mirror of
https://github.com/comfyanonymous/ComfyUI.git
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deepme987/
| Author | SHA1 | Date | |
|---|---|---|---|
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1cd2730b25 | ||
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d4351f77f8 | ||
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9837dd368a | ||
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62ec9a3238 | ||
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b20cb7892e | ||
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b9b24d425b | ||
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d731cb6ae1 |
@@ -1,5 +1,9 @@
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from __future__ import annotations
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import json
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import logging
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import os
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from aiohttp import web
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from typing import TYPE_CHECKING, TypedDict
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@@ -7,7 +11,6 @@ if TYPE_CHECKING:
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from comfy_api.latest._io_public import NodeReplace
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from comfy_execution.graph_utils import is_link
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import nodes
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class NodeStruct(TypedDict):
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inputs: dict[str, str | int | float | bool | tuple[str, int]]
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@@ -43,6 +46,7 @@ class NodeReplaceManager:
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return old_node_id in self._replacements
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def apply_replacements(self, prompt: dict[str, NodeStruct]):
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import nodes
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connections: dict[str, list[tuple[str, str, int]]] = {}
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need_replacement: set[str] = set()
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for node_number, node_struct in prompt.items():
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@@ -94,6 +98,60 @@ class NodeReplaceManager:
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previous_input = prompt[conn_node_number]["inputs"][conn_input_id]
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previous_input[1] = new_output_idx
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def load_from_json(self, module_dir: str, module_name: str, _node_replace_class=None):
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"""Load node_replacements.json from a custom node directory and register replacements.
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Custom node authors can ship a node_replacements.json file in their repo root
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to define node replacements declaratively. The file format matches the output
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of NodeReplace.as_dict(), keyed by old_node_id.
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Fail-open: all errors are logged and skipped so a malformed file never
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prevents the custom node from loading.
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"""
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replacements_path = os.path.join(module_dir, "node_replacements.json")
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if not os.path.isfile(replacements_path):
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return
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try:
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with open(replacements_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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if not isinstance(data, dict):
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logging.warning(f"node_replacements.json in {module_name} must be a JSON object, skipping.")
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return
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if _node_replace_class is None:
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from comfy_api.latest._io import NodeReplace
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_node_replace_class = NodeReplace
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count = 0
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for old_node_id, replacements in data.items():
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if not isinstance(replacements, list):
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logging.warning(f"node_replacements.json in {module_name}: value for '{old_node_id}' must be a list, skipping.")
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continue
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for entry in replacements:
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if not isinstance(entry, dict):
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continue
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new_node_id = entry.get("new_node_id", "")
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if not new_node_id:
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logging.warning(f"node_replacements.json in {module_name}: entry for '{old_node_id}' missing 'new_node_id', skipping.")
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continue
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self.register(_node_replace_class(
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new_node_id=new_node_id,
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old_node_id=entry.get("old_node_id", old_node_id),
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old_widget_ids=entry.get("old_widget_ids"),
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input_mapping=entry.get("input_mapping"),
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output_mapping=entry.get("output_mapping"),
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))
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count += 1
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if count > 0:
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logging.info(f"Loaded {count} node replacement(s) from {module_name}/node_replacements.json")
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except json.JSONDecodeError as e:
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logging.warning(f"Failed to parse node_replacements.json in {module_name}: {e}")
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except Exception as e:
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logging.warning(f"Failed to load node_replacements.json from {module_name}: {e}")
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def as_dict(self):
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"""Serialize all replacements to dict."""
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return {
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -1,322 +1 @@
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{
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"revision": 0,
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"last_node_id": 29,
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"last_link_id": 0,
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"nodes": [
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{
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"id": 29,
