diff --git a/modules/processing.py b/modules/processing.py
index f299e04da2cbbeabacf88eb695822f538a7ab992..e544c2e164d0a7bacbeb7557f731625b2f7ed785 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -186,7 +186,7 @@ class StableDiffusionProcessing:
         return conditioning
 
     def edit_image_conditioning(self, source_image):
-        conditioning_image = self.sd_model.encode_first_stage(source_image).mode()
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
 
         return conditioning_image
 
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 31ee22d3f2f77ae4794a0ff65130131e1771d487..aa7f106b33d92f08e01ef02b9f5f929017b497b0 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -77,9 +77,9 @@ class CFGDenoiser(torch.nn.Module):
         batch_size = len(conds_list)
         repeats = [len(conds_list[i]) for i in range(batch_size)]
 
-        x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
-        sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
-        image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [image_cond])
+        x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+        image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
+        sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
 
         denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
         cfg_denoiser_callback(denoiser_params)
@@ -88,7 +88,7 @@ class CFGDenoiser(torch.nn.Module):
         sigma_in = denoiser_params.sigma
 
         if tensor.shape[1] == uncond.shape[1]:
-            cond_in = torch.cat([tensor, uncond, uncond])
+            cond_in = torch.cat([tensor, uncond])
 
             if shared.batch_cond_uncond:
                 x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})