Duplicate from lora-library/LoRA-DreamBooth-Training-UI

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+training_data/
+experiments/
+wandb/
+
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

.pre-commit-config.yaml

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+exclude: train_dreambooth_lora.py
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v4.2.0
+  hooks:
+  - id: check-executables-have-shebangs
+  - id: check-json
+  - id: check-merge-conflict
+  - id: check-shebang-scripts-are-executable
+  - id: check-toml
+  - id: check-yaml
+  - id: double-quote-string-fixer
+  - id: end-of-file-fixer
+  - id: mixed-line-ending
+    args: ['--fix=lf']
+  - id: requirements-txt-fixer
+  - id: trailing-whitespace
+- repo: https://github.com/myint/docformatter
+  rev: v1.4
+  hooks:
+  - id: docformatter
+    args: ['--in-place']
+- repo: https://github.com/pycqa/isort
+  rev: 5.10.1
+  hooks:
+    - id: isort
+- repo: https://github.com/pre-commit/mirrors-mypy
+  rev: v0.991
+  hooks:
+    - id: mypy
+      args: ['--ignore-missing-imports']
+      additional_dependencies: ['types-python-slugify']
+- repo: https://github.com/google/yapf
+  rev: v0.32.0
+  hooks:
+  - id: yapf
+    args: ['--parallel', '--in-place']

.style.yapf

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+[style]
+based_on_style = pep8
+blank_line_before_nested_class_or_def = false
+spaces_before_comment = 2
+split_before_logical_operator = true

LICENSE

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+MIT License
+
+Copyright (c) 2022 hysts
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+title: LoRA DreamBooth Training UI
+emoji: ⚡
+colorFrom: red
+colorTo: purple
+sdk: gradio
+sdk_version: 3.16.2
+python_version: 3.10.9
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: lora-library/LoRA-DreamBooth-Training-UI
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import os
+
+import gradio as gr
+import torch
+
+from app_inference import create_inference_demo
+from app_training import create_training_demo
+from app_upload import create_upload_demo
+from inference import InferencePipeline
+from trainer import Trainer
+
+TITLE = '# LoRA DreamBooth Training UI'
+
+ORIGINAL_SPACE_ID = 'lora-library/LoRA-DreamBooth-Training-UI'
+SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
+SHARED_UI_WARNING = f'''# Attention - This Space doesn't work in this shared UI. You can duplicate and use it with a paid private T4 GPU.
+
+<center><a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></center>
+'''
+
+if os.getenv('SYSTEM') == 'spaces' and SPACE_ID != ORIGINAL_SPACE_ID:
+    SETTINGS = f'<a href="https://huggingface.co/spaces/{SPACE_ID}/settings">Settings</a>'
+else:
+    SETTINGS = 'Settings'
+CUDA_NOT_AVAILABLE_WARNING = f'''# Attention - Running on CPU.
+<center>
+You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces.
+"T4 small" is sufficient to run this demo.
+</center>
+'''
+
+HF_TOKEN_NOT_SPECIFIED_WARNING = f'''# Attention - The environment variable `HF_TOKEN` is not specified. Please specify your Hugging Face token with write permission as the value of it.
+<center>
+You can check and create your Hugging Face tokens <a href="https://huggingface.co/settings/tokens" target="_blank">here</a>.
+You can specify environment variables in the "Repository secrets" section of the {SETTINGS} tab.
+</center>
+'''
+
+HF_TOKEN = os.getenv('HF_TOKEN')
+
+
+def show_warning(warning_text: str) -> gr.Blocks:
+    with gr.Blocks() as demo:
+        with gr.Box():
+            gr.Markdown(warning_text)
+    return demo
+
+
+pipe = InferencePipeline(HF_TOKEN)
+trainer = Trainer(HF_TOKEN)
+
+with gr.Blocks(css='style.css') as demo:
+    if os.getenv('IS_SHARED_UI'):
+        show_warning(SHARED_UI_WARNING)
+    if not torch.cuda.is_available():
+        show_warning(CUDA_NOT_AVAILABLE_WARNING)
+    if not HF_TOKEN:
+        show_warning(HF_TOKEN_NOT_SPECIFIED_WARNING)
+
+    gr.Markdown(TITLE)
+    with gr.Tabs():
+        with gr.TabItem('Train'):
+            create_training_demo(trainer, pipe)
+        with gr.TabItem('Test'):
+            create_inference_demo(pipe, HF_TOKEN)
+        with gr.TabItem('Upload'):
+            gr.Markdown('''
+            - You can use this tab to upload models later if you choose not to upload models in training time or if upload in training time failed.
+            ''')
+            create_upload_demo(HF_TOKEN)
+
+demo.queue(max_size=1).launch(share=False)