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"type": "4c9d6ea4-b912-40e5-8766-6793a9758c53",
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"pos": [
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1970,
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-230
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],
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"size": [
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180,
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86
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],
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"flags": {},
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"order": 5,
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"mode": 0,
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"inputs": [
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{
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"label": "image",
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"localized_name": "images.image0",
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"name": "images.image0",
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"type": "IMAGE",
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"link": null
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}
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],
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"outputs": [
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{
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"label": "R",
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"localized_name": "IMAGE0",
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"name": "IMAGE0",
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"type": "IMAGE",
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"links": []
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},
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{
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"label": "G",
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"localized_name": "IMAGE1",
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"name": "IMAGE1",
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"type": "IMAGE",
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"links": []
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},
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{
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"label": "B",
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"localized_name": "IMAGE2",
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"name": "IMAGE2",
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"type": "IMAGE",
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"links": []
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},
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{
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"label": "A",
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"localized_name": "IMAGE3",
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"name": "IMAGE3",
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"type": "IMAGE",
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"links": []
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}
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],
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"title": "Image Channels",
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"properties": {
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"proxyWidgets": []
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},
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"widgets_values": []
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}
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],
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"links": [],
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"version": 0.4,
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"definitions": {
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"subgraphs": [
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{
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"id": "4c9d6ea4-b912-40e5-8766-6793a9758c53",
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"version": 1,
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"state": {
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"lastGroupId": 0,
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"lastNodeId": 28,
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"lastLinkId": 39,
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"lastRerouteId": 0
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},
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"revision": 0,
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"config": {},
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"name": "Image Channels",
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"inputNode": {
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"id": -10,
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"bounding": [
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1820,
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-185,
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120,
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60
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]
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},
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"outputNode": {
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"id": -20,
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"bounding": [
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2460,
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-215,
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120,
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120
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]
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},
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"inputs": [
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{
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"id": "3522932b-2d86-4a1f-a02a-cb29f3a9d7fe",
|
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"name": "images.image0",
|
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"type": "IMAGE",
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"linkIds": [
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39
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],
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"localized_name": "images.image0",
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"label": "image",
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"pos": [
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1920,
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-165
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]
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}
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],
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"outputs": [
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{
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||||
"id": "605cb9c3-b065-4d9b-81d2-3ec331889b2b",
|
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"name": "IMAGE0",
|
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"type": "IMAGE",
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"linkIds": [
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26
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],
|
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"localized_name": "IMAGE0",
|
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"label": "R",
|
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"pos": [
|
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2480,
|
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-195
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||||
]
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},
|
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{
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"id": "fb44a77e-0522-43e9-9527-82e7465b3596",
|
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"name": "IMAGE1",
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"type": "IMAGE",
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"linkIds": [
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27
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],
|
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"localized_name": "IMAGE1",
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"label": "G",
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"pos": [
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2480,
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-175
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]
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||||
},
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||||
{
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||||
"id": "81460ee6-0131-402a-874f-6bf3001fc4ff",
|
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"name": "IMAGE2",
|
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"type": "IMAGE",
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"linkIds": [
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28
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],
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"localized_name": "IMAGE2",
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"label": "B",
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"pos": [
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2480,
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-155
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||||
]
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},
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{
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"id": "ae690246-80d4-4951-b1d9-9306d8a77417",
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"name": "IMAGE3",
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"type": "IMAGE",
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"linkIds": [
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29
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],
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"localized_name": "IMAGE3",
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"label": "A",
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"pos": [
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2480,
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-135
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]
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}
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],
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"widgets": [],
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||||
"nodes": [
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{
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"id": 23,
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||||
"type": "GLSLShader",
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||||
"pos": [
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2000,
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||||
-330
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||||
],
|
||||
"size": [
|
||||
400,
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172
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||||
],
|
||||
"flags": {},
|
||||
"order": 0,
|
||||
"mode": 0,
|
||||
"inputs": [
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||||
{
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||||
"label": "image",
|
||||
"localized_name": "images.image0",
|
||||
"name": "images.image0",
|
||||
"type": "IMAGE",
|
||||
"link": 39
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||||
},
|
||||
{
|
||||
"localized_name": "fragment_shader",
|
||||
"name": "fragment_shader",
|
||||
"type": "STRING",
|
||||
"widget": {
|
||||
"name": "fragment_shader"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "size_mode",
|
||||
"name": "size_mode",
|
||||
"type": "COMFY_DYNAMICCOMBO_V3",
|
||||
"widget": {
|
||||
"name": "size_mode"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"label": "image1",
|
||||
"localized_name": "images.image1",
|
||||
"name": "images.image1",
|
||||
"shape": 7,
|
||||
"type": "IMAGE",
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"label": "R",
|
||||
"localized_name": "IMAGE0",
|
||||
"name": "IMAGE0",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
26
|
||||
]
|
||||
},
|
||||
{
|
||||
"label": "G",
|
||||
"localized_name": "IMAGE1",
|
||||
"name": "IMAGE1",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
27
|
||||
]
|
||||
},
|
||||
{
|
||||
"label": "B",
|
||||
"localized_name": "IMAGE2",
|
||||
"name": "IMAGE2",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
28
|
||||
]
|
||||
},
|
||||
{
|
||||
"label": "A",
|
||||
"localized_name": "IMAGE3",
|
||||
"name": "IMAGE3",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
29
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "GLSLShader"
|
||||
},
|
||||
"widgets_values": [
|
||||
"#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\nlayout(location = 1) out vec4 fragColor1;\nlayout(location = 2) out vec4 fragColor2;\nlayout(location = 3) out vec4 fragColor3;\n\nvoid main() {\n vec4 color = texture(u_image0, v_texCoord);\n // Output each channel as grayscale to separate render targets\n fragColor0 = vec4(vec3(color.r), 1.0); // Red channel\n fragColor1 = vec4(vec3(color.g), 1.0); // Green channel\n fragColor2 = vec4(vec3(color.b), 1.0); // Blue channel\n fragColor3 = vec4(vec3(color.a), 1.0); // Alpha channel\n}\n",
|
||||
"from_input"
|
||||
]
|
||||
}
|
||||
],
|
||||
"groups": [],
|
||||
"links": [
|
||||
{
|
||||
"id": 39,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 0,
|
||||
"target_id": 23,
|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 26,
|
||||
"origin_id": 23,
|
||||
"origin_slot": 0,
|
||||
"target_id": -20,
|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 27,
|
||||
"origin_id": 23,
|
||||
"origin_slot": 1,
|
||||
"target_id": -20,
|
||||
"target_slot": 1,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 28,
|
||||
"origin_id": 23,
|
||||
"origin_slot": 2,
|
||||
"target_id": -20,
|
||||
"target_slot": 2,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 29,
|
||||
"origin_id": 23,
|
||||
"origin_slot": 3,
|
||||
"target_id": -20,
|
||||
"target_slot": 3,
|
||||
"type": "IMAGE"
|
||||
}
|
||||
],
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Image Tools/Color adjust"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
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|
||||
"name": "fps"
|
||||
},
|
||||
"link": 12
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "VIDEO",
|
||||
"name": "VIDEO",
|
||||
"type": "VIDEO",
|
||||
"links": [
|
||||
15
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.10.0",
|
||||
"Node name for S&R": "CreateVideo"
|
||||
},
|
||||
"widgets_values": [
|
||||
30
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 10,
|
||||
"type": "GetVideoComponents",
|
||||
"pos": [
|
||||
1110,
|
||||
330
|
||||
],
|
||||
"size": [
|
||||
320,
|
||||
70
|
||||
],
|
||||
"flags": {},
|
||||
"order": 2,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "video",
|
||||
"name": "video",
|
||||
"type": "VIDEO",
|
||||
"link": 10
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "images",
