diff --git a/setup.py b/setup.py
index c74f398..9f75b95 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@ with open('README.md') as f:
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '1.11.1',
+  version = '1.11.2',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   long_description = long_description,
diff --git a/vit_pytorch/accept_video_wrapper.py b/vit_pytorch/accept_video_wrapper.py
index 297a670..108ffe9 100644
--- a/vit_pytorch/accept_video_wrapper.py
+++ b/vit_pytorch/accept_video_wrapper.py
@@ -1,3 +1,5 @@
+from contextlib import nullcontext
+
 import torch
 from torch import is_tensor, randn
 from torch.nn import Module, Parameter
@@ -40,7 +42,8 @@ class AcceptVideoWrapper(Module):
 
     def forward(
         self,
-        video # (b c t h w)
+        video, # (b c t h w)
+        eval_with_no_grad = False
     ):
         add_time_pos_emb = self.add_time_pos_emb
         time = video.shape[2]
@@ -54,9 +57,17 @@ class AcceptVideoWrapper(Module):
 
         video = rearrange(video, 'b t ... -> (b t) ...')
 
+        # forward through image net for outputs
+
         func = getattr(self.image_net, self.forward_function)
 
-        outputs = func(video)
+        if eval_with_no_grad:
+            self.image_net.eval()
+
+        context = torch.no_grad if eval_with_no_grad else nullcontext
+
+        with context():
+            outputs = func(video)
 
         # handle multiple outputs, say logits and embeddings returned from extractor - also handle some reduce aux loss being returned
 
@@ -111,7 +122,7 @@ if __name__ == '__main__':
 
     video_acceptor = AcceptVideoWrapper(v, add_time_pos_emb = True, output_pos_add_pos_emb = 1, time_seq_len = 10, dim_emb = 1024)
 
-    logits, embeddings = video_acceptor(videos) # always (batch, channels, time, height, width) - time is always dimension 2
+    logits, embeddings = video_acceptor(videos, eval_with_no_grad = True) # always (batch, channels, time, height, width) - time is always dimension 2
 
     assert logits.shape == (1, 10, 1000)
     assert embeddings.shape == (1, 10, 65, 1024)