app_inference.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import enum
+
+import gradio as gr
+from huggingface_hub import HfApi
+
+from inference import InferencePipeline
+from utils import find_exp_dirs
+
+SAMPLE_MODEL_IDS = [
+    'patrickvonplaten/lora_dreambooth_dog_example',
+    'sayakpaul/sd-model-finetuned-lora-t4',
+]
+
+
+class ModelSource(enum.Enum):
+    SAMPLE = 'Sample'
+    HUB_LIB = 'Hub (lora-library)'
+    LOCAL = 'Local'
+
+
+class InferenceUtil:
+    def __init__(self, hf_token: str | None):
+        self.hf_token = hf_token
+
+    @staticmethod
+    def load_sample_lora_model_list():
+        return gr.update(choices=SAMPLE_MODEL_IDS, value=SAMPLE_MODEL_IDS[0])
+
+    def load_hub_lora_model_list(self) -> dict:
+        api = HfApi(token=self.hf_token)
+        choices = [
+            info.modelId for info in api.list_models(author='lora-library')
+        ]
+        return gr.update(choices=choices,
+                         value=choices[0] if choices else None)
+
+    @staticmethod
+    def load_local_lora_model_list() -> dict:
+        choices = find_exp_dirs()
+        return gr.update(choices=choices,
+                         value=choices[0] if choices else None)
+
+    def reload_lora_model_list(self, model_source: str) -> dict:
+        if model_source == ModelSource.SAMPLE.value:
+            return self.load_sample_lora_model_list()
+        elif model_source == ModelSource.HUB_LIB.value:
+            return self.load_hub_lora_model_list()
+        elif model_source == ModelSource.LOCAL.value:
+            return self.load_local_lora_model_list()
+        else:
+            raise ValueError
+
+    def load_model_info(self, lora_model_id: str) -> tuple[str, str]:
+        try:
+            card = InferencePipeline.get_model_card(lora_model_id,
+                                                    self.hf_token)
+        except Exception:
+            return '', ''
+        base_model = getattr(card.data, 'base_model', '')
+        instance_prompt = getattr(card.data, 'instance_prompt', '')
+        return base_model, instance_prompt
+
+    def reload_lora_model_list_and_update_model_info(
+            self, model_source: str) -> tuple[dict, str, str]:
+        model_list_update = self.reload_lora_model_list(model_source)
+        model_list = model_list_update['choices']
+        model_info = self.load_model_info(model_list[0] if model_list else '')
+        return model_list_update, *model_info
+
+
+def create_inference_demo(pipe: InferencePipeline,
+                          hf_token: str | None = None) -> gr.Blocks:
+    app = InferenceUtil(hf_token)
+
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                with gr.Box():
+                    model_source = gr.Radio(
+                        label='Model Source',
+                        choices=[_.value for _ in ModelSource],
+                        value=ModelSource.SAMPLE.value)
+                    reload_button = gr.Button('Reload Model List')
+                    lora_model_id = gr.Dropdown(label='LoRA Model ID',
+                                                choices=SAMPLE_MODEL_IDS,
+                                                value=SAMPLE_MODEL_IDS[0])
+                    with gr.Accordion(
+                            label=
+                            'Model info (Base model and instance prompt used for training)',
+                            open=False):
