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Testing how to use explicit load lora weight nodes to achieve global caching

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Jedrzej Kosinski 2025-08-26 20:15:43 -07:00
parent f6b93d41a0
commit 067f99ac49
1 changed files with 114 additions and 0 deletions
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114
nodes.py
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@ -691,6 +691,109 @@ class LoraLoaderModelOnly(LoraLoader):
def load_lora_model_only(self, model, lora_name, strength_model):
return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)
class LoadLora:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
"clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}),
"lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}),
"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."}),
"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."}),
}
}
RETURN_TYPES = ("MODEL", "CLIP")
OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.")
FUNCTION = "load_lora"
CATEGORY = "loaders"
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."
def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
if strength_model == 0 and strength_clip == 0:
return (model, clip)
from comfy_execution.graph_utils import GraphBuilder
g = GraphBuilder()
# create the nodes
load_weights = g.node("LoadLoraWeights", lora_name=lora_name)
apply_weights = g.node("ApplyLoraWeights", model=model, clip=clip, lora=load_weights.out(0), strength_model=strength_model, strength_clip=strength_clip)
return {
"result": (apply_weights.out(0), apply_weights.out(1)),
"expand": g.finalize(),
}
class LoadLoraModelOnly(LoadLora):
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"lora_name": (folder_paths.get_filename_list("loras"), ),
"strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "load_lora_model_only"
def load_lora_model_only(self, model, lora_name, strength_model):
if strength_model == 0:
return (model,)
from comfy_execution.graph_utils import GraphBuilder
g = GraphBuilder()
load_weights = g.node("LoadLoraWeights", lora_name=lora_name)
apply_weights = g.node("ApplyLoraWeights", model=model, clip=None, lora=load_weights.out(0), strength_model=strength_model, strength_clip=0)
return {
"result": (apply_weights.out(0),),
"expand": g.finalize(),
}
class LoadLoraWeights:
@classmethod
def INPUT_TYPES(s):
return {"required": { "lora_name": (folder_paths.get_filename_list("loras"), ),
}}
RETURN_TYPES = ("LORA_MODEL",)
FUNCTION = "load_lora_weights"
DEPRECATED = False
def load_lora_weights(self, lora_name):
lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
return (lora,)
class ApplyLoraWeights:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"clip": ("CLIP",),
"lora": ("LORA_MODEL",),
"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."}),
"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."}),
}}
RETURN_TYPES = ("MODEL", "CLIP")
FUNCTION = "apply_lora_weights"
DEPRECATED = False
def apply_lora_weights(self, model, clip, lora, strength_model: float, strength_clip: float):
model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
return (model_lora, clip_lora)
class ApplyLoraWeightsModelOnly(ApplyLoraWeights):
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"lora": ("LORA_MODEL",),
"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."}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "apply_lora_weights_model_only"
DEPRECATED = False
def apply_lora_weights_model_only(self, model, lora, strength_model: float):
return (self.apply_lora_weights(model, None, lora, strength_model, 0)[0],)
class VAELoader:
@staticmethod
def vae_list():
@ -2007,6 +2110,12 @@ NODE_CLASS_MAPPINGS = {
"ConditioningZeroOut": ConditioningZeroOut,
"ConditioningSetTimestepRange": ConditioningSetTimestepRange,
"LoraLoaderModelOnly": LoraLoaderModelOnly,
"LoadLora": LoadLora,
"LoadLoraModelOnly": LoadLoraModelOnly,
"LoadLoraWeights": LoadLoraWeights,
"ApplyLoraWeights": ApplyLoraWeights,
"ApplyLoraWeightsModelOnly": ApplyLoraWeightsModelOnly,
}
NODE_DISPLAY_NAME_MAPPINGS = {
@ -2025,6 +2134,11 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"CLIPVisionLoader": "Load CLIP Vision",
"UpscaleModelLoader": "Load Upscale Model",
"UNETLoader": "Load Diffusion Model",
"LoadLora": "Load LoRA Expand",
"LoadLoraModelOnly": "Load LoRA Expand (Model Only)",
"LoadLoraWeights": "Load LoRA Weights",
"ApplyLoraWeights": "Apply LoRA Weights",
"ApplyLoraWeightsModelOnly": "Apply LoRA Weights (Model Only)",
# Conditioning
"CLIPVisionEncode": "CLIP Vision Encode",
"StyleModelApply": "Apply Style Model",