Vito
caa9649f32
* feat(db): add pair schema and stage restore migrations for #193 * feat(database): persist reusable ordered prompt pairs (#193) * feat(api): expose reusable pairs and atomic metadata enrichment (#193) * feat(metadata): resolve canonical prompt pairs with bounded provenance (#193) * feat(scan): enrich image pairs without guessing or duplicate links (#193) * fix(database): preserve pair tags during global maintenance (#193) * feat(tracking): enrich live image pairs and cancel scans safely (#193) * feat(nodes): encode paired prompts and count cached recipe runs (#193) * fix(database): validate restored pair ownership and provenance (#193) * feat(admin): report pair scan outcomes and cancellation (#193) * feat(ui): add paired prompt editor and explicit recipe library for #193 * fix(autocomplete): include reusable pair metadata vocabulary (#193) * fix(pairs): honor recent sorting and conservative scan outcomes (#193) * fix(ui): protect paired draft saves and clarify negative variants (#193) * fix(metadata): preserve bounded paired execution evidence (#193) * fix(tracking): follow actual pair output roles and prefer execution evidence (#193) * docs(pairs): document live validation and retain renderer screenshots (#193) * test(api): inject disposable databases before API startup (#194) * test(api): canonicalize portable temporary gallery roots (#194) * docs(ui): prototype branching negative variant history (#193) * docs(ui): show selected negative beneath positive in timeline concept (#193) * docs: retain approved timeline for later gallery redesign (#193) * test(db): close restore fixture connections before cleanup (#194) * test(db): reuse existing handle guards in all pair fixtures (#194) * chore: clarify rollback test and intentional exception handling (#193)
428 lines
17 KiB
Python
428 lines
17 KiB
Python
"""Conservative prompt-pair resolution contracts, independent of ComfyUI."""
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import unittest
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from utils.prompt_pair_resolver import OutputAdapter, resolve_prompt_pair
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def graph(positive: str = "portrait", negative: str = "blurry") -> dict:
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return {
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"1": {"class_type": "CLIPTextEncode", "inputs": {"text": positive}},
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"2": {"class_type": "CLIPTextEncode", "inputs": {"text": negative}},
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"3": {
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"class_type": "KSampler",
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"inputs": {"positive": ["1", 0], "negative": ["2", 0]},
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},
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}
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class TestPromptPairResolver(unittest.TestCase):
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def test_resolves_same_sampler_and_preserves_output_provenance(self):
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result = resolve_prompt_pair({"prompt": graph()})
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self.assertEqual(result["state"], "resolved")
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self.assertEqual(result["positive"]["text"], "portrait")
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self.assertEqual(result["negative"]["text"], "blurry")
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self.assertEqual(result["positive"]["provenance"]["node_id"], "1")
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self.assertEqual(result["positive"]["provenance"]["output_slot"], 0)
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self.assertEqual(result["sampler_id"], "3")
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def test_explicit_empty_negative_is_resolved_but_missing_is_absent(self):
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self.assertEqual(resolve_prompt_pair(graph(negative=""))["state"], "resolved")
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self.assertEqual(
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resolve_prompt_pair(graph(negative=""))["negative"]["text"], ""
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)
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source = graph()
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del source["3"]["inputs"]["negative"]
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result = resolve_prompt_pair(source)
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self.assertEqual(result["state"], "absent")
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self.assertEqual(result["positive"]["text"], "portrait")
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self.assertIsNone(result["negative"])
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def test_multiple_samplers_are_ambiguous_even_when_texts_match(self):
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source = graph()
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source["4"] = {
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"class_type": "KSamplerAdvanced",
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"inputs": source["3"]["inputs"],
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}
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result = resolve_prompt_pair(source)
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self.assertEqual(result["state"], "ambiguous")
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self.assertEqual(len(result["candidates"]), 2)
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self.assertIsNone(result["positive"])
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def test_saver_anchor_selects_only_its_sampler(self):
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source = graph()
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source.update(
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{
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"4": {
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"class_type": "KSampler",
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"inputs": {"positive": ["2", 0], "negative": ["1", 0]},
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},
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"5": {"class_type": "VAEDecode", "inputs": {"samples": ["4", 0]}},
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"6": {"class_type": "SaveImage", "inputs": {"images": ["5", 0]}},
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}
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)
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result = resolve_prompt_pair(source, output_node_id="6")
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self.assertEqual(result["state"], "resolved")
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self.assertEqual(result["sampler_id"], "4")
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self.assertEqual(result["positive"]["text"], "blurry")
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def test_unknown_anchor_transform_and_multistage_are_unresolved(self):
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source = graph()
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source["5"] = {
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"class_type": "MysteryImageTransform",
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"inputs": {"images": ["3", 0]},
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}
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self.assertEqual(
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resolve_prompt_pair(source, output_node_id="5")["state"], "unresolved"
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)
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source["4"] = {
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"class_type": "KSampler",
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"inputs": {**source["3"]["inputs"], "latent_image": ["3", 0]},
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}
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self.assertEqual(
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resolve_prompt_pair(source, output_node_id="4")["state"], "unresolved"
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)
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def test_combines_unknown_encoders_and_sdxl_channels_are_not_guessed(self):
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for name in ("ConditioningCombine", "CLIPTextEncodeSDXL", "UnknownEncoder"):
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with self.subTest(name=name):
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source = graph()
