diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index ff8d63bab..6617a0575 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -173,10 +173,12 @@ class CFGDenoiser(torch.nn.Module):
         else:
             denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
         if self.mask is not None:
-            init_latent = self.init_latent
             if devices.backend == "directml":
-                init_latent = init_latent.float()
-            denoised = init_latent * self.mask + self.nmask * denoised
+                self.init_latent = self.init_latent.float()
+                denoised = self.init_latent * self.mask + self.nmask * denoised
+                self.init_latent = self.init_latent.half()
+            else:
+                denoised = self.init_latent * self.mask + self.nmask * denoised
         after_cfg_callback_params = AfterCFGCallbackParams(denoised, shared.state.sampling_step, shared.state.sampling_steps)
         cfg_after_cfg_callback(after_cfg_callback_params)
         denoised = after_cfg_callback_params.x
@@ -336,7 +338,8 @@ class KDiffusionSampler:
             's_min_uncond': self.s_min_uncond
         }
         samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
-        return samples.type(devices.dtype)
+        samples = samples.type(devices.dtype)
+        return samples
 
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps = steps or p.steps