Vito Sansevero
564de9e3f1
The browser port of the inspection resolver now answers every record set the server does, so no path is left to the server any more: - several sampler candidates, with the identical-branch rule (#270): a compare grid whose branches all resolve to one pair (text and recipe) is resolved, any other mix is ambiguous; - stages whose outputs reach only previews are dropped first; - detailer stages are judged with the samplers in their image ancestry (multistage-differs, cycles, depth and the per-stage visit budgets), and a sampler that only feeds detailers is not a candidate of its own; - A1111 `parameters`, with Python's multiline anchors, `\s` and Unicode `\d`, the bare footer and Template trailers, counted in code points. The parity corpus gains the selection cases and a seeded fuzzer, and every inspection vector now gives the server's prompts, resolution and facts locally. 16 of 17 mutations are caught; the uncaught one (not flattening an ambiguous upstream stage) cannot change any answer.
575 lines
21 KiB
Python
575 lines
21 KiB
Python
"""Candidate-selection record sets for the local resolver parity test (PR L3 Task 9).
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``local_resolver_cases`` covers one sampler's tracing. These cases reach the
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rest of ``_resolve``: several sampler candidates and the identical-branch rule
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(#270), stages that only reach previews, detailer stages and their upstream
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samplers (multistage), and A1111 ``parameters`` with every footer shape. The
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server's ``inspect_prompt_evidence`` is the oracle; nothing here states an
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expected answer. All content is synthetic.
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"""
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import json
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import random
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try:
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from tests.local_resolver_cases import EXECUTION_KEY, SPACES, WORDS, _Builder
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except ImportError: # pragma: no cover - discover -s tests puts tests/ first
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from local_resolver_cases import EXECUTION_KEY, SPACES, WORDS, _Builder
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def _encode(text):
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return {"class_type": "CLIPTextEncode", "inputs": {"text": text, "clip": ["4", 1]}}
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def _sampler(positive, negative, latent=None, name="KSampler"):
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inputs = {"seed": 1, "model": ["4", 0], "positive": positive}
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if negative is not None:
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inputs["negative"] = negative
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inputs["latent_image"] = latent or ["5", 0]
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return {"class_type": name, "inputs": inputs}
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def _decode(sampler_id):
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return {"class_type": "VAEDecode", "inputs": {"samples": [sampler_id, 0]}}
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def _save(image, name="SaveImage"):
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return {"class_type": name, "inputs": {"images": image}}
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def _detailer(image, positive, negative, name="FaceDetailer", **extra):
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inputs = {"image": image, "model": ["4", 0], "positive": positive}
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inputs["negative"] = negative
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inputs.update(extra)
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return {"class_type": name, "inputs": inputs}
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def _base(**nodes):
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graph = {
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"4": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": "m"}},
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"5": {"class_type": "EmptyLatentImage", "inputs": {"width": 64}},
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"6": _encode("a fox"),
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"7": _encode("blurry"),
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"8": _encode("a cat"),
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}
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graph.update(nodes)
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return graph
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def _samplers():
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"""Several unanchored samplers: identical, differing and mixed branches."""
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same = _sampler(["6", 0], ["7", 0])
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cases = {
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"two-identical": _base(**{"3": same, "9": same}),
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"two-differ": _base(**{"3": same, "9": _sampler(["8", 0], ["7", 0])}),
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"two-negative-differs": _base(**{"3": same, "9": _sampler(["6", 0], ["8", 0])}),
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"identical-without-negative": _base(
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**{"3": _sampler(["6", 0], None), "9": _sampler(["6", 0], None)}
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),
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"one-unresolved": _base(**{"3": same, "9": _sampler(["missing", 0], ["7", 0])}),
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"both-unresolved": _base(
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**{"3": _sampler(["x", 0], ["7", 0]), "9": _sampler(["y", 0], ["7", 0])}
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),
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"three-identical-one-advanced": _base(
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**{
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"3": same,
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"9": same,
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"10": _sampler(["6", 0], ["7", 0], name="KSamplerAdvanced"),
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}
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),
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# Equal texts, different recipes: a PromptManager pair against an encoder.
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"same-text-other-recipe": _base(
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**{
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"3": same,
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"9": _sampler(["11", 0], ["7", 0]),
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"11": {
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"class_type": "PromptManager",
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"inputs": {
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"text": "fox",
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"prepend_text": "a",
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"append_text": "",
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"clip": ["4", 1],
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},
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},
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}
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),
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"multistage-latent": _base(
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**{"3": same, "9": _sampler(["6", 0], ["7", 0], latent=["3", 0])}
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),
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}
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selector = {
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"class_type": "easy ifElse",
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"inputs": {"boolean": ["4", 0], "on_true": ["6", 0], "on_false": ["8", 0]},
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}
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cases["selector-and-plain"] = _base(
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**{"3": same, "9": _sampler(["12", 0], ["7", 0]), "12": selector}
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)
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return cases
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def _previews():
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"""Stages whose outputs reach only previews are not the saved file's."""
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same, other = _sampler(["6", 0], ["7", 0]), _sampler(["8", 0], ["7", 0])
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return {
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"preview-dropped": _base(
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**{
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"3": same,
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"9": other,
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"13": _decode("3"),
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"14": _decode("9"),
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"15": _save(["13", 0]),
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"16": _save(["14", 0], "PreviewImage"),
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}
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),
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"all-previews-kept": _base(
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**{
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"3": same,
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"9": other,
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"13": _decode("3"),
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"14": _decode("9"),
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"15": _save(["13", 0], "PreviewImage"),
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"16": _save(["14", 0], "Image Comparer (rgthree)"),
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}
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),
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"preview-and-file": _base(
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**{
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"3": same,
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"9": other,
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"13": _decode("3"),
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"14": _decode("9"),
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"15": _save(["13", 0]),
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"16": _save(["14", 0], "PreviewImage"),
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"17": _save(["14", 0]),
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}
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),
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"unconsumed-stage-kept": _base(
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**{
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"3": same,
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"9": other,
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"14": _decode("9"),
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"16": _save(["14", 0], "PreviewImage"),
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}
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),
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# A list or object class_type is never a preview class (#286).
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"preview-unhashable-class": _base(
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**{
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"3": same,
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"9": other,
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"14": _decode("9"),
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"16": {"class_type": ["PreviewImage"], "inputs": {"images": ["14", 0]}},
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"17": {"class_type": {"a": 1}, "inputs": {"images": ["3", 0]}},
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}
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),
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"preview-through-bad-node": _base(
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**{
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"3": same,
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"9": other,
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"13": _decode("3"),
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"15": _save(["13", 0]),
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"16": _save(["9", 0], "PreviewImage"),
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"18": "not a node",
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"19": {"class_type": "Note", "inputs": {"x": ["18", 0]}},
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}
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),
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}
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def _detailers():
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"""Detailer stages judged with the samplers in their image ancestry."""
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same = _sampler(["6", 0], ["7", 0])
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cases = {
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"detailer-same-prompt": _base(
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**{
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"3": same,
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"10": _decode("3"),
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"11": _detailer(["10", 0], ["6", 0], ["7", 0]),
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"12": _save(["11", 0]),
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}
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),
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"detailer-differs": _base(
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**{
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"3": same,
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"10": _decode("3"),
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"11": _detailer(["10", 0], ["8", 0], ["7", 0]),
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"12": _save(["11", 0]),
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}
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),
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"detailer-and-own-output": _base(
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**{
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"3": same,
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"10": _decode("3"),
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"11": _detailer(["10", 0], ["8", 0], ["7", 0]),
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"12": _save(["11", 0]),
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"13": _save(["10", 0]),
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}
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),
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"detailer-image-literal": _base(
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**{
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"11": _detailer("not a link", ["6", 0], ["7", 0]),
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"12": _save(["11", 0]),
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}
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),
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"detailer-cycle": _base(
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**{
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"11": _detailer(["10", 0], ["6", 0], ["7", 0]),
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"10": {"class_type": "ImageScale", "inputs": {"image": ["11", 0]}},
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"12": _save(["11", 0]),
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}
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),
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"detailer-chain": _base(
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**{
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"3": same,
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"10": _decode("3"),
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"11": _detailer(["10", 0], ["6", 0], ["7", 0]),
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"21": _detailer(
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["11", 0], ["8", 0], ["7", 0], name="UltimateSDUpscale"
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),
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"12": _save(["21", 0]),
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}
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),
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"detailer-chain-same": _base(
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**{
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"3": same,
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"10": _decode("3"),
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"11": _detailer(["10", 0], ["6", 0], ["7", 0]),
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"21": _detailer(["11", 0], ["6", 0], ["7", 0], name="DetailerForEach"),
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"12": _save(["21", 0]),
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}
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),
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"detailer-two-upstream": _base(
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**{
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"3": same,
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"9": _sampler(["8", 0], ["7", 0]),
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"10": {
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"class_type": "ImageBlend",
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"inputs": {"image1": ["13", 0], "image2": ["14", 0]},
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},
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"13": _decode("3"),
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"14": _decode("9"),
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"11": _detailer(["10", 0], ["6", 0], ["7", 0]),
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"12": _save(["11", 0]),
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}
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),
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"detailer-unsupported-stage": _base(
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**{
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"10": {
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"class_type": "MysterySampler",
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"inputs": {"positive": ["6", 0], "model": ["4", 0]},
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},
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"11": _detailer(["10", 0], ["6", 0], ["7", 0]),
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"12": _save(["11", 0]),
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}
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),
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"detailer-upstream-unresolved": _base(
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**{
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"3": _sampler(["gone", 0], ["7", 0]),
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"10": _decode("3"),
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"11": _detailer(["10", 0], ["6", 0], ["7", 0]),
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"12": _save(["11", 0]),
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}
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),
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"detailer-upstream-selector": _base(
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**{
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"3": _sampler(["15", 0], ["7", 0]),
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"15": {
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"class_type": "easy ifElse",
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"inputs": {
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"boolean": ["4", 0],
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"on_true": ["6", 0],
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"on_false": ["8", 0],
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},
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},
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"10": _decode("3"),
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"11": _detailer(["10", 0], ["6", 0], ["7", 0]),
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"12": _save(["11", 0]),
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}
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),
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"detailer-wildcard": _base(
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**{
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"3": same,
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"10": _decode("3"),
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"11": _detailer(["10", 0], ["6", 0], ["7", 0], wildcard="[x|y]"),
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"12": _save(["11", 0]),
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}
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),
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"detailer-no-upscaled-image": _base(
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**{
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"3": same,
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"10": _decode("3"),
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"11": _detailer(
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["10", 0], ["6", 0], ["7", 0], name="UltimateSDUpscaleNoUpscale"
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),
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"12": _save(["11", 0]),
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}
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),
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"detailer-no-sampler-upstream": _base(
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**{
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"10": {"class_type": "LoadImage", "inputs": {"image": "a.png"}},
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"11": _detailer(["10", 0], ["6", 0], ["7", 0]),
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"12": _save(["11", 0]),
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}
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),
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}
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deep = {"3": _sampler(["6", 0], ["7", 0]), "d0": _decode("3")}
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for index in range(1, 70):
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deep[f"d{index}"] = {
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"class_type": "ImageScale",
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"inputs": {"image": [f"d{index - 1}", 0]},
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}
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deep["11"] = _detailer(["d69", 0], ["6", 0], ["7", 0])
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deep["12"] = _save(["11", 0])
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cases["detailer-deep-ancestry"] = _base(**deep)
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# More nested detailer stages than the depth bound.
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stacked = {"3": _sampler(["6", 0], ["7", 0]), "s0": _decode("3")}
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previous = "s0"
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for index in range(1, 67):
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stacked[f"s{index}"] = _detailer([previous, 0], ["6", 0], ["7", 0])
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previous = f"s{index}"
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stacked["12"] = _save([previous, 0])
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cases["detailer-stacked-past-depth"] = _base(**stacked)
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# Each detailer walks a wide shared image ancestry: the total visit budget
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# runs out across stages before any candidate is read.
