Vito
44177974cb
* fix(scan): read sampler-less graphs from the one wired text, retire the heuristic (#252) A graph where no node takes a positive or negative input fell back to the pre-redesign _extract_readable_prompt heuristic: the last node with any string text input won, and with none it read stale UI workflow widgets. It stored LoRA syntax, display-node reports, install notes, model file names and numbers. The scan now asks sampler_less_prompt: only the executed API graph counts; a candidate is a node with a text input that another node consumes; blank and numeric texts are never prompts; this repository's own positive nodes are authoritative; one distinct text links, several (or an unreadable wired text that no candidate builds) stay unlinked and try the recorded-run attribution. The heuristic and its helpers are removed; A1111 files read the parsed positive prompt directly. * fix(scan): refuse loader side outputs, seeded templates and keep A1111 text (#252) Review findings: an unreadable text link counts as built from a candidate only through that node's first output, so a LoRA loader's trigger-word output is unknown, not its text. A node with a seed input holds a template, never the prompt that ran. A sampler-less graph the rule cannot decide still links the A1111 positive a metadata saver wrote beside it, as the old scan did. Only plain numbers are numeric; words such as nan or inf are prompts. * fix(scan): linear plain-number check for sampler-less texts (#252) The numeric pattern had overlapping quantifiers that backtrack quadratically on a long digit run ending in a non-digit; it now has none.
452 lines
19 KiB
Python
452 lines
19 KiB
Python
"""Metadata scanning enriches known pairs and conservatively leaves conflicts."""
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import json
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from pathlib import Path
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from tests.test_api_admin import AdminAPITestCase
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def graph(positive="cat", negative="blur"):
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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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def sampled_graph(positive="cat", negative="blur"):
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"""A KSampler with a model link, as real workflows have."""
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g = graph(positive, negative)
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g["4"] = {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": "m"}}
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g["3"]["inputs"]["model"] = ["4", 0]
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return g
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class TestPairScan(AdminAPITestCase):
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def test_pair_scan_repeated_is_unchanged_and_empty_negative_is_supported(self):
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path = self.output_dir / "pair.png"
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meta = {"prompt": json.dumps(graph(negative=""))}
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self.assertEqual(self.api._ingest_scanned_file(path, meta), "added")
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self.assertEqual(self.api._ingest_scanned_file(path, meta), "unchanged")
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pairs = self.api.db.list_prompt_pairs()["pairs"]
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self.assertEqual(len(pairs), 1)
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self.assertEqual(pairs[0]["negative_text"], "")
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self.assertEqual(pairs[0]["run_count"], 0)
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def _scanned(self, name, prompt_graph):
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meta = {"prompt": json.dumps(prompt_graph)}
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return self.api._ingest_scanned_file(self.output_dir / name, meta)
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def _texts(self):
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return [p["text"] for p in self.api.db.get_recent_prompts()["prompts"]]
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def test_samplers_sharing_one_prompt_link_that_prompt(self):
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# #249: every branch resolves to one exact pair, so the pair is ingested.
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g = sampled_graph()
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g["5"] = {**g["3"]}
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self.assertEqual(self._scanned("multi.png", g), "added")
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self.assertEqual(sorted(self._texts()), ["blur", "cat"])
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pairs = self.api.db.list_prompt_pairs()["pairs"]
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self.assertEqual(
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[(p["positive_text"], p["negative_text"]) for p in pairs], [("cat", "blur")]
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)
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def test_samplers_with_different_prompts_link_the_first_sampler_prompt(self):
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# The prompt the inspector and the legacy panels show for this file.
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g = sampled_graph()
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g["6"] = {"class_type": "CLIPTextEncode", "inputs": {"text": "dog"}}
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g["12"] = {**g["3"], "inputs": {**g["3"]["inputs"], "positive": ["6", 0]}}
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self.assertEqual(self._scanned("different.png", g), "added")
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self.assertEqual(self._texts(), ["cat"])
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def test_an_unsupported_pass_through_still_links_the_traced_prompt(self):
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g = sampled_graph()
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g["7"] = {
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"class_type": "MyConditioningTweak",
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"inputs": {"conditioning": ["1", 0]},
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}
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g["3"]["inputs"]["positive"] = ["7", 0]
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g["5"] = {**g["3"]}
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self.assertEqual(self._scanned("tweak.png", g), "added")
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self.assertEqual(self._texts(), ["cat"])
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def test_a_graph_without_a_traceable_prompt_saves_nothing(self):
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g = sampled_graph()
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g["5"] = {**g["3"]}
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for sampler in ("3", "5"):
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g[sampler]["inputs"]["positive"] = ["9", 0]
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g["9"] = {
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"class_type": "BasicGuider",
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"inputs": {"width": 2048, "height": 2048},
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}
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# Ambiguous with nothing to choose: unresolved, never a review item.
