Issue on docs - CLIPMergeSimple: ratio is described wrong

[morphinapg]

Path: /built-in-nodes/ClipMergeSimple

It says:

Ratio Parameter Explained

ratio = 0.0: Fully uses clip1, ignores clip2
ratio = 0.5: 50% contribution from each model
ratio = 1.0: Fully uses clip2, ignores clip1

While this seems like it would make sense, the ModelMergeSimple node works the opposite way:

When this value is 1, the output model is 100% model1, and when this value is 0, the output model is 100% model2.

This inconsistency made me curious whether the documentation for one of these was incorrect, and it appears ClipMergeSimple is described wrong. The code for ModelMergeSimple and CLIPMergeSimple appear to both handle the ratio parameter the same way:

class ModelMergeSimple:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model1": ("MODEL",),
                              "model2": ("MODEL",),
                              "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                              }}
    RETURN_TYPES = ("MODEL",)
    FUNCTION = "merge"

    CATEGORY = "advanced/model_merging"

    def merge(self, model1, model2, ratio):
        m = model1.clone()
        kp = model2.get_key_patches("diffusion_model.")
        for k in kp:
            m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
        return (m, )

class CLIPMergeSimple:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "clip1": ("CLIP",),
                              "clip2": ("CLIP",),
                              "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                              }}
    RETURN_TYPES = ("CLIP",)
    FUNCTION = "merge"

    CATEGORY = "advanced/model_merging"

    def merge(self, clip1, clip2, ratio):
        m = clip1.clone()
        kp = clip2.get_key_patches()
        for k in kp:
            if k.endswith(".position_ids") or k.endswith(".logit_scale"):
                continue
            m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
        return (m, )

Both of them appear to use ratio where 1.0 = the first model and 0.0 = the second model

Documentation should be updated to reflect this