mirror of
https://github.com/huggingface/diffusers.git
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1683 lines
69 KiB
Python
1683 lines
69 KiB
Python
# coding=utf-8
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# Copyright 2023 HuggingFace Inc..
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import os
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import shutil
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import subprocess
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import sys
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import tempfile
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import unittest
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from typing import List
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import safetensors
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from accelerate.utils import write_basic_config
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from diffusers import DiffusionPipeline, UNet2DConditionModel
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger()
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# These utils relate to ensuring the right error message is received when running scripts
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class SubprocessCallException(Exception):
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pass
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def run_command(command: List[str], return_stdout=False):
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"""
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Runs `command` with `subprocess.check_output` and will potentially return the `stdout`. Will also properly capture
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if an error occurred while running `command`
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"""
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try:
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output = subprocess.check_output(command, stderr=subprocess.STDOUT)
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if return_stdout:
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if hasattr(output, "decode"):
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output = output.decode("utf-8")
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return output
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except subprocess.CalledProcessError as e:
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raise SubprocessCallException(
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f"Command `{' '.join(command)}` failed with the following error:\n\n{e.output.decode()}"
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) from e
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stream_handler = logging.StreamHandler(sys.stdout)
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logger.addHandler(stream_handler)
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class ExamplesTestsAccelerate(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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super().setUpClass()
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cls._tmpdir = tempfile.mkdtemp()
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cls.configPath = os.path.join(cls._tmpdir, "default_config.yml")
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write_basic_config(save_location=cls.configPath)
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cls._launch_args = ["accelerate", "launch", "--config_file", cls.configPath]
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@classmethod
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def tearDownClass(cls):
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super().tearDownClass()
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shutil.rmtree(cls._tmpdir)
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def test_train_unconditional(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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test_args = f"""
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examples/unconditional_image_generation/train_unconditional.py
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--dataset_name hf-internal-testing/dummy_image_class_data
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--model_config_name_or_path diffusers/ddpm_dummy
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--resolution 64
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--output_dir {tmpdir}
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--train_batch_size 2
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--num_epochs 1
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--gradient_accumulation_steps 1
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--ddpm_num_inference_steps 2
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--learning_rate 1e-3
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--lr_warmup_steps 5
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""".split()
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run_command(self._launch_args + test_args, return_stdout=True)
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# save_pretrained smoke test
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.safetensors")))
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
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def test_textual_inversion(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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test_args = f"""
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examples/textual_inversion/textual_inversion.py
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--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
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--train_data_dir docs/source/en/imgs
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--learnable_property object
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--placeholder_token <cat-toy>
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--initializer_token a
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--validation_prompt <cat-toy>
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--validation_steps 1
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--save_steps 1
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--num_vectors 2
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--resolution 64
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 2
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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""".split()
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run_command(self._launch_args + test_args)
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# save_pretrained smoke test
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "learned_embeds.safetensors")))
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def test_dreambooth(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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test_args = f"""
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examples/dreambooth/train_dreambooth.py
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--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
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--instance_data_dir docs/source/en/imgs
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--instance_prompt photo
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--resolution 64
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 2
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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""".split()
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run_command(self._launch_args + test_args)
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# save_pretrained smoke test
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.safetensors")))
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
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def test_dreambooth_if(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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test_args = f"""
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examples/dreambooth/train_dreambooth.py
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--pretrained_model_name_or_path hf-internal-testing/tiny-if-pipe
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--instance_data_dir docs/source/en/imgs
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--instance_prompt photo
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--resolution 64
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 2
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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--pre_compute_text_embeddings
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--tokenizer_max_length=77
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--text_encoder_use_attention_mask
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""".split()
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run_command(self._launch_args + test_args)
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# save_pretrained smoke test
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.safetensors")))
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
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def test_dreambooth_checkpointing(self):
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instance_prompt = "photo"
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pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
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with tempfile.TemporaryDirectory() as tmpdir:
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# Run training script with checkpointing
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# max_train_steps == 5, checkpointing_steps == 2
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# Should create checkpoints at steps 2, 4
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initial_run_args = f"""
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examples/dreambooth/train_dreambooth.py
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--pretrained_model_name_or_path {pretrained_model_name_or_path}
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--instance_data_dir docs/source/en/imgs
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--instance_prompt {instance_prompt}
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--resolution 64
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 5
