Added Gemini Node

__init__.py

@@ -1,8 +1,9 @@
 from .flux_kontext_pro_node import NODE_CLASS_MAPPINGS as PRO_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as PRO_DISPLAY
 from .flux_kontext_max_node import NODE_CLASS_MAPPINGS as MAX_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as MAX_DISPLAY
+from .gemini_node import NODE_CLASS_MAPPINGS as GEMINI_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as GEMINI_DISPLAY
 
 # Combine both mappings
-NODE_CLASS_MAPPINGS = {**PRO_MAPPINGS, **MAX_MAPPINGS}
-NODE_DISPLAY_NAME_MAPPINGS = {**PRO_DISPLAY, **MAX_DISPLAY}
+NODE_CLASS_MAPPINGS = {**PRO_MAPPINGS, **MAX_MAPPINGS, **GEMINI_MAPPINGS}
+NODE_DISPLAY_NAME_MAPPINGS = {**PRO_DISPLAY, **MAX_DISPLAY, **GEMINI_DISPLAY}
 
 __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

gemini_node.py

@@ -0,0 +1,99 @@
+import base64
+import os
+import io
+import numpy as np
+import torch
+from PIL import Image
+from typing import Optional
+from google import genai
+from google.genai import types
+
+class GeminiChatNode:
+    """ComfyUI Node for Gemini API Chat with optional image input"""
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "prompt": ("STRING", {"multiline": True}),
+                "model": ("STRING", {"default": "gemini-2.5-pro", "multiline": False}),
+                "temperature": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 2.0, "step": 0.1}),
+                "thinking": ("BOOLEAN", {"default": True}),
+                "api_key": ("STRING", {"default": "", "multiline": False})
+            },
+            "optional": {
+                "system_instruction": ("STRING", {"multiline": True, "default": ""}),
+                "thinking_budget": ("INT", {"default": -1, "min": -1, "max": 24576, "step": 1}),
+                "image": ("IMAGE",),
+            }
+        }
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("response",)
+    FUNCTION = "generate"
+    CATEGORY = "AI/Gemini"
+    
+    def generate(self, prompt: str, model: str, temperature: float, thinking: bool, api_key: str,
+                 system_instruction: Optional[str] = None, thinking_budget: int = -1, 
+                 image: Optional[torch.Tensor] = None) -> tuple:
+        
+        try:
+            # API key handling
+            key = api_key.strip() or os.environ.get("GEMINI_API_KEY")
+            if not key:
+                return ("Error: No API key provided.",)
+            
+            # Initialize client and build parts
+            client = genai.Client(api_key=key)
+            parts = [types.Part.from_text(text=prompt)]
+            
+            # Handle image input
+            if image is not None:
+                img_array = image.cpu().numpy() if isinstance(image, torch.Tensor) else image
+                if len(img_array.shape) == 4:
+                    img_array = img_array[0]
+                if img_array.dtype in [np.float32, np.float64]:
+                    img_array = (img_array * 255).astype(np.uint8)
+                
+                buffered = io.BytesIO()
+                Image.fromarray(img_array).save(buffered, format="PNG")
+                parts.append(types.Part.from_bytes(mime_type="image/png", data=buffered.getvalue()))
+            
+            model_lower = model.lower()
+            
+            if "gemini-2.0" in model_lower:
+                final_thinking_budget = None
+            elif not thinking:
+                final_thinking_budget = 0
+                if "gemini-2.5-pro" in model_lower:
+                    final_thinking_budget = -1
+            else:
+                final_thinking_budget = thinking_budget
+                if "gemini-2.5-pro" in model_lower and final_thinking_budget == 0:
+                    final_thinking_budget = -1
+            
+            config = types.GenerateContentConfig(
+                temperature=temperature,
+                response_mime_type="text/plain"
+            )
+            
+            if "gemini-2.0" not in model_lower:
+                config.thinking_config = types.ThinkingConfig(thinking_budget=final_thinking_budget)
+            
+            if system_instruction and system_instruction.strip():
+                config.system_instruction = [types.Part.from_text(text=system_instruction.strip())]
+            
+            response = client.models.generate_content(
+                model=model,
+                contents=[types.Content(role="user", parts=parts)],
+                config=config
+            )
+            
+            return (response.text,)
+            
+        except Exception as e:
+            return (f"Error: {str(e)}",)
+
+# Node mappings
+NODE_CLASS_MAPPINGS = {"GeminiChatNode": GeminiChatNode}
+NODE_DISPLAY_NAME_MAPPINGS = {"GeminiChatNode": "Gemini Chat"}

requirements.txt

@@ -1,4 +1,5 @@
 replicate
 pillow
 numpy
-torch
+torch
+google-genai