"""Candidate-selection record sets for the local resolver parity test (PR L3 Task 9). ``local_resolver_cases`` covers one sampler's tracing. These cases reach the rest of ``_resolve``: several sampler candidates and the identical-branch rule (#270), stages that only reach previews, detailer stages and their upstream samplers (multistage), and A1111 ``parameters`` with every footer shape. The server's ``inspect_prompt_evidence`` is the oracle; nothing here states an expected answer. All content is synthetic. """ import json import random try: from tests import passthrough_graphs as shapes from tests.local_resolver_cases import EXECUTION_KEY, SPACES, WORDS, _Builder except ImportError: # pragma: no cover - discover -s tests puts tests/ first import passthrough_graphs as shapes from local_resolver_cases import EXECUTION_KEY, SPACES, WORDS, _Builder def _encode(text): return {"class_type": "CLIPTextEncode", "inputs": {"text": text, "clip": ["4", 1]}} def _sampler(positive, negative, latent=None, name="KSampler"): inputs = {"seed": 1, "model": ["4", 0], "positive": positive} if negative is not None: inputs["negative"] = negative inputs["latent_image"] = latent or ["5", 0] return {"class_type": name, "inputs": inputs} def _decode(sampler_id): return {"class_type": "VAEDecode", "inputs": {"samples": [sampler_id, 0]}} def _save(image, name="SaveImage"): return {"class_type": name, "inputs": {"images": image}} def _detailer(image, positive, negative, name="FaceDetailer", **extra): inputs = {"image": image, "model": ["4", 0], "positive": positive} inputs["negative"] = negative inputs.update(extra) return {"class_type": name, "inputs": inputs} def _base(**nodes): graph = { "4": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": "m"}}, "5": {"class_type": "EmptyLatentImage", "inputs": {"width": 64}}, "6": _encode("a fox"), "7": _encode("blurry"), "8": _encode("a cat"), } graph.update(nodes) return graph def _samplers(): """Several unanchored samplers: identical, differing and mixed branches.""" same = _sampler(["6", 0], ["7", 0]) cases = { "two-identical": _base(**{"3": same, "9": same}), "two-differ": _base(**{"3": same, "9": _sampler(["8", 0], ["7", 0])}), "two-negative-differs": _base(**{"3": same, "9": _sampler(["6", 0], ["8", 0])}), "identical-without-negative": _base( **{"3": _sampler(["6", 0], None), "9": _sampler(["6", 0], None)} ), "one-unresolved": _base(**{"3": same, "9": _sampler(["missing", 0], ["7", 0])}), "both-unresolved": _base( **{"3": _sampler(["x", 0], ["7", 0]), "9": _sampler(["y", 0], ["7", 0])} ), "three-identical-one-advanced": _base( **{ "3": same, "9": same, "10": _sampler(["6", 0], ["7", 0], name="KSamplerAdvanced"), } ), # Equal texts, different recipes: a PromptManager pair against an encoder. "same-text-other-recipe": _base( **{ "3": same, "9": _sampler(["11", 0], ["7", 0]), "11": { "class_type": "PromptManager", "inputs": { "text": "fox", "prepend_text": "a", "append_text": "", "clip": ["4", 1], }, }, } ), "multistage-latent": _base( **{"3": same, "9": _sampler(["6", 0], ["7", 0], latent=["3", 0])} ), } selector = { "class_type": "easy ifElse", "inputs": {"boolean": ["4", 0], "on_true": ["6", 0], "on_false": ["8", 0]}, } cases["selector-and-plain"] = _base( **{"3": same, "9": _sampler(["12", 0], ["7", 0]), "12": selector} ) return cases def _previews(): """Stages whose outputs reach only previews are not the saved file's.""" same, other = _sampler(["6", 0], ["7", 0]), _sampler(["8", 0], ["7", 0]) return { "preview-dropped": _base( **{ "3": same, "9": other, "13": _decode("3"), "14": _decode("9"), "15": _save(["13", 0]), "16": _save(["14", 0], "PreviewImage"), } ), "all-previews-kept": _base( **{ "3": same, "9": other, "13": _decode("3"), "14": _decode("9"), "15": _save(["13", 0], "PreviewImage"), "16": _save(["14", 0], "Image Comparer (rgthree)"), } ), "preview-and-file": _base( **{ "3": same, "9": other, "13": _decode("3"), "14": _decode("9"), "15": _save(["13", 0]), "16": _save(["14", 0], "PreviewImage"), "17": _save(["14", 0]), } ), "unconsumed-stage-kept": _base( **{ "3": same, "9": other, "14": _decode("9"), "16": _save(["14", 0], "PreviewImage"), } ), # A list or object class_type is never a preview class (#286). "preview-unhashable-class": _base( **{ "3": same, "9": other, "14": _decode("9"), "16": {"class_type": ["PreviewImage"], "inputs": {"images": ["14", 0]}}, "17": {"class_type": {"a": 1}, "inputs": {"images": ["3", 0]}}, } ), "preview-through-bad-node": _base( **{ "3": same, "9": other, "13": _decode("3"), "15": _save(["13", 0]), "16": _save(["9", 0], "PreviewImage"), "18": "not a node", "19": {"class_type": "Note", "inputs": {"x": ["18", 0]}}, } ), } def _detailers(): """Detailer stages judged with the samplers in their image ancestry.""" same = _sampler(["6", 0], ["7", 0]) cases = { "detailer-same-prompt": _base( **{ "3": same, "10": _decode("3"), "11": _detailer(["10", 0], ["6", 0], ["7", 0]), "12": _save(["11", 0]), } ), "detailer-differs": _base( **{ "3": same, "10": _decode("3"), "11": _detailer(["10", 0], ["8", 0], ["7", 0]), "12": _save(["11", 0]), } ), "detailer-and-own-output": _base( **{ "3": same, "10": _decode("3"), "11": _detailer(["10", 0], ["8", 0], ["7", 0]), "12": _save(["11", 0]), "13": _save(["10", 0]), } ), "detailer-image-literal": _base( **{ "11": _detailer("not a link", ["6", 0], ["7", 0]), "12": _save(["11", 0]), } ), "detailer-cycle": _base( **{ "11": _detailer(["10", 0], ["6", 0], ["7", 0]), "10": {"class_type": "ImageScale", "inputs": {"image": ["11", 0]}}, "12": _save(["11", 0]), } ), "detailer-chain": _base( **{ "3": same, "10": _decode("3"), "11": _detailer(["10", 0], ["6", 0], ["7", 0]), "21": _detailer( ["11", 0], ["8", 0], ["7", 0], name="UltimateSDUpscale" ), "12": _save(["21", 0]), } ), "detailer-chain-same": _base( **{ "3": same, "10": _decode("3"), "11": _detailer(["10", 0], ["6", 0], ["7", 0]), "21": _detailer(["11", 0], ["6", 0], ["7", 0], name="DetailerForEach"), "12": _save(["21", 0]), } ), "detailer-two-upstream": _base( **{ "3": same, "9": _sampler(["8", 0], ["7", 0]), "10": { "class_type": "ImageBlend", "inputs": {"image1": ["13", 0], "image2": ["14", 0]}, }, "13": _decode("3"), "14": _decode("9"), "11": _detailer(["10", 0], ["6", 0], ["7", 0]), "12": _save(["11", 0]), } ), "detailer-unsupported-stage": _base( **{ "10": { "class_type": "MysterySampler", "inputs": {"positive": ["6", 0], "model": ["4", 0]}, }, "11": _detailer(["10", 0], ["6", 0], ["7", 0]), "12": _save(["11", 0]), } ), "detailer-upstream-unresolved": _base( **{ "3": _sampler(["gone", 0], ["7", 0]), "10": _decode("3"), "11": _detailer(["10", 0], ["6", 0], ["7", 0]), "12": _save(["11", 