mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-08-09 07:20:15 -03:00
573 lines
19 KiB
Python
573 lines
19 KiB
Python
import pytest
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from py.config import config
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from py.recipes.base import RecipeMetadataParser
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from py.recipes.parsers.civitai_image import CivitaiApiMetadataParser
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@pytest.mark.asyncio
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async def test_parse_metadata_creates_loras_from_hashes(monkeypatch):
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async def fake_metadata_provider():
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return None
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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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metadata = {
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"Size": "1536x2688",
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"seed": 3766932689,
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"Model": "indexed_v1",
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"steps": 30,
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"hashes": {
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"model": "692186a14a",
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"LORA:Jedst1": "fb4063c470",
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"LORA:HassaKu_style": "3ce00b926b",
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"LORA:DetailedEyes_V3": "2c1c3f889f",
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"LORA:jiaocha_illustriousXL": "35d3e6f8b0",
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"LORA:绪儿 厚涂构图光影质感增强V3": "d9b5900a59",
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},
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"prompt": "test",
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"Version": "ComfyUI",
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"sampler": "er_sde_ays_30",
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"cfgScale": 5,
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"clipSkip": 2,
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"resources": [
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{
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"hash": "692186a14a",
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"name": "indexed_v1",
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"type": "model",
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}
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],
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"Model hash": "692186a14a",
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"negativePrompt": "bad",
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"username": "LumaRift",
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"baseModel": "Illustrious",
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}
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result = await parser.parse_metadata(metadata)
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assert result["base_model"] == "Illustrious"
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assert len(result["loras"]) == 5
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assert all(lora["weight"] == 1.0 for lora in result["loras"])
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assert {lora["name"] for lora in result["loras"]} == {
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"Jedst1",
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"HassaKu_style",
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"DetailedEyes_V3",
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"jiaocha_illustriousXL",
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"绪儿 厚涂构图光影质感增强V3",
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}
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@pytest.mark.asyncio
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async def test_parse_metadata_handles_nested_meta_and_lowercase_hashes(monkeypatch):
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async def fake_metadata_provider():
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return None
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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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metadata = {
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"id": 106706587,
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"meta": {
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"prompt": "An enigmatic silhouette",
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"hashes": {
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"model": "ee75fd24a4",
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"lora:mj": "de49e1e98c",
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"LORA:Another_Earth_2": "dc11b64a8b",
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},
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"resources": [
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{
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"hash": "ee75fd24a4",
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"name": "stoiqoNewrealityFLUXSD35_f1DAlphaTwo",
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"type": "model",
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}
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],
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},
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}
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assert parser.is_metadata_matching(metadata) # pyright: ignore[reportArgumentType]
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result = await parser.parse_metadata(metadata)
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assert result["gen_params"]["prompt"] == "An enigmatic silhouette"
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assert {l["name"] for l in result["loras"]} == {"mj", "Another_Earth_2"}
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assert {l["hash"] for l in result["loras"]} == {"de49e1e98c", "dc11b64a8b"}
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@pytest.mark.asyncio
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async def test_parse_metadata_populates_checkpoint_and_rewrites_thumbnails(monkeypatch):
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checkpoint_info = {
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"id": 222,
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"modelId": 111,
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"model": {"name": "Checkpoint Example", "type": "checkpoint"},
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"name": "Checkpoint Version",
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"images": [{"url": "https://image.civitai.com/checkpoints/original=true"}],
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"baseModel": "Illustrious",
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"downloadUrl": "https://civitai.com/checkpoint/download",
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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": 1024,
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"name": "Checkpoint Example.safetensors",
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"hashes": {"SHA256": "FFAA0011"},
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}
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],
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}
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lora_info = {
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"id": 444,
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"modelId": 333,
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"model": {"name": "Example Lora Model", "type": "lora"},
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"name": "Example Lora Version",
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"images": [{"url": "https://image.civitai.com/loras/original=true"}],
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"baseModel": "Illustrious",
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"downloadUrl": "https://civitai.com/lora/download",
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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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"hashes": {"SHA256": "abc123"},
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}
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],
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}
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async def fake_metadata_provider():
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class Provider:
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async def get_model_version_info(self, version_id):
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if version_id == "222":
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return checkpoint_info, None
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if version_id == "444":
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return lora_info, None
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return None, "Model not found"
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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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metadata = {
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"prompt": "test prompt",
