(),
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None),)
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class CoreMLLoaderVAE(CoreMLLoader):
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PACKAGE_DIRNAME = "vae"
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RETURN_TYPES = ("VAE",)
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def load(self, coreml_name, compute_unit):
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# TODO: Implement this
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pass
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