0]), } ), "detailer-upstream-selector": _base( **{ "3": _sampler(["15", 0], ["7", 0]), "15": { "class_type": "easy ifElse", "inputs": { "boolean": ["4", 0], "on_true": ["6", 0], "on_false": ["8", 0], }, }, "10": _decode("3"), "11": _detailer(["10", 0], ["6", 0], ["7", 0]), "12": _save(["11", 0]), } ), "detailer-wildcard": _base( **{ "3": same, "10": _decode("3"), "11": _detailer(["10", 0], ["6", 0], ["7", 0], wildcard="[x|y]"), "12": _save(["11", 0]), } ), "detailer-no-upscaled-image": _base( **{ "3": same, "10": _decode("3"), "11": _detailer( ["10", 0], ["6", 0], ["7", 0], name="UltimateSDUpscaleNoUpscale" ), "12": _save(["11", 0]), } ), "detailer-no-sampler-upstream": _base( **{ "10": {"class_type": "LoadImage", "inputs": {"image": "a.png"}}, "11": _detailer(["10", 0], ["6", 0], ["7", 0]), "12": _save(["11", 0]), } ), } deep = {"3": _sampler(["6", 0], ["7", 0]), "d0": _decode("3")} for index in range(1, 70): deep[f"d{index}"] = { "class_type": "ImageScale", "inputs": {"image": [f"d{index - 1}", 0]}, } deep["11"] = _detailer(["d69", 0], ["6", 0], ["7", 0]) deep["12"] = _save(["11", 0]) cases["detailer-deep-ancestry"] = _base(**deep) # More nested detailer stages than the depth bound. stacked = {"3": _sampler(["6", 0], ["7", 0]), "s0": _decode("3")} previous = "s0" for index in range(1, 67): stacked[f"s{index}"] = _detailer([previous, 0], ["6", 0], ["7", 0]) previous = f"s{index}" stacked["12"] = _save([previous, 0]) cases["detailer-stacked-past-depth"] = _base(**stacked) # Each detailer walks a wide shared image ancestry: the total visit budget # runs out across stages before any candidate is read. wide = { f"l{index}": {"class_type": "LoadImage", "inputs": {}} for index in range(1500) } wide["hub"] = { "class_type": "ImageBatchMany", "inputs": {f"i{index}": [f"l{index}", 0] for index in range(1500)}, } for index in range(15): wide[f"w{index}"] = _detailer(["hub", 0], ["6", 0], ["7", 0]) wide[f"o{index}"] = _save([f"w{index}", 0]) cases["detailers-exhaust-total-visits"] = _base(**wide) cases["detailer-mutual-cycle"] = _base( **{ "11": _detailer(["21", 0], ["6", 0], ["7", 0]), "21": _detailer(["11", 0], ["6", 0], ["7", 0]), "12": _save(["21", 0]), } ) # The sampler's output reaches a preview through more nodes than one # detailer's allowance: the walk runs out, so the sampler is not absorbed. long = {"3": _sampler(["6", 0], ["7", 0]), "10": _decode("3")} previous = "10" for index in range(2100): long[f"c{index}"] = { "class_type": "ImageScale", "inputs": {"image": [previous, 0]}, } previous = f"c{index}" long["p"] = _save([previous, 0], "PreviewImage") long["11"] = _detailer(["10", 0], ["6", 0], ["7", 0]) long["12"] = _save(["11", 0]) cases["detailer-output-walk-exhausted"] = _base(**long) # Every detailer's ancestry walk has its own allowance: the first one is # wide, and the second still absorbs the first. chained = {"3": _sampler(["6", 0], ["7", 0]), "10": _decode("3")} chained["h1"] = { "class_type": "ImageBatchMany", "inputs": { "i": ["10", 0], **{f"i{index}": [f"a{index}", 0] for index in range(1500)}, }, } chained["h2"] = { "class_type": "ImageBatchMany", "inputs": { "i": ["11", 0], **{f"i{index}": [f"b{index}", 0] for index in range(600)}, }, } for index in range(1500): chained[f"a{index}"] = {"class_type": "LoadImage", "inputs": {}} for index in