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](../../cg-use-everywhere/Nodes/Anything Everywhere.md)
- [CR Apply LoRA Stack](../../ComfyUI_Comfyroll_CustomNodes/Nodes/CR Apply LoRA Stack.md)
- 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, )

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