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roop/processors/Enhance_CodeFormer.py

@@ -7,10 +7,6 @@ import roop.globals
 from roop.typing import Face, Frame, FaceSet
 from roop.utilities import resolve_relative_path
 
-
-# THREAD_LOCK = threading.Lock()
-
-
 class Enhance_CodeFormer():
     model_codeformer = None
 

roop/processors/Enhance_GFPGAN.py

@@ -7,10 +7,6 @@ import roop.globals
 from roop.typing import Face, Frame, FaceSet
 from roop.utilities import resolve_relative_path
 
-
-# THREAD_LOCK = threading.Lock()
-
-
 class Enhance_GFPGAN():
     plugin_options:dict = None
 

roop/processors/FaceSwapInsightFace.py

@@ -32,30 +32,22 @@ class FaceSwapInsightFace():
             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            
+            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:
-        blob = cv2.dnn.blobFromImage(temp_frame, 1.0 / self.input_std, (128, 128),
-                                      (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
         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", blob)
+        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]
-        img_fake = ort_outs.transpose((0,2,3,1))[0]
-        return np.clip(255 * img_fake, 0, 255).astype(np.uint8)[:,:,::-1]
-
-
-        img_fake, M = self.model_swap_insightface.get(temp_frame, target_face, source_face, paste_back=False)
-    #    target_face.matrix = M
-    #    return img_fake 
+        return ort_outs[0]
 
 
     def Release(self):

roop/processors/Frame_Upscale.py

@@ -2,11 +2,11 @@ import cv2
 import numpy as np
 import onnxruntime
 import roop.globals
-import threading
 
-from roop.utilities import resolve_relative_path
+from roop.utilities import resolve_relative_path, conditional_thread_semaphore
 from roop.typing import Frame
 
+
 class Frame_Upscale():
     plugin_options:dict = None
     model_upscale = None
@@ -16,8 +16,6 @@ class Frame_Upscale():
     processorname = 'upscale'
     type = 'frame_enhancer'
 
-    THREAD_LOCK_UPSCALE = threading.Lock()
-
 
     def Initialize(self, plugin_options:dict):
         if self.plugin_options is not None:
@@ -40,7 +38,7 @@ class Frame_Upscale():
             elif self.prev_type == "lsdirx4":
                 model_path = resolve_relative_path('../models/Frame/lsdir_x4.onnx')
                 self.scale = 4
-
+            onnxruntime.set_default_logger_severity(3)
             self.model_upscale = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers)
             self.model_inputs = self.model_upscale.get_inputs()
             model_outputs = self.model_upscale.get_outputs()
@@ -109,7 +107,7 @@ class Frame_Upscale():
 
         for index, tile_frame in enumerate(upscale_tile_frames):
             tile_frame = self.prepare_tile_frame(tile_frame)
-            with self.THREAD_LOCK_UPSCALE:
+            with conditional_thread_semaphore():
                 self.io_binding.bind_cpu_input(self.model_inputs[0].name, tile_frame)
                 self.model_upscale.run_with_iobinding(self.io_binding)
                 ort_outs = self.io_binding.copy_outputs_to_cpu()

roop/processors/Mask_XSeg.py

@@ -1,13 +1,11 @@
 import numpy as np
 import cv2
 import onnxruntime
-import threading
 import roop.globals
 
 from roop.typing import Frame
-from roop.utilities import resolve_relative_path
+from roop.utilities import resolve_relative_path, conditional_thread_semaphore
 
-THREAD_LOCK_CLIP = threading.Lock()
 
 
 class Mask_XSeg():