range(600): chained[f"b{index}"] = {"class_type": "LoadImage", "inputs": {}} chained["11"] = _detailer(["h1", 0], ["6", 0], ["7", 0]) chained["21"] = _detailer(["h2", 0], ["6", 0], ["7", 0]) chained["12"] = _save(["21", 0]) cases["detailers-each-reset"] = _base(**chained) return cases _FOOTER = "Steps: 20, Sampler: euler, CFG scale: 7" def _parameters(): """A1111 ``parameters`` texts: every footer shape and delimiter rule.""" texts = { "basic": f"a fox\nNegative prompt: blur\n{_FOOTER}", "no-negative": f"a fox\n{_FOOTER}", "two-delimiters": f"a\nNegative prompt: b\nNegative prompt: c\n{_FOOTER}", "leading-delimiter": f"Negative prompt: blur\n{_FOOTER}", "blank-positive": f" \x85\nNegative prompt: blur\n{_FOOTER}", "footer-not-last": f"a fox\n{_FOOTER}\nmore text", "two-footers": f"a\n{_FOOTER}\n{_FOOTER}", "bare-footer": "a fox\nNegative prompt: blur\nSteps: 20", "bare-and-full": f"a fox\nSteps: 5\n{_FOOTER}", "two-bare": "a\nSteps: 1\nSteps: 2", "template": f"a fox\n{_FOOTER}\nTemplate: {{a|b}}", "both-templates": f"a\n{_FOOTER}\nTemplate: x\nNegative Template: y", "templates-reversed": f"a\n{_FOOTER}\nNegative Template: y\nTemplate: x", "two-templates": f"a\n{_FOOTER}\nTemplate: x\nTemplate: y", "template-multiline": f"a\n{_FOOTER}\nTemplate: x\nmore", "crlf": f"a fox\r\nNegative prompt: blur\r\n{_FOOTER}\r\n", "cr-only": f"a fox\rNegative prompt: blur\r{_FOOTER}", "cr-template": f"a\n{_FOOTER}\rTemplate: x", "line-separator": f"a fox
{_FOOTER}", "next-line": f"a fox\x85{_FOOTER}", "spaced-steps": "a fox\nSteps:\n 20 \n, x", "steps-newline-bare": "a fox\nSteps:\n20\n", "arabic-digits": "a fox\nSteps: ٢٠, Sampler: euler", "fullwidth-digits": "a fox\nSteps: ２, x", "no-digits": "a fox\nSteps: , x", "trailing-spaces": f"a fox \nNegative prompt: blur \n{_FOOTER} \n \n", "empty-negative": f"a fox\nNegative prompt:\n{_FOOTER}", "astral-within-limit": "\U0001f600" * 60_000 + f"\n{_FOOTER}", "over-limit": "x" * 100_001, "at-limit": "x" * (100_000 - len(_FOOTER) - 1) + "\n" + _FOOTER, "empty": "", "only-footer": _FOOTER, "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 _passthrough(): """Chains joined through lineage nodes, and the floor for added conditioning.""" cases = { "hires-fix": shapes.hires_fix(), "hires-fix-differs": shapes.hires_fix(second_positive=shapes.OTHER), "hires-fix-unlisted": shapes.hires_fix(filter_class="MysteryImageEffect"), "hires-fix-generated": shapes.generated_text_chain(), "flux-three-stage": shapes.flux_three_stage(), "flux-concat": shapes.flux_three_stage(composite="ConditioningConcat"), "flux-average": shapes.flux_three_stage(composite="ConditioningAverage"), "edit-batch": shapes.edit_batch(3), } graph = shapes.flux_three_stage(combined=(("37", 0), ("31", 0))) graph["37"] = shapes.encode(shapes.OTHER) cases["flux-without-base"] = graph graph = shapes.flux_three_stage() graph["38"] = shapes.encode("watermark") graph["60"]["inputs"]["negative"] = ["38", 0] cases["flux-other-negative"] = graph graph = shapes.hires_fix() graph["20"]["inputs"]["latent_image"] = ["10", 0] graph["10"]["inputs"]["latent_image"] = ["20", 0] cases["hires-fix-cycle"] = graph graph = shapes.hires_fix() graph["17"]["inputs"]["pixels"] = ["18", 0] graph["18"] = {"class_type": "Reroute", "inputs": {"value": ["16", 0]}} cases["hires-fix-reroute"] = graph graph = shapes.hires_fix() graph["24"] = {"class_type": "SaveImage", "inputs": {"images": ["11", 0]}} cases["hires-fix-both-saved"] = graph graph = shapes.hires_fix() graph["20"]["inputs"]["latent_image"] = ["99", 0] cases["hires-fix-missing-latent"] = graph graph = shapes.flux_three_stage() graph["38"] = {"class_type": "MysteryNegative", "inputs": {"seed": 1}} graph["60"]["inputs"]["negative"] = ["38", 0] cases["flux-negative-unsupported"] = graph graph = shapes.flux_three_stage() graph["39"] = {"class_type": "Reroute", "inputs": {"input": ["35", 0]}} graph["60"]["inputs"]["positive"] = ["39", 0] cases["flux-reroute-input"] = graph graph = shapes.flux_three_stage() graph["39"] = { "class_type": "CLIPTextEncodeSDXL", "inputs": {"text_g": shapes.POSITIVE, "text_l": shapes.STYLE}, } graph["60"]["inputs"]["positive"] = ["39", 0] cases["flux-encoder-parts"] = graph cases.update(_passthrough_edges()) cases.update(_runtime_parts()) return cases def _runtime_parts(): """Option A: a PromptManager part fed by a run-time node is left out.""" cases = {"trigger-prepend": shapes.hires_fix_with_trigger_words()} graph = shapes.hires_fix_with_trigger_words() graph["2"]["inputs"].update(prepend_text="masterpiece", append_text=["8", 0]) graph["20"]["inputs"]["latent_image"] = ["4", 0] cases["trigger-append-single"] = graph graph = shapes.hires_fix_with_trigger_words() graph["8"] = {"class_type": "PrimitiveString", "inputs": {"value": "trig9word"}} cases["traced-prepend"] = graph graph = shapes.hires_fix_with_trigger_words() graph["8"]["class_type"] = "MysteryWords" cases["unknown-prepend"] = graph graph = shapes.hires_fix_with_trigger_words() graph["9"] = { "class_type": "StringConcatenate", "inputs": {"string_a": "kept", "string_b": ["8", 0], "delimiter": ""}, } graph["2"]["inputs"]["prepend_text"] = ["9", 0] cases["indirect-prepend"] = graph graph = shapes.hires_fix_with_trigger_words() graph["2"]["inputs"].update(prepend_text="", text=["8", 0]) cases["runtime-main-text"] = graph graph = shapes.hires_fix_with_trigger_words() graph["2"]["class_type"] = "PromptManagerPair" graph["2"]["inputs"]["negative_text"] = shapes.NEGATIVE graph["10"]["inputs"]["negative"] = ["2", 1] graph["20"]["inputs"]["negative"] = ["2", 1] cases["trigger-pair"] = graph graph = shapes.hires_fix_with_trigger_words() graph["30"] = {"class_type": "PromptSearchList", "inputs": {"search": "x"}} graph["2"]["inputs"]["append_text"] = ["30", 0] cases["search-list-append"] = graph for shape in ("text-encode", "chained", "concat"): cases[f"through-{shape}"] = shapes.trigger_words_through(shape) cases["branches-with-and-without"] = ( shapes.branches_with_and_without_trigger_words() ) graph = shapes.hires_fix_with_trigger_words() graph["9"] = shapes.prompt_manager(shapes.POSITIVE) graph["20"]["inputs"]["positive"] = ["9", 0] cases["stage-with-and-without"] = graph graph = shapes.hires_fix() graph["9"] = { "class_type": "JoinStringMulti", "inputs": { "inputcount": 2, "delimiter": " ", "return_list": ["98", 0], "string_1": "static words", "string_2": "more", }, } graph["98"] = {"class_type": "PrimitiveBoolean", "inputs": {"value": False}} graph["2"]["inputs"]["prepend_text"] = ["9", 0] cases["join-multi-flag"] = graph graph = shapes.flux_three_stage() graph["31"] = shapes.encode(shapes.OTHER) cases["flux-other-full-prompt"] = graph graph = shapes.flux_three_stage() graph["7"] = { "class_type": "Lora Loader (LoraManager)", "inputs": {"model": ["1", 0], "clip": ["2", 0], "text": "x"}, } graph["8"] = shapes.trigger_toggle() graph["30"]["inputs"]["prepend_text"] = ["8", 0] cases["flux-trigger"] = graph graph = shapes.flux_three_stage() graph["60"] = { "class_type": "SamplerCustomAdvanced", "inputs": {"guider": ["63", 0], "latent_image": ["66", 0]}, } graph["63"] = { "class_type": "CFGGuider", "inputs": {"positive": ["35", 0], "negative": ["33", 0]}, } graph["66"] = {"class_type": "VAEEncode", "inputs": {"pixels": ["51", 0]}} graph["67"] = {"class_type": "VAEDecode", "inputs": {"samples": ["60", 0]}} graph["61"]["inputs"]["images"] = ["67", 0] cases["flux-custom-advanced-chained"] = graph return {f"runtime-{name}": graph for name, graph in cases.items()} def _passthrough_edges(): """Lineage and floor edges: branching refiners, other slots, cycles, guiders.""" cases = {} graph = shapes.hires_fix() graph["30"] = shapes.ksampler(["2", 0], ["3", 0], ["17", 0], denoise=0.4) graph["31"] = {"class_type": "SaveImage", "inputs": {"images": ["30", 0]}} cases["hires-fix-two-refiners"] = graph graph = shapes.hires_fix() graph["10"]["class_type"] = "SamplerCustom" graph["11"]["inputs"]["samples"] = ["10", 1] cases["hires-fix-denoised-slot"] = graph graph = shapes.hires_fix() graph["30"] = shapes.ksampler(["2", 0], ["3", 0], ["4", 0]) graph["31"] = {"class_type": "VAEDecode", "inputs": {"samples": ["30", 0]}} graph["32"] = {"class_type": "VAEEncode", "inputs": {"pixels": ["31", 0]}} graph["30"]["inputs"]["latent_image"] = ["32", 0] cases["hires-fix-self-latent"] = graph graph = shapes.hires_fix() graph["30"] = shapes.ksampler(["2", 0], ["3", 0], ["32", 0]) graph["31"] = {"class_type": "VAEDecode", "inputs": {"samples": ["32", 0]}} graph["32"] = {"class_type": "VAEEncode", "inputs": {"pixels": ["31", 0]}} graph["33"] = {"class_type": "SaveImage", "inputs": {"images": ["30", 0]}} cases["hires-fix-lineage-cycle"] = graph graph = shapes.flux_three_stage() graph["39"] = shapes.encode(shapes.OTHER) graph["40"]["inputs"]["positive"] = ["39", 0] cases["flux-base-stages-differ"] = graph graph = shapes.flux_three_stage() del graph["60"]["inputs"]["negative"] cases["flux-no-last-negative"] = graph graph = shapes.flux_three_stage() graph["39"] = {"class_type": "MysteryPrompt", "inputs": {"seed": 1}} graph["40"]["inputs"]["positive"] = ["39", 0] graph["50"]["inputs"]["positive"] = ["39", 0] cases["flux-unreadable-base"] = graph graph = shapes.flux_three_stage() graph["39"] = { "class_type": "MysteryUpscaler", "inputs": {"image": ["51", 0], "model": ["1", 0], "positive": ["34", 0]}, } graph["52"]["inputs"]["images"] = ["39", 0] cases["flux-unsupported-ancestry"] = graph graph = shapes.flux_three_stage() graph["60"] = { "class_type": "SamplerCustomAdvanced", "inputs": { "noise": ["62", 0], "guider": ["63", 0], "sampler": ["64", 0], "sigmas": ["65", 0], "latent_image": ["66", 0], }, } graph["62"] = {"class_type": "RandomNoise", "inputs": {"noise_seed": 1}} graph["63"] = { "class_type": "CFGGuider", "inputs": {"model": ["1", 0], "positive": ["35", 0], "negative": ["33", 0]}, } graph["64"] = {"class_type": "KSamplerSelect", "inputs": {"sampler_name": "euler"}} graph["65"] = {"class_type": "BasicScheduler", "inputs": {"steps": 