rename: Similarity-Adaptive Guidance → Positive-Biased Guidance (per reviewer)

- 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

@@ -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=[
@@ -20,16 +20,22 @@ 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']
+            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
@@ -42,6 +48,7 @@ class Mahiro(io.ComfyNode):
             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)