79 lines
3.2 KiB
Python
79 lines
3.2 KiB
Python
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import folder_paths
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import comfy.sd
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import comfy.utils
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from pathlib import Path
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class SLPLoraLoader:
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NAME = "HetimaSLPLoraLoader"
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DISPLAY_NAME = "SLP Lora Loader"
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CATEGORY = "SingleLinePicker"
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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(cls):
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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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"clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}),
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"lora_name_text": ("STRING", {"tooltip": "The name of the LoRA."}),
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"strength_model": ("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": ("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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}
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RETURN_TYPES = ("MODEL", "CLIP", "STRING")
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RETURN_NAMES = ("MODEL", "CLIP", "stem")
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OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.", "The stem of lora file")
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FUNCTION = "load_lora"
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DESCRIPTION = "LoRAs are used to modify diffusion and CLIP models, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together."
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def load_lora(self, model, clip, lora_name_text, strength_model, strength_clip):
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if not lora_name_text:
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return (model, clip, "")
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if strength_model == 0 and strength_clip == 0:
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return (model, clip, "")
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lora_path = folder_paths.get_full_path_or_raise("loras", lora_name_text.splitlines()[0])
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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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self.loaded_lora = 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_lora = (lora_path, 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, Path(lora_path).stem)
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class SLPLoraLoaderModelOnly(SLPLoraLoader):
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NAME = "HetimaSLPLoraLoaderModelOnly"
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DISPLAY_NAME = "SLP Lora Loader (Model Only)"
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CATEGORY = "SingleLinePicker"
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": { "model": ("MODEL",),
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"lora_name_text": ("STRING",),
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"strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("MODEL", "stem")
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RETURN_NAMES = ("MODEL", "stem")
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OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The stem of lora file")
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FUNCTION = "load_lora_model_only"
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def load_lora_model_only(self, model, lora_name_text, strength_model):
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result = self.load_lora(model, None, lora_name_text, strength_model, 0)
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return (result[0], result[2])
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