总工andmultica-agent
e6f761f8c8
- 判型器 classify_usage：按输出节点（sink 检测）与被消费图片槽判定 文生图/图生图/文生视频/图生视频/参考图生视频，不依赖文件名/模型名/ 固定节点 ID；歧义（单散装图+视频、同图双输出）与无输出给中文说明 - 参考图组泛化：任意 <group>.<prefix>N 连续编号且均接图源的输入成组 （MiniMaxH3 特判仅剩标签美化）；patcher 支持通用组回填 - workflows 表新增 usage/usage_source，幂等迁移回填；判不出的存量行 停用待确认；kind 语义收窄为输出媒体，历史无损 - 导入改两步向导：分析→预览（判定依据/输出节点/媒体槽角色可改/ 参数预览/警告）→确认保存；usage_source=user 的纠正不被重分析覆盖 - 生成页按 usage 过滤；待确认工作流不进生成页且提交被拒 - 新增 test_usage_classifier / test_workflow_import_confirm； 两个旧测试夹具补输出节点以匹配新守卫 Co-authored-by: multica-agent <github@multica.ai>
309 lines
12 KiB
Python
309 lines
12 KiB
Python
"""HEL-636 A1–A6: the five-usage classifier judges from graph evidence only.
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Every case feeds real (or minimally mutated) ComfyUI API exports — never a
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file name, model name or fixed node id — and asserts the usage, the media
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slots it implies, and the failure paths for ambiguous / unknown graphs.
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"""
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from __future__ import annotations
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import json
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from pathlib import Path
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from calliope.comfyui.lint import blocking_problem, workflow_warnings
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from calliope.comfyui.parser import (
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classify_usage,
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detect_untagged_inputs,
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parse_dynamic_inputs,
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parse_dynamic_outputs,
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)
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from calliope.comfyui.patcher import patch_workflow
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FIXTURES = Path(__file__).parent / "fixtures" / "comfy_native"
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EXAMPLES = Path(__file__).resolve().parents[2] / "example_ComfyUI_workflows"
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def _load(path: Path) -> dict:
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return json.loads(path.read_text(encoding="utf-8"))
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def _rename_everything(wf: dict, id_shift: int = 7) -> dict:
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"""Change every node id, title and widget value the classifier must not
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depend on (model file names / prompt texts)."""
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def new_id(nid: str) -> str:
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return f"x{int(nid) * id_shift + 3}" if nid.isdigit() else "x" + nid.replace(":", "_")
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out: dict = {}
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for nid, node in wf.items():
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out[new_id(nid)] = json.loads(json.dumps(node))
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for node in out.values():
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for val in (node.get("inputs") or {}).values():
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if isinstance(val, list) and len(val) == 2 and str(val[0]).isdigit():
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val[0] = new_id(str(val[0]))
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if node.get("_meta", {}).get("title"):
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node["_meta"]["title"] = f"改·{node['_meta']['title']}"
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for key, val in list((node.get("inputs") or {}).items()):
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if isinstance(val, str) and val.endswith((".safetensors", ".gguf")):
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node["inputs"][key] = "另一个模型_" + val
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return out
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def _media(inputs: list[dict]) -> list[dict]:
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return [
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i
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for i in inputs
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if i["kind"] in ("image", "image_list", "video", "audio")
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]
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# ---------------------------------------------------------------------------
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# A1 / A2 / A3: the three real shapes, plus id/title/model renames
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# ---------------------------------------------------------------------------
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def test_a1_native_t2i_is_text2image_and_form_has_no_image_slot():
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wf = _load(FIXTURES / "qwen_image_t2i_API.json")
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c = classify_usage(wf)
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assert c["usage"] == "text2image"
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assert c["ambiguous"] is False and c["needs_mapping"] is False
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assert _media(parse_dynamic_inputs(wf)) == []
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outs = parse_dynamic_outputs(wf)
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assert [(o["nodeId"], o["kind"]) for o in outs] == [("60", "image")]
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def test_a2_qwen_edit_variant_with_new_ids_titles_and_model_stays_image2image():
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wf = _rename_everything(_load(FIXTURES / "qwen_image_edit_mask_API.json"))
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c = classify_usage(wf)
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assert c["usage"] == "image2image"
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inputs = parse_dynamic_inputs(wf)
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img = [i for i in _media(inputs) if i.get("role") == "image"]
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assert len(img) == 1 and img[0]["required"] is True
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mask = [i for i in _media(inputs) if i.get("role") == "mask"]
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assert len(mask) == 1 # mask never counts as an input image
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def test_a3_i2v_variant_renamed_still_image2video_first_frame_only():
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wf = _rename_everything(_load(FIXTURES / "h3_i2v_04_API.json"))
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c = classify_usage(wf)
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assert c["usage"] == "image2video"
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media = _media(parse_dynamic_inputs(wf))
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assert [i.get("role") for i in media] == ["first_frame"]
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# A3: no first/last pair → the pair validation must not fire
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assert blocking_problem(wf) is None
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def test_a4_ref2va_variant_with_renamed_class_and_prefix_still_groups():
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wf = _rename_everything(_load(FIXTURES / "h3_ref2va_05_API.json"))
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for node in wf.values():
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if node.get("class_type") == "MiniMaxH3ReferenceToVideo":
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node["class_type"] = "MyFancyRefToVideo"
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inputs = node["inputs"]
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for k in list(inputs):
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if k.startswith("ref_images.ref_image_"):
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inputs[k.replace("ref_images.ref_image_", "refs.pic_")] = inputs.pop(k)
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c = classify_usage(wf)
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assert c["usage"] == "ref2video"
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groups = [i for i in detect_untagged_inputs(wf) if i["kind"] == "image_list"]
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assert len(groups) == 1
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g = groups[0]
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assert g["role"] == "reference_image" and (g["min"], g["max"]) == (1, 9)
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# patch rewires the renamed group in order and refuses a 10th image
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key = g["nodeId"]
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node_id = key.split("@")[0]
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patched = patch_workflow(wf, {key: [f"/u/{i}.png" for i in range(4)]})
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names = sorted(
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(k for k in patched[node_id]["inputs"] if k.startswith("refs.pic_")),
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key=lambda k: int(k.rsplit("_", 1)[-1]) if k.rsplit("_", 1)[-1].isdigit() else int(k[-1]),
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)
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assert names == [f"refs.pic_{i}" for i in range(4)]
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from calliope.comfyui.validate import validate_input_values
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_c, errs = validate_input_values(
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detect_untagged_inputs(wf), {key: [f"/u/{i}.png" for i in range(10)]}
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)
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assert any("最多 9 张" in e for e in errs)
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def test_qwen_single_numbered_encoder_input_is_not_mistaken_for_a_group():
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"""TextEncodeQwenImage21's ``images.image_1`` stays an ordinary input slot."""
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inputs = parse_dynamic_inputs(_load(FIXTURES / "qwen21_edit_sage_API.json"))
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assert not any(i["kind"] == "image_list" for i in inputs)
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assert any(i.get("role") == "image" for i in inputs)
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# ---------------------------------------------------------------------------
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# Judgment table rows from the real example library
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# ---------------------------------------------------------------------------
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def test_all_real_exports_classify():
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expect = {
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"qwen_image_t2i_API.json": "text2image",
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"qwen_image_edit_mask_API.json": "image2image",
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"qwen21_edit_sage_API.json": "image2image",
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"h3_i2v_04_API.json": "image2video",
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"h3_ref2va_05_API.json": "ref2video",
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"minimax_h3_t2v_untagged_API.json": "text2video",
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"wen_sheng_tu_API.json": "text2image",
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"wen_sheng_shipin_API.json": "text2video",
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}
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for name, usage in expect.items():
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assert classify_usage(_load(FIXTURES / name))["usage"] == usage, name
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examples = {
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"Krea2_t2i_20260818_API.json": "text2image",
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"krea2_T2I_CharSheetV1_API.json": "text2image",
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"minimax_h3-Turbo_Text2Video_20260813_API.json": "text2video",
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"minimax_h3-Turbo_Image2Video_20260813_API.json": "text2video",
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"MiniMax-H3-Turbo-R2V-Extend-2Refs1Vid_API.json": "ref2video",
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"video_minimax_h3_r2v_2ref_API.json": "ref2video",
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"video_minimax_h3_r2v_3ref_API.json": "ref2video",
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"video_minimax_h3_r2v_4ref_API.json": "ref2video",
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"video_minimax_h3_r2v_5ref_API.json": "ref2video",
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}
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for name, usage in examples.items():
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assert classify_usage(_load(EXAMPLES / name))["usage"] == usage, name
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def test_r2v_extend_reports_its_video_input_slot():
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wf = _load(EXAMPLES / "MiniMax-H3-Turbo-R2V-Extend-2Refs1Vid_API.json")
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c = classify_usage(wf)
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assert c["usage"] == "ref2video"
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assert c["evidence"]["video_slots"], "the reference-video slot must be reported"
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assert any("视频输入槽" in line for line in c["evidence"]["summary"])
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# ---------------------------------------------------------------------------
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# A5: ambiguity — single loose image into a video graph; multi-output graph
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# ---------------------------------------------------------------------------
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def _load_image(nid: str) -> dict:
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return {
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"class_type": "LoadImage",
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"inputs": {"image": "x.png"},
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"_meta": {"title": f"图 {nid}"},
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}
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def test_single_loose_image_with_video_output_is_ambiguous():
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wf = {
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"1": {"class_type": "PrimitiveStringMultiline", "inputs": {"value": "p"}},
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"5": {"class_type": "LoadImage", "inputs": {"image": "x.png"}},
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"7": {
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"class_type": "SomeVideoSampler",
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"inputs": {"prompt": ["1", 0], "image": ["5", 0]},
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},
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"9": {"class_type": "SaveVideo", "inputs": {"images": ["7", 0]}},
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}
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c = classify_usage(wf)
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assert c["usage"] is None and c["ambiguous"] is True
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assert "首帧还是参考图" in c["problem"]
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def test_first_frame_role_decides_image2video_without_ambiguity():
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wf = {
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"1": {"class_type": "PrimitiveStringMultiline", "inputs": {"value": "p"}},
