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import comfy
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import folder_paths
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class SwarmLoraLoader:
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def __init__(self):
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self.loaded_lora = None
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": ("MODEL", ),
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"clip": ("CLIP", ),
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"lora_names": ("STRING", {"multiline": True, "tooltip": "Comma separated list of lora names to load."}),
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"lora_weights": ("STRING", {"multiline": True, "tooltip": "Comma separated list of lora weights to apply to each lora. Must match the number of loras."}),
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}
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}
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CATEGORY = "SwarmUI/models"
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RETURN_TYPES = ("MODEL", "CLIP")
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FUNCTION = "load_loras"
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DESCRIPTION = "Like a regular LoRA Loader, but designed to take a dynamic list of loras and weights, to allow easier integration with SwarmUI custom workflows."
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def load_loras(self, model, clip, lora_names, lora_weights):
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if lora_names.strip() == "":
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return (model, clip)
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lora_names = lora_names.split(",")
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lora_weights = lora_weights.split(",")
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lora_weights = [float(x.strip()) for x in lora_weights]
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for i in range(len(lora_names)):
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lora_name = lora_names[i].strip()
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weight = lora_weights[i]
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if weight == 0:
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continue
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lora_path = folder_paths.get_full_path("loras", lora_name)
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lora = None
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if self.loaded_lora is not None:
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if self.loaded_lora[0] == lora_path:
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lora = self.loaded_lora[1]
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else:
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temp = self.loaded_lora
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self.loaded_lora = None
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del temp
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if lora is None:
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lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
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self.loaded_lora = (lora_path, lora)
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model, clip = comfy.sd.load_lora_for_models(model, clip, lora, weight, weight)
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return (model, clip)
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NODE_CLASS_MAPPINGS = {
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"SwarmLoraLoader": SwarmLoraLoader,
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}
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