💊 CR Random LoRA Stack¶
Documentation¶
- Class name:
CR Random LoRA Stack - Category:
🧩 Comfyroll Studio/✨ Essential/💊 LoRA - Output node:
False
The CR_RandomLoRAStack node is designed to manage and manipulate stacks of LoRA (Low-Rank Adaptation) instances, specifically focusing on randomizing and applying LoRA modifications to models. It enables the dynamic adjustment of model weights through LoRA techniques, supporting exclusive modes, stride-based re-randomization, and the integration of multiple LoRA instances into a single stack.
Input types¶
Required¶
exclusive_mode- Determines whether only one LoRA instance should be applied at a time, enabling a mode where the instance with the highest chance is selected exclusively.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
stride- Sets the minimum number of cycles before a re-randomization of the LoRA weights is performed, controlling the frequency of weight adjustments.
- Comfy dtype:
INT - Python dtype:
int
force_randomize_after_stride- Forces a re-randomization of the LoRA weights after a specified number of cycles, ensuring dynamic adjustments over time.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
lora_name_1- Specifies the first LoRA instance's name, part of the trio of LoRA instances that can be applied.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
switch_1- Controls whether the first LoRA instance is active or not, allowing for selective application.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
chance_1- Determines the chance of the first LoRA instance being applied, used in exclusive mode to select the most likely instance.
- Comfy dtype:
FLOAT - Python dtype:
float
model_weight_1- Defines the model weight for the first LoRA instance, influencing how it modifies the model.
- Comfy dtype:
FLOAT - Python dtype:
float
clip_weight_1- Sets the clipping weight for the first LoRA instance, affecting its impact on the model.
- Comfy dtype:
FLOAT - Python dtype:
float
lora_name_2- Specifies the second LoRA instance's name, part of the trio of LoRA instances that can be applied.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
switch_2- Controls whether the second LoRA instance is active or not, allowing for selective application.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
chance_2- Determines the chance of the second LoRA instance being applied, used in exclusive mode to select the most likely instance.
- Comfy dtype:
FLOAT - Python dtype:
float
model_weight_2- Defines the model weight for the second LoRA instance, influencing how it modifies the model.
- Comfy dtype:
FLOAT - Python dtype:
float
clip_weight_2- Sets the clipping weight for the second LoRA instance, affecting its impact on the model.
- Comfy dtype:
FLOAT - Python dtype:
float
lora_name_3- Specifies the third LoRA instance's name, part of the trio of LoRA instances that can be applied.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
switch_3- Controls whether the third LoRA instance is active or not, allowing for selective application.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
chance_3- Determines the chance of the third LoRA instance being applied, used in exclusive mode to select the most likely instance.
- Comfy dtype:
FLOAT - Python dtype:
float
model_weight_3- Defines the model weight for the third LoRA instance, influencing how it modifies the model.
- Comfy dtype:
FLOAT - Python dtype:
float
clip_weight_3- Sets the clipping weight for the third LoRA instance, affecting its impact on the model.
- Comfy dtype:
FLOAT - Python dtype:
float
Optional¶
lora_stack- An optional stack of existing LoRA instances to be extended or modified by the current operation.
- Comfy dtype:
LORA_STACK - Python dtype:
List[Tuple[str, float, float]]
Output types¶
lora_stack- Comfy dtype:
LORA_STACK - Returns a modified stack of LoRA instances, incorporating the adjustments and randomizations specified in the input parameters.
- Python dtype:
List[Tuple[str, float, float]]
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes: unknown
Source code¶
class CR_RandomLoRAStack:
@classmethod
def INPUT_TYPES(cls):
loras = ["None"] + folder_paths.get_filename_list("loras")
return {"required": {
"exclusive_mode": (["Off","On"],),
"stride": (("INT", {"default": 1, "min": 1, "max": 1000})),
"force_randomize_after_stride": (["Off","On"],),
"lora_name_1": (loras,),
"switch_1": (["Off","On"],),
"chance_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"model_weight_1": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"clip_weight_1": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora_name_2": (loras,),
"switch_2": (["Off","On"],),
"chance_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"model_weight_2": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"clip_weight_2": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora_name_3": (loras,),
"switch_3": (["Off","On"],),
"chance_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"model_weight_3": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"clip_weight_3": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
},