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source["1"] = {
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"class_type": name,
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"inputs": {
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"text": "guess",
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"text_g": "global",
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"text_l": "local",
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"conditioning_1": ["2", 0],
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},
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}
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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def test_prompt_manager_composition_and_string_slot_are_resolved(self):
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source = graph()
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source["8"] = {
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"class_type": "PromptManagerSplit",
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"inputs": {
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"text": "portrait",
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"prepend_text": " trigger ",
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"append_text": " detail ",
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},
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}
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source["1"]["inputs"]["text"] = ["8", 1]
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result = resolve_prompt_pair(source)
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self.assertEqual(result["positive"]["text"], "trigger portrait detail")
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self.assertIn(
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{"node_id": "8", "output_slot": 1}, result["positive"]["provenance"]["path"]
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)
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source["3"]["inputs"]["positive"] = ["8", 0]
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self.assertEqual(
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resolve_prompt_pair(source)["positive"]["text"], "trigger portrait detail"
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)
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source["1"]["inputs"]["text"] = ["8", 0]
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source["3"]["inputs"]["positive"] = ["1", 0]
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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def test_concat_and_reroute_resolve_safe_linked_strings(self):
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source = graph()
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source["8"] = {"class_type": "PrimitiveString", "inputs": {"value": "portrait"}}
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source["9"] = {
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"class_type": "StringConcatenate",
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"inputs": {"string_a": ["8", 0], "string_b": ["8", 0], "delimiter": ", "},
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}
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source["10"] = {"class_type": "Reroute", "inputs": {"value": ["9", 0]}}
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source["1"]["inputs"]["text"] = ["10", 0]
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self.assertEqual(
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resolve_prompt_pair(source)["positive"]["text"], "portrait, portrait"
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)
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def test_slot_adapter_does_not_conflate_two_outputs_of_one_node(self):
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source = graph()
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source["8"] = {
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"class_type": "FuturePair",
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"inputs": {"first": "positive", "second": "negative"},
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}
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source["3"]["inputs"] = {"positive": ["8", 0], "negative": ["8", 1]}
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adapters = {
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("FuturePair", 0): OutputAdapter("first", "conditioning"),
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("FuturePair", 1): OutputAdapter("second", "conditioning"),
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}
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result = resolve_prompt_pair(source, adapters=adapters)
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self.assertEqual(result["positive"]["text"], "positive")
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self.assertEqual(result["negative"]["text"], "negative")
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self.assertNotEqual(
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result["positive"]["provenance"]["output_slot"],
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result["negative"]["provenance"]["output_slot"],
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)
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def test_prompt_manager_text_has_only_its_real_string_output_zero(self):
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source = graph()
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source["8"] = {
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"class_type": "PromptManagerText",
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"inputs": {"text": "text node", "prepend_text": "before"},
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}
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source["1"]["inputs"]["text"] = ["8", 0]
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self.assertEqual(
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resolve_prompt_pair(source)["positive"]["text"], "before text node"
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)
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source["1"]["inputs"]["text"] = ["8", 1]
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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source["3"]["inputs"]["positive"] = ["8", 0]
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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def test_malformed_class_names_and_unknown_samplers_do_not_claim_absence(self):
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for name in ([], {}, None, "UnknownSampler"):
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source = graph()
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source["3"]["class_type"] = name
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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def test_pair_adapter_composes_only_positive_and_retains_recipe_text(self):
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source = graph()
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source["8"] = {
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"class_type": "PromptManagerPair",
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"inputs": {
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"text": "positive",
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"negative_text": "negative",
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"prepend_text": "trigger",
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"append_text": "detail",
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},
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}
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source["3"]["inputs"] = {"positive": ["8", 0], "negative": ["8", 1]}
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result = resolve_prompt_pair(source)
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self.assertEqual(result["positive"]["text"], "trigger positive detail")
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self.assertEqual(result["positive"]["storage_text"], "positive")
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self.assertEqual(result["negative"]["text"], "negative")
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self.assertEqual(result["negative"]["storage_text"], "negative")
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source["1"]["inputs"]["text"] = ["8", 2]
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source["2"]["inputs"]["text"] = ["8", 3]
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source["3"]["inputs"] = {"positive": ["1", 0], "negative": ["2", 0]}
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result = resolve_prompt_pair(source)
