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ComfyAssets-ComfyUI_PromptM…/tests/test_prompt_pair_resolver.py
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Vito Sansevero 80a4ba3ba4 fix(inspect): drop preview-only stages from unanchored candidates 
A stage whose outputs reach only non-file outputs (PreviewImage and the other
non_file_outputs) cannot have written a saved file, yet it stayed an
unanchored candidate, so a preview-only FaceDetailer made the saved branch
ambiguous (f01b).

The inspection profile now drops such stages before detailer absorption,
using two linear backward passes over the graph (charged like the consumer
map; a failed pass keeps every stage). A stage without consumers, or one that
also reaches any other terminal, is kept, and nothing is dropped when every
stage is preview-only. Raw plus detailed double saves both write files and
stay ambiguous. A file in the host temp folder may itself be a preview, so it
is inspected with PREVIEW_INSPECTION_PROFILE, which keeps those stages.

Workspace facts follow the same choice: an absent or unresolved result that
names a sampler merges facts across that sampler's stages only, so the
dropped detailer's settings no longer blur the saved file's facts.

f01b now resolves (absent negative, guider has none); its vector is updated
and the expected failure removed. The default profile is unchanged:
differential against 39dcc40 identical over 160,256 cases. Over 60,054
inspection cases no previously resolved or absent result changed.
2026-10-19 16:08:34 -07:00

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"""Conservative prompt-pair resolution contracts, independent of ComfyUI."""
import collections
import json
import random
import subprocess # nosec B404 - fixed interpreter command, no shell
import sys
import tempfile
import unittest
from dataclasses import replace
from pathlib import Path
from unittest import mock
from utils import inspection_semantics
from utils import prompt_pair_resolver as resolver
from utils.prompt_pair_resolver import OutputAdapter, resolve_prompt_pair
try:
from tests import inspection_parity as parity
except ImportError: # discovered with ``-s tests``
import inspection_parity as parity
def graph(positive: str = "portrait", negative: str = "blurry") -> dict:
return {
"1": {"class_type": "CLIPTextEncode", "inputs": {"text": positive}},
"2": {"class_type": "CLIPTextEncode", "inputs": {"text": negative}},
"3": {
"class_type": "KSampler",
"inputs": {"positive": ["1", 0], "negative": ["2", 0]},
},
}
class TestPromptPairResolver(unittest.TestCase):
def test_resolves_same_sampler_and_preserves_output_provenance(self):
result = resolve_prompt_pair({"prompt": graph()})
self.assertEqual(result["state"], "resolved")
self.assertEqual(result["positive"]["text"], "portrait")
self.assertEqual(result["negative"]["text"], "blurry")
self.assertEqual(result["positive"]["provenance"]["node_id"], "1")
self.assertEqual(result["positive"]["provenance"]["output_slot"], 0)
self.assertEqual(result["sampler_id"], "3")
def test_explicit_empty_negative_is_resolved_but_missing_is_absent(self):
self.assertEqual(resolve_prompt_pair(graph(negative=""))["state"], "resolved")
self.assertEqual(
resolve_prompt_pair(graph(negative=""))["negative"]["text"], ""
)
source = graph()
del source["3"]["inputs"]["negative"]
result = resolve_prompt_pair(source)
self.assertEqual(result["state"], "absent")
self.assertEqual(result["positive"]["text"], "portrait")
self.assertIsNone(result["negative"])
def test_multiple_samplers_are_ambiguous_even_when_texts_match(self):
source = graph()
source["4"] = {
"class_type": "KSamplerAdvanced",
"inputs": source["3"]["inputs"],
}
result = resolve_prompt_pair(source)
self.assertEqual(result["state"], "ambiguous")
self.assertEqual(len(result["candidates"]), 2)
self.assertIsNone(result["positive"])
def test_saver_anchor_selects_only_its_sampler(self):
source = graph()
source.update(
{
"4": {
"class_type": "KSampler",
"inputs": {"positive": ["2", 0], "negative": ["1", 0]},
},
"5": {"class_type": "VAEDecode", "inputs": {"samples": ["4", 0]}},
"6": {"class_type": "SaveImage", "inputs": {"images": ["5", 0]}},
}
)
result = resolve_prompt_pair(source, output_node_id="6")
self.assertEqual(result["state"], "resolved")
self.assertEqual(result["sampler_id"], "4")
self.assertEqual(result["positive"]["text"], "blurry")
def test_unknown_anchor_transform_and_multistage_are_unresolved(self):
source = graph()
source["5"] = {
"class_type": "MysteryImageTransform",
"inputs": {"images": ["3", 0]},
}
self.assertEqual(
resolve_prompt_pair(source, output_node_id="5")["state"], "unresolved"
)
source["4"] = {
"class_type": "KSampler",
"inputs": {**source["3"]["inputs"], "latent_image": ["3", 0]},
}
self.assertEqual(
resolve_prompt_pair(source, output_node_id="4")["state"], "unresolved"
)
def test_combines_unknown_encoders_and_sdxl_channels_are_not_guessed(self):
for name in ("ConditioningCombine", "CLIPTextEncodeSDXL", "UnknownEncoder"):
with self.subTest(name=name):
source = graph()
source["1"] = {
"class_type": name,
"inputs": {
"text": "guess",
"text_g": "global",
"text_l": "local",
"conditioning_1": ["2", 0],
},
}
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
def test_prompt_manager_composition_and_string_slot_are_resolved(self):
source = graph()
source["8"] = {
"class_type": "PromptManagerSplit",
"inputs": {
"text": "portrait",
"prepend_text": " trigger ",
"append_text": " detail ",
},
}
source["1"]["inputs"]["text"] = ["8", 1]
result = resolve_prompt_pair(source)
self.assertEqual(result["positive"]["text"], "trigger portrait detail")
self.assertIn(
{"node_id": "8", "output_slot": 1}, result["positive"]["provenance"]["path"]
)
source["3"]["inputs"]["positive"] = ["8", 0]
self.assertEqual(
resolve_prompt_pair(source)["positive"]["text"], "trigger portrait detail"
)
source["1"]["inputs"]["text"] = ["8", 0]
source["3"]["inputs"]["positive"] = ["1", 0]
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
def test_concat_and_reroute_resolve_safe_linked_strings(self):
source = graph()
source["8"] = {"class_type": "PrimitiveString", "inputs": {"value": "portrait"}}
source["9"] = {
"class_type": "StringConcatenate",
"inputs": {"string_a": ["8", 0], "string_b": ["8", 0], "delimiter": ", "},
}
source["10"] = {"class_type": "Reroute", "inputs": {"value": ["9", 0]}}
source["1"]["inputs"]["text"] = ["10", 0]
self.assertEqual(
resolve_prompt_pair(source)["positive"]["text"], "portrait, portrait"
)
def test_slot_adapter_does_not_conflate_two_outputs_of_one_node(self):
source = graph()
source["8"] = {
"class_type": "FuturePair",
"inputs": {"first": "positive", "second": "negative"},
}
source["3"]["inputs"] = {"positive": ["8", 0], "negative": ["8", 1]}
adapters = {
("FuturePair", 0): OutputAdapter("first", "conditioning"),
("FuturePair", 1): OutputAdapter("second", "conditioning"),
}
result = resolve_prompt_pair(source, adapters=adapters)
self.assertEqual(result["positive"]["text"], "positive")
self.assertEqual(result["negative"]["text"], "negative")
self.assertNotEqual(
result["positive"]["provenance"]["output_slot"],
result["negative"]["provenance"]["output_slot"],
)
def test_prompt_manager_text_has_only_its_real_string_output_zero(self):
source = graph()
source["8"] = {
"class_type": "PromptManagerText",
"inputs": {"text": "text node", "prepend_text": "before"},
}
source["1"]["inputs"]["text"] = ["8", 0]
self.assertEqual(
resolve_prompt_pair(source)["positive"]["text"], "before text node"
)
source["1"]["inputs"]["text"] = ["8", 1]
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
source["3"]["inputs"]["positive"] = ["8", 0]
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
def test_malformed_class_names_and_unknown_samplers_do_not_claim_absence(self):
for name in ([], {}, None, "UnknownSampler"):
source = graph()
source["3"]["class_type"] = name
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
def test_pair_adapter_composes_only_positive_and_retains_recipe_text(self):
source = graph()
source["8"] = {
"class_type": "PromptManagerPair",
"inputs": {
"text": "positive",
"negative_text": "negative",
"prepend_text": "trigger",
"append_text": "detail",
},
}
source["3"]["inputs"] = {"positive": ["8", 0], "negative": ["8", 1]}
result = resolve_prompt_pair(source)
self.assertEqual(result["positive"]["text"], "trigger positive detail")
self.assertEqual(result["positive"]["storage_text"], "positive")
self.assertEqual(result["negative"]["text"], "negative")
self.assertEqual(result["negative"]["storage_text"], "negative")
source["1"]["inputs"]["text"] = ["8", 2]
source["2"]["inputs"]["text"] = ["8", 3]
source["3"]["inputs"] = {"positive": ["1", 0], "negative": ["2", 0]}
result = resolve_prompt_pair(source)
self.assertEqual(result["positive"]["storage_text"], "positive")
self.assertEqual(result["negative"]["text"], "negative")
source["3"]["inputs"]["negative"] = ["8", 3]
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
def test_linked_recipe_and_ordinary_clip_storage_values_are_explicit(self):
source = graph()
result = resolve_prompt_pair(source)
self.assertEqual(result["positive"]["storage_text"], "portrait")
source["8"] = {
"class_type": "PrimitiveString",
"inputs": {"value": "linked main"},
}
source["1"] = {
"class_type": "PromptManager",
"inputs": {"text": ["8", 0], "prepend_text": "before"},
}
result = resolve_prompt_pair(source)
self.assertEqual(result["positive"]["text"], "before linked main")
self.assertEqual(result["positive"]["storage_text"], "linked main")
a1111 = resolve_prompt_pair(
{
"parameters": (
"portrait\nNegative prompt: blurry\n" "Steps: 20, Sampler: Euler"
)
}
)
self.assertEqual(a1111["positive"]["storage_text"], "portrait")
self.assertEqual(a1111["negative"]["storage_text"], "blurry")
def test_cycles_depth_visits_and_text_growth_are_bounded(self):
source = graph()
source["8"] = {"class_type": "Reroute", "inputs": {"value": ["8", 0]}}
source["1"]["inputs"]["text"] = ["8", 0]
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
self.assertEqual(
resolve_prompt_pair(graph(), max_visits=1)["state"], "unresolved"
)
self.assertEqual(
resolve_prompt_pair(graph(), max_depth=0)["state"], "unresolved"
)
self.assertEqual(
resolve_prompt_pair(graph(), max_text_length=3)["state"], "unresolved"
)
def test_malformed_metadata_and_links_never_raise(self):
for value in (
None,
[],
"raw text",
{},
{"prompt": []},
{"prompt": {"1": None}},
):
with self.subTest(value=value):
self.assertIn(
resolve_prompt_pair(value)["state"], ("absent", "unresolved")
)
for link in (["missing", 0], ["1", -1], ["1", True], ["1", 17], ["1"], 5):
source = graph()
source["3"]["inputs"]["positive"] = link
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
def test_a1111_explicit_parameters_and_empty_negative(self):
for negative in ("bad anatomy\nblurry", ""):
result = resolve_prompt_pair(
{
"parameters": (
f"portrait\nNegative prompt: {negative}\n"
"Steps: 20, Sampler: Euler, Seed: 1"
)
}
)
self.assertEqual(result["state"], "resolved")
self.assertEqual(result["negative"]["text"], negative)
self.assertEqual(result["positive"]["provenance"]["format"], "a1111")
self.assertEqual(
resolve_prompt_pair({"parameters": "portrait\nSteps: 20, Sampler: Euler"})[
"state"
],
"absent",
)
self.assertEqual(
resolve_prompt_pair({"parameters": "unstructured arbitrary text"})["state"],
"unresolved",
)
def test_was_concat_preserves_configured_whitespace_and_rejects_bad_delimiters(
self,
):
source = graph()
source["8"] = {
"class_type": "Text Concatenate",
"inputs": {
"text_a": " portrait ",
"text_b": "",
"text_c": "studio",
"delimiter": ", ",
},
}
source["1"]["inputs"]["text"] = ["8", 0]
self.assertEqual(
resolve_prompt_pair(source)["positive"]["text"], "portrait, studio"
)
source["8"]["inputs"]["clean_whitespace"] = "false"
self.assertEqual(
resolve_prompt_pair(source)["positive"]["text"], " portrait , studio"
)
source["8"]["inputs"]["delimiter"] = []
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
source["8"]["inputs"] = {1: "bad key"}
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
def test_valid_empty_latent_and_reroute_but_not_unknown_or_cyclic_latents(self):
source = graph()
source["8"] = {"class_type": "EmptyLatentImage", "inputs": {}}
source["9"] = {"class_type": "Reroute", "inputs": {"value": ["8", 0]}}
source["3"]["inputs"]["latent_image"] = ["9", 0]
self.assertEqual(resolve_prompt_pair(source)["state"], "resolved")
source["9"]["inputs"]["value"] = ["9", 0]
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
source["3"]["inputs"]["latent_image"] = ["8", 0]
source["8"]["class_type"] = "UnknownLatentTransform"
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
source["3"]["inputs"]["latent_image"] = None
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
def test_cyclic_or_invalid_output_anchors_never_guess(self):
source = graph()
source["8"] = {"class_type": "Reroute", "inputs": {"value": ["8", 0]}}
self.assertEqual(
resolve_prompt_pair(source, output_node_id="8")["state"], "unresolved"
)
self.assertEqual(
resolve_prompt_pair(source, output_node_id="missing")["state"], "unresolved"
)
source["8"] = {"class_type": "SaveImage", "inputs": {"images": ["3", 1]}}
self.assertEqual(
resolve_prompt_pair(source, output_node_id="8")["state"], "unresolved"
)
source["8"] = {"class_type": "SaveImage", "inputs": {"images": ["3", 0]}}
self.assertEqual(
resolve_prompt_pair(source, output_node_id="8", max_depth=1)["state"],
"unresolved",
)
def test_malformed_reroute_empty_positive_and_duplicate_string_ids(self):
source = graph(positive="")
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
source = graph()
source["8"] = {
"class_type": "Reroute",
"inputs": {"value": ["1", 0], "input": ["2", 0]},
}
source["3"]["inputs"]["positive"] = ["8", 0]
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
source[3] = source["3"]
self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
def test_a1111_malformed_ambiguous_and_bounded_parameters(self):
self.assertEqual(resolve_prompt_pair({"parameters": None})["state"], "absent")
self.assertEqual(
resolve_prompt_pair({"parameters": "x" * 20}, max_text_length=10)["state"],
"unresolved",
)
self.assertEqual(
resolve_prompt_pair(
{"parameters": "\nNegative prompt: bad\nSteps: 20, Sampler: Euler"}
)["state"],
"unresolved",
)
self.assertEqual(
resolve_prompt_pair(
{
"parameters": (
"portrait\nNegative prompt: bad\nNegative prompt: worse\n"
"Steps: 20, Sampler: Euler"
)
}
)["state"],
"ambiguous",
)
self.assertEqual(
resolve_prompt_pair(
{
"parameters": (
"portrait\nNegative prompt: bad\n"
"Steps: 20, Sampler: Euler\nextra text"
)
}
)["state"],
"unresolved",
)
def test_exponential_concat_text_is_bounded_before_building_a_large_string(self):
source = graph()
source["8"] = {"class_type": "PrimitiveString", "inputs": {"value": "xx"}}
for index in range(9, 19):
source[str(index)] = {
"class_type": "StringConcatenate",
"inputs": {
"string_a": [str(index - 1), 0],
"string_b": [str(index - 1), 0],
},
}
source["1"]["inputs"]["text"] = ["18", 0]
result = resolve_prompt_pair(source, max_text_length=100)
self.assertEqual(result["state"], "unresolved")
self.assertIn("length limit", result["reason"])
self.assertEqual(
resolve_prompt_pair(graph(), max_depth=1)["state"], "unresolved"
)
def test_api_graph_is_authoritative_over_unrelated_parameters(self):
source = graph()
source["1"]["class_type"] = "UnknownEncoder"
result = resolve_prompt_pair(
{
"prompt": source,
"parameters": (
"fallback\nNegative prompt: wrong\n" "Steps: 20, Sampler: Euler"
),
}
)
self.assertEqual(result["state"], "unresolved")
def _f03_metadata() -> dict:
return {"prompt": parity.build("f03")["prompt"]}
def _vector(name: str) -> dict:
return next(v for v in parity.load_vectors() if v["name"] == name)["expect"]
def _diamond(prefix: str = "d", leaves: int = 575, middles: int = 24) -> dict:
"""A ~600-node string diamond: 24 fans over 575 leaves, one leaf shared."""