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
14
|
||||
]
|
||||
},
|
||||
{
|
||||
"localized_name": "audio",
|
||||
"name": "audio",
|
||||
"type": "AUDIO",
|
||||
"links": [
|
||||
16
|
||||
]
|
||||
},
|
||||
{
|
||||
"localized_name": "fps",
|
||||
"name": "fps",
|
||||
"type": "FLOAT",
|
||||
"links": [
|
||||
12
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.10.0",
|
||||
"Node name for S&R": "GetVideoComponents"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "UpscaleModelLoader",
|
||||
"pos": [
|
||||
750,
|
||||
450
|
||||
],
|
||||
"size": [
|
||||
280,
|
||||
60
|
||||
],
|
||||
"flags": {},
|
||||
"order": 0,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "model_name",
|
||||
"name": "model_name",
|
||||
"type": "COMBO",
|
||||
"widget": {
|
||||
"name": "model_name"
|
||||
},
|
||||
"link": 19
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "UPSCALE_MODEL",
|
||||
"name": "UPSCALE_MODEL",
|
||||
"type": "UPSCALE_MODEL",
|
||||
"links": [
|
||||
1
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.10.0",
|
||||
"Node name for S&R": "UpscaleModelLoader",
|
||||
"models": [
|
||||
{
|
||||
"name": "RealESRGAN_x4plus.safetensors",
|
||||
"url": "https://huggingface.co/Comfy-Org/Real-ESRGAN_repackaged/resolve/main/RealESRGAN_x4plus.safetensors",
|
||||
"directory": "upscale_models"
|
||||
}
|
||||
]
|
||||
},
|
||||
"widgets_values": [
|
||||
"RealESRGAN_x4plus.safetensors"
|
||||
]
|
||||
}
|
||||
],
|
||||
"groups": [],
|
||||
"links": [
|
||||
{
|
||||
"id": 1,
|
||||
"origin_id": 1,
|
||||
"origin_slot": 0,
|
||||
"target_id": 2,
|
||||
"target_slot": 0,
|
||||
"type": "UPSCALE_MODEL"
|
||||
},
|
||||
{
|
||||
"id": 14,
|
||||
"origin_id": 10,
|
||||
"origin_slot": 0,
|
||||
"target_id": 2,
|
||||
"target_slot": 1,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 13,
|
||||
"origin_id": 2,
|
||||
"origin_slot": 0,
|
||||
"target_id": 11,
|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 16,
|
||||
"origin_id": 10,
|
||||
"origin_slot": 1,
|
||||
"target_id": 11,
|
||||
"target_slot": 1,
|
||||
"type": "AUDIO"
|
||||
},
|
||||
{
|
||||
"id": 12,
|
||||
"origin_id": 10,
|
||||
"origin_slot": 2,
|
||||
"target_id": 11,
|
||||
"target_slot": 2,
|
||||
"type": "FLOAT"
|
||||
},
|
||||
{
|
||||
"id": 10,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 0,
|
||||
"target_id": 10,
|
||||
"target_slot": 0,
|
||||
"type": "VIDEO"
|
||||
},
|
||||
{
|
||||
"id": 15,
|
||||
"origin_id": 11,
|
||||
"origin_slot": 0,
|
||||
"target_id": -20,
|
||||
"target_slot": 0,
|
||||
"type": "VIDEO"
|
||||
},
|
||||
{
|
||||
"id": 19,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 1,
|
||||
"target_id": 1,
|
||||
"target_slot": 0,
|
||||
"type": "COMBO"
|
||||
}
|
||||
],
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Video generation and editing/Enhance video"
|
||||
}
|
||||
]
|
||||
},
|
||||
"extra": {}
|
||||
}
|
||||
{"revision": 0, "last_node_id": 13, "last_link_id": 0, "nodes": [{"id": 13, "type": "cf95b747-3e17-46cb-8097-cac60ff9b2e1", "pos": [1120, 330], "size": [240, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [{"localized_name": "video", "name": "video", "type": "VIDEO", "link": null}, {"name": "model_name", "type": "COMBO", "widget": {"name": "model_name"}, "link": null}], "outputs": [{"localized_name": "VIDEO", "name": "VIDEO", "type": "VIDEO", "links": []}], "title": "Video Upscale(GAN x4)", "properties": {"proxyWidgets": [["-1", "model_name"]], "cnr_id": "comfy-core", "ver": "0.14.1"}, "widgets_values": ["RealESRGAN_x4plus.safetensors"]}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "cf95b747-3e17-46cb-8097-cac60ff9b2e1", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 13, "lastLinkId": 19, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Video Upscale(GAN x4)", "inputNode": {"id": -10, "bounding": [550, 460, 120, 80]}, "outputNode": {"id": -20, "bounding": [1490, 460, 120, 60]}, "inputs": [{"id": "666d633e-93e7-42dc-8d11-2b7b99b0f2a6", "name": "video", "type": "VIDEO", "linkIds": [10], "localized_name": "video", "pos": [650, 480]}, {"id": "2e23a087-caa8-4d65-99e6-662761aa905a", "name": "model_name", "type": "COMBO", "linkIds": [19], "pos": [650, 500]}], "outputs": [{"id": "0c1768ea-3ec2-412f-9af6-8e0fa36dae70", "name": "VIDEO", "type": "VIDEO", "linkIds": [15], "localized_name": "VIDEO", "pos": [1510, 480]}], "widgets": [], "nodes": [{"id": 2, "type": "ImageUpscaleWithModel", "pos": [1110, 450], "size": [320, 46], "flags": {}, "order": 1, "mode": 0, "inputs": [{"localized_name": "upscale_model", "name": "upscale_model", "type": "UPSCALE_MODEL", "link": 1}, {"localized_name": "image", "name": "image", "type": "IMAGE", "link": 14}], "outputs": [{"localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "links": [13]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "ImageUpscaleWithModel"}}, {"id": 11, "type": "CreateVideo", "pos": [1110, 550], "size": [320, 78], "flags": {}, "order": 3, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "link": 13}, {"localized_name": "audio", "name": "audio", "shape": 7, "type": "AUDIO", "link": 16}, {"localized_name": "fps", "name": "fps", "type": "FLOAT", "widget": {"name": "fps"}, "link": 12}], "outputs": [{"localized_name": "VIDEO", "name": "VIDEO", "type": "VIDEO", "links": [15]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "CreateVideo"}, "widgets_values": [30]}, {"id": 10, "type": "GetVideoComponents", "pos": [1110, 330], "size": [320, 70], "flags": {}, "order": 2, "mode": 0, "inputs": [{"localized_name": "video", "name": "video", "type": "VIDEO", "link": 10}], "outputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "links": [14]}, {"localized_name": "audio", "name": "audio", "type": "AUDIO", "links": [16]}, {"localized_name": "fps", "name": "fps", "type": "FLOAT", "links": [12]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "GetVideoComponents"}}, {"id": 1, "type": "UpscaleModelLoader", "pos": [750, 450], "size": [280, 60], "flags": {}, "order": 0, "mode": 0, "inputs": [{"localized_name": "model_name", "name": "model_name", "type": "COMBO", "widget": {"name": "model_name"}, "link": 19}], "outputs": [{"localized_name": "UPSCALE_MODEL", "name": "UPSCALE_MODEL", "type": "UPSCALE_MODEL", "links": [1]}], "properties": {"cnr_id": "comfy-core", "ver": "0.10.0", "Node name for S&R": "UpscaleModelLoader", "models": [{"name": "RealESRGAN_x4plus.safetensors", "url": "https://huggingface.co/Comfy-Org/Real-ESRGAN_repackaged/resolve/main/RealESRGAN_x4plus.safetensors", "directory": "upscale_models"}]}, "widgets_values": ["RealESRGAN_x4plus.safetensors"]}], "groups": [], "links": [{"id": 1, "origin_id": 1, "origin_slot": 0, "target_id": 2, "target_slot": 0, "type": "UPSCALE_MODEL"}, {"id": 14, "origin_id": 10, "origin_slot": 0, "target_id": 2, "target_slot": 1, "type": "IMAGE"}, {"id": 13, "origin_id": 2, "origin_slot": 0, "target_id": 11, "target_slot": 0, "type": "IMAGE"}, {"id": 16, "origin_id": 10, "origin_slot": 1, "target_id": 11, "target_slot": 1, "type": "AUDIO"}, {"id": 12, "origin_id": 10, "origin_slot": 2, "target_id": 11, "target_slot": 2, "type": "FLOAT"}, {"id": 10, "origin_id": -10, "origin_slot": 0, "target_id": 10, "target_slot": 0, "type": "VIDEO"}, {"id": 15, "origin_id": 11, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "VIDEO"}, {"id": 19, "origin_id": -10, "origin_slot": 1, "target_id": 1, "target_slot": 0, "type": "COMBO"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Video generation and editing/Enhance video"}]}, "extra": {}}
|
||||
|
||||
@@ -611,7 +611,6 @@ class AceStepDiTModel(nn.Module):
|
||||
intermediate_size,
|
||||
patch_size,
|
||||
audio_acoustic_hidden_dim,
|
||||
condition_dim=None,
|
||||
layer_types=None,
|
||||
sliding_window=128,
|
||||
rms_norm_eps=1e-6,
|
||||
