mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-06-10 04:49:24 -03:00
34791c2ad7
When importing a CivitAI image as a recipe, modelVersionIds[0] was blindly used as the checkpoint version ID. This array mixes checkpoints and LoRAs without ordering guarantees, causing LoRAs to be saved as the recipe checkpoint. Fix by: 1. Removing the modelVersionIds[0] fallback in _download_remote_media 2. Parsing resources entries with type:"model" as the checkpoint 3. Adding model type validation in populate_checkpoint_from_civitai Also add 2 tests for the new behavior and fix 3 tests whose mocks lacked the required model.type field.
411 lines
13 KiB
Python
411 lines
13 KiB
Python
import pytest
|
|
|
|
from py.recipes.parsers.civitai_image import CivitaiApiMetadataParser
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_parse_metadata_creates_loras_from_hashes(monkeypatch):
|
|
async def fake_metadata_provider():
|
|
return None
|
|
|
|
monkeypatch.setattr(
|
|
"py.recipes.parsers.civitai_image.get_default_metadata_provider",
|
|
fake_metadata_provider,
|
|
)
|
|
|
|
parser = CivitaiApiMetadataParser()
|
|
|
|
metadata = {
|
|
"Size": "1536x2688",
|
|
"seed": 3766932689,
|
|
"Model": "indexed_v1",
|
|
"steps": 30,
|
|
"hashes": {
|
|
"model": "692186a14a",
|
|
"LORA:Jedst1": "fb4063c470",
|
|
"LORA:HassaKu_style": "3ce00b926b",
|
|
"LORA:DetailedEyes_V3": "2c1c3f889f",
|
|
"LORA:jiaocha_illustriousXL": "35d3e6f8b0",
|
|
"LORA:绪儿 厚涂构图光影质感增强V3": "d9b5900a59",
|
|
},
|
|
"prompt": "test",
|
|
"Version": "ComfyUI",
|
|
"sampler": "er_sde_ays_30",
|
|
"cfgScale": 5,
|
|
"clipSkip": 2,
|
|
"resources": [
|
|
{
|
|
"hash": "692186a14a",
|
|
"name": "indexed_v1",
|
|
"type": "model",
|
|
}
|
|
],
|
|
"Model hash": "692186a14a",
|
|
"negativePrompt": "bad",
|
|
"username": "LumaRift",
|
|
"baseModel": "Illustrious",
|
|
}
|
|
|
|
result = await parser.parse_metadata(metadata)
|
|
|
|
assert result["base_model"] == "Illustrious"
|
|
assert len(result["loras"]) == 5
|
|
assert all(lora["weight"] == 1.0 for lora in result["loras"])
|
|
assert {lora["name"] for lora in result["loras"]} == {
|
|
"Jedst1",
|
|
"HassaKu_style",
|
|
"DetailedEyes_V3",
|
|
"jiaocha_illustriousXL",
|
|
"绪儿 厚涂构图光影质感增强V3",
|
|
}
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_parse_metadata_handles_nested_meta_and_lowercase_hashes(monkeypatch):
|
|
async def fake_metadata_provider():
|
|
return None
|
|
|
|
monkeypatch.setattr(
|
|
"py.recipes.parsers.civitai_image.get_default_metadata_provider",
|
|
fake_metadata_provider,
|
|
)
|
|
|
|
parser = CivitaiApiMetadataParser()
|
|
|
|
metadata = {
|
|
"id": 106706587,
|
|
"meta": {
|
|
"prompt": "An enigmatic silhouette",
|
|
"hashes": {
|
|
"model": "ee75fd24a4",
|
|
"lora:mj": "de49e1e98c",
|
|
"LORA:Another_Earth_2": "dc11b64a8b",
|
|
},
|
|
"resources": [
|
|
{
|
|
"hash": "ee75fd24a4",
|
|
"name": "stoiqoNewrealityFLUXSD35_f1DAlphaTwo",
|
|
"type": "model",
|
|
}
|
|
],
|
|
},
|
|
}
|
|
|
|
assert parser.is_metadata_matching(metadata)
|
|
|
|
result = await parser.parse_metadata(metadata)
|
|
|
|
assert result["gen_params"]["prompt"] == "An enigmatic silhouette"
|
|
