2023-02-26 02:19:48 +01:00
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def test_load_model():
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2023-03-22 05:57:18 +01:00
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from lama_cleaner.plugins import InteractiveSeg
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2023-02-26 02:19:48 +01:00
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from lama_cleaner.model_manager import ModelManager
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2023-05-12 11:28:35 +02:00
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interactive_seg_model = InteractiveSeg('vit_l', 'cpu')
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2023-02-26 02:19:48 +01:00
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models = [
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"lama",
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"ldm",
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"zits",
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"mat",
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"fcf",
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"manga",
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]
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for m in models:
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ModelManager(
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name=m,
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device="cpu",
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no_half=False,
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hf_access_token="",
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disable_nsfw=False,
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sd_cpu_textencoder=True,
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sd_run_local=True,
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local_files_only=True,
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cpu_offload=True,
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enable_xformers=False,
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)
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# def create_empty_file(tmp_dir, name):
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# tmp_model_dir = os.path.join(tmp_dir, "torch", "hub", "checkpoints")
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# Path(tmp_model_dir).mkdir(exist_ok=True, parents=True)
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# path = os.path.join(tmp_model_dir, name)
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# with open(path, "w") as f:
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# f.write("1")
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#
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#
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# def test_load_model_error():
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# MODELS = [
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# ("big-lama.pt", "e3aa4aaa15225a33ec84f9f4bc47e500"),
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# ("cond_stage_model_encode.pt", "23239fc9081956a3e70de56472b3f296"),
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# ("cond_stage_model_decode.pt", "fe419cd15a750d37a4733589d0d3585c"),
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# ("diffusion.pt", "b0afda12bf790c03aba2a7431f11d22d"),
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# ]
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# with tempfile.TemporaryDirectory() as tmp_dir:
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# os.environ["XDG_CACHE_HOME"] = tmp_dir
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# for name, md5 in MODELS:
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# create_empty_file(tmp_dir, name)
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# test_load_model()
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