mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-09-21 11:11:26 -03:00
feat(recipes): reconnect suggestions, undo, and base-model family tolerance
Enhance the deleted-LoRA reconnect flow in the recipe modal: - Suggest local reconnect candidates when the panel opens, ranked by identity (same hash / same CivitAI version) then filename/name similarity, with a hard filter on confident base-model mismatches; the input gets a Combobox backed by the same endpoint as you type. - Snapshot the pre-reconnect entry and offer a permanent restore: reconnected entries show an undo icon at the right end of the info row, with the original filename in the tooltip. - Relax the manual reconnect base-model guard to a three-tier check: exact/unknown labels pass silently, same-architecture families (e.g. Pony <-> Illustrious) pass with a warning toast, and only cross-architecture mismatches stay hard-rejected.
This commit is contained in:
Will Miao
@@ -16,6 +16,7 @@ from py.services.recipe_scanner import RecipeScanner
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from py.services import settings_manager as settings_manager_module
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from py.utils.models import BaseModelMetadata
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from py.utils.utils import calculate_recipe_fingerprint
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from py.services.recipes.errors import RecipeValidationError
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async def _wait_for_resort(scanner: RecipeScanner) -> None:
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@@ -164,6 +165,285 @@ async def test_local_lora_lookup_requires_unambiguous_name_and_matching_base_mod
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assert await scanner.get_local_lora_by_hash("b" * 64) is models[1]
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def _suggestion_item(**overrides):
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item = {
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"sha256": "ab" * 32,
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"file_name": "style.safetensors",
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"file_path": "/models/loras/style.safetensors",
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"folder": "",
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"model_name": "Style LoRA",
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"base_model": "SD 1.5",
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"preview_url": "/preview/style.png",
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}
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item.update(overrides)
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return item
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@pytest.mark.asyncio
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async def test_suggest_reconnect_candidates_same_hash_ranks_first(recipe_scanner):
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scanner, stub = recipe_scanner
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stub.cache_version = 1
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same_hash = _suggestion_item(
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file_name="zzz-unrelated.safetensors",
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file_path="/models/loras/zzz-unrelated.safetensors",
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model_name="Unrelated",
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)
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similar = _suggestion_item(
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sha256="cd" * 32,
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file_name="anime-style-v2.safetensors",
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file_path="/models/loras/anime-style-v2.safetensors",
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model_name="Anime Style",
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)
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stub._cache.raw_data = [same_hash, similar]
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suggestions = await scanner.suggest_reconnect_candidates(
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entry={"hash": "ab" * 32, "file_name": "anime-style-v2.safetensors"},
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recipe_base_model="SD 1.5",
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)
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assert suggestions[0]["match_reason"] == "same_hash"
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assert suggestions[0]["file_path"] == same_hash["file_path"]
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assert suggestions[0]["score"] >= 1.0
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assert any(s["match_reason"] == "similar_filename" for s in suggestions[1:])
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@pytest.mark.asyncio
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async def test_suggest_reconnect_candidates_same_version(recipe_scanner):
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scanner, stub = recipe_scanner
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item = _suggestion_item()
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stub._cache.raw_data = [item]
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stub._cache.version_index[456] = item
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suggestions = await scanner.suggest_reconnect_candidates(
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entry={"modelVersionId": 456},
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recipe_base_model="SD 1.5",
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)
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assert len(suggestions) == 1
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assert suggestions[0]["match_reason"] == "same_version"
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assert suggestions[0]["score"] >= 0.95
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@pytest.mark.asyncio
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async def test_suggest_reconnect_candidates_base_model_mismatch_excluded(recipe_scanner):
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scanner, stub = recipe_scanner
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matching = _suggestion_item(
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file_name="anime-style.safetensors",
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file_path="/models/loras/anime-style.safetensors",
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model_name="Anime Style",
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base_model="SD 1.5",
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)
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mismatched = _suggestion_item(
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sha256="cd" * 32,
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file_name="anime-style.safetensors",
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file_path="/models/loras/sdxl/anime-style.safetensors",
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folder="sdxl",
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model_name="Anime Style",
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base_model="SDXL 1.0",
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)
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stub._cache.raw_data = [matching, mismatched]
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suggestions = await scanner.suggest_reconnect_candidates(
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entry={"file_name": "anime-style.safetensors"},
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recipe_base_model="SD 1.5",
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)
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# A confident base-model mismatch is a hard rejection — reconnect itself
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# enforces that rule, so suggesting the mismatch would guarantee failure.
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assert [s["file_path"] for s in suggestions] == [matching["file_path"]]
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assert suggestions[0]["target_name"] == "anime-style"
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@pytest.mark.asyncio
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async def test_suggest_reconnect_candidates_base_model_unknown_stays_eligible(recipe_scanner):
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scanner, stub = recipe_scanner
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unknown_item = _suggestion_item(
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file_name="anime-style.safetensors",
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file_path="/models/loras/anime-style.safetensors",
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model_name="Anime Style",
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base_model="",
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)
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stub._cache.raw_data = [unknown_item]
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# Unknown base model on the item side must not be rejected — reconnect
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# accepts it too (find_matching_models lenient guard).
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suggestions = await scanner.suggest_reconnect_candidates(
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entry={"file_name": "anime-style.safetensors"},
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recipe_base_model="SD 1.5",
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)
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assert [s["file_path"] for s in suggestions] == [unknown_item["file_path"]]
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@pytest.mark.asyncio
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async def test_suggest_reconnect_candidates_same_hash_mismatched_base_model_excluded(
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recipe_scanner,
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):
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scanner, stub = recipe_scanner
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stub.cache_version = 1
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mismatched = _suggestion_item(
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file_name="zzz-unrelated.safetensors",
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file_path="/models/loras/zzz-unrelated.safetensors",
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model_name="Unrelated",
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base_model="SDXL 1.0",
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)
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stub._cache.raw_data = [mismatched]
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# Even the strongest identity signal (same hash) must not surface a
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# candidate that reconnect would reject on base-model grounds.
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suggestions = await scanner.suggest_reconnect_candidates(
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entry={"hash": "ab" * 32, "file_name": "other.safetensors"},
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recipe_base_model="SD 1.5",
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)
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assert suggestions == []
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@pytest.mark.asyncio
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async def test_suggest_reconnect_candidates_basename_collision_uses_folder_path(recipe_scanner):
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scanner, stub = recipe_scanner
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first = _suggestion_item(
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file_name="anime-style.safetensors",
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file_path="/models/loras/anime-style.safetensors",
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model_name="Anime Style",
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base_model="SD 1.5",
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)
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second = _suggestion_item(
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sha256="cd" * 32,
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file_name="anime-style.safetensors",
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file_path="/models/loras/sd15/anime-style.safetensors",
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folder="sd15",
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model_name="Anime Style v2",
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base_model="SD 1.5",
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)
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stub._cache.raw_data = [first, second]
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suggestions = await scanner.suggest_reconnect_candidates(
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entry={"file_name": "anime-style.safetensors"},
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recipe_base_model="SD 1.5",
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)
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# Duplicate basenames disambiguate target_name with the folder path.
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assert {s["target_name"] for s in suggestions} == {"anime-style", "sd15/anime-style"}
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scanner, stub = recipe_scanner
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checkpoint = _suggestion_item(sub_type="checkpoint")
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lora = _suggestion_item(
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sha256="cd" * 32,
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file_path="/models/loras/other/style.safetensors",
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folder="other",
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)
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stub._cache.raw_data = [checkpoint, lora]
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suggestions = await scanner.suggest_reconnect_candidates(
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entry={"file_name": "style.safetensors"},
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recipe_base_model=None,
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)
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assert all(s["file_path"] != checkpoint["file_path"] for s in suggestions)
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assert any(s["file_path"] == lora["file_path"] for s in suggestions)
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@pytest.mark.asyncio
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async def test_suggest_reconnect_candidates_respects_limit(recipe_scanner):
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scanner, stub = recipe_scanner
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stub._cache.raw_data = [
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_suggestion_item(
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sha256=f"{i:064x}",
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file_name=f"anime-style-{i}.safetensors",
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file_path=f"/models/loras/anime-style-{i}.safetensors",
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model_name=f"Anime Style {i}",
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)
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for i in range(10)
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]
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suggestions = await scanner.suggest_reconnect_candidates(
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entry={"file_name": "anime-style.safetensors"},
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recipe_base_model="SD 1.5",
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limit=3,
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)
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assert len(suggestions) == 3
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@pytest.mark.asyncio
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async def test_suggest_reconnect_candidates_query_substring(recipe_scanner):
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scanner, stub = recipe_scanner
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item = _suggestion_item(
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file_name="anime-style.safetensors",
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file_path="/models/loras/anime-style.safetensors",
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model_name="Anime Style",
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)
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stub._cache.raw_data = [item]
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suggestions = await scanner.suggest_reconnect_candidates(
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entry={"file_name": "unrelated.safetensors"},
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recipe_base_model="SD 1.5",
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query="anime",
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)
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assert len(suggestions) == 1
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assert suggestions[0]["match_reason"] == "similar_filename"
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# Substring hits floor the ratio at 0.8: 0.5 + 0.4 * 0.8 + 0.1 base boost.
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assert suggestions[0]["score"] == 0.92
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assert suggestions[0]["target_name"] == "anime-style"
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@pytest.mark.asyncio
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async def test_suggest_reconnect_candidates_skips_items_without_hash(recipe_scanner):
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scanner, stub = recipe_scanner
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no_hash = _suggestion_item(sha256="")
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stub._cache.raw_data = [no_hash]
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suggestions = await scanner.suggest_reconnect_candidates(
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entry={"file_name": "style.safetensors"},
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recipe_base_model="SD 1.5",
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)
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assert suggestions == []
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@pytest.mark.asyncio
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async def test_suggest_reconnect_candidates_short_query_no_substring_floor(recipe_scanner):
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scanner, stub = recipe_scanner
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item = _suggestion_item(
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file_name="anime-style.safetensors",
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file_path="/models/loras/anime-style.safetensors",
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model_name="Anime Style",
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)
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stub._cache.raw_data = [item]
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# A 1-2 character query is a substring of nearly everything; it must NOT
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# floor the ratio, otherwise every library item surfaces as a suggestion.
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suggestions = await scanner.suggest_reconnect_candidates(
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entry={"file_name": "unrelated.safetensors"},
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recipe_base_model="SD 1.5",
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query="a",
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)
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assert suggestions == []
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@pytest.mark.asyncio
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async def test_suggest_reconnect_candidates_name_threshold_filters_generic_overlap(recipe_scanner):
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scanner, stub = recipe_scanner
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item = _suggestion_item(
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file_name="not-artists-styles-pony.safetensors",
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file_path="/models/loras/not-artists-styles-pony.safetensors",
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model_name="Not Artists Styles for Pony Diffusion V6 XL",
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)
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stub._cache.raw_data = [item]
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# Long names sharing generic tokens ("style", "pony", "diffusion") score
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# ~0.638 — below the name-similarity threshold, so unrelated models stay
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# out of the suggestions.
