ModelMergeSimple

Documentation

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

The ModelMergeSimple node is designed for merging two models by blending their parameters based on a specified ratio. This node facilitates the creation of hybrid models that combine the strengths or characteristics of both input models.

Input types

Required

• model1
  • The first model to be merged. It serves as the base model onto which patches from the second model are applied.
  • Comfy dtype: MODEL
  • Python dtype: comfy.model_base.Model
• model2
  • The second model whose patches are applied onto the first model, influenced by the specified ratio.
  • Comfy dtype: MODEL
  • Python dtype: comfy.model_base.Model
• ratio
  • Determines the blend ratio between the two models' parameters, affecting the degree to which each model influences the merged output.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

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

Usage tips

• Infra type: CPU
• Common nodes:
  • KSampler
  • Anything Everywhere
  • CR Apply LoRA Stack
  • ModelMergeSimple
  • Reroute

Source code

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, )