Sayak Paul
0c693ca240
* add: dreambooth lora script for Playground v2.5 * fix: kwarg * address suraj's comments. * Apply suggestions from code review Co-authored-by: Suraj Patil <surajp815@gmail.com> * apply suraj's suggestion * incorporate changes in the canonical script./ * tracker naming * fix: schedule determination * add: two simple tests * remove playground script * note about edm-style training * address pedro's comments. * address part of Suraj's comments. * Apply suggestions from code review Co-authored-by: Suraj Patil <surajp815@gmail.com> * remove guidance_scale. * use mse_loss. * add comments for preconditioning. * quality * Update examples/dreambooth/train_dreambooth_lora_sdxl.py Co-authored-by: Suraj Patil <surajp815@gmail.com> * tackle v-pred. * Empty-Commit * support edm for sdxl too. * address suraj's comments. * Empty-Commit --------- Co-authored-by: Suraj Patil <surajp815@gmail.com>
100 lines
4.0 KiB
Python
100 lines
4.0 KiB
Python
# coding=utf-8
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# Copyright 2024 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 sys
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import tempfile
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import safetensors
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sys.path.append("..")
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from test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger()
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stream_handler = logging.StreamHandler(sys.stdout)
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logger.addHandler(stream_handler)
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class DreamBoothLoRASDXLWithEDM(ExamplesTestsAccelerate):
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def test_dreambooth_lora_sdxl_with_edm(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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--do_edm_style_training
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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_playground(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-playground-v2-5-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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