AI/diffusers
1
0
Fork 0
mirror of https://github.com/huggingface/diffusers.git synced 2026-01-27 17:22:53 +03:00
Code Activity

Width was typod as weight (#1800)

Browse Source 
* Width was typod as weight

* Run Black
This commit is contained in:
William Held
2022-12-27 09:09:21 -05:00
committed by GitHub
parent 68e24259af
commit 8a4c3e50bd
1 changed files with 5 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files

14
src/diffusers/models/attention.py
View File

@@ -204,17 +204,17 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
"""
# 1. Input
if self.is_input_continuous:
batch, channel, height, weight = hidden_states.shape
batch, channel, height, width = hidden_states.shape
residual = hidden_states
hidden_states = self.norm(hidden_states)
if not self.use_linear_projection:
hidden_states = self.proj_in(hidden_states)
inner_dim = hidden_states.shape[1]
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
else:
inner_dim = hidden_states.shape[1]
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
hidden_states = self.proj_in(hidden_states)
elif self.is_input_vectorized:
hidden_states = self.latent_image_embedding(hidden_states)
@@ -231,15 +231,11 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
# 3. Output
if self.is_input_continuous:
if not self.use_linear_projection:
hidden_states = (
hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
)
hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
hidden_states = self.proj_out(hidden_states)
else:
hidden_states = self.proj_out(hidden_states)
hidden_states = (
hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
)
hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
output = hidden_states + residual
elif self.is_input_vectorized: