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101 lines
4.4 KiB
Python
101 lines
4.4 KiB
Python
# Copyright 2022-2023 The Alibaba Fundamental Vision Team Authors. All rights reserved.
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import os
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import tempfile
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import unittest
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import cv2
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from modelscope.models import Model
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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 EfficientDiffusionTuningTestSwift(unittest.TestCase):
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def setUp(self) -> None:
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os.system('pip install ms-swift -U')
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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_swift_lora_run_pipeline(self):
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model_id = 'damo/multi-modal_efficient-diffusion-tuning-swift-lora'
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model_revision = 'v1.0.2'
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inputs = {
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'prompt':
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'a street scene with a cafe and a restaurant sign in anime style'
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}
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sd_tuner_pipeline = pipeline(
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self.task, model_id, model_revision=model_revision)
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result = sd_tuner_pipeline(inputs, generator_seed=0)
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output_image_path = tempfile.NamedTemporaryFile(suffix='.png').name
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cv2.imwrite(output_image_path, result['output_imgs'][0])
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print(
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f'Efficient-diffusion-tuning-swift-lora output: {output_image_path}'
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)
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_efficient_diffusion_tuning_swift_lora_load_model_from_pretrained(
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self):
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model_id = 'damo/multi-modal_efficient-diffusion-tuning-swift-lora'
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model_revision = 'v1.0.2'
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model = Model.from_pretrained(model_id, model_revision=model_revision)
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from modelscope.models.multi_modal import EfficientStableDiffusion
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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_swift_adapter_run_pipeline(self):
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model_id = 'damo/multi-modal_efficient-diffusion-tuning-swift-adapter'
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model_revision = 'v1.0.2'
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inputs = {
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'prompt':
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'a street scene with a cafe and a restaurant sign in anime style'
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}
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sd_tuner_pipeline = pipeline(
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self.task, model_id, model_revision=model_revision)
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result = sd_tuner_pipeline(inputs, generator_seed=0)
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output_image_path = tempfile.NamedTemporaryFile(suffix='.png').name
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cv2.imwrite(output_image_path, result['output_imgs'][0])
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print(
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f'Efficient-diffusion-tuning-swift-adapter output: {output_image_path}'
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)
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_efficient_diffusion_tuning_swift_adapter_load_model_from_pretrained(
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self):
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model_id = 'damo/multi-modal_efficient-diffusion-tuning-swift-adapter'
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model_revision = 'v1.0.2'
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model = Model.from_pretrained(model_id, model_revision=model_revision)
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from modelscope.models.multi_modal import EfficientStableDiffusion
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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_swift_prompt_run_pipeline(self):
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model_id = 'damo/multi-modal_efficient-diffusion-tuning-swift-prompt'
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model_revision = 'v1.0.2'
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inputs = {
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'prompt':
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'a street scene with a cafe and a restaurant sign in anime style'
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}
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sd_tuner_pipeline = pipeline(
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self.task, model_id, model_revision=model_revision)
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result = sd_tuner_pipeline(inputs, generator_seed=0)
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output_image_path = tempfile.NamedTemporaryFile(suffix='.png').name
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cv2.imwrite(output_image_path, result['output_imgs'][0])
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print(
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f'Efficient-diffusion-tuning-swift-prompt output: {output_image_path}'
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)
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_efficient_diffusion_tuning_swift_prompt_load_model_from_pretrained(
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self):
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model_id = 'damo/multi-modal_efficient-diffusion-tuning-swift-prompt'
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model_revision = 'v1.0.2'
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model = Model.from_pretrained(model_id, model_revision=model_revision)
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from modelscope.models.multi_modal import EfficientStableDiffusion
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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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