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self.assertEqual(self._scanned("untraceable.png", g), "unresolved")
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self.assertEqual(self.api.db.list_prompt_pairs()["total"], 0)
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self.assertEqual(self._texts(), [])
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def test_numeric_widgets_are_never_stored_as_a_prompt(self):
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# The pre-redesign scan stored "2048" from a resolution widget here.
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g = {
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"1": {"class_type": "CLIPTextEncode", "inputs": {"text": ["8", 0]}},
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"8": {"class_type": "DisplayText", "inputs": {"text_size": 2048}},
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"3": {
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"class_type": "SamplerCustomAdvanced",
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"inputs": {"guider": ["4", 0], "sigmas": ["5", 0]},
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},
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"4": {"class_type": "BasicGuider", "inputs": {"conditioning": ["1", 0]}},
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}
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self.assertIn(
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self._scanned("numbers.png", g), ("ambiguous", "unresolved", "found")
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)
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self.assertEqual(self._texts(), [])
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def test_batch_exposes_pair_counts_and_actual_unchanged_status(self):
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meta = {"parameters": "cat\nNegative prompt: blur\nSteps: 20, Sampler: Euler"}
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batch = [(Path(self.output_dir / "a.png"), meta)]
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first = self.api._ingest_scan_batch_sync(batch)
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self.assertEqual(first["pairs_added"], 1)
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self.assertEqual(first["pair_links_added"], 1)
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second = self.api._ingest_scan_batch_sync(batch)
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self.assertEqual(second["unchanged"], 1)
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self.assertEqual(second["linked"], 0)
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self.assertEqual(second["pairs_added"], 0)
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def test_legacy_single_prompt_second_pass_not_reported_as_new_link(self):
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# A sampler-less graph: one encoder wired into a guider (#252).
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single = {
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"1": graph()["1"],
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"2": {"class_type": "BasicGuider", "inputs": {"conditioning": ["1", 0]}},
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}
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meta = {"prompt": json.dumps(single)}
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path = self.output_dir / "old.png"
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self.assertEqual(self.api._ingest_scanned_file(path, meta), "added")
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self.assertEqual(self.api._ingest_scanned_file(path, meta), "unchanged")
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async def test_cancel_finishes_at_safe_boundary_and_restart_is_idempotent(self):
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from py.api.jobs import Job
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job = Job("scan", "Scan")
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self.api._scan_job = job
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response = await self.client.post("/prompt_manager/scan/cancel")
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self.assertEqual(response.status, 200)
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self.assertTrue((await response.json())["cancellation_requested"])
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counts = self.api._ingest_scan_batch_sync(
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[(self.output_dir / "cancel.png", {"prompt": json.dumps(graph())})]
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)
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self.assertEqual(counts["processed"], 0)
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self.assertEqual(self.api.db.list_prompt_pairs()["total"], 0)
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job.finish()
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self.api._scan_job = Job("scan", "Scan")
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first = self.api._ingest_scan_batch_sync(
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[(self.output_dir / "cancel.png", {"prompt": json.dumps(graph())})]
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)
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self.assertEqual(first["pairs_added"], 1)
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self.assertEqual(
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self.api._ingest_scan_batch_sync(
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[(self.output_dir / "cancel.png", {"prompt": json.dumps(graph())})]
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)["unchanged"],
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1,
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)
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def test_legacy_found_outcome_is_not_counted_twice(self):
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result = self.api._ingest_scan_batch_sync(
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[(self.output_dir / "blank.png", {"parameters": "Negative prompt: blur"})]
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)
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self.assertEqual(result["processed"], 1)
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self.assertEqual(result["found"], 1)
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def test_ambiguous_a1111_does_not_fall_back_to_legacy_guess(self):
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meta = {
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"parameters": (
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"cat\nNegative prompt: blur\nNegative prompt: noise"
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"\nSteps: 20, Sampler: Euler"
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)
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}
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# No candidate to choose: reported unresolved, still not guessed.
