Vito Sansevero
b83c3556d8
Adds utils/generation_params.py, a line-for-line port of extractGenerationParams in web/js/comfy-metadata.js, the parser behind the gallery, admin and metadata pages. The scan and live monitor can use it so every surface reads prompts the same way. The JavaScript parser is unchanged. The port reproduces the JavaScript value semantics the result depends on (property order, truthiness, String(), trim(), strict equality, Map keys, JSON.parse number rounding, and the inputs on which it throws), and runs its recursive tracing on an explicit stack so crafted graphs cannot exhaust the Python stack. Tests: - behaviour tests for each tracing rule, with JavaScript results as expected values, including the visit-limit boundary; - golden vectors of the JavaScript parser's results for every named case and every PNG fixture, so the comparison runs without Node; - a live comparison against the JavaScript parser when Node is present; - a randomized differential test over 9,000 generated graphs and workflows, coherent and corrupted, including inputs where the JavaScript throws. Five deliberate porting mistakes (trim, truthiness, prepend handling, visit limit, pair slot) each fail the tests. On a 38,602-file output folder, all 18,491 files with a workflow give identical results.
445 lines
13 KiB
Python
445 lines
13 KiB
Python
"""Named workflow cases shared by the generation-parameter parser tests.
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Each case is the raw metadata records a file would carry, as text, so the cases
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exercise ``parse_js_json`` as well as the tracing. The JS parity test runs every
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case through the legacy browser parser too. Prompt texts and seeds are invented.
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"""
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import json
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def _text(value):
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return None if value is None else json.dumps(value)
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def case(prompt=None, workflow=None):
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return {"prompt": _text(prompt), "workflow": _text(workflow)}
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def encoder(text, clip=("4", 1)):
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return {
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"class_type": "CLIPTextEncode",
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"inputs": {"text": text, "clip": list(clip)},
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}
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def ksampler(positive, negative, model=("4", 0), **settings):
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inputs = {
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"seed": 5,
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"steps": 20,
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"cfg": 7,
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"sampler_name": "euler",
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"scheduler": "normal",
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"denoise": 1.0,
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"model": list(model),
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"positive": list(positive),
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"negative": list(negative),
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"latent_image": ["2", 0],
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}
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inputs.update(settings)
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return {"class_type": "KSampler", "inputs": inputs}
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CHECKPOINT = {
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"class_type": "CheckpointLoaderSimple",
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"inputs": {"ckpt_name": "example.safetensors"},
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}
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LATENT = {
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"class_type": "EmptyLatentImage",
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"inputs": {"width": 512, "height": 512, "batch_size": 1},
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}
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def _basic():
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return {
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"4": CHECKPOINT,
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"2": LATENT,
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"6": encoder("a cat on a windowsill"),
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"7": encoder("blurry, watermark"),
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"3": ksampler(("6", 0), ("7", 0)),
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"9": {"class_type": "SaveImage", "inputs": {"images": ["8", 0]}},
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}
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def _prompt_manager(text, prepend="", append=""):
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return {
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"class_type": "PromptManager",
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"inputs": {
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"text": text,
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"category": "",
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"tags": "",
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"search_text": "",
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"prepend_text": prepend,
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"append_text": append,
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"clip": ["4", 1],
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},
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}
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def _shared_prompt_pipeline():
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"""One PromptManager pair feeds a base pass, an upscale pass and a detailer."""
