57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
import folder_paths
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from nodes import LoraLoader
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class LoraRangeLoaderModelOnly(LoraLoader):
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@classmethod
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def INPUT_TYPES(cls):
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loras = folder_paths.get_filename_list("loras")
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return {
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"required": {
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"model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
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"first": (loras, {"tooltip": "First LoRA in the allowed filename range."}),
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"last": (loras, {"tooltip": "Last LoRA in the allowed filename range."}),
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"current": (loras, {"tooltip": "The LoRA that will be loaded for the current run."}),
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"mode": (
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["fixed", "increment", "decrement", "random"],
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{"default": "fixed", "tooltip": "How current should change after a run is queued."},
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),
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"strength_model": (
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"FLOAT",
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{
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"default": 1.0,
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"min": -100.0,
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"max": 100.0,
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"step": 0.01,
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"tooltip": "How strongly to modify the diffusion model. This value can be negative.",
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},
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),
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}
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}
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RETURN_TYPES = ("MODEL",)
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OUTPUT_TOOLTIPS = ("The modified diffusion model.",)
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FUNCTION = "load_lora_model_only"
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CATEGORY = "loaders"
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DESCRIPTION = "Loads the current LoRA like Load LoRA (Model Only) and lets the frontend advance through a filename range after each queued run."
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SEARCH_ALIASES = ["lora range", "lora sweep", "load lora range", "checkpoint training lora"]
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def load_lora_model_only(self, model, first, last, current, mode, strength_model):
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del first
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del last
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del mode
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return (self.load_lora(model, None, current, strength_model, 0)[0],)
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NODE_CLASS_MAPPINGS = {
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"LoraRangeLoaderModelOnly": LoraRangeLoaderModelOnly,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LoraRangeLoaderModelOnly": "Load LoRA Range (Model Only)",
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}
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WEB_DIRECTORY = "./web"
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"] |