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self.assertEqual(
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self.api._ingest_scanned_file(self.output_dir / "ambiguous.png", meta),
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"unresolved",
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)
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self.assertEqual(self.api.db.get_recent_prompts()["prompts"], [])
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def _nan_scanned(self, name, prompt_graph):
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"""Scan ``prompt_graph`` as ComfyUI writes it: ``is_changed`` is NaN."""
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stamped = {
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node_id: {**node, "is_changed": [float("nan")]}
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for node_id, node in prompt_graph.items()
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}
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text = json.dumps(stamped)
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self.assertIn("NaN", text)
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return self._scanned_text(name, text)
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def _scanned_text(self, name, prompt_text):
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meta = {"prompt": prompt_text}
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return self.api._ingest_scanned_file(self.output_dir / name, meta)
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@staticmethod
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def _hires_fix_graph(positive):
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"""A PromptManager prompt on a base sampler and a hires-fix sampler.
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The base latent comes from a custom batch node and the hires pass from
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a custom image filter: no sampler branch traces, so the scan links the
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prompt the legacy pages show.
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"""
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return {
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"1": {
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"class_type": "CheckpointLoaderSimple",
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"inputs": {"ckpt_name": "m.safetensors"},
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},
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"2": {
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"class_type": "MyTriggerWords",
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"inputs": {"trigger_words": ["1", 2]},
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},
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"3": {
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"class_type": "PromptManager",
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"inputs": {
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"text": positive,
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"prepend_text": ["2", 0],
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"append_text": "",
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"clip": ["1", 1],
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},
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},
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"4": {
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"class_type": "CLIPTextEncode",
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"inputs": {"text": "blur", "clip": ["1", 1]},
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},
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"5": {"class_type": "MyLatentBatch", "inputs": {"batch_size": 6}},
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"6": {
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"class_type": "KSampler",
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"inputs": {
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"seed": 7,
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"model": ["1", 0],
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"positive": ["3", 0],
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"negative": ["4", 0],
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"latent_image": ["5", 0],
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},
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},
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"7": {
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"class_type": "VAEDecode",
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"inputs": {"samples": ["6", 0], "vae": ["1", 2]},
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},
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"8": {"class_type": "MyImageFilter", "inputs": {"images": ["7", 0]}},
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"9": {
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"class_type": "VAEEncode",
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"inputs": {"pixels": ["8", 0], "vae": ["1", 2]},
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},
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"14": {
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"class_type": "KSampler",
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"inputs": {
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"seed": 7,
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"denoise": 0.25,
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"model": ["1", 0],
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"positive": ["3", 0],
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"negative": ["4", 0],
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"latent_image": ["9", 0],
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},
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},
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"15": {
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"class_type": "VAEDecode",
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"inputs": {"samples": ["14", 0], "vae": ["1", 2]},
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},
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"16": {"class_type": "SaveImage", "inputs": {"images": ["15", 0]}},
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}
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def test_hires_fix_prompt_links_with_or_without_nan_is_changed(self):
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# Legacy parity: the legacy pages read NaN as null before parsing, so they
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# showed this prompt; the scan left NaN-stamped files in review.
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for name, scan in (("plain.png", None), ("stamped.png", "nan")):
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with self.subTest(name=name):
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g = self._hires_fix_graph("cat on a sofa")
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if scan:
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status = self._nan_scanned(name, g)
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else:
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status = self._scanned(name, g)
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self.assertIn(status, ("added", "linked"))
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self.assertEqual(self._texts(), ["cat on a sofa"])
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def test_untraced_single_sampler_links_the_legacy_prompt_with_nan(self):
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g = self._hires_fix_graph("cat on a sofa")
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for node_id in ("7", "8", "9", "14", "15"):
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del g[node_id]
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g["16"]["inputs"]["images"] = ["6", 0]
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self.assertEqual(self._nan_scanned("single.png", g), "added")
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self.assertEqual(self._texts(), ["cat on a sofa"])
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def test_nan_inside_prompt_text_is_kept(self):
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# Only the NaN number reads as null; legacy's regex also rewrote text.
