diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 7b23afc..0bf6550 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -7,7 +7,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.2
+    rev: v0.15.16
     hooks:
       # Run the linter.
       - id: ruff
@@ -18,7 +18,7 @@ repos:
         types_or: [python, pyi, jupyter]
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
       - id: trailing-whitespace
         exclude_types:
diff --git a/src/neurosis/modules/attention.py b/src/neurosis/modules/attention.py
index 2a7f615..d4c5139 100644
--- a/src/neurosis/modules/attention.py
+++ b/src/neurosis/modules/attention.py
@@ -325,11 +325,13 @@ def forward(
 
         b, _, _ = q.shape
         q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(b, t.shape[1], self.heads, self.dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b * self.heads, t.shape[1], self.dim_head)
-            .contiguous(),
+            lambda t: (
+                t.unsqueeze(3)
+                .reshape(b, t.shape[1], self.heads, self.dim_head)
+                .permute(0, 2, 1, 3)
+                .reshape(b * self.heads, t.shape[1], self.dim_head)
+                .contiguous()
+            ),
             (q, k, v),
         )
 
diff --git a/src/neurosis/modules/diffusion/model.py b/src/neurosis/modules/diffusion/model.py
index aa9cd80..571e662 100644
--- a/src/neurosis/modules/diffusion/model.py
+++ b/src/neurosis/modules/diffusion/model.py
@@ -202,11 +202,13 @@ def attention(self, h_: Tensor) -> Tensor:
         q, k, v = map(lambda x: rearrange(x, "b c h w -> b (h w) c"), (q, k, v))
 
         q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(B, t.shape[1], 1, C)
-            .permute(0, 2, 1, 3)
-            .reshape(B * 1, t.shape[1], C)
-            .contiguous(),
+            lambda t: (
+                t.unsqueeze(3)
+                .reshape(B, t.shape[1], 1, C)
+                .permute(0, 2, 1, 3)
+                .reshape(B * 1, t.shape[1], C)
+                .contiguous()
+            ),
             (q, k, v),
         )
         out = xops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)