From 93d90b38326bd693a9f057bdb42642c8bcb4cdb8 Mon Sep 17 00:00:00 2001 From: Hayk Martiros Date: Tue, 17 Jan 2023 02:17:23 +0000 Subject: [PATCH] revup virtual diff target 2fc9b8d37961cc8cab5579b8c58df1af79371553 d98efa55fe6e64c65f3344a22cad8db1111240ff 450a9b6fad158cfefa12df4dc3d17880de8d0263 a7b41d90bd843e23fa2c430e1d17e1bf4d69c718 --- .github/workflows/ci.yml | 44 + .gitignore | 141 +++ CITATION | 6 + LICENSE | 16 + README.md | 220 ++++ cog.yaml | 38 + dev_requirements.txt | 7 + integrations/README.md | 23 + integrations/__init__.py | 0 integrations/baseten.py | 83 ++ integrations/cog_riffusion.py | 158 +++ pyproject.toml | 99 ++ requirements.txt | 21 + riffusion/__init__.py | 0 riffusion/audio_splitter.py | 187 ++++ riffusion/cli.py | 138 +++ riffusion/datatypes.py | 73 ++ riffusion/external/README.md | 3 + riffusion/external/__init__.py | 0 riffusion/external/lora/__init__.py | 0 .../external/lora/run_lora_db_unet_only.sh | 24 + riffusion/external/lora/train_lora.py | 49 + 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create mode 100644 requirements.txt create mode 100644 riffusion/__init__.py create mode 100644 riffusion/audio_splitter.py create mode 100644 riffusion/cli.py create mode 100644 riffusion/datatypes.py create mode 100644 riffusion/external/README.md create mode 100644 riffusion/external/__init__.py create mode 100644 riffusion/external/lora/__init__.py create mode 100644 riffusion/external/lora/run_lora_db_unet_only.sh create mode 100644 riffusion/external/lora/train_lora.py create mode 100755 riffusion/external/lora/train_lora.sh create mode 100644 riffusion/external/lora/train_lora_dreambooth.py create mode 100644 riffusion/external/prompt_weighting.py create mode 100644 riffusion/riffusion_pipeline.py create mode 100644 riffusion/server.py create mode 100644 riffusion/spectrogram_converter.py create mode 100644 riffusion/spectrogram_image_converter.py create mode 100644 riffusion/spectrogram_params.py create mode 100644 riffusion/streamlit/README.md create mode 100644 riffusion/streamlit/__init__.py create mode 100644 riffusion/streamlit/pages/audio_to_audio.py create mode 100644 riffusion/streamlit/pages/image_to_audio.py create mode 100644 riffusion/streamlit/pages/interpolation.py create mode 100644 riffusion/streamlit/pages/sample_clips.py create mode 100644 riffusion/streamlit/pages/split_audio.py create mode 100644 riffusion/streamlit/pages/text_to_audio.py create mode 100644 riffusion/streamlit/pages/text_to_audio_batch.py create mode 100644 riffusion/streamlit/playground.py create mode 100644 riffusion/streamlit/util.py create mode 100644 riffusion/util/__init__.py create mode 100644 riffusion/util/audio_util.py create mode 100644 riffusion/util/base64_util.py create mode 100644 riffusion/util/fft_util.py create mode 100644 riffusion/util/image_util.py create mode 100644 riffusion/util/torch_util.py create mode 100644 seed_images/agile.png create mode 100644 seed_images/marim.png create mode 100644 seed_images/mask_beat_lines_80.png create 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mode 100644 test/test_data/tired_traveler/clips/clip_1_start_860_ms_duration_5678_ms.wav create mode 100644 test/test_data/tired_traveler/clips/clip_2_start_103694_ms_duration_5678_ms.wav create mode 100644 test/test_data/tired_traveler/images/clip_2_start_103694_ms_duration_5678_ms.png create mode 100644 test/test_data/tired_traveler/images/clip_2_start_103694_ms_duration_5678_ms_stereo.png create mode 100644 test/test_data/tired_traveler/tired_traveler.mp3 diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..bbc2aca --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,44 @@ +name: CI +run-name: ${{ github.actor }} is running Riffusion CI + +on: + push: + branches: + - 'main' + pull_request: + types: [opened, synchronize, reopened, ready_for_review] + +jobs: + riffusion-ci: + runs-on: ubuntu-latest + + strategy: + matrix: + python-version: ["3.9", "3.10"] + + steps: + - name: Checkout + uses: actions/checkout@v3 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + + - name: Install system packages + run: | + sudo apt-get update + sudo apt-get install -y ffmpeg libsndfile1 + + - name: Install pip packages from requirements.txt + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + + - name: Install pip packages from dev_requirements.txt + run: | + pip install -r dev_requirements.txt + + - name: Test with unittest + run: | + RIFFUSION_TEST_DEVICE=cpu python -m unittest test/*_test.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..60dae11 --- /dev/null +++ b/.gitignore @@ -0,0 +1,141 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# VSCode +.vscode + +# Cog +.cog/ + +# Random stuff I don't care about +.graveyard/ + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# OSX cruft +.DS_Store + +# 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/ + +# 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 +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.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 + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__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/ diff --git a/CITATION b/CITATION new file mode 100644 index 0000000..9f4ae3a --- /dev/null +++ b/CITATION @@ -0,0 +1,6 @@ +@article{Forsgren_Martiros_2022, + author = {Forsgren, Seth* and Martiros, Hayk*}, + title = {{Riffusion - Stable diffusion for real-time music generation}}, + url = {https://riffusion.com/about}, + year = {2022} +} diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..0bb776b --- /dev/null +++ b/LICENSE @@ -0,0 +1,16 @@ +Copyright 2022 Hayk Martiros and Seth Forsgren + +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. diff --git a/README.md b/README.md new file mode 100644 index 0000000..59dd2c1 --- /dev/null +++ b/README.md @@ -0,0 +1,220 @@ +# :guitar: Riffusion + +CI status +Python 3.9 | 3.10 +MIT License + +Riffusion is a library for real-time music and audio generation with stable diffusion. + +Read about it at https://www.riffusion.com/about and try it at https://www.riffusion.com/. + +This is the core repository for riffusion image and audio processing code. + + * Diffusion pipeline that performs prompt interpolation combined with image conditioning + * Conversions between spectrogram images and audio clips + * Command-line interface for common tasks + * Interactive app using streamlit + * Flask server to provide model inference via API + * Various third party integrations + +Related repositories: +* Web app: https://github.com/riffusion/riffusion-app +* Model checkpoint: https://huggingface.co/riffusion/riffusion-model-v1 + +## Citation + +If you build on this work, please cite it as follows: + +``` +@article{Forsgren_Martiros_2022, + author = {Forsgren, Seth* and Martiros, Hayk*}, + title = {{Riffusion - Stable diffusion for real-time music generation}}, + url = {https://riffusion.com/about}, + year = {2022} +} +``` + +## Install + +Tested in CI with Python 3.9 and 3.10. + +It's highly recommended to set up a virtual Python environment with `conda` or `virtualenv`: +``` +conda create --name riffusion python=3.9 +conda activate riffusion +``` + +Install Python dependencies: +``` +python -m pip install -r requirements.txt +``` + +In order to use audio formats other than WAV, [ffmpeg](https://ffmpeg.org/download.html) is required. +``` +sudo apt-get install ffmpeg # linux +brew install ffmpeg # mac +conda install -c conda-forge ffmpeg # conda +``` + +If torchaudio has no backend, you may need to install `libsndfile`. See [this issue](https://github.com/riffusion/riffusion/issues/12). + +If you have an issue, try upgrading [diffusers](https://github.com/huggingface/diffusers). Tested with 0.9 - 0.11. + +Guides: +* [Simple Install Guide for Windows](https://www.reddit.com/r/riffusion/comments/zrubc9/installation_guide_for_riffusion_app_inference/) + +## Backends + +### CPU +`cpu` is supported but is quite slow. + +### CUDA +`cuda` is the recommended and most performant backend. + +To use with CUDA, make sure you have torch and torchaudio installed with CUDA support. See the +[install guide](https://pytorch.org/get-started/locally/) or +[stable wheels](https://download.pytorch.org/whl/torch_stable.html). + +To generate audio in real-time, you need a GPU that can run stable diffusion with approximately 50 +steps in under five seconds, such as a 3090 or A10G. + +Test availability with: + +```python3 +import torch +torch.cuda.is_available() +``` + +### MPS +The `mps` backend on Apple Silicon is supported for inference but some operations fall back to CPU, +particularly for audio processing. You may need to set +`PYTORCH_ENABLE_MPS_FALLBACK=1`. + +In addition, this backend is not deterministic. + +Test availability with: + +```python3 +import torch +torch.backends.mps.is_available() +``` + +## Command-line interface + +Riffusion comes with a command line interface for performing