diff --git a/examples/multimodal/multimodal_llm/neva/sequence_packing/preprocess_dataset.py b/examples/multimodal/multimodal_llm/neva/sequence_packing/preprocess_dataset.py
index ee96ff6489d3..60f882fa9821 100644
--- a/examples/multimodal/multimodal_llm/neva/sequence_packing/preprocess_dataset.py
+++ b/examples/multimodal/multimodal_llm/neva/sequence_packing/preprocess_dataset.py
@@ -60,6 +60,7 @@
 from tqdm import tqdm
 
 from nemo.collections.multimodal.data.neva.neva_dataset import make_supervised_data_module
+from nemo.collections.multimodal.parts.utils import create_image_processor
 from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
 from nemo.utils import logging
 
@@ -254,8 +255,14 @@ def main():
     nemo_config.model.data.conv_template = args.conv_template
     nemo_config.model.data.image_aspect_ratio = args.image_aspect_ratio
 
-    tokenizer = get_nmt_tokenizer(library="sentencepiece", tokenizer_model=args.tokenizer_path,)
-    train_ds = make_supervised_data_module(tokenizer=tokenizer, model_cfg=nemo_config.model)["train_dataset"]
+    tokenizer = get_nmt_tokenizer(
+        library="sentencepiece",
+        tokenizer_model=args.tokenizer_path,
+    )
+    image_processor = create_image_processor(nemo_config.model.mm_cfg)
+    train_ds = make_supervised_data_module(
+        tokenizer=tokenizer, image_processor=image_processor, model_cfg=nemo_config.model
+    )["train_dataset"]
     train_dl = DataLoader(train_ds, num_workers=32, collate_fn=None, shuffle=False)
     # Example shape: {'tokens': torch.Size([1, 344]), 'labels': torch.Size([1, 344]), 'image': torch.Size([1, 1, 3, 224, 224])}
 
diff --git a/nemo/collections/multimodal/models/multimodal_llm/neva/neva_model.py b/nemo/collections/multimodal/models/multimodal_llm/neva/neva_model.py
index c4daa13877f5..2fc9bb7e2022 100644
--- a/nemo/collections/multimodal/models/multimodal_llm/neva/neva_model.py
+++ b/nemo/collections/multimodal/models/multimodal_llm/neva/neva_model.py
@@ -23,10 +23,9 @@
 from omegaconf.dictconfig import DictConfig
 from pkg_resources import packaging
 from pytorch_lightning.trainer.trainer import Trainer
-from transformers import CLIPImageProcessor, CLIPVisionModel, SiglipImageProcessor, SiglipVisionModel
+from transformers import CLIPVisionModel, SiglipVisionModel
 
 from nemo.collections.common.parts.utils import extend_instance
-from nemo.collections.multimodal.data.clip.augmentations.augmentations import image_transform
 from nemo.collections.multimodal.data.neva.conversation import DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN
 from nemo.collections.multimodal.data.neva.neva_dataset import (
     DataCollatorForSupervisedDataset,
@@ -37,7 +36,7 @@
     CLIPVisionTransformer,
     MegatronCLIPModel,
 )
-from nemo.collections.multimodal.parts.utils import load_nemo_model_weights
+from nemo.collections.multimodal.parts.utils import create_image_processor, load_nemo_model_weights
 from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import MegatronPretrainingSampler
 from nemo.collections.nlp.models.language_modeling.megatron.gpt_model import GPTModel
 from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel, get_specs
@@ -308,9 +307,6 @@ def create_vision_encoder_and_processor(self, mm_cfg):
                     for param in vision_encoder.parameters():
                         param.requires_grad = False
                     vision_encoder = vision_encoder.eval()
-                image_processor = CLIPImageProcessor.from_pretrained(
-                    mm_cfg.vision_encoder.from_pretrained, torch_dtype=torch.bfloat16
-                )
             elif "siglip" in mm_cfg.vision_encoder.from_pretrained:
                 vision_encoder = SiglipVisionModel.from_pretrained(
                     mm_cfg.vision_encoder.from_pretrained,
@@ -321,9 +317,6 @@ def create_vision_encoder_and_processor(self, mm_cfg):
                     for param in vision_encoder.parameters():
                         param.requires_grad = False
                     vision_encoder = vision_encoder.eval()
-                image_processor = SiglipImageProcessor.from_pretrained(
-                    mm_cfg.vision_encoder.from_pretrained, torch_dtype=torch.bfloat16
-                )
             else:
                 raise (ValueError("Currently only support CLIPVisionModel and SigLipVisionModel from Huggingface"))
         else:
@@ -334,13 +327,8 @@ def create_vision_encoder_and_processor(self, mm_cfg):
             self.load_vision_encoder_weights(vision_encoder, mm_cfg.vision_encoder.from_pretrained)
             if mm_cfg.vision_encoder.freeze:
                 vision_encoder.freeze()
-            crop_size = mm_cfg.get("crop_size", (224, 224))
-            image_processor = image_transform(
-                crop_size,
-                is_train=False,
-                mean=None,
-                std=None,
-            )
+
+        image_processor = create_image_processor(mm_cfg)
 
         return vision_encoder, image_processor
 
diff --git a/nemo/collections/multimodal/parts/utils.py b/nemo/collections/multimodal/parts/utils.py
index 6d55ad015444..dc7ad86a0f23 100644
--- a/nemo/collections/multimodal/parts/utils.py
+++ b/nemo/collections/multimodal/parts/utils.py
@@ -21,7 +21,8 @@
 from PIL import Image
 from pytorch_lightning import Trainer
 from pytorch_lightning.plugins.environments import TorchElasticEnvironment
-from transformers import CLIPImageProcessor
+from transformers import CLIPImageProcessor, SiglipImageProcessor
+from nemo.collections.multimodal.data.clip.augmentations.augmentations import image_transform
 
 from nemo.collections.multimodal.data.neva.neva_dataset import process_image
 from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel
@@ -508,3 +509,27 @@ def expand2square(pil_img, background_color):
         return media_tensors.unsqueeze(dim=0).unsqueeze(dim=0)
 
     return model, image_processor, video_processor
+
+
+def create_image_processor(mm_cfg):
+    if mm_cfg.vision_encoder.get("from_hf", False):
+        if "clip" in mm_cfg.vision_encoder.from_pretrained:
+            image_processor = CLIPImageProcessor.from_pretrained(
+                mm_cfg.vision_encoder.from_pretrained, torch_dtype=torch.bfloat16
+            )
+        elif "siglip" in mm_cfg.vision_encoder.from_pretrained:
+            image_processor = SiglipImageProcessor.from_pretrained(
+                mm_cfg.vision_encoder.from_pretrained, torch_dtype=torch.bfloat16
+            )
+        else:
+            raise (ValueError("Currently only support CLIPImageProcessor and SiglipImageProcessor from Huggingface"))
+    else:
+        #Corresponds to MegatronCLIPModel
+        crop_size = mm_cfg.get("crop_size", (224, 224))
+        image_processor = image_transform(
+            crop_size,
+            is_train=False,
+            mean=None,
+            std=None,
+        )
+    return image_processor