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wide = {
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f"l{index}": {"class_type": "LoadImage", "inputs": {}} for index in range(1500)
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}
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wide["hub"] = {
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"class_type": "ImageBatchMany",
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"inputs": {f"i{index}": [f"l{index}", 0] for index in range(1500)},
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}
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for index in range(15):
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wide[f"w{index}"] = _detailer(["hub", 0], ["6", 0], ["7", 0])
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wide[f"o{index}"] = _save([f"w{index}", 0])
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cases["detailers-exhaust-total-visits"] = _base(**wide)
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cases["detailer-mutual-cycle"] = _base(
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**{
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"11": _detailer(["21", 0], ["6", 0], ["7", 0]),
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"21": _detailer(["11", 0], ["6", 0], ["7", 0]),
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"12": _save(["21", 0]),
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}
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)
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# The sampler's output reaches a preview through more nodes than one
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# detailer's allowance: the walk runs out, so the sampler is not absorbed.
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long = {"3": _sampler(["6", 0], ["7", 0]), "10": _decode("3")}
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previous = "10"
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for index in range(2100):
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long[f"c{index}"] = {
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"class_type": "ImageScale",
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"inputs": {"image": [previous, 0]},
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}
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previous = f"c{index}"
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long["p"] = _save([previous, 0], "PreviewImage")
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long["11"] = _detailer(["10", 0], ["6", 0], ["7", 0])
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long["12"] = _save(["11", 0])
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cases["detailer-output-walk-exhausted"] = _base(**long)
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# Every detailer's ancestry walk has its own allowance: the first one is
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# wide, and the second still absorbs the first.
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chained = {"3": _sampler(["6", 0], ["7", 0]), "10": _decode("3")}
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chained["h1"] = {
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"class_type": "ImageBatchMany",
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"inputs": {
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"i": ["10", 0],
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**{f"i{index}": [f"a{index}", 0] for index in range(1500)},
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},
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}
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chained["h2"] = {
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"class_type": "ImageBatchMany",
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"inputs": {
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"i": ["11", 0],
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**{f"i{index}": [f"b{index}", 0] for index in range(600)},
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},
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}
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for index in range(1500):
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chained[f"a{index}"] = {"class_type": "LoadImage", "inputs": {}}
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for index in range(600):
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chained[f"b{index}"] = {"class_type": "LoadImage", "inputs": {}}
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chained["11"] = _detailer(["h1", 0], ["6", 0], ["7", 0])
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chained["21"] = _detailer(["h2", 0], ["6", 0], ["7", 0])
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chained["12"] = _save(["21", 0])
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cases["detailers-each-reset"] = _base(**chained)
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return cases
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_FOOTER = "Steps: 20, Sampler: euler, CFG scale: 7"
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def _parameters():
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"""A1111 ``parameters`` texts: every footer shape and delimiter rule."""