common tasks. + +See available commands: +``` +python -m riffusion.cli -h +``` + +Get help for a specific command: +``` +python -m riffusion.cli image-to-audio -h +``` + +Execute: +``` +python -m riffusion.cli image-to-audio --image spectrogram_image.png --audio clip.wav +``` + +## Riffusion Playground + +Riffusion contains a [streamlit](https://streamlit.io/) app for interactive use and exploration. + +Run with: +``` +python -m streamlit run riffusion/streamlit/playground.py --browser.serverAddress 127.0.0.1 --browser.serverPort 8501 +``` + +And access at http://127.0.0.1:8501/ + +Riffusion Playground + +## Run the model server + +Riffusion can be run as a flask server that provides inference via API. This server enables the [web app](https://github.com/riffusion/riffusion-app) to run locally. + +Run with: + +``` +python -m riffusion.server --host 127.0.0.1 --port 3013 +``` + +You can specify `--checkpoint` with your own directory or huggingface ID in diffusers format. + +Use the `--device` argument to specify the torch device to use. + +The model endpoint is now available at `http://127.0.0.1:3013/run_inference` via POST request. + +Example input (see [InferenceInput](https://github.com/hmartiro/riffusion-inference/blob/main/riffusion/datatypes.py#L28) for the API): +``` +{ + "alpha": 0.75, + "num_inference_steps": 50, + "seed_image_id": "og_beat", + + "start": { + "prompt": "church bells on sunday", + "seed": 42, + "denoising": 0.75, + "guidance": 7.0 + }, + + "end": { + "prompt": "jazz with piano", + "seed": 123, + "denoising": 0.75, + "guidance": 7.0 + } +} +``` + +Example output (see [InferenceOutput](https://github.com/hmartiro/riffusion-inference/blob/main/riffusion/datatypes.py#L54) for the API): +``` +{ + "image": "< base64 encoded JPEG image >", + "audio": "< base64 encoded MP3 clip >" +} +``` + +## Tests +Tests live in the `test/` directory and are implemented with `unittest`. + +To run all tests: +``` +python -m unittest test/*_test.py +``` + +To run a single test: +``` +python -m unittest test.audio_to_image_test +``` + +To preserve temporary outputs for debugging, set `RIFFUSION_TEST_DEBUG`: +``` +RIFFUSION_TEST_DEBUG=1 python -m unittest test.audio_to_image_test +``` + +To run a single test case within a test: +``` +python -m unittest test.audio_to_image_test -k AudioToImageTest.test_stereo +``` + +To run tests using a specific torch device, set `RIFFUSION_TEST_DEVICE`. Tests should pass with +`cpu`, `cuda`, and `mps` backends. + +## Development Guide +Install additional packages for dev with `python -m pip install -r dev_requirements.txt`. + +* Linter: `ruff` +* Formatter: `black` +* Type checker: `mypy` + +These are configured in `pyproject.toml`. + +The results of `mypy .`, `black .`, and `ruff .` *must* be clean to accept a PR. + +CI is run through GitHub Actions from `.github/workflows/ci.yml`. + +Contributions are welcome through pull requests. diff --git a/cog.yaml b/cog.yaml new file mode 100644 index 0000000..b1b81e4 --- /dev/null +++ b/cog.yaml @@ -0,0 +1,38 @@ +# Configuration for Cog ⚙️ +# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md + +build: + # set to true if your model requires a GPU + gpu: true + + # a list of ubuntu apt packages to install + system_packages: + - "ffmpeg" + - "libsndfile1" + + # python version in the form '3.8' or '3.8.12' + python_version: "3.9" + + # a list of packages in the format == + python_packages: + - "accelerate==0.15.0" + - "argh==0.26.2" + - "dacite==1.6.0" + - "diffusers==0.10.2" + - "flask_cors==3.0.10" + - "flask==1.1.2" + - "numpy==1.19.4" + - "pillow==9.1.0" + - "pydub==0.25.1" + - "scipy==1.6.3" + - "torch==1.13.0" + - "torchaudio==0.13.0" + - "transformers==4.25.1" + + # commands run after the environment is setup + # run: + # - "echo env is ready!" + # - "echo another command if needed" + +# predict.py defines how predictions are run on your model +predict: "integrations/cog_riffusion.py:RiffusionPredictor" diff --git a/dev_requirements.txt b/dev_requirements.txt new file mode 100644 index 0000000..a37435b --- /dev/null +++ b/dev_requirements.txt @@ -0,0 +1,7 @@ +black +ipdb +mypy +ruff +types-Flask-Cors +types-Pillow +types-requests diff --git a/integrations/README.md b/integrations/README.md new file mode 100644 index 0000000..2437418 --- /dev/null +++ b/integrations/README.md @@ -0,0 +1,23 @@ +# Integrations + +This package contains integrations of Riffusion into third party apps and deployments. + +## Baseten + +[Baseten](https://baseten.com) is a platform for building and deploying machine learning models. + +## Replicate + +To run riffusion as a Cog model, first, [install Cog](https://github.com/replicate/cog) and +download the model weights: + + cog run python -m integrations.cog_riffusion --download_weights + +Then you can run predictions: + + cog predict -i prompt_a="funky synth solo" + +You can also view the model on replicate [here](https://replicate.com/hmartiro/riffusion). Owners +can push an updated version of the model like so: + + cog push r8.im/hmartiro/riffusion diff --git a/integrations/__init__.py b/integrations/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/integrations/baseten.py b/integrations/baseten.py new file mode 100644 index 0000000..9fd863b --- /dev/null +++ b/integrations/baseten.py @@ -0,0 +1,83 @@ +""" +This file can be used to build a Truss for deployment with Baseten. +If used, it should be renamed to model.py and placed alongside the other +files from /riffusion in the standard /model directory of the Truss. + +For more on the Truss file format, see https://truss.baseten.co/ +""" + +import typing as T + +import dacite +import torch +from huggingface_hub import snapshot_download + +from riffusion.datatypes import InferenceInput +from riffusion.riffusion_pipeline import RiffusionPipeline +from riffusion.server import compute_request + + +class Model: + """ + Baseten Truss model class for riffusion. + + See: https://truss.baseten.co/reference/structure#model.py + """ + + def __init__(self, **kwargs) -> None: + self._data_dir = kwargs["data_dir"] + self._config = kwargs["config"] + self._pipeline = None + self._vae = None + + self.checkpoint_name = "riffusion/riffusion-model-v1" + + # Download entire seed image folder from huggingface hub + self._seed_images_dir = snapshot_download(self.checkpoint_name, allow_patterns="*.png") + + def load(self): + """ + Load the model. Guaranteed to be called before `predict`. + """ + self._pipeline = RiffusionPipeline.load_checkpoint( + checkpoint=self.checkpoint_name, + use_traced_unet=True, + device=torch.device("cuda" if torch.cuda.is_available() else "cpu"), + ) + + def preprocess(self, request: T.Dict) -> T.Dict: + """ + Incorporate pre-processing required by the model if desired here. + + These might be feature transformations that are tightly coupled to the model. + """ + return request + + def predict(self, request: T.Dict) -> T.Dict[str, T.List]: + """ + This is the main function that is called. + """ + assert self._pipeline is not None, "Model pipeline not loaded" + + try: + inputs = dacite.from_dict(InferenceInput, request) + except dacite.exceptions.WrongTypeError as exception: + return str(exception), 400 + except dacite.exceptions.MissingValueError as exception: + return str(exception), 400 + + # NOTE: Autocast disabled to speed up inference, previous inference time was 10s on T4 + with torch.inference_mode() and torch.cuda.amp.autocast(enabled=False): + response = compute_request( + inputs=inputs, + pipeline=self._pipeline, + seed_images_dir=self._seed_images_dir, + ) + + return response + + def postprocess(self, request: T.Dict) -> T.Dict: + """ + Incorporate post-processing required by the model if desired here. + """ + return request diff --git a/integrations/cog_riffusion.py b/integrations/cog_riffusion.py new file mode 100644 index 0000000..aad79ef --- /dev/null +++ b/integrations/cog_riffusion.py @@ -0,0 +1,158 @@ +""" +Prediction interface for Cog ⚙️ +https://github.com/replicate/cog/blob/main/docs/python.md +""" + +import argparse +import os +import shutil +import typing as T + +import numpy as np +import PIL +import torch +from cog import BaseModel, BasePredictor, Input, Path + +from riffusion.datatypes import InferenceInput, PromptInput +from riffusion.riffusion_pipeline import RiffusionPipeline +from riffusion.spectrogram_image_converter import SpectrogramImageConverter +from riffusion.spectrogram_params import SpectrogramParams + +MODEL_ID = "riffusion/riffusion-model-v1" +MODEL_CACHE = "riffusion-cache" + +# Where built-in seed images are stored +SEED_IMAGES_DIR = Path("./seed_images") +SEED_IMAGES = [val.split(".")