letters = "abcdefghijklmnopqrstuvwxyz"
nodes = {
f"{prefix}l{index}": {"class_type": "PrimitiveString", "inputs": {"value": "a"}}
for index in range(leaves)
}
for middle in range(middles):
nodes[f"{prefix}m{middle}"] = {
"class_type": "Text Concatenate",
"inputs": {
f"text_{letters[index]}": [
f"{prefix}l{(middle * middles + index) % leaves}",
0,
]
for index in range(middles)
},
}
nodes[f"{prefix}r"] = {
"class_type": "Text Concatenate",
"inputs": {
f"text_{letters[index]}": [f"{prefix}m{index}", 0]
for index in range(middles)
},
}
return nodes
def _diamond_samplers(count: int) -> dict:
"""``count`` samplers, each encoding the same diamond through its own encoder."""
source = {"neg": {"class_type": "CLIPTextEncode", "inputs": {"text": "bad"}}}
source.update(_diamond())
for index in range(count):
source[f"e{index}"] = {
"class_type": "CLIPTextEncode",
"inputs": {"text": ["dr", 0]},
}
source[f"s{index}"] = {
"class_type": "KSampler",
"inputs": {"positive": [f"e{index}", 0], "negative": ["neg", 0]},
}
return source
def _distinct_diamond_samplers(count: int, chain: int = 60) -> dict:
"""``count`` samplers, each with its own 590-node diamond, sharing a latent chain.
Real work exceeds the shared 20,000-visit total while the graph stays under
the node ceiling: every candidate re-walks the (unmemoized) latent chain.
"""
source = {
"neg": {"class_type": "CLIPTextEncode", "inputs": {"text": "bad"}},
"z0": {"class_type": "EmptyLatentImage", "inputs": {}},
}
for index in range(1, chain + 1):
source[f"z{index}"] = {
"class_type": "Reroute",
"inputs": {"value": [f"z{index - 1}", 0]},
}
for index in range(count):
prefix = f"d{index}_"
source.update(_diamond(prefix, leaves=565))
source[f"e{index}"] = {
"class_type": "CLIPTextEncode",
"inputs": {"text": [f"{prefix}r", 0]},
}
source[f"s{index}"] = {
"class_type": "KSampler",
"inputs": {
"positive": [f"e{index}", 0],
"negative": ["neg", 0],
"latent_image": [f"z{chain}", 0],
},
}
return source
def _depth_straddling_samplers() -> dict:
"""A at depth 9 below its sampler, B four Reroutes deeper on the same chain."""
source = {"p": {"class_type": "PrimitiveString", "inputs": {"value": "cat"}}}
previous = "p"
for index in range(6):
source[f"c{index}"] = {
"class_type": "Reroute",
"inputs": {"value": [previous, 0]},
}
previous = f"c{index}"
source["encA"] = {"class_type": "CLIPTextEncode", "inputs": {"text": [previous, 0]}}
source["A"] = {
"class_type": "KSampler",
"inputs": {"positive": ["encA", 0], "negative": ["encA", 0]},
}
for index in range(4):
source[f"b{index}"] = {
"class_type": "Reroute",
"inputs": {"value": [previous, 0]},
}
previous = f"b{index}"
source["encB"] = {"class_type": "CLIPTextEncode", "inputs": {"text": [previous, 0]}}
source["B"] = {
"class_type": "KSampler",
"inputs": {"positive": ["encB", 0], "negative": ["encB", 0]},
}
return source
def _nested_shared_chain_samplers() -> dict:
"""A, B and C share one chain; each later sampler hangs below a memo hit."""
source = {"p": {"class_type": "PrimitiveString", "inputs": {"value": "cat"}}}
def chain(start: str, prefix: str, count: int) -> str:
previous = start
for index in range(count):
source[f"{prefix}{index}"] = {
"class_type": "Reroute",
"inputs": {"value": [previous, 0]},
}
previous = f"{prefix}{index}"
return previous
def sampler(name: str, text_source: str) -> None:
source[f"enc{name}"] = {
"class_type": "CLIPTextEncode",
"inputs": {"text": [text_source, 0]},
}
source[name] = {
"class_type": "KSampler",
"inputs": {"positive": [f"enc{name}", 0], "negative": [f"enc{name}", 0]},
}
top = chain("p", "c", 6) # A memoizes c5 with height 6
sampler("A", top)
middle = chain(top, "r", 1) # r0 is built over a memo hit on c5
sampler("B", middle)
sampler("C", chain(middle, "d", 3)) # C reaches r0 three Reroutes deeper
return source
def _random_graph(rng: random.Random) -> dict:
ids = [str(index) for index in range(rng.randrange(5, 60))]
classes = (
"StringConcatenate",
"Text Concatenate",
"Reroute",
"PrimitiveString",
"CLIPTextEncode",
"KSampler",
"PromptManager",
"EmptyLatentImage",
)
def link():
return [rng.choice(ids), 0]
source = {}
for node_id in ids:
name = rng.choice(classes)
if name == "StringConcatenate":
inputs = {"string_a": link(), "string_b": rng.choice([link(), "x"])}
elif name == "Text Concatenate":
inputs = {"text_a": link(), "text_b": link(), "delimiter": ""}
elif name == "Reroute":
inputs = {"value": link()}
elif name == "PrimitiveString":
inputs = {"value": rng.choice(["a", " b "])}
elif name == "CLIPTextEncode":
inputs = {"text": rng.choice([link(), "p"])}
elif name == "KSampler":
inputs = {"positive": link(), "negative": link()}
if rng.random() < 0.3:
inputs["latent_image"] = link()
elif name == "PromptManager":
inputs = {"text": rng.choice(["q", link()]), "prepend_text": ""}
else:
inputs = {}
source[node_id] = {"class_type": name, "inputs": inputs}
return source
def _outcomes(result: dict) -> dict:
items = result["candidates"] or [result]
return {
item["sampler_id"]: (item["state"], item["reason_code"], item["reason"])
for item in items
}
class _CountingInputs(dict):
"""Node inputs that count how often a resolver iterates (scans) their keys."""
scans = 0
def __iter__(self):
type(self).scans += 1
return super().__iter__()
class _CountingText(str):
"""A literal that tallies the characters a resolver strips from it."""
stripped = 0
def strip(self, *args):
type(self).stripped += len(self)
return super().strip(*args)
class TestResolverProfiles(unittest.TestCase):
"""Inspection bounds work, not graph size; the default profile is pinned."""
def test_inspection_profile_resolves_reachable_branch_in_506_node_graph(self):
expected = _vector("f03-default")
self.assertGreater(len(_f03_metadata()["prompt"]), 500)
result = resolve_prompt_pair(
_f03_metadata(), profile=resolver.INSPECTION_PROFILE
)
self.assertEqual(result["state"], "resolved")
self.assertIsNone(result["reason_code"])
self.assertEqual(result["positive"]["text"], parity.POS)
self.assertEqual(result["negative"]["text"], parity.NEG)
self.assertEqual(expected["state"], "unresolved") # ingestion baseline
def test_default_profile_keeps_500_node_gate(self):
for result in (
resolve_prompt_pair(_f03_metadata()),
resolve_prompt_pair(_f03_metadata(), profile=resolver.DEFAULT_PROFILE),
):
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason"], "Resolution limit reached")
self.assertIsNone(result["reason_code"])
self.assertNotIn("node", result)
def test_total_visit_budget_is_shared_across_candidates(self):
source = _distinct_diamond_samplers(33)
self.assertEqual(resolver.INSPECTION_PROFILE.total_visits, 20_000)
self.assertLess(len(source), resolver.INSPECTION_PROFILE.node_ceiling)
result = resolve_prompt_pair(
{"prompt": source}, profile=resolver.INSPECTION_PROFILE
)
# While selection is off (until PR3) many samplers are always ambiguous;
# PR3 must add "work-limit never upgrades to resolved" once one is chosen.
self.assertNotEqual(result["state"], "resolved")
self.assertEqual(result["state"], "ambiguous")
candidates = result["candidates"]
self.assertEqual(len(candidates), 33)
self.assertEqual(candidates[0]["state"], "resolved")
codes = [candidate["reason_code"] for candidate in candidates]
first_limited = codes.index("work-limit")
self.assertGreater(first_limited, 0)
self.assertTrue(all(code == "work-limit" for code in codes[first_limited:]))
self.assertTrue(
all(c["state"] == "unresolved" for c in candidates[first_limited:])
)
# The default profile keeps its historical gate on the same graph.
self.assertEqual(
resolve_prompt_pair({"prompt": source})["reason"],
"Resolution limit reached",
)
def test_memo_hit_respects_the_depth_left_at_the_reusing_candidate(self):
source = _depth_straddling_samplers()
reverse = dict(reversed(list(source.items())))
profile = resolver.INSPECTION_PROFILE
forward = _outcomes(
resolve_prompt_pair({"prompt": source}, profile=profile, max_depth=10)
)
backward = _outcomes(
resolve_prompt_pair({"prompt": reverse}, profile=profile, max_depth=10)
)
self.assertEqual(forward, backward)
self.assertEqual(forward["A"][:2], ("resolved", None))
self.assertEqual(forward["B"][:2], ("unresolved", "depth-limit"))
anchored = resolve_prompt_pair(
{"prompt": source}, profile=profile, max_depth=10, output_node_id="B"
)
self.assertEqual(
(anchored["state"], anchored["reason_code"]), ("unresolved", "depth-limit")
)
def test_memo_height_propagates_through_a_nested_memo_hit(self):
source = _nested_shared_chain_samplers()
reverse = dict(reversed(list(source.items())))
profile = resolver.INSPECTION_PROFILE
for graph in (source, reverse):
outcomes = _outcomes(
resolve_prompt_pair({"prompt": graph}, profile=profile, max_depth=12)
)
self.assertEqual(outcomes["C"][:2], ("unresolved", "depth-limit"))
anchored = resolve_prompt_pair(
{"prompt": source}, profile=profile, max_depth=12, output_node_id="C"
)
self.assertEqual(
(anchored["state"], anchored["reason_code"]), ("unresolved", "depth-limit")
)
def test_candidate_outcomes_match_anchored_and_ignore_node_order(self):
profile = resolver.INSPECTION_PROFILE
limits = ({}, {"max_depth": 4}, {"max_depth": 8}, {"max_visits": 6})
compared = 0
for seed in range(240, 246): # 6,000 deterministic graphs, about 1.5 s
rng = random.Random(seed)
for _ in range(1_000):
source = _random_graph(rng)
kwargs = rng.choice(limits)
base = _outcomes(
resolve_prompt_pair({"prompt": source}, profile=profile, **kwargs)
)
items = list(source.items())
rng.shuffle(items)
shuffled = _outcomes(
resolve_prompt_pair(
{"prompt": dict(items)}, profile=profile, **kwargs
)
)
self.assertEqual(base, shuffled, (seed, source, kwargs))
if len(base) < 2:
continue
for sampler_id, outcome in base.items():
anchored = resolve_prompt_pair(
{"prompt": source},
profile=profile,
output_node_id=sampler_id,
**kwargs,
)
self.assertEqual(
_outcomes(anchored)[sampler_id],
outcome,
(seed, sampler_id, source, kwargs),
)
compared += 1
self.assertGreater(compared, 1_000)
def test_provenance_entry_budget_reports_the_path_limit(self):
letters = "abcdefghijklmnopqrstuvwxyz"
source = {
"p": {"class_type": "PrimitiveString", "inputs": {"value": "cat"}},
"neg": {"class_type": "CLIPTextEncode", "inputs": {"text": "bad"}},
}
previous = "p"
for index in range(60):
source[f"c{index}"] = {
"class_type": "Reroute",
"inputs": {"value": [previous, 0]},
}
previous = f"c{index}"
for index in range(500):
source[f"w{index}"] = {
"class_type": "Text Concatenate",
"inputs": {f"text_{c}": [previous, 0] for c in letters},
}
source[f"e{index}"] = {
"class_type": "CLIPTextEncode",
"inputs": {"text": [f"w{index}", 0]},
}
source[f"s{index}"] = {
"class_type": "KSampler",
"inputs": {"positive": [f"e{index}", 0], "negative": ["neg", 0]},
}
result = resolve_prompt_pair(
{"prompt": source}, profile=resolver.INSPECTION_PROFILE
)
reasons = [candidate["reason"] for candidate in result["candidates"]]
self.assertEqual(result["candidates"][0]["state"], "resolved")
first = reasons.index("Provenance path limit reached")
self.assertGreater(first, 0)
self.assertLessEqual(
set(reasons[first:]),
{"Provenance path limit reached", "Resolution visit limit reached"},
)
self.assertEqual(reasons[first], "Provenance path limit reached")
self.assertEqual(
{c["reason_code"] for c in result["candidates"][first:]}, {"work-limit"}
)
# A small entry budget pins the same reason on one candidate.
small = replace(resolver.INSPECTION_PROFILE, max_path_entries=10)
result = resolve_prompt_pair({"prompt": source}, profile=small)
self.assertEqual(
(result["candidates"][0]["reason"], result["candidates"][0]["reason_code"]),
("Provenance path limit reached", "work-limit"),
)
def test_shared_node_is_scanned_once_however_many_candidates_use_it(self):
_CountingInputs.scans = 0
fat = _CountingInputs({"text": "portrait"})
fat.update({f"extra_{index}": 0 for index in range(20_000)})
source = {
"fat": {"class_type": "CLIPTextEncode", "inputs": fat},
"neg": {"class_type": "CLIPTextEncode", "inputs": {"text": "bad"}},
}
for index in range(1_000):
source[f"s{index}"] = {
"class_type": "KSampler",
"inputs": {"positive": ["fat", 0], "negative": ["neg", 0]},
}
result = resolve_prompt_pair(
{"prompt": source}, profile=resolver.INSPECTION_PROFILE
)
states = {candidate["state"] for candidate in result["candidates"]}
self.assertEqual(states, {"resolved"})
