mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-27 17:22:53 +03:00
fdd003d8e2
* control and custom diffusion * dreambooth * instructpix2pix and dreambooth ckpting * t2i adapters. * text to image ft * textual inversion * unconditional * workflows * import fix * fix import
131 lines
4.8 KiB
Python
131 lines
4.8 KiB
Python
# coding=utf-8
|
|
# Copyright 2023 HuggingFace Inc.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import logging
|
|
import os
|
|
import sys
|
|
import tempfile
|
|
|
|
|
|
sys.path.append("..")
|
|
from test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402
|
|
|
|
|
|
logging.basicConfig(level=logging.DEBUG)
|
|
|
|
logger = logging.getLogger()
|
|
stream_handler = logging.StreamHandler(sys.stdout)
|
|
logger.addHandler(stream_handler)
|
|
|
|
|
|
class CustomDiffusion(ExamplesTestsAccelerate):
|
|
def test_custom_diffusion(self):
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
test_args = f"""
|
|
examples/custom_diffusion/train_custom_diffusion.py
|
|
--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
|
|
--instance_data_dir docs/source/en/imgs
|
|
--instance_prompt <new1>
|
|
--resolution 64
|
|
--train_batch_size 1
|
|
--gradient_accumulation_steps 1
|
|
--max_train_steps 2
|
|
--learning_rate 1.0e-05
|
|
--scale_lr
|
|
--lr_scheduler constant
|
|
--lr_warmup_steps 0
|
|
--modifier_token <new1>
|
|
--no_safe_serialization
|
|
--output_dir {tmpdir}
|
|
""".split()
|
|
|
|
run_command(self._launch_args + test_args)
|
|
# save_pretrained smoke test
|
|
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_custom_diffusion_weights.bin")))
|
|
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "<new1>.bin")))
|
|
|
|
def test_custom_diffusion_checkpointing_checkpoints_total_limit(self):
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
test_args = f"""
|
|
examples/custom_diffusion/train_custom_diffusion.py
|
|
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
|
|
--instance_data_dir=docs/source/en/imgs
|
|
--output_dir={tmpdir}
|
|
--instance_prompt=<new1>
|
|
--resolution=64
|
|
--train_batch_size=1
|
|
--modifier_token=<new1>
|
|
--dataloader_num_workers=0
|
|
--max_train_steps=6
|
|
--checkpoints_total_limit=2
|
|
--checkpointing_steps=2
|
|
--no_safe_serialization
|
|
""".split()
|
|
|
|
run_command(self._launch_args + test_args)
|
|
|
|
self.assertEqual(
|
|
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
|
|
{"checkpoint-4", "checkpoint-6"},
|
|
)
|
|
|
|
def test_custom_diffusion_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
test_args = f"""
|
|
examples/custom_diffusion/train_custom_diffusion.py
|
|
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
|
|
--instance_data_dir=docs/source/en/imgs
|
|
--output_dir={tmpdir}
|
|
--instance_prompt=<new1>
|
|
--resolution=64
|
|
--train_batch_size=1
|
|
--modifier_token=<new1>
|
|
--dataloader_num_workers=0
|
|
--max_train_steps=9
|
|
--checkpointing_steps=2
|
|
--no_safe_serialization
|
|
""".split()
|
|
|
|
run_command(self._launch_args + test_args)
|
|
|
|
self.assertEqual(
|
|
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
|
|
{"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
|
|
)
|
|
|
|
resume_run_args = f"""
|
|
examples/custom_diffusion/train_custom_diffusion.py
|
|
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
|
|
--instance_data_dir=docs/source/en/imgs
|
|
--output_dir={tmpdir}
|
|
--instance_prompt=<new1>
|
|
--resolution=64
|
|
--train_batch_size=1
|
|
--modifier_token=<new1>
|
|
--dataloader_num_workers=0
|
|
--max_train_steps=11
|
|
--checkpointing_steps=2
|
|
--resume_from_checkpoint=checkpoint-8
|
|
--checkpoints_total_limit=3
|
|
--no_safe_serialization
|
|
""".split()
|
|
|
|
run_command(self._launch_args + resume_run_args)
|
|
|
|
self.assertEqual(
|
|
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
|
|
{"checkpoint-6", "checkpoint-8", "checkpoint-10"},
|
|
)
|