[0] for val in os.listdir(SEED_IMAGES_DIR) if "png" in val] +SEED_IMAGES.sort() + + +class Output(BaseModel): + """ + Output class for riffusion predictions + """ + + audio: Path + spectrogram: Path + error: T.Optional[str] = None + + +class RiffusionPredictor(BasePredictor): + """ + Implementation of cog predictor object s.t. we can run riffusion predictions w/cog. + + See README & https://github.com/replicate/cog for details + """ + + def setup(self, local_files_only=True): + """ + Loads the model onto GPU from local cache. + """ + self.device = "cuda" if torch.cuda.is_available() else "cpu" + + self.model = RiffusionPipeline.load_checkpoint( + checkpoint=MODEL_ID, + use_traced_unet=True, + device=self.device, + local_files_only=local_files_only, + cache_dir=MODEL_CACHE, + ) + + def predict( + self, + prompt_a: str = Input(description="The prompt for your audio", default="funky synth solo"), + denoising: float = Input( + description="How much to transform input spectrogram", + default=0.75, + ge=0, + le=1, + ), + prompt_b: str = Input( + description="The second prompt to interpolate with the first," + "leave blank if no interpolation", + default=None, + ), + alpha: float = Input( + description="Interpolation alpha if using two prompts." + "A value of 0 uses prompt_a fully, a value of 1 uses prompt_b fully", + default=0.5, + ge=0, + le=1, + ), + num_inference_steps: int = Input( + description="Number of steps to run the diffusion model", default=50, ge=1 + ), + seed_image_id: str = Input( + description="Seed spectrogram to use", default="vibes", choices=SEED_IMAGES + ), + ) -> Output: + """ + Runs riffusion inference. + """ + # Load the seed image by ID + init_image_path = Path(SEED_IMAGES_DIR, f"{seed_image_id}.png") + if not init_image_path.is_file(): + return Output(error=f"Invalid seed image: {seed_image_id}") + init_image = PIL.Image.open(str(init_image_path)).convert("RGB") + + # fake max ints + seed_a = np.random.randint(0, 2147483647) + seed_b = np.random.randint(0, 2147483647) + + start = PromptInput(prompt=prompt_a, seed=seed_a, denoising=denoising) + if not prompt_b: # no transition + prompt_b = prompt_a + alpha = 0 + end = PromptInput(prompt=prompt_b, seed=seed_b, denoising=denoising) + riffusion_input = InferenceInput( + start=start, + end=end, + alpha=alpha, + num_inference_steps=num_inference_steps, + seed_image_id=seed_image_id, + ) + + # Execute the model to get the spectrogram image + image = self.model.riffuse(riffusion_input, init_image=init_image, mask_image=None) + + # Reconstruct audio from the image + params = SpectrogramParams() + converter = SpectrogramImageConverter(params=params, device=self.device) + segment = converter.audio_from_spectrogram_image(image) + + if not os.path.exists("out/"): + os.mkdir("out") + + out_img_path = "out/spectrogram.jpg" + image.save("out/spectrogram.jpg", exif=image.getexif()) + + out_wav_path = "out/gen_sound.wav" + segment.export(out_wav_path, format="wav") + + return Output(audio=Path(out_wav_path), spectrogram=Path(out_img_path)) + + +# TODO(hayk): Can we get rid of the below functions and incorporate into +# RiffusionPipeline.load_checkpoint? + + +def download_weights(): + """ + Clears local cache & downloads riffusion weights + """ + if os.path.exists(MODEL_CACHE): + shutil.rmtree(MODEL_CACHE) + os.makedirs(MODEL_CACHE) + + pred = RiffusionPredictor() + pred.setup(local_files_only=False) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--download_weights", action="store_true", help="Download and cache weights" + ) + args = parser.parse_args() + if args.download_weights: + download_weights() diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..987789e --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,99 @@ +[tool.black] +line-length = 100 + +[tool.ruff] +line-length = 100 + +# Which rules to run +select = [ + # Pyflakes + "F", + # Pycodestyle + "E", + "W", + # isort + "I001" +] + +ignore = [] + +# Exclude a variety of commonly ignored directories. +exclude = [ + ".bzr", + ".direnv", + ".eggs", + ".git", + ".hg", + ".mypy_cache", + ".nox", + ".pants.d", + ".ruff_cache", + ".svn", + ".tox", + ".venv", + "__pypackages__", + "_build", + "buck-out", + "build", + "dist", + "node_modules", + "venv", +] +per-file-ignores = {} + +# Allow unused variables when underscore-prefixed. +dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" + +# Assume Python 3.10. +target-version = "py310" + +[tool.ruff.mccabe] +# Unlike Flake8, default to a complexity level of 10. +max-complexity = 10 + +[tool.mypy] +python_version = "3.10" + +[[tool.mypy.overrides]] +module = "argh.*" +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = "cog.*" +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = "diffusers.