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1663 lines (1443 loc) 路 68.1 KB
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# 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 shutil
import subprocess
import sys
import tempfile
import unittest
from typing import List
import safetensors
from accelerate.utils import write_basic_config
from diffusers import DiffusionPipeline, UNet2DConditionModel
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
# These utils relate to ensuring the right error message is received when running scripts
class SubprocessCallException(Exception):
pass
def run_command(command: List[str], return_stdout=False):
"""
Runs `command` with `subprocess.check_output` and will potentially return the `stdout`. Will also properly capture
if an error occurred while running `command`
"""
try:
output = subprocess.check_output(command, stderr=subprocess.STDOUT)
if return_stdout:
if hasattr(output, "decode"):
output = output.decode("utf-8")
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
f"Command `{' '.join(command)}` failed with the following error:\n\n{e.output.decode()}"
) from e
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class ExamplesTestsAccelerate(unittest.TestCase):
@classmethod
def setUpClass(cls):
super().setUpClass()
cls._tmpdir = tempfile.mkdtemp()
cls.configPath = os.path.join(cls._tmpdir, "default_config.yml")
write_basic_config(save_location=cls.configPath)
cls._launch_args = ["accelerate", "launch", "--config_file", cls.configPath]
@classmethod
def tearDownClass(cls):
super().tearDownClass()
shutil.rmtree(cls._tmpdir)
def test_train_unconditional(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/unconditional_image_generation/train_unconditional.py
--dataset_name hf-internal-testing/dummy_image_class_data
--model_config_name_or_path diffusers/ddpm_dummy
--resolution 64
--output_dir {tmpdir}
--train_batch_size 2
--num_epochs 1
--gradient_accumulation_steps 1
--ddpm_num_inference_steps 2
--learning_rate 1e-3
--lr_warmup_steps 5
""".split()
run_command(self._launch_args + test_args, return_stdout=True)
# 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_textual_inversion(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/textual_inversion/textual_inversion.py
--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
--train_data_dir docs/source/en/imgs
--learnable_property object
--placeholder_token <cat-toy>
--initializer_token a
--validation_prompt <cat-toy>
--validation_steps 1
--save_steps 1
--num_vectors 2
--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, "learned_embeds.safetensors")))
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 == 5, 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 5
--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=2)
# 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=2)
# 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 7
--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=2)
# 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_lora(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/dreambooth/train_dreambooth_lora.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, "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 `"unet"` in their names.
starts_with_unet = all(key.startswith("unet") for key in lora_state_dict.keys())
self.assertTrue(starts_with_unet)
def test_dreambooth_lora_with_text_encoder(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/dreambooth/train_dreambooth_lora.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
--train_text_encoder
--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")))
# check `text_encoder` is present at all.
lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
keys = lora_state_dict.keys()
is_text_encoder_present = any(k.startswith("text_encoder") for k in keys)
self.assertTrue(is_text_encoder_present)
# the names of the keys of the state dict should either start with `unet`
# or `text_encoder`.
is_correct_naming = all(k.startswith("unet") or k.startswith("text_encoder") for k in keys)
self.assertTrue(is_correct_naming)
def test_dreambooth_lora_if_model(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/dreambooth/train_dreambooth_lora.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, "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 `"unet"` in their names.
starts_with_unet = all(key.startswith("unet") for key in lora_state_dict.keys())
self.assertTrue(starts_with_unet)
def test_dreambooth_lora_sdxl(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/dreambooth/train_dreambooth_lora_sdxl.py
--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-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, "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 `"unet"` in their names.
starts_with_unet = all(key.startswith("unet") for key in lora_state_dict.keys())
self.assertTrue(starts_with_unet)
def test_dreambooth_lora_sdxl_with_text_encoder(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/dreambooth/train_dreambooth_lora_sdxl.py
--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-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}
--train_text_encoder
""".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 `"unet"` or `"text_encoder"` or `"text_encoder_2"` in their names.
keys = lora_state_dict.keys()
starts_with_unet = all(
k.startswith("unet") or k.startswith("text_encoder") or k.startswith("text_encoder_2") for k in keys
)
self.assertTrue(starts_with_unet)
def test_dreambooth_lora_sdxl_checkpointing_checkpoints_total_limit(self):
pipeline_path = "hf-internal-testing/tiny-stable-diffusion-xl-pipe"
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/dreambooth/train_dreambooth_lora_sdxl.py
--pretrained_model_name_or_path {pipeline_path}
--instance_data_dir docs/source/en/imgs
--instance_prompt photo
--resolution 64
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 7
--checkpointing_steps=2
--checkpoints_total_limit=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)
pipe = DiffusionPipeline.from_pretrained(pipeline_path)
pipe.load_lora_weights(tmpdir)
pipe("a prompt", num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
# checkpoint-2 should have been deleted
{"checkpoint-4", "checkpoint-6"},
)
def test_dreambooth_lora_sdxl_text_encoder_checkpointing_checkpoints_total_limit(self):
pipeline_path = "hf-internal-testing/tiny-stable-diffusion-xl-pipe"
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/dreambooth/train_dreambooth_lora_sdxl.py
--pretrained_model_name_or_path {pipeline_path}
--instance_data_dir docs/source/en/imgs
--instance_prompt photo
--resolution 64
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 7
--checkpointing_steps=2
--checkpoints_total_limit=2
--train_text_encoder
--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)
pipe = DiffusionPipeline.from_pretrained(pipeline_path)
pipe.load_lora_weights(tmpdir)
pipe("a prompt", num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
# checkpoint-2 should have been deleted
{"checkpoint-4", "checkpoint-6"},
)
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_text_to_image(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--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_text_to_image_checkpointing(self):
