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cc3c384d5e
1. Optimize downloading meta-csv files for large-scale dataset like mPLUG-youku (> 1GB for meta csv mapping) 2. Add head and overall progress bar for NativeIterableDataset 3. Modify the try-catch info for oss_utils Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/12952842
54 lines
2.3 KiB
Python
54 lines
2.3 KiB
Python
# Copyright 2022-2023 The Alibaba Fundamental Vision Team Authors. All rights reserved.
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import unittest
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from modelscope.models import Model
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from modelscope.models.multi_modal import EfficientStableDiffusion
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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from modelscope.utils.test_utils import test_level
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class EfficientDiffusionTuningTest(unittest.TestCase):
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def setUp(self) -> None:
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self.task = Tasks.efficient_diffusion_tuning
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_efficient_diffusion_tuning_lora_run_pipeline(self):
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model_id = 'damo/multi-modal_efficient-diffusion-tuning-lora'
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inputs = {'prompt': 'pale golden rod circle with old lace background'}
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edt_pipeline = pipeline(self.task, model_id)
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result = edt_pipeline(inputs)
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print(f'Efficient-diffusion-tuning-lora output: {result}.')
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_efficient_diffusion_tuning_lora_load_model_from_pretrained(self):
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model_id = 'damo/multi-modal_efficient-diffusion-tuning-lora'
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model = Model.from_pretrained(model_id)
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self.assertTrue(model.__class__ == EfficientStableDiffusion)
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_efficient_diffusion_tuning_control_lora_run_pipeline(self):
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# TODO: to be fixed in the future
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model_id = 'damo/multi-modal_efficient-diffusion-tuning-control-lora'
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inputs = {
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'prompt':
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'pale golden rod circle with old lace background',
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'cond':
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'data/test/images/efficient_diffusion_tuning_sd_control_lora_source.png'
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}
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edt_pipeline = pipeline(self.task, model_id)
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result = edt_pipeline(inputs)
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print(f'Efficient-diffusion-tuning-control-lora output: {result}.')
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_efficient_diffusion_tuning_control_lora_load_model_from_pretrained(
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self):
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model_id = 'damo/multi-modal_efficient-diffusion-tuning-control-lora'
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model = Model.from_pretrained(model_id)
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self.assertTrue(model.__class__ == EfficientStableDiffusion)
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if __name__ == '__main__':
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unittest.main()
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