641 lines
22 KiB
Python
641 lines
22 KiB
Python
from types import SimpleNamespace
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from unittest.mock import patch
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from core import resolver as resolver_core
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from core.contracts import ModelReference, Resolution
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from core.workflow import analysis, references, subgraphs, traversal
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from core.workflow_updater import update_workflow_nodes
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def _loader_node(node_id=10, value="model.safetensors", link=None):
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return {
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"id": node_id,
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"type": "CheckpointLoaderSimple",
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"title": "Loader",
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"widgets_values": [value],
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"inputs": [
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{
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"name": "ckpt_name",
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"widget": {"name": "ckpt_name"},
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"link": link,
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}
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],
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"outputs": [],
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}
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def test_workflow_node_traversal_preserves_node_order_and_scope():
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top_level_node = {"id": 1, "type": "TopLevel"}
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subgraph_node = {"id": 2, "type": "Inner"}
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workflow = {
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"nodes": [top_level_node, "invalid-top-level-node"],
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"definitions": {
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"subgraphs": [
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None,
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{
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"id": "group-1",
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"name": "Group one",
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"nodes": [subgraph_node, "invalid-subgraph-node"],
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},
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]
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},
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}
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contexts = list(traversal.iter_workflow_nodes_with_scope(workflow))
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assert [context.node for context in contexts] == [
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top_level_node,
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"invalid-top-level-node",
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subgraph_node,
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"invalid-subgraph-node",
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]
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assert contexts[0].is_top_level is True
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assert contexts[0].subgraph_id == ""
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assert contexts[1].is_top_level is True
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assert contexts[2].is_top_level is False
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assert contexts[2].subgraph_id == "group-1"
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assert contexts[2].subgraph_name == "Group one"
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assert contexts[3].subgraph_id == "group-1"
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def test_workflow_cache_helpers_share_scope_keys_and_normalized_hashing():
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node = {"id": 2, "type": "Inner", "pos": [10, 20]}
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assert analysis._get_workflow_node_cache_key(
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node,
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subgraph_id="group-1",
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is_top_level=False,
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) == analysis._get_workflow_scope_key(
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2,
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subgraph_id="group-1",
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is_top_level=False,
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)
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assert analysis._get_workflow_node_cache_key(
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node,
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is_top_level=True,
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) == ("top", "", "2")
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assert analysis._get_workflow_node_fingerprint(node) == (
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analysis._hash_normalized_workflow_value(node)
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)
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assert analysis._get_workflow_node_fingerprint(
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{"type": "Inner", "id": 2, "pos": [999, 999]}
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) == analysis._get_workflow_node_fingerprint(node)
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def test_active_workflow_node_traversal_follows_nested_referenced_subgraphs_only():
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workflow = {
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"nodes": [
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{"id": "top", "type": "subgraph-1", "widgets_values": []},
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"invalid-top-level-node",
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],
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"definitions": {
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"subgraphs": [
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{
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"id": "subgraph-1",
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"name": "Active group",
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"nodes": [
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{"id": "inner", "type": "subgraph-2"},
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"invalid-inner-node",
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],
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},
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{
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"id": "subgraph-2",
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"name": "Nested active group",
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"nodes": [{"id": "nested", "type": "NestedLoader"}],
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},
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{
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"id": "unused",
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"name": "Unused group",
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"nodes": [{"id": "unused-node", "type": "UnusedLoader"}],
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},
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]
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},
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}
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active_contexts = list(
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traversal.iter_active_workflow_nodes_with_scope(workflow)
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)
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assert [context.node["id"] for context in active_contexts] == [
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"top",
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"inner",
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"nested",
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]
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assert active_contexts[1].subgraph_id == "subgraph-1"
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assert active_contexts[2].subgraph_id == "subgraph-2"
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def test_extract_workflow_urls_includes_all_serialized_subgraph_nodes():
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workflow = {
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"nodes": [
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{
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"id": "top",
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"type": "TopLoader",
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"properties": {
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"models": [
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{
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"name": "top.safetensors",
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"url": "https://civitai.com/api/download/models/1",
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"directory": "checkpoints",
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}
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]
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},
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}
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],
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"definitions": {
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"subgraphs": [
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{
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"id": "unused",
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"nodes": [
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{