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suggestions = await scanner.suggest_reconnect_candidates(
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entry={"modelName": "Concept Art Twilight Style SDXL_LoRA_Pony Diffusion"},
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recipe_base_model="Pony",
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)
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assert suggestions == []
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def test_recipes_dir_uses_custom_settings_path(tmp_path: Path, monkeypatch):
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RecipeScanner._instance = None
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settings_manager_module.reset_settings_manager()
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@@ -331,6 +611,120 @@ async def test_update_lora_entry_updates_cache_and_file(tmp_path: Path, recipe_s
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assert cached_recipe["fingerprint"] == expected_fingerprint
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async def test_update_lora_entry_snapshots_previous_state(tmp_path: Path, recipe_scanner):
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scanner, stub = recipe_scanner
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recipes_dir = Path(config.loras_roots[0]) / "recipes"
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recipes_dir.mkdir(parents=True, exist_ok=True)
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recipe_id = "recipe-snapshot"
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recipe_path = recipes_dir / f"{recipe_id}.recipe.json"
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original_entry = {
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"file_name": "old",
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"strength": 1.0,
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"hash": "",
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"isDeleted": True,
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"exclude": True,
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}
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recipe_data = {
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"id": recipe_id,
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"file_path": str(tmp_path / "image.png"),
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"title": "Original",
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"modified": 0.0,
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"created_date": 0.0,
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"loras": [dict(original_entry)],
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}
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recipe_path.write_text(json.dumps(recipe_data))
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await scanner.add_recipe(dict(recipe_data))
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target_info = {
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"sha256": "abc123",
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"file_path": str(tmp_path / "loras" / "target.safetensors"),
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"preview_url": "preview.png",
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"civitai": {"id": 42, "name": "v1", "model": {"name": "Target"}},
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}
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stub.register_model("target", target_info)
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await scanner.update_lora_entry(
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recipe_id, 0, target_name="target", target_lora=target_info
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)
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with recipe_path.open("r", encoding="utf-8") as file_obj:
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persisted = json.load(file_obj)
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snapshot = persisted["loras"][0]["reconnectSnapshot"]
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assert snapshot == original_entry
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# Snapshots never nest
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assert "reconnectSnapshot" not in snapshot
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async def test_restore_lora_entry_round_trip(tmp_path: Path, recipe_scanner):
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scanner, stub = recipe_scanner
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recipes_dir = Path(config.loras_roots[0]) / "recipes"
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recipes_dir.mkdir(parents=True, exist_ok=True)
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recipe_id = "recipe-restore"
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recipe_path = recipes_dir / f"{recipe_id}.recipe.json"
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original_entry = {
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"file_name": "old",
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"strength": 1.0,
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"hash": "",
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"isDeleted": True,
