fix: 用 transformers 替代 modelscope 导入，从根源消除副作用

- infer_v2.py: from modelscope → from transformers import AutoModelForCausalLM
  模型已在本地，不需要 modelscope 的 hub 下载包装
  这样 modelscope 根本不会被 import，消除所有 monkey-patching 副作用
- aiia_vibevoice_nodes.py: 还原 device_map=auto（此文件无需修改）

aiia_vibevoice_nodes.py

@@ -146,7 +146,7 @@ class AIIA_VibeVoice_Loader:
         # 5. Load Model
         print(f"[AIIA] Loading VibeVoice model variant: {VibeVoiceClass.__name__}")
         config = VibeVoiceConfig.from_pretrained(load_path)
-        model = VibeVoiceClass.from_pretrained(load_path, config=config, torch_dtype=dtype, trust_remote_code=False).to(device)
+        model = VibeVoiceClass.from_pretrained(load_path, config=config, torch_dtype=dtype, device_map="auto", trust_remote_code=False)
         
         # Load Generation Config
         try:

libs/index-tts/indextts/infer_v2.py

@@ -28,7 +28,7 @@ from indextts.s2mel.modules.campplus.DTDNN import CAMPPlus
 from indextts.s2mel.modules.audio import mel_spectrogram
 
 from transformers import AutoTokenizer
-from modelscope import AutoModelForCausalLM
+from transformers import AutoModelForCausalLM
 from huggingface_hub import hf_hub_download
 import safetensors
 from transformers import SeamlessM4TFeatureExtractor