converted 6 more files

comfy_extras/v3/nodes_mahiro.py

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+from __future__ import annotations
+
+import torch
+import torch.nn.functional as F
+
+from comfy_api.latest import io
+
+
+class Mahiro(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Mahiro_V3",
+            display_name="Mahiro is so cute that she deserves a better guidance function!! (。・ω・。) _V3",
+            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.",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model")
+            ],
+            outputs=[
+                io.Model.Output(display_name="patched_model")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, model):
+        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
+            leap = cond_p * scale
+            #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
+            return wm
+        m.set_model_sampler_post_cfg_function(mahiro_normd)
+        return io.NodeOutput(m)
+
+
+NODES_LIST = [
+    Mahiro,
+]