4}} graph["66"] = {"class_type": "VAEEncode", "inputs": {"pixels": ["52", 0]}} graph["67"] = {"class_type": "VAEDecode", "inputs": {"samples": ["60", 0]}} graph["61"]["inputs"]["images"] = ["67", 0] cases["flux-custom-advanced-last"] = graph return {f"edge-{name}": graph for name, graph in cases.items()} def selection_cases(): """Named record sets for every Task 9 path, each a ``read_inspection_file`` dict.""" cases = {} for group in (_samplers(), _previews(), _detailers(), _passthrough()): 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 _LINEAGE_HOPS = ( ("VAEDecode", "samples"), ("VAEDecodeTiled", "samples"), ("Image Filter", "images"), ("ImageUpscaleWithModel", "image"), ("ImageScale", "image"), ("ImageScaleBy", "image"), ("VAEEncode", "pixels"), ("LatentUpscaleBy", "samples"), ("Reroute", "value"), ("MysteryImageEffect", "image"), ) def passthrough_fuzz_cases(seed, count): """Seeded sampler chains joined by random lineage (and unlisted) hops. Each later stage reads the earlier one's output through zero to four hops, with a shared, a different, an added-to (combine, concat, average) or a run-time positive, and a shared or different negative, under a saver or a preview. The server is the oracle; nothing states the expected answer. """ rng = random.Random(seed) cases = {} for index in range(count): graph = { "1": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": "m"}}, "2": shapes.prompt_manager(rng.choice([shapes.POSITIVE, "fox"])), "3": shapes.encode(shapes.NEGATIVE), "4": {"class_type": "EmptyLatentBatch", "inputs": {"batch_size": 2}}, "5": shapes.encode(shapes.STYLE), "6": shapes.encode(shapes.OTHER), } previous, next_id = ["4", 0], 10 for stage in range(rng.randint(2, 3)): positive = ["2", 0] choice = rng.randrange(8) if stage and choice == 1: positive = ["6", 0] elif stage and choice in (2, 3, 4): name = ( "ConditioningCombine", "ConditioningConcat", "ConditioningAverage", )[choice - 2] names = shapes._COMPOSITE_INPUTS[name] parts = [["2", 0], rng.choice([["5", 0], ["6", 0]])] rng.shuffle(parts) graph[str(next_id)] = { "class_type": name, "inputs": dict(zip(names, parts)), } positive, next_id = [str(next_id), 0], next_id + 1 elif stage and choice == 5: graph[str(next_id)] = { "class_type": "FluxGuidance", "inputs": {"guidance": 3.5, "conditioning": ["2", 0]}, } positive, next_id = [str(next_id), 0], next_id + 1 negative = ["6", 0] if stage and rng.random() < 0.15 else ["3", 0] latent = previous if stage: image = previous for _ in range(rng.randrange(5)): name, key = rng.choice(_LINEAGE_HOPS) graph[str(next_id)] = {"class_type": name, "inputs": {key: image}} image, next_id = [str(next_id), 0], next_id + 1 latent = image sampler = shapes.ksampler(positive, negative, latent) if stage and rng.random() < 0.2: sampler = { "class_type": "UltimateSDUpscale", "inputs": { "image": latent, "model": ["1", 0], "positive": positive, "negative": negative, }, } graph[str(next_id)] = sampler previous, next_id = [str(next_id), 0], next_id + 1 if rng.random() < 0.3: saver = rng.choice(["SaveImage", "PreviewImage"]) graph[str(next_id)] = { "class_type": saver, "inputs": {"images": previous}, } next_id += 1 graph[str(next_id)] = { "class_type": "SaveImage", "inputs": {"images": previous}, } cases[f"select-passthrough-fuzz-{seed}-{index}"] = { "prompt": json.dumps(graph, ensure_ascii=False) } return cases