*" +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = "lora_diffusion.*" +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = "numpy.*" +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = "plotly.*" +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = "pydub.*" +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = "scipy.fft.*" +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = "scipy.io.*" +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = "torchaudio.*" +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = "transformers.*" +ignore_missing_imports = true diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..dbfe559 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,21 @@ +accelerate +argh +dacite +demucs +diffusers>=0.9.0 +flask +flask_cors +numpy +pillow>=9.1.0 +plotly +pydub +pysoundfile +scipy +soundfile +sox +streamlit>=1.10.0 +torch +torchaudio +torchvision +transformers +git+https://github.com/cloneofsimo/lora.git diff --git a/riffusion/__init__.py b/riffusion/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/riffusion/audio_splitter.py b/riffusion/audio_splitter.py new file mode 100644 index 0000000..8d90ff1 --- /dev/null +++ b/riffusion/audio_splitter.py @@ -0,0 +1,187 @@ +import shutil +import subprocess +import tempfile +import typing as T +from pathlib import Path + +import numpy as np +import pydub +import torch +import torchaudio +from torchaudio.transforms import Fade + +from riffusion.util import audio_util + + +def split_audio( + segment: pydub.AudioSegment, + model_name: str = "htdemucs_6s", + extension: str = "wav", + jobs: int = 4, + device: str = "cuda", +) -> T.Dict[str, pydub.AudioSegment]: + """ + Split audio into stems using demucs. + """ + tmp_dir = Path(tempfile.mkdtemp(prefix="split_audio_")) + + # Save the audio to a temporary file + audio_path = tmp_dir / "audio.mp3" + segment.export(audio_path, format="mp3") + + # Assemble command + command = [ + "demucs", + str(audio_path), + "--name", + model_name, + "--out", + str(tmp_dir), + "--jobs", + str(jobs), + "--device", + device if device != "mps" else "cpu", + ] + print(" ".join(command)) + + if extension == "mp3": + command.append("--mp3") + + # Run demucs + subprocess.run( + command, + check=True, + ) + + # Load the stems + stems = {} + for stem_path in tmp_dir.glob(f"{model_name}/audio/*.{extension}"): + stem = pydub.AudioSegment.from_file(stem_path) + stems[stem_path.stem] = stem + + # Delete tmp dir + shutil.rmtree(tmp_dir) + + return stems + + +class AudioSplitter: + """ + Split audio into instrument stems like {drums, bass, vocals, etc.} + + NOTE(hayk): This is deprecated as it has inferior performance to the newer hybrid transformer + model in the demucs repo. See the function above. Probably just delete this. + + See: + https://pytorch.org/audio/main/tutorials/hybrid_demucs_tutorial.html + """ + + def __init__( + self, + segment_length_s: float = 10.0, + overlap_s: float = 0.1, + device: str = "cuda", + ): + self.segment_length_s = segment_length_s + self.overlap_s = overlap_s + self.device = device + + self.model = self.load_model().to(device) + + @staticmethod + def load_model(model_path: str = "models/hdemucs_high_trained.pt") -> torchaudio.models.HDemucs: + """ + Load the trained HDEMUCS pytorch model. + """ + # NOTE(hayk): The sources are baked into the pretrained model and can't be changed + model = torchaudio.models.hdemucs_high(sources=["drums", "bass", "other", "vocals"]) + + path = torchaudio.utils.download_asset(model_path) + state_dict = torch.load(path) + model.load_state_dict(state_dict) + model.eval() + + return model + + def split(self, audio: pydub.AudioSegment) -> T.Dict[str, pydub.AudioSegment]: + """ + Split the given audio segment into instrument stems. + """ + if audio.channels == 1: + audio_stereo = audio.set_channels(2) + elif audio.channels == 2: + audio_stereo = audio + else: + raise ValueError(f"Audio must be stereo, but got {audio.channels} channels") + + # Get as (samples, channels) float numpy array + waveform_np = np.array(audio_stereo.get_array_of_samples()) + waveform_np = waveform_np.reshape(-1, audio_stereo.channels) + waveform_np_float = waveform_np.astype(np.float32) + + # To torch and channels-first + waveform = torch.from_numpy(waveform_np_float).to(self.device) + waveform = waveform.transpose(1, 0) + + # Normalize + ref = waveform.mean(0) + waveform = (waveform - ref.mean()) / ref.std() + + # Split + sources = self.separate_sources( + waveform[None], + sample_rate=audio.frame_rate, + )[0] + + # De-normalize + sources = sources * ref.std() + ref.mean() + + # To numpy + sources_np = sources.cpu().numpy().astype(waveform_np.dtype) + + # Convert to pydub + stem_segments = [ + audio_util.audio_from_waveform(waveform, audio.frame_rate) for waveform in sources_np + ] + + # Convert back to mono if necessary + if audio.channels == 1: + stem_segments = [stem.set_channels(1) for stem in stem_segments] + + return dict(zip(self.model.sources, stem_segments)) + + def separate_sources( + self, + waveform: torch.Tensor, + sample_rate: int = 44100, + ): + """ + Apply model to a given waveform in chunks. Use fade and overlap to smooth the edges. + """ + batch, channels, length = waveform.shape + + chunk_len = int(sample_rate * self.segment_length_s * (1 + self.overlap_s)) + start = 0 + end = chunk_len + overlap_frames = self.overlap_s * sample_rate + fade = Fade(fade_in_len=0, fade_out_len=int(overlap_frames), fade_shape="linear") + + final = torch.zeros(batch, len(self.model.sources), channels, length, device=self.device) + + # TODO(hayk): Improve this code, which came from the torchaudio docs + while start < length - overlap_frames: + chunk = waveform[:, :, start:end] + with torch.no_grad(): + out = self.model.forward(chunk) + out = fade(out) + final[:, :, :, start:end] += out + if start == 0: + fade.fade_in_len = int(overlap_frames) + start += int(chunk_len - overlap_frames) + else: + start += chunk_len + end += chunk_len + if end >= length: + fade.fade_out_len = 0 + + return final diff --git a/riffusion/cli.py b/riffusion/cli.py new file mode 100644 index 0000000..266d12b --- /dev/null +++ b/riffusion/cli.py @@ -0,0 +1,138 @@ +""" +Command line tools for riffusion. +""" + +from pathlib import Path + +import argh +import numpy as np +import pydub +from PIL import Image + +from riffusion.spectrogram_image_converter import SpectrogramImageConverter +from riffusion.spectrogram_params import SpectrogramParams +from riffusion.util import image_util + + +@argh.arg("--step-size-ms", help="Duration of one pixel in the X axis of the spectrogram image") +@argh.arg("--num-frequencies", help="Number of Y axes in the spectrogram image") +def audio_to_image( + *, + audio: str, + image: str, + step_size_ms: int = 10, + num_frequencies: int = 512, + min_frequency: int = 0, + max_frequency: int = 10000, + window_duration_ms: int = 100, + padded_duration_ms: int = 400, + power_for_image: float = 0.25, + stereo: bool = False, + device: str = "cuda", +): + """ + Compute a spectrogram image from a waveform. + """ + segment = pydub.AudioSegment.from_file(audio) + + params = SpectrogramParams( + sample_rate=segment.frame_rate, + stereo=stereo, + window_duration_ms=window_duration_ms, + padded_duration_ms=padded_duration_ms, + step_size_ms=step_size_ms, + min_frequency=min_frequency, + max_frequency=max_frequency, + num_frequencies=num_frequencies, + power_for_image=power_for_image, + ) + + converter = SpectrogramImageConverter(params=params, device=device) + + pil_image = converter.spectrogram_image_from_audio(segment) + + pil_image.save(image, exif=pil_image.getexif(), format="PNG") + print(f"Wrote {image}") + + +def print_exif(*, image: str) -> None: + """ + Print the params of a spectrogram image as saved in the exif data. + """ + pil_image = Image.open(image) + exif_data = image_util.exif_from_image(pil_image) + + for name, value in exif_data.items(): + print(f"{name:<20} = {value:>15}") + + +def image_to_audio(*, image: str, audio: str, device: str = "cuda"): + """ + Reconstruct an audio clip from a spectrogram image. + """ + pil_image = Image.open(image) + + # Get parameters from image exif + img_exif = pil_image.getexif() + assert img_exif is not None + + try: + params = SpectrogramParams.from_exif(exif=img_exif) + except (KeyError, AttributeError): + print("WARNING: Could not find spectrogram parameters in exif data. Using defaults.") + params = SpectrogramParams() + + converter = SpectrogramImageConverter(params=params, device=device) + segment = converter.audio_from_spectrogram_image(pil_image) + + extension = Path(audio).suffix[1:] + segment.export(audio, format=extension) + + print(f"Wrote {audio} ({segment.duration_seconds:.2f} seconds)") + + +def sample_clips( + *, + audio: str, + output_dir: str, + num_clips: int = 1, + duration_ms: int = 5000, + mono: bool = False, + extension: str = "wav", + seed: int = -1, +): + """ + Slice an audio file into clips of the given duration. + """ + if seed >= 0: + np.random.seed(seed) + + segment = pydub.AudioSegment.from_file(audio) + + if mono: + segment = segment.set_channels(1) + + output_dir_path = Path(output_dir) + if not output_dir_path.exists(): + output_dir_path.mkdir(parents=True) + + segment_duration_ms = int(segment.duration_seconds * 1000) + for i in range(num_clips): + clip_start_ms = np.random.randint(0, segment_duration_ms - duration_ms) + clip = segment[clip_start_ms : clip_start_ms + duration_ms] + + clip_name = f"clip_{i}_start_{clip_start_ms}_ms_duration_{duration_ms}_ms.