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fdd003d8e2
* control and custom diffusion * dreambooth * instructpix2pix and dreambooth ckpting * t2i adapters. * text to image ft * textual inversion * unconditional * workflows * import fix * fix import
121 lines
4.4 KiB
Python
121 lines
4.4 KiB
Python
# coding=utf-8
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# Copyright 2023 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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 # noqa: E402
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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 ControlNet(ExamplesTestsAccelerate):
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def test_controlnet_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/controlnet/train_controlnet.py
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--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
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--dataset_name=hf-internal-testing/fill10
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--output_dir={tmpdir}
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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=6
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--checkpoints_total_limit=2
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--checkpointing_steps=2
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--controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet
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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-4", "checkpoint-6"},
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)
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def test_controlnet_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/controlnet/train_controlnet.py
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--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
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--dataset_name=hf-internal-testing/fill10
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--output_dir={tmpdir}
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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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--controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet
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--max_train_steps=9
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--checkpointing_steps=2
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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", "checkpoint-6", "checkpoint-8"},
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)
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resume_run_args = f"""
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examples/controlnet/train_controlnet.py
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--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
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--dataset_name=hf-internal-testing/fill10
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--output_dir={tmpdir}
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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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--controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet
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--max_train_steps=11
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--checkpointing_steps=2
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--resume_from_checkpoint=checkpoint-8
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--checkpoints_total_limit=3
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""".split()
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run_command(self._launch_args + resume_run_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-8", "checkpoint-10", "checkpoint-12"},
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)
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class ControlNetSDXL(ExamplesTestsAccelerate):
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def test_controlnet_sdxl(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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test_args = f"""
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examples/controlnet/train_controlnet_sdxl.py
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--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-xl-pipe
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--dataset_name=hf-internal-testing/fill10
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--output_dir={tmpdir}
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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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--controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet-sdxl
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--max_train_steps=9
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--checkpointing_steps=2
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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, "diffusion_pytorch_model.safetensors")))
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