# Work is O(nodes), not O(candidates x node size).
self.assertLessEqual(_CountingInputs.scans, 2)
def test_stripped_concatenation_text_is_charged_to_a_work_budget(self):
_CountingText.stripped = 0
letters = "abcdefghijklmnopqrstuvwxyz"
hub = _CountingText(" " * 100_000)
source = graph()
source["hub"] = {"class_type": "PrimitiveString", "inputs": {"value": hub}}
for leaf in range(300):
source[f"l{leaf}"] = {
"class_type": "Text Concatenate",
"inputs": {f"text_{c}": ["hub", 0] for c in letters},
}
for middle in range(12):
source[f"m{middle}"] = {
"class_type": "Text Concatenate",
"inputs": {
f"text_{letters[i]}": [f"l{middle * 25 + i}", 0] for i in range(25)
},
}
source["root"] = {
"class_type": "Text Concatenate",
"inputs": {f"text_{letters[i]}": [f"m{i}", 0] for i in range(12)},
}
source["1"]["inputs"]["text"] = ["root", 0]
result = resolve_prompt_pair(source, profile=resolver.INSPECTION_PROFILE)
self.assertEqual(
(result["state"], result["reason_code"]), ("unresolved", "work-limit")
)
self.assertLessEqual(
_CountingText.stripped, resolver.INSPECTION_PROFILE.max_scanned_units
)
def test_per_candidate_visit_budget_resets_for_each_sampler(self):
source = _diamond_samplers(3)
result = resolve_prompt_pair(
{"prompt": source}, profile=resolver.INSPECTION_PROFILE, max_visits=700
)
self.assertEqual(
[candidate["state"] for candidate in result["candidates"]],
["resolved"] * 3,
)
result = resolve_prompt_pair(
{"prompt": source}, profile=resolver.INSPECTION_PROFILE, max_visits=300
)
self.assertEqual(
[candidate["reason_code"] for candidate in result["candidates"]],
["work-limit"] * 3,
)
def test_node_ceiling_reports_graph_ceiling(self):
source = graph()
for index in range(20_001 - len(source)):
source[f"p{index}"] = {"class_type": "PrimitiveInt", "inputs": {"value": 1}}
self.assertEqual(len(source), 20_001)
result = resolve_prompt_pair(
{"prompt": source}, profile=resolver.INSPECTION_PROFILE
)
self.assertEqual(result["reason_code"], "graph-ceiling")
self.assertNotEqual(result["state"], "resolved")
del source["p0"]
result = resolve_prompt_pair(
{"prompt": source}, profile=resolver.INSPECTION_PROFILE
)
self.assertEqual(result["state"], "resolved")
def test_prompt_graph_without_supported_sampler_is_unresolved_not_absent(self):
# f17 with its sampler renamed to a class the inspector does not know.
source = parity.build("f17")["prompt"]
source["13"]["class_type"] = "FutureSampler"
metadata = {"prompt": source}
result = resolve_prompt_pair(metadata, profile=resolver.INSPECTION_PROFILE)
self.assertEqual(
(result["state"], result["reason_code"]), ("unresolved", "no-sampler")
)
self.assertIn("FutureSampler", result["reason"])
self.assertIsNone(result["positive"])
default = resolve_prompt_pair(metadata)
self.assertEqual(default["state"], "absent")
self.assertEqual(default["reason"], "No supported sampler pair found")
self.assertIsNone(default["reason_code"])
def test_no_sampler_reason_names_at_most_three_sampler_like_classes(self):
source = {
str(index): {
"class_type": f"FutureSampler{index % 5}",
"inputs": {"latent_image": ["x", 0]},
}
for index in range(10)
}
result = resolve_prompt_pair(
{"prompt": source}, profile=resolver.INSPECTION_PROFILE
)
self.assertEqual(result["reason_code"], "no-sampler")
self.assertEqual(
result["reason"],
"No supported sampler pair found "
"(saw FutureSampler0, FutureSampler1, FutureSampler2)",
)
def test_absent_only_without_prompt_or_parameters(self):
for value in (None, [], "raw text", {}, {"parameters": None}):
with self.subTest(value=value):
result = resolve_prompt_pair(value, profile=resolver.INSPECTION_PROFILE)
self.assertEqual(result["state"], "absent")
self.assertIsNone(result["positive"])
note = {"1": {"class_type": "Note", "inputs": {"text": "hello"}}}
for value in (
{"prompt": note},
note,
{"prompt": {}},
{"prompt": []},
{"prompt": {"1": None}},
{"parameters": "unstructured arbitrary text"},
):
with self.subTest(value=value):
result = resolve_prompt_pair(value, profile=resolver.INSPECTION_PROFILE)
self.assertEqual(result["state"], "unresolved")
self.assertIsNotNone(result["reason_code"])
self.assertEqual(resolve_prompt_pair({"prompt": note})["state"], "absent")
# A pair-level "absent" names the missing negative; it always has a positive.
source = graph()
del source["3"]["inputs"]["negative"]
result = resolve_prompt_pair(source, profile=resolver.INSPECTION_PROFILE)
self.assertEqual(result["state"], "absent")
self.assertEqual(result["positive"]["text"], "portrait")
def test_existing_limits_map_to_reason_codes(self):
cases = (
({"max_visits": 1}, "work-limit", "Resolution visit limit reached"),
({"max_depth": 0}, "depth-limit", None),
({"max_depth": 1}, "depth-limit", "Resolution depth limit reached"),
({"max_text_length": 3}, "text-limit", None),
)
for limits, code, reason in cases:
with self.subTest(limits=limits):
default = resolve_prompt_pair(graph(), **limits)
self.assertEqual(default["state"], "unresolved")
self.assertIsNone(default["reason_code"])
result = resolve_prompt_pair(
graph(), profile=resolver.INSPECTION_PROFILE, **limits
)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason_code"], code)
if reason:
self.assertEqual(result["reason"], reason)
result = resolve_prompt_pair(
{"parameters": "x" * 20},
profile=resolver.INSPECTION_PROFILE,
max_text_length=10,
)
self.assertEqual(result["reason_code"], "text-limit")
def test_unsupported_node_names_the_blocking_node(self):
source = graph()
source["1"]["class_type"] = "UnknownEncoder"
result = resolve_prompt_pair(source, profile=resolver.INSPECTION_PROFILE)
self.assertEqual(result["reason_code"], "unsupported-node")
self.assertEqual(result["node"], {"id": "1", "class_type": "UnknownEncoder"})
default = resolve_prompt_pair(source)
self.assertIsNone(default["reason_code"])
self.assertNotIn("node", default)
self.assertEqual(default["reason"], result["reason"])
def test_default_profile_emits_only_a_null_reason_code_beyond_todays_keys(self):
source = graph()
source["4"] = {"class_type": "KSampler", "inputs": source["3"]["inputs"]}
for value in (graph(), source, {"parameters": "a\nSteps: 1, Sampler: x"}):
result = resolve_prompt_pair(value)
self.assertEqual(
set(result),
{
"state",
"positive",
"negative",
"sampler_id",
"reason",
"candidates",
"reason_code",
},
)
self.assertIsNone(result["reason_code"])
for candidate in result["candidates"]:
self.assertNotIn("node", candidate)
self.assertIsNone(candidate["reason_code"])
def test_diamond_provenance_paths_are_bounded_in_inspection(self):
source = graph()
source["8"] = {"class_type": "PrimitiveString", "inputs": {"value": ""}}
for index in range(9, 49):
source[str(index)] = {
"class_type": "StringConcatenate",
"inputs": {
"string_a": [str(index - 1), 0],
"string_b": [str(index - 1), 0],
"delimiter": "",
},
}
source["1"]["inputs"]["text"] = ["48", 0]
result = resolve_prompt_pair(source, profile=resolver.INSPECTION_PROFILE)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason_code"], "work-limit")
# A memo hit charges its unfolded cost: the 2^k unfolded visits exhaust
# the 2,000 allowance (per-entry paths never outgrow the allowance).
self.assertEqual(result["reason"], "Resolution visit limit reached")
def _guided(guider: dict = None) -> dict:
"""``f07b`` (SamplerCustomAdvanced + CFGGuider), optionally another guider."""
source = parity.build("f07b")["prompt"]
if guider is not None:
source["21"] = guider
return source
def _basic_guided() -> dict:
"""``f17`` with its BasicGuider reading the CLIPTextEncode directly."""
source = parity.build("f17")["prompt"]
source["22"]["inputs"]["conditioning"] = ["6", 0]
del source["26"]
return source
def _inspect(source: dict, **options) -> dict:
return resolve_prompt_pair(
{"prompt": source}, profile=resolver.INSPECTION_PROFILE, **options
)
class TestGuiderSamplers(unittest.TestCase):
"""SamplerCustomAdvanced follows its guider through the semantics table."""
def test_sampler_custom_advanced_with_cfg_guider_resolves(self):
result = _inspect(_guided())
self.assertEqual(result["state"], "resolved")
self.assertIsNone(result["reason_code"])
self.assertEqual(result["sampler_id"], "3")
self.assertEqual(result["positive"]["text"], parity.POS)
self.assertEqual(result["negative"]["text"], parity.NEG)
provenance = result["positive"]["provenance"]
self.assertEqual(
(provenance["sampler_id"], provenance["guider_id"]), ("3", "21")
)
self.assertEqual(provenance["node_id"], "6")
self.assertEqual(provenance["path"], [{"node_id": "6", "output_slot": 0}])
self.assertEqual(result["negative"]["provenance"]["node_id"], "7")
# The saver anchor reaches the same sampler.
anchored = _inspect(_guided(), output_node_id="9")
self.assertEqual(anchored, result)
def test_basic_guider_negative_is_absent_by_design(self):
from utils.prompt_inspector import inspect_prompt_evidence
result = _inspect(_basic_guided())
self.assertEqual(result["state"], "absent")
self.assertEqual(result["reason"], "Guider has no negative input")
self.assertIsNone(result["reason_code"])
self.assertEqual(result["positive"]["text"], parity.POS)
self.assertIsNone(result["negative"])
evidence = inspect_prompt_evidence({"prompt": _basic_guided()})
self.assertEqual(evidence["prompts"]["positive"]["status"], "resolved")
self.assertEqual(evidence["prompts"]["positive"]["text"], parity.POS)
self.assertEqual(evidence["prompts"]["negative"]["status"], "absent")
self.assertEqual(
evidence["resolution"]["reason"], "Guider has no negative input"
)
def test_basic_guider_empty_positive_is_not_absent(self):
source = _basic_guided()
source["6"]["inputs"]["text"] = " "
result = _inspect(source)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason"], "Positive prompt is empty")
def test_dual_cfg_guider_is_unsupported_node(self):
dual = {
"class_type": "DualCFGGuider",
"inputs": {
"model": ["4", 0],
"cond1": ["6", 0],
"cond2": ["6", 0],
"negative": ["7", 0],
"cfg_conds": 8.0,
"cfg_cond2_negative": 8.0,
},
}
result = _inspect(_guided(dual))
self.assertEqual(
(result["state"], result["reason_code"], result["node"]["class_type"]),
("unresolved", "unsupported-node", "DualCFGGuider"),
)
self.assertEqual(result["node"], {"id": "21", "class_type": "DualCFGGuider"})
self.assertEqual(result["sampler_id"], "3")
self.assertIsNone(result["positive"])
def test_unsupported_node_names_first_blocking_node(self):
source = _guided()
source["30"] = {
"class_type": "FutureConditioner",
"inputs": {"conditioning": ["6", 0]},
}
source["31"] = {
"class_type": "OtherConditioner",
"inputs": {"conditioning": ["7", 0]},
}
source["21"]["inputs"]["positive"] = ["30", 0]
source["21"]["inputs"]["negative"] = ["31", 0]
result = _inspect(source)
self.assertEqual(result["reason_code"], "unsupported-node")
self.assertEqual(
result["node"], {"id": "30", "class_type": "FutureConditioner"}
)
# With the positive fixed, the negative's node is the first blocker.
source["21"]["inputs"]["positive"] = ["6", 0]
result = _inspect(source)
self.assertEqual(result["node"], {"id": "31", "class_type": "OtherConditioner"})
self.assertEqual(result["positive"]["text"], parity.POS)
def test_default_profile_ignores_semantics(self):
for source in (_guided(), _basic_guided()):
result = resolve_prompt_pair({"prompt": source})
self.assertIn(result["state"], ("unresolved", "absent"))
self.assertEqual(result["reason"], "No supported sampler pair found")
self.assertIsNone(result["reason_code"])
self.assertIsNone(result["positive"])
# Without semantics the inspection bounds alone do not follow guiders.
plain = replace(resolver.INSPECTION_PROFILE, semantics=False)
result = resolve_prompt_pair({"prompt": _guided()}, profile=plain)
self.assertEqual(result["reason_code"], "no-sampler")
def test_guider_reached_through_a_reroute(self):
source = _guided()
source["40"] = {"class_type": "Reroute", "inputs": {"value": ["21", 0]}}
source["3"]["inputs"]["guider"] = ["40", 0]
result = _inspect(source)
self.assertEqual(result["state"], "resolved")
self.assertEqual(result["positive"]["provenance"]["guider_id"], "21")
def test_malformed_guider_links_and_slots_never_guess(self):
cases = {
"missing": None,
"literal": "CFGGuider",
"dangling": ["99", 0],
"slot": ["21", 1],
}
for label, value in cases.items():
with self.subTest(label=label):
source = _guided()
if value is None:
del source["3"]["inputs"]["guider"]
else:
source["3"]["inputs"]["guider"] = value
result = _inspect(source)
self.assertEqual(result["state"], "unresolved")
self.assertIsNone(result["positive"])
expected = "unsupported-node" if label == "slot" else "malformed"
self.assertEqual(result["reason_code"], expected)
source = _guided()
source["21"]["inputs"]["model"] = ["21", 0]
source["40"] = {"class_type": "Reroute", "inputs": {"value": ["40", 0]}}
source["3"]["inputs"]["guider"] = ["40", 0]
self.assertEqual(_inspect(source)["reason"], "Cyclic guider ancestry")
source["40"]["inputs"] = {"value": ["21", 0], "input": ["21", 0]}
result = _inspect(source)
self.assertEqual(result["reason"], "Reroute input is not uniquely identifiable")
self.assertEqual(result["reason_code"], "malformed")
def test_guider_hops_draw_from_the_work_budgets(self):
self.assertEqual(_inspect(_guided(), max_visits=2)["reason_code"], "work-limit")
self.assertEqual(_inspect(_guided())["state"], "resolved")
source = _guided()
previous = "21"
for index in range(70):
source[f"r{index}"] = {
"class_type": "Reroute",
"inputs": {"value": [previous, 0]},
}
previous = f"r{index}"
source["3"]["inputs"]["guider"] = [previous, 0]
self.assertEqual(_inspect(source)["reason_code"], "depth-limit")
def test_custom_advanced_upstream_of_a_sampler_is_multistage(self):
source = _guided()
source["41"] = {
"class_type": "KSampler",
"inputs": {
"positive": ["6", 0],
"negative": ["7", 0],
"latent_image": ["3", 0],
},
}
source["8"]["inputs"]["samples"] = ["41", 0]
result = _inspect(source, output_node_id="9")
self.assertEqual(result["sampler_id"], "41")
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason"], "Multistage sampler ancestry is ambiguous")
unanchored = _inspect(source)
self.assertEqual(unanchored["state"], "ambiguous")
self.assertEqual(
sorted(item["sampler_id"] for item in unanchored["candidates"]),
["3", "41"],
)
WOLF = "a grey wolf in fog"
def _encoded(text: str) -> dict:
return {"class_type": "CLIPTextEncode", "inputs": {"text": text, "clip": ["4", 1]}}
def _with_positive(node: dict, extra: dict = None) -> dict:
"""``f02``'s KSampler graph whose positive input reads ``node`` (as node 6)."""
source = parity.build("f02")["prompt"]
source["6"] = node
source.update(extra or {})
return source
def _if_else(condition, on_true, on_false) -> dict:
return {
"class_type": "easy ifElse",
"inputs": {"boolean": condition, "on_true": on_true, "on_false": on_false},
}
def _image_exists() -> dict:
return {"class_type": "ImageExists", "inputs": {"image": ["13", 0]}}
def _sides(result: dict) -> tuple:
return tuple(
result[role]["text"] if isinstance(result[role], dict) else None
for role in ("positive", "negative")
)
class TestConditioningSemantics(unittest.TestCase):
"""Pass-throughs, zeroed conditioning, selectors and composites (inspection)."""
