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import ast
import os.path
import torch
from modules import devices, shared
from .hnutil import find_self
from .shared import version_flag
lazy_load = False # when this is enabled, HNs will be loaded when required.
if not hasattr(devices, 'cond_cast_unet'):
raise RuntimeError("Cannot find cond_cast_unet attribute, please update your webui version!")
class DynamicDict(dict): # Brief dict that dynamically unloads Hypernetworks if required.
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.current = None
self.hash = None
self.dict = {**kwargs}
def prepare(self, key, value):
if lazy_load and self.current is not None and (
key != self.current): # or filename is identical, but somehow hash is changed?
self.current.to('cpu')
self.current = value
if self.current is not None:
self.current.to(devices.device)
def __getitem__(self, item):
value = self.dict[item]
self.prepare(item, value)
return value
def __setitem__(self, key, value):
if key in self.dict:
return
self.dict[key] = value
def __contains__(self, item):
return item in self.dict
available_opts = DynamicDict() # string -> HN itself.
# Behavior definition.
# [[], [], []] -> sequential processing
# [{"A" : 0.8, "B" : 0.1}] -> parallel processing. with weighted sum in this case, A = 8/9 effect, B = 1/9 effect.
# [("A", 0.2), ("B", 0.4)] -> tuple is used to specify strength.
# [{"A", "B", "C"}] -> parallel, but having same effects (set)
# ["A", "B", []] -> sequential processing
# [{"A":0.6}, "B", "C"] -> sequential, dict with single value will be considered as strength modification.
# [["A"], {"B"}, "C"] -> singletons are equal to items without covers, nested singleton will not be parsed, because its inefficient.
# {{'Aa' : 0.2, 'Ab' : 0.8} : 0.8, 'B' : 0.1} (X) -> {"{'Aa' : 0.2, 'Ab' : 0.8}" : 0.8, 'B' : 0.1} (O), When you want complex setups in parallel, you need to cover them with "". You can use backslash too.
# Testing parsing function.
def test_parsing(string=None):
def test(arg):
print(arg)
try:
obj = str(Forward.parse(arg))
print(obj)
except Exception as e:
print(e)
if string:
test(string)
else:
for strings in ["[[], [], []]", "[{\"A\" : 0.8, \"B\" : 0.1}]", '[("A", 0.2), ("B", 0.4)]', '[{"A", "B", "C"}]',
'[{"A":0.6}, "B", "C"]', '[["A"], {"B"}, "C"]',
'{"{\'Aa\' : 0.2, \'Ab\' : 0.8}" : 0.8, \'B\' : 0.1}']:
test(strings)
class Forward:
def __init__(self, **kwargs):
self.name = "defaultForward" if 'name' not in kwargs else kwargs['name']
pass
def __call__(self, *args, **kwargs):
raise NotImplementedError
def set_multiplier(self, *args, **kwargs):
pass
def extra_name(self):
if version_flag:
return ""
found = find_self(self)
if found is not None:
return f" <hypernet:{found}:1.0>"
return f" <hypernet:{self.name}:1.0>"
@staticmethod
def parse(arg, name=None):
arg = Forward.unpack(arg)
arg = Forward.eval(arg)
if Forward.isSingleTon(arg):
return SingularForward(*Forward.parseSingleTon(arg))
elif Forward.isParallel(arg):
return ParallelForward(Forward.parseParallel(arg), name=name)
elif Forward.isSequential(arg):
return SequentialForward(Forward.parseSequential(arg), name=name)
raise ValueError(f"Cannot parse {arg} into sequences!")
