diff --git a/nodes.py b/nodes.py
index 2ab205d..ec3694a 100644
--- a/nodes.py
+++ b/nodes.py
@@ -320,6 +320,7 @@ class DynamiCrafterI2V:
                 "mask": ("MASK",),
                 "frame_window_size": ("INT", {"default": 16, "min": 1, "max": 200, "step": 1}),
                 "frame_window_stride": ("INT", {"default": 4, "min": 1, "max": 200, "step": 1}),
+                "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.0001})
             }
         }
 
@@ -328,7 +329,8 @@ class DynamiCrafterI2V:
     FUNCTION = "process"
     CATEGORY = "DynamiCrafterWrapper"
 
-    def process(self, model, image, clip_vision, positive, negative, cfg, steps, eta, seed, fs, keep_model_loaded, frames, vae_dtype, frame_window_size=16, frame_window_stride=4, mask=None, image2=None):
+    def process(self, model, image, clip_vision, positive, negative, cfg, steps, eta, seed, fs, keep_model_loaded, 
+                frames, vae_dtype, frame_window_size=16, frame_window_stride=4, mask=None, image2=None, augmentation_level=0):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
         mm.unload_all_models()
@@ -354,6 +356,8 @@ class DynamiCrafterI2V:
         with torch.autocast(comfy.model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
             image = image * 2 - 1
             image = image.permute(0, 3, 1, 2).to(dtype).to(device)
+            if augmentation_level > 0:
+                image += torch.randn_like(image) * augmentation_level
 
             B, C, H, W = image.shape
             orig_H, orig_W = H, W
@@ -374,6 +378,10 @@ class DynamiCrafterI2V:
             if image2 is not None:
                 image2 = image2 * 2 - 1
                 image2 = image2.permute(0, 3, 1, 2).to(dtype).to(device)
+
+                if augmentation_level > 0:
+                    image2 += torch.randn_like(image2) * augmentation_level
+
                 if image2.shape != image.shape:
                     image2 = F.interpolate(image, size=(H, W), mode="bicubic")
                 z2 = get_latent_z(self.model, image2.unsqueeze(2)) #bc,1,hw
@@ -516,6 +524,7 @@ class ToonCrafterInterpolation:
             },
             "optional": {
                 "image_embed_ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.0001})
             }
         }
 
@@ -524,7 +533,7 @@ class ToonCrafterInterpolation:
     FUNCTION = "process"
     CATEGORY = "DynamiCrafterWrapper"
 
-    def process(self, model, clip_vision, images, positive, negative, cfg, steps, eta, seed, fs, frames, vae_dtype, image_embed_ratio=1.0):
+    def process(self, model, clip_vision, images, positive, negative, cfg, steps, eta, seed, fs, frames, vae_dtype, image_embed_ratio=1.0, augmentation_level=0):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
         mm.unload_all_models()
@@ -574,13 +583,22 @@ class ToonCrafterInterpolation:
 
                 self.model.first_stage_model.to(device)
 
+
+                if augmentation_level > 0:
+                    image += torch.randn_like(image) * augmentation_level
+                    image2 += torch.randn_like(image) * augmentation_level
+
                 videos = image.unsqueeze(2) # bc1hw
                 videos = repeat(videos, 'b c t h w -> b c (repeat t) h w', repeat=frames//2)
                 videos2 = image2.unsqueeze(2) # bc1hw
                 videos2 = repeat(videos2, 'b c t h w -> b c (repeat t) h w', repeat=frames//2)
                 videos = torch.cat([videos, videos2], dim=2)
 
-                z, hs = get_latent_z_with_hidden_states(self.model, videos)
+                encode_pixels = videos
+                # if augmentation_level > 0:
+                #     encode_pixels += torch.randn_like(videos) * augmentation_level                    
+
+                z, hs = get_latent_z_with_hidden_states(self.model, encode_pixels)
                 hidden_states.append(hs)
 
                 img_tensor_repeat = torch.zeros_like(z)