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texts = {
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"basic": f"a fox\nNegative prompt: blur\n{_FOOTER}",
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"no-negative": f"a fox\n{_FOOTER}",
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"two-delimiters": f"a\nNegative prompt: b\nNegative prompt: c\n{_FOOTER}",
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"leading-delimiter": f"Negative prompt: blur\n{_FOOTER}",
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"blank-positive": f" \x85\nNegative prompt: blur\n{_FOOTER}",
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"footer-not-last": f"a fox\n{_FOOTER}\nmore text",
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"two-footers": f"a\n{_FOOTER}\n{_FOOTER}",
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"bare-footer": "a fox\nNegative prompt: blur\nSteps: 20",
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"bare-and-full": f"a fox\nSteps: 5\n{_FOOTER}",
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"two-bare": "a\nSteps: 1\nSteps: 2",
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"template": f"a fox\n{_FOOTER}\nTemplate: {{a|b}}",
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"both-templates": f"a\n{_FOOTER}\nTemplate: x\nNegative Template: y",
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"templates-reversed": f"a\n{_FOOTER}\nNegative Template: y\nTemplate: x",
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"two-templates": f"a\n{_FOOTER}\nTemplate: x\nTemplate: y",
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"template-multiline": f"a\n{_FOOTER}\nTemplate: x\nmore",
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"crlf": f"a fox\r\nNegative prompt: blur\r\n{_FOOTER}\r\n",
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"cr-only": f"a fox\rNegative prompt: blur\r{_FOOTER}",
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"cr-template": f"a\n{_FOOTER}\rTemplate: x",
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"line-separator": f"a fox
{_FOOTER}",
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"next-line": f"a fox\x85{_FOOTER}",
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"spaced-steps": "a fox\nSteps:\n 20 \n, x",
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"steps-newline-bare": "a fox\nSteps:\n20\n",
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"arabic-digits": "a fox\nSteps: ٢٠, Sampler: euler",
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"fullwidth-digits": "a fox\nSteps: ２, x",
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"no-digits": "a fox\nSteps: , x",
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"trailing-spaces": f"a fox \nNegative prompt: blur \n{_FOOTER} \n \n",
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"empty-negative": f"a fox\nNegative prompt:\n{_FOOTER}",
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"astral-within-limit": "\U0001f600" * 60_000 + f"\n{_FOOTER}",
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"over-limit": "x" * 100_001,
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"at-limit": "x" * (100_000 - len(_FOOTER) - 1) + "\n" + _FOOTER,
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"empty": "",
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"only-footer": _FOOTER,
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"lone-surrogate": f"\ud800 fox\nNegative prompt: \udfff\n{_FOOTER}",
|
||
}
|
||
cases = {f"parameters-{name}": {"parameters": text} for name, text in texts.items()}
|
||
cases["parameters-with-execution"] = {
|
||
"parameters": texts["basic"],
|
||
EXECUTION_KEY: json.dumps({"version": 1, "nodes": {}}),
|
||
}
|
||
return cases
|
||
|
||
|
||
def selection_cases():
|
||
"""Named record sets for every Task 9 path, each a ``read_inspection_file`` dict."""
|
||
cases = {}
|
||
for group in (_samplers(), _previews(), _detailers()):
|
||
for name, graph in group.items():
|
||
cases[f"select-{name}"] = {"prompt": json.dumps(graph, ensure_ascii=False)}
|
||
cases.update(_parameters())
|
||
return cases
|
||
|
||
|
||
class _Selection(_Builder):
|
||
"""Random graphs with several samplers, outputs, previews and detailers."""
|
||
|
||
def sampler(self):
|
||
rng = self.rng
|
||
positive = self.conditioning(0)
|
||
negative = self.conditioning(0) if rng.random() < 0.85 else None
|
||
name = rng.choice(["KSampler", "KSampler", "KSamplerAdvanced", "SamplerCustom"])
|
||
node_id = self.new_id()
|
||
self.nodes[node_id] = _sampler(positive, negative, name=name)
|
||
return node_id
|
||
|
||
def shared(self, ids):
|
||
"""A copy of an earlier sampler: an identical branch."""