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
prompt = "a prompt"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 5, checkpointing_steps == 2
# Should create checkpoints at steps 2, 4
initial_run_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 5
--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)
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "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(prompt, num_inference_steps=2)
# 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/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 7
--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(prompt, num_inference_steps=2)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{
# no checkpoint-2 -> check old checkpoints do not exist
# check new checkpoints exist
"checkpoint-4",
"checkpoint-6",
},
)
def test_text_to_image_checkpointing_use_ema(self):
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
prompt = "a prompt"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 5, checkpointing_steps == 2
# Should create checkpoints at steps 2, 4
initial_run_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 5
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--use_ema
--seed=0
""".split()
run_command(self._launch_args + initial_run_args)
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "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(prompt, num_inference_steps=2)
# 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/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 7
--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
--use_ema
--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(prompt, num_inference_steps=2)
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{
# no checkpoint-2 -> check old checkpoints do not exist
# check new checkpoints exist
"checkpoint-4",
"checkpoint-6",
},
)
def test_text_to_image_checkpointing_checkpoints_total_limit(self):
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
prompt = "a prompt"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 7, checkpointing_steps == 2, checkpoints_total_limit == 2
# Should create checkpoints at steps 2, 4, 6
# with checkpoint at step 2 deleted
initial_run_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 7
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--checkpoints_total_limit=2
--seed=0
""".split()
run_command(self._launch_args + initial_run_args)
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
# checkpoint-2 should have been deleted
{"checkpoint-4", "checkpoint-6"},
)
def test_text_to_image_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
prompt = "a prompt"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 9, checkpointing_steps == 2
# Should create checkpoints at steps 2, 4, 6, 8
initial_run_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 9
--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)
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
)
# resume and we should try to checkpoint at 10, where we'll have to remove
# checkpoint-2 and checkpoint-4 instead of just a single previous checkpoint
resume_run_args = f"""
examples/text_to_image/train_text_to_image.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 11
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--resume_from_checkpoint=checkpoint-8
--checkpoints_total_limit=3
--seed=0
""".split()
run_command(self._launch_args + resume_run_args)
pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-6", "checkpoint-8", "checkpoint-10"},
)
def test_text_to_image_sdxl(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/text_to_image/train_text_to_image_sdxl.py
--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--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_text_to_image_lora_checkpointing_checkpoints_total_limit(self):
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
prompt = "a prompt"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 7, checkpointing_steps == 2, checkpoints_total_limit == 2
# Should create checkpoints at steps 2, 4, 6
# with checkpoint at step 2 deleted
initial_run_args = f"""
examples/text_to_image/train_text_to_image_lora.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 7
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--checkpoints_total_limit=2
--seed=0
--num_validation_images=0
""".split()
run_command(self._launch_args + initial_run_args)
pipe = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
)
pipe.load_lora_weights(tmpdir)
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
# checkpoint-2 should have been deleted
{"checkpoint-4", "checkpoint-6"},
)
def test_text_to_image_lora_sdxl_checkpointing_checkpoints_total_limit(self):
prompt = "a prompt"
pipeline_path = "hf-internal-testing/tiny-stable-diffusion-xl-pipe"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 7, checkpointing_steps == 2, checkpoints_total_limit == 2
# Should create checkpoints at steps 2, 4, 6
# with checkpoint at step 2 deleted
initial_run_args = f"""
examples/text_to_image/train_text_to_image_lora_sdxl.py
--pretrained_model_name_or_path {pipeline_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 7
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--checkpoints_total_limit=2
""".split()
run_command(self._launch_args + initial_run_args)
pipe = DiffusionPipeline.from_pretrained(pipeline_path)
pipe.load_lora_weights(tmpdir)
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
# checkpoint-2 should have been deleted
{"checkpoint-4", "checkpoint-6"},
)
def test_text_to_image_lora_sdxl_text_encoder_checkpointing_checkpoints_total_limit(self):
prompt = "a prompt"
pipeline_path = "hf-internal-testing/tiny-stable-diffusion-xl-pipe"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 7, checkpointing_steps == 2, checkpoints_total_limit == 2
# Should create checkpoints at steps 2, 4, 6
# with checkpoint at step 2 deleted
initial_run_args = f"""
examples/text_to_image/train_text_to_image_lora_sdxl.py
--pretrained_model_name_or_path {pipeline_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--train_batch_size 1
--gradient_accumulation_steps 1
--max_train_steps 7
--learning_rate 5.0e-04
--scale_lr
--lr_scheduler constant
--train_text_encoder
--lr_warmup_steps 0
--output_dir {tmpdir}
--checkpointing_steps=2
--checkpoints_total_limit=2
""".split()
run_command(self._launch_args + initial_run_args)
pipe = DiffusionPipeline.from_pretrained(pipeline_path)
pipe.load_lora_weights(tmpdir)
pipe(prompt, num_inference_steps=2)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
# checkpoint-2 should have been deleted
{"checkpoint-4", "checkpoint-6"},
)
def test_text_to_image_lora_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
prompt = "a prompt"
with tempfile.TemporaryDirectory() as tmpdir:
# Run training script with checkpointing
# max_train_steps == 9, checkpointing_steps == 2
# Should create checkpoints at steps 2, 4, 6, 8
initial_run_args = f"""
examples/text_to_image/train_text_to_image_lora.py
--pretrained_model_name_or_path {pretrained_model_name_or_path}
--dataset_name hf-internal-testing/dummy_image_text_data
--resolution 64
--center_crop
--random_flip
--train_batch_size 1
--gradient_accumulation_steps 1