def test_flux_guidance_passthrough_resolves(self):
result = _inspect(parity.build("f06c")["prompt"])
self.assertEqual(result["state"], "resolved")
self.assertIsNone(result["reason_code"])
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
provenance = result["positive"]["provenance"]
self.assertEqual(
provenance["path"],
[{"node_id": "6", "output_slot": 0}, {"node_id": "10", "output_slot": 0}],
)
self.assertEqual(provenance["text_source"], "graph-inferred")
# f17: BasicGuider <- FluxGuidance <- CLIPTextEncode, negative absent.
guided = _inspect(parity.build("f17")["prompt"])
self.assertEqual(guided["state"], "absent")
self.assertEqual(guided["reason"], "Guider has no negative input")
self.assertEqual(guided["positive"]["text"], parity.POS)
def test_controlnet_apply_advanced_maps_slots(self):
source = parity.build("f06e")["prompt"]
result = _inspect(source)
self.assertEqual(result["state"], "resolved")
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
self.assertEqual(
result["negative"]["provenance"]["path"],
[{"node_id": "7", "output_slot": 1}, {"node_id": "11", "output_slot": 0}],
)
# Slot 0 of the negative's node is its positive output, never the negative.
source["3"]["inputs"]["negative"] = ["7", 0]
self.assertEqual(_sides(_inspect(source)), (parity.POS, parity.POS))
# An output slot the table does not list is unsupported, named.
source["3"]["inputs"]["negative"] = ["7", 2]
result = _inspect(source)
self.assertEqual(result["reason_code"], "unsupported-node")
self.assertEqual(
result["node"], {"id": "7", "class_type": "ControlNetApplyAdvanced"}
)
def test_reference_latent_and_set_area_pass_through(self):
chain = [
("ReferenceLatent", {"latent": ["5", 0]}),
("ConditioningSetArea", {"width": 64, "height": 64, "x": 0, "y": 0}),
("ConditioningSetAreaPercentage", {"width": 1.0, "height": 1.0}),
("ConditioningSetAreaStrength", {"strength": 1.0}),
("ConditioningSetMask", {"mask": ["40", 0], "strength": 1.0}),
("ConditioningSetTimestepRange", {"start": 0.0, "end": 1.0}),
("ControlNetApply", {"control_net": ["41", 0], "strength": 1.0}),
]
extra, previous = {"10": _encoded(parity.POS)}, "10"
for index, (name, inputs) in enumerate(chain):
node_id = f"2{index}"
extra[node_id] = {
"class_type": name,
"inputs": {**inputs, "conditioning": [previous, 0]},
}
previous = node_id
source = _with_positive(extra.pop(previous), extra)
result = _inspect(source)
self.assertEqual(result["state"], "resolved")
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
path = result["positive"]["provenance"]["path"]
self.assertEqual(
[entry["node_id"] for entry in path],
["6", "25", "24", "23", "22", "21", "20", "10"],
)
def test_inpaint_model_conditioning_slots(self):
inpaint = {
"class_type": "InpaintModelConditioning",
"inputs": {
"positive": ["10", 0],
"negative": ["11", 0],
"vae": ["4", 2],
"pixels": ["13", 0],
"mask": ["14", 0],
"noise_mask": True,
},
}
extra = {"10": _encoded(parity.POS), "11": _encoded(parity.NEG)}
source = _with_positive(inpaint, extra)
source["3"]["inputs"]["negative"] = ["6", 1]
result = _inspect(source)
self.assertEqual(result["state"], "resolved")
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
self.assertEqual(result["positive"]["provenance"]["output_slot"], 0)
self.assertEqual(result["negative"]["provenance"]["output_slot"], 1)
def test_zeroed_negative_is_explicit_empty_with_distinct_source(self):
from utils.prompt_inspector import inspect_prompt_evidence
source = parity.build("f23")["prompt"]
result = _inspect(source)
self.assertEqual(result["state"], "resolved")
negative = result["negative"]
self.assertEqual((negative["text"], negative["storage_text"]), ("", ""))
self.assertEqual(negative["provenance"]["text_source"], "zeroed-conditioning")
self.assertEqual(
negative["provenance"]["path"], [{"node_id": "7", "output_slot": 0}]
)
self.assertEqual(result["positive"]["text"], parity.POS)
self.assertEqual(
result["positive"]["provenance"]["text_source"], "graph-inferred"
)
evidence = inspect_prompt_evidence({"prompt": source})["prompts"]
self.assertEqual(
evidence["negative"],
{"status": "resolved", "text": "", "source": "zeroed-conditioning"},
)
# The source survives pass-throughs and reroutes in front of it.
source["30"] = {
"class_type": "FluxGuidance",
"inputs": {"guidance": 3.5, "conditioning": ["7", 0]},
}
source["31"] = {"class_type": "Reroute", "inputs": {"value": ["30", 0]}}
source["3"]["inputs"]["negative"] = ["31", 0]
negative = _inspect(source)["negative"]
self.assertEqual(negative["text"], "")
self.assertEqual(negative["provenance"]["text_source"], "zeroed-conditioning")
# A zeroed positive is an empty positive, never a resolved prompt.
source["3"]["inputs"]["positive"] = ["7", 0]
result = _inspect(source)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason"], "Positive prompt is empty")
def test_if_else_literal_boolean_selects_branch(self):
source = parity.build("f20")["prompt"]
result = _inspect(source)
self.assertEqual(result["state"], "resolved")
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
self.assertEqual(
[entry["node_id"] for entry in result["positive"]["provenance"]["path"]],
["6", "10"],
)
source["6"]["inputs"]["boolean"] = False
self.assertEqual(_inspect(source)["positive"]["text"], WOLF)
# The branch that is not taken is never traced.
source["6"]["inputs"]["on_true"] = ["99", 0]
self.assertEqual(_inspect(source)["state"], "resolved")
for literal in (1, "true", None):
with self.subTest(literal=literal):
source["6"]["inputs"]["boolean"] = literal
result = _inspect(source)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason_code"], "malformed")
self.assertIsNone(result["positive"])
def test_if_else_linked_boolean_converging_branches_resolve(self):
guidance = {
"class_type": "FluxGuidance",
"inputs": {"guidance": 3.5, "conditioning": ["10", 0]},
}
extra = {"10": _encoded(parity.POS), "11": guidance, "12": _image_exists()}
source = _with_positive(_if_else(["12", 1], ["11", 0], ["10", 0]), extra)
result = _inspect(source)
self.assertEqual(result["state"], "resolved")
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
self.assertEqual(result["sampler_id"], "3")
self.assertEqual(result["candidates"], [])
# Two encoders with the same text converge as well.
source["11"] = _encoded(parity.POS)
self.assertEqual(_inspect(source)["state"], "resolved")
def test_if_else_linked_boolean_diverging_branches_are_ambiguous_with_candidates(
self,
):
extra = {
"10": _encoded(parity.POS),
"11": _encoded(WOLF),
"12": _image_exists(),
}
source = _with_positive(_if_else(["12", 1], ["10", 0], ["11", 0]), extra)
result = _inspect(source)
self.assertEqual(result["state"], "ambiguous")
self.assertEqual(result["sampler_id"], "3")
self.assertIsNone(result["positive"])
self.assertIsNone(result["negative"])
candidates = result["candidates"]
self.assertEqual([item["state"] for item in candidates], ["resolved"] * 2)
self.assertEqual(
[_sides(item) for item in candidates],
[(parity.POS, parity.NEG), (WOLF, parity.NEG)],
)
self.assertEqual(
[
item["positive"]["provenance"]["path"][-1]["node_id"]
for item in candidates
],
["10", "11"],
)
# One selector read by both roles is one runtime choice, not four pairs.
source["3"]["inputs"]["negative"] = ["6", 0]
candidates = _inspect(source)["candidates"]
self.assertEqual(
[_sides(item) for item in candidates],
[(parity.POS, parity.POS), (WOLF, WOLF)],
)
# Independent selectors on each role multiply.
source["7"] = _if_else(["16", 1], ["10", 0], ["14", 0])
source["14"] = _encoded(parity.NEG)
source["16"] = _image_exists()
source["3"]["inputs"]["negative"] = ["7", 0]
candidates = _inspect(source)["candidates"]
self.assertEqual(len(candidates), 4)
self.assertEqual(len({_sides(item) for item in candidates}), 4)
def test_selectors_sharing_one_condition_choose_together(self):
extra = {
"10": _encoded("pos A"),
"11": _encoded("pos B"),
"14": _encoded("neg A"),
"15": _encoded("neg B"),
"12": _image_exists(),
"16": _image_exists(),
}
source = _with_positive(_if_else(["12", 1], ["10", 0], ["11", 0]), extra)
source["7"] = _if_else(["12", 1], ["14", 0], ["15", 0])
result = _inspect(source)
self.assertEqual(result["state"], "ambiguous")
# One runtime value drives both selectors: never "pos A" with "neg B".
self.assertEqual(
[_sides(item) for item in result["candidates"]],
[("pos A", "neg A"), ("pos B", "neg B")],
)
# Another output slot of the same node is another value.
source["7"]["inputs"]["boolean"] = ["12", 0]
self.assertEqual(len(_inspect(source)["candidates"]), 4)
# Two independent conditions give the full cross product.
source["7"]["inputs"]["boolean"] = ["16", 1]
candidates = _inspect(source)["candidates"]
self.assertEqual(
sorted(_sides(item) for item in candidates),
[
("pos A", "neg A"),
("pos A", "neg B"),
("pos B", "neg A"),
("pos B", "neg B"),
],
)
def test_diverging_branch_that_cannot_be_traced_is_never_guessed(self):
extra = {
"10": _encoded(parity.POS),
"11": {"class_type": "FutureEncoder", "inputs": {"text": WOLF}},
"12": _image_exists(),
}
source = _with_positive(_if_else(["12", 1], ["10", 0], ["11", 0]), extra)
result = _inspect(source)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["node"], {"id": "11", "class_type": "FutureEncoder"})
self.assertIsNone(result["positive"])
def test_selector_branches_are_bounded(self):
extra, previous = {"12": _image_exists()}, None
for index in range(6):
extra[f"a{index}"] = _encoded(f"left {index}")
extra[f"b{index}"] = _encoded(f"right {index}")
# Each selector reads its own condition: independent choices.
extra[f"e{index}"] = _image_exists()
extra[f"s{index}"] = _if_else(
[f"e{index}", 1], [f"a{index}", 0], [f"b{index}", 0]
)
extra[f"c{index}"] = {
"class_type": "ConditioningCombine",
"inputs": {
"conditioning_1": [f"s{index}", 0],
"conditioning_2": [previous or f"s{index}", 0],
},
}
previous = f"c{index}"
source = _with_positive(
{"class_type": "Reroute", "inputs": {"value": [previous, 0]}}, extra
)
result = _inspect(source)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason_code"], "work-limit")
self.assertEqual(result["candidates"], [])
# Branch tracing draws from the per-candidate visit allowance too.
small = _with_positive(
_if_else(["12", 1], ["10", 0], ["11", 0]),
{"10": _encoded(parity.POS), "11": _encoded(WOLF), "12": _image_exists()},
)
self.assertEqual(_inspect(small, max_visits=3)["reason_code"], "work-limit")
def test_conditioning_combine_distinct_texts_is_composite(self):
source = parity.build("f06d")["prompt"]
result = _inspect(source)
self.assertEqual(
(result["state"], result["reason_code"]), ("unresolved", "composite")
)
self.assertEqual(
result["node"], {"id": "6", "class_type": "ConditioningCombine"}
)
self.assertIsNone(result["positive"])
for name, inputs in (
("ConditioningConcat", ("conditioning_to", "conditioning_from")),
("ConditioningAverage", ("conditioning_to", "conditioning_from")),
):
with self.subTest(name=name):
source["6"] = {
"class_type": name,
"inputs": {inputs[0]: ["10", 0], inputs[1]: ["11", 0]},
}
result = _inspect(source)
self.assertEqual(result["reason_code"], "composite")
self.assertEqual(result["node"], {"id": "6", "class_type": name})
# A zeroed part is a different text too.
source["12"] = {
"class_type": "ConditioningZeroOut",
"inputs": {"conditioning": ["10", 0]},
}
source["6"]["inputs"]["conditioning_from"] = ["12", 0]
self.assertEqual(_inspect(source)["reason_code"], "composite")
def test_conditioning_combine_identical_texts_resolve(self):
source = parity.build("f06d")["prompt"]
source["11"]["inputs"]["text"] = parity.POS
result = _inspect(source)
self.assertEqual(result["state"], "resolved")
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
source["6"]["inputs"]["conditioning_2"] = ["10", 0]
self.assertEqual(_inspect(source)["state"], "resolved")
# A missing part is malformed, never "the other part".
del source["6"]["inputs"]["conditioning_2"]
result = _inspect(source)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason_code"], "malformed")
def test_selector_sources_and_string_branches(self):
# Converging branches with different text sources name no single source.
source = parity.build("f23")["prompt"]
source["12"] = _image_exists()
source["14"] = _encoded("")
source["15"] = _if_else(["12", 1], ["7", 0], ["14", 0])
source["3"]["inputs"]["negative"] = ["15", 0]
negative = _inspect(source)["negative"]
self.assertEqual(negative["text"], "")
self.assertEqual(negative["provenance"]["text_source"], "graph-inferred")
source["3"]["inputs"]["negative"] = ["7", 0]
source["15"]["inputs"]["on_false"] = ["7", 0]
source["3"]["inputs"]["negative"] = ["15", 0]
negative = _inspect(source)["negative"]
self.assertEqual(negative["provenance"]["text_source"], "zeroed-conditioning")
# A selector also forwards string inputs, including literal text.
source = parity.build("f02")["prompt"]
source["12"] = _image_exists()
source["20"] = _if_else(["12", 1], "same words", "same words")
source["6"]["inputs"]["text"] = ["20", 0]
result = _inspect(source)
self.assertEqual(result["positive"]["text"], "same words")
source["20"]["inputs"]["on_false"] = "other words"
result = _inspect(source)
self.assertEqual(result["state"], "ambiguous")
self.assertEqual(
[item["positive"]["text"] for item in result["candidates"]],
["same words", "other words"],
)
def test_default_profile_keeps_conditioning_nodes_unsupported(self):
for name in ("f06c", "f06d", "f06e", "f20", "f23"):
with self.subTest(fixture=name):
result = resolve_prompt_pair({"prompt": parity.build(name)["prompt"]})
self.assertEqual(result["state"], "unresolved")
self.assertEqual(
result["reason"], "Unsupported node semantics or output slot"
)
self.assertIsNone(result["reason_code"])
def _primitive(value: str) -> dict:
return {"class_type": "PrimitiveString", "inputs": {"value": value}}
def _text_encoded(link: list) -> dict:
"""A CLIPTextEncode (node 6 in ``_with_positive``) reading a linked string."""
return {"class_type": "CLIPTextEncode", "inputs": {"text": link, "clip": ["4", 1]}}
def _string_positive(node: dict, extra: dict = None) -> dict:
"""``f02`` whose positive encoder reads string node 10 (``node``)."""
return _with_positive(_text_encoded(["10", 0]), {"10": node, **(extra or {})})
def _multiline(text, flag) -> dict:
return {
"class_type": "StringConstantMultiline",
"inputs": {"string": text, "strip_newlines": flag},
}
def _join(count, delimiter, return_list=False, **strings) -> dict:
return {
"class_type": "JoinStringMulti",
"inputs": {
"inputcount": count,
"delimiter": delimiter,
"return_list": return_list,
**strings,
},
}
def _sd3(clip_l: str, clip_g: str, t5xxl: str) -> dict:
return {
"class_type": "CLIPTextEncodeSD3",
"inputs": {
"clip": ["4", 1],
"clip_l": clip_l,
"clip_g": clip_g,
"t5xxl": t5xxl,
"empty_padding": "none",
},
}
def _positive(result: dict) -> tuple:
return result["state"], _role_text_of(result, "positive"), result["reason_code"]
def _role_text_of(result: dict, role: str):
side = result[role]
return side["text"] if isinstance(side, dict) else None
F01B_POSITIVE = f"{parity.POS}, Scene 0: unique shot description soft winter light,"
class TestEncoderAndStringSemantics(unittest.TestCase):
"""Text encoders, string sources and runtime nodes (inspection, Task 2.4)."""