@@ -641,7 +640,7 @@ class AceStepDiTModel(nn.Module):
|
||||
|
||||
self.time_embed = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations)
|
||||
self.time_embed_r = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations)
|
||||
self.condition_embedder = Linear(condition_dim, hidden_size, dtype=dtype, device=device)
|
||||
self.condition_embedder = Linear(hidden_size, hidden_size, dtype=dtype, device=device)
|
||||
|
||||
if layer_types is None:
|
||||
layer_types = ["full_attention"] * num_layers
|
||||
@@ -1036,9 +1035,6 @@ class AceStepConditionGenerationModel(nn.Module):
|
||||
fsq_dim=2048,
|
||||
fsq_levels=[8, 8, 8, 5, 5, 5],
|
||||
fsq_input_num_quantizers=1,
|
||||
encoder_hidden_size=2048,
|
||||
encoder_intermediate_size=6144,
|
||||
encoder_num_heads=16,
|
||||
audio_model=None,
|
||||
dtype=None,
|
||||
device=None,
|
||||
@@ -1058,24 +1054,24 @@ class AceStepConditionGenerationModel(nn.Module):
|
||||
|
||||
self.decoder = AceStepDiTModel(
|
||||
in_channels, hidden_size, num_dit_layers, num_heads, num_kv_heads, head_dim,
|
||||
intermediate_size, patch_size, audio_acoustic_hidden_dim, condition_dim=encoder_hidden_size,
|
||||
intermediate_size, patch_size, audio_acoustic_hidden_dim,
|
||||
layer_types=layer_types, sliding_window=sliding_window, rms_norm_eps=rms_norm_eps,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
self.encoder = AceStepConditionEncoder(
|
||||
text_hidden_dim, timbre_hidden_dim, encoder_hidden_size, num_lyric_layers, num_timbre_layers,
|
||||
encoder_num_heads, num_kv_heads, head_dim, encoder_intermediate_size, rms_norm_eps,
|
||||
text_hidden_dim, timbre_hidden_dim, hidden_size, num_lyric_layers, num_timbre_layers,
|
||||
num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
self.tokenizer = AceStepAudioTokenizer(
|
||||
audio_acoustic_hidden_dim, encoder_hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps,
|
||||
audio_acoustic_hidden_dim, hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
self.detokenizer = AudioTokenDetokenizer(
|
||||
encoder_hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim,
|
||||
hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
self.null_condition_emb = nn.Parameter(torch.empty(1, 1, encoder_hidden_size, dtype=dtype, device=device))
|
||||
self.null_condition_emb = nn.Parameter(torch.empty(1, 1, hidden_size, dtype=dtype, device=device))
|
||||
|
||||
def prepare_condition(
|
||||
self,
|
||||
|
||||
@@ -155,7 +155,6 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
|
||||
def __init__(self, embed_dim: int, **kwargs):
|
||||
self.max_batch_size = kwargs.pop("max_batch_size", None)
|
||||
ddconfig = kwargs.pop("ddconfig")
|
||||
decoder_ddconfig = kwargs.pop("decoder_ddconfig", ddconfig)
|
||||
super().__init__(
|
||||
encoder_config={
|
||||
"target": "comfy.ldm.modules.diffusionmodules.model.Encoder",
|
||||
@@ -163,7 +162,7 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
|
||||
},
|
||||
decoder_config={
|
||||
"target": "comfy.ldm.modules.diffusionmodules.model.Decoder",
|
||||
"params": decoder_ddconfig,
|
||||
"params": ddconfig,
|
||||
},
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@@ -696,15 +696,6 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
|
||||
if '{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix) in state_dict_keys:
|
||||
dit_config = {}
|
||||
dit_config["audio_model"] = "ace1.5"
|
||||
head_dim = 128
|
||||
dit_config["hidden_size"] = state_dict['{}decoder.layers.0.self_attn_norm.weight'.format(key_prefix)].shape[0]
|
||||
dit_config["intermediate_size"] = state_dict['{}decoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0]
|
||||
dit_config["num_heads"] = state_dict['{}decoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim
|
||||
|
||||
dit_config["encoder_hidden_size"] = state_dict['{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix)].shape[0]
|
||||
dit_config["encoder_num_heads"] = state_dict['{}encoder.lyric_encoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim
|
||||
dit_config["encoder_intermediate_size"] = state_dict['{}encoder.lyric_encoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0]
|
||||
dit_config["num_dit_layers"] = count_blocks(state_dict_keys, '{}decoder.layers.'.format(key_prefix) + '{}.')
|
||||
return dit_config
|
||||
|
||||
if '{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix) in state_dict_keys: # RT-DETR_v4
|
||||
|
||||
11
comfy/sd.py
11
comfy/sd.py
@@ -556,19 +556,12 @@ class VAE:
|
||||
old_memory_used_decode = self.memory_used_decode
|
||||
self.memory_used_decode = lambda shape, dtype: old_memory_used_decode(shape, dtype) * 4.0
|
||||
|
||||
decoder_ch = sd['decoder.conv_in.weight'].shape[0] // ddconfig['ch_mult'][-1]
|
||||
if decoder_ch != ddconfig['ch']:
|
||||
decoder_ddconfig = ddconfig.copy()
|
||||
decoder_ddconfig['ch'] = decoder_ch
|
||||
else:
|
||||
decoder_ddconfig = None
|
||||
|
||||
if 'post_quant_conv.weight' in sd:
|
||||
self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1], **({"decoder_ddconfig": decoder_ddconfig} if decoder_ddconfig is not None else {}))
|
||||
self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
|
||||
else:
|
||||
self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
|
||||
encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
|
||||
decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': decoder_ddconfig if decoder_ddconfig is not None else ddconfig})
|
||||
decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
|
||||
elif "decoder.layers.1.layers.0.beta" in sd:
|
||||
config = {}
|
||||
param_key = None
|
||||
|
||||
@@ -9,7 +9,6 @@ from comfy_api.latest._input import (
|
||||
CurveInput,
|
||||
MonotoneCubicCurve,
|
||||
LinearCurve,
|
||||
RangeInput,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
@@ -22,5 +21,4 @@ __all__ = [
|
||||
"CurveInput",
|
||||
"MonotoneCubicCurve",
|
||||
"LinearCurve",
|
||||
"RangeInput",
|
||||
]
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
from .basic_types import ImageInput, AudioInput, MaskInput, LatentInput
|
||||
from .curve_types import CurvePoint, CurveInput, MonotoneCubicCurve, LinearCurve
|
||||
from .range_types import RangeInput
|
||||
from .video_types import VideoInput
|
||||
|
||||
__all__ = [
|
||||
@@ -13,5 +12,4 @@ __all__ = [
|
||||
"CurveInput",
|
||||
"MonotoneCubicCurve",
|
||||
"LinearCurve",
|
||||
"RangeInput",
|
||||
]
|
||||
|
||||
@@ -1,70 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class RangeInput:
|
||||
"""Represents a levels/range adjustment: input range [min, max] with
|
||||
optional midpoint (gamma control).
|
||||
|
||||
Generates a 1D LUT identical to GIMP's levels mapping:
|
||||
1. Normalize input to [0, 1] using [min, max]
|
||||
2. Apply gamma correction: pow(value, 1/gamma)
|
||||
3. Clamp to [0, 1]
|
||||
|
||||
The midpoint field is a position in [0, 1] representing where the
|
||||
midtone falls within [min, max]. It maps to gamma via:
|
||||
gamma = -log2(midpoint)
|
||||
So midpoint=0.5 → gamma=1.0 (linear).
|
||||
"""
|
||||
|
||||
def __init__(self, min_val: float, max_val: float, midpoint: float | None = None):
|
||||
self.min_val = min_val
|
||||
self.max_val = max_val
|
||||
self.midpoint = midpoint
|
||||
|
||||
@staticmethod
|
||||
def from_raw(data) -> RangeInput:
|
||||
if isinstance(data, RangeInput):
|
||||
return data
|
||||
if isinstance(data, dict):
|
||||
return RangeInput(
|
||||
min_val=float(data.get("min", 0.0)),
|
||||
max_val=float(data.get("max", 1.0)),
|
||||
midpoint=float(data["midpoint"]) if data.get("midpoint") is not None else None,
|
||||
)
|
||||
raise TypeError(f"Cannot convert {type(data)} to RangeInput")
|
||||
|
||||
def to_lut(self, size: int = 256) -> np.ndarray:
|
||||
"""Generate a float64 lookup table mapping [0, 1] input through this
|
||||
levels adjustment.
