mirror of
https://github.com/justUmen/Bjornulf_custom_nodes.git
synced 2026-03-21 12:42:11 -03:00
69 lines
2.6 KiB
Python
69 lines
2.6 KiB
Python
import os
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import random
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from folder_paths import get_filename_list, get_full_path
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import comfy.sd
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import comfy.utils
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class RandomLoraSelector:
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@classmethod
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def INPUT_TYPES(cls):
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lora_list = get_filename_list("loras")
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optional_inputs = {}
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# Add a default value if lora_list is empty
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if not lora_list:
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lora_list = ["none"]
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for i in range(1, 11):
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optional_inputs[f"lora_{i}"] = (lora_list, {"default": lora_list[0]})
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optional_inputs["seed"] = ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff})
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return {
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"required": {
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"number_of_loras": ("INT", {"default": 3, "min": 1, "max": 20, "step": 1}),
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"model": ("MODEL",),
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"clip": ("CLIP",),
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"strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
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"strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
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},
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"optional": optional_inputs
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}
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RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING", "STRING")
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RETURN_NAMES = ("model", "clip", "lora_path", "lora_name", "lora_folder")
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FUNCTION = "random_select_lora"
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CATEGORY = "Bjornulf"
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def random_select_lora(self, number_of_loras, model, clip, strength_model, strength_clip, seed, **kwargs):
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random.seed(seed)
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# Collect available Loras from kwargs, excluding "none"
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available_loras = [
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kwargs[f"lora_{i}"] for i in range(1, number_of_loras + 1)
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if f"lora_{i}" in kwargs and kwargs[f"lora_{i}"] and kwargs[f"lora_{i}"] != "none"
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]
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# Return original model and clip if no valid LoRAs are available
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if not available_loras:
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return (model, clip, "", "", "")
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# Randomly select a Lora
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selected_lora = random.choice(available_loras)
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# Get the Lora name (without folders or extensions)
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lora_name = os.path.splitext(os.path.basename(selected_lora))[0]
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# Get the full path to the selected Lora
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lora_path = get_full_path("loras", selected_lora)
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# Get the folder name where the Lora is located
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lora_folder = os.path.basename(os.path.dirname(lora_path))
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# Load the Lora file
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lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
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# Apply the Lora
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model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
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return (model_lora, clip_lora, lora_path, lora_name, lora_folder) |