diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index f8aaac59..136aa8e5 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -156,11 +156,10 @@ class CFGDenoiser(torch.nn.Module):
             if not skip_uncond:
                 x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
 
+        denoised_image_indexes = [x[0][0] for x in conds_list]
         if skip_uncond:
-            #x_out = torch.cat([x_out, x_out[0:batch_size]])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
-            denoised_image_indexes = [x[0][0] for x in conds_list]
             fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
-            x_out = torch.cat([x_out, fake_uncond])
+            x_out = torch.cat([x_out, fake_uncond])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
 
         denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
         cfg_denoised_callback(denoised_params)