assert {l["name"] for l in result["loras"]} == {"mj", "Another_Earth_2"}
|
|
assert {l["hash"] for l in result["loras"]} == {"de49e1e98c", "dc11b64a8b"}
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_parse_metadata_populates_checkpoint_and_rewrites_thumbnails(monkeypatch):
|
|
checkpoint_info = {
|
|
"id": 222,
|
|
"modelId": 111,
|
|
"model": {"name": "Checkpoint Example", "type": "checkpoint"},
|
|
"name": "Checkpoint Version",
|
|
"images": [{"url": "https://image.civitai.com/checkpoints/original=true"}],
|
|
"baseModel": "Illustrious",
|
|
"downloadUrl": "https://civitai.com/checkpoint/download",
|
|
"files": [
|
|
{
|
|
"type": "Model",
|
|
"primary": True,
|
|
"sizeKB": 1024,
|
|
"name": "Checkpoint Example.safetensors",
|
|
"hashes": {"SHA256": "FFAA0011"},
|
|
}
|
|
],
|
|
}
|
|
|
|
lora_info = {
|
|
"id": 444,
|
|
"modelId": 333,
|
|
"model": {"name": "Example Lora Model", "type": "lora"},
|
|
"name": "Example Lora Version",
|
|
"images": [{"url": "https://image.civitai.com/loras/original=true"}],
|
|
"baseModel": "Illustrious",
|
|
"downloadUrl": "https://civitai.com/lora/download",
|
|
"files": [
|
|
{
|
|
"type": "Model",
|
|
"primary": True,
|
|
"sizeKB": 512,
|
|
"hashes": {"SHA256": "abc123"},
|
|
}
|
|
],
|
|
}
|
|
|
|
async def fake_metadata_provider():
|
|
class Provider:
|
|
async def get_model_version_info(self, version_id):
|
|
if version_id == "222":
|
|
return checkpoint_info, None
|
|
if version_id == "444":
|
|
return lora_info, None
|
|
return None, "Model not found"
|
|
|
|
return Provider()
|
|
|
|
monkeypatch.setattr(
|
|
"py.recipes.parsers.civitai_image.get_default_metadata_provider",
|
|
fake_metadata_provider,
|
|
)
|
|
|
|
parser = CivitaiApiMetadataParser()
|
|
|
|
metadata = {
|
|
"prompt": "test prompt",
|
|
"negativePrompt": "test negative prompt",
|
|
"civitaiResources": [
|
|
{
|
|
"type": "checkpoint",
|
|
"modelId": 111,
|
|
"modelVersionId": 222,
|
|
"modelName": "Checkpoint Example",
|
|
"modelVersionName": "Checkpoint Version",
|
|
},
|
|
{
|
|
"type": "lora",
|
|
"modelId": 333,
|
|
"modelVersionId": 444,
|
|
"modelName": "Example Lora",
|
|
"modelVersionName": "Lora Version",
|
|
"weight": 0.7,
|
|
},
|
|
],
|
|
}
|
|
|
|
result = await parser.parse_metadata(metadata)
|
|
|
|
assert result["model"] is not None
|
|
assert result["model"]["name"] == "Checkpoint Example"
|
|
assert result["model"]["type"] == "checkpoint"
|
|
assert (
|
|
result["model"]["thumbnailUrl"]
|
|
== "https://image.civitai.com/checkpoints/width=450,optimized=true"
|
|
)
|
|
assert result["model"]["modelId"] == 111
|
|
assert result["model"]["size"] == 1024 * 1024
|
|
assert result["model"]["hash"] == "ffaa0011"
|
|
assert result["model"]["file_name"] == "Checkpoint Example"
|
|
|
|
assert result["loras"]
|
|
assert result["loras"][0]["name"] == "Example Lora Model"
|
|
assert (
|
|
result["loras"][0]["thumbnailUrl"]
|
|
== "https://image.civitai.com/loras/width=450,optimized=true"
|
|
)
|
|
assert result["loras"][0]["hash"] == "abc123"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_parse_metadata_handles_modelVersionIds(monkeypatch):
|
|
"""Test that modelVersionIds from Civitai image API are properly processed."""
