使用PySide6-Fluent-Widgets重构整套UI

添加任务列表组件并优化视频加载逻辑
支持可视化显示字幕区域
整理所有模型, 分别为STTN智能擦除, STTN字幕检测, LAMA, ProPainter, OpenCV
提高处理性能
新增CPU运行模式并优化多语言支持
修复Propainter模式部分视频报错

本次提交新增了CPU运行模式，适用于无GPU加速的场景。同时，优化了多语言支持，新增了日语、韩语、越南语等语言配置文件，并更新了README文档以反映新的运行模式和多语言支持。此外，修复了部分代码逻辑，提升了系统的稳定性和兼容性。

backend/tools/hardware_accelerator.py

@@ -0,0 +1,120 @@
+import traceback
+import importlib.util
+
+import torch
+
+from backend.config import tr
+
+class HardwareAccelerator:
+
+    # 类变量，用于存储单例实例
+    _instance = None
+
+    @classmethod
+    def instance(cls):
+        """获取单例实例"""
+        if cls._instance is None:
+            cls._instance = HardwareAccelerator()
+            cls._instance.initialize()
+        return cls._instance
+
+    def __init__(self):
+        self.__cuda = False
+        self.__dml = False
+        self.__onnx_providers = []
+        self.__enabled = True
+        self.__device = None
+
+    def initialize(self):
+        self.check_directml_available()
+        self.check_cuda_available()
+        self.load_onnx_providers()
+
+    def check_directml_available(self):
+        self.__dml = importlib.util.find_spec("torch_directml")
+
+    def check_cuda_available(self):
+        self.__cuda = torch.cuda.is_available()
+
+    def load_onnx_providers(self):
+        try:
+            import onnxruntime as ort
+            available_providers = ort.get_available_providers()
+            for provider in available_providers:
+                if provider in [
+                    "CPUExecutionProvider"
+                ]:
+                    continue
+                if provider not in [
+                    "DmlExecutionProvider",         # DirectML，适用于 Windows GPU
+                    "ROCMExecutionProvider",        # AMD ROCm
+                    "MIGraphXExecutionProvider",    # AMD MIGraphX
+                    "VitisAIExecutionProvider",     # AMD VitisAI，适用于 RyzenAI & Windows, 实测和DirectML性能似乎差不多
+                    "OpenVINOExecutionProvider",    # Intel GPU
+                    "MetalExecutionProvider",       # Apple macOS
+                    "CoreMLExecutionProvider",      # Apple macOS
+                    "CUDAExecutionProvider",        # Nvidia GPU
+                ]:
+                    print(tr['Main']['OnnxExectionProviderNotSupportedSkipped'].format(provider))
+                    continue
+                print(tr['Main']['OnnxExecutionProviderDetected'].format(provider))
+                self.__onnx_providers.append(provider)
+        except ModuleNotFoundError as e:
+            print(tr['Main']['OnnxRuntimeNotInstall'])
+
+    def has_accelerator(self):
+        if not self.__enabled:
+            return False
+        return self.__cuda or self.__dml or len(self.__onnx_providers) > 0
+
+    @property
+    def accelerator_name(self):
+        if not self.__enabled:
+            return "CPU"
+        if self.__dml:
+            return "DirectML"
+        if self.__cuda:
+            return "GPU"
+        elif len(self.__onnx_providers) > 0:
+            return ", ".join(self.__onnx_providers)
+        else:
+            return "CPU"
+
+    @property
+    def onnx_providers(self):
+        if not self.__enabled:
+            return []
+        return self.__onnx_providers
+
+    def has_cuda(self):
+        if not self.__enabled:
+            return False
+        return self.__cuda
+    
+    def set_enabled(self, enable):
+        self.__enabled = enable
+
+    @property
+    def device(self):
+        """
+        onnxruntime-directml 1.21.1-1.22.0(往上未测试) 和 torch-directml 不能同时初始化, 会相互影响
+        提示site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 266, in run
+                return self._sess.run(output_names, input_feed, run_options)
+                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+            UnicodeDecodeError: 'utf-8' codec can't decode byte 0xb2 in position 344: invalid start bn 344: invalid start byte
+        onnxruntime-directml 1.21.1 则正常, 但Win10跑不起来, Win11正常
+        为了避免冲突以及避免重写一个QPT智能部署流程, 这里采用延迟初始化的方式+继续使用onnxruntime-directml 1.20.1
+        当然SubtitleDetect放到一个独立进程去操作也是可以的
+        """
+        if self.__enabled:
+            if self.__dml:
+                try:
+                    import torch_directml
+                    return torch_directml.device(torch_directml.default_device())
+                    self.__dml = True
+                except:
+                    traceback.print_exc()
+                    self.__dml = False
+            if self.__cuda:
+                return torch.device("cuda:0")
+        return torch.device("cpu")