def test_sdxl_equal_parts_resolve(self):
result = _inspect(parity.build("f06a")["prompt"])
self.assertEqual(result["state"], "resolved")
self.assertIsNone(result["reason_code"])
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
provenance = result["positive"]["provenance"]
self.assertEqual(provenance["path"], [{"node_id": "6", "output_slot": 0}])
self.assertEqual(provenance["text_source"], "graph-inferred")
self.assertEqual(result["positive"]["storage_text"], parity.POS)
# Linked parts are followed; equal texts still resolve, path deduplicated.
source = parity.build("f06a")["prompt"]
source["6"]["inputs"]["text_g"] = ["10", 0]
source["6"]["inputs"]["text_l"] = ["10", 0]
source["10"] = _primitive(parity.POS)
result = _inspect(source)
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
self.assertEqual(
result["positive"]["provenance"]["path"],
[{"node_id": "6", "output_slot": 0}, {"node_id": "10", "output_slot": 0}],
)
def test_sdxl_distinct_parts_are_composite(self):
source = parity.build("f06a")["prompt"]
source["6"]["inputs"]["text_l"] = "a second style"
result = _inspect(source)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason_code"], "composite")
self.assertEqual(result["reason"], "Encoder parts have different texts")
self.assertEqual(
result["node"], {"id": "6", "class_type": "CLIPTextEncodeSDXL"}
)
self.assertIsNone(result["positive"])
# Whitespace is text: emptiness is exact, never trimmed into agreement.
source["6"]["inputs"]["text_l"] = " "
self.assertEqual(_inspect(source)["reason_code"], "composite")
source["6"]["inputs"]["text_l"] = parity.POS + " "
self.assertEqual(_inspect(source)["reason_code"], "composite")
def test_sdxl_single_nonempty_part_resolves(self):
for empty in ("text_l", "text_g"):
with self.subTest(empty=empty):
source = parity.build("f06a")["prompt"]
source["6"]["inputs"][empty] = ""
result = _inspect(source)
self.assertEqual(result["state"], "resolved")
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
self.assertEqual(result["positive"]["storage_text"], parity.POS)
# A linked empty part counts as empty; both paths are recorded.
source = parity.build("f06a")["prompt"]
source["6"]["inputs"]["text_l"] = ["10", 0]
source["10"] = _primitive("")
result = _inspect(source)
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
self.assertEqual(
result["positive"]["provenance"]["path"],
[{"node_id": "6", "output_slot": 0}, {"node_id": "10", "output_slot": 0}],
)
# All parts empty: an empty positive, never a resolved prompt.
source["6"]["inputs"]["text_g"] = ""
self.assertEqual(_inspect(source)["reason"], "Positive prompt is empty")
# A missing part is malformed, never read as empty.
source = parity.build("f06a")["prompt"]
del source["6"]["inputs"]["text_l"]
self.assertEqual(_inspect(source)["reason_code"], "malformed")
def test_flux_and_sd3_encoders(self):
result = _inspect(parity.build("f06b")["prompt"])
self.assertEqual(result["state"], "resolved")
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
source = parity.build("f06b")["prompt"]
source["6"]["inputs"]["t5xxl"] = "a longer natural language description"
result = _inspect(source)
self.assertEqual(result["reason_code"], "composite")
self.assertEqual(result["node"]["class_type"], "CLIPTextEncodeFlux")
cases = [
((parity.POS, parity.POS, parity.POS), ("resolved", parity.POS, None)),
(("", "", parity.POS), ("resolved", parity.POS, None)),
(("", parity.POS, ""), ("resolved", parity.POS, None)),
((parity.POS, "", WOLF), ("unresolved", None, "composite")),
((parity.POS, parity.POS, WOLF), ("unresolved", None, "composite")),
# Spec 7.2: only "all equal" or "exactly one non-empty" resolve.
((parity.POS, parity.POS, ""), ("unresolved", None, "composite")),
]
for parts, expected in cases:
with self.subTest(parts=parts):
self.assertEqual(
_positive(_inspect(_with_positive(_sd3(*parts)))), expected
)
def test_qwen_edit_plus_prompt(self):
extra = {
"13": {"class_type": "LoadImage", "inputs": {"image": "reference.png"}}
}
for name in ("TextEncodeQwenImageEditPlus", "TextEncodeQwenImageEdit"):
with self.subTest(node=name):
inputs = {"clip": ["4", 1], "prompt": parity.POS, "vae": ["4", 2]}
images = ("image1", "image2") if name.endswith("Plus") else ("image",)
inputs.update({image: ["13", 0] for image in images})
node = {"class_type": name, "inputs": inputs}
result = _inspect(_with_positive(node, extra))
self.assertEqual(result["state"], "resolved")
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
self.assertEqual(
result["positive"]["provenance"]["path"],
[{"node_id": "6", "output_slot": 0}],
)
def test_qwen_image21_two_outputs(self):
source = parity.build("f24")["prompt"]
result = _inspect(source)
self.assertEqual(result["state"], "resolved")
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
self.assertEqual(
result["negative"]["provenance"]["path"],
[{"node_id": "6", "output_slot": 1}],
)
self.assertEqual(result["negative"]["storage_text"], parity.NEG)
# Slot 2 is the latent output, never a prompt.
source["3"]["inputs"]["negative"] = ["6", 2]
result = _inspect(source)
self.assertEqual(result["reason_code"], "unsupported-node")
self.assertEqual(
result["node"], {"id": "6", "class_type": "TextEncodeQwenImage21"}
)
def test_kiko_text_encode_plain_text(self):
node = {"class_type": "KikoTextEncode", "inputs": {"clip": ["4", 1]}}
node["inputs"]["text"] = parity.POS
result = _inspect(_with_positive(node))
self.assertEqual(result["state"], "resolved")
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
self.assertEqual(result["positive"]["storage_text"], parity.POS)
# No composition: a linked text arrives unchanged.
node["inputs"]["text"] = ["10", 0]
extra = {"10": {"class_type": "PrimitiveStringMultiline"}}
extra["10"]["inputs"] = {"value": " " + parity.POS + "\n"}
result = _inspect(_with_positive(node, extra))
self.assertEqual(result["positive"]["text"], " " + parity.POS + "\n")
def test_string_constant_multiline_strip_newlines_exact(self):
result = _inspect(_string_positive(_multiline("a\n b \n", True)))
self.assertEqual(_positive(result), ("resolved", "a b", None))
self.assertEqual(result["positive"]["storage_text"], "a b")
self.assertEqual(
result["positive"]["provenance"]["path"],
[{"node_id": "6", "output_slot": 0}, {"node_id": "10", "output_slot": 0}],
)
result = _inspect(_string_positive(_multiline("a\n b \n", False)))
self.assertEqual(_positive(result), ("resolved", "a\n b \n", None))
# Only "\n" is removed; other whitespace inside the text is kept.
result = _inspect(_string_positive(_multiline("\ta\r\n\tb\n\n", True)))
self.assertEqual(result["positive"]["text"], "a\r\tb")
# A linked text is followed; a linked flag is unsupported (not guessed).
linked = _string_positive(
_multiline(["11", 0], True), {"11": _primitive("x\ny")}
)
self.assertEqual(_inspect(linked)["positive"]["text"], "xy")
flag = _string_positive(
_multiline("a\nb", ["12", 0]),
{"12": {"class_type": "PrimitiveBoolean", "inputs": {"value": True}}},
)
result = _inspect(flag)
self.assertEqual(result["reason_code"], "unsupported-node")
self.assertEqual(
result["node"], {"id": "10", "class_type": "StringConstantMultiline"}
)
for value in ("true", 1, None):
with self.subTest(flag=value):
result = _inspect(_string_positive(_multiline("a\nb", value)))
self.assertEqual(result["reason_code"], "malformed")
missing = _multiline("a", True)
del missing["inputs"]["strip_newlines"]
self.assertEqual(
_inspect(_string_positive(missing))["reason_code"], "malformed"
)
# StringConstant is the literal strategy: its string, unchanged.
constant = {"class_type": "StringConstant", "inputs": {"string": " x\n"}}
result = _inspect(_string_positive(constant))
self.assertEqual(_positive(result), ("resolved", " x\n", None))
def test_join_string_multi_exact_semantics(self):
strings = {
"string_1": ["11", 0],
"string_2": "b",
"string_3": ["13", 0],
"string_4": ["14", 0],
"string_6": "beyond the count",
}
extra = {
"11": _primitive(""),
"13": _primitive(""),
"14": _primitive(" d "),
}
join = _join(5, "\\n|", **strings)
result = _inspect(_string_positive(join, extra))
# string_1 is kept although empty; empty and missing later parts are
# skipped; inputs past ``inputcount`` are ignored; the delimiter is
# used exactly as written (no escape processing, no trimming).
self.assertEqual(_positive(result), ("resolved", "\\n|b\\n| d ", None))
self.assertEqual(result["positive"]["storage_text"], "\\n|b\\n| d ")
self.assertEqual(
result["positive"]["provenance"]["path"],
[
{"node_id": "6", "output_slot": 0},
{"node_id": "10", "output_slot": 0},
{"node_id": "11", "output_slot": 0},
{"node_id": "13", "output_slot": 0},
{"node_id": "14", "output_slot": 0},
],
)
# One link read by several parts is listed once in the provenance path.
twice = _join(3, " ", string_1=["11", 0], string_2=["11", 0], string_3="c")
result = _inspect(_string_positive(twice, {"11": _primitive("w")}))
self.assertEqual(result["positive"]["text"], "w w c")
self.assertEqual(
result["positive"]["provenance"]["path"],
[{"node_id": "6", "output_slot": 0}, {"node_id": "10", "output_slot": 0}]
+ [{"node_id": "11", "output_slot": 0}],
)
two = _join(2, " ", string_1="a red fox", string_2="in snow")
self.assertEqual(
_inspect(_string_positive(two))["positive"]["text"], "a red fox in snow"
)
# Settings must be literal values the node would accept.
linked = {"12": {"class_type": "PrimitiveInt", "inputs": {"value": 2}}}
for field, value, code in (
("inputcount", ["12", 0], "unsupported-node"),
("delimiter", ["12", 0], "unsupported-node"),
("inputcount", 0, "malformed"),
("inputcount", 1001, "malformed"),
("inputcount", True, "malformed"),
("inputcount", "2", "malformed"),
("delimiter", 1, "malformed"),
("return_list", "false", "malformed"),
("return_list", 0, "malformed"),
):
with self.subTest(field=field, value=value):
node = _join(2, " ", string_1="a", string_2="b")
node["inputs"][field] = value
result = _inspect(_string_positive(node, linked))
self.assertEqual(result["reason_code"], code)
if code == "unsupported-node":
self.assertEqual(result["node"]["class_type"], "JoinStringMulti")
for field in ("inputcount", "delimiter", "return_list", "string_1"):
with self.subTest(missing=field):
node = _join(2, " ", string_1="a", string_2="b")
del node["inputs"][field]
result = _inspect(_string_positive(node))
self.assertEqual(result["reason_code"], "malformed")
def test_join_string_multi_return_list_is_runtime_value(self):
source = parity.build("f21")["prompt"]
result = _inspect(source)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason_code"], "runtime-value")
self.assertEqual(result["reason"], "Text is decided at run time")
self.assertEqual(result["node"], {"id": "10", "class_type": "JoinStringMulti"})
# A linked list flag is a run-time value too.
source["10"]["inputs"]["return_list"] = ["20", 0]
source["20"] = {"class_type": "PrimitiveBoolean", "inputs": {"value": False}}
self.assertEqual(_inspect(source)["reason_code"], "runtime-value")
# With the flag literally false the same parts join.
source["10"]["inputs"]["return_list"] = False
self.assertEqual(_inspect(source)["positive"]["text"], "a red fox, in snow")
def test_prompt_search_list_is_runtime_value(self):
from utils.prompt_inspector import inspect_prompt_evidence
source = parity.build("f22")["prompt"]
result = _inspect(source)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason_code"], "runtime-value")
self.assertEqual(result["node"], {"id": "10", "class_type": "PromptSearchList"})
self.assertIsNone(result["positive"])
# Never a list item: every output slot is decided at run time.
source["6"]["inputs"]["text"] = ["10", 3]
self.assertEqual(_inspect(source)["reason_code"], "runtime-value")
evidence = inspect_prompt_evidence({"prompt": parity.build("f22")["prompt"]})
self.assertEqual(evidence["prompts"]["positive"]["status"], "unresolved")
self.assertIsNone(evidence["prompts"]["positive"]["text"])
# The preview-only FaceDetailer no longer competes with the saved branch.
def test_f01b_single_flux2_branch_resolves_with_absent_negative(self):
result = _inspect(parity.build("f01b")["prompt"])
self.assertEqual(result["state"], "absent")
self.assertEqual(result["positive"]["text"], F01B_POSITIVE)
self.assertIsNone(result["negative"])
def test_f01b_branch_text_is_the_exact_join(self):
# Anchored at the branch's saver, over its EmptyFlux2LatentImage origin:
# the join, multiline strings and encoder alone give the exact text.
source = parity.build("f01b")["prompt"]
self.assertEqual(source["1300:7"]["class_type"], "EmptyFlux2LatentImage")
result = _inspect(source, output_node_id="1400")
self.assertEqual(result["state"], "absent")
self.assertEqual(result["reason"], "Guider has no negative input")
self.assertEqual(result["positive"]["text"], F01B_POSITIVE)
self.assertEqual(result["positive"]["storage_text"], F01B_POSITIVE)
self.assertIsNone(result["negative"])
def test_encoder_and_string_outputs_keep_their_kinds(self):
sdxl = parity.build("f06a")["prompt"]["6"]
# A conditioning output read as a string, and a string output read as
# conditioning, are malformed links, not unsupported nodes.
join = _join(2, " ", string_1=["11", 0], string_2="b")
result = _inspect(_string_positive(join, {"11": sdxl}))
self.assertEqual(result["reason_code"], "malformed")
self.assertEqual(result["reason"], "Output slot has an incompatible type")
for node in (_multiline("a", True), _join(2, " ", string_1="a")):
with self.subTest(node=node["class_type"]):
result = _inspect(_with_positive(node))
self.assertEqual(result["reason_code"], "malformed")
def test_unverified_text_nodes_stay_unsupported_and_named(self):
# TextBoxMira was verified in its source in Task 2.7b and is listed.
for name in ("LoRAfromText", "RegexReplace", "XYZPrompt"):
with self.subTest(node=name):
node = {"class_type": name, "inputs": {"text": parity.POS}}
result = _inspect(_string_positive(node))
self.assertEqual(result["reason_code"], "unsupported-node")
self.assertEqual(result["node"], {"id": "10", "class_type": name})
def test_string_strategies_bound_every_intermediate(self):
limited = replace(resolver.INSPECTION_PROFILE, max_text_length=10)
join = _join(2, " ", string_1="abcdef", string_2="ghijk")
result = resolve_prompt_pair(
{"prompt": _string_positive(join)}, profile=limited
)
self.assertEqual(result["reason_code"], "text-limit")
# Exactly at the limit is fine.
join["inputs"]["string_2"] = "ghi"
result = resolve_prompt_pair(
{"prompt": _string_positive(join)}, profile=limited
)
self.assertEqual(result["positive"]["text"], "abcdef ghi")