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g = self._hires_fix_graph("NaN cat, value: NaN")
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self.assertEqual(self._nan_scanned("text.png", g), "added")
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self.assertEqual(self._texts(), ["NaN cat, value: NaN"])
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def test_infinity_is_still_unreadable_like_the_legacy_pages(self):
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text = json.dumps(self._hires_fix_graph("cat")).replace(
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'"seed": 7', '"seed": Infinity'
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)
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self.assertIn(self._scanned_text("inf.png", text), ("ambiguous", "unresolved"))
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self.assertEqual(self._texts(), [])
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def test_raw_png_workflow_is_stored_as_object_and_repeat_is_unchanged(self):
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path = self.output_dir / "workflow.png"
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workflow = {"nodes": [{"id": 1, "type": "PromptManagerPair"}]}
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metadata = {"prompt": json.dumps(graph()), "workflow": json.dumps(workflow)}
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self.assertEqual(self.api._ingest_scanned_file(path, metadata), "added")
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self.assertEqual(
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self.api.db.get_image_prompt_info(str(path))["workflow_data"], workflow
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)
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self.assertEqual(self.api._ingest_scanned_file(path, metadata), "unchanged")
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def flux_graph(text="a red fox", encoder="CLIPTextEncode"):
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"""A sampler-less Flux graph: the prompt reaches a guider's conditioning (#252)."""
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return {
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"1": {"class_type": "UNETLoader", "inputs": {"unet_name": "flux.safetensors"}},
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"2": {"class_type": "DualCLIPLoader", "inputs": {"clip_name1": "t5"}},
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"6": {"class_type": encoder, "inputs": {"text": text, "clip": ["2", 0]}},
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"22": {
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"class_type": "BasicGuider",
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"inputs": {"model": ["1", 0], "conditioning": ["6", 0]},
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},
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"13": {
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"class_type": "SamplerCustomAdvanced",
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"inputs": {"guider": ["22", 0], "sigmas": ["17", 0]},
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},
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"17": {"class_type": "BasicScheduler", "inputs": {"steps": 20}},
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"9": {"class_type": "SaveImage", "inputs": {"images": ["13", 0]}},
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}
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class TestSamplerLessScan(AdminAPITestCase):
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"""Graphs where no node takes a positive or negative input (#252)."""
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def _scanned(self, name, prompt_graph, workflow=None):
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meta = {"prompt": json.dumps(prompt_graph)}
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if workflow is not None:
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meta["workflow"] = json.dumps(workflow)
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return self.api._ingest_scanned_file(self.output_dir / name, meta)
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def _texts(self):
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return [p["text"] for p in self.api.db.get_recent_prompts()["prompts"]]
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def test_the_one_wired_prompt_is_linked(self):
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self.assertEqual(self._scanned("flux.png", flux_graph()), "added")
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self.assertEqual(self._texts(), ["a red fox"])
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def test_a_text_node_feeding_the_encoder_is_one_prompt(self):
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g = flux_graph(text=["30", 0])
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g["30"] = {"class_type": "TextInput", "inputs": {"text": "a red fox"}}
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self.assertEqual(self._scanned("textinput.png", g), "added")
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self.assertEqual(self._texts(), ["a red fox"])
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def test_a_display_node_that_feeds_nothing_is_never_the_prompt(self):
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# A real scan stored a "=== GIT CLONE REPORT ===" display text this way.
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g = flux_graph()
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g["40"] = {
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"class_type": "easy showAnything",
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"inputs": {"text": "=== GIT CLONE REPORT ===\nTotal repositories: 1"},
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}
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self.assertEqual(self._scanned("report.png", g), "added")
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self.assertEqual(self._texts(), ["a red fox"])
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def test_only_unconsumed_texts_store_nothing(self):
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g = {
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"40": {"class_type": "easy showAnything", "inputs": {"text": "report"}},
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"41": {"class_type": "Textbox", "inputs": {"text": "install note"}},
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}
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self.assertEqual(self._scanned("notes.png", g), "found")
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self.assertEqual(self._texts(), [])
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def test_two_wired_texts_are_ambiguous_and_stay_unlinked(self):
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# A LoRA loader's text input is wired too: the scan stored "<lora:...>".