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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--checkpointing_steps=2
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--seed=0
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""".split()
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run_command(self._launch_args + initial_run_args)
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# check can run the original fully trained output pipeline
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pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
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pipe(instance_prompt, num_inference_steps=2)
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# check checkpoint directories exist
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
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# check can run an intermediate checkpoint
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unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder="checkpoint-2/unet")
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)
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pipe(instance_prompt, num_inference_steps=2)
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# Remove checkpoint 2 so that we can check only later checkpoints exist after resuming
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shutil.rmtree(os.path.join(tmpdir, "checkpoint-2"))
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# Run training script for 7 total steps resuming from checkpoint 4
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resume_run_args = f"""
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examples/dreambooth/train_dreambooth.py
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--pretrained_model_name_or_path {pretrained_model_name_or_path}
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--instance_data_dir docs/source/en/imgs
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--instance_prompt {instance_prompt}
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--resolution 64
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 7
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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--checkpointing_steps=2
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--resume_from_checkpoint=checkpoint-4
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--seed=0
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""".split()
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run_command(self._launch_args + resume_run_args)
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# check can run new fully trained pipeline
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pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
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pipe(instance_prompt, num_inference_steps=2)
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# check old checkpoints do not exist
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self.assertFalse(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
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# check new checkpoints exist
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-6")))
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def test_dreambooth_lora(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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test_args = f"""
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examples/dreambooth/train_dreambooth_lora.py
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--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
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--instance_data_dir docs/source/en/imgs
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--instance_prompt photo
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--resolution 64
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 2
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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""".split()
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run_command(self._launch_args + test_args)
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# save_pretrained smoke test
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
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# make sure the state_dict has the correct naming in the parameters.
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lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
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is_lora = all("lora" in k for k in lora_state_dict.keys())
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self.assertTrue(is_lora)
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# when not training the text encoder, all the parameters in the state dict should start
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# with `"unet"` in their names.
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starts_with_unet = all(key.startswith("unet") for key in lora_state_dict.keys())
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self.assertTrue(starts_with_unet)
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def test_dreambooth_lora_with_text_encoder(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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test_args = f"""
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examples/dreambooth/train_dreambooth_lora.py
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--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
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--instance_data_dir docs/source/en/imgs
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--instance_prompt photo
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--resolution 64
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 2
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--train_text_encoder
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--output_dir {tmpdir}
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""".split()
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run_command(self._launch_args + test_args)
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# save_pretrained smoke test
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
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# check `text_encoder` is present at all.
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lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
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keys = lora_state_dict.keys()
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is_text_encoder_present = any(k.startswith("text_encoder") for k in keys)
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self.assertTrue(is_text_encoder_present)
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# the names of the keys of the state dict should either start with `unet`
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# or `text_encoder`.
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is_correct_naming = all(k.startswith("unet") or k.startswith("text_encoder") for k in keys)
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self.assertTrue(is_correct_naming)
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def test_dreambooth_lora_if_model(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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test_args = f"""
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examples/dreambooth/train_dreambooth_lora.py
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--pretrained_model_name_or_path hf-internal-testing/tiny-if-pipe
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--instance_data_dir docs/source/en/imgs
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--instance_prompt photo
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--resolution 64
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 2
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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--pre_compute_text_embeddings
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--tokenizer_max_length=77
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--text_encoder_use_attention_mask
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""".split()
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run_command(self._launch_args + test_args)
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# save_pretrained smoke test
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
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# make sure the state_dict has the correct naming in the parameters.
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lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
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is_lora = all("lora" in k for k in lora_state_dict.keys())
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self.assertTrue(is_lora)
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# when not training the text encoder, all the parameters in the state dict should start
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# with `"unet"` in their names.
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starts_with_unet = all(key.startswith("unet") for key in lora_state_dict.keys())
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self.assertTrue(starts_with_unet)
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def test_dreambooth_lora_sdxl(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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test_args = f"""
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examples/dreambooth/train_dreambooth_lora_sdxl.py
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--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
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--instance_data_dir docs/source/en/imgs
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--instance_prompt photo
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--resolution 64
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 2
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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""".split()
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run_command(self._launch_args + test_args)
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# save_pretrained smoke test
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
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# make sure the state_dict has the correct naming in the parameters.