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self.assertEqual(result["positive"]["storage_text"], "positive")
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self.assertEqual(result["negative"]["text"], "negative")
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source["3"]["inputs"]["negative"] = ["8", 3]
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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def test_linked_recipe_and_ordinary_clip_storage_values_are_explicit(self):
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source = graph()
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result = resolve_prompt_pair(source)
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self.assertEqual(result["positive"]["storage_text"], "portrait")
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source["8"] = {
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"class_type": "PrimitiveString",
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"inputs": {"value": "linked main"},
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}
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source["1"] = {
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"class_type": "PromptManager",
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"inputs": {"text": ["8", 0], "prepend_text": "before"},
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}
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result = resolve_prompt_pair(source)
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self.assertEqual(result["positive"]["text"], "before linked main")
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self.assertEqual(result["positive"]["storage_text"], "linked main")
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a1111 = resolve_prompt_pair(
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{
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"parameters": (
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"portrait\nNegative prompt: blurry\n" "Steps: 20, Sampler: Euler"
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)
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}
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)
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self.assertEqual(a1111["positive"]["storage_text"], "portrait")
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self.assertEqual(a1111["negative"]["storage_text"], "blurry")
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def test_cycles_depth_visits_and_text_growth_are_bounded(self):
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source = graph()
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source["8"] = {"class_type": "Reroute", "inputs": {"value": ["8", 0]}}
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source["1"]["inputs"]["text"] = ["8", 0]
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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self.assertEqual(
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resolve_prompt_pair(graph(), max_visits=1)["state"], "unresolved"
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)
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self.assertEqual(
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resolve_prompt_pair(graph(), max_depth=0)["state"], "unresolved"
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)
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self.assertEqual(
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resolve_prompt_pair(graph(), max_text_length=3)["state"], "unresolved"
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)
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def test_malformed_metadata_and_links_never_raise(self):
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for value in (
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None,
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[],
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"raw text",
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{},
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{"prompt": []},
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{"prompt": {"1": None}},
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):
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with self.subTest(value=value):
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self.assertIn(
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resolve_prompt_pair(value)["state"], ("absent", "unresolved")
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)
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for link in (["missing", 0], ["1", -1], ["1", True], ["1", 17], ["1"], 5):
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source = graph()
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source["3"]["inputs"]["positive"] = link
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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def test_a1111_explicit_parameters_and_empty_negative(self):
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for negative in ("bad anatomy\nblurry", ""):
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result = resolve_prompt_pair(
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{
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"parameters": (
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f"portrait\nNegative prompt: {negative}\n"
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"Steps: 20, Sampler: Euler, Seed: 1"
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)
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}
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)
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self.assertEqual(result["state"], "resolved")
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self.assertEqual(result["negative"]["text"], negative)
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self.assertEqual(result["positive"]["provenance"]["format"], "a1111")
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self.assertEqual(
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resolve_prompt_pair({"parameters": "portrait\nSteps: 20, Sampler: Euler"})[
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"state"
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],
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"absent",
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)
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self.assertEqual(
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resolve_prompt_pair({"parameters": "unstructured arbitrary text"})["state"],
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"unresolved",
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)
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def test_was_concat_preserves_configured_whitespace_and_rejects_bad_delimiters(
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self,
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):
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source = graph()
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source["8"] = {
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"class_type": "Text Concatenate",
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"inputs": {
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"text_a": " portrait ",
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"text_b": "",
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"text_c": "studio",
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"delimiter": ", ",
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},
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}
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source["1"]["inputs"]["text"] = ["8", 0]
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self.assertEqual(
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resolve_prompt_pair(source)["positive"]["text"], "portrait, studio"
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)
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source["8"]["inputs"]["clean_whitespace"] = "false"
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self.assertEqual(
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resolve_prompt_pair(source)["positive"]["text"], " portrait , studio"
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)
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source["8"]["inputs"]["delimiter"] = []
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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source["8"]["inputs"] = {1: "bad key"}
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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def test_valid_empty_latent_and_reroute_but_not_unknown_or_cyclic_latents(self):
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source = graph()
