diff --git a/ldm/data/laion.py b/ldm/data/laion.py
index bfc7d3c..3593545 100644
--- a/ldm/data/laion.py
+++ b/ldm/data/laion.py
@@ -18,6 +18,59 @@ from webdataset.handlers import warn_and_continue
 from ldm.util import instantiate_from_config
 
 
+class DataWithWings(data.IterableDataset):
+    def __init__(self, min_size, transform=None, target_transform=None):
+        self.min_size = min_size
+        self.transform = transform if transform is not None else nn.Identity()
+        self.target_transform = target_transform if target_transform is not None else nn.Identity()
+        self.kv = OnDiskKV(file='/home/ubuntu/laion5B-watermark-safety-ordered', key_format='q', value_format='ee')
+        self.kv_aesthetic = OnDiskKV(file='/home/ubuntu/laion5B-aesthetic-tags-kv', key_format='q', value_format='e')
+        self.pwatermark_threshold = 0.8
+        self.punsafe_threshold = 0.5
+        self.aesthetic_threshold = 5.
+        self.total_samples = 0
+        self.samples = 0
+        location = 'pipe:aws s3 cp --quiet s3://s-datasets/laion5b/laion2B-data/{000000..231349}.tar -'
+
+        self.inner_dataset = wds.DataPipeline(
+            wds.ResampledShards(location),
+            wds.tarfile_to_samples(handler=wds.warn_and_continue),
+            wds.shuffle(1000, handler=wds.warn_and_continue),
+            wds.decode('pilrgb', handler=wds.warn_and_continue),
+            wds.map(self._add_tags, handler=wds.ignore_and_continue),
+            wds.select(self._filter_predicate),
+            wds.map_dict(jpg=self.transform, txt=self.target_transform, punsafe=self._punsafe_to_class, handler=wds.warn_and_continue),
+            wds.to_tuple('jpg', 'txt', 'punsafe', handler=wds.warn_and_continue),
+        )
+
+    @staticmethod
+    def _compute_hash(url, text):
+        if url is None:
+            url = ''
+        if text is None:
+            text = ''
+        total = (url + text).encode('utf-8')
+        return mmh3.hash64(total)[0]
+
+    def _add_tags(self, x):
+        hsh = self._compute_hash(x['json']['url'], x['txt'])
+        pwatermark, punsafe = self.kv[hsh]
+        aesthetic = self.kv_aesthetic[hsh][0]
+        return {**x, 'pwatermark': pwatermark, 'punsafe': punsafe, 'aesthetic': aesthetic}
+
+    def _punsafe_to_class(self, punsafe):
+        return torch.tensor(punsafe >= self.punsafe_threshold).long()
+
+    def _filter_predicate(self, x):
+        try:
+            return x['pwatermark'] < self.pwatermark_threshold and x['aesthetic'] >= self.aesthetic_threshold and x['json']['original_width'] >= self.min_size and x['json']['original_height'] >= self.min_size
+        except:
+            return False
+
+    def __iter__(self):
+        return iter(self.inner_dataset)
+
+
 def dict_collation_fn(samples, combine_tensors=True, combine_scalars=True):
     """Take a list  of samples (as dictionary) and create a batch, preserving the keys.
     If `tensors` is True, `ndarray` objects are combined into