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"exclude": True,
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}
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recipe_data = {
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"id": recipe_id,
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"file_path": str(tmp_path / "image.png"),
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"title": "Original",
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"modified": 0.0,
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"created_date": 0.0,
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"loras": [dict(original_entry)],
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}
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recipe_path.write_text(json.dumps(recipe_data))
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await scanner.add_recipe(dict(recipe_data))
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target_info = {
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"sha256": "abc123",
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"file_path": str(tmp_path / "loras" / "target.safetensors"),
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"preview_url": "preview.png",
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"civitai": {"id": 42, "name": "v1", "model": {"name": "Target"}},
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}
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stub.register_model("target", target_info)
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await scanner.update_lora_entry(
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recipe_id, 0, target_name="target", target_lora=target_info
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)
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restored_recipe, restored_lora = await scanner.restore_lora_entry(recipe_id, 0)
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entry = restored_recipe["loras"][0]
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assert entry == original_entry
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assert "reconnectSnapshot" not in entry
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assert restored_lora["isDeleted"] is True
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assert restored_lora["inLibrary"] is False
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assert restored_recipe["fingerprint"] == calculate_recipe_fingerprint([original_entry])
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with recipe_path.open("r", encoding="utf-8") as file_obj:
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persisted = json.load(file_obj)
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assert persisted["loras"][0] == original_entry
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assert persisted["fingerprint"] == restored_recipe["fingerprint"]
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async def test_restore_lora_entry_without_snapshot_rejected(tmp_path: Path, recipe_scanner):
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scanner, _ = recipe_scanner
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recipes_dir = Path(config.loras_roots[0]) / "recipes"
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recipes_dir.mkdir(parents=True, exist_ok=True)
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recipe_id = "recipe-no-snapshot"
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recipe_path = recipes_dir / f"{recipe_id}.recipe.json"
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recipe_path.write_text(
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json.dumps({"id": recipe_id, "loras": [{"file_name": "plain"}]})
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)
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with pytest.raises(RecipeValidationError):
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await scanner.restore_lora_entry(recipe_id, 0)
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async def test_set_lora_entry_hash_invalid_persists_flag(tmp_path: Path, recipe_scanner):
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scanner, _ = recipe_scanner
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recipes_dir = Path(config.loras_roots[0]) / "recipes"
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@@ -1315,6 +1315,189 @@ async def test_reconnect_lora_distinguishes_ambiguous_mismatched_and_missing(tmp
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)
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@pytest.mark.asyncio
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async def test_reconnect_lora_family_compatible_succeeds_with_warning(tmp_path):
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service = RecipePersistenceService(
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exif_utils=DummyExifUtils(),
|
||||
card_preview_width=512,
|
||||
logger=logging.getLogger("test"),
|
||||
)
|
||||
|
||||
pony_item = {
|
||||
"file_name": "style.safetensors",
|
||||
"folder": "",
|
||||
"file_path": "/models/loras/style.safetensors",
|
||||
"base_model": "Pony",
|
||||
"sha256": "ab" * 32,
|
||||
}
|
||||
|
||||
recipe_path = tmp_path / "recipe.json"
|
||||
recipe_path.write_text(
|
||||