+                        with gr.Row():
+                            base_model_used_for_training = gr.Text(
+                                label='Base model', interactive=False)
+                            instance_prompt_used_for_training = gr.Text(
+                                label='Instance prompt', interactive=False)
+                prompt = gr.Textbox(
+                    label='Prompt',
+                    max_lines=1,
+                    placeholder='Example: "A picture of a sks dog in a bucket"'
+                )
+                alpha = gr.Slider(label='LoRA alpha',
+                                  minimum=0,
+                                  maximum=2,
+                                  step=0.05,
+                                  value=1)
+                seed = gr.Slider(label='Seed',
+                                 minimum=0,
+                                 maximum=100000,
+                                 step=1,
+                                 value=0)
+                with gr.Accordion('Other Parameters', open=False):
+                    num_steps = gr.Slider(label='Number of Steps',
+                                          minimum=0,
+                                          maximum=100,
+                                          step=1,
+                                          value=25)
+                    guidance_scale = gr.Slider(label='CFG Scale',
+                                               minimum=0,
+                                               maximum=50,
+                                               step=0.1,
+                                               value=7.5)
+
+                run_button = gr.Button('Generate')
+
+                gr.Markdown('''
+                - After training, you can press "Reload Model List" button to load your trained model names.
+                ''')
+            with gr.Column():
+                result = gr.Image(label='Result')
+
+        model_source.change(
+            fn=app.reload_lora_model_list_and_update_model_info,
+            inputs=model_source,
+            outputs=[
+                lora_model_id,
+                base_model_used_for_training,
+                instance_prompt_used_for_training,
+            ])
+        reload_button.click(
+            fn=app.reload_lora_model_list_and_update_model_info,
+            inputs=model_source,
+            outputs=[
+                lora_model_id,
+                base_model_used_for_training,
+                instance_prompt_used_for_training,
+            ])
+        lora_model_id.change(fn=app.load_model_info,
+                             inputs=lora_model_id,
+                             outputs=[
+                                 base_model_used_for_training,
+                                 instance_prompt_used_for_training,
+                             ])
+        inputs = [
+            lora_model_id,
+            prompt,
+            alpha,
+            seed,
+            num_steps,
+            guidance_scale,
+        ]
+        prompt.submit(fn=pipe.run, inputs=inputs, outputs=result)
+        run_button.click(fn=pipe.run, inputs=inputs, outputs=result)
+    return demo
+
+
+if __name__ == '__main__':
+    import os
+
+    hf_token = os.getenv('HF_TOKEN')
+    pipe = InferencePipeline(hf_token)
+    demo = create_inference_demo(pipe, hf_token)
+    demo.queue(max_size=10).launch(share=False)