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"negativePrompt": "test negative prompt",
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"civitaiResources": [
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{
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"type": "checkpoint",
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"modelId": 111,
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"modelVersionId": 222,
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"modelName": "Checkpoint Example",
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"modelVersionName": "Checkpoint Version",
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},
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{
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"type": "lora",
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"modelId": 333,
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"modelVersionId": 444,
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"modelName": "Example Lora",
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"modelVersionName": "Lora Version",
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"weight": 0.7,
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},
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],
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}
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result = await parser.parse_metadata(metadata)
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assert result["model"] is not None
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assert result["model"]["name"] == "Checkpoint Example"
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assert result["model"]["type"] == "checkpoint"
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assert (
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result["model"]["thumbnailUrl"]
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== "https://image.civitai.com/checkpoints/width=450,optimized=true"
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)
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assert result["model"]["modelId"] == 111
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assert result["model"]["size"] == 1024 * 1024
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assert result["model"]["hash"] == "ffaa0011"
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assert result["model"]["file_name"] == "Checkpoint Example"
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assert result["loras"]
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assert result["loras"][0]["name"] == "Example Lora Model"
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assert (
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result["loras"][0]["thumbnailUrl"]
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== "https://image.civitai.com/loras/width=450,optimized=true"
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)
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assert result["loras"][0]["hash"] == "abc123"
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@pytest.mark.asyncio
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async def test_parse_metadata_handles_modelVersionIds(monkeypatch):
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"""Test that modelVersionIds from Civitai image API are properly processed."""
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lora_info_1 = {
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"id": 2398829,
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"modelId": 123456,
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"model": {"name": "Dance LoRA 1", "type": "lora"},
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"name": "Version 1.0",
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"images": [{"url": "https://image.civitai.com/lora1/original=true"}],
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"baseModel": "SDXL",
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"downloadUrl": "https://civitai.com/lora1/download",
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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": 10240,
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"name": "dance_lora_1.safetensors",
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"hashes": {"SHA256": "aabbccdd0011"},
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}
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],
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}
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lora_info_2 = {
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"id": 2398838,
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"modelId": 123457,
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"model": {"name": "Style LoRA 2", "type": "lora"},
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"name": "Version 2.0",
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"images": [{"url": "https://image.civitai.com/lora2/original=true"}],
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"baseModel": "SDXL",
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"downloadUrl": "https://civitai.com/lora2/download",
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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": 20480,
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"name": "style_lora_2.safetensors",
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"hashes": {"SHA256": "aabbccdd0022"},
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}
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],
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}
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async def fake_metadata_provider():
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class Provider:
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async def get_model_version_info(self, version_id):
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if version_id == "2398829":
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return lora_info_1, None
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if version_id == "2398838":
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return lora_info_2, None
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return None, "Model not found"
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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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# This simulates the metadata from Civitai image API where modelVersionIds
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# is at the root level and meta only contains basic prompt info
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metadata = {
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"id": 109882763,
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"meta": {
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"id": 109882763,
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"meta": {"prompt": "A woman does the hip bump dance."},
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},
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"modelVersionIds": [2398829, 2398838],
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}
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assert parser.is_metadata_matching(metadata) # pyright: ignore[reportArgumentType]
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result = await parser.parse_metadata(metadata)
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# Verify both LoRAs were created from modelVersionIds
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assert len(result["loras"]) == 2
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# Check first LoRA
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lora1 = result["loras"][0]
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assert lora1["id"] == 2398829
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assert lora1["name"] == "Dance LoRA 1"
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assert lora1["type"] == "lora"
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assert lora1["hash"] == "aabbccdd0011"
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assert lora1["baseModel"] == "SDXL"
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assert (
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lora1["thumbnailUrl"]
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== "https://image.civitai.com/lora1/width=450,optimized=true"
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)
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# Check second LoRA
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lora2 = result["loras"][1]
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assert lora2["id"] == 2398838
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assert lora2["name"] == "Style LoRA 2"
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assert lora2["type"] == "lora"
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assert lora2["hash"] == "aabbccdd0022"
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assert lora2["baseModel"] == "SDXL"
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@pytest.mark.asyncio
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async def test_parse_metadata_extracts_checkpoint_from_resources_model_type(monkeypatch):
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"""resources entries with type:"model" should be captured as the checkpoint,
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not skipped (which was the old buggy behavior), and not mixed into loras."""
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captured_hashes = []
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async def fake_metadata_provider():
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class Provider:
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async def get_model_by_hash(self, model_hash):
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captured_hashes.append(model_hash)
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if model_hash == "a1b2c3d4e5":
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return ({
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"id": 999,
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"modelId": 888,
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"name": "v1.0",
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"model": {"name": "Real Checkpoint", "type": "Checkpoint"},
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"baseModel": "SDXL 1.0",
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"images": [{"url": "https://image.civitai.com/cp/original=true"}],
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"files": [{"type": "Model", "primary": True, "sizeKB": 1024, "name": "cp.safetensors"}]
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}, None)
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return None, "Model not found"
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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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metadata = {
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"prompt": "test",
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"resources": [
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{"hash": "a1b2c3d4e5", "name": "Real Checkpoint", "type": "model"},
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{"hash": "f6g7h8i9j0", "name": "Some LoRA", "type": "lora", "weight": 0.8},
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],
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"Model hash": "a1b2c3d4e5",
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}
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result = await parser.parse_metadata(metadata)
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# The type:"model" resource should be in result["model"], not in result["loras"]
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assert result["model"] is not None, "checkpoint model should be extracted"
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assert result["model"]["name"] == "Real Checkpoint"
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assert result["model"]["hash"] == "a1b2c3d4e5"
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assert result["model"]["type"] == "model"
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# The LoRA resource should be in result["loras"]
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assert len(result["loras"]) == 1
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assert result["loras"][0]["name"] == "Some LoRA"
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# The checkpoint hash should have triggered a lookup
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assert "a1b2c3d4e5" in captured_hashes
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@pytest.mark.asyncio
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async def test_parse_metadata_resources_model_type_does_not_duplicate_checkpoint_in_loras(monkeypatch):
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"""When a resources entry has type:"model", it should NOT also appear in loras.
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Regression test for the bug where the checkpoint model appeared in both places."""
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async def fake_metadata_provider():
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class Provider:
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async def get_model_by_hash(self, model_hash):
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if model_hash == "cp123hash":
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return ({
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"id": 100,
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"modelId": 200,
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"name": "v2",
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"model": {"name": "My Checkpoint", "type": "Checkpoint"},
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"baseModel": "SDXL",
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"files": [{"type": "Model", "primary": True, "sizeKB": 1024, "name": "cp.safetensors"}]
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}, None)
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if model_hash == "lora1hash":
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return ({
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"id": 300,
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"modelId": 400,
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"name": "v1",
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"model": {"name": "Style LoRA", "type": "LORA"},
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"baseModel": "SDXL",
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"files": [{"type": "Model", "primary": True, "sizeKB": 512, "name": "style.safetensors"}]
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}, None)
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return None, "Model not found"
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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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metadata = {
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"resources": [
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{"hash": "cp123hash", "name": "My Checkpoint", "type": "model"},
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{"hash": "lora1hash", "name": "Style LoRA", "type": "lora", "weight": 0.5},
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],
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}
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result = await parser.parse_metadata(metadata)
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# Checkpoint must NOT appear in loras
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lora_names = {l["name"] for l in result["loras"]}
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assert "My Checkpoint" not in lora_names
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assert "Style LoRA" in lora_names
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# Checkpoint must be in result["model"]
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assert result["model"] is not None
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assert result["model"]["name"] == "My Checkpoint"
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def _make_lora_civitai_info(hash_value):
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"""Build a minimal Civitai response for populate_lora_from_civitai.
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The files entry carries no SHA256 so the entry hash comes from the
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hash_value fallback, letting each test control the hash form (autov3,
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autov2 prefix, or full sha256) directly.
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"""
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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": "SDXL",
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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": {},
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}
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],
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}
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class _FakeCache:
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def __init__(self, entries):
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self.raw_data = entries
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class _FakeLoraScanner:
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def __init__(self, cache, local_path):
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self._cache = cache
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self._local_path = local_path
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def has_hash(self, sha256):
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return True
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def get_path_by_hash(self, sha256):
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return self._local_path
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async def get_cached_data(self):
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return self._cache
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class _FakeRecipeScanner:
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def __init__(self, lora_scanner):
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self._lora_scanner = lora_scanner
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async def _run_backfill(cached_items, hash_value, local_path="/loras/style.safetensors"):
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"""Drive populate_lora_from_civitai through the local-exists backfill block."""
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lora_scanner = _FakeLoraScanner(_FakeCache(cached_items), local_path)
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lora_entry = {"file_name": "style"}
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return await RecipeMetadataParser.populate_lora_from_civitai(
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lora_entry,
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_make_lora_civitai_info(hash_value),
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recipe_scanner=_FakeRecipeScanner(lora_scanner),
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hash_value=hash_value,
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)
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@pytest.mark.asyncio
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async def test_backfill_lora_item_by_file_path():
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# The primary resolution: the cache item's file_path equals the local
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# path resolved by get_path_by_hash, so it is found without hashing.
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autov3_hash = "a1b2c3d4e5f6"
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cached_item = {
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"file_path": "/loras/style.safetensors",
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"file_name": "Style LoRA",
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"preview_url": "/previews/style.png",
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}
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result = await _run_backfill([cached_item], autov3_hash)
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assert result is not None
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assert result["existsLocally"] is True
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assert result["localPath"] == "/loras/style.safetensors"
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assert result["thumbnailUrl"] == config.get_preview_static_url(
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cached_item["preview_url"]
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)
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@pytest.mark.asyncio
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|
async def test_backfill_lora_item_by_autov3_hash():
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# 12-char autov3 hash that does NOT match the cache item's sha256,
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# but matches its stored autov3 — would fail with the sha256-only lookup.
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|
autov3_hash = "a1b2c3d4e5f6"
|
|
cached_item = {
|
|
"file_path": "/loras/style_stored.safetensors",
|
|
"file_name": "Style LoRA",
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|
"sha256": "deadbeefdeadbeefdeadbeefdeadbeefdeadbeefdeadbeefdeadbeefdeadbeef",
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|
"autov3": autov3_hash,
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|
"preview_url": "/previews/style.png",
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|
}
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|
result = await _run_backfill([cached_item], autov3_hash)
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|
assert result is not None
|
|
assert result["existsLocally"] is True
|
|
assert result["localPath"] == "/loras/style.safetensors"
|
|
assert result["thumbnailUrl"] == config.get_preview_static_url(
|
|
cached_item["preview_url"]
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_backfill_lora_item_by_autov2_prefix():
|
|
# 10-char autov2 hash matches the cache item's sha256 prefix.
|
|
autov2_hash = "0123456789"
|
|
cached_item = {
|
|
"file_path": "/loras/style_stored.safetensors",
|
|
"file_name": "Style LoRA",
|
|
"sha256": "0123456789abcdef0123456789abcdef0123456789abcdef0123456789abcdef",
|
|
"autov3": "",
|
|
"preview_url": "/previews/style.png",
|
|
}
|
|
result = await _run_backfill([cached_item], autov2_hash)
|
|
assert result is not None
|
|
assert result["existsLocally"] is True
|
|
assert result["thumbnailUrl"] == config.get_preview_static_url(
|
|
cached_item["preview_url"]
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_backfill_lora_item_by_full_sha256():
|
|
# Full sha256 hash matches the cache item as before the change.
|
|
sha256_hash = "0123456789abcdef0123456789abcdef0123456789abcdef0123456789abcdef"
|
|
cached_item = {
|
|
"file_path": "/loras/style_stored.safetensors",
|
|
"file_name": "Style LoRA",
|
|
"sha256": sha256_hash,
|
|
"preview_url": "/previews/style.png",
|
|
}
|
|
result = await _run_backfill([cached_item], sha256_hash)
|
|
assert result is not None
|
|
assert result["existsLocally"] is True
|
|
assert result["thumbnailUrl"] == config.get_preview_static_url(
|
|
cached_item["preview_url"]
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_backfill_lora_cache_item_without_sha256_does_not_crash():
|
|
# Cache item missing the sha256 field: no KeyError, no match, no crash.
|
|
autov3_hash = "a1b2c3d4e5f6"
|
|
cached_item = {
|
|
"file_path": "/loras/unrelated.safetensors",
|
|
"file_name": "Unrelated",
|
|
}
|
|
result = await _run_backfill([cached_item], autov3_hash)
|
|
assert result is not None
|
|
assert result["existsLocally"] is True
|
|
# No match, so thumbnailUrl stays the CivitAI image URL.
|
|
assert result["thumbnailUrl"] != config.get_preview_static_url(
|
|
cached_item.get("preview_url", "/previews/unrelated.png")
|
|
)
|
|
assert result["thumbnailUrl"].startswith("https://image.civitai.com/")
|
|
|