Clybius
f23bb9b0c5
This node loads multiple LoRAs in one action to prevent re-quantizing the model in Comfy. Good for quantized formats like FP8, Int8, and NVFP4.
126 lines
4.8 KiB
Python
126 lines
4.8 KiB
Python
import torch
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import logging
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import comfy.sd
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import folder_paths
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import comfy.utils
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import comfy.lora
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import comfy.lora_convert
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class ClybAdaptiveLoraLoader:
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def __init__(self):
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self.loaded_loras = {}
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@classmethod
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def INPUT_TYPES(s):
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file_list = folder_paths.get_filename_list("loras")
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file_list.insert(0, "none")
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inputs = {
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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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"clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}),
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"lora_name_1": (file_list, {"tooltip": "The name of the LoRA."}),
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"strength_model_1": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}),
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"strength_clip_1": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the CLIP model. This value can be negative."}),
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},
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"optional": {}
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}
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for i in range(2, 21):
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inputs["optional"][f"lora_name_{i}"] = (file_list, {"default": "none"})
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inputs["optional"][f"strength_model_{i}"] = ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01})
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inputs["optional"][f"strength_clip_{i}"] = ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01})
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return inputs
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RETURN_TYPES = ("MODEL", "CLIP")
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OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.")
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FUNCTION = "load_lora"
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CATEGORY = "loaders"
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DESCRIPTION = "Apply multiple LoRAs adaptively by merging patches into a single model clone."
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EXPERIMENTAL = True
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def load_lora(self, model, clip, **kwargs):
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lora_keys = [k for k in kwargs.keys() if k.startswith("lora_name_")]
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try:
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lora_keys.sort(key=lambda x: int(x.split("_")[-1]))
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except:
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pass
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model_lora = model.clone() if model is not None else None
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clip_lora = clip.clone() if clip is not None else None
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for k in lora_keys:
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lora_name = kwargs[k]
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if lora_name == "none":
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continue
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idx = k.split("_")[-1]
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strength_model = kwargs.get(f"strength_model_{idx}", 1.0)
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strength_clip = kwargs.get(f"strength_clip_{idx}", 1.0)
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if strength_model == 0 and strength_clip == 0:
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continue
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lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
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lora = None
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if idx in self.loaded_loras:
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if self.loaded_loras[idx][0] == lora_path:
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lora = self.loaded_loras[idx][1]
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else:
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self.loaded_loras.pop(idx, None)
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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_loras[idx] = (lora_path, lora)
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key_map = {}
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if model_lora is not None:
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key_map = comfy.lora.model_lora_keys_unet(model_lora.model, key_map)
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if clip_lora is not None:
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key_map = comfy.lora.model_lora_keys_clip(clip_lora.cond_stage_model, key_map)
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lora_converted = comfy.lora_convert.convert_lora(lora)
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loaded = comfy.lora.load_lora(lora_converted, key_map)
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if model_lora is not None:
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model_lora.add_patches(loaded, strength_model)
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if clip_lora is not None:
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clip_lora.add_patches(loaded, strength_clip)
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return (model_lora, clip_lora)
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class ClybAdaptiveLoraLoaderModelOnly(ClybAdaptiveLoraLoader):
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@classmethod
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def INPUT_TYPES(s):
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file_list = folder_paths.get_filename_list("loras")
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file_list.insert(0, "none")
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inputs = {
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"required": {
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"model": ("MODEL",),
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"lora_name_1": (file_list, ),
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"strength_model_1": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
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},
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"optional": {}
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}
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for i in range(2, 21):
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inputs["optional"][f"lora_name_{i}"] = (file_list, {"default": "none"})
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inputs["optional"][f"strength_model_{i}"] = ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01})
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return inputs
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "load_lora_model_only"
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def load_lora_model_only(self, model, **kwargs):
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new_kwargs = kwargs.copy()
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lora_keys = [k for k in kwargs.keys() if k.startswith("lora_name_")]
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for k in lora_keys:
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idx = k.split("_")[-1]
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new_kwargs[f"strength_clip_{idx}"] = 0.0
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return (self.load_lora(model, None, **new_kwargs)[0],)
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