diff --git a/diffusion/datasets/image_caption_latents.py b/diffusion/datasets/image_caption_latents.py
index dd2cbcc8..18fb2ffa 100644
--- a/diffusion/datasets/image_caption_latents.py
+++ b/diffusion/datasets/image_caption_latents.py
@@ -33,6 +33,7 @@ class StreamingImageCaptionLatentsDataset(StreamingDataset):
         image_key (str): Key associated with the image in the streaming dataset. Default: ``'image'``.
         caption_keys (Tuple[str, ...]): Key(s) associated with captions in the streaming dataset. Default: ``('caption',)``.
         caption_selection_probs (Tuple[float, ...]): The probability of selecting each caption key. Default: ``(1.0,)``.
+        aspect_ratio_bucket_key (str, optional): Key associated with the aspect ratio bucket in the streaming dataset. Default: ``None``.
         text_latent_keys (Tuple[str, ...]): Key(s) associated with text latents in the streaming dataset.
             Default: ``('T5_LATENTS', 'CLIP_LATENTS')``.
         text_latent_shapes (Tuple[Tuple[int, int], ...]): The shape(s) of the text latents in the streaming dataset.
@@ -55,6 +56,7 @@ def __init__(
         image_key: str = 'image',
         caption_keys: Tuple[str, ...] = ('caption',),
         caption_selection_probs: Tuple[float, ...] = (1.0,),
+        aspect_ratio_bucket_key: Optional[str] = None,
         text_latent_keys: Tuple[str, ...] = ('T5_LATENTS', 'CLIP_LATENTS'),
         text_latent_shapes: Tuple[Tuple[int, int], ...] = ((512, 4096), (77, 768)),
         attention_mask_keys: Tuple[str, ...] = ('T5_ATTENTION_MASK', 'CLIP_ATTENTION_MASK'),
@@ -75,6 +77,9 @@ def __init__(
         self.image_key = image_key
         self.caption_keys = caption_keys
         self.caption_selection_probs = caption_selection_probs
+        self.aspect_ratio_bucket_key = aspect_ratio_bucket_key
+        if isinstance(self.crop, RandomCropBucketedAspectRatioTransform):
+            assert self.aspect_ratio_bucket_key is not None, 'aspect_ratio_bucket_key must be provided when using RandomCropBucketedAspectRatioTransform'
         self.text_latent_keys = text_latent_keys
         self.text_latent_shapes = text_latent_shapes
         self.attention_mask_keys = attention_mask_keys
@@ -94,15 +99,16 @@ def __getitem__(self, index):
         out['cond_original_size'] = torch.tensor(img.size)
 
         # Image transforms
-        if self.crop is not None:
+        if isinstance(self.crop, RandomCropBucketedAspectRatioTransform):
+            img, crop_top, crop_left = self.crop(img, sample[self.aspect_ratio_bucket_key])
+        elif self.crop is not None:
             img, crop_top, crop_left = self.crop(img)
         else:
             crop_top, crop_left = 0, 0
-        out['cond_crops_coords_top_left'] = torch.tensor([crop_top, crop_left])
-
         if self.transform is not None:
             img = self.transform(img)
         out['image'] = img
+        out['cond_crops_coords_top_left'] = torch.tensor([crop_top, crop_left])
 
         # Get the new height and width
         if isinstance(img, torch.Tensor):
@@ -264,6 +270,7 @@ def build_streaming_image_caption_latents_dataloader(
         image_key=image_key,
         caption_keys=caption_keys,
         caption_selection_probs=caption_selection_probs,
+        aspect_ratio_bucket_key=aspect_ratio_bucket_key,
         text_latent_keys=text_latent_keys,
         text_latent_shapes=text_latent_shapes,
         attention_mask_keys=attention_mask_keys,