diff --git a/magicanimate/models/orig_attention.py b/magicanimate/models/orig_attention.py
index 9c3eba09..fd4ab237 100644
--- a/magicanimate/models/orig_attention.py
+++ b/magicanimate/models/orig_attention.py
@@ -114,7 +114,7 @@ def __init__(
         self.attention_head_dim = attention_head_dim
         inner_dim = num_attention_heads * attention_head_dim
 
-        # 1. Transformer2DModel can process both standard continous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
+        # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
         # Define whether input is continuous or discrete depending on configuration
         self.is_input_continuous = in_channels is not None
         self.is_input_vectorized = num_vector_embeds is not None
@@ -185,7 +185,7 @@ def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, retu
         """
         Args:
             hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`.
-                When continous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input
+                When continuous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input
                 hidden_states
             encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
                 Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
@@ -985,4 +985,4 @@ def forward(
         if not return_dict:
             return (output_states,)
 
-        return Transformer2DModelOutput(sample=output_states)
\ No newline at end of file
+        return Transformer2DModelOutput(sample=output_states)