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# coding=utf-8
# Copyright 2024 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 shutil
import sys
import tempfile
from diffusers import DiffusionPipeline, UNet2DConditionModel
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 DreamBooth(ExamplesTestsAccelerate):
def test_dreambooth(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/dreambooth/train_dreambooth.py
--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
--instance_data_dir docs/source/en/imgs
--instance_prompt photo
--resolution 64
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 2
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
""".split()
run_command(self._launch_args + test_args)
# save_pretrained smoke test
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.safetensors")))
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
def test_dreambooth_if(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/dreambooth/train_dreambooth.py
--pretrained_model_name_or_path hf-internal-testing/tiny-if-pipe
--instance_data_dir docs/source/en/imgs
--instance_prompt photo
--resolution 64
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 2
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--pre_compute_text_embeddings
--tokenizer_max_length=77
--text_encoder_use_attention_mask
""".split()
run_command(self._launch_args + test_args)
# save_pretrained smoke test
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.safetensors")))
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
def test_dreambooth_checkpointing(self):
instance_prompt = "photo"
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 4, checkpointing_steps == 2
# Should create checkpoints at steps 2, 4
initial_run_args = f"""
examples/dreambooth/train_dreambooth.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--instance_data_dir docs/source/en/imgs
--instance_prompt {instance_prompt}
--resolution 64
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 4
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--seed=0
""".split()
run_command(self._launch_args + initial_run_args)
# check can run the original fully trained output pipeline
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(instance_prompt, num_inference_steps=1)
# check checkpoint directories exist
self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
# check can run an intermediate checkpoint
unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder="checkpoint-2/unet")
pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)
pipe(instance_prompt, num_inference_steps=1)
# Remove checkpoint 2 so that we can check only later checkpoints exist after resuming
shutil.rmtree(os.path.join(tmpdir, "checkpoint-2"))
# Run training script for 7 total steps resuming from checkpoint 4
resume_run_args = f"""
examples/dreambooth/train_dreambooth.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--instance_data_dir docs/source/en/imgs
--instance_prompt {instance_prompt}
--resolution 64
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 6
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--resume_from_checkpoint=checkpoint-4
--seed=0
""".split()
run_command(self._launch_args + resume_run_args)
# check can run new fully trained pipeline
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(instance_prompt, num_inference_steps=1)
# check old checkpoints do not exist
self.assertFalse(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
# check new checkpoints exist
self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-6")))
def test_dreambooth_checkpointing_checkpoints_total_limit(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/dreambooth/train_dreambooth.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=prompt
--resolution=64
--train_batch_size=1
--gradient_accumulation_steps=1
--max_train_steps=6
--checkpoints_total_limit=2
--checkpointing_steps=2
""".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_dreambooth_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/dreambooth/train_dreambooth.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=prompt
--resolution=64
--train_batch_size=1
--gradient_accumulation_steps=1
--max_train_steps=4
--checkpointing_steps=2
""".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"},
)
resume_run_args = f"""
examples/dreambooth/train_dreambooth.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=prompt
--resolution=64
--train_batch_size=1
--gradient_accumulation_steps=1
--max_train_steps=8
--checkpointing_steps=2
--resume_from_checkpoint=checkpoint-4
--checkpoints_total_limit=2
""".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"})
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