mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
78ef946e01
- Implement new model loader and modify invocations and embeddings - Finish implementation loaders for all models currently supported by InvokeAI. - Move lora, textual_inversion, and model patching support into backend/embeddings. - Restore support for model cache statistics collection (a little ugly, needs work). - Fixed up invocations that load and patch models. - Move seamless and silencewarnings utils into better location
31 lines
1009 B
Python
31 lines
1009 B
Python
from pathlib import Path
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import pytest
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from invokeai.backend.model_manager import BaseModelType, ModelRepoVariant
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from invokeai.backend.model_manager.probe import VaeFolderProbe
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@pytest.mark.parametrize(
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"vae_path,expected_type",
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[
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("sd-vae-ft-mse", BaseModelType.StableDiffusion1),
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("sdxl-vae", BaseModelType.StableDiffusionXL),
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("taesd", BaseModelType.StableDiffusion1),
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("taesdxl", BaseModelType.StableDiffusionXL),
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],
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)
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def test_get_base_type(vae_path: str, expected_type: BaseModelType, datadir: Path):
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sd1_vae_path = datadir / "vae" / vae_path
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probe = VaeFolderProbe(sd1_vae_path)
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base_type = probe.get_base_type()
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assert base_type == expected_type
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repo_variant = probe.get_repo_variant()
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assert repo_variant == ModelRepoVariant.DEFAULT
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def test_repo_variant(datadir: Path):
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probe = VaeFolderProbe(datadir / "vae" / "taesdxl-fp16")
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repo_variant = probe.get_repo_variant()
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assert repo_variant == ModelRepoVariant.FP16
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