CavinHuang-comfyui-nodes-docs/nodes-docs/en-US/Comfy/docs/CLIPMergeSimple.md

tags

tags
ModelMerge

CLIPMergeSimple

Documentation

• Class name: CLIPMergeSimple
• Category: advanced/model_merging
• Output node: False

This node specializes in merging two CLIP models based on a specified ratio, effectively blending their characteristics. It selectively applies patches from one model to another, excluding specific components like position IDs and logit scale, to create a hybrid model that combines features from both source models.

Input types

Required

• clip1
  • The first CLIP model to be merged. It serves as the base model for the merging process.
  • Comfy dtype: CLIP
  • Python dtype: comfy.model_base.Model
• clip2
  • The second CLIP model to be merged. Its key patches, except for position IDs and logit scale, are applied to the first model based on the specified ratio.
  • Comfy dtype: CLIP
  • Python dtype: comfy.model_base.Model
• ratio
  • Determines the proportion of features from the second model to blend into the first model. A ratio of 1.0 means fully adopting the second model's features, while 0.0 retains only the first model's features.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• clip
  • Comfy dtype: CLIP
  • The resulting merged CLIP model, incorporating features from both input models according to the specified ratio.
  • Python dtype: comfy.model_base.Model

Usage tips

• Infra type: GPU
• Common nodes:
  • [CR Apply LoRA Stack](../../ComfyUI_Comfyroll_CustomNodes/Nodes/CR Apply LoRA Stack.md)
  • CLIPSetLastLayer

Source code

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, )