diff --git a/dwimage2.py b/dwimage2.py
deleted file mode 100644
index 16ac3d8..0000000
--- a/dwimage2.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from PIL import Image
-from pathlib import Path
-import folder_paths
-import os
-import shutil
-import re
-from torchvision.transforms import ToPILImage
-
-# 定义模型存储目录
-models_dir = Path(folder_paths.base_path) / "models"
-llava_checkpoints_dir = models_dir / "LLavacheckpoints"
-files_for_moondream2 = llava_checkpoints_dir / "files_for_moondream2"
-files_for_moondream2.mkdir(parents=True, exist_ok=True)
-
-class Moondream2Predictor:
-    _instance = None
-
-    def __new__(cls):
-        if cls._instance is None:
-            cls._instance = super(Moondream2Predictor, cls).__new__(cls)
-            cls._instance.model = None
-            cls._instance.tokenizer = None
-        return cls._instance
-
-    def load_model(self):
-        if self.model is None:
-            self.device = "cuda" if torch.cuda.is_available() else "cpu"
-            print(f"使用设备: {self.device}")
-            print("加载模型中...")
-            self.model = AutoModelForCausalLM.from_pretrained(files_for_moondream2, trust_remote_code=True).to(self.device)
-            self.tokenizer = AutoTokenizer.from_pretrained(files_for_moondream2)
-            print("模型加载成功")
-
-    def generate_predictions(self, image_path, question):
-        self.load_model()  # 确保模型已加载
-        try:
-            image_input = Image.open(image_path).convert("RGB")
-            enc_image = self.model.encode_image(image_input)
-            generated_text = self.model.answer_question(enc_image, question, self.tokenizer)
-            return generated_text
-        finally:
-            if os.path.exists(image_path):
-                os.remove(image_path)
-
-    def clear_memory(self):
-        if self.model:
-            del self.model
-            del self.tokenizer
-            self.model = None
-            self.tokenizer = None
-            torch.cuda.empty_cache()
-        print("模型已卸载，内存已清理")
-
-class Moondream2model:
-    def __init__(self):
-        self.predictor = Moondream2Predictor()
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "text_input": ("STRING", {"multiline": True, "default": ""}),
-            },
-        }
-
-    RETURN_TYPES = ("STRING",)
-    FUNCTION = "moondream2_generate_predictions"
-    CATEGORY = "🌙DW/dwimage2"
-
-    def moondream2_generate_predictions(self, image, text_input):
-        try:
-            pil_image = ToPILImage()(image[0].permute(2, 0, 1))
-            temp_path = files_for_moondream2 / "temp_image.png"
-            pil_image.save(temp_path)
-            
-            response = self.predictor.generate_predictions(temp_path, text_input)
-            response = ' '.join(response.strip().split())
-           
-            # 使用正则表达式移除开头的特定短语
-            response = re.sub(r'^(The image contains|The image contains|This picture contains|This picture contains|The image contains|The image contains|This image contains|This image contains)\s*', '', response, flags=re.IGNORECASE)
-            
-            return (response,)
-        finally:
-            # 确保在函数结束时卸载模型
-            self.predictor.clear_memory()
-
-NODE_CLASS_MAPPINGS = {
-    "dwimage2": Moondream2model
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "dwimage2": "DW Image2 Chat"
-}
\ No newline at end of file