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src/diffusers/modular_pipelines/components_manager.py

Lines changed: 5 additions & 13 deletions
Original file line numberDiff line numberDiff line change
@@ -864,6 +864,8 @@ def get_ids(self, names: Union[str, List[str]] = None, collection: Optional[str]
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Get component IDs by a list of names, optionally filtered by collection.
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"""
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ids = set()
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if not isinstance(names, list):
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names = [names]
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for name in names:
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ids.update(self._lookup_ids(name=name, collection=collection))
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return list(ids)
@@ -895,25 +897,15 @@ def get_components_by_names(self, names: List[str], collection: Optional[str] =
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def summarize_dict_by_value_and_parts(d: Dict[str, Any]) -> Dict[str, Any]:
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"""Summarizes a dictionary by finding common prefixes that share the same value.
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This function is particularly useful for IP-Adapter attention processor patterns, where multiple
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attention layers may share the same scale value. It groups dot-separated keys by their values
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and finds the shortest common prefix for each group.
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For example, given a dictionary with IP-Adapter attention processor patterns like:
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{
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For a dictionary with dot-separated keys like: {
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'down_blocks.1.attentions.1.transformer_blocks.0.attn2.processor': [0.6],
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'down_blocks.1.attentions.1.transformer_blocks.1.attn2.processor': [0.6],
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'up_blocks.1.attentions.0.transformer_blocks.0.attn2.processor': [0.3],
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'up_blocks.1.attentions.0.transformer_blocks.1.attn2.processor': [0.3],
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}
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Returns a dictionary where keys are the shortest common prefixes and values are their shared values:
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{
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'down_blocks.1.attentions.1.transformer_blocks': [0.6],
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'up_blocks.1.attentions.0.transformer_blocks': [0.3]
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Returns a dictionary where keys are the shortest common prefixes and values are their shared values: {
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'down_blocks': [0.6], 'up_blocks': [0.3]
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}
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This helps identify which attention layers share the same IP-Adapter scale values.
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"""
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# First group by values - convert lists to tuples to make them hashable
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value_to_keys = {}

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