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return {
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"1": CHECKPOINT,
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"5": ksampler(
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("25", 0),
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("26", 0),
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("1", 0),
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seed=111,
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steps=4,
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cfg=1.0,
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sampler_name="lcm",
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latent_image=["20", 0],
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),
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"6": {
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"class_type": "VAEDecode",
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"inputs": {"samples": ["5", 0], "vae": ["1", 2]},
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},
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"11": {
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"class_type": "VAEEncode",
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"inputs": {"pixels": ["6", 0], "vae": ["1", 2]},
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},
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"12": ksampler(
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("25", 0),
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("26", 0),
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("1", 0),
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seed=222,
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steps=6,
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cfg=1.0,
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sampler_name="lcm",
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denoise=0.45,
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latent_image=["11", 0],
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),
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"13": {
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"class_type": "VAEDecode",
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"inputs": {"samples": ["12", 0], "vae": ["1", 2]},
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},
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"17": {
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"class_type": "FaceDetailer",
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"inputs": {
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"seed": 333,
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"steps": 4,
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"cfg": 1.0,
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"sampler_name": "lcm",
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"image": ["13", 0],
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"model": ["1", 0],
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"clip": ["1", 1],
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"vae": ["1", 2],
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"positive": ["25", 0],
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"negative": ["26", 0],
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},
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},
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"20": {
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"class_type": "EmptyLatentImage",
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"inputs": {"width": 640, "height": 1536, "batch_size": 1},
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},
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"25": _prompt_manager("a lighthouse at dusk, oil painting"),
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"26": _prompt_manager("blurry, watermark"),
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"48": {
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"class_type": "KikoSaveImage",
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"inputs": {"filename_prefix": ["49", 0], "images": ["17", 0]},
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},
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"49": {
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"class_type": "KikoProjectFilePath",
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"inputs": {"project_name": "example"},
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},
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}
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def _custom_sampler(guider):
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return {
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"13": CHECKPOINT,
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"20": encoder("a red fox in snow", clip=("13", 1)),
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"21": encoder("lowres", clip=("13", 1)),
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"22": guider,
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"25": {"class_type": "RandomNoise", "inputs": {"noise_seed": 42}},
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"16": {"class_type": "KSamplerSelect", "inputs": {"sampler_name": "dpmpp_2m"}},
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"17": {
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"class_type": "BasicScheduler",
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"inputs": {
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"scheduler": "karras",
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"steps": 8,
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"denoise": 1.0,
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"model": ["13", 0],
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},
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},
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"1300:8": {
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"class_type": "SamplerCustomAdvanced",
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"inputs": {
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"noise": ["25", 0],
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"guider": ["22", 0],
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"sampler": ["16", 0],
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"sigmas": ["17", 0],
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"latent_image": ["5", 0],
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},
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},
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}
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def _workflow_only():
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return {
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"nodes": [
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{
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"id": 4,
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"type": "CheckpointLoaderSimple",
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"mode": 0,
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"inputs": [],
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"widgets_values": ["example.safetensors"],
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},
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{
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"id": 6,
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"type": "CLIPTextEncode",
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"mode": 0,
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"inputs": [{"name": "clip", "link": 3}],
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"widgets_values": ["a tidepool at noon"],
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},
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{
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"id": 7,
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"type": "CLIPTextEncode",
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"mode": 0,
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"inputs": [{"name": "clip", "link": 5}],
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"widgets_values": ["noisy"],
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},
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{
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"id": 10,
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"type": "Reroute",
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"mode": 0,
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"inputs": [{"name": "", "link": 7}],
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},
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{
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"id": 3,
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"type": "KSampler",
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"mode": 0,
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"inputs": [
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{"name": "model", "link": 1},
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{"name": "positive", "link": 8},
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{"name": "negative", "link": 4},
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{"name": "latent_image", "link": 2},
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],
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"widgets_values": [314, "randomize", 25, 6.5, "dpmpp_2m", "karras", 1],
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},
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{
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"id": 11,
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"type": "CLIPTextEncode",
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"mode": 2,
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"inputs": [],
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"widgets_values": ["muted node text"],
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},
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],
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"links": [
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[1, 4, 0, 3, 0, "MODEL"],
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[3, 4, 1, 6, 0, "CLIP"],
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[5, 4, 1, 7, 0, "CLIP"],
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[7, 6, 0, 10, 0, "CONDITIONING"],
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[8, 10, 0, 3, 1, "CONDITIONING"],
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[4, 7, 0, 3, 2, "CONDITIONING"],
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{
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"id": 2,
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"origin_id": 99,
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"origin_slot": 0,
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"target_id": 3,
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"target_slot": 3,
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"type": "LATENT",
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},
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],
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}
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def _chain(length):
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graph = {"4": CHECKPOINT, "1": encoder("deep chain text")}
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previous = "1"
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for index in range(length):
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node_id = str(100 + index)
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graph[node_id] = {
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"class_type": "ConditioningPass",
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"inputs": {"conditioning": [previous, 0]},
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}
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previous = node_id
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graph["3"] = ksampler((previous, 0), ("1", 0))
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return graph
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CASES = {
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"basic": case(_basic()),
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"prompt-manager-prepend-append": case(
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{
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"4": CHECKPOINT,
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"6": _prompt_manager("a cat", prepend="masterpiece", append="4k"),
