diff --git a/k_diffusion/augmentation.py b/k_diffusion/augmentation.py
index 920ef86..917473a 100644
--- a/k_diffusion/augmentation.py
+++ b/k_diffusion/augmentation.py
@@ -93,7 +93,7 @@ class KarrasAugmentWrapper(nn.Module):
     def __init__(self, model):
         super().__init__()
         self.inner_model = model
-    
+
     def forward(self, input, sigma, aug_cond=None, mapping_cond=None, **kwargs):
         if aug_cond is None:
             aug_cond = input.new_zeros([input.shape[0], 9])
diff --git a/k_diffusion/external.py b/k_diffusion/external.py
index 79b51ce..25110de 100644
--- a/k_diffusion/external.py
+++ b/k_diffusion/external.py
@@ -27,14 +27,14 @@ def t_to_sigma(self, t):
         return (t * math.pi / 2).tan()
 
     def loss(self, input, noise, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
+        c_skip, c_out, c_in = (utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma))
         noised_input = input + noise * utils.append_dims(sigma, input.ndim)
         model_output = self.inner_model(noised_input * c_in, self.sigma_to_t(sigma), **kwargs)
         target = (input - c_skip * noised_input) / c_out
         return (model_output - target).pow(2).flatten(1).mean(1)
 
     def forward(self, input, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
+        c_skip, c_out, c_in = (utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma))
         return self.inner_model(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip
 
 
@@ -102,13 +102,13 @@ def get_eps(self, *args, **kwargs):
         return self.inner_model(*args, **kwargs)
 
     def loss(self, input, noise, sigma, **kwargs):
-        c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
+        c_out, c_in = (utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma))
         noised_input = input + noise * utils.append_dims(sigma, input.ndim)
         eps = self.get_eps(noised_input * c_in, self.sigma_to_t(sigma), **kwargs)
         return (eps - noise).pow(2).flatten(1).mean(1)
 
     def forward(self, input, sigma, **kwargs):
-        c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
+        c_out, c_in = (utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma))
         eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
         return input + eps * c_out
 
@@ -156,14 +156,14 @@ def get_v(self, *args, **kwargs):
         return self.inner_model(*args, **kwargs)
 
     def loss(self, input, noise, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
+        c_skip, c_out, c_in = (utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma))
         noised_input = input + noise * utils.append_dims(sigma, input.ndim)
         model_output = self.get_v(noised_input * c_in, self.sigma_to_t(sigma), **kwargs)
         target = (input - c_skip * noised_input) / c_out
         return (model_output - target).pow(2).flatten(1).mean(1)
 
     def forward(self, input, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
+        c_skip, c_out, c_in = (utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma))
         return self.get_v(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip
 
 
diff --git a/k_diffusion/layers.py b/k_diffusion/layers.py
index dfe2a0e..34d7a4d 100644
--- a/k_diffusion/layers.py
+++ b/k_diffusion/layers.py
@@ -73,7 +73,7 @@ def get_scalings(self, sigma):
         return c_skip, c_out, c_in
 
     def loss(self, input, noise, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
+        c_skip, c_out, c_in = (utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma))
         c_weight = self.weighting(sigma)
         noised_input = input + noise * utils.append_dims(sigma, input.ndim)
         model_output = self.inner_model(noised_input * c_in, sigma, **kwargs)
@@ -85,13 +85,13 @@ def loss(self, input, noise, sigma, **kwargs):
         return (sq_error * f_weight).flatten(1).mean(1) * c_weight
 
     def forward(self, input, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
+        c_skip, c_out, c_in = (utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma))
         return self.inner_model(input * c_in, sigma, **kwargs) * c_out + input * c_skip
 
 
 class DenoiserWithVariance(Denoiser):
     def loss(self, input, noise, sigma, **kwargs):
-        c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
+        c_skip, c_out, c_in = (utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma))
         noised_input = input + noise * utils.append_dims(sigma, input.ndim)
         model_output, logvar = self.inner_model(noised_input * c_in, sigma, return_variance=True, **kwargs)
         logvar = utils.append_dims(logvar, model_output.ndim)
@@ -234,10 +234,10 @@ def forward(self, input, cond):
 _kernels = {
     'linear':
         [1 / 8, 3 / 8, 3 / 8, 1 / 8],
-    'cubic': 
+    'cubic':
         [-0.01171875, -0.03515625, 0.11328125, 0.43359375,
         0.43359375, 0.11328125, -0.03515625, -0.01171875],
-    'lanczos3': 
+    'lanczos3':
         [0.003689131001010537, 0.015056144446134567, -0.03399861603975296,
         -0.066637322306633, 0.13550527393817902, 0.44638532400131226,
         0.44638532400131226, 0.13550527393817902, -0.066637322306633,
diff --git a/pyproject.toml b/pyproject.toml
index fed528d..eca1e82 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,3 +1,18 @@
 [build-system]
 requires = ["setuptools"]
 build-backend = "setuptools.build_meta"
+
+[tool.ruff]
+target-version = "py38"
+
+[tool.ruff.lint]
+select = [
+    "B",
+    "E",
+    "F",
+    "W",
+    "UP",
+]
+ignore = [
+    "E501",
+]