support for batched input images

openai_node.py

@@ -7,7 +7,7 @@ from PIL import Image
 from openai import OpenAI
 
 class OpenAILLMNode:
-    
+
     @classmethod
     def INPUT_TYPES(cls):
         return {
@@ -24,66 +24,55 @@ class OpenAILLMNode:
                 "image": ("IMAGE",),
             }
         }
-    
+
     RETURN_TYPES = ("STRING",)
     RETURN_NAMES = ("response",)
     FUNCTION = "generate"
     CATEGORY = "text/generation"
-    
+
     def generate(self, prompt, model, temperature, reasoning_effort, api_key, max_output_tokens,
                  system_instruction="", image=None):
-        
+
         if not prompt.strip():
             raise ValueError("Prompt cannot be empty.")
-        
+
         key = os.environ.get(api_key.strip(), api_key.strip()) or os.environ.get("OPENAI_API_KEY")
         if not key:
             raise ValueError("No API key provided.")
-        
+
         client = OpenAI(api_key=key)
-        
-        # Build input content
+
         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")
-            base64_image = base64.b64encode(buffered.getvalue()).decode("utf-8")
-            
-            input_content = [{
-                "role": "user",
-                "content": [
-                    {"type": "input_text", "text": prompt},
-                    {"type": "input_image", "image_url": f"data:image/png;base64,{base64_image}"}
-                ]
-            }]
+            content = [{"type": "input_text", "text": prompt}]
+            for i in range(image.shape[0]):
+                arr = (image[i].cpu().numpy() * 255).astype(np.uint8)
+                buf = io.BytesIO()
+                Image.fromarray(arr).save(buf, format="PNG")
+                b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
+                content.append({"type": "input_image", "image_url": f"data:image/png;base64,{b64}"})
+            input_content = [{"role": "user", "content": content}]
         else:
             input_content = prompt
-        
-        # Build request
+
         request_params = {
             "model": model,
             "input": input_content,
             "temperature": temperature,
             "max_output_tokens": max_output_tokens
         }
-        
-        # Only add reasoning for o-series and gpt-5+ models
+
         if not model.startswith("gpt-4"):
             request_params["reasoning"] = {"effort": reasoning_effort}
         else:
             print(f"Skipping reasoning parameter for {model} (not supported)")
-        
+
         if system_instruction.strip():
             request_params["instructions"] = system_instruction
-        
+
         response = client.responses.create(**request_params)
-        
+
         return (response.output_text,)
 
+
 NODE_CLASS_MAPPINGS = {"OpenAILLMNode": OpenAILLMNode}
 NODE_DISPLAY_NAME_MAPPINGS = {"OpenAILLMNode": "OpenAI LLM"}

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "externalapi-helpers"
 description = "Various ComfyUI nodes for Gemini, Replicate and OpenAI"
-version = "1.1.3"
+version = "1.1.4"
 license = {file = "LICENSE"} 
 # classifiers = [
 #     # For OS-independent nodes (works on all operating systems)