121 lines
4.6 KiB
Python
121 lines
4.6 KiB
Python
"""
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XB-BOX 三连 LoRA 加载
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=====================
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两个节点，都是「三个官方 LoRA 加载节点串联」的套皮：
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· XB_TripleLoraLoader —— 串联三个官方 Load LoRA (Model and CLIP)：输入 model + clip，输出 MODEL + CLIP
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· XB_TripleLoraLoaderModelOnly —— 串联三个官方 Load LoRA（仅模型）：输入 model，输出 MODEL
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每个槽位的 LoRA 下拉都多一个「关闭」选项：选中即跳过该槽位（不加载、不改变模型）。
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前者每个槽位的强度同时作用于 MODEL 与 CLIP（strength_model = strength_clip）。
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"""
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import copy
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import nodes
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_OFF = "关闭"
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class XB_TripleLoraLoader:
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"""三连 LoRA — 三个官方 Load LoRA 串联，任一槽位可选「关闭」跳过。"""
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def __init__(self):
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# 官方实例只缓存「最后一次」加载的 LoRA，所以每个槽位各用一个实例
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self._loaders = [nodes.LoraLoader() for _ in range(3)]
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@classmethod
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def INPUT_TYPES(cls):
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base = copy.deepcopy(nodes.LoraLoader.INPUT_TYPES()["required"])
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loras = [_OFF] + [name for name in base["lora_name"][0] if name != _OFF]
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strength_def = copy.deepcopy(base["strength_model"])
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required = {
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"model": copy.deepcopy(base["model"]),
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"clip": copy.deepcopy(base["clip"]),
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}
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for i in (1, 2, 3):
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required[f"lora_{i:02d}"] = (loras, {
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"default": _OFF,
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"tooltip": f"LORA-{i:02d}：选择「关闭」= 跳过该槽位",
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})
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required[f"strength_{i:02d}"] = copy.deepcopy(strength_def)
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return {"required": required}
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RETURN_TYPES = ("MODEL", "CLIP")
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RETURN_NAMES = ("MODEL", "CLIP")
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FUNCTION = "load"
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CATEGORY = "XB_ToolBox/原版优化"
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DESCRIPTION = (
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"等效于串联三个官方 Load LoRA 节点。\n"
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"每个槽位（LORA-01 / LORA-02 / LORA-03）选择「关闭」时直接跳过，不加载也不改模型。\n"
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"强度同时作用于 MODEL 与 CLIP（官方节点的 strength_model = strength_clip）。"
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)
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def load(self, model, clip, lora_01=_OFF, strength_01=1.0,
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lora_02=_OFF, strength_02=1.0, lora_03=_OFF, strength_03=1.0):
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slots = (
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(lora_01, strength_01),
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(lora_02, strength_02),
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(lora_03, strength_03),
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)
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for idx, (name, strength) in enumerate(slots):
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if not name or name == _OFF:
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continue
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try:
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value = float(strength)
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except (TypeError, ValueError):
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value = 1.0
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model, clip = self._loaders[idx].load_lora(model, clip, name, value, value)
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return (model, clip)
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class XB_TripleLoraLoaderModelOnly:
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"""三连 LoRA（仅模型）— 三个官方 Load LoRA（仅模型）串联，任一槽位可选「关闭」跳过。"""
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def __init__(self):
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# 官方实例只缓存「最后一次」加载的 LoRA，所以每个槽位各用一个实例
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self._loaders = [nodes.LoraLoaderModelOnly() for _ in range(3)]
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@classmethod
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def INPUT_TYPES(cls):
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base = copy.deepcopy(nodes.LoraLoaderModelOnly.INPUT_TYPES()["required"])
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loras = [_OFF] + [name for name in base["lora_name"][0] if name != _OFF]
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strength_def = copy.deepcopy(base["strength_model"])
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required = {"model": copy.deepcopy(base["model"])}
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for i in (1, 2, 3):
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required[f"lora_{i:02d}"] = (loras, {
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"default": _OFF,
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"tooltip": f"LORA-{i:02d}：选择「关闭」= 跳过该槽位",
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})
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required[f"strength_{i:02d}"] = copy.deepcopy(strength_def)
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return {"required": required}
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RETURN_TYPES = ("MODEL",)
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RETURN_NAMES = ("MODEL",)
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FUNCTION = "load"
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CATEGORY = "XB_ToolBox/原版优化"
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DESCRIPTION = (
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"等效于串联三个官方 Load LoRA（仅模型）节点。\n"
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"每个槽位（LORA-01 / LORA-02 / LORA-03）选择「关闭」时直接跳过，不加载也不改模型。"
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)
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def load(self, model, lora_01=_OFF, strength_01=1.0,
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lora_02=_OFF, strength_02=1.0, lora_03=_OFF, strength_03=1.0):
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slots = (
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(lora_01, strength_01),
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(lora_02, strength_02),
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(lora_03, strength_03),
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)
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for idx, (name, strength) in enumerate(slots):
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if not name or name == _OFF:
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continue
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try:
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value = float(strength)
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except (TypeError, ValueError):
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value = 1.0
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model = self._loaders[idx].load_lora_model_only(model, name, value)[0]
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return (model,)
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