| import roop.globals
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| import numpy as np
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| import onnx
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| import onnxruntime
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| from roop.typing import Face, Frame
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| from roop.utilities import resolve_relative_path
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| class FaceSwapInsightFace():
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| plugin_options:dict = None
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| model_swap_insightface = None
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|
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| processorname = 'faceswap'
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| type = 'swap'
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| def Initialize(self, plugin_options:dict):
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| if self.plugin_options is not None:
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| if self.plugin_options["devicename"] != plugin_options["devicename"] or self.plugin_options["modelname"] != plugin_options["modelname"]:
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| self.Release()
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|
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| self.plugin_options = plugin_options
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| if self.model_swap_insightface is None:
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| model_path = resolve_relative_path('../models/' + self.plugin_options["modelname"])
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| graph = onnx.load(model_path).graph
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| self.emap = onnx.numpy_helper.to_array(graph.initializer[-1])
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| self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu')
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| self.input_mean = 0.0
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| self.input_std = 255.0
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|
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| sess_options = onnxruntime.SessionOptions()
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| sess_options.enable_cpu_mem_arena = False
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| self.model_swap_insightface = onnxruntime.InferenceSession(model_path, sess_options, providers=roop.globals.execution_providers)
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| def Run(self, source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
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| latent = source_face.normed_embedding.reshape((1,-1))
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| latent = np.dot(latent, self.emap)
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| latent /= np.linalg.norm(latent)
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| io_binding = self.model_swap_insightface.io_binding()
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| io_binding.bind_cpu_input("target", temp_frame)
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| io_binding.bind_cpu_input("source", latent)
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| io_binding.bind_output("output", self.devicename)
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| self.model_swap_insightface.run_with_iobinding(io_binding)
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| ort_outs = io_binding.copy_outputs_to_cpu()[0]
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| return ort_outs[0]
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| def Release(self):
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| del self.model_swap_insightface
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| self.model_swap_insightface = None
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