diff --git a/comfy_extras/nodes_video_model.py b/comfy_extras/nodes_video_model.py
index 97792b92..92c5ddeb 100644
--- a/comfy_extras/nodes_video_model.py
+++ b/comfy_extras/nodes_video_model.py
@@ -33,10 +33,9 @@ class SVD_img2vid_Conditioning:
                               "video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}),
                               "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}),
                               "fps": ("INT", {"default": 6, "min": 1, "max": 1024}),
-                              "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01})
-                             },
-                "optional":  { "noise_seed": ("INT", {"default": 0, "min": 0, "max": 2**32-1}),
-                             }
+                              "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "noise_seed": ("INT", {"default": 0, "min": 0, "max": 2**32-1}),
+                    }
                 }
     RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
     RETURN_NAMES = ("positive", "negative", "latent")
@@ -46,15 +45,14 @@ class SVD_img2vid_Conditioning:
     CATEGORY = "conditioning/video_models"
 
     def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id,
-               fps, augmentation_level, noise_seed=None):
+               fps, augmentation_level, noise_seed):
         output = clip_vision.encode_image(init_image)
         pooled = output.image_embeds.unsqueeze(0)
         pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
         encode_pixels = pixels[:,:,:,:3]
         if augmentation_level > 0:
-            if noise_seed is not None:
-                torch.manual_seed(noise_seed)
-            encode_pixels += torch.randn_like(pixels) * augmentation_level
+            generator = torch.manual_seed(noise_seed)
+            encode_pixels += torch.randn(pixels.shape, generator=generator) * augmentation_level
         t = vae.encode(encode_pixels)
         positive = [[pooled, {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": t}]]
         negative = [[torch.zeros_like(pooled), {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": torch.zeros_like(t)}]]