app_training.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import os
+
+import gradio as gr
+
+from constants import UploadTarget
+from inference import InferencePipeline
+from trainer import Trainer
+
+
+def create_training_demo(trainer: Trainer,
+                         pipe: InferencePipeline | None = None) -> gr.Blocks:
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                with gr.Box():
+                    gr.Markdown('Training Data')
+                    instance_images = gr.Files(label='Instance images')
+                    instance_prompt = gr.Textbox(label='Instance prompt',
+                                                 max_lines=1)
+                    gr.Markdown('''
+                        - Upload images of the style you are planning on training on.
+                        - For an instance prompt, use a unique, made up word to avoid collisions.
+                        ''')
+                with gr.Box():
+                    gr.Markdown('Output Model')
+                    output_model_name = gr.Text(label='Name of your model',
+                                                max_lines=1)
+                    delete_existing_model = gr.Checkbox(
+                        label='Delete existing model of the same name',
+                        value=False)
+                    validation_prompt = gr.Text(label='Validation Prompt')
+                with gr.Box():
+                    gr.Markdown('Upload Settings')
+                    with gr.Row():
+                        upload_to_hub = gr.Checkbox(
+                            label='Upload model to Hub', value=True)
+                        use_private_repo = gr.Checkbox(label='Private',
+                                                       value=True)
+                        delete_existing_repo = gr.Checkbox(
+                            label='Delete existing repo of the same name',
+                            value=False)
+                    upload_to = gr.Radio(
+                        label='Upload to',
+                        choices=[_.value for _ in UploadTarget],
+                        value=UploadTarget.LORA_LIBRARY.value)
+                    gr.Markdown('''
+                    - By default, trained models will be uploaded to [LoRA Library](https://huggingface.co/lora-library) (see [this example model](https://huggingface.co/lora-library/lora-dreambooth-sample-dog)).
+                    - You can also choose "Personal Profile", in which case, the model will be uploaded to https://huggingface.co/{your_username}/{model_name}.
+                    ''')
+
+            with gr.Box():
+                gr.Markdown('Training Parameters')
+                with gr.Row():
+                    base_model = gr.Text(
+                        label='Base Model',
+                        value='stabilityai/stable-diffusion-2-1-base',
+                        max_lines=1)
+                    resolution = gr.Dropdown(choices=['512', '768'],
+                                             value='512',
+                                             label='Resolution')
+                num_training_steps = gr.Number(
+                    label='Number of Training Steps', value=1000, precision=0)
+                learning_rate = gr.Number(label='Learning Rate', value=0.0001)
+                gradient_accumulation = gr.Number(
+                    label='Number of Gradient Accumulation',
+                    value=1,
+                    precision=0)
+                seed = gr.Slider(label='Seed',
+                                 minimum=0,
+                                 maximum=100000,
+                                 step=1,
+                                 value=0)
+                fp16 = gr.Checkbox(label='FP16', value=True)
+                use_8bit_adam = gr.Checkbox(label='Use 8bit Adam', value=True)
+                checkpointing_steps = gr.Number(label='Checkpointing Steps',
+                                                value=100,
+                                                precision=0)
+                use_wandb = gr.Checkbox(label='Use W&B',
+                                        value=False,
+                                        interactive=bool(
+                                            os.getenv('WANDB_API_KEY')))
+                validation_epochs = gr.Number(label='Validation Epochs',
+                                              value=100,
+                                              precision=0)
+                gr.Markdown('''
+                    - The base model must be a model that is compatible with [diffusers](https://github.com/huggingface/diffusers) library.
+                    - It takes a few minutes to download the base model first.
+                    - It will take about 8 minutes to train for 1000 steps with a T4 GPU.
+                    - You may want to try a small number of steps first, like 1, to see if everything works fine in your environment.
+                    - You can check the training status by pressing the "Open logs" button if you are running this on your Space.
+                    - You need to set the environment variable `WANDB_API_KEY` if you'd like to use [W&B](https://wandb.ai/site). See [W&B documentation](https://docs.wandb.ai/guides/track/advanced/environment-variables).
+                    - **Note:** Due to [this issue](https://github.com/huggingface/accelerate/issues/944), currently, training will not terminate properly if you use W&B.
+                    ''')
+
+        remove_gpu_after_training = gr.Checkbox(
+            label='Remove GPU after training',
+            value=False,
+            interactive=bool(os.getenv('SPACE_ID')),
+            visible=False)
+        run_button = gr.Button('Start Training')
+
+        with gr.Box():
+            gr.Markdown('Output message')
+            output_message = gr.Markdown()
+
+        if pipe is not None:
+            run_button.click(fn=pipe.clear)
+        run_button.click(fn=trainer.run,
+                         inputs=[
+                             instance_images,
+                             instance_prompt,
+                             output_model_name,
+                             delete_existing_model,
+                             validation_prompt,
+                             base_model,
+                             resolution,
+                             num_training_steps,
+                             learning_rate,
+                             gradient_accumulation,
+                             seed,
+                             fp16,
+                             use_8bit_adam,
+                             checkpointing_steps,
+                             use_wandb,
+                             validation_epochs,
+                             upload_to_hub,
+                             use_private_repo,
+                             delete_existing_repo,
+                             upload_to,
+                             remove_gpu_after_training,
+                         ],
+                         outputs=output_message)
+    return demo
+
+
+if __name__ == '__main__':
+    hf_token = os.getenv('HF_TOKEN')
+    trainer = Trainer(hf_token)
+    demo = create_training_demo(trainer)
+    demo.queue(max_size=1).launch(share=False)

