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Add custom nodes, Civitai loras (LFS), and vast.ai setup script
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>
2026-02-09 00:56:42 +00:00

34 lines
1.1 KiB
Python

"""The Context node."""
from .context_utils import (ORIG_CTX_OPTIONAL_INPUTS, ORIG_CTX_RETURN_NAMES, ORIG_CTX_RETURN_TYPES,
get_orig_context_return_tuple, new_context)
from .constants import get_category, get_name
class RgthreeContext:
"""The initial Context node.
For now, this nodes' outputs will remain as-is, as they are perfect for most 1.5 application, but
is also backwards compatible with other Context nodes.
"""
NAME = get_name("Context")
CATEGORY = get_category()
@classmethod
def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring
return {
"required": {},
"optional": ORIG_CTX_OPTIONAL_INPUTS,
"hidden": {
"version": "FLOAT"
},
}
RETURN_TYPES = ORIG_CTX_RETURN_TYPES
RETURN_NAMES = ORIG_CTX_RETURN_NAMES
FUNCTION = "convert"
def convert(self, base_ctx=None, **kwargs): # pylint: disable = missing-function-docstring
ctx = new_context(base_ctx, **kwargs)
return get_orig_context_return_tuple(ctx)