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lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
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is_lora = all("lora" in k for k in lora_state_dict.keys())
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self.assertTrue(is_lora)
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# when not training the text encoder, all the parameters in the state dict should start
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# with `"unet"` in their names.
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starts_with_unet = all(key.startswith("unet") for key in lora_state_dict.keys())
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self.assertTrue(starts_with_unet)
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def test_dreambooth_lora_sdxl_with_text_encoder(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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test_args = f"""
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examples/dreambooth/train_dreambooth_lora_sdxl.py
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--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
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--instance_data_dir docs/source/en/imgs
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--instance_prompt photo
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--resolution 64
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 2
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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--train_text_encoder
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""".split()
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run_command(self._launch_args + test_args)
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# save_pretrained smoke test
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
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# make sure the state_dict has the correct naming in the parameters.
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lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
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is_lora = all("lora" in k for k in lora_state_dict.keys())
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self.assertTrue(is_lora)
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# when not training the text encoder, all the parameters in the state dict should start
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# with `"unet"` or `"text_encoder"` or `"text_encoder_2"` in their names.
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keys = lora_state_dict.keys()
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starts_with_unet = all(
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k.startswith("unet") or k.startswith("text_encoder") or k.startswith("text_encoder_2") for k in keys
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)
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self.assertTrue(starts_with_unet)
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def test_dreambooth_lora_sdxl_checkpointing_checkpoints_total_limit(self):
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pipeline_path = "hf-internal-testing/tiny-stable-diffusion-xl-pipe"
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with tempfile.TemporaryDirectory() as tmpdir:
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test_args = f"""
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examples/dreambooth/train_dreambooth_lora_sdxl.py
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--pretrained_model_name_or_path {pipeline_path}
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--instance_data_dir docs/source/en/imgs
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--instance_prompt photo
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--resolution 64
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 7
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--checkpointing_steps=2
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--checkpoints_total_limit=2
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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""".split()
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run_command(self._launch_args + test_args)
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pipe = DiffusionPipeline.from_pretrained(pipeline_path)
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pipe.load_lora_weights(tmpdir)
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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
|
|
--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
|
|
--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", "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_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 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
|
|
--num_validation_images=0
|
|
""".split()
|
|
|
|
run_command(self._launch_args + resume_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-6", "checkpoint-8", "checkpoint-10"},
