diff --git a/README.md b/README.md
index db1fdaf3c928..b0731db33bb0 100644
--- a/README.md
+++ b/README.md
@@ -255,7 +255,7 @@ This is the command to install the Pytorch xpu nightly which might have some per
 
 Nvidia users should install stable pytorch using this command:
 
-```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu129```
+```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130```
 
 This is the command to install pytorch nightly instead which might have performance improvements.
 
diff --git a/comfy/patcher_extension.py b/comfy/patcher_extension.py
index 46cc7b2a8858..5ee4d5ee5523 100644
--- a/comfy/patcher_extension.py
+++ b/comfy/patcher_extension.py
@@ -150,7 +150,7 @@ def merge_nested_dicts(dict1: dict, dict2: dict, copy_dict1=True):
     for key, value in dict2.items():
         if isinstance(value, dict):
             curr_value = merged_dict.setdefault(key, {})
-            merged_dict[key] = merge_nested_dicts(value, curr_value)
+            merged_dict[key] = merge_nested_dicts(curr_value, value)
         elif isinstance(value, list):
             merged_dict.setdefault(key, []).extend(value)
         else:
diff --git a/comfy/samplers.py b/comfy/samplers.py
index c59e296a1ad3..e7efaf4705d3 100755
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -306,17 +306,10 @@ def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tens
                                                                                  copy_dict1=False)
 
             if patches is not None:
-                # TODO: replace with merge_nested_dicts function
-                if "patches" in transformer_options:
-                    cur_patches = transformer_options["patches"].copy()
-                    for p in patches:
-                        if p in cur_patches:
-                            cur_patches[p] = cur_patches[p] + patches[p]
-                        else:
-                            cur_patches[p] = patches[p]
-                    transformer_options["patches"] = cur_patches
-                else:
-                    transformer_options["patches"] = patches
+                transformer_options["patches"] = comfy.patcher_extension.merge_nested_dicts(
+                    transformer_options.get("patches", {}),
+                    patches
+                )
 
             transformer_options["cond_or_uncond"] = cond_or_uncond[:]
             transformer_options["uuids"] = uuids[:]
diff --git a/comfy_api_nodes/nodes_veo2.py b/comfy_api_nodes/nodes_veo2.py
index 4588a7991c9d..4ab5c518614d 100644
--- a/comfy_api_nodes/nodes_veo2.py
+++ b/comfy_api_nodes/nodes_veo2.py
@@ -27,6 +27,13 @@
 )
 
 AVERAGE_DURATION_VIDEO_GEN = 32
+MODELS_MAP = {
+    "veo-2.0-generate-001": "veo-2.0-generate-001",
+    "veo-3.1-generate": "veo-3.1-generate-preview",
+    "veo-3.1-fast-generate": "veo-3.1-fast-generate-preview",
+    "veo-3.0-generate-001": "veo-3.0-generate-001",
+    "veo-3.0-fast-generate-001": "veo-3.0-fast-generate-001",
+}
 
 def convert_image_to_base64(image: torch.Tensor):
     if image is None:
@@ -158,6 +165,7 @@ async def execute(
         model="veo-2.0-generate-001",
         generate_audio=False,
     ):
+        model = MODELS_MAP[model]
         # Prepare the instances for the request
         instances = []
 
@@ -385,7 +393,7 @@ def define_schema(cls):
                 ),
                 IO.Combo.Input(
                     "model",
-                    options=["veo-3.0-generate-001", "veo-3.0-fast-generate-001"],
+                    options=list(MODELS_MAP.keys()),
                     default="veo-3.0-generate-001",
                     tooltip="Veo 3 model to use for video generation",
                     optional=True,
diff --git a/requirements.txt b/requirements.txt
index a450579708bc..82457df54a74 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.28.6
+comfyui-frontend-package==1.28.7
 comfyui-workflow-templates==0.1.95
 comfyui-embedded-docs==0.3.0
 torch