diff --git a/comfy_extras/nodes_longcat_image.py b/comfy_extras/nodes_longcat_image.py
index 5541c5ee3..6e8178cdb 100644
--- a/comfy_extras/nodes_longcat_image.py
+++ b/comfy_extras/nodes_longcat_image.py
@@ -1,3 +1,4 @@
+import torch
 from typing_extensions import override
 from comfy_api.latest import ComfyExtension, io
 
@@ -28,11 +29,71 @@ class CLIPTextEncodeLongCatImage(io.ComfyNode):
     encode = execute
 
 
+class CFGRenormLongCatImage(io.ComfyNode):
+    """Per-patch CFG renormalization matching HuggingFace's LongCat-Image pipeline.
+
+    After standard CFG combination, rescales the noise prediction at each 2x2 patch
+    so its norm doesn't exceed the conditional prediction's norm.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CFGRenormLongCatImage",
+            display_name="CFG Renorm (LongCat-Image)",
+            category="advanced/model/longcat",
+            description="Applies per-patch CFG renormalization used by the LongCat-Image pipeline. Connect between the model loader and the sampler.",
+            inputs=[
+                io.Model.Input("model"),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model) -> io.NodeOutput:
+        def cfg_renorm_post(args):
+            denoised = args["denoised"]
+            cond_denoised = args["cond_denoised"]
+            x = args["input"]
+
+            B, C, H, W = denoised.shape
+            ps = 2
+
+            noise = x - denoised
+            noise_cond = x - cond_denoised
+
+            noise_packed = noise.reshape(B, C, H // ps, ps, W // ps, ps) \
+                               .permute(0, 2, 4, 1, 3, 5) \
+                               .reshape(B, -1, C * ps * ps)
+            cond_packed = noise_cond.reshape(B, C, H // ps, ps, W // ps, ps) \
+                                    .permute(0, 2, 4, 1, 3, 5) \
+                                    .reshape(B, -1, C * ps * ps)
+
+            noise_norm = torch.norm(noise_packed, dim=-1, keepdim=True)
+            cond_norm = torch.norm(cond_packed, dim=-1, keepdim=True)
+
+            scale = (cond_norm / (noise_norm + 1e-8)).clamp(min=0.0, max=1.0)
+
+            renormed = (noise_packed * scale) \
+                .reshape(B, H // ps, W // ps, C, ps, ps) \
+                .permute(0, 3, 1, 4, 2, 5) \
+                .reshape(B, C, H, W)
+
+            return x - renormed
+
+        m = model.clone()
+        m.set_model_sampler_post_cfg_function(cfg_renorm_post, disable_cfg1_optimization=True)
+        return io.NodeOutput(m)
+
+
 class LongCatImageExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
         return [
             CLIPTextEncodeLongCatImage,
+            CFGRenormLongCatImage,
         ]