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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
"""The Dynamic Context node."""
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from mimetypes import add_type
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from .constants import get_category, get_name
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from .utils import ByPassTypeTuple, FlexibleOptionalInputType
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class RgthreeDynamicContext:
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"""The Dynamic Context node.
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Similar to the static Context and Context Big nodes, this allows users to add any number and
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variety of inputs to a Dynamic Context node, and return the outputs by key name.
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"""
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NAME = get_name("Dynamic Context")
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CATEGORY = get_category()
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@classmethod
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def INPUT_TYPES(cls): # pylint: disable = invalid-name,missing-function-docstring
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return {
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"required": {},
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"optional": FlexibleOptionalInputType(add_type),
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"hidden": {},
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}
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RETURN_TYPES = ByPassTypeTuple(("RGTHREE_DYNAMIC_CONTEXT",))
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RETURN_NAMES = ByPassTypeTuple(("CONTEXT",))
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FUNCTION = "main"
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def main(self, **kwargs):
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"""Creates a new context from the provided data, with an optional base ctx to start.
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This node takes a list of named inputs that are the named keys (with an optional "+ " prefix)
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which are to be stored within the ctx dict as well as a list of keys contained in `output_keys`
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to determine the list of output data.
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"""
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base_ctx = kwargs.get('base_ctx', None)
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output_keys = kwargs.get('output_keys', None)
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new_ctx = base_ctx.copy() if base_ctx is not None else {}
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for key_raw, value in kwargs.items():
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if key_raw in ['base_ctx', 'output_keys']:
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continue
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key = key_raw.upper()
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if key.startswith('+ '):
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key = key[2:]
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new_ctx[key] = value
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print(new_ctx)
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res = [new_ctx]
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output_keys = output_keys.split(',') if output_keys is not None else []
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for key in output_keys:
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res.append(new_ctx[key] if key in new_ctx else None)
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return tuple(res)
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