diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
index b8ff309..edf1eaf 100644
--- a/ldm/models/diffusion/ddim.py
+++ b/ldm/models/diffusion/ddim.py
@@ -166,11 +166,14 @@ class DDIMSampler(object):
                       temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
                       unconditional_guidance_scale=1., unconditional_conditioning=None):
         b, *_, device = *x.shape, x.device
-        e_t = self.model.apply_model(x, t, c)
 
-        if unconditional_guidance_scale != 1.:
-            assert unconditional_conditioning is not None
-            e_t_uncond =  self.model.apply_model(x, t, unconditional_conditioning)
+        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+            e_t = self.model.apply_model(x, t, c)
+        else:
+            x_in = torch.cat([x] * 2)
+            t_in = torch.cat([t] * 2)
+            c_in = torch.cat([unconditional_conditioning, c])
+            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
             e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
 
         if score_corrector is not None:
diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py
index 91a792f..78eeb10 100644
--- a/ldm/models/diffusion/plms.py
+++ b/ldm/models/diffusion/plms.py
@@ -176,11 +176,13 @@ class PLMSSampler(object):
         b, *_, device = *x.shape, x.device
 
         def get_model_output(x, t):
-            e_t = self.model.apply_model(x, t, c)
-
-            if unconditional_guidance_scale != 1.:
-                assert unconditional_conditioning is not None
-                e_t_uncond =  self.model.apply_model(x, t, unconditional_conditioning)
+            if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+                e_t = self.model.apply_model(x, t, c)
+            else:
+                x_in = torch.cat([x] * 2)
+                t_in = torch.cat([t] * 2)
+                c_in = torch.cat([unconditional_conditioning, c])
+                e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
                 e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
 
             if score_corrector is not None: