Update app.py

app.py

@@ -1,32 +1,61 @@
 import os
 import sys
-import shutil
 
 # ==========================================
-# 1. 物理层攻击：篡改模型文件
+# 1. 核心补丁：全局搜索并摧毁 Flash Attention
 # ==========================================
-print("🔨 正在执行物理覆盖手术...")
+print("🛰️ 启动全盘扫描，寻找 GPU 毒瘤文件...")
 
-cwd = os.getcwd()
-cpu_model_path = os.path.join(cwd, "AR", "models", "t2s_model.py")
-gpu_model_path = os.path.join(cwd, "AR", "models", "t2s_model_flash_attn.py")
+# 定义我们要写入的“CPU 伪装代码”
+# 这段代码会把所有对 GPU 模型的调用，转发给 CPU 模型
+CPU_HACK_CODE = """
+import sys
+import logging
+print("🛡️ [HACK] 成功拦截 FlashAttn 调用，强制重定向至 CPU 模型！")
 
-if os.path.exists(cpu_model_path) and os.path.exists(gpu_model_path):
+# 试图导入普通的 CPU 模型
+try:
+    # 尝试多种可能的路径
     try:
-        # 读取 CPU 代码
-        with open(cpu_model_path, "r", encoding="utf-8") as f:
-            cpu_code = f.read()
+        from AR.models.t2s_model import Text2SemanticDecoder
+    except ImportError:
+        from GPT_SoVITS.AR.models.t2s_model import Text2SemanticDecoder
+except ImportError:
+    # 如果都找不到，手动定义一个空的 CPU 兼容类（防止报错）
+    import torch.nn as nn
+    class Text2SemanticDecoder(nn.Module):
+        def __init__(self, *args, **kwargs):
+            super().__init__()
+            print("⚠️ 使用了紧急备用 CPU Decoder 类")
+        def forward(self, *args, **kwargs):
+            print("⚠️ 紧急备用 Forward 被调用")
+            return None
+
+# 导出这个类，骗过调用者
+Text2SemanticDecoderFlashAttn = Text2SemanticDecoder
+"""
+
+# 递归搜索并覆盖
+target_filename = "t2s_model_flash_attn.py"
+hacked_count = 0
+
+for root, dirs, files in os.walk("."):
+    if target_filename in files:
+        full_path = os.path.join(root, target_filename)
+        print(f"🎯 锁定目标: {full_path}")
         
-        # 写入 GPU 文件 (鸠占鹊巢)
-        with open(gpu_model_path, "w", encoding="utf-8") as f:
-            f.write(cpu_code)
-            
-        print(f"✅ 覆盖成功！\n源: {cpu_model_path}\n目标: {gpu_model_path}")
-        print("现在 't2s_model_flash_attn.py' 里的代码实际上是 CPU 版代码。")
-    except Exception as e:
-        print(f"❌ 覆盖失败 (权限不足?): {e}")
+        try:
+            with open(full_path, "w", encoding="utf-8") as f:
+                f.write(CPU_HACK_CODE)
+            print("✅ 已执行物理覆盖 (写入 CPU 伪装代码)")
+            hacked_count += 1
+        except Exception as e:
+            print(f"❌ 覆盖失败: {e}")
+
+if hacked_count == 0:
+    print("⚠️ 警告：未找到任何 FlashAttn 文件！可能目录结构极其特殊。")
 else:
-    print("⚠️ 找不到模型文件路径，跳过覆盖步骤 (可能目录结构变了)")
+    print(f"🎉 成功处理了 {hacked_count} 个 GPU 文件。")
 
 # ==========================================
 # 2. 基础环境设置
@@ -39,18 +68,15 @@ def no_op(self, *args, **kwargs): return self
 torch.Tensor.cuda = no_op
 torch.nn.Module.cuda = no_op
 
-print("💉 CUDA 已屏蔽。")
-
 # ==========================================
-# 3. 导入业务逻辑
+# 3. 导入推理核心
 # ==========================================
-sys.path.append(cwd)
+sys.path.append(os.getcwd())
 
 try:
     import inference_webui as core
     print("✅ 成功导入 inference_webui")
     
-    # 双重保险：配置修改
     if hasattr(core, "is_half"): core.is_half = False
     if hasattr(core, "device"): core.device = "cpu"
         
@@ -59,7 +85,7 @@ except ImportError:
     sys.exit(1)
 
 # ==========================================
-# 4. 自动寻找模型
+# 4. 自动寻找模型文件
 # ==========================================
 def find_real_model(pattern, search_path="."):
     candidates = []
@@ -83,21 +109,17 @@ sovits_path = find_real_model("s2Gv2ProPlus.pth") or find_real_model("s2G")
 # ==========================================
 try:
     if gpt_path and sovits_path:
-        # 三重保险
         core.is_half = False
-        
-        if hasattr(core, "change_gpt_weights"):
-            core.change_gpt_weights(gpt_path=gpt_path)
-        if hasattr(core, "change_sovits_weights"):
-            core.change_sovits_weights(sovits_path=sovits_path)
-        print(f"🎉 模型加载成功！(HARDWARE HACKED)")
+        if hasattr(core, "change_gpt_weights"): core.change_gpt_weights(gpt_path=gpt_path)
+        if hasattr(core, "change_sovits_weights"): core.change_sovits_weights(sovits_path=sovits_path)
+        print(f"🎉 模型加载成功！")
     else:
         print("❌ 未找到模型文件")
 except Exception as e:
     print(f"⚠️ 模型加载报错: {e}")
 
 # ==========================================
-# 6. 推理逻辑
+# 6. 推理接口
 # ==========================================
 import soundfile as sf
 import gradio as gr
@@ -108,8 +130,7 @@ REF_TEXT = "你好"
 REF_LANG = "中文"
 
 def run_predict(text):
-    if not os.path.exists(REF_AUDIO):
-        return None, "❌ 错误：请上传 ref.wav"
+    if not os.path.exists(REF_AUDIO): return None, "❌ 请上传 ref.wav"
     
     print(f"📥 任务: {text}")
     try:
@@ -131,7 +152,6 @@ def run_predict(text):
             sr, data = result_list[0]
             out_path = f"out_{os.urandom(4).hex()}.wav"
             sf.write(out_path, data, sr)
-            print(f"✅ 生成完毕: {out_path}")
             return out_path, "✅ 成功"
             
     except Exception as e:
@@ -140,19 +160,16 @@ def run_predict(text):
         return None, f"💥 报错: {e}"
 
 # ==========================================
-# 7. 界面
+# 7. 启动界面
 # ==========================================
 with gr.Blocks() as app:
-    gr.Markdown(f"### GPT-SoVITS V2 (File System Override)")
-    
+    gr.Markdown(f"### GPT-SoVITS V2 (CPU HACKED)")
     with gr.Row():
-        inp = gr.Textbox(label="文本", value="别报错了，求求了。")
+        inp = gr.Textbox(label="文本", value="这下舒服了，终于跑通了。")
         btn = gr.Button("生成")
-    
     with gr.Row():
         out = gr.Audio(label="结果")
         log = gr.Textbox(label="日志")
-        
     btn.click(run_predict, [inp], [out, log], api_name="predict")
 
 if __name__ == "__main__":