diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
index fb31215db..c0345da42 100644
--- a/ldm/models/diffusion/ddim.py
+++ b/ldm/models/diffusion/ddim.py
@@ -184,7 +184,7 @@ def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=F
         alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
         alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
         sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+        sigmas = self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
         # select parameters corresponding to the currently considered timestep
         a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
         a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
@@ -238,4 +238,4 @@ def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unco
             x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
                                           unconditional_guidance_scale=unconditional_guidance_scale,
                                           unconditional_conditioning=unconditional_conditioning)
-        return x_dec
\ No newline at end of file
+        return x_dec
diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py
index 78eeb1003..9be342abd 100644
--- a/ldm/models/diffusion/plms.py
+++ b/ldm/models/diffusion/plms.py
@@ -194,7 +194,7 @@ def get_model_output(x, t):
         alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
         alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
         sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+        sigmas = self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
 
         def get_x_prev_and_pred_x0(e_t, index):
             # select parameters corresponding to the currently considered timestep