diff --git a/pipeline_stable_diffusion_xl_instantid.py b/pipeline_stable_diffusion_xl_instantid.py
index 664f3fa..64bbb12 100644
--- a/pipeline_stable_diffusion_xl_instantid.py
+++ b/pipeline_stable_diffusion_xl_instantid.py
@@ -743,15 +743,31 @@ class StableDiffusionXLInstantIDPipeline(StableDiffusionXLControlNetPipeline):
         if not output_type == "latent":
             # make sure the VAE is in float32 mode, as it overflows in float16
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+
             if needs_upcasting:
                 self.upcast_vae()
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
-            
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+
+            # unscale/denormalize the latents
+            # denormalize with the mean and std if available and not None
+            has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
+            has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
+            if has_latents_mean and has_latents_std:
+                latents_mean = (
+                    torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents_std = (
+                    torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
+            else:
+                latents = latents / self.vae.config.scaling_factor
+
+            image = self.vae.decode(latents, return_dict=False)[0]
 
             # cast back to fp16 if needed
             if needs_upcasting:
-                self.vae.to(dtype=torch.float16)            
+                self.vae.to(dtype=torch.float16)
         else:
             image = latents
 
diff --git a/pipeline_stable_diffusion_xl_instantid_full.py b/pipeline_stable_diffusion_xl_instantid_full.py
index 328aee7..55bfa9f 100644
--- a/pipeline_stable_diffusion_xl_instantid_full.py
+++ b/pipeline_stable_diffusion_xl_instantid_full.py
@@ -1180,15 +1180,31 @@ class StableDiffusionXLInstantIDPipeline(StableDiffusionXLControlNetPipeline):
         if not output_type == "latent":
             # make sure the VAE is in float32 mode, as it overflows in float16
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+
             if needs_upcasting:
                 self.upcast_vae()
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
-            
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+
+            # unscale/denormalize the latents
+            # denormalize with the mean and std if available and not None
+            has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
+            has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
+            if has_latents_mean and has_latents_std:
+                latents_mean = (
+                    torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents_std = (
+                    torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
+            else:
+                latents = latents / self.vae.config.scaling_factor
+
+            image = self.vae.decode(latents, return_dict=False)[0]
 
             # cast back to fp16 if needed
             if needs_upcasting:
-                self.vae.to(dtype=torch.float16)            
+                self.vae.to(dtype=torch.float16)
         else:
             image = latents