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import logging |
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import os |
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import sys |
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import tempfile |
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sys.path.append("..") |
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from test_examples_utils import ExamplesTestsAccelerate, run_command |
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logging.basicConfig(level=logging.DEBUG) |
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logger = logging.getLogger() |
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stream_handler = logging.StreamHandler(sys.stdout) |
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logger.addHandler(stream_handler) |
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class CustomDiffusion(ExamplesTestsAccelerate): |
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def test_custom_diffusion(self): |
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with tempfile.TemporaryDirectory() as tmpdir: |
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test_args = f""" |
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examples/custom_diffusion/train_custom_diffusion.py |
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--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe |
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--instance_data_dir docs/source/en/imgs |
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--instance_prompt <new1> |
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--resolution 64 |
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--train_batch_size 1 |
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--gradient_accumulation_steps 1 |
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--max_train_steps 2 |
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--learning_rate 1.0e-05 |
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--scale_lr |
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--lr_scheduler constant |
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--lr_warmup_steps 0 |
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--modifier_token <new1> |
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--no_safe_serialization |
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--output_dir {tmpdir} |
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""".split() |
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run_command(self._launch_args + test_args) |
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_custom_diffusion_weights.bin"))) |
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "<new1>.bin"))) |
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def test_custom_diffusion_checkpointing_checkpoints_total_limit(self): |
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with tempfile.TemporaryDirectory() as tmpdir: |
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test_args = f""" |
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examples/custom_diffusion/train_custom_diffusion.py |
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--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe |
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--instance_data_dir=docs/source/en/imgs |
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--output_dir={tmpdir} |
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--instance_prompt=<new1> |
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--resolution=64 |
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--train_batch_size=1 |
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--modifier_token=<new1> |
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--dataloader_num_workers=0 |
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--max_train_steps=6 |
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--checkpoints_total_limit=2 |
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--checkpointing_steps=2 |
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--no_safe_serialization |
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""".split() |
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run_command(self._launch_args + test_args) |
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self.assertEqual({x for x in os.listdir(tmpdir) if "checkpoint" in x}, {"checkpoint-4", "checkpoint-6"}) |
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def test_custom_diffusion_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self): |
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with tempfile.TemporaryDirectory() as tmpdir: |
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test_args = f""" |
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examples/custom_diffusion/train_custom_diffusion.py |
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--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe |
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--instance_data_dir=docs/source/en/imgs |
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--output_dir={tmpdir} |
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--instance_prompt=<new1> |
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--resolution=64 |
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--train_batch_size=1 |
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--modifier_token=<new1> |
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--dataloader_num_workers=0 |
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--max_train_steps=4 |
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--checkpointing_steps=2 |
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--no_safe_serialization |
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""".split() |
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run_command(self._launch_args + test_args) |
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self.assertEqual( |
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{x for x in os.listdir(tmpdir) if "checkpoint" in x}, |
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{"checkpoint-2", "checkpoint-4"}, |
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) |
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resume_run_args = f""" |
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examples/custom_diffusion/train_custom_diffusion.py |
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--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe |
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--instance_data_dir=docs/source/en/imgs |
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--output_dir={tmpdir} |
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--instance_prompt=<new1> |
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--resolution=64 |
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--train_batch_size=1 |
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--modifier_token=<new1> |
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--dataloader_num_workers=0 |
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--max_train_steps=8 |
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--checkpointing_steps=2 |
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--resume_from_checkpoint=checkpoint-4 |
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--checkpoints_total_limit=2 |
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--no_safe_serialization |
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""".split() |
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run_command(self._launch_args + resume_run_args) |
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self.assertEqual({x for x in os.listdir(tmpdir) if "checkpoint" in x}, {"checkpoint-6", "checkpoint-8"}) |
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