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"id": "subgraph-node",
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"type": "SubgraphLoader",
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"properties": {
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"models": [
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{
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"name": "subgraph.safetensors",
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"url": "https://civitai.com/api/download/models/2",
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"directory": "loras",
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}
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]
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},
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}
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],
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}
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]
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},
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}
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url_map = resolver_core.extract_workflow_urls(workflow)
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assert url_map["top.safetensors"] == {
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"url": "https://civitai.com/api/download/models/1",
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"model_url": "https://civitai.com/api/download/models/1",
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"directory": "checkpoints",
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"node_type": "TopLoader",
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"source": "node_properties",
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}
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assert url_map["subgraph.safetensors"] == {
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"url": "https://civitai.com/api/download/models/2",
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"model_url": "https://civitai.com/api/download/models/2",
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"directory": "loras",
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"node_type": "SubgraphLoader",
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"source": "node_properties",
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}
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def test_subgraph_widget_indexes_support_named_inputs_and_links():
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node = {
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**_loader_node(),
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"inputs": [
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{"name": "model", "widget": None, "link": 1},
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{"name": "ckpt_name", "widget": {"name": "ckpt_name"}},
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],
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"widgets_values": ["model.safetensors"],
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}
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assert subgraphs._get_widget_index_for_input_index(node, 0) is None
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assert subgraphs._get_widget_index_for_input_index(node, 1) == 0
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assert subgraphs._get_widget_index_by_name(node, "ckpt-name") == 0
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assert subgraphs._get_node_by_id([node], "10") is node
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subgraph = {
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"inputs": [{"name": "ckpt_name", "linkIds": [7, "8"]}],
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}
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assert subgraphs._get_subgraph_input_link_ids(subgraph, "ckpt-name") == {
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"7",
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"8",
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}
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assert subgraphs._get_subgraph_input_link_ids(subgraph, "missing") == set()
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def test_find_promoted_targets_prefers_explicit_node_then_linked_input():
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explicit_node = _loader_node(node_id=10)
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linked_node = _loader_node(node_id=11, link=7)
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subgraph = {
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"inputs": [{"name": "ckpt_name", "linkIds": [7]}],
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"nodes": [explicit_node, linked_node],
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}
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with patch.object(
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subgraphs.references,
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"get_effective_model_category_hint",
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return_value="checkpoints",
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):
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explicit = subgraphs._find_promoted_widget_targets(
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subgraph, "10", "ckpt_name"
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)
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linked = subgraphs._find_promoted_widget_targets(
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subgraph, "-1", "ckpt_name"
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)
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assert [(item["node_id"], item["widget_index"]) for item in explicit] == [(10, 0)]
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assert [(item["node_id"], item["widget_index"]) for item in linked] == [(11, 0)]
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assert linked[0]["category"] == "checkpoints"
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def test_build_promoted_contexts_records_instance_and_inner_locators():
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workflow = {
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"nodes": [
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{
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"id": 1,
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"type": "subgraph-1",
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"widgets_values": ["promoted.safetensors"],
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"properties": {"proxyWidgets": [["10", "ckpt_name"]]},
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}
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]
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}
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subgraph = {
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"id": "subgraph-1",
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"name": "Promoted loader",
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"nodes": [_loader_node()],
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}
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with patch.object(
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subgraphs.references,
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"get_effective_model_category_hint",
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return_value="checkpoints",
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):
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contexts = subgraphs._build_promoted_widget_contexts(workflow, [subgraph])
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instance = contexts["instance_widgets"][("1", 0)]
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assert instance["subgraph_name"] == "Promoted loader"
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assert instance["promoted_value"] == "promoted.safetensors"
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assert contexts["inner_widgets"][("subgraph-1", "10", 0)][0]["node_id"] == 1
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assert contexts["inner_widget_names"][("subgraph-1", "10", "ckpt_name")]
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def test_promoted_context_uses_named_widget_indexes_when_inputs_omit_hidden_values():
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model_name = "4x_NMKD-Superscale-SP_178000_G.pth"
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workflow = {
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"nodes": [
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{
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"id": 157,
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"type": "subgraph-1",
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"mode": 4,
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"inputs": [
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{"name": "image", "link": 1},
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{"name": "positive", "link": 2},
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{"name": "negative", "link": 3},
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{"name": "vae", "link": 4},
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{"name": "model", "link": 5},
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{
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"name": "value",
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"widget": {"name": "value"},
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"link": None,
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},
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{
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"name": "model_name",
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"widget": {"name": "model_name"},
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"link": None,
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},
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],
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# The hidden seed is present in the positional values but not
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# in the visible instance inputs.
