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test_dreambooth_lora_sd3.py
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test_dreambooth_lora_sd3.py
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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 DreamBoothLoRASD3(ExamplesTestsAccelerate):
instance_data_dir = "docs/source/en/imgs"
instance_prompt = "photo"
pretrained_model_name_or_path = "hf-internal-testing/tiny-sd3-pipe"
script_path = "examples/dreambooth/train_dreambooth_lora_sd3.py"
transformer_block_idx = 0
layer_type = "attn.to_k"
def test_dreambooth_lora_sd3(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
{self.script_path}
--pretrained_model_name_or_path {self.pretrained_model_name_or_path}
--instance_data_dir {self.instance_data_dir}
--instance_prompt {self.instance_prompt}
--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, "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)
# when not training the text encoder, all the parameters in the state dict should start
# with `"transformer"` in their names.
starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
self.assertTrue(starts_with_transformer)
def test_dreambooth_lora_text_encoder_sd3(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
{self.script_path}
--pretrained_model_name_or_path {self.pretrained_model_name_or_path}
--instance_data_dir {self.instance_data_dir}
--instance_prompt {self.instance_prompt}
--resolution 64
--train_batch_size 1
--train_text_encoder
--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)
starts_with_expected_prefix = all(
(key.startswith("transformer") or key.startswith("text_encoder")) for key in lora_state_dict.keys()
)
self.assertTrue(starts_with_expected_prefix)
def test_dreambooth_lora_latent_caching(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
{self.script_path}
--pretrained_model_name_or_path {self.pretrained_model_name_or_path}
--instance_data_dir {self.instance_data_dir}
--instance_prompt {self.instance_prompt}
--resolution 64
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 2
--cache_latents
--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)
# when not training the text encoder, all the parameters in the state dict should start
# with `"transformer"` in their names.
starts_with_transformer = all(key.startswith("transformer") for key in lora_state_dict.keys())
self.assertTrue(starts_with_transformer)
def test_dreambooth_lora_block(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
{self.script_path}
--pretrained_model_name_or_path {self.pretrained_model_name_or_path}
--instance_data_dir {self.instance_data_dir}
--instance_prompt {self.instance_prompt}
--resolution 64
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 2
--lora_blocks {self.transformer_block_idx}
--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)
# when not training the text encoder, all the parameters in the state dict should start
# with `"transformer"` in their names.
# In this test, only params of transformer block 0 should be in the state dict
starts_with_transformer = all(
key.startswith("transformer.transformer_blocks.0") for key in lora_state_dict.keys()
)
self.assertTrue(starts_with_transformer)
def test_dreambooth_lora_layer(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
{self.script_path}
--pretrained_model_name_or_path {self.pretrained_model_name_or_path}
--instance_data_dir {self.instance_data_dir}
--instance_prompt {self.instance_prompt}
--resolution 64
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 2
--lora_layers {self.layer_type}
--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)
# In this test, only transformer params of attention layers `attn.to_k` should be in the state dict
starts_with_transformer = all("attn.to_k" in key for key in lora_state_dict.keys())
self.assertTrue(starts_with_transformer)
def test_dreambooth_lora_sd3_checkpointing_checkpoints_total_limit(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
{self.script_path}
--pretrained_model_name_or_path={self.pretrained_model_name_or_path}
--instance_data_dir={self.instance_data_dir}
--output_dir={tmpdir}
--instance_prompt={self.instance_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_lora_sd3_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
{self.script_path}
--pretrained_model_name_or_path={self.pretrained_model_name_or_path}
--instance_data_dir={self.instance_data_dir}
--output_dir={tmpdir}
--instance_prompt={self.instance_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"""
{self.script_path}
--pretrained_model_name_or_path={self.pretrained_model_name_or_path}
--instance_data_dir={self.instance_data_dir}
--output_dir={tmpdir}
--instance_prompt={self.instance_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"})