Lora List Stacker

Documentation

• Class name: AV_LoraListStacker
• Category: Art Venture/Loaders
• Output node: False

The AV_LoraListStacker node is designed to manage and stack configurations for Lora models, facilitating the dynamic integration and application of multiple Lora models into a processing pipeline. It abstracts the complexity of handling Lora model parameters and ensures that the models are applied in a sequence that respects the intended layering and strength adjustments.

Input types

Required

• data
  • The 'data' input contains the list of Lora models and their configurations in JSON format, dictating how each Lora model should be applied to the base model.
  • Comfy dtype: STRING
  • Python dtype: str

Optional

• base_url
  • The 'base_url' optional input provides a default URL prefix for fetching Lora models not locally available, facilitating remote model retrieval.
  • Comfy dtype: STRING
  • Python dtype: str
• lora_stack
  • The 'lora_stack' input allows for the inclusion of an existing stack of Lora models to be further processed or integrated with new Lora configurations.
  • Comfy dtype: LORA_STACK
  • Python dtype: list

Output types

• lora_stack
  • Comfy dtype: LORA_STACK
  • Returns the updated stack of Lora models after integrating the specified Lora model adjustments.
  • Python dtype: list

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class AVLoraListStacker:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "data": ("STRING", {"default": "", "multiline": True, "dynamicPrompts": False}),
            },
            "optional": {"base_url": ("STRING", {"default": lora_cloud_front_url}), "lora_stack": ("LORA_STACK",)},
        }

    RETURN_TYPES = ("LORA_STACK",)
    FUNCTION = "load_list_lora"
    CATEGORY = "Art Venture/Loaders"

    def parse_lora_list(self, data: str, base_url: str):
        # data is a list of lora model (lora_name, strength_model, strength_clip, url) in json format
        # trim data
        data = data.strip()
        if data == "" or data == "[]" or data is None:
            return []

        print(f"Loading lora list: {data}")

        lora_list = json.loads(data)
        if len(lora_list) == 0:
            return []

        available_loras = folder_paths.get_filename_list("loras")
        model_path = os.path.join(folder_paths.models_dir, "loras")

        lora_params = []
        for lora in lora_list:
            lora_name = lora["name"]
            strength_model = lora["strength"]
            strength_clip = lora["strength"]
            lora_url = lora.get("url", None)

            if strength_model == 0 and strength_clip == 0:
                continue

            if lora_name not in available_loras:
                lora_url = lora_url or f"{base_url}/models/loras/{lora_name}"
                load_file_from_url(lora_url, model_dir=model_path, file_name=lora_name)

            lora_params.append((lora_name, strength_model, strength_clip))

        return lora_params

    def load_list_lora(self, data, base_url=lora_cloud_front_url, lora_stack=None):
        loras = self.parse_lora_list(data, base_url=base_url)

        if lora_stack is not None:
            loras.extend([l for l in lora_stack if l[0] != "None"])

        return (loras,)