# The scanned-character budget covers stripping, joining and comparing.
long_text = "x\n" * 5_000
cases = {
"strip": _string_positive(_multiline(long_text, True)),
"join": _string_positive(
_join(2, " ", string_1=long_text, string_2=long_text)
),
"parts": _with_positive(_sd3(long_text, long_text, long_text)),
}
scanned = replace(resolver.INSPECTION_PROFILE, max_scanned_units=8_000)
for label, source in cases.items():
with self.subTest(strategy=label):
self.assertEqual(_inspect(source)["state"], "resolved")
result = resolve_prompt_pair({"prompt": source}, profile=scanned)
self.assertEqual(result["reason_code"], "work-limit")
def test_listed_strategies_without_code_here_are_unsupported(self):
# ``pm-compose`` and ``concat`` are schema-valid but implemented by the
# existing adapters only; a table row using them is never guessed at.
table = inspection_semantics.load_semantics()
encoders = dict(table.encoders)
encoders[("CLIPTextEncodeSDXL", 0)] = {
"strategy": "pm-compose",
"inputs": ("text",),
}
strings = dict(table.strings)
strings[("StringConstant", 0)] = {"strategy": "concat", "inputs": ("string",)}
patched = replace(table, encoders=encoders, strings=strings)
constant = {"class_type": "StringConstant", "inputs": {"string": "x"}}
with mock.patch.object(resolver, "_inspection_semantics", return_value=patched):
for source, name in (
(parity.build("f06a")["prompt"], "CLIPTextEncodeSDXL"),
(_string_positive(constant), "StringConstant"),
):
with self.subTest(node=name):
result = _inspect(source)
self.assertEqual(result["reason_code"], "unsupported-node")
self.assertEqual(result["node"]["class_type"], name)
def test_unreadable_string_sources_keep_pair_execution_evidence(self):
from utils.pair_execution import EXECUTION_KEY
record = {
"text": "recorded words",
"negative_text": "neg",
"positive_text": "recorded words",
"negative_effective_text": "neg",
"prepend_text": "",
"append_text": "",
}
evidence = {
EXECUTION_KEY: {"version": 1, "nodes": {"8": {"records": [record]}}}
}
def paired(source: dict, extra: dict) -> dict:
graph = parity.build("f02")["prompt"]
graph["8"] = {
"class_type": "PromptManagerPair",
"inputs": {
"text": ["10", 0],
"negative_text": "neg",
"prepend_text": "",
"append_text": "",
},
}
graph["3"]["inputs"]["positive"] = ["8", 0]
graph["3"]["inputs"]["negative"] = ["8", 1]
graph.update({"10": source, **extra})
return graph
flag = {"11": {"class_type": "PrimitiveBoolean", "inputs": {"value": True}}}
unknown = {"11": {"class_type": "FutureNode", "inputs": {}}}
# Text that cannot be read statically is unverifiable, not a mismatch:
# the pair's unique execution record still decides (as it did before
# these nodes were listed).
cases = {
"linked strip flag": (_multiline("recorded words", ["11", 0]), flag),
"linked delimiter": (
_join(2, ["11", 0], string_1="recorded", string_2="words"),
unknown,
),
"linked count": (
_join(["11", 0], " ", string_1="recorded", string_2="words"),
unknown,
),
"list output": (
_join(2, " ", True, string_1="recorded", string_2="words"),
{},
),
"search list": ({"class_type": "PromptSearchList", "inputs": {}}, {}),
}
for label, (source, extra) in cases.items():
with self.subTest(source=label):
result = resolve_prompt_pair(
{"prompt": paired(source, extra), **evidence},
profile=resolver.INSPECTION_PROFILE,
)
self.assertEqual(result["state"], "resolved")
self.assertEqual(_sides(result), ("recorded words", "neg"))
provenance = result["positive"]["provenance"]
self.assertEqual(provenance["text_source"], "execution")
# A source that does resolve is still verified against the record.
readable = _join(2, " ", string_1="other", string_2="words")
result = resolve_prompt_pair(
{"prompt": paired(readable, {}), **evidence},
profile=resolver.INSPECTION_PROFILE,
)
self.assertEqual(result["reason_code"], "malformed")
self.assertEqual(
result["reason"], "Execution evidence does not match recorded node inputs"
)
matching = _join(2, " ", string_1="recorded", string_2="words")
result = resolve_prompt_pair(
{"prompt": paired(matching, {}), **evidence},
profile=resolver.INSPECTION_PROFILE,
)
self.assertEqual(result["positive"]["provenance"]["text_source"], "execution")
def test_default_profile_keeps_encoder_and_string_nodes_unsupported(self):
for name in ("f06a", "f06b", "f21", "f22", "f24"):
with self.subTest(fixture=name):
result = resolve_prompt_pair({"prompt": parity.build(name)["prompt"]})
self.assertEqual(result["state"], "unresolved")
self.assertEqual(
result["reason"], "Unsupported node semantics or output slot"
)
self.assertIsNone(result["reason_code"])
def _detailed(same_prompt: bool = False) -> dict:
"""``f19``: KSampler 3 -> VAEDecode 8 -> FaceDetailer 50 -> SaveImage 9."""
source = parity.build("f19")["prompt"]
if same_prompt:
source["50"]["inputs"]["positive"] = ["6", 0]
return source
def _detailer(image: list, positive: list, negative: list = None) -> dict:
return {
"class_type": "FaceDetailer",
"inputs": {
"image": image,
"model": ["4", 0],
"positive": positive,
"negative": negative or ["7", 0],
"seed": 1,
"steps": 8,
},
}
class TestDetailerStages(unittest.TestCase):
"""D19: a detailer is a sampler stage judged with its upstream sampler."""
def test_detailer_with_the_upstream_prompt_resolves(self):
result = _inspect(_detailed(same_prompt=True))
self.assertEqual(result["state"], "resolved")
self.assertEqual(result["sampler_id"], "50")
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
self.assertEqual(result["candidates"], [])
self.assertEqual(
_inspect(_detailed(same_prompt=True), output_node_id="9"), result
)
def test_detailer_with_a_different_prompt_is_ambiguous_with_both_stages(self):
from utils.prompt_inspector import inspect_prompt_evidence
result = _inspect(_detailed())
self.assertEqual(
(result["state"], result["reason_code"]),
("ambiguous", "multistage-differs"),
)
self.assertEqual(result["sampler_id"], "50")
self.assertIsNone(result["positive"])
self.assertIsNone(result["negative"])
self.assertEqual(
[(item["sampler_id"], _sides(item)) for item in result["candidates"]],
[
("50", (parity.DETAILER_POS, parity.NEG)),
("3", (parity.POS, parity.NEG)),
],
)
self.assertEqual(_inspect(_detailed(), output_node_id="9"), result)
# Neither the upstream prompt nor the detailer prompt is shown as the answer.
prompts = inspect_prompt_evidence({"prompt": _detailed()})["prompts"]
self.assertEqual(
[prompts[role]["status"] for role in ("positive", "negative")],
["ambiguous", "ambiguous"],
)
self.assertNotIn(parity.POS, str(prompts))
def test_detailer_of_a_loaded_image_is_a_single_stage(self):
source = _detailed()
source["13"] = {"class_type": "LoadImage", "inputs": {"image": "input.png"}}
source["50"]["inputs"]["image"] = ["13", 0]
source["9"]["inputs"]["images"] = ["50", 0]
source["8"]["inputs"]["samples"] = ["3", 0]
source["14"] = {"class_type": "SaveImage", "inputs": {"images": ["8", 0]}}
result = _inspect(source, output_node_id="9")
self.assertEqual(result["state"], "resolved")
self.assertEqual(_sides(result), (parity.DETAILER_POS, parity.NEG))
self.assertEqual(result["sampler_id"], "50")
def test_raw_and_detailed_saves_stay_ambiguous_without_an_anchor(self):
source = _detailed(same_prompt=True)
source["14"] = {"class_type": "SaveImage", "inputs": {"images": ["8", 0]}}
result = _inspect(source)
self.assertEqual(result["state"], "ambiguous")
self.assertEqual(result["reason"], "Multiple sampler branches")
self.assertEqual(
sorted(item["sampler_id"] for item in result["candidates"]), ["3", "50"]
)
raw = _inspect(source, output_node_id="14")
self.assertEqual((raw["state"], raw["sampler_id"]), ("resolved", "3"))
detailed = _inspect(source, output_node_id="9")
self.assertEqual(
(detailed["state"], detailed["sampler_id"]), ("resolved", "50")
)
def test_a_preview_of_the_upstream_image_is_not_another_output(self):
source = _detailed()
source["14"] = {"class_type": "PreviewImage", "inputs": {"images": ["8", 0]}}
result = _inspect(source)
self.assertEqual(result["reason_code"], "multistage-differs")
self.assertEqual(len(result["candidates"]), 2)
def test_chained_detailers_compare_every_stage(self):
source = _detailed(same_prompt=True)
source["51"] = _detailer(["50", 0], ["6", 0])
source["9"]["inputs"]["images"] = ["51", 0]
result = _inspect(source)
self.assertEqual((result["state"], result["sampler_id"]), ("resolved", "51"))
source["50"]["inputs"]["positive"] = ["10", 0]
result = _inspect(source)
self.assertEqual(result["reason_code"], "multistage-differs")
self.assertEqual(
[item["sampler_id"] for item in result["candidates"]], ["51", "50", "3"]
)
def test_unresolved_stage_blocks_the_chain(self):
source = _detailed(same_prompt=True)
source["30"] = {"class_type": "FutureConditioner", "inputs": {"c": ["6", 0]}}
source["3"]["inputs"]["positive"] = ["30", 0]
source["50"]["inputs"]["positive"] = ["10", 0]
result = _inspect(source)
self.assertEqual((result["state"], result["sampler_id"]), ("unresolved", "50"))
self.assertEqual(
result["node"], {"id": "30", "class_type": "FutureConditioner"}
)
self.assertIsNone(result["positive"])
def test_malformed_and_cyclic_detailer_ancestry_never_guesses(self):
source = _detailed()
del source["50"]["inputs"]["image"]
result = _inspect(source, output_node_id="9")
self.assertEqual(
(result["state"], result["reason_code"]), ("unresolved", "malformed")
)
source = _detailed()
source["8"]["inputs"]["samples"] = ["51", 0]
source["51"] = _detailer(["50", 0], ["6", 0])
result = _inspect(source, output_node_id="9")
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason_code"], "malformed")
def test_detailer_image_ancestry_is_walked_generically(self):
source = _detailed(same_prompt=True)
source["60"] = {
"class_type": "ImageBlend",
"inputs": {"image1": ["8", 0], "image2": ["8", 0], "blend_factor": 0.5},
}
source["61"] = {
"class_type": "ImageBlend",
"inputs": {"image1": ["8", 0], "image2": ["60", 0]},
}
source["62"] = {"class_type": "ImageScale", "inputs": {"image": ["60", 0]}}
source["50"]["inputs"]["image"] = ["61", 0]
source["63"] = {"class_type": "PreviewImage", "inputs": {"images": ["62", 0]}}
source["junk"] = "not a node"
result = _inspect(source)
self.assertEqual((result["state"], result["sampler_id"]), ("resolved", "50"))
# A detailer whose image ancestry returns to itself is malformed.
source = _detailed()
source["64"] = {"class_type": "ImageScale", "inputs": {"image": ["50", 0]}}
source["50"]["inputs"]["image"] = ["64", 0]
result = _inspect(source, output_node_id="9")
self.assertEqual(result["reason"], "Cyclic sampler stage ancestry")
self.assertEqual(result["reason_code"], "malformed")
def test_detailer_stage_walks_draw_from_the_work_budget(self):
self.assertEqual(
_inspect(_detailed(), max_visits=6)["reason_code"], "work-limit"
)
# Too little budget to absorb the upstream sampler keeps both as
# candidates: more ambiguity, never a chosen prompt.
starved = _inspect(_detailed(), max_visits=3)
self.assertEqual(starved["state"], "ambiguous")
self.assertEqual(len(starved["candidates"]), 2)
self.assertIsNone(starved["positive"])
source = _detailed()
previous = "8"
for index in range(70):
source[f"u{index}"] = {
"class_type": "ImageScale",
"inputs": {"image": [previous, 0]},
}
previous = f"u{index}"
source["50"]["inputs"]["image"] = [previous, 0]
result = _inspect(source, output_node_id="9")
self.assertEqual(result["reason_code"], "depth-limit")
def test_long_detailer_chains_are_depth_limited(self):
for count in (70, 600):
source = _detailed(same_prompt=True)
previous = "50"
for index in range(count):
node_id = f"d{index}"
source[node_id] = _detailer([previous, 0], ["6", 0])
previous = node_id
source["9"]["inputs"]["images"] = [previous, 0]
for options in ({}, {"output_node_id": "9"}):
with self.subTest(count=count, options=options):
result = _inspect(source, **options)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason_code"], "depth-limit")
self.assertIsNone(result["positive"])
# A chain within the bound still resolves.
source = _detailed(same_prompt=True)
previous = "50"
for index in range(20):
source[f"d{index}"] = _detailer([previous, 0], ["6", 0])
previous = f"d{index}"
source["9"]["inputs"]["images"] = [previous, 0]
result = _inspect(source, output_node_id="9")
self.assertEqual((result["state"], result["sampler_id"]), ("resolved", "d19"))
def test_a_preview_only_detailer_never_blocks_the_saved_branch(self):
# Codex P2: the detailer feeds only a PreviewImage; the raw image is saved.
source = _detailed()
source["9"]["class_type"] = "PreviewImage"
source["14"] = {"class_type": "SaveImage", "inputs": {"images": ["8", 0]}}
result = _inspect(source)
self.assertEqual((result["state"], result["sampler_id"]), ("resolved", "3"))
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
self.assertEqual(result["candidates"], [])
# Every non-file output counts as a preview, through any image chain.
for name in sorted(inspection_semantics.load_semantics().non_file_outputs):
with self.subTest(output=name):
source["15"] = {
"class_type": "ImageScale",
"inputs": {"image": ["50", 0]},
}
source["9"] = {"class_type": name, "inputs": {"images": ["15", 0]}}
self.assertEqual(_inspect(source)["sampler_id"], "3")
def test_a_preview_only_sampler_never_blocks_the_saved_sampler(self):
source = _detailed()
source["9"]["inputs"]["images"] = ["8", 0]
source["16"] = {"class_type": "VAEDecode", "inputs": {"samples": ["50", 0]}}
del source["50"]
source["50"] = {
"class_type": "KSampler",
"inputs": {**source["3"]["inputs"], "positive": ["10", 0]},
}
source["17"] = {"class_type": "PreviewImage", "inputs": {"images": ["16", 0]}}
result = _inspect(source)
self.assertEqual((result["state"], result["sampler_id"]), ("resolved", "3"))
# Ingestion keeps its historical answer (D1).
legacy = resolve_prompt_pair({"prompt": source})
self.assertEqual(legacy["state"], "ambiguous")
def test_stages_that_write_files_stay_candidates(self):
# Raw and detailed double saves both write files (the H1 exception).
source = _detailed()
source["14"] = {"class_type": "SaveImage", "inputs": {"images": ["8", 0]}}
source["15"] = {"class_type": "PreviewImage", "inputs": {"images": ["50", 0]}}
result = _inspect(source)
self.assertEqual(result["state"], "ambiguous")
self.assertEqual(
sorted(item["sampler_id"] for item in result["candidates"]), ["3", "50"]
)
# A stage with no output at all is not known to be preview-only.
source = _detailed()
source["14"] = {"class_type": "SaveImage", "inputs": {"images": ["8", 0]}}
del source["9"]
self.assertEqual(_inspect(source)["state"], "ambiguous")
def test_when_every_stage_is_preview_only_none_is_dropped(self):
source = _detailed()
source["9"]["class_type"] = "PreviewImage"
source["14"] = {"class_type": "PreviewImage", "inputs": {"images": ["8", 0]}}
result = _inspect(source)
self.assertEqual(result["reason_code"], "multistage-differs")
self.assertEqual(len(result["candidates"]), 2)
def test_preview_files_keep_preview_only_stages(self):
# A preview file in the temp folder may be the preview stage's own output.
source = _detailed()
source["9"]["class_type"] = "PreviewImage"
source["14"] = {"class_type": "SaveImage", "inputs": {"images": ["8", 0]}}
result = resolve_prompt_pair(
{"prompt": source}, profile=resolver.PREVIEW_INSPECTION_PROFILE
)
self.assertEqual(result["state"], "ambiguous")
self.assertEqual(len(result["candidates"]), 2)
def test_a_failed_preview_walk_keeps_every_stage(self):
source = _detailed()
source["9"]["class_type"] = "PreviewImage"
source["14"] = {"class_type": "SaveImage", "inputs": {"images": ["8", 0]}}
limit = resolver._Unresolved("Resolution work limit reached", "work-limit")
with mock.patch.object(
resolver._GraphResolver, "producers_of", side_effect=limit
):
result = _inspect(source)
# More ambiguity, never a chosen prompt.
self.assertEqual(result["state"], "ambiguous")
self.assertEqual(len(result["candidates"]), 2)
self.assertIsNone(result["positive"])
def test_preview_only_walks_are_linear_in_long_chains(self):
# 600 chained detailers ending in a preview, beside a saved raw image:
# one pass decides every stage, so the walk itself never runs out.
source = _detailed(same_prompt=True)
previous = "50"
for index in range(600):
source[f"d{index}"] = _detailer([previous, 0], ["6", 0])
previous = f"d{index}"
source["9"] = {
"class_type": "PreviewImage",
"inputs": {"images": [previous, 0]},
}
source["14"] = {"class_type": "SaveImage", "inputs": {"images": ["8", 0]}}
result = _inspect(source)
self.assertEqual((result["state"], result["sampler_id"]), ("resolved", "3"))
def test_default_profile_keeps_the_upstream_sampler(self):
# Ingestion is unchanged (D1): the detailer is not a sampler there.
result = resolve_prompt_pair({"prompt": _detailed()})
self.assertEqual((result["state"], result["sampler_id"]), ("resolved", "3"))
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
MULTISTAGE = "Multistage sampler ancestry is ambiguous"
OTHER = "a white owl at dusk"
def _latent_from(node: dict, extra: dict = None) -> dict:
"""``f02``'s KSampler graph whose ``latent_image`` reads ``node`` (as node 5)."""
