mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-22 13:42:12 -03:00
53c4165d82
- Add MODEL_NAME_PATTERN regex to extract model names from parameters - Extract model hash from parsed hashes when available in metadata - Add checkpoint model hash and name extraction from parameters section - Implement checkpoint resource processing from Civitai metadata - Improve model information completeness for better recipe tracking
68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
import json
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import pytest
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from py.recipes.parsers.recipe_format import RecipeFormatParser
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@pytest.mark.asyncio
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async def test_recipe_format_parser_populates_checkpoint(monkeypatch):
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checkpoint_info = {
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"id": 777111,
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"modelId": 333222,
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"model": {"name": "Z Image", "type": "checkpoint"},
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"name": "Turbo",
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"images": [{"url": "https://image.civitai.com/checkpoints/original=true"}],
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"baseModel": "sdxl",
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"downloadUrl": "https://civitai.com/api/download/checkpoint",
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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": 2048,
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"name": "Z_Image_Turbo.safetensors",
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"hashes": {"SHA256": "ABC123FF"},
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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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assert version_id == "777111"
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return checkpoint_info, None
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return Provider()
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monkeypatch.setattr(
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"py.recipes.parsers.recipe_format.get_default_metadata_provider",
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fake_metadata_provider,
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)
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parser = RecipeFormatParser()
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recipe_metadata = {
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"title": "Z Recipe",
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"base_model": "",
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"loras": [],
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"gen_params": {"steps": 20},
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"tags": ["test"],
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"checkpoint": {
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"modelVersionId": 777111,
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"modelId": 333222,
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"name": "Z Image",
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"version": "Turbo",
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},
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}
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metadata_text = f"Recipe metadata: {json.dumps(recipe_metadata)}"
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result = await parser.parse_metadata(metadata_text)
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checkpoint = result.get("checkpoint")
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assert checkpoint is not None
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assert checkpoint["name"] == "Z Image"
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assert checkpoint["version"] == "Turbo"
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assert checkpoint["hash"] == "abc123ff"
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assert checkpoint["file_name"] == "Z_Image_Turbo"
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assert result["base_model"] == "sdxl"
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assert result["model"] == checkpoint
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