"optional": {"lora_stack": ("LORA_STACK",)
},
}
RETURN_TYPES = ("LORA_STACK",)
FUNCTION = "random_lora_stacker"
CATEGORY = icons.get("Comfyroll/LoRA")
UsedLorasMap = {}
StridesMap = {}
LastHashMap = {}
@staticmethod
def getIdHash(lora_name_1: str, lora_name_2: str, lora_name_3: str) -> int:
id_set = set([lora_name_1, lora_name_2, lora_name_3])
id_hash = hash(frozenset(id_set))
return id_hash
@staticmethod
def deduplicateLoraNames(lora_name_1: str, lora_name_2: str, lora_name_3: str):
is_same_1 = False
is_same_2 = False
is_same_3 = False
if lora_name_1 == lora_name_2:
is_same_1 = True
is_same_2 = True
if lora_name_1 == lora_name_3:
is_same_1 = True
is_same_3 = True
if lora_name_2 == lora_name_3:
is_same_2 = True
is_same_3 = True
if is_same_1:
lora_name_1 = lora_name_1 + "CR_RandomLoRAStack_1"
if is_same_2:
lora_name_2 = lora_name_2 + "CR_RandomLoRAStack_2"
if is_same_3:
lora_name_3 = lora_name_3 + "CR_RandomLoRAStack_3"
return lora_name_1, lora_name_2, lora_name_3
@staticmethod
def cleanLoraName(lora_name) -> str:
if "CR_RandomLoRAStack_1" in lora_name:
lora_name = lora_name.replace("CR_RandomLoRAStack_1", "")
elif "CR_RandomLoRAStack_2" in lora_name:
lora_name = lora_name.replace("CR_RandomLoRAStack_2", "")
elif "CR_RandomLoRAStack_3" in lora_name:
lora_name = lora_name.replace("CR_RandomLoRAStack_3", "")
return lora_name
@classmethod
def IS_CHANGED(cls, exclusive_mode, stride, force_randomize_after_stride, lora_name_1, model_weight_1, clip_weight_1, switch_1, chance_1, lora_name_2,
model_weight_2, clip_weight_2, switch_2, chance_2, lora_name_3, model_weight_3, clip_weight_3, switch_3, chance_3, lora_stack=None):
lora_set = set()
lora_name_1, lora_name_2, lora_name_3 = CR_RandomLoRAStack.deduplicateLoraNames(lora_name_1, lora_name_2, lora_name_3)
id_hash = CR_RandomLoRAStack.getIdHash(lora_name_1, lora_name_2, lora_name_3)
if id_hash not in CR_RandomLoRAStack.StridesMap:
CR_RandomLoRAStack.StridesMap[id_hash] = 0
CR_RandomLoRAStack.StridesMap[id_hash] += 1
if stride > 1 and CR_RandomLoRAStack.StridesMap[id_hash] < stride and id_hash in CR_RandomLoRAStack.LastHashMap:
return CR_RandomLoRAStack.LastHashMap[id_hash]
else:
CR_RandomLoRAStack.StridesMap[id_hash] = 0
total_on = 0
if lora_name_1 != "None" and switch_1 == "On" and chance_1 > 0.0: total_on += 1
if lora_name_2 != "None" and switch_2 == "On" and chance_2 > 0.0: total_on += 1
if lora_name_3 != "None" and switch_3 == "On" and chance_3 > 0.0: total_on += 1
def perform_randomization() -> set:
_lora_set = set()
rand_1 = random()
rand_2 = random()
rand_3 = random()
apply_1 = True if (rand_1 <= chance_1 and switch_1 == "On") else False
apply_2 = True if (rand_2 <= chance_2 and switch_2 == "On") else False
apply_3 = True if (rand_3 <= chance_3 and switch_3 == "On") else False
num_to_apply = sum([apply_1, apply_2, apply_3])
if exclusive_mode == "On" and num_to_apply > 1:
rand_dict = {}
if apply_1: rand_dict[1] = rand_1
if apply_2: rand_dict[2] = rand_2
if apply_3: rand_dict[3] = rand_3
sorted_rands = sorted(rand_dict.keys(), key=lambda k: rand_dict[k])
if sorted_rands[0] == 1:
apply_2 = False
apply_3 = False
elif sorted_rands[0] == 2:
apply_1 = False
apply_3 = False
elif sorted_rands[0] == 3:
apply_1 = False
apply_2 = False
if lora_name_1 != "None" and switch_1 == "On" and apply_1:
_lora_set.add(lora_name_1)
if lora_name_2 != "None" and switch_2 == "On" and apply_2:
_lora_set.add(lora_name_2)
if lora_name_3 != "None" and switch_3 == "On" and apply_3:
_lora_set.add(lora_name_3)
return _lora_set
last_lora_set = CR_RandomLoRAStack.UsedLorasMap.get(id_hash, set())
lora_set = perform_randomization()
if force_randomize_after_stride == "On" and len(last_lora_set) > 0 and total_on > 1:
while lora_set == last_lora_set:
lora_set = perform_randomization()
CR_RandomLoRAStack.UsedLorasMap[id_hash] = lora_set
hash_str = str(hash(frozenset(lora_set)))
CR_RandomLoRAStack.LastHashMap[id_hash] = hash_str
return hash_str
def random_lora_stacker(self, exclusive_mode, stride, force_randomize_after_stride, lora_name_1, model_weight_1, clip_weight_1, switch_1, chance_1, lora_name_2,
model_weight_2, clip_weight_2, switch_2, chance_2, lora_name_3, model_weight_3, clip_weight_3, switch_3, chance_3, lora_stack=None):
# Initialise the list
lora_list=list()
if lora_stack is not None:
lora_list.extend([l for l in lora_stack if l[0] != "None"])
lora_name_1, lora_name_2, lora_name_3 = CR_RandomLoRAStack.deduplicateLoraNames(lora_name_1, lora_name_2, lora_name_3)
id_hash = CR_RandomLoRAStack.getIdHash(lora_name_1, lora_name_2, lora_name_3)
used_loras = CR_RandomLoRAStack.UsedLorasMap.get(id_hash, set())
if lora_name_1 != "None" and switch_1 == "On" and lora_name_1 in used_loras:
lora_list.extend([(CR_RandomLoRAStack.cleanLoraName(lora_name_1), model_weight_1, clip_weight_1)]),
if lora_name_2 != "None" and switch_2 == "On" and lora_name_2 in used_loras:
lora_list.extend([(CR_RandomLoRAStack.cleanLoraName(lora_name_2), model_weight_2, clip_weight_2)]),
if lora_name_3 != "None" and switch_3 == "On" and lora_name_3 in used_loras:
lora_list.extend([(CR_RandomLoRAStack.cleanLoraName(lora_name_3), model_weight_3, clip_weight_3)]),
return (lora_list,)