return parity._ksampler_graph(
parity._enc(parity.POS), parity._enc(parity.NEG), latent=node, extra=extra
)
def _image_to_image(positive: str = OTHER) -> dict:
"""KSampler 3 -> VAEDecode 8 -> VAEEncode 60 -> KSampler 61 -> SaveImage 63."""
source = _latent_from(None)
source.update(
{
"60": {
"class_type": "VAEEncode",
"inputs": {"pixels": ["8", 0], "vae": ["4", 2]},
},
"62": parity._enc(positive),
"61": {
"class_type": "KSampler",
"inputs": {
**source["3"]["inputs"],
"positive": ["62", 0],
"latent_image": ["60", 0],
"denoise": 0.6,
},
},
"64": {
"class_type": "VAEDecode",
"inputs": {"samples": ["61", 0], "vae": ["4", 2]},
},
"63": {
"class_type": "SaveImage",
"inputs": {"filename_prefix": "img2img", "images": ["64", 0]},
},
}
)
# The first stage's own output is only a preview.
source["9"]["class_type"] = "PreviewImage"
return source
class TestLatentAncestry(unittest.TestCase):
"""Any latent origin without an upstream sampler is a single stage."""
def test_vaeencode_from_load_image_is_single_stage(self):
source = parity.build("f10")["prompt"]
for anchor in (None, "9"):
with self.subTest(anchor=anchor):
result = _inspect(source, output_node_id=anchor)
self.assertEqual(result["state"], "resolved")
self.assertIsNone(result["reason_code"])
self.assertEqual(result["sampler_id"], "3")
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
# Ingestion is unchanged (D1).
legacy = resolve_prompt_pair({"prompt": source})
self.assertEqual(legacy["state"], "unresolved")
self.assertEqual(legacy["reason"], "Unsupported latent ancestry")
def test_model_specific_empty_latents_are_single_stage(self):
size = {"width": 1024, "height": 1024, "batch_size": 1}
origins = {
"f11": parity.build("f11")["prompt"],
"EmptyLatentBatch": _latent_from(
{"class_type": "EmptyLatentBatch", "inputs": dict(size)}
),
"EmptyFlux2LatentImage": _latent_from(
{"class_type": "EmptyFlux2LatentImage", "inputs": dict(size)}
),
"SetLatentNoiseMask": _latent_from(
{
"class_type": "SetLatentNoiseMask",
"inputs": {"samples": ["60", 0], "mask": ["50", 1]},
},
{
"50": {"class_type": "LoadImage", "inputs": {"image": "a.png"}},
"60": {
"class_type": "VAEEncode",
"inputs": {"pixels": ["50", 0], "vae": ["4", 2]},
},
},
),
"Reroute": _latent_from(
{"class_type": "Reroute", "inputs": {"value": ["60", 0]}},
{"60": {"class_type": "EmptyLatentBatch", "inputs": dict(size)}},
),
}
for name in ("InpaintModelConditioning", "WanImageToVideo"):
# Slots 0 and 1 carry the conditioning, slot 2 is the latent origin.
source = _latent_from(
{
"class_type": name,
"inputs": {
"positive": ["6", 0],
"negative": ["7", 0],
"vae": ["4", 2],
"pixels": ["50", 0],
"start_image": ["50", 0],
"mask": ["50", 1],
"width": 832,
"height": 480,
},
},
{"50": {"class_type": "LoadImage", "inputs": {"image": "a.png"}}},
)
source["3"]["inputs"].update(
positive=["5", 0], negative=["5", 1], latent_image=["5", 2]
)
origins[name] = source
for name, source in origins.items():
with self.subTest(origin=name):
result = _inspect(source)
self.assertEqual(result["state"], "resolved")
self.assertEqual(result["sampler_id"], "3")
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
self.assertEqual(_inspect(source, output_node_id="9"), result)
def test_upstream_sampler_through_vaedecode_vaeencode_is_multistage(self):
for positive in (OTHER, parity.POS):
with self.subTest(positive=positive):
result = _inspect(_image_to_image(positive), output_node_id="63")
# Until PR3 compares stage texts, never one stage's prompt.
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["sampler_id"], "61")
self.assertEqual(result["reason"], MULTISTAGE)
self.assertIsNone(result["reason_code"])
self.assertIsNone(result["positive"])
self.assertIsNone(result["negative"])
# An upstream sampler reached through a non-latent input is a stage too.
source = _latent_from(
{
"class_type": "InpaintModelConditioning",
"inputs": {
"positive": ["6", 0],
"negative": ["7", 0],
"vae": ["4", 2],
"pixels": ["71", 0],
"mask": ["50", 1],
},
},
{
"50": {"class_type": "LoadImage", "inputs": {"image": "a.png"}},
"70": {
"class_type": "KSampler",
"inputs": {
"model": ["4", 0],
"positive": ["6", 0],
"negative": ["7", 0],
"latent_image": ["72", 0],
},
},
"71": {
"class_type": "VAEDecode",
"inputs": {"samples": ["70", 0], "vae": ["4", 2]},
},
"72": {"class_type": "EmptyLatentImage", "inputs": {}},
},
)
source["3"]["inputs"]["latent_image"] = ["5", 2]
result = _inspect(source, output_node_id="9")
self.assertEqual(
(result["state"], result["reason"]), ("unresolved", MULTISTAGE)
)
# A latent that reaches its own sampler is malformed, not a stage.
source = _latent_from(
{"class_type": "SetLatentNoiseMask", "inputs": {"samples": ["3", 0]}}
)
result = _inspect(source, output_node_id="9")
self.assertEqual(
(result["state"], result["reason_code"]), ("unresolved", "malformed")
)
self.assertEqual(result["reason"], "Cyclic latent ancestry")
source["3"]["inputs"]["latent_image"] = None
self.assertEqual(_inspect(source)["reason_code"], "malformed")
def test_unlisted_pipe_and_custom_samplers_in_the_ancestry_are_unsupported(self):
# Review I-1: stages whose inputs are pipes, embeds or sampler parts
# (easy-use, Impact pipes, video and custom samplers) must never be
# walked through to a silent single-stage pick of the final prompt.
easy = _latent_from(None)
easy["3"]["inputs"]["positive"] = ["62", 0]
easy.update(
{
"62": parity._enc(OTHER),
"20": {
"class_type": "easy fullLoader",
"inputs": {"positive": "first pass", "negative": "bad"},
},
"21": {"class_type": "easy preSampling", "inputs": {"pipe": ["20", 0]}},
"22": {"class_type": "easy kSampler", "inputs": {"pipe": ["21", 0]}},
"23": {
"class_type": "VAEEncode",
"inputs": {"pixels": ["22", 1], "vae": ["4", 2]},
},
}
)
easy["3"]["inputs"]["latent_image"] = ["23", 0]
pipes = _latent_from(None)
pipes.update(
{
"50": {"class_type": "LoadImage", "inputs": {"image": "a.png"}},
"20": {
"class_type": "easy fullLoader",
"inputs": {"positive": "segs prompt", "negative": "n"},
},
"24": {
"class_type": "easy pipeToBasicPipe",
"inputs": {"pipe": ["20", 0]},
},
"26": {
"class_type": "BasicPipeToDetailerPipe",
"inputs": {"basic_pipe": ["24", 0]},
},
"25": {
"class_type": "FaceDetailerPipe",
"inputs": {"image": ["50", 0], "detailer_pipe": ["26", 0]},
},
"23": {
"class_type": "VAEEncode",
"inputs": {"pixels": ["25", 0], "vae": ["4", 2]},
},
}
)
pipes["3"]["inputs"]["latent_image"] = ["23", 0]
cases = {"easy": (easy, "22", "easy kSampler")}
# Breadth-first, the first unlisted pipe consumer met is named.
cases["pipes"] = (pipes, "25", "FaceDetailerPipe")
for label, embeds in (
("text_embeds", "WanVideoSampler"),
("hyvid_embeds", "HyVideoSampler"),
("sigmas", "MysteryCustomSampler"),
("noise", "MysteryNoiseSampler"),
("sampler", "MysterySamplerStage"),
):
source = _latent_from(None)
source.update(
{
"30": {"class_type": "MysteryEmbeds", "inputs": {}},
"31": {"class_type": embeds, "inputs": {label: ["30", 0]}},
"32": {
"class_type": "VAEDecode",
"inputs": {"samples": ["31", 0], "vae": ["4", 2]},
},
"23": {
"class_type": "VAEEncode",
"inputs": {"pixels": ["32", 0], "vae": ["4", 2]},
},
}
)
source["3"]["inputs"]["latent_image"] = ["23", 0]
cases[label] = (source, "31", embeds)
for label, (source, node_id, class_type) in cases.items():
with self.subTest(case=label):
result = _inspect(source)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason_code"], "unsupported-node")
self.assertEqual(result["reason"], "Unsupported latent ancestry")
self.assertEqual(
result["node"], {"id": node_id, "class_type": class_type}
)
self.assertIsNone(result["positive"])
# A LoRA in a walked CLIP chain is not a stage (``model`` is not one).
source = _latent_from(
{
"class_type": "InpaintModelConditioning",
"inputs": {
"positive": ["6", 0],
"negative": ["7", 0],
"vae": ["4", 2],
"pixels": ["50", 0],
"mask": ["50", 1],
},
},
{
"50": {"class_type": "LoadImage", "inputs": {"image": "a.png"}},
"40": {
"class_type": "LoraLoader",
"inputs": {"model": ["4", 0], "clip": ["4", 1]},
},
},
)
source["6"]["inputs"]["clip"] = ["40", 1]
source["3"]["inputs"].update(
positive=["5", 0], negative=["5", 1], latent_image=["5", 2]
)
self.assertEqual(_inspect(source)["state"], "resolved")
def test_unlisted_model_driven_samplers_in_the_ancestry_are_unsupported(self):
# Re-review I-1b: a sampler linking ``model`` with conditioning or a
# latent under other names (XlabsSampler, generic video samplers).
xlabs = _latent_from(None)
xlabs["3"]["inputs"]["positive"] = ["62", 0]
xlabs.update(
{
"62": parity._enc(OTHER),
"30": {
"class_type": "CLIPTextEncodeFlux",
"inputs": {
"clip": ["4", 1],
"t5xxl": "first stage",
"clip_l": "first stage",
"guidance": 3.5,
},
},
"31": parity._enc("neg"),
"22": {
"class_type": "XlabsSampler",
"inputs": {
"model": ["4", 0],
"conditioning": ["30", 0],
"neg_conditioning": ["31", 0],
"noise_seed": 1,
},
},
"28": {
"class_type": "VAEDecode",
"inputs": {"samples": ["22", 0], "vae": ["4", 2]},
},
"23": {
"class_type": "VAEEncode",
"inputs": {"pixels": ["28", 0], "vae": ["4", 2]},
},
}
)
xlabs["3"]["inputs"]["latent_image"] = ["23", 0]
video = _latent_from(None)
video.update(
{
"40": {"class_type": "EmptyLatentImage", "inputs": {}},
"22": {
"class_type": "SomeVideoSampler",
"inputs": {"model": ["4", 0], "samples": ["40", 0], "seed": 1},
},
"23": {
"class_type": "LatentUpscale",
"inputs": {"samples": ["22", 0], "width": 768, "height": 768},
},
}
)
video["3"]["inputs"]["latent_image"] = ["23", 0]
reference = _latent_from(None)
reference.update(
{
"22": {
"class_type": "MysteryLatentStage",
"inputs": {"model": ["4", 0], "latent": ["5", 0]},
},
}
)
reference["3"]["inputs"]["latent_image"] = ["22", 0]
cases = {
"XlabsSampler": (xlabs, "22"),
"SomeVideoSampler": (video, "22"),
"MysteryLatentStage": (reference, "22"),
}
for class_type, (source, node_id) in cases.items():
with self.subTest(case=class_type):
result = _inspect(source)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason_code"], "unsupported-node")
self.assertEqual(
result["node"], {"id": node_id, "class_type": class_type}
)
self.assertIsNone(result["positive"])
# ``model`` alone (ModelSampling*) or with ``clip`` (LoraLoader) is a
# model patch, never a stage.
patched = _latent_from(
{
"class_type": "MysteryLatentFromModel",
"inputs": {"model": ["41", 0], "vae": ["4", 2]},
},
{
"40": {
"class_type": "LoraLoader",
"inputs": {"model": ["4", 0], "clip": ["4", 1]},
},
"41": {
"class_type": "ModelSamplingFlux",
"inputs": {"model": ["40", 0], "max_shift": 1.15},
},
},
)
self.assertEqual(_inspect(patched)["state"], "resolved")
def test_unlisted_stage_in_a_detailer_image_ancestry_is_unsupported(self):
# Review M-1: the detailer's upstream-stage walk uses the same rule.
source = _detailed(same_prompt=True)
source.update(
{
"20": {"class_type": "easy fullLoader", "inputs": {"positive": "x"}},
"22": {"class_type": "easy kSampler", "inputs": {"pipe": ["20", 0]}},
}
)
source["50"]["inputs"]["image"] = ["22", 1]
result = _inspect(source, output_node_id="9")
self.assertEqual(result["reason_code"], "unsupported-node")
self.assertEqual(result["node"], {"id": "22", "class_type": "easy kSampler"})
def test_unclassified_sampler_shaped_latent_node_is_unsupported_and_named(self):
# An unlisted node with sampler inputs may itself be an earlier stage:
# never call the chain single-stage across it.
source = _latent_from(
{
"class_type": "MysteryLatentSampler",
"inputs": {
"model": ["4", 0],
"positive": ["6", 0],
"latent_image": ["60", 0],
},
},
{"60": {"class_type": "EmptyLatentImage", "inputs": {}}},
)
result = _inspect(source)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason_code"], "unsupported-node")
self.assertEqual(result["reason"], "Unsupported latent ancestry")
self.assertEqual(
result["node"], {"id": "5", "class_type": "MysteryLatentSampler"}
)
self.assertIsNone(result["positive"])