|
||||
|
||||
The LUT maps normalized input values (0..1) to output values (0..1),
|
||||
matching the GIMP levels formula.
|
||||
"""
|
||||
xs = np.linspace(0.0, 1.0, size, dtype=np.float64)
|
||||
|
||||
in_range = self.max_val - self.min_val
|
||||
if abs(in_range) < 1e-10:
|
||||
return np.where(xs >= self.min_val, 1.0, 0.0).astype(np.float64)
|
||||
|
||||
# Normalize: map [min, max] → [0, 1]
|
||||
result = (xs - self.min_val) / in_range
|
||||
result = np.clip(result, 0.0, 1.0)
|
||||
|
||||
# Gamma correction from midpoint
|
||||
if self.midpoint is not None and self.midpoint > 0 and self.midpoint != 0.5:
|
||||
gamma = max(-math.log2(self.midpoint), 0.001)
|
||||
inv_gamma = 1.0 / gamma
|
||||
mask = result > 0
|
||||
result[mask] = np.power(result[mask], inv_gamma)
|
||||
|
||||
return result
|
||||
|
||||
def __repr__(self) -> str:
|
||||
mid = f", midpoint={self.midpoint}" if self.midpoint is not None else ""
|
||||
return f"RangeInput(min={self.min_val}, max={self.max_val}{mid})"
|
||||
@@ -1266,43 +1266,6 @@ class Histogram(ComfyTypeIO):
|
||||
Type = list[int]
|
||||
|
||||
|
||||
@comfytype(io_type="RANGE")
|
||||
class Range(ComfyTypeIO):
|
||||
from comfy_api.input import RangeInput
|
||||
if TYPE_CHECKING:
|
||||
Type = RangeInput
|
||||
|
||||
class Input(WidgetInput):
|
||||
def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
|
||||
socketless: bool=True, default: dict=None,
|
||||
display: str=None,
|
||||
gradient_stops: list=None,
|
||||
show_midpoint: bool=None,
|
||||
midpoint_scale: str=None,
|
||||
value_min: float=None,
|
||||
value_max: float=None,
|
||||
advanced: bool=None):
|
||||
super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
|
||||
if default is None:
|
||||
self.default = {"min": 0.0, "max": 1.0}
|
||||
self.display = display
|
||||
self.gradient_stops = gradient_stops
|
||||
self.show_midpoint = show_midpoint
|
||||
self.midpoint_scale = midpoint_scale
|
||||
self.value_min = value_min
|
||||
self.value_max = value_max
|
||||
|
||||
def as_dict(self):
|
||||
return super().as_dict() | prune_dict({
|
||||
"display": self.display,
|
||||
"gradient_stops": self.gradient_stops,
|
||||
"show_midpoint": self.show_midpoint,
|
||||
"midpoint_scale": self.midpoint_scale,
|
||||
"value_min": self.value_min,
|
||||
"value_max": self.value_max,
|
||||
})
|
||||
|
||||
|
||||
DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {}
|
||||
def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]):
|
||||
DYNAMIC_INPUT_LOOKUP[io_type] = func
|
||||
@@ -2313,6 +2276,5 @@ __all__ = [
|
||||
"BoundingBox",
|
||||
"Curve",
|
||||
"Histogram",
|
||||
"Range",
|
||||
"NodeReplace",
|
||||
]
|
||||
|
||||
@@ -80,7 +80,7 @@ class EmptyAceStepLatentAudio(io.ComfyNode):
|
||||
@classmethod
|
||||
def execute(cls, seconds, batch_size) -> io.NodeOutput:
|
||||
length = int(seconds * 44100 / 512 / 8)
|
||||
latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
|
||||
latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device())
|
||||
return io.NodeOutput({"samples": latent, "type": "audio"})
|
||||
|
||||
|
||||
@@ -103,7 +103,7 @@ class EmptyAceStep15LatentAudio(io.ComfyNode):
|
||||
@classmethod
|
||||
def execute(cls, seconds, batch_size) -> io.NodeOutput:
|
||||
length = round((seconds * 48000 / 1920))
|
||||
latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
|
||||
latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
|
||||
return io.NodeOutput({"samples": latent, "type": "audio"})
|
||||
|
||||
class ReferenceAudio(io.ComfyNode):
|
||||
|
||||
@@ -1,7 +1,5 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import numpy as np
|
||||
|
||||
from comfy_api.latest import ComfyExtension, io
|
||||
from comfy_api.input import CurveInput
|
||||
from typing_extensions import override
|
||||
@@ -34,58 +32,10 @@ class CurveEditor(io.ComfyNode):
|
||||
return io.NodeOutput(result, ui=ui) if ui else io.NodeOutput(result)
|
||||
|
||||
|
||||
class ImageHistogram(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="ImageHistogram",
|
||||
display_name="Image Histogram",
|
||||
category="utils",
|
||||
inputs=[
|
||||
io.Image.Input("image"),
|
||||
],
|
||||
outputs=[
|
||||
io.Histogram.Output("rgb"),
|
||||
io.Histogram.Output("luminance"),
|
||||
io.Histogram.Output("red"),
|
||||
io.Histogram.Output("green"),
|
||||
io.Histogram.Output("blue"),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, image) -> io.NodeOutput:
|
||||
img = image[0].cpu().numpy()
|
||||
img_uint8 = np.clip(img * 255, 0, 255).astype(np.uint8)
|
||||
|
||||
def bincount(data):
|
||||
return np.bincount(data.ravel(), minlength=256)[:256]
|
||||
|
||||
hist_r = bincount(img_uint8[:, :, 0])
|
||||
hist_g = bincount(img_uint8[:, :, 1])
|
||||
hist_b = bincount(img_uint8[:, :, 2])
|
||||
|
||||
# Average of R, G, B histograms (same as Photoshop's RGB composite)
|
||||
rgb = ((hist_r + hist_g + hist_b) // 3).tolist()
|
||||
|
||||
# ITU-R BT.709-6, Item 3.2 (p.6) — Derivation of luminance signal
|
||||
# https://www.itu.int/rec/R-REC-BT.709-6-201506-I/en
|
||||
lum = 0.2126 * img[:, :, 0] + 0.7152 * img[:, :, 1] + 0.0722 * img[:, :, 2]
|
||||
luminance = bincount(np.clip(lum * 255, 0, 255).astype(np.uint8)).tolist()
|
||||
|
||||
return io.NodeOutput(
|
||||
rgb,
|
||||
luminance,
|
||||
hist_r.tolist(),
|
||||
hist_g.tolist(),
|
||||
hist_b.tolist(),
|
||||
)
|
||||
|
||||
|
||||
class CurveExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self):
|
||||
return [CurveEditor, ImageHistogram]
|
||||
return [CurveEditor]
|
||||
|
||||
|
||||
async def comfy_entrypoint():
|
||||
|
||||
6
nodes.py
6
nodes.py
@@ -2228,6 +2228,12 @@ async def load_custom_node(module_path: str, ignore=set(), module_parent="custom
|
||||
|
||||
LOADED_MODULE_DIRS[module_name] = os.path.abspath(module_dir)
