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refactor: rename Mahiro CFG to Similarity-Adaptive Guidance (#12172)

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* refactor: rename Mahiro CFG to Similarity-Adaptive Guidance

Rename the display name to better describe what the node does:
adaptively blends guidance based on cosine similarity between
positive and negative conditions.

Amp-Thread-ID: https://ampcode.com/threads/T-019c0d36-8b43-745f-b7b2-e35b53f17fa1
Co-authored-by: Amp <amp@ampcode.com>

* feat: add search aliases for old mahiro name

Amp-Thread-ID: https://ampcode.com/threads/T-019c0d36-8b43-745f-b7b2-e35b53f17fa1

* 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

---------

Co-authored-by: Amp <amp@ampcode.com>
Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
This commit is contained in:
Christian Byrne 2026-02-28 20:59:24 -08:00 committed by GitHub
parent 1080bd442a
commit d159142615
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1 changed files with 16 additions and 8 deletions
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24
comfy_extras/nodes_mahiro.py
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@ -10,7 +10,7 @@ class Mahiro(io.ComfyNode):
def define_schema(cls): def define_schema(cls):
return io.Schema( return io.Schema(
node_id="Mahiro", node_id="Mahiro",
display_name="Mahiro CFG", display_name="Positive-Biased Guidance",
category="_for_testing", 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.", description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.",
inputs=[ inputs=[
@ -20,27 +20,35 @@ class Mahiro(io.ComfyNode):
io.Model.Output(display_name="patched_model"), io.Model.Output(display_name="patched_model"),
], ],
is_experimental=True, is_experimental=True,
search_aliases=[
"mahiro",
"mahiro cfg",
"similarity-adaptive guidance",
"positive-biased cfg",
],
) )
@classmethod @classmethod
def execute(cls, model) -> io.NodeOutput: def execute(cls, model) -> io.NodeOutput:
m = model.clone() m = model.clone()
def mahiro_normd(args): def mahiro_normd(args):
scale: float = args['cond_scale'] scale: float = args["cond_scale"]
cond_p: torch.Tensor = args['cond_denoised'] cond_p: torch.Tensor = args["cond_denoised"]
uncond_p: torch.Tensor = args['uncond_denoised'] uncond_p: torch.Tensor = args["uncond_denoised"]
#naive leap # naive leap
leap = cond_p * scale leap = cond_p * scale
#sim with uncond leap # sim with uncond leap
u_leap = uncond_p * scale u_leap = uncond_p * scale
cfg = args["denoised"] cfg = args["denoised"]
merge = (leap + cfg) / 2 merge = (leap + cfg) / 2
normu = torch.sqrt(u_leap.abs()) * u_leap.sign() normu = torch.sqrt(u_leap.abs()) * u_leap.sign()
normm = torch.sqrt(merge.abs()) * merge.sign() normm = torch.sqrt(merge.abs()) * merge.sign()
sim = F.cosine_similarity(normu, normm).mean() sim = F.cosine_similarity(normu, normm).mean()
simsc = 2 * (sim+1) simsc = 2 * (sim + 1)
wm = (simsc*cfg + (4-simsc)*leap) / 4 wm = (simsc * cfg + (4 - simsc) * leap) / 4
return wm return wm
m.set_model_sampler_post_cfg_function(mahiro_normd) m.set_model_sampler_post_cfg_function(mahiro_normd)
return io.NodeOutput(m) return io.NodeOutput(m)