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"7": _prompt_manager(
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"masterpiece, a dog", prepend="masterpiece", append="4k"
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),
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"3": ksampler(("6", 0), ("7", 0)),
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}
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),
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"linked-text-concat": case(
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{
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"4": CHECKPOINT,
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"10": {"class_type": "PrimitiveString", "inputs": {"value": "first part"}},
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"11": {
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"class_type": "StringConcatenate",
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"inputs": {
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"string_a": ["10", 0],
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"string_b": "second part",
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"delimiter": ", ",
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"seed": 3,
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"string_c": " ",
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},
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},
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"6": {
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"class_type": "CLIPTextEncode",
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"inputs": {"text": ["11", 0], "clip": ["4", 1]},
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},
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"7": encoder(""),
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"3": ksampler(("6", 0), ("7", 0)),
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}
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),
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"prompt-manager-pair": case(
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{
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"4": CHECKPOINT,
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"8": {
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"class_type": "PromptManagerPair",
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"inputs": {
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"text": "pair positive",
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"negative_text": "pair negative",
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"prepend_text": "",
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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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"3": ksampler(("8", 0), ("8", 1)),
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}
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),
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"conditioning-passthrough": case(
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{
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"4": CHECKPOINT,
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"6": encoder("through a controlnet"),
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"7": encoder("negative through it"),
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"12": {
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"class_type": "ControlNetApplyAdvanced",
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"inputs": {
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"positive": ["6", 0],
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"negative": ["7", 0],
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"control_net": ["13", 0],
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},
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},
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"3": ksampler(("12", 0), ("12", 1)),
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}
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),
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"first-sampler-in-js-key-order": case(
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{
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"4": CHECKPOINT,
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"6": encoder("from sampler twelve"),
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"7": encoder("from sampler five"),
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"x": ksampler(("6", 0), ("6", 0)),
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"12": ksampler(("6", 0), ("6", 0), seed=12),
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"5": ksampler(("7", 0), ("7", 0), seed=55),
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}
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),
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"custom-sampler-cfg-guider": case(
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_custom_sampler(
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{
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"class_type": "CFGGuider",
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"inputs": {
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"model": ["13", 0],
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"positive": ["20", 0],
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"negative": ["21", 0],
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"cfg": 3.5,
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},
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}
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)
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),
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"custom-sampler-basic-guider": case(
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_custom_sampler(
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{
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"class_type": "BasicGuider",
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"inputs": {"model": ["13", 0], "conditioning": ["20", 0]},
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}
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)
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),
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"no-sampler-loose-settings": case(
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{
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"4": CHECKPOINT,
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"3": {
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"class_type": "MyKSamplerVariant",
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"inputs": {
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"noise_seed": 9,
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"steps": 12,
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"cfg": 2,
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"sampler_name": "ddim",
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},
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},
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}
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),
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"primitive-seed": case(
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{
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"4": CHECKPOINT,
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"6": encoder("seeded"),
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"30": {
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"class_type": "Seed (rgthree)",
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"inputs": {"other": "x", "seed": 77},
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},
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"3": ksampler(("6", 0), ("6", 0), seed=["30", 0]),
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}
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),
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"workflow-only": case(workflow=_workflow_only()),
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"overwritten-prompt-manager-text": case(
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{
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"4": CHECKPOINT,
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"25": _prompt_manager("same saved text"),
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"26": _prompt_manager("same saved text"),
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"3": ksampler(("25", 0), ("26", 0)),
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},
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{
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"nodes": [
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{
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"id": 25,
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"type": "PromptManager",
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"widgets_values": ["the real positive"],
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},
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{
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"id": 26,
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"type": "PromptManager",
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"widgets_values": ["the real negative"],
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},
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],
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"links": [],
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},
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),
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"conditioning-cycle": case(
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{
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"4": CHECKPOINT,
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"20": {
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"class_type": "ConditioningCombine",
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"inputs": {"conditioning_1": ["21", 0]},
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},
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"21": {
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"class_type": "ConditioningCombine",
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"inputs": {"conditioning_1": ["20", 0]},
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},
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"3": ksampler(("20", 0), ("21", 0)),
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}
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),
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"chain-within-visit-limit": case(_chain(450)),
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"chain-beyond-visit-limit": case(_chain(600)),
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"chain-at-last-visit": case(_chain(499)),
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"chain-one-past-last-visit": case(_chain(500)),
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"blank-text-and-zero-seed": case(
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{
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"4": CHECKPOINT,
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"6": encoder(" "),
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"7": encoder("kept negative"),
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"3": ksampler(("6", 0), ("7", 0), seed=0),
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}
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),
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"shared-prompt-pipeline": case(_shared_prompt_pipeline()),
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"null-workflow-node": case(_basic(), {"nodes": [None], "links": []}),
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"unparsable-records": {"prompt": '{"3": NaN}', "workflow": "not json"},
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"seed-beyond-double-precision": case(
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{
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"4": CHECKPOINT,
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"6": encoder("x"),
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"3": ksampler(("6", 0), ("6", 0), cfg=1.0, seed=4974052037430000001),
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}
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),
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"nothing": case(),
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}
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