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Feathered Border, Vignette Filter and new demos
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@Suzie1
Suzie1 committed Dec 21, 2023
1 parent 738ee7d commit acd8126
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2 changes: 0 additions & 2 deletions .gitignore
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14 changes: 10 additions & 4 deletions __init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -115,12 +115,14 @@
#"CR Display Font": CR_DisplayFont,
### Graphics Filter
"CR Halftone Filter": CR_HalftoneFilter,
"CR Color Tint": CR_ColorTint,
"CR Color Tint": CR_ColorTint,
"CR Vignette Filter": CR_VignetteFilter,
### Graphics Layout
"CR Page Layout": CR_PageLayout,
"CR Image Panel": CR_ImagePanel,
"CR Image Grid Panel": CR_ImageGridPanel,
"CR Image Border": CR_ImageBorder,
"CR Feathered Border": CR_FeatheredBorder,
"CR Simple Text Panel": CR_SimpleTextPanel,
"CR Color Panel": CR_ColorPanel,
"CR Overlay Transparent Image": CR_OverlayTransparentImage,
Expand Down Expand Up @@ -302,14 +304,16 @@
"CR Font File List": "🔤️ CR Font File List",
### Graphics Filter
"CR Halftone Filter": "🎨 Halftone Filter",
"CR Color Tint": "🎨 CR Color Tint",
"CR Color Tint": "🎨 CR Color Tint",
"CR Vignette Filter": "🎨 CR Vignette Filter",
### Graphics Layout
"CR Image Panel": "🌁 CR Image Panel",
"CR Image Grid Panel": "🌁 CR Image Grid Panel",
"CR Simple Text Panel": "🌁 CR Simple Text Panel",
"CR Color Panel": "🌁 CR Color Panel",
"CR Page Layout": "🌁 CR Page Layout",
"CR Image Border": "🌁 CR Image Border",
"CR Image Border": "🌁 CR Image Border",
"CR Feathered Border": "🌁 CR Feathered Border",
"CR Overlay Transparent Image": "🌁 CR Overlay Transparent Image",
#"CR Simple Titles": "🌁 CR Simple Titles",
### Graphics Template
Expand Down Expand Up @@ -423,8 +427,10 @@
except ImportError:
NODE_CLASS_MAPPINGS = LIVE_NODE_CLASS_MAPPINGS
NODE_DISPLAY_NAME_MAPPINGS = LIVE_NODE_DISPLAY_NAME_MAPPINGS


print("\033[34m------------------------------")
print("\033[34mComfyroll Custom Nodes: \033[92mLoaded\033[0m")
print("\033[34m------------------------------\033[0m")

import shutil
import folder_paths
Expand Down
272 changes: 2 additions & 270 deletions nodes/lora.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,8 +24,10 @@ def __init__(self):

@classmethod
def INPUT_TYPES(s):

file_list = folder_paths.get_filename_list("loras")
file_list.insert(0, "None")

return {"required": { "model": ("MODEL",),
"clip": ("CLIP", ),
"switch": (["On","Off"],),
Expand Down Expand Up @@ -114,274 +116,6 @@ def lora_stacker(self, lora_name_1, model_weight_1, clip_weight_1, switch_1, lor
show_help = "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/wiki/LoRA-Nodes#cr-lora-stack"

return (lora_list, show_help, )

#---------------------------------------------------------------------------------------------------------------------#
# This is adds to a LoRA stack chain, which produces a LoRA instance with a randomized weight within a range.
# Stride sets the number of iterations before weight is re-randomized.
class CR_RandomWeightLoRA:

@classmethod
def INPUT_TYPES(cls):

loras = ["None"] + folder_paths.get_filename_list("loras")

return {"required": {
"stride": (("INT", {"default": 1, "min": 1, "max": 1000})),
"force_randomize_after_stride": (["Off","On"],),
"lora_name": (loras,),
"switch": (["Off","On"],),
"weight_min": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"weight_max": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
},
"optional": {"lora_stack": ("LORA_STACK",)
},
}

RETURN_TYPES = ("LORA_STACK",)
FUNCTION = "random_weight_lora"
CATEGORY = icons.get("Comfyroll/LoRA")

LastWeightMap = {}
StridesMap = {}
LastHashMap = {}

@staticmethod
def getIdHash(lora_name: str, force_randomize_after_stride, stride, weight_min, weight_max, clip_weight) -> int:
fl_str = f"{lora_name}_{force_randomize_after_stride}_{stride}_{weight_min:.2f}_{weight_max:.2f}_{clip_weight:.2f}"
return hashlib.sha256(fl_str.encode('utf-8')).hexdigest()

@classmethod
def IS_CHANGED(cls, stride, force_randomize_after_stride, lora_name, switch, weight_min, weight_max, clip_weight, lora_stack=None):
id_hash = CR_RandomWeightLoRA.getIdHash(lora_name, force_randomize_after_stride, stride, weight_min, weight_max, clip_weight)

if switch == "Off":
return id_hash + "_Off"
if lora_name == "None":
return id_hash

if id_hash not in CR_RandomWeightLoRA.StridesMap:
CR_RandomWeightLoRA.StridesMap[id_hash] = 0

CR_RandomWeightLoRA.StridesMap[id_hash] += 1

if stride > 1 and CR_RandomWeightLoRA.StridesMap[id_hash] < stride and id_hash in CR_RandomWeightLoRA.LastHashMap:
return CR_RandomWeightLoRA.LastHashMap[id_hash]
else:
CR_RandomWeightLoRA.StridesMap[id_hash] = 0

last_weight = CR_RandomWeightLoRA.LastWeightMap.get(id_hash, None)
weight = uniform(weight_min, weight_max)

if last_weight is not None:
while weight == last_weight:
weight = uniform(weight_min, weight_max)

CR_RandomWeightLoRA.LastWeightMap[id_hash] = weight

hash_str = f"{id_hash}_{weight:.3f}"
CR_RandomWeightLoRA.LastHashMap[id_hash] = hash_str
return hash_str

def random_weight_lora(self, stride, force_randomize_after_stride, lora_name, switch, weight_min, weight_max, clip_weight, lora_stack=None):
id_hash = CR_RandomWeightLoRA.getIdHash(lora_name, force_randomize_after_stride, stride, weight_min, weight_max, clip_weight)

# 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"])

weight = CR_RandomWeightLoRA.LastWeightMap.get(id_hash, 0.0)

if lora_name != "None" and switch == "On":
lora_list.extend([(lora_name, weight, clip_weight)]),

return (lora_list,)

#---------------------------------------------------------------------------------------------------------------------#
# This is a lora stack where a single node has 3 different loras which can be applied randomly. Exclusive mode causes only one lora to be applied.
# If exclusive mode is on, each LoRA's chance of being applied is evaluated, and the lora with the highest chance is applied
# Stride sets the minimum number of cycles before a re-randomization is performed.
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,)


#---------------------------------------------------------------------------------------------------------------------#
# This applies the lora stack.
Expand Down Expand Up @@ -702,8 +436,6 @@ def random_lora_stacker(self, exclusive_mode, stride, force_randomize_after_stri
NODE_CLASS_MAPPINGS = {
"CR Load LoRA": CR_LoraLoader,
"CR LoRA Stack":CR_LoRAStack,
"CR Random LoRA Stack":CR_RandomLoRAStack,
"CR Random Weight LoRA":CR_RandomWeightLoRA,
"CR Apply LoRA Stack":CR_ApplyLoRAStack,
"CR Random LoRA Stack":CR_RandomLoRAStack,
"CR Random Weight LoRA":CR_RandomWeightLoRA,
Expand Down
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