mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-08-08 23:10:15 -03:00
feat(recipes): match civitai image hash sections against local hash cache
This commit is contained in:
Will Miao
@@ -570,3 +570,331 @@ async def test_backfill_lora_cache_item_without_sha256_does_not_crash():
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)
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assert result["thumbnailUrl"].startswith("https://image.civitai.com/")
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class _RecordingProvider:
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"""Metadata provider stub that records get_model_by_hash calls.
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With raise_on_call=True any hash lookup fails the test loudly — used to
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prove that local_cache hits skip the CivitAI API. Otherwise the result
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is returned for the cache-miss path.
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"""
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def __init__(self, result=None, raise_on_call=False):
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self.hash_calls = []
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self._result = result
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self._raise_on_call = raise_on_call
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async def get_model_by_hash(self, model_hash):
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self.hash_calls.append(model_hash)
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if self._raise_on_call:
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raise AssertionError(
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f"get_model_by_hash should not be called on local_cache hit, got {model_hash}"
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)
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return self._result
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async def get_model_version_info(self, version_id):
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return None, "Model not found"
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def _cache_item(name="Local Style", base_model="SDXL 1.0", model_type="LORA"):
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"""Build a scanner cache item shaped like the local hash cache values."""
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return {
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"file_path": f"/loras/{name.lower().replace(' ', '_')}.safetensors",
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"file_name": name,
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"sha256": "aabbccddeeff00112233445566778899aabbccddeeff00112233445566778899",
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"autov3": "",
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"preview_url": "/previews/style.png",
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"base_model": base_model,
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"civitai": {
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"id": 300,
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"modelId": 400,
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"name": "v1",
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"model": {"name": name, "type": model_type},
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},
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}
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def _make_lora_info(base_model="SDXL 1.0"):
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"""Civitai response for a lora hash lookup on the cache-miss path."""
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return {
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"id": 300,
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"modelId": 400,
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"model": {"name": "Style LoRA", "type": "lora"},
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"name": "v1",
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"images": [{"url": "https://image.civitai.com/lora/original=true"}],
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"baseModel": base_model,
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"downloadUrl": "https://civitai.com/api/download/300",
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"files": [
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{
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"type": "Model",
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"primary": True,
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"sizeKB": 512,
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"name": "style.safetensors",
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"hashes": {"SHA256": "ff00112233445566778899aabbccddeeff00112233445566778899aabbccddee"},
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}
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],
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}
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async def _parse_with_cache(monkeypatch, provider, metadata, local_cache=None):
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"""Run parse_metadata with a fixed metadata provider and optional local_cache."""
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async def fake_metadata_provider():
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return provider
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monkeypatch.setattr(
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"py.recipes.parsers.civitai_image.get_default_metadata_provider",
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fake_metadata_provider,
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)
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parser = CivitaiApiMetadataParser()
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return await parser.parse_metadata(metadata, local_cache=local_cache)
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@pytest.mark.asyncio
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async def test_local_cache_hashes_section_populates_from_cache_and_skips_api(monkeypatch):
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"""Hashes-section lora whose hash is a local_cache key is populated from
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the cache and the metadata provider is never consulted."""
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provider = _RecordingProvider(raise_on_call=True)
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item = _cache_item(name="Local Style", base_model="SDXL 1.0")
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local_cache = {"a1b2c3d4e5f6": item}
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metadata = {"hashes": {"LORA:Local Style": "A1B2C3D4E5F6"}}
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result = await _parse_with_cache(monkeypatch, provider, metadata, local_cache=local_cache)
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assert provider.hash_calls == []
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assert len(result["loras"]) == 1
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lora = result["loras"][0]
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assert lora["existsLocally"] is True
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assert lora["localPath"] == item["file_path"]
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assert lora["hash"] == item["sha256"]
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assert lora["name"] == "Local Style"
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@pytest.mark.asyncio
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async def test_local_cache_lora_n_section_populates_from_cache_and_skips_api(monkeypatch):
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"""Lora_N section lora whose hash is a local_cache key is populated from
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the cache and the metadata provider is never consulted."""