# The rule holds at any depth (review M-3): behind a VAEEncode.
source = _latent_from(
{
"class_type": "VAEEncode",
"inputs": {"pixels": ["60", 0], "vae": ["4", 2]},
},
{
"50": {"class_type": "LoadImage", "inputs": {"image": "a.png"}},
"60": {
# Task 2.7b lists UltimateSDUpscale; an unlisted upscaler
# of the same shape keeps this rule's coverage.
"class_type": "MysteryUpscaler",
"inputs": {
"image": ["50", 0],
"model": ["4", 0],
"positive": ["6", 0],
"negative": ["7", 0],
},
},
},
)
result = _inspect(source, output_node_id="9")
self.assertEqual(result["reason_code"], "unsupported-node")
self.assertEqual(result["node"], {"id": "60", "class_type": "MysteryUpscaler"})
# The listed stage is now an earlier sampler stage in the ancestry.
source["60"]["class_type"] = "UltimateSDUpscale"
result = _inspect(source, output_node_id="9")
self.assertEqual(result["reason"], resolver.MULTISTAGE_UNATTRIBUTED_REASON)
def test_latent_walk_budget_exhaustion_is_work_limit(self):
# 2,100 distinct images in the latent ancestry: more than one
# candidate's 2,000 visits, and never reported as single stage.
extra = {
f"i{index}": {"class_type": "LoadImage", "inputs": {"image": f"{index}"}}
for index in range(2_100)
}
extra["50"] = {
"class_type": "ImageBatchMulti",
"inputs": {f"image_{index}": [f"i{index}", 0] for index in range(2_100)},
}
source = _latent_from(
{
"class_type": "VAEEncode",
"inputs": {"pixels": ["50", 0], "vae": ["4", 2]},
},
extra,
)
result = _inspect(source)
self.assertEqual(result["state"], "unresolved")
self.assertEqual(result["reason_code"], "work-limit")
self.assertIsNone(result["positive"])
self.assertEqual(
_inspect(parity.build("f10")["prompt"], max_visits=2)["reason_code"],
"work-limit",
)
# A chain longer than the depth limit is ``depth-limit``.
source = _latent_from({"class_type": "EmptyLatentImage", "inputs": {}})
previous = "5"
for index in range(70):
source[f"u{index}"] = {
"class_type": "LatentUpscaleBy",
"inputs": {"samples": [previous, 0], "scale_by": 1.0},
}
previous = f"u{index}"
source["3"]["inputs"]["latent_image"] = [previous, 0]
self.assertEqual(_inspect(source)["reason_code"], "depth-limit")
source["3"]["inputs"]["latent_image"] = ["u10", 0]
self.assertEqual(_inspect(source)["state"], "resolved")
class _CountingDeque(collections.deque):
"""Counts every item queued by the resolver's breadth-first walks."""
queued = 0
def append(self, item):
_CountingDeque.queued += 1
super().append(item)
def extend(self, items):
for item in items:
self.append(item)
def _dense_layers(
prefix: str, bottom: list, layers: int = 4, width: int = 40, fan: int = None
):
"""A hub over ``layers`` x ``width`` nodes; each links to ``fan`` (default:
all) nodes of the next layer, so every node is shared by many consumers."""
nodes = {}
fan = fan or width
for layer in range(layers):
for index in range(width):
below = (
[
[f"{prefix}{layer + 1}_{(index + step) % width}", 0]
for step in range(fan)
]
if layer + 1 < layers
else [bottom]
)
nodes[f"{prefix}{layer}_{index}"] = {
"class_type": "DenseBlend",
"inputs": {f"in_{k}": link for k, link in enumerate(below)},
}
# One hub reads the whole first layer.
nodes[f"{prefix}top"] = {
"class_type": "DenseBlend",
"inputs": {f"in_{k}": [f"{prefix}0_{k}", 0] for k in range(width)},
}
return nodes, [f"{prefix}top", 0], layers * width + 1
class TestBreadthFirstWalkWork(unittest.TestCase):
"""Dense shared fan-in: each node is queued at most once per walk."""
def counted(self, source: dict, **options) -> tuple:
_CountingDeque.queued = 0
with mock.patch.object(resolver, "deque", _CountingDeque):
result = _inspect(source, **options)
return result, _CountingDeque.queued
def test_dense_latent_ancestry_queues_each_node_once(self):
source = _latent_from({"class_type": "EmptyLatentImage", "inputs": {}})
dense, top, count = _dense_layers("L", ["5", 0])
source.update(dense)
source["3"]["inputs"]["latent_image"] = top
result, queued = self.counted(source)
self.assertEqual(result["state"], "resolved")
self.assertEqual(_sides(result), (parity.POS, parity.NEG))
# The dense layers plus the empty latent, each queued once.
self.assertLessEqual(queued, count + 1)
def test_dense_detailer_image_ancestry_queues_each_node_once(self):
source = _detailed(same_prompt=True)
expected = _inspect(source, output_node_id="9")
dense, top, count = _dense_layers("D", source["50"]["inputs"]["image"])
source.update(dense)
source["50"]["inputs"]["image"] = top
result, queued = self.counted(source, output_node_id="9")
self.assertEqual(_outcomes(result), _outcomes(expected))
# Dense layers + the decoder + its sampler, plus the latent walk of
# that sampler (one EmptyLatentImage).
self.assertLessEqual(queued, count + 3)
def test_dense_fan_in_beyond_the_budget_is_work_limit(self):
source = _latent_from({"class_type": "EmptyLatentImage", "inputs": {}})
dense, top, count = _dense_layers("W", ["5", 0], layers=3, width=800, fan=40)
source.update(dense)
source["3"]["inputs"]["latent_image"] = top
result, queued = self.counted(source)
self.assertEqual(result["reason_code"], "work-limit")
self.assertLessEqual(queued, count + 1)
def _wan_selector_graph(width: int, selectors: int, latent_origin: bool) -> dict:
"""Review I-3: WanImageToVideo over ``width``-input joins and LoRAs, with
``selectors`` diverging runtime selectors on the positive side."""
source = _latent_from(None)
source["50"] = {"class_type": "LoadImage", "inputs": {"image": "a.png"}}
clip = ["4", 1]
for index in range(5):
model = ["4", 0] if index == 0 else [f"lo{index - 1}", 0]
source[f"lo{index}"] = {
"class_type": "LoraLoader",
"inputs": {"model": model, "clip": clip, "lora_name": f"l{index}"},
}
clip = [f"lo{index}", 1]
ends = []
for prefix, text in (("P", "pos"), ("N", "neg")):
parts = {}
for index in range(width):
source[f"{prefix}l{index}"] = _multiline(f"{text} {index}", False)
parts[f"string_{index + 1}"] = [f"{prefix}l{index}", 0]
source[f"{prefix}j"] = _join(width, " ", **parts)
source[f"{prefix}e"] = {
"class_type": "CLIPTextEncode",
"inputs": {"text": [f"{prefix}j", 0], "clip": clip},
}
ends.append(f"{prefix}e")
previous = ends[0]
for index in range(selectors):
source[f"alt{index}"] = _encoded(f"alternative {index}")
source[f"ie{index}"] = {
"class_type": "ImageExists",
"inputs": {"image": ["50", 0]},
}
source[f"sel{index}"] = _if_else(
[f"ie{index}", 1], [previous, 0], [f"alt{index}", 0]
)
previous = f"sel{index}"
source["5"] = {
"class_type": "WanImageToVideo",
"inputs": {
"positive": [previous, 0],
"negative": [ends[1], 0],
"vae": ["4", 2],
"start_image": ["50", 0],
"width": 832,
"height": 480,
},
}
source["3"]["inputs"].update(positive=["5", 0], negative=["5", 1])
if latent_origin:
source["3"]["inputs"]["latent_image"] = ["5", 2]
else:
source["5x"] = {"class_type": "EmptyHunyuanLatentVideo", "inputs": {}}
source["3"]["inputs"]["latent_image"] = ["5x", 0]
return source
class TestLatentWalkReuse(unittest.TestCase):
"""Review I-3: one latent walk per sampler per resolution, reuse charged."""
def counted(self, source: dict) -> tuple:
walks, visits = [], []
walk, spend = resolver._GraphResolver.latent_stages, resolver._Budget.spend
def counted_walk(self, *args):
walks.append(args[0])
return walk(self, *args)
def counted_spend(self):
visits.append(1)
return spend(self)
with (
mock.patch.object(resolver._GraphResolver, "latent_stages", counted_walk),
mock.patch.object(resolver._Budget, "spend", counted_spend),
):
result = _inspect(source)
return result, len(walks), len(visits)
def test_selector_runs_reuse_one_latent_walk(self):
result, walks, visits = self.counted(_wan_selector_graph(100, 3, True))
self.assertEqual(result["state"], "ambiguous")
self.assertEqual(len(result["candidates"]), 8)
self.assertEqual(walks, 1)
separate, _, baseline = self.counted(_wan_selector_graph(100, 3, False))
self.assertEqual(_outcomes(separate).keys(), _outcomes(result).keys())
# One walk over the conditioning subgraph on top of the text tracing.
self.assertLess(visits, baseline + 300)
def test_reused_walk_cost_is_charged_to_each_candidate(self):
# Two samplers share one latent origin: the second candidate is charged
# the walk's cost, so its outcome does not depend on tracing order.
source = _latent_from(
{
"class_type": "VAEEncode",
"inputs": {"pixels": ["50", 0], "vae": ["4", 2]},
},
{"50": {"class_type": "LoadImage", "inputs": {"image": "a.png"}}},
)
source["41"] = {"class_type": "KSampler", "inputs": dict(source["3"]["inputs"])}
source["42"] = {
"class_type": "VAEDecode",
"inputs": {"samples": ["41", 0], "vae": ["4", 2]},
}
source["43"] = {
"class_type": "SaveImage",
"inputs": {"filename_prefix": "b", "images": ["42", 0]},
}
# Both candidates share the latent walk's cached cost.
full = _inspect(source)
self.assertEqual(
[item["state"] for item in full["candidates"]], ["resolved", "resolved"]
)
for budget in range(1, 12):
with self.subTest(max_visits=budget):
both = _inspect(source, max_visits=budget)
states = [
(item["state"], item["reason_code"]) for item in both["candidates"]
]
alone = [
_inspect(source, max_visits=budget, output_node_id=anchor)
for anchor in ("9", "43")
]
self.assertEqual(
states, [(item["state"], item["reason_code"]) for item in alone]
)
REPO = Path(__file__).resolve().parents[1]
SEMANTICS_UNAVAILABLE = "Inspection node semantics are unavailable"
class TestSemanticsIsolation(unittest.TestCase):
"""Only inspection reads the semantics table; a bad table never breaks ingestion."""
def resolve_with_table(self, text: str, *values):
# Windows: the table is written and closed before it is read or removed.
with tempfile.TemporaryDirectory() as root:
path = Path(root) / "semantics.json"
path.write_text(text, encoding="utf-8")
inspection_semantics.get_semantics.cache_clear()
try:
with (
mock.patch.object(inspection_semantics, "SEMANTICS_PATH", path),
mock.patch.object(resolver, "_LOGGER") as logger,
):
self.logged = logger.error.call_args_list
return [
resolve_prompt_pair(value, profile=profile)
for value in values
for profile in (
resolver.DEFAULT_PROFILE,
resolver.INSPECTION_PROFILE,
)
]
finally:
inspection_semantics.get_semantics.cache_clear()
def test_stage_shaped_inputs_come_from_the_table(self):
# Review I-2: the stage-walk input lists live in the semantics table, so
# a key added there changes the inspection walk and never the default one.
source = _latent_from(None)
source.update(
{
"30": {"class_type": "MysteryEmbeds", "inputs": {}},
"31": {"class_type": "MysteryStage", "inputs": {"stage_x": ["30", 0]}},
"32": {
"class_type": "VAEDecode",
"inputs": {"samples": ["31", 0], "vae": ["4", 2]},
},
"23": {
"class_type": "VAEEncode",
"inputs": {"pixels": ["32", 0], "vae": ["4", 2]},
},
}
)
source["3"]["inputs"]["latent_image"] = ["23", 0]
table = json.loads(
(REPO / "web" / "js" / "inspection-semantics.json").read_text("utf-8")
)
plain_default, plain = self.resolve_with_table(json.dumps(table), source)
table["stage_inputs"]["shaped"].append("stage_x")
default, inspected = self.resolve_with_table(json.dumps(table), source)
self.assertEqual(plain["state"], "resolved")
self.assertEqual(inspected["state"], "unresolved")
self.assertEqual(inspected["reason_code"], "unsupported-node")
self.assertEqual(inspected["node"], {"id": "31", "class_type": "MysteryStage"})
self.assertEqual(default, plain_default)
def test_corrupt_table_leaves_default_resolution_working(self):
for text in ("{not json", "[" * 100_000, "{}"):
with self.subTest(text=text[:10]):
default, inspected = self.resolve_with_table(text, graph())
self.assertEqual(default["state"], "resolved")
self.assertEqual(default["positive"]["text"], "portrait")
self.assertEqual(inspected["state"], "unresolved")
self.assertEqual(inspected["reason"], SEMANTICS_UNAVAILABLE)
self.assertIsNone(inspected["reason_code"])
self.assertIsNone(inspected["positive"])
self.assertEqual(len(self.logged), 1)
def test_corrupt_table_only_affects_graph_inspection(self):
parameters = {"parameters": "cat\nNegative prompt: dog\nSteps: 1, Sampler: x"}
results = self.resolve_with_table("{not json", parameters, {})
self.assertEqual([item["state"] for item in results[:2]], ["resolved"] * 2)
self.assertEqual([item["state"] for item in results[2:]], ["absent"] * 2)
self.assertEqual(self.logged, [])
def test_corrupt_table_is_a_clear_inspector_outcome(self):
from utils.prompt_inspector import inspect_prompt_evidence
with tempfile.TemporaryDirectory() as root:
path = Path(root) / "semantics.json"
path.write_text("{not json", encoding="utf-8")
inspection_semantics.get_semantics.cache_clear()
try:
with (
mock.patch.object(inspection_semantics, "SEMANTICS_PATH", path),
self.assertLogs("prompt_manager.prompt_pair_resolver", "ERROR"),
):
evidence = inspect_prompt_evidence({"prompt": graph()})
finally:
inspection_semantics.get_semantics.cache_clear()
self.assertEqual(evidence["resolution"]["state"], "unresolved")
self.assertEqual(evidence["resolution"]["reason"], SEMANTICS_UNAVAILABLE)
self.assertEqual(
[evidence["prompts"][role]["status"] for role in ("positive", "negative")],
["unresolved", "unresolved"],
)
def test_default_resolution_never_imports_the_semantics_table(self):
code = (
"import sys\n"
"from utils.prompt_pair_resolver import resolve_prompt_pair\n"
"graph = {'1': {'class_type': 'CLIPTextEncode', 'inputs': {'text': 'a'}},"
" '3': {'class_type': 'KSampler',"
" 'inputs': {'positive': ['1', 0], 'negative': ['1', 0]}}}\n"
"assert resolve_prompt_pair(graph)['state'] == 'resolved'\n"
"assert 'utils.inspection_semantics' not in sys.modules\n"
)
# Fixed interpreter and literal code, no shell.
completed = subprocess.run( # nosec B603
[sys.executable, "-c", code],
cwd=REPO,
capture_output=True,
timeout=60,
check=False,
)
self.assertEqual(completed.returncode, 0, completed.stderr.decode("utf-8"))
if __name__ == "__main__":
unittest.main()
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