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"5": {"class_type": "LoadImage", "inputs": {"image": "x.png"}},
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"7": {
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"class_type": "SomeVideoSampler",
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"inputs": {"prompt": ["1", 0], "first_frame": ["5", 0]},
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},
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"9": {"class_type": "SaveVideo", "inputs": {"images": ["7", 0]}},
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}
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assert classify_usage(wf)["usage"] == "image2video"
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def test_same_graph_both_image_and_video_outputs_is_ambiguous():
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wf = {
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"1": {"class_type": "PrimitiveStringMultiline", "inputs": {"value": "p"}},
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"5": {"class_type": "LoadImage", "inputs": {"image": "x.png"}},
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"7": {
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"class_type": "SomeVideoSampler",
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"inputs": {"prompt": ["1", 0], "image": ["5", 0]},
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},
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"9": {"class_type": "SaveVideo", "inputs": {"images": ["7", 0]}},
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"10": {"class_type": "SaveImage", "inputs": {"images": ["7", 1]}},
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}
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c = classify_usage(wf)
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assert c["usage"] is None and c["ambiguous"] is True
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assert "同时输出图片和视频" in c["problem"]
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def test_two_reference_images_into_a_custom_node_become_a_group_ref2video():
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wf = {
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"1": {"class_type": "PrimitiveStringMultiline", "inputs": {"value": "p"}},
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"5": {"class_type": "LoadImage", "inputs": {"image": "a.png"}},
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"6": {"class_type": "LoadImage", "inputs": {"image": "b.png"}},
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"7": {
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"class_type": "GenericRefToVideo",
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"inputs": {
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"prompt": ["1", 0],
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"refs.pic_1": ["5", 0],
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"refs.pic_2": ["6", 0],
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},
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},
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"9": {"class_type": "SaveVideo", "inputs": {"images": ["7", 0]}},
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}
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c = classify_usage(wf)
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assert c["usage"] == "ref2video"
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groups = [i for i in detect_untagged_inputs(wf) if i["kind"] == "image_list"]
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assert [g["nodeId"] for g in groups] == ["7@refs"]
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# ---------------------------------------------------------------------------
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# A6: failure paths — no output, unknown custom output nodes
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# ---------------------------------------------------------------------------
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def test_no_output_graph_reports_chinese_error_and_blocks():
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wf = {
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"1": {"class_type": "CLIPTextEncode", "inputs": {"text": "p"}},
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"2": {"class_type": "VAEDecode", "inputs": {"samples": ["1", 0]}},
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}
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c = classify_usage(wf)
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assert c["usage"] is None and c["ambiguous"] is False
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assert "未在图中找到输出节点" in c["problem"]
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assert blocking_problem(wf) and "未在图中找到输出节点" in blocking_problem(wf)
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def test_unknown_custom_save_node_needs_mapping_and_keeps_a_runnable_path():
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wf = {
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"1": {"class_type": "PrimitiveStringMultiline", "inputs": {"value": "p"}},
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"7": {
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"class_type": "MyCloudExporter",
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"inputs": {"filename_prefix": "out", "render": ["8", 0]},
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},
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"8": {"class_type": "VAEDecode", "inputs": {"samples": ["1", 0]}},
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}
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c = classify_usage(wf)
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assert c["needs_mapping"] is True
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assert c["usage"] is None and "无法识别" in (c["problem"] or "")
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# unknown savers are a warning (user maps them at import), not a block —
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# once the user confirms the usage the job can still run and results are
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# pulled from ComfyUI history's images/gifs/videos keys.
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codes = {(w["level"], w["code"]) for w in workflow_warnings(wf)}
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assert ("warning", "unknown_output_node") in codes
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assert ("error", "no_output") not in codes
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def test_utility_sink_tail_is_not_an_output_and_does_not_break_detection():
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"""VideoCombine feeding a cache-cleanup tail still counts as the output."""
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wf = _load(FIXTURES / "minimax_h3_t2v_untagged_API.json")
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wf["9999"] = {
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"class_type": "easy cleanGpuUsed",
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"inputs": {"anything": ["5978", 0]},
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}
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c = classify_usage(wf)
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assert c["usage"] == "text2video"
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assert c["needs_mapping"] is False
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assert workflow_warnings(wf) == []
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def test_evidence_lists_output_nodes_and_slots():
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wf = _load(FIXTURES / "h3_ref2va_05_API.json")
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c = classify_usage(wf)
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ev = c["evidence"]
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assert any(o["nodeId"] == "92" and o["kind"] == "video" for o in ev["outputs"])
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assert any(s["kind"] == "image_list" for s in ev["image_slots"])
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assert any("输出" in line for line in ev["summary"])
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