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"widgets_values": [27499, 1.2, model_name],
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"widgets_values_named": {
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"seed": 27499,
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"value": 1.2,
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"model_name": model_name,
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},
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}
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],
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"definitions": {
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"subgraphs": [
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{
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"id": "subgraph-1",
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"name": "Second Pass (Upscale)",
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"inputs": [
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{"name": "seed", "linkIds": [10]},
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{"name": "value", "linkIds": [11]},
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{"name": "model_name", "linkIds": [12]},
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],
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"nodes": [
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{
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"id": 142,
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"type": "UpscaleModelLoader",
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"mode": 4,
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"inputs": [
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{
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"name": "model_name",
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"widget": {"name": "model_name"},
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"link": 12,
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}
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],
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"outputs": [
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{
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"type": "UPSCALE_MODEL",
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"links": [20],
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}
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],
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"widgets_values": [model_name],
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"widgets_values_named": {"model_name": model_name},
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},
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{
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"id": 154,
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"type": "PrimitiveFloat",
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"inputs": [
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{
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"name": "value",
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"widget": {"name": "value"},
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"link": 11,
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}
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],
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"outputs": [],
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"widgets_values": [1.2],
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"widgets_values_named": {"value": 1.2},
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},
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],
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}
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]
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},
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}
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refs = analysis.analyze_workflow_models(workflow, available_models=[])
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assert len(refs) == 1
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assert refs[0].original_path == model_name
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assert refs[0].category == "upscale_models"
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assert refs[0].node_id == 157
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assert refs[0].widget_index == 2
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assert refs[0].extra_value("promoted_inner_node_id") == 142
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assert refs[0].extra_value("promoted_widget_name") == "model_name"
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assert refs[0].extra_value("connected") is False
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def test_apply_instance_promoted_context_resolves_existing_model(tmp_path):
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model_path = tmp_path / "promoted.safetensors"
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model_path.write_bytes(b"model")
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available_models = [
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{
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"category": "checkpoints",
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"relative_path": "promoted.safetensors",
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"path": str(model_path),
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}
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]
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reference = ModelReference.from_mapping(
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{"original_path": "promoted.safetensors", "exists": False}
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)
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context = {
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"proxy_widget_name": "ckpt_name",
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"node_id": 10,
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"node_type": "CheckpointLoaderSimple",
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"node_title": "Loader",
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"widget_index": 0,
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"widget_name": "ckpt_name",
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"category": "checkpoints",
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}
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reference = subgraphs._apply_instance_promoted_widget_context(
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reference,
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context,
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available_models,
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)
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assert reference.extra_value("promoted_inner_node_id") == 10
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assert reference.category == "checkpoints"
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assert reference.extra_value("full_path") == str(model_path)
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assert reference.exists is True
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def test_apply_promoted_locator_supports_name_fallback_and_value_selection():
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contexts = {
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"inner_widgets": {},
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"inner_widget_names": {
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("subgraph-1", "10", "ckpt_name"): [
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{"node_id": 1, "node_type": "Subgraph", "subgraph_name": "One", "promoted_value": "first"},
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{"node_id": 2, "node_type": "Subgraph", "subgraph_name": "Two", "promoted_value": "second"},
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]
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},
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}
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reference = ModelReference.from_mapping(
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{
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"subgraph_id": "subgraph-1",
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"node_id": 10,
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"widget_index": 99,
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"widget_name": "ckpt_name",
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"original_path": "second",
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}
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)
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reference = subgraphs._apply_promoted_widget_locator(reference, contexts)
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assert reference.extra_value("locate_node_id") == 2
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assert reference.extra_value("locate_subgraph_name") == "Two"
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assert reference.extra_value("locate_is_top_level") is True
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assert reference.extra_value("locate_via_promoted_widget") is True
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def test_subgraph_input_reference_targets_instance_for_update_and_location():
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workflow = {
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"nodes": [
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{
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"id": 1,
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"type": "subgraph-1",
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"title": "Model subgraph",
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"inputs": [
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{
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"name": "ckpt_name",
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"type": "COMBO",
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"widget": {"name": "ckpt_name"},
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"link": None,
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}
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],
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"outputs": [{"links": [20]}],
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"properties": {"proxyWidgets": [["10", "ckpt_name"]]},
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# Some serialized subgraph instances have no values here;
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# the connected inner widget still contains the old value.