json.dumps({"id": "r1", "base_model": "Illustrious", "loras": [{}]})
|
||||
)
|
||||
|
||||
class DummyScanner:
|
||||
async def get_recipe_json_path(self, recipe_id):
|
||||
return str(recipe_path)
|
||||
|
||||
async def find_local_loras_by_name(self, name, base_model=None):
|
||||
return [pony_item]
|
||||
|
||||
async def update_lora_entry(self, recipe_id, lora_index, *, target_name, target_lora):
|
||||
assert target_lora is pony_item
|
||||
return ({"id": "r1"}, {"file_name": target_lora["file_name"]})
|
||||
|
||||
async def find_recipes_by_fingerprint(self, fingerprint):
|
||||
return []
|
||||
|
||||
result = await service.reconnect_lora(
|
||||
recipe_scanner=DummyScanner(), recipe_id="r1", lora_index=0, target_name="style"
|
||||
)
|
||||
|
||||
assert result.payload["success"] is True
|
||||
assert result.payload["base_model_mismatch"] == {
|
||||
"recipe_base_model": "Illustrious",
|
||||
"lora_base_model": "Pony",
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_reconnect_lora_exact_base_model_has_no_warning(tmp_path):
|
||||
service = RecipePersistenceService(
|
||||
exif_utils=DummyExifUtils(),
|
||||
card_preview_width=512,
|
||||
logger=logging.getLogger("test"),
|
||||
)
|
||||
|
||||
item = {
|
||||
"file_name": "style.safetensors",
|
||||
"folder": "",
|
||||
"file_path": "/models/loras/style.safetensors",
|
||||
"base_model": "SDXL 1.0",
|
||||
"sha256": "ab" * 32,
|
||||
}
|
||||
|
||||
recipe_path = tmp_path / "recipe.json"
|
||||
recipe_path.write_text(
|
||||
json.dumps({"id": "r1", "base_model": "SDXL 1.0", "loras": [{}]})
|
||||
)
|
||||
|
||||
class DummyScanner:
|
||||
async def get_recipe_json_path(self, recipe_id):
|
||||
return str(recipe_path)
|
||||
|
||||
async def find_local_loras_by_name(self, name, base_model=None):
|
||||
return [item]
|
||||
|
||||
async def update_lora_entry(self, recipe_id, lora_index, *, target_name, target_lora):
|
||||
return ({"id": "r1"}, {"file_name": target_lora["file_name"]})
|
||||
|
||||
async def find_recipes_by_fingerprint(self, fingerprint):
|
||||
return []
|
||||
|
||||
result = await service.reconnect_lora(
|
||||
recipe_scanner=DummyScanner(), recipe_id="r1", lora_index=0, target_name="style"
|
||||
)
|
||||
|
||||
assert result.payload["success"] is True
|
||||
assert "base_model_mismatch" not in result.payload
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_reconnect_suggestions_loads_entry_and_delegates(tmp_path):
|
||||
service = RecipePersistenceService(
|
||||
exif_utils=DummyExifUtils(),
|
||||
card_preview_width=512,
|
||||
logger=logging.getLogger("test"),
|
||||
)
|
||||
|
||||
recipe_path = tmp_path / "recipe.json"
|
||||
recipe_path.write_text(
|
||||
json.dumps(
|
||||
{
|
||||
"id": "r1",
|
||||
"base_model": "SD 1.5",
|
||||
"loras": [
|
||||
{"file_name": "a.safetensors", "hash": "aaa"},
|
||||
{"file_name": "b.safetensors", "hash": "bbb", "isDeleted": True},
|
||||
],
|
||||
}
|
||||
)
|
||||
)
|
||||
|
||||
class DummyScanner:
|
||||
def __init__(self):
|
||||
self.calls = []
|
||||
|
||||
async def get_recipe_json_path(self, recipe_id):
|
||||
assert recipe_id == "r1"
|
||||
return str(recipe_path)
|
||||
|
||||
async def suggest_reconnect_candidates(
|
||||
self, *, entry, recipe_base_model, query=None, limit=5
|
||||
):
|
||||
self.calls.append(
|
||||
{
|
||||
"entry": entry,
|
||||
"recipe_base_model": recipe_base_model,
|
||||
"query": query,
|
||||
}
|
||||
)
|
||||
return [
|
||||
{
|
||||
"file_name": "b.safetensors",
|
||||
"score": 1.0,
|
||||
"match_reason": "same_hash",
|
||||
"target_name": "b",
|
||||
}
|
||||
]
|
||||
|
||||
scanner = DummyScanner()
|
||||
result = await service.get_reconnect_suggestions(
|
||||
recipe_scanner=scanner, recipe_id="r1", lora_index=1, query="b"
|
||||
)
|
||||
|
||||
assert result.payload["success"] is True
|
||||
assert result.payload["suggestions"][0]["target_name"] == "b"
|
||||
assert scanner.calls == [
|
||||
{
|
||||
"entry": {"file_name": "b.safetensors", "hash": "bbb", "isDeleted": True},
|
||||
"recipe_base_model": "SD 1.5",
|
||||
"query": "b",
|
||||
}
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_reconnect_suggestions_validates_recipe_and_index(tmp_path):
|
||||
service = RecipePersistenceService(
|
||||
exif_utils=DummyExifUtils(),
|
||||
card_preview_width=512,
|
||||
logger=logging.getLogger("test"),
|
||||
)
|
||||
|
||||
class MissingScanner:
|
||||
async def get_recipe_json_path(self, recipe_id):
|
||||
return str(tmp_path / "missing.json")
|
||||
|
||||
with pytest.raises(RecipeNotFoundError):
|
||||
await service.get_reconnect_suggestions(
|
||||
recipe_scanner=MissingScanner(), recipe_id="nope", lora_index=0
|
||||
)
|
||||
|
||||
recipe_path = tmp_path / "recipe.json"
|
||||
recipe_path.write_text(json.dumps({"id": "r1", "loras": []}))
|
||||
|
||||
class EmptyScanner:
|
||||
async def get_recipe_json_path(self, recipe_id):
|
||||
return str(recipe_path)
|
||||
|
||||
with pytest.raises(RecipeValidationError, match="lora_index"):
|
||||
await service.get_reconnect_suggestions(
|
||||
recipe_scanner=EmptyScanner(), recipe_id="r1", lora_index=0
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_mark_lora_hash_invalid_delegates_and_reports(tmp_path):
|
||||
service = RecipePersistenceService(
|
||||
|
||||
Reference in New Issue
Block a user