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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 sys
import tempfile
import safetensors
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 TextToImageLCM(ExamplesTestsAccelerate):
def test_text_to_image_lcm_lora_sdxl(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/consistency_distillation/train_lcm_distill_lora_sdxl.py
--pretrained_teacher_model hf-internal-testing/tiny-stable-diffusion-xl-pipe
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--lora_rank 4
--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, "pytorch_lora_weights.safetensors")))
# make sure the state_dict has the correct naming in the parameters.
lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
is_lora = all("lora" in k for k in lora_state_dict.keys())
self.assertTrue(is_lora)
def test_text_to_image_lcm_lora_sdxl_checkpointing(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/consistency_distillation/train_lcm_distill_lora_sdxl.py
--pretrained_teacher_model hf-internal-testing/tiny-stable-diffusion-xl-pipe
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--lora_rank 4
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 7
--checkpointing_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)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4", "checkpoint-6"},
)
test_args = f"""
examples/consistency_distillation/train_lcm_distill_lora_sdxl.py
--pretrained_teacher_model hf-internal-testing/tiny-stable-diffusion-xl-pipe
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--lora_rank 4
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 9
--checkpointing_steps 2
--resume_from_checkpoint latest
--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)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
)
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