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"widgets_values": [],
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}
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],
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"definitions": {
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"subgraphs": [
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{
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"id": "subgraph-1",
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"name": "Model subgraph",
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"inputs": [
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{"name": "ckpt_name", "type": "COMBO", "linkIds": [7]}
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],
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"nodes": [
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{
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"id": 10,
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"type": "CheckpointLoaderSimple",
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"inputs": [
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{
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"name": "ckpt_name",
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"type": "COMBO",
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"widget": {"name": "ckpt_name"},
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"link": 7,
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}
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],
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"outputs": [{"type": "MODEL", "links": [20]}],
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"widgets_values": ["inner-stale.safetensors"],
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}
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],
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}
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]
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},
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}
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refs = analysis.analyze_workflow_models(workflow, available_models=[])
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assert len(refs) == 1
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assert refs[0].node_id == 1
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assert refs[0].widget_index == 0
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assert refs[0].subgraph_id == "subgraph-1"
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assert refs[0].is_top_level is True
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assert refs[0].extra_value("subgraph_path") is None
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assert refs[0].extra_value("locate_node_id") == 1
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assert refs[0].extra_value("locate_subgraph_id") == ""
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assert refs[0].extra_value("promoted_inner_node_id") == 10
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no_proxy_workflow = {
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**workflow,
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"nodes": [
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{
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**workflow["nodes"][0],
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"properties": {},
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}
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],
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}
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no_proxy_refs = analysis.analyze_workflow_models(
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no_proxy_workflow, available_models=[]
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)
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assert no_proxy_refs[0].node_id == 1
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assert no_proxy_refs[0].extra_value("promoted_inner_node_id") == 10
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resolution = Resolution.from_mapping(
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{
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"node_id": refs[0].node_id,
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"widget_index": refs[0].widget_index,
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"resolved_path": "replacement.safetensors",
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"category": "checkpoints",
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"subgraph_id": refs[0].subgraph_id,
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"is_top_level": refs[0].is_top_level,
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"promoted_widget_name": refs[0].extra_value("promoted_widget_name"),
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}
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)
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update_workflow_nodes(workflow, [resolution])
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assert workflow["nodes"][0]["widgets_values"] == ["replacement.safetensors"]
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assert workflow["definitions"]["subgraphs"][0]["nodes"][0]["widgets_values"] == [
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"inner-stale.safetensors"
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]
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|
def test_reused_subgraph_instance_refreshes_promoted_model_context():
|
|
workflow = {
|
|
"nodes": [
|
|
{
|
|
"id": 1,
|
|
"type": "subgraph-1",
|
|
"inputs": [
|
|
{
|
|
"name": "ckpt_name",
|
|
"type": "COMBO",
|
|
"widget": {"name": "ckpt_name"},
|
|
"link": None,
|
|
}
|
|
],
|
|
"properties": {"proxyWidgets": [["10", "ckpt_name"]]},
|
|
"widgets_values": ["old.safetensors"],
|
|
}
|
|
],
|
|
"definitions": {
|
|
"subgraphs": [
|
|
{
|
|
"id": "subgraph-1",
|
|
"name": "Promoted loader",
|
|
"nodes": [_loader_node(value="old.safetensors")],
|
|
}
|
|
]
|
|
},
|
|
}
|
|
previous_node_cache = {}
|
|
analysis.analyze_workflow_models(
|
|
workflow,
|
|
available_models=[],
|
|
node_cache_out=previous_node_cache,
|
|
)
