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Update orig_attention.py #60

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Update orig_attention.py #60

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6 changes: 3 additions & 3 deletions magicanimate/models/orig_attention.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -985,4 +985,4 @@ def forward(
if not return_dict:
return (output_states,)

return Transformer2DModelOutput(sample=output_states)
return Transformer2DModelOutput(sample=output_states)