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provider = _RecordingProvider(raise_on_call=True)
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item = _cache_item(name="Lora N Style", base_model="SDXL 1.0")
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local_cache = {"abc123def456": item}
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metadata = {
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"Lora_0 Model hash": "ABC123DEF456",
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"Lora_0 Model name": "Lora N Style",
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"Lora_0 Strength model": 0.7,
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}
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result = await _parse_with_cache(monkeypatch, provider, metadata, local_cache=local_cache)
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assert provider.hash_calls == []
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assert len(result["loras"]) == 1
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lora = result["loras"][0]
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assert lora["existsLocally"] is True
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assert lora["localPath"] == item["file_path"]
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assert lora["weight"] == 0.7
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assert lora["hash"] == item["sha256"]
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@pytest.mark.asyncio
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async def test_local_cache_uppercase_hash_matches_lowercase_key(monkeypatch):
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"""Resources lora with an UPPERCASE hash still matches the lowercase key."""
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provider = _RecordingProvider(raise_on_call=True)
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sha256 = "aabbccddeeff00112233445566778899aabbccddeeff00112233445566778899"
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item = _cache_item(name="Upper Case", base_model="SDXL 1.0")
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local_cache = {sha256: item}
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metadata = {
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"resources": [
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{"hash": sha256.upper(), "name": "Upper Case", "type": "lora", "weight": 0.5},
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],
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}
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result = await _parse_with_cache(monkeypatch, provider, metadata, local_cache=local_cache)
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assert provider.hash_calls == []
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assert len(result["loras"]) == 1
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assert result["loras"][0]["existsLocally"] is True
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assert result["loras"][0]["hash"] == sha256
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@pytest.mark.asyncio
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async def test_local_cache_lora_hits_increment_base_model_counts_for_fallback(monkeypatch):
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"""When ALL loras hit the cache, base_model_counts is populated so the
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max(counts) fallback still resolves the base model (parity with the API path)."""
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provider = _RecordingProvider(raise_on_call=True)
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item1 = _cache_item(name="Lora One", base_model="SDXL 1.0")
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item2 = _cache_item(name="Lora Two", base_model="SDXL 1.0")
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local_cache = {"hash1111111111": item1, "hash2222222222": item2}
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metadata = {
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"resources": [
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{"hash": "hash1111111111", "name": "Lora One", "type": "lora"},
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{"hash": "hash2222222222", "name": "Lora Two", "type": "lora"},
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],
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}
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result = await _parse_with_cache(monkeypatch, provider, metadata, local_cache=local_cache)
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assert provider.hash_calls == []
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assert len(result["loras"]) == 2
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assert result["base_model"] == "SDXL 1.0"
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@pytest.mark.asyncio
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async def test_local_cache_checkpoint_hit_does_not_increment_base_model_counts(monkeypatch):
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"""Parity pin: a checkpoint cache hit never contributes to base_model_counts.
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Scenario 1 sets result["base_model"] directly (like the API path); scenario 2
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proves a base_model-less checkpoint added nothing to the counts fallback."""
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provider = _RecordingProvider(raise_on_call=True)
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cp_item = _cache_item(name="My Checkpoint", base_model="CP Base", model_type="Checkpoint")
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lora_item = _cache_item(name="Style LoRA", base_model="Lora Base", model_type="LORA")
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metadata1 = {
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"resources": [
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{"hash": "cp1234567890", "name": "My Checkpoint", "type": "model"},
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{"hash": "lora123456789", "name": "Style LoRA", "type": "lora"},
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],
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}
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result1 = await _parse_with_cache(
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monkeypatch,
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provider,
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metadata1,
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local_cache={"cp1234567890": cp_item, "lora123456789": lora_item},
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)
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assert result1["base_model"] == "CP Base"
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cp_no_bm = _cache_item(name="No Bm Checkpoint", base_model="", model_type="Checkpoint")
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metadata2 = {
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"resources": [
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{"hash": "cp9999999999", "name": "No Bm Checkpoint", "type": "model"},
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{"hash": "lora123456789", "name": "Style LoRA", "type": "lora"},
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],
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}
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result2 = await _parse_with_cache(
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monkeypatch,
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provider,
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metadata2,
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local_cache={"cp9999999999": cp_no_bm, "lora123456789": lora_item},
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)
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assert result2["base_model"] == "Lora Base"
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@pytest.mark.asyncio
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async def test_local_cache_type_gate_skips_checkpoint_cache_item_in_lora_section(monkeypatch):
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"""A cache item whose civitai.model.type is a checkpoint is skipped in a
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lora section — no entry is appended for it."""
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provider = _RecordingProvider(raise_on_call=True)
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cp_item = _cache_item(name="Disguised Checkpoint", base_model="CP Base", model_type="Checkpoint")
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lora_item = _cache_item(name="Real Lora", base_model="Lora Base", model_type="LORA")
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metadata = {
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"resources": [
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{"hash": "cp1111111111", "name": "Disguised Checkpoint", "type": "lora"},
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{"hash": "lora111111111", "name": "Real Lora", "type": "lora"},
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],
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}
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result = await _parse_with_cache(
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monkeypatch,
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provider,
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metadata,
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local_cache={"cp1111111111": cp_item, "lora111111111": lora_item},
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)
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assert provider.hash_calls == []
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assert [l["name"] for l in result["loras"]] == ["Real Lora"]
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@pytest.mark.asyncio
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async def test_local_cache_type_gate_accepts_uppercase_lora_type(monkeypatch):
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"""Cache items storing the type as UPPERCASE 'LORA' must not be skipped
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(false-skip guard — stored types are verbatim, VALID_LORA_TYPES is lowercase)."""
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provider = _RecordingProvider(raise_on_call=True)
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item = _cache_item(name="Uppercase Lora", base_model="SDXL 1.0", model_type="LORA")
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local_cache = {"upcasehash12": item}
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metadata = {
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"resources": [
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{"hash": "upcasehash12", "name": "Uppercase Lora", "type": "lora"},
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],
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}
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result = await _parse_with_cache(monkeypatch, provider, metadata, local_cache=local_cache)
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assert provider.hash_calls == []
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assert len(result["loras"]) == 1
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assert result["loras"][0]["existsLocally"] is True
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@pytest.mark.asyncio
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async def test_local_cache_type_gate_accepts_item_without_civitai_type(monkeypatch):
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"""Local-only cache items without civitai type info are treated as valid."""
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provider = _RecordingProvider(raise_on_call=True)
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item = {
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"file_path": "/loras/local_only.safetensors",
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"file_name": "Local Only",
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"sha256": "00112233445566778899aabbccddeeff00112233445566778899aabbccddeeff",
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"preview_url": "/previews/local_only.png",
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"base_model": "SDXL 1.0",
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}
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local_cache = {"localonly123": item}
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metadata = {
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"resources": [
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{"hash": "localonly123", "name": "Local Only", "type": "lora"},
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],
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}
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result = await _parse_with_cache(monkeypatch, provider, metadata, local_cache=local_cache)
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assert provider.hash_calls == []
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assert len(result["loras"]) == 1
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assert result["loras"][0]["existsLocally"] is True
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@pytest.mark.asyncio
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async def test_local_cache_miss_calls_provider(monkeypatch):
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"""A hash absent from local_cache falls through to the provider as before."""
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lora_info = _make_lora_info(base_model="SDXL 1.0")
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provider = _RecordingProvider(result=lora_info)
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metadata = {
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"resources": [
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{"hash": "missedhash123", "name": "Missed LoRA", "type": "lora", "weight": 0.8},
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],
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}
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result = await _parse_with_cache(monkeypatch, provider, metadata, local_cache={})
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assert provider.hash_calls == ["missedhash123"]
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assert len(result["loras"]) == 1
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assert result["loras"][0]["name"] == "Style LoRA"
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@pytest.mark.asyncio
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async def test_local_cache_dedup_same_hash_produces_one_entry_on_hit(monkeypatch):
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"""Repeated same-hash resources produce a single entry on the cache-hit path."""
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provider = _RecordingProvider(raise_on_call=True)
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item = _cache_item(name="Dedup Lora", base_model="SDXL 1.0")
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local_cache = {"deduphash123": item}
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metadata = {
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"resources": [
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{"hash": "deduphash123", "name": "Dedup Lora", "type": "lora"},
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{"hash": "deduphash123", "name": "Dedup Lora", "type": "lora"},
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],
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}
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result = await _parse_with_cache(monkeypatch, provider, metadata, local_cache=local_cache)
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assert provider.hash_calls == []
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assert len(result["loras"]) == 1
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@pytest.mark.asyncio
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async def test_local_cache_dedup_same_hash_produces_one_entry_on_miss(monkeypatch):
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"""Repeated same-hash resources produce a single entry on the cache-miss path."""
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provider = _RecordingProvider(result=_make_lora_info(base_model="SDXL 1.0"))
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metadata = {
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"resources": [
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{"hash": "missdedup123", "name": "Dedup Miss", "type": "lora"},
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{"hash": "missdedup123", "name": "Dedup Miss", "type": "lora"},
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],
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}
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result = await _parse_with_cache(monkeypatch, provider, metadata, local_cache={})
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assert provider.hash_calls == ["missdedup123"]
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assert len(result["loras"]) == 1
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