diff --git a/generate.py b/generate.py
index 9a47d4f..876c34a 100755
--- a/generate.py
+++ b/generate.py
@@ -528,8 +528,6 @@ def generate_lowmem(args):
 
 
 if __name__ == "__main__":
-    # 限制模型使用的显存为0.6
-    torch.cuda.set_per_process_memory_fraction(0.55)
     torch.backends.cudnn.enabled = False
     OmegaConf.register_new_resolver("eval", lambda x: eval(x))
     OmegaConf.register_new_resolver("concat", lambda *x: [xxx for xx in x for xxx in xx])
diff --git a/tmp/sample_prompt_audio_vocal.flac b/tmp/sample_prompt_audio_vocal.flac
deleted file mode 100644
index 6630678..0000000
Binary files a/tmp/sample_prompt_audio_vocal.flac and /dev/null differ
diff --git a/tools/gradio/app.py b/tools/gradio/app.py
index 76b976b..dc8eedc 100644
--- a/tools/gradio/app.py
+++ b/tools/gradio/app.py
@@ -6,6 +6,7 @@ import yaml
 import time
 import re
 import os.path as op
+import torch
 from levo_inference_lowmem import LeVoInference
 
 EXAMPLE_LYRICS = """
@@ -98,7 +99,7 @@ def generate_song(lyric, description=None, prompt_audio=None, genre=None, cfg_co
     progress(0.0, "Start Generation")
     start = time.time()
     
-    audio_data = MODEL(lyric_norm, description, prompt_audio, genre, op.join(APP_DIR, "ckpt/prompt.pt"), gen_type, params).cpu().permute(1, 0).float().numpy()
+    audio_data = MODEL(lyric_norm, description, prompt_audio, genre, op.join(APP_DIR, "tools/new_prompt.pt"), gen_type, params).cpu().permute(1, 0).float().numpy()
 
     end = time.time()
     
@@ -239,4 +240,5 @@ lyrics
 
 # 启动应用
 if __name__ == "__main__":
+    torch.set_num_threads(1)
     demo.launch(server_name="0.0.0.0", server_port=8081)
diff --git a/tools/gradio/levo_inference.py b/tools/gradio/levo_inference.py
index 87d5241..4699cb0 100644
--- a/tools/gradio/levo_inference.py
+++ b/tools/gradio/levo_inference.py
@@ -71,11 +71,7 @@ class LeVoInference(torch.nn.Module):
             melody_is_wav = True
         elif genre is not None and auto_prompt_path is not None:
             auto_prompt = torch.load(auto_prompt_path)
-            merge_prompt = [item for sublist in auto_prompt.values() for item in sublist]
-            if genre == "Auto": 
-                prompt_token = merge_prompt[np.random.randint(0, len(merge_prompt))]
-            else:
-                prompt_token = auto_prompt[genre][np.random.randint(0, len(auto_prompt[genre]))]
+            prompt_token = auto_prompt[genre][np.random.randint(0, len(auto_prompt[genre]))]
             pmt_wav = prompt_token[:,[0],:]
             vocal_wav = prompt_token[:,[1],:]
             bgm_wav = prompt_token[:,[2],:]
diff --git a/tools/gradio/levo_inference_lowmem.py b/tools/gradio/levo_inference_lowmem.py
index e97f131..e59148b 100644
--- a/tools/gradio/levo_inference_lowmem.py
+++ b/tools/gradio/levo_inference_lowmem.py
@@ -66,11 +66,7 @@ class LeVoInference(torch.nn.Module):
             torch.cuda.empty_cache()
         elif genre is not None and auto_prompt_path is not None:
             auto_prompt = torch.load(auto_prompt_path)
-            merge_prompt = [item for sublist in auto_prompt.values() for item in sublist]
-            if genre == "Auto": 
-                prompt_token = merge_prompt[np.random.randint(0, len(merge_prompt))]
-            else:
-                prompt_token = auto_prompt[genre][np.random.randint(0, len(auto_prompt[genre]))]
+            prompt_token = auto_prompt[genre][np.random.randint(0, len(auto_prompt[genre]))]
             pmt_wav = prompt_token[:,[0],:]
             vocal_wav = prompt_token[:,[1],:]
             bgm_wav = prompt_token[:,[2],:]
diff --git a/tools/gradio/run.sh b/tools/gradio/run.sh
index 6961792..a90e62e 100644
--- a/tools/gradio/run.sh
+++ b/tools/gradio/run.sh
@@ -1,3 +1,7 @@
+export OMP_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+export CUDA_LAUNCH_BLOCKING=0
+
 export USER=root
 export PYTHONDONTWRITEBYTECODE=1
 export TRANSFORMERS_CACHE="$(pwd)/third_party/hub"