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source["8"] = {"class_type": "EmptyLatentImage", "inputs": {}}
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source["9"] = {"class_type": "Reroute", "inputs": {"value": ["8", 0]}}
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source["3"]["inputs"]["latent_image"] = ["9", 0]
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self.assertEqual(resolve_prompt_pair(source)["state"], "resolved")
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source["9"]["inputs"]["value"] = ["9", 0]
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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source["3"]["inputs"]["latent_image"] = ["8", 0]
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source["8"]["class_type"] = "UnknownLatentTransform"
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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source["3"]["inputs"]["latent_image"] = None
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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def test_cyclic_or_invalid_output_anchors_never_guess(self):
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source = graph()
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source["8"] = {"class_type": "Reroute", "inputs": {"value": ["8", 0]}}
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self.assertEqual(
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resolve_prompt_pair(source, output_node_id="8")["state"], "unresolved"
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)
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self.assertEqual(
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resolve_prompt_pair(source, output_node_id="missing")["state"], "unresolved"
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)
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source["8"] = {"class_type": "SaveImage", "inputs": {"images": ["3", 1]}}
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self.assertEqual(
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resolve_prompt_pair(source, output_node_id="8")["state"], "unresolved"
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)
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source["8"] = {"class_type": "SaveImage", "inputs": {"images": ["3", 0]}}
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self.assertEqual(
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resolve_prompt_pair(source, output_node_id="8", max_depth=1)["state"],
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"unresolved",
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)
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def test_malformed_reroute_empty_positive_and_duplicate_string_ids(self):
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source = graph(positive="")
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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source = graph()
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source["8"] = {
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"class_type": "Reroute",
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"inputs": {"value": ["1", 0], "input": ["2", 0]},
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}
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source["3"]["inputs"]["positive"] = ["8", 0]
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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source[3] = source["3"]
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self.assertEqual(resolve_prompt_pair(source)["state"], "unresolved")
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def test_a1111_malformed_ambiguous_and_bounded_parameters(self):
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self.assertEqual(resolve_prompt_pair({"parameters": None})["state"], "absent")
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self.assertEqual(
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resolve_prompt_pair({"parameters": "x" * 20}, max_text_length=10)["state"],
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"unresolved",
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)
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self.assertEqual(
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resolve_prompt_pair(
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{"parameters": "\nNegative prompt: bad\nSteps: 20, Sampler: Euler"}
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)["state"],
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"unresolved",
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)
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self.assertEqual(
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resolve_prompt_pair(
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{
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"parameters": (
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"portrait\nNegative prompt: bad\nNegative prompt: worse\n"
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"Steps: 20, Sampler: Euler"
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)
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}
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)["state"],
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"ambiguous",
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)
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self.assertEqual(
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resolve_prompt_pair(
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{
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"parameters": (
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"portrait\nNegative prompt: bad\n"
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"Steps: 20, Sampler: Euler\nextra text"
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)
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}
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)["state"],
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"unresolved",
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)
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def test_exponential_concat_text_is_bounded_before_building_a_large_string(self):
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source = graph()
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source["8"] = {"class_type": "PrimitiveString", "inputs": {"value": "xx"}}
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for index in range(9, 19):
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source[str(index)] = {
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"class_type": "StringConcatenate",
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"inputs": {
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"string_a": [str(index - 1), 0],
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"string_b": [str(index - 1), 0],
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},
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}
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source["1"]["inputs"]["text"] = ["18", 0]
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result = resolve_prompt_pair(source, max_text_length=100)
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self.assertEqual(result["state"], "unresolved")
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self.assertIn("length limit", result["reason"])
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self.assertEqual(
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resolve_prompt_pair(graph(), max_depth=1)["state"], "unresolved"
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)
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def test_api_graph_is_authoritative_over_unrelated_parameters(self):
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source = graph()
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source["1"]["class_type"] = "UnknownEncoder"
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result = resolve_prompt_pair(
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{
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"prompt": source,
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"parameters": (
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"fallback\nNegative prompt: wrong\n" "Steps: 20, Sampler: Euler"
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),
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}
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)
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self.assertEqual(result["state"], "unresolved")
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if __name__ == "__main__":
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unittest.main()
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