|
|
)
|
|
|
|
def test_unconditional_checkpointing_checkpoints_total_limit(self):
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
initial_run_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 1
|
|
--num_epochs 1
|
|
--gradient_accumulation_steps 1
|
|
--ddpm_num_inference_steps 2
|
|
--learning_rate 1e-3
|
|
--lr_warmup_steps 5
|
|
--checkpointing_steps=2
|
|
--checkpoints_total_limit=2
|
|
""".split()
|
|
|
|
run_command(self._launch_args + initial_run_args)
|
|
|
|
# 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_unconditional_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
initial_run_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 1
|
|
--num_epochs 1
|
|
--gradient_accumulation_steps 1
|
|
--ddpm_num_inference_steps 2
|
|
--learning_rate 1e-3
|
|
--lr_warmup_steps 5
|
|
--checkpointing_steps=1
|
|
""".split()
|
|
|
|
run_command(self._launch_args + initial_run_args)
|
|
|
|
# check checkpoint directories exist
|
|
self.assertEqual(
|
|
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
|
|
{"checkpoint-1", "checkpoint-2", "checkpoint-3", "checkpoint-4", "checkpoint-5", "checkpoint-6"},
|
|
)
|
|
|
|
resume_run_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 1
|
|
--num_epochs 2
|
|
--gradient_accumulation_steps 1
|
|
--ddpm_num_inference_steps 2
|
|
--learning_rate 1e-3
|
|
--lr_warmup_steps 5
|
|
--resume_from_checkpoint=checkpoint-6
|
|
--checkpointing_steps=2
|
|
--checkpoints_total_limit=3
|
|
""".split()
|
|
|
|
run_command(self._launch_args + resume_run_args)
|
|
|
|
# check checkpoint directories exist
|
|
self.assertEqual(
|
|
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
|
|
{"checkpoint-8", "checkpoint-10", "checkpoint-12"},
|
|
)
|
|
|
|
def test_textual_inversion_checkpointing(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 3
|
|
--learning_rate 5.0e-04
|
|
--scale_lr
|
|
--lr_scheduler constant
|
|
--lr_warmup_steps 0
|
|
--output_dir {tmpdir}
|
|
--checkpointing_steps=1
|
|
--checkpoints_total_limit=2
|
|
""".split()
|
|
|
|
run_command(self._launch_args + test_args)
|
|
|
|
# check checkpoint directories exist
|
|
self.assertEqual(
|
|
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
|
|
{"checkpoint-2", "checkpoint-3"},
|
|
)
|
|
|
|
def test_textual_inversion_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(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 3
|
|
--learning_rate 5.0e-04
|
|
--scale_lr
|
|
--lr_scheduler constant
|
|
--lr_warmup_steps 0
|
|
--output_dir {tmpdir}
|
|
--checkpointing_steps=1
|
|
""".split()
|
|
|
|
run_command(self._launch_args + test_args)
|
|
|
|
# check checkpoint directories exist
|
|
self.assertEqual(
|
|
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
|
|
{"checkpoint-1", "checkpoint-2", "checkpoint-3"},
|
|
)
|
|
|
|
resume_run_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 4
|
|
--learning_rate 5.0e-04
|
|
--scale_lr
|
|
--lr_scheduler constant
|
|
--lr_warmup_steps 0
|
|
--output_dir {tmpdir}
|
|
--checkpointing_steps=1
|
|
--resume_from_checkpoint=checkpoint-3
|
|
--checkpoints_total_limit=2
|
|
""".split()
|
|
|
|
run_command(self._launch_args + resume_run_args)
|
|
|
|
# check checkpoint directories exist
|
|
self.assertEqual(
|
|
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
|
|
{"checkpoint-3", "checkpoint-4"},
|
|
)
|
|
|
|
def test_instruct_pix2pix_checkpointing_checkpoints_total_limit(self):
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
test_args = f"""
|
|
examples/instruct_pix2pix/train_instruct_pix2pix.py
|
|
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
|
|
--dataset_name=hf-internal-testing/instructpix2pix-10-samples
|
|
--resolution=64
|
|
--random_flip
|
|
--train_batch_size=1
|
|
--max_train_steps=7
|
|
--checkpointing_steps=2
|
|
--checkpoints_total_limit=2
|
|
--output_dir {tmpdir}
|
|
--seed=0
|
|
""".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_instruct_pix2pix_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
test_args = f"""
|
|
examples/instruct_pix2pix/train_instruct_pix2pix.py
|
|
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
|
|
--dataset_name=hf-internal-testing/instructpix2pix-10-samples
|
|
--resolution=64
|
|
--random_flip
|
|
--train_batch_size=1
|
|
--max_train_steps=9
|
|
--checkpointing_steps=2
|
|
--output_dir {tmpdir}
|
|
--seed=0
|
|
""".split()
|
|
|
|
run_command(self._launch_args + test_args)
|
|
|
|
# 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_run_args = f"""
|
|
examples/instruct_pix2pix/train_instruct_pix2pix.py
|
|
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
|
|
--dataset_name=hf-internal-testing/instructpix2pix-10-samples
|
|
--resolution=64
|
|
--random_flip
|
|
--train_batch_size=1
|
|
--max_train_steps=11
|
|
--checkpointing_steps=2
|
|
--output_dir {tmpdir}
|
|
--seed=0
|
|
--resume_from_checkpoint=checkpoint-8
|
|
--checkpoints_total_limit=3
|
|
""".split()
|
|
|
|
run_command(self._launch_args + resume_run_args)
|
|
|
|
# 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_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=9
|
|
