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import roop.globals |
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import cv2 |
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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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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"]: |
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self.Release() |
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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/inswapper_128.onnx') |
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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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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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