multi-image support

nodes.py

@@ -145,16 +145,20 @@ class LLaVA_OneVision_Run:
         image_processor = llava_model["image_processor"]
         device = llava_model["device"]
         dtype = llava_model["dtype"]
+
         
         B, H, W, C = image.shape
         image = image.permute(0, 3, 1, 2)  # Change shape to (B, C, H, W)
         transform = transforms.ToPILImage()
-        
         image_pils = [transform(image[i]) for i in range(B)]  # Convert each image to PIL format
         
         image_sizes = [img.size for img in image_pils]  # Get sizes for all images
-        image_tensors = process_images(image_pils, image_processor, model.config)  # Process all images
-        image_tensors = [_image.to(dtype=dtype, device=device) for _image in image_tensors]  # Move to appropriate device and dtype
+        
+        image_tensors = []
+        for image_pil in image_pils:
+            processed_image = process_images([image_pil], image_processor, model.config)  # Process individual image
+            processed_image = processed_image[0].to(dtype=dtype, device=device)  # Move to appropriate device and dtype
+            image_tensors.append(processed_image)
 
         conv_template = "qwen_1_5"
         question = DEFAULT_IMAGE_TOKEN + prompt
@@ -187,9 +191,9 @@ class LLaVA_OneVision_Run:
                 mm.soft_empty_cache()
         text_outputs = tokenizer.batch_decode(result, skip_special_tokens=True)
         print(text_outputs)
-        
+
        
-        return (text_outputs[0],)
+        return (text_outputs[0])
      
 NODE_CLASS_MAPPINGS = {
     "DownloadAndLoadLLaVAOneVisionModel": DownloadAndLoadLLaVAOneVisionModel,