--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", "checkpoint-6", "checkpoint-8"},
|
|
)
|
|
|
|
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=11
|
|
--checkpointing_steps=2
|
|
--resume_from_checkpoint=checkpoint-8
|
|
--checkpoints_total_limit=3
|
|
""".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"},
|
|
)
|
|
|
|
def test_dreambooth_lora_checkpointing_checkpoints_total_limit(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
|
|
--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_lora_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(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
|
|
--output_dir={tmpdir}
|
|
--instance_prompt=prompt
|
|
--resolution=64
|
|
--train_batch_size=1
|
|
--gradient_accumulation_steps=1
|
|
--max_train_steps=9
|
|
--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", "checkpoint-6", "checkpoint-8"},
|
|
)
|
|
|
|
resume_run_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
|
|
--output_dir={tmpdir}
|
|
--instance_prompt=prompt
|
|
--resolution=64
|
|
--train_batch_size=1
|
|
--gradient_accumulation_steps=1
|
|
--max_train_steps=11
|
|
--checkpointing_steps=2
|
|
--resume_from_checkpoint=checkpoint-8
|
|
--checkpoints_total_limit=3
|
|
""".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"},
|
|
)
|
|
|
|
def test_controlnet_checkpointing_checkpoints_total_limit(self):
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
test_args = f"""
|
|
examples/controlnet/train_controlnet.py
|
|
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
|
|
--dataset_name=hf-internal-testing/fill10
|
|
--output_dir={tmpdir}
|
|
--resolution=64
|
|
--train_batch_size=1
|
|
--gradient_accumulation_steps=1
|
|
--max_train_steps=6
|
|
--checkpoints_total_limit=2
|
|
--checkpointing_steps=2
|
|
--controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet
|
|
""".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_controlnet_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
test_args = f"""
|
|
examples/controlnet/train_controlnet.py
|
|
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
|
|
--dataset_name=hf-internal-testing/fill10
|
|
--output_dir={tmpdir}
|
|
--resolution=64
|
|
--train_batch_size=1
|
|
--gradient_accumulation_steps=1
|
|
--controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet
|
|
--max_train_steps=9
|
|
--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", "checkpoint-6", "checkpoint-8"},
|
|
)
|
|
|
|
resume_run_args = f"""
|
|
examples/controlnet/train_controlnet.py
|
|
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
|
|
--dataset_name=hf-internal-testing/fill10
|
|
--output_dir={tmpdir}
|
|
--resolution=64
|
|
--train_batch_size=1
|
|
--gradient_accumulation_steps=1
|
|
--controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet
|
|
--max_train_steps=11
|
|
--checkpointing_steps=2
|
|
--resume_from_checkpoint=checkpoint-8
|
|
--checkpoints_total_limit=3
|
|
""".split()
|
|
|
|
run_command(self._launch_args + resume_run_args)
|
|
|
|
self.assertEqual(
|
|
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
|
|
{"checkpoint-8", "checkpoint-10", "checkpoint-12"},
|
|
)
|
|
|
|
def test_controlnet_sdxl(self):
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
test_args = f"""
|
|
examples/controlnet/train_controlnet_sdxl.py
|
|
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-xl-pipe
|
|
--dataset_name=hf-internal-testing/fill10
|
|
--output_dir={tmpdir}
|
|
--resolution=64
|
|
--train_batch_size=1
|
|
--gradient_accumulation_steps=1
|
|
--controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet-sdxl
|
|
--max_train_steps=9
|
|
--checkpointing_steps=2
|
|
""".split()
|
|
|
|
run_command(self._launch_args + test_args)
|
|
|
|
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "diffusion_pytorch_model.safetensors")))
|
|
|
|
def test_t2i_adapter_sdxl(self):
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
test_args = f"""
|
|
examples/t2i_adapter/train_t2i_adapter_sdxl.py
|
|
--pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-xl-pipe
|
|
--adapter_model_name_or_path=hf-internal-testing/tiny-adapter
|
|
--dataset_name=hf-internal-testing/fill10
|
|
--output_dir={tmpdir}
|
|
--resolution=64
|
|
--train_batch_size=1
|
|
--gradient_accumulation_steps=1
|
|
--max_train_steps=9
|
|
--checkpointing_steps=2
|
|
""".split()
|
|
|
|
run_command(self._launch_args + test_args)
|
|
|
|
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "diffusion_pytorch_model.safetensors")))
|
|
|
|
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"},
|
|
)
|
|
|
|
def test_text_to_image_lora_sdxl(self):
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
test_args = f"""
|
|
examples/text_to_image/train_text_to_image_lora_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
|
|
--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_lora_sdxl_with_text_encoder(self):
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
test_args = f"""
|
|
examples/text_to_image/train_text_to_image_lora_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
|
|
--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)
|