|
|
lora_info_1 = {
|
|
"id": 2398829,
|
|
"modelId": 123456,
|
|
"model": {"name": "Dance LoRA 1", "type": "lora"},
|
|
"name": "Version 1.0",
|
|
"images": [{"url": "https://image.civitai.com/lora1/original=true"}],
|
|
"baseModel": "SDXL",
|
|
"downloadUrl": "https://civitai.com/lora1/download",
|
|
"files": [
|
|
{
|
|
"type": "Model",
|
|
"primary": True,
|
|
"sizeKB": 10240,
|
|
"name": "dance_lora_1.safetensors",
|
|
"hashes": {"SHA256": "aabbccdd0011"},
|
|
}
|
|
],
|
|
}
|
|
|
|
lora_info_2 = {
|
|
"id": 2398838,
|
|
"modelId": 123457,
|
|
"model": {"name": "Style LoRA 2", "type": "lora"},
|
|
"name": "Version 2.0",
|
|
"images": [{"url": "https://image.civitai.com/lora2/original=true"}],
|
|
"baseModel": "SDXL",
|
|
"downloadUrl": "https://civitai.com/lora2/download",
|
|
"files": [
|
|
{
|
|
"type": "Model",
|
|
"primary": True,
|
|
"sizeKB": 20480,
|
|
"name": "style_lora_2.safetensors",
|
|
"hashes": {"SHA256": "aabbccdd0022"},
|
|
}
|
|
],
|
|
}
|
|
|
|
async def fake_metadata_provider():
|
|
class Provider:
|
|
async def get_model_version_info(self, version_id):
|
|
if version_id == "2398829":
|
|
return lora_info_1, None
|
|
if version_id == "2398838":
|
|
return lora_info_2, None
|
|
return None, "Model not found"
|
|
|
|
return Provider()
|
|
|
|
monkeypatch.setattr(
|
|
"py.recipes.parsers.civitai_image.get_default_metadata_provider",
|
|
fake_metadata_provider,
|
|
)
|
|
|
|
parser = CivitaiApiMetadataParser()
|
|
|
|
# This simulates the metadata from Civitai image API where modelVersionIds
|
|
# is at the root level and meta only contains basic prompt info
|
|
metadata = {
|
|
"id": 109882763,
|
|
"meta": {
|
|
"id": 109882763,
|
|
"meta": {"prompt": "A woman does the hip bump dance."},
|
|
},
|
|
"modelVersionIds": [2398829, 2398838],
|
|
}
|
|
|
|
assert parser.is_metadata_matching(metadata)
|
|
|
|
result = await parser.parse_metadata(metadata)
|
|
|
|
# Verify both LoRAs were created from modelVersionIds
|
|
assert len(result["loras"]) == 2
|
|
|
|
# Check first LoRA
|
|
lora1 = result["loras"][0]
|
|
assert lora1["id"] == 2398829
|
|
assert lora1["name"] == "Dance LoRA 1"
|
|
assert lora1["type"] == "lora"
|
|
assert lora1["hash"] == "aabbccdd0011"
|
|
assert lora1["baseModel"] == "SDXL"
|
|
assert (
|
|
lora1["thumbnailUrl"]
|
|
== "https://image.civitai.com/lora1/width=450,optimized=true"
|
|
)
|
|
|
|
# Check second LoRA
|
|
lora2 = result["loras"][1]
|
|
assert lora2["id"] == 2398838
|
|
assert lora2["name"] == "Style LoRA 2"
|
|
assert lora2["type"] == "lora"
|
|
assert lora2["hash"] == "aabbccdd0022"
|
|
assert lora2["baseModel"] == "SDXL"
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_parse_metadata_extracts_checkpoint_from_resources_model_type(monkeypatch):
|
|
"""resources entries with type:"model" should be captured as the checkpoint,
|
|
not skipped (which was the old buggy behavior), and not mixed into loras."""
