diff --git a/nodes.py b/nodes.py
index 34750ba..0141122 100644
--- a/nodes.py
+++ b/nodes.py
@@ -3314,7 +3314,56 @@ class ImageTransformByNormalizedAmplitude:
         
         return (transformed_batch,)
     
-    
+class GLIGENTextBoxApplyBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning_to": ("CONDITIONING", ),
+                              "latents": ("LATENT", ),
+                              "clip": ("CLIP", ),
+                              "gligen_textbox_model": ("GLIGEN", ),
+                              "text": ("STRING", {"multiline": True}),
+                              "width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
+                              "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                              "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                              "x_increment": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 8}),
+                              "y_increment": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 8}),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "append"
+
+    CATEGORY = "conditioning/gligen"
+
+    def append(self, latents, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y, x_increment, y_increment):
+        batch_size = sum(tensor.size(0) for tensor in latents.values())
+        print(batch_size)
+        c = []
+        cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True)
+        
+        for t in conditioning_to:
+            n = [t[0], t[1].copy()]
+            
+            position_params_batch = [[] for _ in range(batch_size)]  # Initialize a list of empty lists for each batch item
+            
+            for i in range(batch_size):
+                x_position = x + i * x_increment
+                y_position = y + i * y_increment
+                position_param = (cond_pooled, height // 8, width // 8, y_position // 8, x_position // 8)
+                position_params_batch[i].append(position_param)  # Append position_param to the correct sublist
+            
+            prev = []
+            if "gligen" in n[1]:
+                prev = n[1]['gligen'][2]
+            else:
+                prev = [[] for _ in range(batch_size)]
+            # Concatenate prev and position_params_batch, ensuring both are lists of lists
+            # and each sublist corresponds to a batch item
+            combined_position_params = [prev_item + batch_item for prev_item, batch_item in zip(prev, position_params_batch)]
+            n[1]['gligen'] = ("position", gligen_textbox_model, combined_position_params)
+            c.append(n)
+        
+        return (c, )
+        
 NODE_CLASS_MAPPINGS = {
     "INTConstant": INTConstant,
     "FloatConstant": FloatConstant,
@@ -3376,7 +3425,8 @@ NODE_CLASS_MAPPINGS = {
     "OffsetMaskByNormalizedAmplitude": OffsetMaskByNormalizedAmplitude,
     "ImageTransformByNormalizedAmplitude": ImageTransformByNormalizedAmplitude,
     "GetLatentsFromBatchIndexed": GetLatentsFromBatchIndexed,
-    "StringConstant": StringConstant
+    "StringConstant": StringConstant,
+    "GLIGENTextBoxApplyBatch": GLIGENTextBoxApplyBatch
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
     "INTConstant": "INT Constant",
@@ -3438,5 +3488,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "OffsetMaskByNormalizedAmplitude": "OffsetMaskByNormalizedAmplitude",
     "ImageTransformByNormalizedAmplitude": "ImageTransformByNormalizedAmplitude",
     "GetLatentsFromBatchIndexed": "GetLatentsFromBatchIndexed",
-    "StringConstant": "StringConstant"
+    "StringConstant": "StringConstant",
+    "GLIGENTextBoxApplyBatch": "GLIGENTextBoxApplyBatch"
 }
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