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| import roop.globals | |
| import cv2 | |
| import numpy as np | |
| import onnx | |
| import onnxruntime | |
| from roop.typing import Face, Frame | |
| from roop.utilities import resolve_relative_path | |
| class FaceSwapInsightFace(): | |
| plugin_options:dict = None | |
| model_swap_insightface = None | |
| processorname = 'faceswap' | |
| type = 'swap' | |
| def Initialize(self, plugin_options:dict): | |
| if self.plugin_options is not None: | |
| if self.plugin_options["devicename"] != plugin_options["devicename"]: | |
| self.Release() | |
| self.plugin_options = plugin_options | |
| if self.model_swap_insightface is None: | |
| model_path = resolve_relative_path('../models/inswapper_128.onnx') | |
| graph = onnx.load(model_path).graph | |
| self.emap = onnx.numpy_helper.to_array(graph.initializer[-1]) | |
| self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu') | |
| self.input_mean = 0.0 | |
| self.input_std = 255.0 | |
| #cuda_options = {"arena_extend_strategy": "kSameAsRequested", 'cudnn_conv_algo_search': 'DEFAULT'} | |
| sess_options = onnxruntime.SessionOptions() | |
| sess_options.enable_cpu_mem_arena = False | |
| self.model_swap_insightface = onnxruntime.InferenceSession(model_path, sess_options, providers=roop.globals.execution_providers) | |
| def Run(self, source_face: Face, target_face: Face, temp_frame: Frame) -> Frame: | |
| latent = source_face.normed_embedding.reshape((1,-1)) | |
| latent = np.dot(latent, self.emap) | |
| latent /= np.linalg.norm(latent) | |
| io_binding = self.model_swap_insightface.io_binding() | |
| io_binding.bind_cpu_input("target", temp_frame) | |
| io_binding.bind_cpu_input("source", latent) | |
| io_binding.bind_output("output", self.devicename) | |
| self.model_swap_insightface.run_with_iobinding(io_binding) | |
| ort_outs = io_binding.copy_outputs_to_cpu()[0] | |
| return ort_outs[0] | |
| def Release(self): | |
| del self.model_swap_insightface | |
| self.model_swap_insightface = None | |