|
||||
|
||||
# Only load node_replacements.json from directory-based custom nodes (proper packs).
|
||||
# Single-file .py nodes share a parent dir, so checking there would be incorrect.
|
||||
if os.path.isdir(module_path):
|
||||
from server import PromptServer
|
||||
PromptServer.instance.node_replace_manager.load_from_json(module_dir, module_name)
|
||||
|
||||
try:
|
||||
from comfy_config import config_parser
|
||||
|
||||
|
||||
217
tests/test_node_replacements_json.py
Normal file
217
tests/test_node_replacements_json.py
Normal file
@@ -0,0 +1,217 @@
|
||||
"""Tests for NodeReplaceManager.load_from_json — auto-registration of
|
||||
node_replacements.json from custom node directories."""
|
||||
import json
|
||||
import os
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
from app.node_replace_manager import NodeReplaceManager
|
||||
|
||||
|
||||
class SimpleNodeReplace:
|
||||
"""Lightweight stand-in for comfy_api.latest._io.NodeReplace (avoids torch import)."""
|
||||
def __init__(self, new_node_id, old_node_id, old_widget_ids=None,
|
||||
input_mapping=None, output_mapping=None):
|
||||
self.new_node_id = new_node_id
|
||||
self.old_node_id = old_node_id
|
||||
self.old_widget_ids = old_widget_ids
|
||||
self.input_mapping = input_mapping
|
||||
self.output_mapping = output_mapping
|
||||
|
||||
def as_dict(self):
|
||||
return {
|
||||
"new_node_id": self.new_node_id,
|
||||
"old_node_id": self.old_node_id,
|
||||
"old_widget_ids": self.old_widget_ids,
|
||||
"input_mapping": list(self.input_mapping) if self.input_mapping else None,
|
||||
"output_mapping": list(self.output_mapping) if self.output_mapping else None,
|
||||
}
|
||||
|
||||
|
||||
class TestLoadFromJson(unittest.TestCase):
|
||||
"""Test auto-registration of node_replacements.json from custom node directories."""
|
||||
|
||||
def setUp(self):
|
||||
self.tmpdir = tempfile.mkdtemp()
|
||||
self.manager = NodeReplaceManager()
|
||||
|
||||
def _write_json(self, data):
|
||||
path = os.path.join(self.tmpdir, "node_replacements.json")
|
||||
with open(path, "w") as f:
|
||||
json.dump(data, f)
|
||||
|
||||
def _load(self):
|
||||
self.manager.load_from_json(self.tmpdir, "test-node-pack", _node_replace_class=SimpleNodeReplace)
|
||||
|
||||
def test_no_file_does_nothing(self):
|
||||
"""No node_replacements.json — should silently do nothing."""
|
||||
self._load()
|
||||
self.assertEqual(self.manager.as_dict(), {})
|
||||
|
||||
def test_empty_object(self):
|
||||
"""Empty {} — should do nothing."""
|
||||
self._write_json({})
|
||||
self._load()
|
||||
self.assertEqual(self.manager.as_dict(), {})
|
||||
|
||||
def test_single_replacement(self):
|
||||
"""Single replacement entry registers correctly."""
|
||||
self._write_json({
|
||||
"OldNode": [{
|
||||
"new_node_id": "NewNode",
|
||||
"old_node_id": "OldNode",
|
||||
"input_mapping": [{"new_id": "model", "old_id": "ckpt_name"}],
|
||||
"output_mapping": [{"new_idx": 0, "old_idx": 0}],
|
||||
}]
|
||||
})
|
||||
self._load()
|
||||
result = self.manager.as_dict()
|
||||
self.assertIn("OldNode", result)
|
||||
self.assertEqual(len(result["OldNode"]), 1)
|
||||
entry = result["OldNode"][0]
|
||||
self.assertEqual(entry["new_node_id"], "NewNode")
|
||||
self.assertEqual(entry["old_node_id"], "OldNode")
|
||||
self.assertEqual(entry["input_mapping"], [{"new_id": "model", "old_id": "ckpt_name"}])
|
||||
self.assertEqual(entry["output_mapping"], [{"new_idx": 0, "old_idx": 0}])
|
||||
|
||||
def test_multiple_replacements(self):
|
||||
"""Multiple old_node_ids each with entries."""
