mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-09-21 03:01:27 -03:00
feat(modelscope): read the model-detail API for card extras
ModelScope's model card is not just README.md: the author's summary (Description), the site-curated tags (OfficialTags), the internal architecture enums (VisionFoundation/SubVisionFoundation) and — per published version — the model filenames with that file's example images (coverImages) and trigger words all live in the model-detail API. AIGC repositories there frequently ship an auto-generated boilerplate README and put the only useful text in Description, so reading just the README yielded almost nothing. Add `ModelSource.fetch_model_card_context()` returning a new `ModelCardContext`, implemented by ModelScopeSource against the public (no API key) detail endpoint. Example images are matched to the model's basename through each version's `stats.fileList`, so every checkpoint in a collection repository gets its own images rather than a sibling's. Consume the context in the post-processor: * example images seed `civitai.images` and, being per-file, take priority in the preview fallback chain * the author summary becomes a paragraph in `modelDescription` and fills `civitai.description` when the LLM returns no short description * site-curated tags are always merged in, which also fixes the official `character-enhancement` being dropped by the prompt's no-hyphen rule * per-file trigger words are used before the repo-wide YAML `instance_prompt` * an explicitly stated strength range is recovered by regex so `usage_tips` is populated even without an LLM The prompt gains a Site-Provided Metadata section so the LLM can prefer the site's first-hand data over its own guesses.
This commit is contained in:
Will Miao
@@ -332,6 +332,190 @@ class TestAssetBaseUrl:
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Model card context (site extras kept outside the README)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _modelscope_detail_payload() -> dict:
|
||||
"""A trimmed-but-faithful ModelScope model-detail response.
|
||||
|
||||
Mirrors the shape of ``/api/v1/models/{id}`` for an AIGC LoRA repo whose
|
||||
README is auto-generated boilerplate, so the author summary and the
|
||||
per-file example images are only reachable through this API.
|
||||
"""
|
||||
|
||||
return {
|
||||
"Code": 200,
|
||||
"Data": {
|
||||
"Name": "Krea-2-LORA",
|
||||
"ChineseName": "krea脸模",
|
||||
"Description": "权重0.5-1.2。配合《风格滤镜》lora一起使用。",
|
||||
"BaseModel": ["krea/Krea-2-Turbo"],
|
||||
"License": "Apache License 2.0",
|
||||
"OfficialTags": [
|
||||
{"Tag": "photography", "ChineseName": "写实摄影"},
|
||||
{"Tag": "woman", "ChineseName": "女生"},
|
||||
{"Tag": "photography", "ChineseName": "重复项"},
|
||||
],
|
||||
"MuseInfo": {
|
||||
"versions": [
|
||||
{
|
||||
"stats": {"fileList": ["Krea-2-LORA_c1-st8000.safetensors"]},
|
||||
"modelVersion": {"showName": "c1-st8000", "triggerWords": '[""]'},
|
||||
"coverImages": [
|
||||
{"url": "https://resources.modelscope.cn/cover-images/a.png"}
|
||||
],
|
||||
},
|
||||
{
|
||||
"stats": {"fileList": ["Krea-2-LORA_c1-st1000.safetensors"]},
|
||||
"modelVersion": {
|
||||
"showName": "c1-st1000",
|
||||
"triggerWords": '["kreaface","kreamodel"]',
|
||||
},
|
||||
"coverImages": [
|
||||
{"url": "https://resources.modelscope.cn/cover-images/b.png"},
|
||||
{"url": "https://resources.modelscope.cn/cover-images/c.png"},
|
||||
],
|
||||
},
|
||||
]
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
class TestFetchModelCardContext:
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_reads_description_tags_and_base_model(self, monkeypatch):
|
||||
async def fake_fetch_json(url, **_kwargs):
|
||||
assert url == "https://modelscope.cn/api/v1/models/u/r"
|
||||
return 200, _modelscope_detail_payload()
|
||||
|
||||
monkeypatch.setattr(
|
||||
"py.services.model_sources.modelscope.fetch_json", fake_fetch_json
|
||||
)
|
||||
|
||||
context = await ModelScopeSource().fetch_model_card_context("u/r")
|
||||
|
||||
assert context.description == "权重0.5-1.2。配合《风格滤镜》lora一起使用。"
|
||||
assert context.base_model == "krea/Krea-2-Turbo"
|
||||
# OfficialTag values only, de-duplicated, order preserved.
|
||||
assert context.official_tags == ["photography", "woman"]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_matches_example_images_by_filename(self, monkeypatch):
|
||||
async def fake_fetch_json(url, **_kwargs):
|
||||
return 200, _modelscope_detail_payload()
|
||||
|
||||
monkeypatch.setattr(
|
||||
"py.services.model_sources.modelscope.fetch_json", fake_fetch_json
|
||||
)
|
||||
|
||||
context = await ModelScopeSource().fetch_model_card_context(
|
||||
"u/r", "Krea-2-LORA_c1-st1000.safetensors"
|
||||
)
|
||||
|
||||
# Only the requested file's images, never a sibling checkpoint's.
|
||||
assert context.example_images == [
|
||||
"https://resources.modelscope.cn/cover-images/b.png",
|
||||
"https://resources.modelscope.cn/cover-images/c.png",
|
||||
]
|
||||
assert context.trigger_words == ["kreaface", "kreamodel"]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_never_borrows_images_for_an_unknown_file(
|
||||
self, monkeypatch
|
||||
):
|
||||
async def fake_fetch_json(url, **_kwargs):
|
||||
return 200, _modelscope_detail_payload()
|
||||
|
||||
monkeypatch.setattr(
|
||||
"py.services.model_sources.modelscope.fetch_json", fake_fetch_json
|
||||
)
|
||||
|
||||
context = await ModelScopeSource().fetch_model_card_context(
|
||||
"u/r", "other.safetensors"
|
||||
)
|
||||
|
||||
assert context.example_images == []
|
||||
assert context.trigger_words == []
|
||||
# The repo-wide fields are still returned.
|
||||
assert context.base_model == "krea/Krea-2-Turbo"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_single_version_repo_without_filename(self, monkeypatch):
|
||||
payload = _modelscope_detail_payload()
|
||||
versions = payload["Data"]["MuseInfo"]["versions"]
|
||||
payload["Data"]["MuseInfo"]["versions"] = versions[:1]
|
||||
|
||||
async def fake_fetch_json(url, **_kwargs):
|
||||
return 200, payload
|
||||
|
||||
monkeypatch.setattr(
|
||||
"py.services.model_sources.modelscope.fetch_json", fake_fetch_json
|
||||
)
|
||||
|
||||
context = await ModelScopeSource().fetch_model_card_context("u/r")
|
||||
|
||||
assert context.example_images == [
|
||||
"https://resources.modelscope.cn/cover-images/a.png"
|
||||
]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_tolerates_failures_and_odd_payloads(self, monkeypatch):
|
||||
payloads = (None, {"Code": 500}, {"Data": "nope"}, {"Data": {}})
|
||||
for payload in payloads:
|
||||
|
||||
async def fake_fetch_json(url, _payload=payload, **_kwargs):
|
||||
return (0 if _payload is None else 200), _payload
|
||||
|
||||
monkeypatch.setattr(
|
||||
"py.services.model_sources.modelscope.fetch_json", fake_fetch_json
|
||||
)
|
||||
context = await ModelScopeSource().fetch_model_card_context(
|
||||
"u/r", "a.safetensors"
|
||||
)
|
||||
assert context.is_empty(), payload
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_modelscope_reads_stats_from_json_encoded_fallback(self, monkeypatch):
|
||||
payload = {
|
||||
"Data": {
|
||||
"MuseInfo": {
|
||||
"versions": [
|
||||
{
|
||||
"modelVersion": {
|
||||
"showName": "v1",
|
||||
"stats": '{"fileList": ["model.safetensors"]}',
|
||||
"triggerWords": '["hi"]',
|
||||
},
|
||||
"coverImages": [{"url": "https://cdn.example/x.png"}],
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
async def fake_fetch_json(url, **_kwargs):
|
||||
return 200, payload
|
||||
|
||||
monkeypatch.setattr(
|
||||
"py.services.model_sources.modelscope.fetch_json", fake_fetch_json
|
||||
)
|
||||
|
||||
context = await ModelScopeSource().fetch_model_card_context(
|
||||
"u/r", "model.safetensors"
|
||||
)
|
||||
|
||||
assert context.example_images == ["https://cdn.example/x.png"]
|
||||
assert context.trigger_words == ["hi"]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_default_context_is_empty_for_other_sources(self):
|
||||
assert (await HuggingFaceSource().fetch_model_card_context("u/r")).is_empty()
|
||||
assert (await TensorArtSource().fetch_model_card_context("123")).is_empty()
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Download support
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@@ -6,12 +6,14 @@ functions and verify the business logic (conditions, merges, dispatch).
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from datetime import datetime, timezone
|
||||
from unittest import mock
|
||||
|
||||
import pytest
|
||||
|
||||
from py.services.agent.post_processor import PostProcessor
|
||||
from py.services.model_sources import ModelCardContext
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
@@ -523,3 +525,481 @@ class TestMergeTags:
|
||||
result = PostProcessor._merge_tags(existing, new)
|
||||
# All tags are lowercased (matching TagUpdateService behaviour)
|
||||
assert result == ["anime", "flux", "lora"]
|
||||
|
||||
|
||||
# ======================================================================
|
||||
# enrich_hf_metadata — site-provided card extras (ModelCardContext)
|
||||
# ======================================================================
|
||||
|
||||
|
||||
class TestSiteProvidedContext:
|
||||
"""ModelScope keeps the author summary, the curated tags and the per-file
|
||||
example images outside the README; these tests pin how they are applied.
|
||||
"""
|
||||
|
||||
MODELSCOPE_METADATA = {
|
||||
"from_civitai": False,
|
||||
"source_platform": "modelscope",
|
||||
"source_url": "https://modelscope.cn/models/user/repo",
|
||||
}
|
||||
|
||||
LLM_OUTPUT = {
|
||||
"base_model": "",
|
||||
"trigger_words": [],
|
||||
"short_description": "",
|
||||
"tags": [],
|
||||
"recommended_width": 0,
|
||||
"recommended_height": 0,
|
||||
"preview_url": "",
|
||||
"confidence": "medium",
|
||||
}
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_example_images_become_gallery_and_preview(self, processor):
|
||||
"""A boilerplate README still yields images and a downloaded preview."""
|
||||
context = ModelCardContext(
|
||||
example_images=[
|
||||
"https://resources.modelscope.cn/cover-images/a.png",
|
||||
"https://resources.modelscope.cn/cover-images/b.png",
|
||||
]
|
||||
)
|
||||
boilerplate = "### 当前模型的贡献者未提供更加详细的模型介绍。\n"
|
||||
|
||||
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=self.LLM_OUTPUT,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
readme_content=boilerplate,
|
||||
source_context=context,
|
||||
)
|
||||
|
||||
applied = mock_apply.call_args[0][1]
|
||||
images = applied["civitai"]["images"]
|
||||
assert [img["url"] for img in images] == context.example_images
|
||||
assert images[0]["type"] == "image"
|
||||
# The first (per-file) site image is used as the preview.
|
||||
mock_dl.assert_awaited_once_with(
|
||||
"/p.safetensors", "https://resources.modelscope.cn/cover-images/a.png"
|
||||
)
|
||||
assert applied["preview_url"] == "/p.webp"
|
||||
assert result["preview_downloaded"] is True
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_example_images_work_without_any_readme(self, processor):
|
||||
"""The site images alone are enough — the README may be unreachable."""
|
||||
context = ModelCardContext(
|
||||
example_images=["https://resources.modelscope.cn/cover-images/a.png"]
|
||||
)
|
||||
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.LLM_OUTPUT,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
readme_content="",
|
||||
source_context=context,
|
||||
)
|
||||
|
||||
images = mock_apply.call_args[0][1]["civitai"]["images"]
|
||||
assert [img["url"] for img in images] == context.example_images
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_site_description_precedes_readme_in_model_description(self, processor):
|
||||
context = ModelCardContext(description="权重0.5-1.2。配合滤镜lora一起使用。")
|
||||
readme = "# 模型介绍\n\n本模型依托魔搭社区完成训练。\n"
|
||||
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.LLM_OUTPUT,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
readme_content=readme,
|
||||
source_context=context,
|
||||
)
|
||||
|
||||
description = mock_apply.call_args[0][1]["modelDescription"]
|
||||
assert description.startswith(f"<p>{context.description}</p>")
|
||||
assert "<h1>模型介绍</h1>" in description
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_site_description_is_html_escaped(self, processor):
|
||||
context = ModelCardContext(description="a < b & c")
|
||||
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.LLM_OUTPUT,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
readme_content="",
|
||||
source_context=context,
|
||||
)
|
||||
|
||||
assert mock_apply.call_args[0][1]["modelDescription"] == "<p>a < b & c</p>"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_site_trigger_words_fill_in_when_llm_finds_none(self, processor):
|
||||
context = ModelCardContext(trigger_words=["kreaface", "kreamodel"])
|
||||
readme = "---\ninstance_prompt: yamlword\n---\nbody\n"
|
||||
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.LLM_OUTPUT,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
readme_content=readme,
|
||||
source_context=context,
|
||||
)
|
||||
|
||||
# The per-file site value wins over the repo-wide YAML instance_prompt.
|
||||
assert mock_apply.call_args[0][1]["civitai"]["trainedWords"] == [
|
||||
"kreaface",
|
||||
"kreamodel",
|
||||
]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_yaml_instance_prompt_still_used_when_site_has_none(self, processor):
|
||||
context = ModelCardContext(description="summary only")
|
||||
readme = "---\ninstance_prompt: yamlword\n---\nbody\n"
|
||||
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.LLM_OUTPUT,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
readme_content=readme,
|
||||
source_context=context,
|
||||
)
|
||||
|
||||
assert mock_apply.call_args[0][1]["civitai"]["trainedWords"] == ["yamlword"]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_site_images_are_skipped_for_a_model_with_no_external_source(
|
||||
self, processor
|
||||
):
|
||||
"""A CivitAI-only model must not pick up ModelScope images."""
|
||||
context = ModelCardContext(
|
||||
example_images=["https://resources.modelscope.cn/cover-images/a.png"]
|
||||
)
|
||||
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.LLM_OUTPUT,
|
||||
metadata={"from_civitai": True},
|
||||
readme_content="",
|
||||
source_context=context,
|
||||
)
|
||||
|
||||
assert "images" not in mock_apply.call_args[0][1].get("civitai", {})
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_site_images_deduplicate_against_readme_images(self, processor):
|
||||
"""A URL present in both the site data and the README appears once."""
|
||||
shared = "https://modelscope.cn/models/user/repo/resolve/master/sample.png"
|
||||
context = ModelCardContext(example_images=[shared])
|
||||
readme = f"\n"
|
||||
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.LLM_OUTPUT,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
readme_content=readme,
|
||||
source_context=context,
|
||||
)
|
||||
|
||||
images = mock_apply.call_args[0][1]["civitai"]["images"]
|
||||
assert [img["url"] for img in images] == [shared]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_empty_context_keeps_readme_only_behaviour(self, processor):
|
||||
"""An empty site context must not change existing HF behaviour."""
|
||||
readme = "---\nwidget:\n- text: a cat\n output:\n url: images/cat.png\n---\n"
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.LLM_OUTPUT,
|
||||
metadata={
|
||||
"from_civitai": False,
|
||||
"hf_url": "https://huggingface.co/user/repo",
|
||||
},
|
||||
readme_content=readme,
|
||||
source_context=ModelCardContext(),
|
||||
)
|
||||
images = mock_apply.call_args[0][1]["civitai"]["images"]
|
||||
assert [img["url"] for img in images] == [
|
||||
"https://huggingface.co/user/repo/resolve/main/images/cat.png"
|
||||
]
|
||||
|
||||
|
||||
|
||||
# ======================================================================
|
||||
# enrich_hf_metadata — deterministic fallbacks used when the LLM is skipped
|
||||
# ======================================================================
|
||||
|
||||
|
||||
class TestDeterministicFallbacks:
|
||||
"""With the LLM skipped, these fields must still be produced from the API."""
|
||||
|
||||
MODELSCOPE_METADATA = {
|
||||
"from_civitai": False,
|
||||
"source_platform": "modelscope",
|
||||
"source_url": "https://modelscope.cn/models/user/repo",
|
||||
}
|
||||
|
||||
EMPTY_LLM = {
|
||||
"base_model": "",
|
||||
"trigger_words": [],
|
||||
"short_description": "",
|
||||
"tags": [],
|
||||
"recommended_width": 0,
|
||||
"recommended_height": 0,
|
||||
"preview_url": "",
|
||||
"notes": "",
|
||||
"usage_tips": "{}",
|
||||
"confidence": "",
|
||||
}
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_resolved_base_model_used_when_llm_gave_none(self, processor):
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.EMPTY_LLM,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
source_context=ModelCardContext(base_model="krea/Krea-2-Turbo"),
|
||||
resolved_base_model="Krea 2",
|
||||
)
|
||||
assert mock_apply.call_args[0][1]["base_model"] == "Krea 2"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_llm_base_model_still_wins_over_the_resolver(self, processor):
|
||||
llm = {**self.EMPTY_LLM, "base_model": "Flux.1 D"}
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=llm,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
resolved_base_model="Krea 2",
|
||||
)
|
||||
assert mock_apply.call_args[0][1]["base_model"] == "Flux.1 D"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_site_description_fills_civitai_description(self, processor):
|
||||
context = ModelCardContext(description="一个 Krea 2 人像 LoRA。")
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.EMPTY_LLM,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
source_context=context,
|
||||
)
|
||||
assert (
|
||||
mock_apply.call_args[0][1]["civitai"]["description"]
|
||||
== "一个 Krea 2 人像 LoRA。"
|
||||
)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_llm_short_description_wins_over_site_description(self, processor):
|
||||
llm = {**self.EMPTY_LLM, "short_description": "from the LLM"}
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=llm,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
source_context=ModelCardContext(description="from the site"),
|
||||
)
|
||||
assert mock_apply.call_args[0][1]["civitai"]["description"] == "from the LLM"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_official_tags_are_applied_without_the_llm(self, processor):
|
||||
context = ModelCardContext(
|
||||
official_tags=["photography", "character-enhancement", "woman"]
|
||||
)
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.EMPTY_LLM,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
source_context=context,
|
||||
)
|
||||
assert mock_apply.call_args[0][1]["tags"] == [
|
||||
"photography",
|
||||
"character-enhancement",
|
||||
"woman",
|
||||
]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_official_tags_are_kept_alongside_llm_tags(self, processor):
|
||||
context = ModelCardContext(official_tags=["photography", "woman"])
|
||||
llm = {**self.EMPTY_LLM, "tags": ["portrait", "photography"]}
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=llm,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
source_context=context,
|
||||
)
|
||||
# Site tags first, then the LLM's extra ones, no duplicates.
|
||||
assert mock_apply.call_args[0][1]["tags"] == [
|
||||
"photography",
|
||||
"woman",
|
||||
"portrait",
|
||||
]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_usage_tips_recovered_from_the_author_summary(self, processor):
|
||||
context = ModelCardContext(
|
||||
description="权重0.5-1.2。2个一起时，权重建议都用1.0-1.1。"
|
||||
)
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.EMPTY_LLM,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
source_context=context,
|
||||
)
|
||||
tips = json.loads(mock_apply.call_args[0][1]["usage_tips"])
|
||||
assert tips == {
|
||||
"strength_min": 0.5,
|
||||
"strength_max": 1.2,
|
||||
"strength_range": "0.5-1.2",
|
||||
}
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_llm_usage_tips_win_over_the_regex(self, processor):
|
||||
llm = {**self.EMPTY_LLM, "usage_tips": '{"strength": 0.9}'}
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=llm,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
source_context=ModelCardContext(description="权重0.5-1.2"),
|
||||
)
|
||||
assert mock_apply.call_args[0][1]["usage_tips"] == '{"strength": 0.9}'
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_notes_are_not_rewritten_when_the_llm_is_skipped(self, processor):
|
||||
"""Notes are LLM-only; skipping must not clobber or duplicate them."""
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.EMPTY_LLM,
|
||||
metadata={**self.MODELSCOPE_METADATA, "notes": "existing notes"},
|
||||
source_context=ModelCardContext(description="权重0.5-1.2"),
|
||||
)
|
||||
assert "notes" not in mock_apply.call_args[0][1]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_no_site_data_leaves_llm_only_fields_untouched(self, processor):
|
||||
"""An empty context must behave exactly like the pre-existing pipeline."""
|
||||
with (
|
||||
mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.metadata_ops.download_preview", return_value=None),
|
||||
mock.patch("py.metadata_ops.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.EMPTY_LLM,
|
||||
metadata=dict(self.MODELSCOPE_METADATA),
|
||||
source_context=ModelCardContext(),
|
||||
resolved_base_model="",
|
||||
)
|
||||
applied = mock_apply.call_args[0][1]
|
||||
assert "base_model" not in applied
|
||||
assert "tags" not in applied
|
||||
assert "notes" not in applied
|
||||
assert "usage_tips" not in applied
|
||||
|
||||
Reference in New Issue
Block a user