mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-09-21 03:01:27 -03:00
Will Miao
5ab0e88abc
A model file could only ever be linked to huggingface.co: `set_hf_url` validated the URL with a huggingface-only regex, the agent fetched the card from a hardcoded HF URL, and the readme processor built every relative image path off `https://huggingface.co/{repo}/resolve/main`. ModelScope publishes the same model-card convention (README.md + YAML frontmatter, often carrying `base_model:` and `trigger_words:`) behind a public, key-less API, so the enrichment pipeline could already serve it - it was the plumbing that was HF-shaped, not the idea. Make the external source a first-class, provider-driven concept: - New `py/services/model_sources/` registry. A `ModelSource` owns URL recognition (lenient for stored values, strict for user input), the canonical page URL, model-card fetching, the asset base URL and the capability flags. `HuggingFaceSource` is the previous logic relocated; `ModelScopeSource` reads `/models/{o}/{n}/resolve/{master|main}/README.md` and falls back to `/api/v1/models/{o}/{n}/repo`. `TensorArtSource` is link-only on purpose: tensor.art answers plain HTTP clients with a Cloudflare challenge and its internal API (ap-east-1.tensorart.cloud / cn.tensorart.net) rejects every /v1/model/* route with "invalid authorization header", so it declares supports_enrichment=False rather than failing silently later. - Metadata gains `source_platform` + `source_url`; `hf_url` stays as a read/write alias, written only for Hugging Face, so existing sidecars, cached rows and third-party consumers keep working. Normalisation runs at the scanner, the persistent cache (both directions, plus two new columns behind an ALTER migration) and the linking handler - which is what stops a user who switches sources from leaving a stale `hf_url` on a ModelScope model. - The agent pipeline keys off the provider instead of `hf_url`: the fast-fail gate now explains *why* a model is skipped (no source / unknown source / source without a reachable card), the prompt context exposes source_url/source_id/source_label/asset_base_url while still filling the legacy hf_url/repo aliases, and the four README image extractors take a base_url (defaulting to HF) so relative paths resolve against the right site. Version grouping generalises to hf: / ms: / ta: keys. - `POST /api/lm/set-hf-url` keeps its path and its legacy payload keys but accepts `source_url`, validates against every provider and returns the platform. `GET /api/lm/model-sources` lets the UI render the supported-site list from the server. - Frontend: a `modelSourceHelpers` mirror of the registry drives the link dialog, the card/modal globe (branded "View on ModelScope/TensorArt"), the version-group key and the enrichment gate; the versions tab no longer sends ms:/ta: keys to the CivitAI API. TensorArt stays in the list because provenance is worth keeping even when the card is unreadable - the dialog says so plainly ("Sites that don't expose one (currently TensorArt) can only be linked") and the context menu disables enrichment with a matching tooltip, instead of the user getting "Unsupported URL". Verified against the real ModelScope API: jj3550945163/Krea-2-LORA returns a 1882-byte card whose frontmatter carries base_model/tags/trigger_words, and relative images resolve to .../resolve/master/.... Tests: backend 2815 passed; frontend 1130 JS + 91 Vue passed; pytest tests/i18n and a Jinja compile pass over templates/. The nine locales carry [TODO: Translate] for the new strings, completed in the next commit.
526 lines
21 KiB
Python
526 lines
21 KiB
Python
"""Tests for the PostProcessor (py/services/agent/post_processor.py).
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PostProcessor delegates all I/O to AgentCLI — these tests mock AgentCLI
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functions and verify the business logic (conditions, merges, dispatch).
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"""
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from __future__ import annotations
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from datetime import datetime, timezone
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from unittest import mock
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import pytest
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from py.services.agent.post_processor import PostProcessor
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@pytest.fixture
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def processor():
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return PostProcessor()
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# ======================================================================
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# process() — routing
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# ======================================================================
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class TestProcessDispatch:
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@pytest.mark.asyncio
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async def test_unknown_skill_returns_error(self, processor):
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result = await processor.process(
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skill_name="nonexistent",
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model_path="/p.safetensors",
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llm_output={},
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metadata={},
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)
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assert result["success"] is False
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assert "nonexistent" in result["errors"][0]
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@pytest.mark.asyncio
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async def test_enrich_hf_metadata_routes_correctly(self, processor):
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview") as mock_dl,
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mock.patch("py.metadata_ops.refresh_cache") as mock_ref,
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):
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mock_apply.return_value = ["metadata_source"]
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mock_dl.return_value = None
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result = await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output={},
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metadata={"from_civitai": True},
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)
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assert result["success"] is True
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# ======================================================================
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# enrich_hf_metadata — field-level logic
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# ======================================================================
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class TestEnrichHfMetadata:
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"""Business logic tests for the enrich_hf_metadata post-processor."""
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MIN_LLM_OUTPUT = {
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"base_model": "",
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"trigger_words": [],
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"short_description": "",
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"tags": [],
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"recommended_width": 0,
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"recommended_height": 0,
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"preview_url": "",
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"confidence": "low",
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}
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# -- base_model ------------------------------------------------------
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@pytest.mark.asyncio
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async def test_base_model_overwrites_empty(self, processor):
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"""Empty current base_model → new value is applied."""
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llm = {**self.MIN_LLM_OUTPUT, "base_model": "Flux.1 D"}
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview", return_value=False),
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mock.patch("py.metadata_ops.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=llm,
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metadata={"base_model": ""},
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)
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applied = mock_apply.call_args[0][1]
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assert applied["base_model"] == "Flux.1 D"
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@pytest.mark.asyncio
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async def test_base_model_does_not_overwrite_existing_civitai(self, processor):
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"""Existing base_model from CivitAI → not overwritten."""
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llm = {**self.MIN_LLM_OUTPUT, "base_model": "Flux.1 D"}
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview", return_value=False),
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mock.patch("py.metadata_ops.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=llm,
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metadata={"base_model": "SDXL 1.0", "from_civitai": True},
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)
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# apply IS called (metadata_source, llm_enriched_at) but base_model not in it
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applied = mock_apply.call_args[0][1]
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assert "base_model" not in applied
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@pytest.mark.asyncio
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async def test_base_model_overwrites_existing_hf_model(self, processor):
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"""Existing base_model from HF → overwritten (LLM is more reliable)."""
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llm = {**self.MIN_LLM_OUTPUT, "base_model": "Flux.1 D"}
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview", return_value=False),
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mock.patch("py.metadata_ops.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=llm,
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metadata={
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"base_model": "SD 1.5",
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"from_civitai": False,
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"hf_url": "https://huggingface.co/user/repo",
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},
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)
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applied = mock_apply.call_args[0][1]
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assert applied["base_model"] == "Flux.1 D"
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@pytest.mark.asyncio
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async def test_base_model_skipped_when_llm_empty(self, processor):
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"""LLM returns empty base_model → nothing written."""
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview", return_value=False),
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mock.patch("py.metadata_ops.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=self.MIN_LLM_OUTPUT,
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metadata={"base_model": ""},
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)
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applied = mock_apply.call_args[0][1]
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assert "base_model" not in applied
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# -- trigger_words ---------------------------------------------------
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@pytest.mark.asyncio
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async def test_trigger_words_merged(self, processor):
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"""New trigger words written when current list is empty."""
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llm = {**self.MIN_LLM_OUTPUT, "trigger_words": ["trigger1", "trigger2"]}
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview", return_value=None),
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mock.patch("py.metadata_ops.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=llm,
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metadata={},
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)
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applied = mock_apply.call_args[0][1]
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assert applied["civitai"]["trainedWords"] == ["trigger1", "trigger2"]
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# -- short_description → civitai.description -------------------------
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@pytest.mark.asyncio
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async def test_short_description_written_to_civitai(self, processor):
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"""short_description written to civitai.description for HF models."""
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llm = {**self.MIN_LLM_OUTPUT, "short_description": "A short summary"}
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview", return_value=None),
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mock.patch("py.metadata_ops.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=llm,
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metadata={
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"from_civitai": False,
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"hf_url": "https://huggingface.co/user/repo",
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},
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)
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applied = mock_apply.call_args[0][1]
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assert applied["civitai"]["description"] == "A short summary"
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@pytest.mark.asyncio
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async def test_short_description_skipped_without_hf_url(self, processor):
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"""short_description NOT written when the model has no HF source."""
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llm = {**self.MIN_LLM_OUTPUT, "short_description": "A short summary"}
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview", return_value=None),
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mock.patch("py.metadata_ops.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=llm,
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metadata={"from_civitai": True},
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)
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applied = mock_apply.call_args[0][1]
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assert "civitai" not in applied or "description" not in applied.get("civitai", {})
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# -- readme_content → modelDescription -------------------------------
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@pytest.mark.asyncio
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async def test_readme_content_converted_to_model_description(self, processor):
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"""Raw README converted to HTML and stored as modelDescription."""
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview", return_value=None),
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mock.patch("py.metadata_ops.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=self.MIN_LLM_OUTPUT,
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metadata={
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"from_civitai": False,
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"hf_url": "https://huggingface.co/user/repo",
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},
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readme_content="# Hello\n\nThis is **bold**.",
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)
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applied = mock_apply.call_args[0][1]
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assert "<h1>Hello</h1>" in applied.get("modelDescription", "")
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assert "<strong>bold</strong>" in applied.get("modelDescription", "")
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@pytest.mark.asyncio
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async def test_readme_content_skipped_without_hf_url(self, processor):
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"""README content NOT converted when the model has no HF source."""
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview", return_value=None),
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mock.patch("py.metadata_ops.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=self.MIN_LLM_OUTPUT,
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metadata={"from_civitai": True},
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readme_content="# Hello",
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)
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applied = mock_apply.call_args[0][1]
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assert "modelDescription" not in applied
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# -- gallery images → civitai.images ---------------------------------
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@pytest.mark.asyncio
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async def test_gallery_images_extracted_from_readme(self, processor):
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"""Widget entries in README → civitai.images."""
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readme = """---
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widget:
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- text: "a cat"
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output:
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url: images/cat.png
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---
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Content
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"""
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview", return_value=None),
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mock.patch("py.metadata_ops.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=self.MIN_LLM_OUTPUT,
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metadata={
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"from_civitai": False,
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"hf_url": "https://huggingface.co/user/repo",
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},
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readme_content=readme,
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)
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applied = mock_apply.call_args[0][1]
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images = applied.get("civitai", {}).get("images", [])
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assert len(images) == 1
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assert images[0]["url"] == (
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"https://huggingface.co/user/repo/resolve/main/images/cat.png"
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)
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assert images[0]["meta"]["prompt"] == "a cat"
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@pytest.mark.asyncio
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async def test_gallery_images_use_modelscope_asset_base_url(self, processor):
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"""A ModelScope-linked model resolves relative images against ModelScope."""
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readme = """---
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widget:
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- text: "a cat"
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output:
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url: images/cat.png
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---
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Content
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"""
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview", return_value=None),
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mock.patch("py.metadata_ops.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=self.MIN_LLM_OUTPUT,
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metadata={
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"from_civitai": False,
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"source_platform": "modelscope",
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"source_url": "https://modelscope.cn/models/user/repo",
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},
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readme_content=readme,
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)
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applied = mock_apply.call_args[0][1]
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images = applied.get("civitai", {}).get("images", [])
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assert len(images) == 1
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assert images[0]["url"] == (
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"https://modelscope.cn/models/user/repo/resolve/master/images/cat.png"
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)
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@pytest.mark.asyncio
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async def test_base_model_overwrites_existing_modelscope_model(self, processor):
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"""ModelScope is an external source, so the LLM may overwrite base_model."""
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llm = {**self.MIN_LLM_OUTPUT, "base_model": "Flux.1 D"}
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview", return_value=False),
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mock.patch("py.metadata_ops.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=llm,
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metadata={
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"base_model": "SD 1.5",
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"source_platform": "modelscope",
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"source_url": "https://modelscope.cn/models/user/repo",
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},
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)
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assert mock_apply.call_args[0][1]["base_model"] == "Flux.1 D"
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@pytest.mark.asyncio
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async def test_gallery_images_skipped_without_hf_url(self, processor):
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"""Gallery images NOT extracted when the model has no HF source."""
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview", return_value=None),
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mock.patch("py.metadata_ops.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=self.MIN_LLM_OUTPUT,
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metadata={"from_civitai": True},
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readme_content="---\nwidget:\n- text: a\n output:\n url: x.png\n---\n",
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)
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applied = mock_apply.call_args[0][1]
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civitai = applied.get("civitai", {})
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assert "images" not in civitai
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@pytest.mark.asyncio
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async def test_gallery_images_extracted_for_civitai_linked_model(self, processor):
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"""A model may be on CivitAI and HuggingFace at once (#1094).
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HF enrichment is gated on ``hf_url``, not on ``from_civitai``, so the
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README gallery is still applied when both sources are present.
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"""
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview", return_value=None),
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mock.patch("py.metadata_ops.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=self.MIN_LLM_OUTPUT,
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metadata={
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"from_civitai": True,
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"hf_url": "https://huggingface.co/user/repo",
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},
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readme_content="---\nwidget:\n- text: a\n output:\n url: x.png\n---\n",
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)
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applied = mock_apply.call_args[0][1]
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images = applied.get("civitai", {}).get("images", [])
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assert len(images) == 1
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assert images[0]["url"] == (
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"https://huggingface.co/user/repo/resolve/main/x.png"
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)
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# -- tags ------------------------------------------------------------
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@pytest.mark.asyncio
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async def test_tags_merged_and_deduplicated(self, processor):
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llm = {**self.MIN_LLM_OUTPUT, "tags": ["flux", "lora", "STYLE"]}
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with (
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mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
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mock.patch("py.metadata_ops.download_preview", return_value=False),
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mock.patch("py.metadata_ops.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=llm,
|
|
metadata={"tags": ["anime"], "from_civitai": False},
|
|
)
|
|
merged = mock_apply.call_args[0][1]["tags"]
|
|
assert "anime" in merged
|
|
assert "flux" in merged
|
|
assert "style" in merged # lowercased
|
|
# "lora" and "STYLE" → "lora" and "style"
|
|
assert len(merged) == 4 # anime, flux, lora, style
|
|
|
|
# -- metadata_source & llm_enriched_at --------------------------------
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_audit_fields_always_set(self, processor):
|
|
with (
|
|
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
|
mock.patch("py.metadata_ops.download_preview", return_value=False),
|
|
mock.patch("py.metadata_ops.refresh_cache"),
|
|
):
|
|
await processor.process(
|
|
skill_name="enrich_hf_metadata",
|
|
model_path="/p.safetensors",
|
|
llm_output=self.MIN_LLM_OUTPUT,
|
|
metadata={},
|
|
)
|
|
applied = mock_apply.call_args[0][1]
|
|
assert applied["metadata_source"] == "agent:enrich_hf_metadata"
|
|
assert "llm_enriched_at" in applied
|
|
|
|
# -- preview download ------------------------------------------------
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_preview_downloaded_when_url_provided(self, processor):
|
|
llm = {**self.MIN_LLM_OUTPUT, "preview_url": "https://ex.com/img.png"}
|
|
with (
|
|
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
|
mock.patch("py.metadata_ops.download_preview") as mock_dl,
|
|
mock.patch("py.metadata_ops.refresh_cache"),
|
|
):
|
|
mock_dl.return_value = "/p.webp"
|
|
result = await processor.process(
|
|
skill_name="enrich_hf_metadata",
|
|
model_path="/p.safetensors",
|
|
llm_output=llm,
|
|
metadata={},
|
|
)
|
|
assert result["preview_downloaded"] is True
|
|
mock_dl.assert_awaited_once_with("/p.safetensors", "https://ex.com/img.png")
|
|
applied = mock_apply.call_args[0][1]
|
|
assert applied["preview_url"] == "/p.webp"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_preview_skipped_when_exists(self, processor):
|
|
"""If current_preview file exists on disk, skip download."""
|
|
llm = {**self.MIN_LLM_OUTPUT, "preview_url": "https://ex.com/img.png"}
|
|
with (
|
|
mock.patch("py.metadata_ops.apply_metadata_updates"),
|
|
mock.patch("py.metadata_ops.download_preview") as mock_dl,
|
|
mock.patch("py.metadata_ops.refresh_cache"),
|
|
mock.patch("os.path.exists", return_value=True),
|
|
):
|
|
await processor.process(
|
|
skill_name="enrich_hf_metadata",
|
|
model_path="/p.safetensors",
|
|
llm_output=llm,
|
|
metadata={"preview_url": "/existing/preview.webp"},
|
|
)
|
|
mock_dl.assert_not_called()
|
|
|
|
# -- cache refresh ---------------------------------------------------
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_cache_refreshed_when_updates_applied(self, processor):
|
|
llm = {**self.MIN_LLM_OUTPUT, "base_model": "Flux.1 D"}
|
|
with (
|
|
mock.patch("py.metadata_ops.apply_metadata_updates", return_value=["base_model"]),
|
|
mock.patch("py.metadata_ops.download_preview", return_value=False),
|
|
mock.patch("py.metadata_ops.refresh_cache") as mock_ref,
|
|
):
|
|
await processor.process(
|
|
skill_name="enrich_hf_metadata",
|
|
model_path="/p.safetensors",
|
|
llm_output=llm,
|
|
metadata={"base_model": ""},
|
|
)
|
|
mock_ref.assert_awaited_once_with("/p.safetensors")
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_cache_not_refreshed_when_nothing_changed(self, processor):
|
|
with (
|
|
mock.patch("py.metadata_ops.apply_metadata_updates", return_value=[]),
|
|
mock.patch("py.metadata_ops.download_preview", return_value=False),
|
|
mock.patch("py.metadata_ops.refresh_cache") as mock_ref,
|
|
):
|
|
await processor.process(
|
|
skill_name="enrich_hf_metadata",
|
|
model_path="/p.safetensors",
|
|
llm_output=self.MIN_LLM_OUTPUT,
|
|
metadata={"base_model": ""},
|
|
)
|
|
mock_ref.assert_not_called()
|
|
|
|
|
|
# ======================================================================
|
|
# Unit: _merge_tags
|
|
# ======================================================================
|
|
|
|
|
|
class TestMergeTags:
|
|
def test_deduplicates_case_insensitive(self):
|
|
existing = ["anime", "Flux"]
|
|
new = ["flux", "LORA", "anime"]
|
|
result = PostProcessor._merge_tags(existing, new)
|
|
# All tags are lowercased (matching TagUpdateService behaviour)
|
|
assert result == ["anime", "flux", "lora"]
|