|
||||
self._write_json({
|
||||
"NodeA": [{"new_node_id": "NodeB", "old_node_id": "NodeA"}],
|
||||
"NodeC": [{"new_node_id": "NodeD", "old_node_id": "NodeC"}],
|
||||
})
|
||||
self._load()
|
||||
result = self.manager.as_dict()
|
||||
self.assertEqual(len(result), 2)
|
||||
self.assertIn("NodeA", result)
|
||||
self.assertIn("NodeC", result)
|
||||
|
||||
def test_multiple_alternatives_for_same_node(self):
|
||||
"""Multiple replacement options for the same old node."""
|
||||
self._write_json({
|
||||
"OldNode": [
|
||||
{"new_node_id": "AltA", "old_node_id": "OldNode"},
|
||||
{"new_node_id": "AltB", "old_node_id": "OldNode"},
|
||||
]
|
||||
})
|
||||
self._load()
|
||||
result = self.manager.as_dict()
|
||||
self.assertEqual(len(result["OldNode"]), 2)
|
||||
|
||||
def test_null_mappings(self):
|
||||
"""Null input/output mappings (trivial replacement)."""
|
||||
self._write_json({
|
||||
"OldNode": [{
|
||||
"new_node_id": "NewNode",
|
||||
"old_node_id": "OldNode",
|
||||
"input_mapping": None,
|
||||
"output_mapping": None,
|
||||
}]
|
||||
})
|
||||
self._load()
|
||||
entry = self.manager.as_dict()["OldNode"][0]
|
||||
self.assertIsNone(entry["input_mapping"])
|
||||
self.assertIsNone(entry["output_mapping"])
|
||||
|
||||
def test_old_node_id_defaults_to_key(self):
|
||||
"""If old_node_id is missing from entry, uses the dict key."""
|
||||
self._write_json({
|
||||
"OldNode": [{"new_node_id": "NewNode"}]
|
||||
})
|
||||
self._load()
|
||||
entry = self.manager.as_dict()["OldNode"][0]
|
||||
self.assertEqual(entry["old_node_id"], "OldNode")
|
||||
|
||||
def test_invalid_json_skips(self):
|
||||
"""Invalid JSON file — should warn and skip, not crash."""
|
||||
path = os.path.join(self.tmpdir, "node_replacements.json")
|
||||
with open(path, "w") as f:
|
||||
f.write("{invalid json")
|
||||
self._load()
|
||||
self.assertEqual(self.manager.as_dict(), {})
|
||||
|
||||
def test_non_object_json_skips(self):
|
||||
"""JSON array instead of object — should warn and skip."""
|
||||
self._write_json([1, 2, 3])
|
||||
self._load()
|
||||
self.assertEqual(self.manager.as_dict(), {})
|
||||
|
||||
def test_non_list_value_skips(self):
|
||||
"""Value is not a list — should warn and skip that key."""
|
||||
self._write_json({
|
||||
"OldNode": "not a list",
|
||||
"GoodNode": [{"new_node_id": "NewNode", "old_node_id": "GoodNode"}],
|
||||
})
|
||||
self._load()
|
||||
result = self.manager.as_dict()
|
||||
self.assertNotIn("OldNode", result)
|
||||
self.assertIn("GoodNode", result)
|
||||
|
||||
def test_with_old_widget_ids(self):
|
||||
"""old_widget_ids are passed through."""
|
||||
self._write_json({
|
||||
"OldNode": [{
|
||||
"new_node_id": "NewNode",
|
||||
"old_node_id": "OldNode",
|
||||
"old_widget_ids": ["width", "height"],
|
||||
}]
|
||||
})
|
||||
self._load()
|
||||
entry = self.manager.as_dict()["OldNode"][0]
|
||||
self.assertEqual(entry["old_widget_ids"], ["width", "height"])
|
||||
|
||||
def test_set_value_in_input_mapping(self):
|
||||
"""input_mapping with set_value entries."""
|
||||
self._write_json({
|
||||
"OldNode": [{
|
||||
"new_node_id": "NewNode",
|
||||
"old_node_id": "OldNode",
|
||||
"input_mapping": [
|
||||
{"new_id": "method", "set_value": "lanczos"},
|
||||
{"new_id": "size", "old_id": "dimension"},
|
||||
],
|
||||
}]
|
||||
})
|
||||
self._load()
|
||||
entry = self.manager.as_dict()["OldNode"][0]
|
||||
self.assertEqual(len(entry["input_mapping"]), 2)
|
||||
|
||||
def test_missing_new_node_id_skipped(self):
|
||||
"""Entry without new_node_id is skipped."""
|
||||
self._write_json({
|
||||
"OldNode": [
|
||||
{"old_node_id": "OldNode"},
|
||||
{"new_node_id": "", "old_node_id": "OldNode"},
|
||||
{"new_node_id": "ValidNew", "old_node_id": "OldNode"},
|
||||
]
|
||||
})
|
||||
self._load()
|
||||
result = self.manager.as_dict()
|
||||
self.assertEqual(len(result["OldNode"]), 1)
|
||||
self.assertEqual(result["OldNode"][0]["new_node_id"], "ValidNew")
|
||||
|
||||
def test_non_dict_entry_skipped(self):
|
||||
"""Non-dict entries in the list are silently skipped."""
|
||||
self._write_json({
|
||||
"OldNode": [
|
||||
"not a dict",
|
||||
{"new_node_id": "NewNode", "old_node_id": "OldNode"},
|
||||
]
|
||||
})
|
||||
self._load()
|
||||
result = self.manager.as_dict()
|
||||
self.assertEqual(len(result["OldNode"]), 1)
|
||||
|
||||
def test_has_replacement_after_load(self):
|
||||
"""Manager reports has_replacement correctly after JSON load."""
|
||||
self._write_json({
|
||||
"OldNode": [{"new_node_id": "NewNode", "old_node_id": "OldNode"}],
|
||||
})
|
||||
self.assertFalse(self.manager.has_replacement("OldNode"))
|
||||
self._load()
|
||||
self.assertTrue(self.manager.has_replacement("OldNode"))
|
||||
self.assertFalse(self.manager.has_replacement("UnknownNode"))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user