@staticmethod
def unpack(arg): # stop using ({({{((a))}})}) please
if len(arg) == 1 and type(arg) in (set, list, tuple):
return Forward.unpack(list(arg)[0])
if len(arg) == 1 and type(arg) is dict:
key = list(arg.keys())[0]
if arg[key] == 1:
return Forward.unpack(key)
return arg
@staticmethod
def eval(arg): # from "{something}", parse as etc form.
if arg is None:
raise ValueError("None cannot be evaluated!")
try:
newarg = ast.literal_eval(arg)
if type(arg) is str and arg.startswith(("{", "[", "(")) and newarg is not None:
if not newarg:
raise RuntimeError(f"Cannot eval false object {arg}!")
return newarg
except ValueError:
return arg
return arg
@staticmethod
def isSingleTon(
arg): # Very strict. This applies strength to HN, which cannot happen in combined networks. Only weighting is allowed in complex process.
if type(arg) is str and not arg.startswith(('[', '(', '{')): # Strict. only accept str
return True
elif type(
arg) is dict: # Strict. only accept {str : int/float} - Strength modification can only happen for str.
return len(arg) == 1 and all(type(value) in (int, float) for value in arg.values()) and all(
type(k) is str for k in arg)
elif type(arg) in (list, set):
return len(arg) == 1 and all(type(x) is str for x in arg)
elif type(arg) is tuple:
return len(arg) == 2 and type(arg[0]) is str and type(arg[1]) in (int, float)
return False
@staticmethod
def parseSingleTon(sequence): # accepts sequence, returns str, float pair. This is Strict.
if type(sequence) in (list, dict, set):
assert len(sequence) == 1, f"SingularForward only accepts singletons, but given {sequence}!"
key = list(sequence)[0]
if type(sequence) is dict:
assert type(key) is str, f"Strength modification only accepts single Hypernetwork, but given {key}!"
return key, sequence[key]
else:
key = list(key)[0]
return key, 1
elif type(sequence) is tuple:
assert len(sequence) == 2, f"Tuple with non-couple {sequence} encountered in SingularForward!"
assert type(
sequence[0]) is str, f"Strength modification only accepts single Hypernetwork, but given {sequence[0]}!"
assert type(sequence[1]) in (int, float), f"Strength tuple only accepts Numbers, but given {sequence[1]}!"
return sequence[0], sequence[1]
else:
assert type(
sequence) is str, f"Strength modification only accepts single Hypernetwork, but given {sequence}!"
return sequence, 1
@staticmethod
def isParallel(
arg): # Parallel, or Sequential processing is not strict, it can have {"String covered sequence or just HN String" : weight, ...
if type(arg) in (dict, set) and len(arg) > 1:
if type(arg) is set:
return all(type(key) is str for key in
arg), f"All keys should be Hypernetwork Name/Sequence for Set but given :{arg}"
else:
arg: dict
return all(type(key) is str for key in
arg.keys()), f"All keys should be Hypernetwork Name/Sequence for Set but given :{arg}"
else:
return False
@staticmethod
def parseParallel(sequence): # accepts sequence, returns {"Name or sequence" : weight...}
assert len(sequence) > 1, f"Length of sequence {sequence} was not enough for parallel!"
if type(sequence) is set: # only allows hashable types. otherwise it should be supplied as string cover
assert all(type(key) in (str, tuple) for key in
sequence), f"All keys should be Hypernetwork Name/Sequence for Set but given :{sequence}"
return {key: 1 / len(sequence) for key in sequence}
elif type(sequence) is dict:
assert all(type(key) in (str, tuple) for key in
sequence.keys()), f"All keys should be Hypernetwork Name/Sequence for Dict but given :{sequence}"
assert all(type(value) in (int, float) for value in
sequence.values()), f"All values should be int/float for Dict but given :{sequence}"
return sequence
else:
raise ValueError(f"Cannot parse parallel sequence {sequence}!")
