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synced 2025-03-15 14:09:36 +00:00
added docstrings to parse_parentheses() and token_weights()
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@ -232,6 +232,27 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
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return self.transformer.load_state_dict(sd, strict=False)
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def parse_parentheses(string):
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"""
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Split a string based off top-level nested parentheses.
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Parameters
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----------
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string : string
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The string to be split into its top nested groups.
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Returns
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-------
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result : list
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A list of each element in string, split by top-level elements
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Examples
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--------
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>>> string = "(foo)(bar)"
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['(foo)', '(bar)']
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>>> string = "(foo(bar)(test1))(test2(test3))"
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['(foo(bar)(test1))', '(test2(test3))']
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"""
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result = []
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current_item = ""
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nesting_level = 0
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@ -260,6 +281,55 @@ def parse_parentheses(string):
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return result
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def token_weights(string, current_weight):
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"""
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Find the requested weight of a token, and multiply it by the current weight. For parentheses groupings with no set weight, multiply by 1.1.
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Parameters
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----------
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string : string
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The input of tokens to calculate requested weights for
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current_weight : float
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The current weight of all tokens
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Returns
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-------
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out : list
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A list of each token paired with the calculated weight
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Examples
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--------
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>>> string = "(foo)"
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>>> current_weight = 1.0
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[('foo', 1.1)]
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>>> string = "(foo)(bar)"
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>>> current_weight = 1.0
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[('foo', 1.1), ('bar', 1.1)]
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>>> string = "(foo:2.0)"
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>>> current_weight = 1.0
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[('foo', 2.0)]
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>>> string = "((foo))"
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>>> current_weight = 1.0
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[('foo', 1.21)]
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>>> string = "((foo):1.1)"
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>>> current_weight = 1.0
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[('foo', 1.21)]
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>>> string = "((foo:1.1))"
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>>> current_weight = 1.0
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[('foo', 1.1)]
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>>> string = "(foo:0.0)"
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>>> current_weight = 1.0
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[('foo', 0.0)]
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>>> string = "((foo:1.0):0.0)"
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>>> current_weight = 1.0
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[('foo', 1.0)]
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"""
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a = parse_parentheses(string)
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out = []
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for x in a:
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@ -267,6 +337,7 @@ def token_weights(string, current_weight):
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if len(x) >= 2 and x[-1] == ')' and x[0] == '(':
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x = x[1:-1]
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xx = x.rfind(":")
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# This line makes *all nestings* multiply the weight by 1.1
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weight *= 1.1
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if xx > 0:
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try:
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