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g = flux_graph()
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g["50"] = {
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"class_type": "Lora Loader (LoraManager)",
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"inputs": {
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"model": ["1", 0],
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"clip": ["2", 0],
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"text": "<lora:style:1.00>",
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},
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}
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g["22"]["inputs"]["model"] = ["50", 0]
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g["6"]["inputs"]["clip"] = ["50", 1]
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self.assertEqual(self._scanned("lora.png", g), "unresolved")
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self.assertEqual(self._texts(), [])
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def test_an_untraceable_wired_text_makes_the_graph_ambiguous(self):
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# The encoder's text comes from a node no rule reads: never guess the other.
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g = flux_graph(text=["60", 0])
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g["60"] = {"class_type": "SomeLLMNode", "inputs": {"seed": 1}}
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g["50"] = {
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"class_type": "Lora Loader (LoraManager)",
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"inputs": {"model": ["1", 0], "text": "<lora:style:1.00>"},
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}
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g["22"]["inputs"]["model"] = ["50", 0]
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self.assertEqual(self._scanned("llm.png", g), "unresolved")
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self.assertEqual(self._texts(), [])
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def test_this_repositorys_prompt_node_is_authoritative(self):
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g = flux_graph(text="a red fox", encoder="PromptManager")
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g["50"] = {
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"class_type": "Lora Loader (LoraManager)",
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"inputs": {"model": ["1", 0], "text": "<lora:style:1.00>"},
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}
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g["22"]["inputs"]["model"] = ["50", 0]
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self.assertEqual(self._scanned("own.png", g), "added")
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self.assertEqual(self._texts(), ["a red fox"])
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def test_a_numeric_text_is_never_stored_as_a_prompt(self):
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self.assertEqual(self._scanned("number.png", flux_graph(text="2048")), "found")
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self.assertEqual(self._texts(), [])
|
|
|
|
def test_workflow_widget_values_are_never_read(self):
|
|
# The API text is linked from a node no rule reads; the UI workflow
|
|
# still holds a stale widget value, which the old scan stored.
|
|
g = flux_graph(text=["70", 0])
|
|
g["70"] = {"class_type": "PromptBuilder", "inputs": {"prompt": "x", "n": 2}}
|
|
workflow = {
|
|
"nodes": [
|
|
{"id": 6, "type": "CLIPTextEncode", "widgets_values": ["stale text"]}
|
|
]
|
|
}
|
|
self.assertEqual(self._scanned("stale.png", g, workflow), "unresolved")
|
|
self.assertEqual(self._texts(), [])
|
|
|
|
def test_a_lora_loaders_trigger_word_output_is_not_its_text(self):
|
|
# The encoder reads the loader's trigger-word output (slot 2), not its
|
|
# text input: the prompt is unknown, never the "<lora:...>" syntax.
|
|
g = flux_graph(text=["50", 2])
|
|
g["50"] = {
|
|
"class_type": "Lora Loader (LoraManager)",
|
|
"inputs": {"model": ["1", 0], "text": "<lora:style:1.00>"},
|
|
}
|
|
g["22"]["inputs"]["model"] = ["50", 0]
|
|
self.assertEqual(self._scanned("trigger.png", g), "unresolved")
|
|
self.assertEqual(self._texts(), [])
|
|
|
|
def test_a_seeded_text_node_holds_a_template_not_the_prompt(self):
|
|
g = flux_graph(text=["80", 0])
|
|
g["80"] = {
|
|
"class_type": "RandomPrompt",
|
|
"inputs": {"text": "a {red|blue} fox", "seed": 5},
|
|
}
|
|
self.assertEqual(self._scanned("random.png", g), "unresolved")
|
|
self.assertEqual(self._texts(), [])
|
|
|
|
def test_a1111_parameters_beside_an_undecided_graph_are_read(self):
|
|
# Metadata savers write both; the old scan read the A1111 positive.
|
|
g = flux_graph()
|
|
g["50"] = {
|
|
"class_type": "Lora Loader (LoraManager)",
|
|
"inputs": {"model": ["1", 0], "text": "<lora:style:1.00>"},
|
|
}
|
|
g["22"]["inputs"]["model"] = ["50", 0]
|
|
meta = {
|
|
"prompt": json.dumps(g),
|
|
"parameters": "a red fox\nNegative prompt: blur\nSteps: 20",
|
|
}
|
|
path = self.output_dir / "saver.png"
|
|
self.assertEqual(self.api._ingest_scanned_file(path, meta), "added")
|
|
self.assertEqual(self._texts(), ["a red fox"])
|