first commit

.gitignore

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+/.venv/

__init__.py

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+from .llm_node import LLMImageDescription
+
+NODE_CLASS_MAPPINGS = {
+    "LLMImageDescription": LLMImageDescription
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LLMImageDescription": "LLM Image Description"
+}
+
+__version__ = "1.0.0"

llm_node.py

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+import numpy as np
+import base64
+from PIL import Image
+from io import BytesIO
+from openai import OpenAI
+
+
+class LLMImageDescription:
+    def __init__(self):
+        self.output_dir = "output"
+        self.type = "output"
+        self._client = None
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "model": (["gpt-4o", "claude-3-5-sonnet-20241022", "gemini-1.5-pro-latest", "gemini-2.0-flash-exp"],),
+                "api_url": ("STRING", {
+                    "default": "",
+                    "multiline": False
+                }),
+                "api_key": ("STRING", {
+                    "default": "",
+                    "multiline": False
+                }),
+                "prompt_template": ("STRING", {
+                    "default": "Please describe this image in detail:",
+                    "multiline": True
+                })
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("description",)
+    FUNCTION = "process_image"
+    CATEGORY = "image/text"
+
+    def get_client(self, api_key, api_url=None):
+        """Get or create OpenAI client"""
+        if not self._client:
+            kwargs = {"api_key": api_key}
+            if api_url:
+                kwargs["base_url"] = api_url
+            self._client = OpenAI(**kwargs)
+        return self._client
+
+    @staticmethod
+    def convert_image_to_base64(image):
+        # Convert PyTorch tensor to PIL Image
+        image = image.cpu().numpy()
+        image = (image * 255).astype(np.uint8)
+        if image.shape[0] == 3:  # If image is in CHW format
+            image = np.transpose(image, (1, 2, 0))
+        pil_image = Image.fromarray(image)
+
+        # Convert PIL Image to base64
+        buffered = BytesIO()
+        pil_image.save(buffered, format="PNG")
+        img_str = base64.b64encode(buffered.getvalue()).decode()
+        return img_str
+
+    @staticmethod
+    def process_with_openai_compatible(prompt_template, base64_image, client, model):
+        """Process image using OpenAI API format"""
+        try:
+            response = client.chat.completions.create(
+                model=model,
+                messages=[
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "text",
+                                "text": prompt_template
+                            },
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": f"data:image/png;base64,{base64_image}"
+                                }
+                            }
+                        ]
+                    }
+                ],
+                max_tokens=300
+            )
+            return response.choices[0].message.content
+        except Exception as e:
+            print(f"API error: {str(e)}")
+            return f"Error: API request failed - {str(e)}"
+
+    def process_image(self, image, model, api_url, api_key, prompt_template):
+        try:
+            # Convert the first image in batch to base64
+            if len(image.shape) == 4:
+                image = image[0]
+            base64_image = self.convert_image_to_base64(image)
+
+            # Get appropriate API URL based on model
+            if not api_url:
+                api_urls = {
+                    "gpt-4o": "https://api.openai.com/v1/chat/completions",
+                    "claude-3-5-sonnet-20241022": "https://api.anthropic.com/v1/messages",
+                    "gemini-1.5-pro-latest": "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro-latest:generateContent",
+                    "gemini-2.0-flash-exp": "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-exp:generateContent"
+                }
+                api_url = api_urls.get(model, "")
+
+            # Process using unified OpenAI SDK
+            client = self.get_client(api_key, api_url)
+            description = self.process_with_openai_compatible(prompt_template, base64_image, client, model)
+
+            return (description,)
+
+        except Exception as e:
+            print(f"Error in image description: {str(e)}")
+            return (f"Error: Failed to generate image description. {str(e)}",)
+
+    def __del__(self):
+        """Cleanup client on deletion"""
+        if self._client:
+            self._client.close()

requirements.txt

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+openai
+numpy
+requests
+pillow