@staticmethod
def isSequential(arg):
if type(arg) is list and len(arg) > 0:
return True
return False
@staticmethod
def parseSequential(sequence): # accepts sequence, only checks if its list, then returns sequence.
if type(sequence) is list and len(sequence) > 0:
return sequence
else:
raise ValueError(f"Cannot parse non-list sequence {sequence}!")
def shorthash(self):
return '0000000000'
from .hypernetwork import Hypernetwork
def find_non_hash_key(target):
closest = [x for x in shared.hypernetworks if x.rsplit('(', 1)[0] == target or x == target]
if closest:
return shared.hypernetworks[closest[0]]
raise KeyError(f"{target} is not found in Hypernetworks!")
class SingularForward(Forward):
def __init__(self, processor, strength):
assert processor != 'defaultForward', "Cannot use name defaultForward!"
super(SingularForward, self).__init__()
self.name = processor
self.processor = processor
self.strength = strength
# parse. We expect parsing Singletons or (k,v) pair here, which is HN Name and Strength.
hn = Hypernetwork()
try:
hn.load(find_non_hash_key(self.processor))
except:
global lazy_load
lazy_load = True
print("Encountered CUDA Memory Error, will unload HNs, speed might go down severely!")
hn.load(find_non_hash_key(self.processor))
available_opts[self.processor] = hn
# assert self.processor in available_opts, f"Hypernetwork named {processor} is not ready!"
assert 0 <= self.strength <= 1, "Strength must be between 0 and 1!"
print(f"SingularForward <{self.name}, {self.strength}>")
def __call__(self, context_k, context_v=None, layer=None):
if self.processor in available_opts:
context_layers = available_opts[self.processor].layers.get(context_k.shape[2], None)
if context_v is None:
context_v = context_k
if context_layers is None:
return context_k, context_v
#if layer is not None and hasattr(layer, 'hyper_k') and hasattr(layer, 'hyper_v'):
# layer.hyper_k = context_layers[0], layer.hyper_v = context_layers[1]
return devices.cond_cast_unet(context_layers[0](devices.cond_cast_float(context_k), multiplier=self.strength)),\
devices.cond_cast_unet(context_layers[1](devices.cond_cast_float(context_v), multiplier=self.strength))
# define forward_strength, which invokes HNModule with specified strength.
# Note : we share same HN if it is called multiple time, which means you might not be able to train it via this structure.
raise KeyError(f"Key {self.processor} is not found in cached Hypernetworks!")
def __str__(self):
return "SingularForward>" + str(self.processor)
class ParallelForward(Forward):
def __init__(self, sequence, name=None):
self.name = "ParallelForwardHypernet" if name is None else name
self.callers = {}
self.weights = {}
super(ParallelForward, self).__init__()
# parse
for keys in sequence:
self.callers[keys] = Forward.parse(keys)
self.weights[keys] = sequence[keys] / sum(sequence.values())
print(str(self))
def __call__(self, context, context_v=None, layer=None):
ctx_k, ctx_v = torch.zeros_like(context, device=context.device), torch.zeros_like(context,
device=context.device)
for key in self.callers:
k, v = self.callers[key](context, context_v, layer=layer)
ctx_k += k * self.weights[key]
ctx_v += v * self.weights[key]
return ctx_k, ctx_v
def __str__(self):
return "ParallelForward>" + str({str(k): str(v) for (k, v) in self.callers.items()})
class SequentialForward(Forward):
def __init__(self, sequence, name=None):
self.name = "SequentialForwardHypernet" if name is None else name
self.callers = []
super(SequentialForward, self).__init__()
for keys in sequence:
self.callers.append(Forward.parse(keys))
print(str(self))
def __call__(self, context, context_v=None, layer=None):
if context_v is None:
context_v = context
for keys in self.callers:
context, context_v = keys(context, context_v, layer=layer)
return context, context_v
def __str__(self):
return "SequentialForward>" + str([str(x) for x in self.callers])
class EmptyForward(Forward):
def __init__(self):
super().__init__()
self.name = None
def __call__(self, context, context_v=None, layer=None):
if context_v is None:
context_v = context
return context, context_v
def __str__(self):
return "EmptyForward"
def load(filename):
with open(filename, 'r') as file:
return Forward.parse(file.read(), name=os.path.basename(filename))
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