|
|
|
|
updated_workflow = {
|
|
**workflow,
|
|
"nodes": [
|
|
{
|
|
**workflow["nodes"][0],
|
|
"widgets_values": ["new.safetensors"],
|
|
}
|
|
],
|
|
}
|
|
# Simulate a serialized proxy widget change whose node cache entry was
|
|
# retained while the promoted context changed.
|
|
previous_node_cache[("top", "", "1")]["fingerprint"] = (
|
|
analysis._get_workflow_node_fingerprint(updated_workflow["nodes"][0])
|
|
)
|
|
|
|
refs = analysis.analyze_workflow_models(
|
|
updated_workflow,
|
|
available_models=[],
|
|
previous_node_cache=previous_node_cache,
|
|
)
|
|
|
|
assert len(refs) == 1
|
|
assert refs[0].node_id == 1
|
|
assert refs[0].original_path == "new.safetensors"
|
|
assert refs[0].extra_value("promoted_inner_node_id") == 10
|
|
|
|
|
|
def test_reference_matching_handles_placeholders_paths_and_scanner_records(tmp_path):
|
|
model_path = tmp_path / "folder" / "model.safetensors"
|
|
model_path.parent.mkdir()
|
|
model_path.write_bytes(b"model")
|
|
available_models = [
|
|
{
|
|
"category": "checkpoints",
|
|
"relative_path": "folder/model.safetensors",
|
|
"path": str(model_path),
|
|
}
|
|
]
|
|
|
|
assert references.is_model_filename("model.safetensors") is True
|
|
assert references.is_model_filename("urn:air:sd:checkpoint:civitai:1@2") is True
|
|
assert references.should_scan_as_model_reference("None", True) is False
|
|
assert references.should_scan_as_model_reference("plain-name", True) is True
|
|
assert references.should_scan_as_model_reference("plain-name", False) is False
|
|
assert references.is_static_or_hybrid_widget_choice(
|
|
"Folder\\Model.safetensors",
|
|
{"source": "static", "choices": ["folder/model.safetensors"]},
|
|
)
|
|
assert references.static_or_hybrid_choice_looks_like_model(
|
|
"folder/model.safetensors",
|
|
{"source": "hybrid", "choices": []},
|
|
)
|
|
|
|
resolved = references.try_resolve_model_path(
|
|
"folder/model.safetensors",
|
|
["checkpoints", "configs"],
|
|
available_models=available_models,
|
|
)
|
|
assert resolved == ("checkpoints", str(model_path))
|
|
assert references.try_resolve_model_path(
|
|
"folder/MODEL.safetensors",
|
|
["checkpoints"],
|
|
available_models=available_models,
|
|
) is None
|
|
|
|
|
|
def test_reference_resolution_uses_folder_paths_when_scanner_is_not_available(tmp_path):
|
|
model_path = tmp_path / "model.safetensors"
|
|
model_path.write_bytes(b"model")
|
|
fake_folder_paths = SimpleNamespace(
|
|
folder_names_and_paths={"checkpoints": ([], set()), "configs": ([], set())},
|
|
get_filename_list=lambda category: ["model.safetensors"],
|
|
get_full_path=lambda category, filename: str(model_path),
|
|
)
|
|
|
|
with patch.object(references, "folder_paths", fake_folder_paths):
|
|
assert references.try_resolve_model_path("model.safetensors") == (
|
|
"checkpoints",
|
|
str(model_path),
|
|
)
|