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from collections.abc import Iterable
from typing import Union
import torch
from torch import Tensor
from .utils_motion import create_multival_combo, linear_conversion, normalize_min_max, extend_to_batch_size, extend_list_to_batch_size
class ScaleType:
ABSOLUTE = "absolute"
RELATIVE = "relative"
LIST = [ABSOLUTE, RELATIVE]
class MultivalDynamicNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"float_val": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001},),
},
"optional": {
"mask_optional": ("MASK",),
},
"hidden": {
"autosize": ("ADEAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("MULTIVAL",)
CATEGORY = "Animate Diff 🎭🅐🅓/multival"
FUNCTION = "create_multival"
def create_multival(self, float_val: Union[float, list[float]]=1.0, mask_optional: Tensor=None):
return (create_multival_combo(float_val=float_val, mask_optional=mask_optional),)
class MultivalScaledMaskNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"min_float_val": ("FLOAT", {"default": 0.0, "min": 0.0, "step": 0.001}),
"max_float_val": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
"mask": ("MASK",),
},
"optional": {
"scaling": (ScaleType.LIST,),
},
"hidden": {
"autosize": ("ADEAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("MULTIVAL",)
CATEGORY = "Animate Diff 🎭🅐🅓/multival"
FUNCTION = "create_multival"
def create_multival(self, min_float_val: float, max_float_val: float, mask: Tensor, scaling: str=ScaleType.ABSOLUTE):
lengths = [mask.shape[0]]
iterable_inputs = [False, False]
val_inputs = [min_float_val, max_float_val]
if isinstance(min_float_val, Iterable):
iterable_inputs[0] = True
val_inputs[0] = list(min_float_val)
lengths.append(len(min_float_val))
if isinstance(max_float_val, Iterable):
iterable_inputs[1] = True
val_inputs[1] = list(max_float_val)
lengths.append(len(max_float_val))
# make sure mask and any iterable float_vals match max length
max_length = max(lengths)
mask = extend_to_batch_size(mask, max_length)
for i in range(len(iterable_inputs)):
if iterable_inputs[i] == True:
# make sure tensors will match dimensions of mask
val_inputs[i] = torch.tensor(extend_list_to_batch_size(val_inputs[i], max_length)).unsqueeze(-1).unsqueeze(-1)
min_float_val, max_float_val = val_inputs
if scaling == ScaleType.ABSOLUTE:
mask = linear_conversion(mask.clone(), new_min=min_float_val, new_max=max_float_val)
elif scaling == ScaleType.RELATIVE:
mask = normalize_min_max(mask.clone(), new_min=min_float_val, new_max=max_float_val)
else:
raise ValueError(f"scaling '{scaling}' not recognized.")
return MultivalDynamicNode.create_multival(self, mask_optional=mask)
class MultivalDynamicFloatInputNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"float_val": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "forceInput": True},),
},
"optional": {
"mask_optional": ("MASK",),
},
"hidden": {
"autosize": ("ADEAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("MULTIVAL",)
CATEGORY = "Animate Diff 🎭🅐🅓/multival"
FUNCTION = "create_multival"
def create_multival(self, float_val: Union[float, list[float]]=None, mask_optional: Tensor=None):
return MultivalDynamicNode.create_multival(self, float_val=float_val, mask_optional=mask_optional)
class MultivalDynamicFloatsNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"floats": ("FLOATS", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001},),
},
"optional": {
"mask_optional": ("MASK",),
},
"hidden": {
"autosize": ("ADEAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("MULTIVAL",)
CATEGORY = "Animate Diff 🎭🅐🅓/multival"
FUNCTION = "create_multival"
def create_multival(self, floats: Union[float, list[float]]=None, mask_optional: Tensor=None):
return MultivalDynamicNode.create_multival(self, float_val=floats, mask_optional=mask_optional)
class MultivalFloatNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"float_val": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001},),
},
"hidden": {
"autosize": ("ADEAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("MULTIVAL",)
CATEGORY = "Animate Diff 🎭🅐🅓/multival"
FUNCTION = "create_multival"
def create_multival(self, float_val: Union[float, list[float]]=None):
return MultivalDynamicNode.create_multival(self, float_val=float_val)
class MultivalConvertToMaskNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"multival": ("MULTIVAL",),
},
"hidden": {
"autosize": ("ADEAUTOSIZE", {"padding": 0}),
}
}
RETURN_TYPES = ("MASK",)
CATEGORY = "Animate Diff 🎭🅐🅓/multival"
FUNCTION = "convert_multival_to_mask"
def convert_multival_to_mask(self, multival: Union[float, Tensor]):
# if already tensor, assume is a valid mask
if type(multival) == Tensor:
return (multival,)
# otherwise, make a single 1x1 mask with the proper value
shape = (1,1,1)
converted_multival = torch.ones(shape) * multival
return (converted_multival,)
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