From 8c41e2393b07842b7f00461c52002293b63d27a2 Mon Sep 17 00:00:00 2001
From: bymyself <cbyrne@comfy.org>
Date: Fri, 27 Feb 2026 17:13:40 -0800
Subject: [PATCH] =?UTF-8?q?rename:=20Similarity-Adaptive=20Guidance=20?=
 =?UTF-8?q?=E2=86=92=20Positive-Biased=20Guidance=20(per=20reviewer)?=
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- display_name changed to 'Positive-Biased Guidance' to avoid SAG acronym collision
- search_aliases expanded: mahiro, mahiro cfg, similarity-adaptive guidance, positive-biased cfg
- ruff format applied
---
 comfy_extras/nodes_mahiro.py | 25 ++++++++++++++++---------
 1 file changed, 16 insertions(+), 9 deletions(-)

diff --git a/comfy_extras/nodes_mahiro.py b/comfy_extras/nodes_mahiro.py
index d01d69429..a25226e6d 100644
--- a/comfy_extras/nodes_mahiro.py
+++ b/comfy_extras/nodes_mahiro.py
@@ -10,7 +10,7 @@ class Mahiro(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Mahiro",
-            display_name="Similarity-Adaptive Guidance",
+            display_name="Positive-Biased Guidance",
             category="_for_testing",
             description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.",
             inputs=[
@@ -20,28 +20,35 @@ class Mahiro(io.ComfyNode):
                 io.Model.Output(display_name="patched_model"),
             ],
             is_experimental=True,
-            search_aliases=["mahiro", "mahiro cfg"],
+            search_aliases=[
+                "mahiro",
+                "mahiro cfg",
+                "similarity-adaptive guidance",
+                "positive-biased cfg",
+            ],
         )
 
     @classmethod
     def execute(cls, model) -> io.NodeOutput:
         m = model.clone()
+
         def mahiro_normd(args):
-            scale: float = args['cond_scale']
-            cond_p: torch.Tensor = args['cond_denoised']
-            uncond_p: torch.Tensor = args['uncond_denoised']
-            #naive leap
+            scale: float = args["cond_scale"]
+            cond_p: torch.Tensor = args["cond_denoised"]
+            uncond_p: torch.Tensor = args["uncond_denoised"]
+            # naive leap
             leap = cond_p * scale
-            #sim with uncond leap
+            # sim with uncond leap
             u_leap = uncond_p * scale
             cfg = args["denoised"]
             merge = (leap + cfg) / 2
             normu = torch.sqrt(u_leap.abs()) * u_leap.sign()
             normm = torch.sqrt(merge.abs()) * merge.sign()
             sim = F.cosine_similarity(normu, normm).mean()
-            simsc = 2 * (sim+1)
-            wm = (simsc*cfg + (4-simsc)*leap) / 4
+            simsc = 2 * (sim + 1)
+            wm = (simsc * cfg + (4 - simsc) * leap) / 4
             return wm
+
         m.set_model_sampler_post_cfg_function(mahiro_normd)
         return io.NodeOutput(m)