|
||
node_id = self.new_id()
|
||
self.nodes[node_id] = json.loads(json.dumps(self.nodes[self.rng.choice(ids)]))
|
||
return node_id
|
||
|
||
def output(self, image):
|
||
rng = self.rng
|
||
choice = rng.randrange(5)
|
||
if choice == 4:
|
||
return
|
||
name = ["SaveImage", "SaveImage", "PreviewImage", "PreviewAny"][choice]
|
||
self.nodes[self.new_id()] = _save(image, name)
|
||
|
||
def detailer(self, image):
|
||
rng = self.rng
|
||
node_id = self.new_id()
|
||
positive = self.conditioning(1) if rng.random() < 0.5 else None
|
||
name = rng.choice(list(_DETAILER_NAMES))
|
||
node = _detailer(image, positive, self.conditioning(1), name=name)
|
||
if positive is None:
|
||
node["inputs"]["positive"] = self.reuse_positive()
|
||
if name == "UltimateSDUpscaleNoUpscale":
|
||
node["inputs"]["upscaled_image"] = node["inputs"].pop("image")
|
||
if name in ("FaceDetailer", "DetailerForEach") and rng.random() < 0.15:
|
||
node["inputs"]["wildcard"] = rng.choice(["", "x", ["4", 0]])
|
||
self.nodes[node_id] = node
|
||
return node_id
|
||
|
||
def reuse_positive(self):
|
||
samplers = [
|
||
node
|
||
for node in self.nodes.values()
|
||
if node.get("class_type")
|
||
in ("KSampler", "KSamplerAdvanced", "SamplerCustom")
|
||
]
|
||
if samplers:
|
||
return self.rng.choice(samplers)["inputs"]["positive"]
|
||
return self.conditioning(1)
|
||
|
||
def build(self):
|
||
rng = self.rng
|
||
ids = [self.sampler()]
|
||
for _ in range(rng.randrange(3)):
|
||
ids.append(self.shared(ids) if rng.random() < 0.4 else self.sampler())
|
||
for sampler_id in list(ids):
|
||
decoded = self.new_id()
|
||
self.nodes[decoded] = _decode(sampler_id)
|
||
image = [decoded, 0]
|
||
for _ in range(rng.randrange(3)):
|
||
if rng.random() < 0.5:
|
||
image = [self.detailer(image), 0]
|
||
else:
|
||
self.output(image)
|
||
self.output(image)
|
||
if rng.random() < 0.1:
|
||
self.nodes[sampler_id]["inputs"]["latent_image"] = [rng.choice(ids), 0]
|
||
graph = {
|
||
"4": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": "m"}},
|
||
"5": {"class_type": "EmptyLatentImage", "inputs": {"width": 64}},
|
||
}
|
||
graph.update(self.nodes)
|
||
return graph
|
||
|
||
|
||
_DETAILER_NAMES = (
|
||
"FaceDetailer",
|
||
"DetailerForEach",
|
||
"UltimateSDUpscale",
|
||
"UltimateSDUpscaleNoUpscale",
|
||
)
|
||
_PARAMETER_LINES = [
|
||
"Negative prompt:",
|
||
"Negative prompt: blur",
|
||
"Steps: 20, Sampler: euler",
|
||
"Steps: 7",
|
||
"Steps: x",
|
||
"Template: t",
|
||
"Negative Template: n",
|
||
"Steps:٣, a",
|
||
]
|
||
|
||
|
||
def _parameter_text(rng):
|
||
parts = []
|
||
for _ in range(rng.randint(1, 5)):
|
||
if rng.random() < 0.5:
|
||
parts.append(rng.choice(_PARAMETER_LINES))
|
||
else:
|
||
parts.append(rng.choice(SPACES) + rng.choice(WORDS) + rng.choice(SPACES))
|
||
joints = ["\n", "\n", "\r\n", "\r", "
", " "]
|
||
return "".join(part + rng.choice(joints) for part in parts).rstrip(
|
||
rng.choice(["", "\n", " "])
|
||
)
|
||
|
||
|
||
def selection_fuzz_cases(seed, count):
|
||
"""Seeded several-sampler graphs (sometimes corrupted) and parameters texts."""
|
||
rng = random.Random(seed)
|
||
cases = {}
|
||
for index in range(count):
|
||
if rng.random() < 0.2:
|
||
cases[f"select-fuzz-{seed}-{index}"] = {"parameters": _parameter_text(rng)}
|
||
continue
|
||
builder = _Selection(rng)
|
||
graph = builder.build()
|
||
if rng.random() < 0.2:
|
||
builder.nodes = graph
|
||
builder.corrupt()
|
||
records = {"prompt": json.dumps(graph, ensure_ascii=rng.random() < 0.5)}
|
||
records.update(builder.records)
|
||
cases[f"select-fuzz-{seed}-{index}"] = records
|
||
return cases
|