|
|
captured_hashes = []
|
|
|
|
async def fake_metadata_provider():
|
|
class Provider:
|
|
async def get_model_by_hash(self, model_hash):
|
|
captured_hashes.append(model_hash)
|
|
if model_hash == "a1b2c3d4e5":
|
|
return ({
|
|
"id": 999,
|
|
"modelId": 888,
|
|
"name": "v1.0",
|
|
"model": {"name": "Real Checkpoint", "type": "Checkpoint"},
|
|
"baseModel": "SDXL 1.0",
|
|
"images": [{"url": "https://image.civitai.com/cp/original=true"}],
|
|
"files": [{"type": "Model", "primary": True, "sizeKB": 1024, "name": "cp.safetensors"}]
|
|
}, None)
|
|
return None, "Model not found"
|
|
|
|
return Provider()
|
|
|
|
monkeypatch.setattr(
|
|
"py.recipes.parsers.civitai_image.get_default_metadata_provider",
|
|
fake_metadata_provider,
|
|
)
|
|
|
|
parser = CivitaiApiMetadataParser()
|
|
|
|
metadata = {
|
|
"prompt": "test",
|
|
"resources": [
|
|
{"hash": "a1b2c3d4e5", "name": "Real Checkpoint", "type": "model"},
|
|
{"hash": "f6g7h8i9j0", "name": "Some LoRA", "type": "lora", "weight": 0.8},
|
|
],
|
|
"Model hash": "a1b2c3d4e5",
|
|
}
|
|
|
|
result = await parser.parse_metadata(metadata)
|
|
|
|
# The type:"model" resource should be in result["model"], not in result["loras"]
|
|
assert result["model"] is not None, "checkpoint model should be extracted"
|
|
assert result["model"]["name"] == "Real Checkpoint"
|
|
assert result["model"]["hash"] == "a1b2c3d4e5"
|
|
assert result["model"]["type"] == "model"
|
|
|
|
# The LoRA resource should be in result["loras"]
|
|
assert len(result["loras"]) == 1
|
|
assert result["loras"][0]["name"] == "Some LoRA"
|
|
|
|
# The checkpoint hash should have triggered a lookup
|
|
assert "a1b2c3d4e5" in captured_hashes
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_parse_metadata_resources_model_type_does_not_duplicate_checkpoint_in_loras(monkeypatch):
|
|
"""When a resources entry has type:"model", it should NOT also appear in loras.
|
|
Regression test for the bug where the checkpoint model appeared in both places."""
|
|
async def fake_metadata_provider():
|
|
class Provider:
|
|
async def get_model_by_hash(self, model_hash):
|
|
if model_hash == "cp123hash":
|
|
return ({
|
|
"id": 100,
|
|
"modelId": 200,
|
|
"name": "v2",
|
|
"model": {"name": "My Checkpoint", "type": "Checkpoint"},
|
|
"baseModel": "SDXL",
|
|
"files": [{"type": "Model", "primary": True, "sizeKB": 1024, "name": "cp.safetensors"}]
|
|
}, None)
|
|
if model_hash == "lora1hash":
|
|
return ({
|
|
"id": 300,
|
|
"modelId": 400,
|
|
"name": "v1",
|
|
"model": {"name": "Style LoRA", "type": "LORA"},
|
|
"baseModel": "SDXL",
|
|
"files": [{"type": "Model", "primary": True, "sizeKB": 512, "name": "style.safetensors"}]
|
|
}, None)
|
|
return None, "Model not found"
|
|
|
|
return Provider()
|
|
|
|
monkeypatch.setattr(
|
|
"py.recipes.parsers.civitai_image.get_default_metadata_provider",
|
|
fake_metadata_provider,
|
|
)
|
|
|
|
parser = CivitaiApiMetadataParser()
|
|
metadata = {
|
|
"resources": [
|
|
{"hash": "cp123hash", "name": "My Checkpoint", "type": "model"},
|
|
{"hash": "lora1hash", "name": "Style LoRA", "type": "lora", "weight": 0.5},
|
|
],
|
|
}
|
|
|
|
result = await parser.parse_metadata(metadata)
|
|
|
|
# Checkpoint must NOT appear in loras
|
|
lora_names = {l["name"] for l in result["loras"]}
|
|
assert "My Checkpoint" not in lora_names
|
|
assert "Style LoRA" in lora_names
|
|
|
|
# Checkpoint must be in result["model"]
|
|
assert result["model"] is not None
|
|
assert result["model"]["name"] == "My Checkpoint"
|