diff --git a/src/refiners/foundationals/latent_diffusion/solvers/ddim.py b/src/refiners/foundationals/latent_diffusion/solvers/ddim.py
index 77423de..5f6c576 100644
--- a/src/refiners/foundationals/latent_diffusion/solvers/ddim.py
+++ b/src/refiners/foundationals/latent_diffusion/solvers/ddim.py
@@ -65,7 +65,7 @@ class DDIM(Solver):
         Returns:
             The denoised version of the input data `x`.
         """
-        assert self.first_inference_step <= step < self.num_inference_steps, "invalid step {step}"
+        assert self.first_inference_step <= step < self.num_inference_steps, f"invalid step {step}"
 
         timestep, previous_timestep = (
             self.timesteps[step],
diff --git a/src/refiners/foundationals/latent_diffusion/solvers/dpm.py b/src/refiners/foundationals/latent_diffusion/solvers/dpm.py
index b2296dc..01caf3d 100644
--- a/src/refiners/foundationals/latent_diffusion/solvers/dpm.py
+++ b/src/refiners/foundationals/latent_diffusion/solvers/dpm.py
@@ -308,7 +308,7 @@ class DPMSolver(Solver):
         Returns:
             The denoised version of the input data `x`.
         """
-        assert self.first_inference_step <= step < self.num_inference_steps, "invalid step {step}"
+        assert self.first_inference_step <= step < self.num_inference_steps, f"invalid step {step}"
 
         scale_factor = self.cumulative_scale_factors[step]
         noise_ratio = self.noise_std[step]
diff --git a/src/refiners/foundationals/latent_diffusion/solvers/euler.py b/src/refiners/foundationals/latent_diffusion/solvers/euler.py
index 1f2aa7c..4945a22 100644
--- a/src/refiners/foundationals/latent_diffusion/solvers/euler.py
+++ b/src/refiners/foundationals/latent_diffusion/solvers/euler.py
@@ -89,7 +89,7 @@ class Euler(Solver):
         Returns:
             The denoised version of the input data `x`.
         """
-        assert self.first_inference_step <= step < self.num_inference_steps, "invalid step {step}"
+        assert self.first_inference_step <= step < self.num_inference_steps, f"invalid step {step}"
 
         if self.params.model_prediction_type == ModelPredictionType.SAMPLE:
             x0 = predicted_noise  # the model does not actually predict the noise but x0