diff --git a/nodes.py b/nodes.py
index a6e2de8..8818ace 100644
--- a/nodes.py
+++ b/nodes.py
@@ -571,6 +571,9 @@ class MochiDecode:
             "tile_overlap_factor_height": ("FLOAT", {"default": 0.1666, "min": 0.0, "max": 1.0, "step": 0.001}),
             "tile_overlap_factor_width": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001}),
             },
+            "optional": {
+                "unnormalize": ("BOOLEAN", {"default": False, "tooltip": "Unnormalize the latents before decoding"}),
+                },
         }
 
     RETURN_TYPES = ("IMAGE",)
@@ -579,12 +582,13 @@ class MochiDecode:
     CATEGORY = "MochiWrapper"
 
     def decode(self, vae, samples, enable_vae_tiling, tile_sample_min_height, tile_sample_min_width, tile_overlap_factor_height, 
-               tile_overlap_factor_width, auto_tile_size, frame_batch_size):
+               tile_overlap_factor_width, auto_tile_size, frame_batch_size, unnormalize=False):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
         intermediate_device = mm.intermediate_device()
         samples = samples["samples"]
-        samples = dit_latents_to_vae_latents(samples)
+        if unnormalize:
+            samples = dit_latents_to_vae_latents(samples)
         samples = samples.to(vae.dtype).to(device)
 
         B, C, T, H, W = samples.shape
@@ -699,6 +703,9 @@ class MochiDecodeSpatialTiling:
             "min_block_size": ("INT", {"default": 1, "min": 1, "max": 256, "step": 1, "tooltip": "Minimum number of pixels in each dimension when subdividing"}),
             "per_batch": ("INT", {"default": 6, "min": 1, "max": 256, "step": 1, "tooltip": "Number of samples per batch, in latent space (6 frames in 1 latent)"}),
             },
+            "optional": {
+                "unnormalize": ("BOOLEAN", {"default": True, "tooltip": "Unnormalize the latents before decoding"}),
+                },
         }
 
     RETURN_TYPES = ("IMAGE",)
@@ -707,12 +714,13 @@ class MochiDecodeSpatialTiling:
     CATEGORY = "MochiWrapper"
 
     def decode(self, vae, samples, enable_vae_tiling, num_tiles_w, num_tiles_h, overlap, 
-               min_block_size, per_batch):
+               min_block_size, per_batch, unnormalize=True):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
         intermediate_device = mm.intermediate_device()
         samples = samples["samples"]
-        samples = dit_latents_to_vae_latents(samples)
+        if unnormalize:
+            samples = dit_latents_to_vae_latents(samples)
         samples = samples.to(vae.dtype).to(device)
 
         B, C, T, H, W = samples.shape
@@ -768,14 +776,17 @@ class MochiImageEncode:
             "overlap": ("INT", {"default": 16, "min": 0, "max": 256, "step": 1, "tooltip": "Number of pixel of overlap between adjacent tiles"}),
             "min_block_size": ("INT", {"default": 1, "min": 1, "max": 256, "step": 1, "tooltip": "Minimum number of pixels in each dimension when subdividing"}),
             },
+            "optional": {
+                "normalize": ("BOOLEAN", {"default": True, "tooltip": "Normalize the images before encoding"}),
+                },
         }
 
     RETURN_TYPES = ("LATENT",)
     RETURN_NAMES = ("samples",)
-    FUNCTION = "decode"
+    FUNCTION = "encode"
     CATEGORY = "MochiWrapper"
 
-    def decode(self, encoder, images, enable_vae_tiling, num_tiles_w, num_tiles_h, overlap, min_block_size):
+    def encode(self, encoder, images, enable_vae_tiling, num_tiles_w, num_tiles_h, overlap, min_block_size, normalize=True):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
         intermediate_device = mm.intermediate_device()
@@ -800,7 +811,8 @@ class MochiImageEncode:
                     latents = apply_tiled(encoder, video, num_tiles_w = num_tiles_w, num_tiles_h = num_tiles_h, overlap=overlap, min_block_size=min_block_size)
                 else:
                     latents = encoder(video)
-        latents = vae_latents_to_dit_latents(latents)
+        if normalize:
+            latents = vae_latents_to_dit_latents(latents)
         print("encoder output",latents.shape)
 
         return ({"samples": latents},)