app_upload.py

@@ -0,0 +1,100 @@
+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import pathlib
+
+import gradio as gr
+import slugify
+
+from constants import UploadTarget
+from uploader import Uploader
+from utils import find_exp_dirs
+
+
+class LoRAModelUploader(Uploader):
+    def upload_lora_model(
+        self,
+        folder_path: str,
+        repo_name: str,
+        upload_to: str,
+        private: bool,
+        delete_existing_repo: bool,
+    ) -> str:
+        if not folder_path:
+            raise ValueError
+        if not repo_name:
+            repo_name = pathlib.Path(folder_path).name
+        repo_name = slugify.slugify(repo_name)
+
+        if upload_to == UploadTarget.PERSONAL_PROFILE.value:
+            organization = ''
+        elif upload_to == UploadTarget.LORA_LIBRARY.value:
+            organization = 'lora-library'
+        else:
+            raise ValueError
+
+        return self.upload(folder_path,
+                           repo_name,
+                           organization=organization,
+                           private=private,
+                           delete_existing_repo=delete_existing_repo)
+
+
+def load_local_lora_model_list() -> dict:
+    choices = find_exp_dirs(ignore_repo=True)
+    return gr.update(choices=choices, value=choices[0] if choices else None)
+
+
+def create_upload_demo(hf_token: str | None) -> gr.Blocks:
+    uploader = LoRAModelUploader(hf_token)
+    model_dirs = find_exp_dirs(ignore_repo=True)
+
+    with gr.Blocks() as demo:
+        with gr.Box():
+            gr.Markdown('Local Models')
+            reload_button = gr.Button('Reload Model List')
+            model_dir = gr.Dropdown(
+                label='Model names',
+                choices=model_dirs,
+                value=model_dirs[0] if model_dirs else None)
+        with gr.Box():
+            gr.Markdown('Upload Settings')
+            with gr.Row():
+                use_private_repo = gr.Checkbox(label='Private', value=True)
+                delete_existing_repo = gr.Checkbox(
+                    label='Delete existing repo of the same name', value=False)
+            upload_to = gr.Radio(label='Upload to',
+                                 choices=[_.value for _ in UploadTarget],
+                                 value=UploadTarget.LORA_LIBRARY.value)
+            model_name = gr.Textbox(label='Model Name')
+        upload_button = gr.Button('Upload')
+        gr.Markdown('''
+            - You can upload your trained model to your personal profile (i.e. https://huggingface.co/{your_username}/{model_name}) or to the public [LoRA Concepts Library](https://huggingface.co/lora-library) (i.e. https://huggingface.co/lora-library/{model_name}).
+            ''')
+        with gr.Box():
+            gr.Markdown('Output message')
+            output_message = gr.Markdown()
+
+        reload_button.click(fn=load_local_lora_model_list,
+                            inputs=None,
+                            outputs=model_dir)
+        upload_button.click(fn=uploader.upload_lora_model,
+                            inputs=[
+                                model_dir,
+                                model_name,
+                                upload_to,
+                                use_private_repo,
+                                delete_existing_repo,
+                            ],
+                            outputs=output_message)
+
+    return demo
+
+
+if __name__ == '__main__':
+    import os
+
+    hf_token = os.getenv('HF_TOKEN')
+    demo = create_upload_demo(hf_token)
+    demo.queue(max_size=1).launch(share=False)

constants.py

@@ -0,0 +1,6 @@
+import enum
+
+
+class UploadTarget(enum.Enum):
+    PERSONAL_PROFILE = 'Personal Profile'
+    LORA_LIBRARY = 'LoRA Library'

inference.py

@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+import gc
+import pathlib
+
+import gradio as gr
+import PIL.Image
+import torch
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+from huggingface_hub import ModelCard
+
+
+class InferencePipeline:
+    def __init__(self, hf_token: str | None = None):
+        self.hf_token = hf_token
+        self.pipe = None
+        self.device = torch.device(
+            'cuda:0' if torch.cuda.is_available() else 'cpu')
+        self.lora_model_id = None
+        self.base_model_id = None
+
+    def clear(self) -> None:
+        self.lora_model_id = None
+        self.base_model_id = None
+        del self.pipe
+        self.pipe = None
+        torch.cuda.empty_cache()
+        gc.collect()
+
+    @staticmethod
+    def check_if_model_is_local(lora_model_id: str) -> bool:
+        return pathlib.Path(lora_model_id).exists()
+
+    @staticmethod
+    def get_model_card(model_id: str,
+                       hf_token: str | None = None) -> ModelCard:
+        if InferencePipeline.check_if_model_is_local(model_id):
+            card_path = (pathlib.Path(model_id) / 'README.md').as_posix()
+        else:
+            card_path = model_id
+        return ModelCard.load(card_path, token=hf_token)
+
+    @staticmethod
+    def get_base_model_info(lora_model_id: str,
+                            hf_token: str | None = None) -> str:
+        card = InferencePipeline.get_model_card(lora_model_id, hf_token)
+        return card.data.base_model
+
+    def load_pipe(self, lora_model_id: str) -> None:
+        if lora_model_id == self.lora_model_id:
+            return
+        base_model_id = self.get_base_model_info(lora_model_id, self.hf_token)
+        if base_model_id != self.base_model_id:
+            if self.device.type == 'cpu':
+                pipe = DiffusionPipeline.from_pretrained(
+                    base_model_id, use_auth_token=self.hf_token)
+            else:
+                pipe = DiffusionPipeline.from_pretrained(
+                    base_model_id,
+                    torch_dtype=torch.float16,
+                    use_auth_token=self.hf_token)
+                pipe = pipe.to(self.device)
+            pipe.scheduler = DPMSolverMultistepScheduler.from_config(
+                pipe.scheduler.config)
+            self.pipe = pipe
+        self.pipe.unet.load_attn_procs(  # type: ignore
+            lora_model_id, use_auth_token=self.hf_token)
+
+        self.lora_model_id = lora_model_id  # type: ignore
+        self.base_model_id = base_model_id  # type: ignore
+
+    def run(
+        self,
+        lora_model_id: str,
+        prompt: str,
+        lora_scale: float,
+        seed: int,
+        n_steps: int,
+        guidance_scale: float,
+    ) -> PIL.Image.Image:
+        if not torch.cuda.is_available():
+            raise gr.Error('CUDA is not available.')
+
+        self.load_pipe(lora_model_id)
+
+        generator = torch.Generator(device=self.device).manual_seed(seed)
+        out = self.pipe(
+            prompt,
+            num_inference_steps=n_steps,
+            guidance_scale=guidance_scale,
+            generator=generator,
+            cross_attention_kwargs={'scale': lora_scale},
+        )  # type: ignore
+        return out.images[0]

requirements.txt

@@ -0,0 +1,14 @@
+accelerate==0.15.0
+bitsandbytes==0.36.0.post2
+datasets==2.8.0
+git+https://github.com/huggingface/diffusers@31be42209ddfdb69d9640a777b32e9b5c6259bf0#egg=diffusers
+ftfy==6.1.1
+gradio==3.16.2
+huggingface-hub==0.12.0
+Pillow==9.4.0
+python-slugify==7.0.0
+tensorboard==2.11.2
+torch==1.13.1
+torchvision==0.14.1
+transformers==4.26.0
+wandb==0.13.9

style.css

@@ -0,0 +1,3 @@
+h1 {
+  text-align: center;
+}