{extension}" + clip_path = output_dir_path / clip_name + clip.export(clip_path, format=extension) + print(f"Wrote {clip_path}") + + +if __name__ == "__main__": + argh.dispatch_commands( + [ + audio_to_image, + image_to_audio, + sample_clips, + print_exif, + ] + ) diff --git a/riffusion/datatypes.py b/riffusion/datatypes.py new file mode 100644 index 0000000..99f1c2d --- /dev/null +++ b/riffusion/datatypes.py @@ -0,0 +1,73 @@ +""" +Data model for the riffusion API. +""" +from __future__ import annotations + +import typing as T +from dataclasses import dataclass + + +@dataclass(frozen=True) +class PromptInput: + """ + Parameters for one end of interpolation. + """ + + # Text prompt fed into a CLIP model + prompt: str + + # Random seed for denoising + seed: int + + # Negative prompt to avoid (optional) + negative_prompt: T.Optional[str] = None + + # Denoising strength + denoising: float = 0.75 + + # Classifier-free guidance strength + guidance: float = 7.0 + + +@dataclass(frozen=True) +class InferenceInput: + """ + Parameters for a single run of the riffusion model, interpolating between + a start and end set of PromptInputs. This is the API required for a request + to the model server. + """ + + # Start point of interpolation + start: PromptInput + + # End point of interpolation + end: PromptInput + + # Interpolation alpha [0, 1]. A value of 0 uses start fully, a value of 1 + # uses end fully. + alpha: float + + # Number of inner loops of the diffusion model + num_inference_steps: int = 50 + + # Which seed image to use + seed_image_id: str = "og_beat" + + # ID of mask image to use + mask_image_id: T.Optional[str] = None + + +@dataclass(frozen=True) +class InferenceOutput: + """ + Response from the model inference server. + """ + + # base64 encoded spectrogram image as a JPEG + image: str + + # base64 encoded audio clip as an MP3 + audio: str + + # The duration of the audio clip + duration_s: float diff --git a/riffusion/external/README.md b/riffusion/external/README.md new file mode 100644 index 0000000..698f0b6 --- /dev/null +++ b/riffusion/external/README.md @@ -0,0 +1,3 @@ +# external + +This package contains scripts and tools from external sources. diff --git a/riffusion/external/__init__.py b/riffusion/external/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/riffusion/external/lora/__init__.py b/riffusion/external/lora/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/riffusion/external/lora/run_lora_db_unet_only.sh b/riffusion/external/lora/run_lora_db_unet_only.sh new file mode 100644 index 0000000..722c397 --- /dev/null +++ b/riffusion/external/lora/run_lora_db_unet_only.sh @@ -0,0 +1,24 @@ +export MODEL_NAME="riffusion/riffusion-model-v1" +export INSTANCE_DIR="/tmp/sample_clips_tdlcqdfi/images" +export OUTPUT_DIR="/home/ubuntu/lora_dreambooth_waterfalls_2k" + +accelerate launch\ + --num_machines 1 \ + --num_processes 8 \ + --dynamo_backend=no \ + --mixed_precision="fp16" \ + riffusion/external/lora/train_lora_dreambooth.py \ + --pretrained_model_name_or_path=$MODEL_NAME \ + --instance_data_dir=$INSTANCE_DIR \ + --output_dir=$OUTPUT_DIR \ + --instance_prompt="style of sks" \ + --resolution=512 \ + --train_batch_size=1 \ + --gradient_accumulation_steps=1 \ + --learning_rate=1e-4 \ + --lr_scheduler="constant" \ + --lr_warmup_steps=0 \ + --max_train_steps=2000 + +# TODO try mixed_precision=fp16 +# TODO try num_processes = 8 diff --git a/riffusion/external/lora/train_lora.py b/riffusion/external/lora/train_lora.py new file mode 100644 index 0000000..7fa76c8 --- /dev/null +++ b/riffusion/external/lora/train_lora.py @@ -0,0 +1,49 @@ +from lora_diffusion.cli_lora_pti import train +from lora_diffusion.dataset import STYLE_TEMPLATE + +MODEL_NAME = "riffusion/riffusion-model-v1" +INSTANCE_DIR = "/tmp/sample_clips_xzv8p57g/images" +OUTPUT_DIR = "./lora_output_acoustic" + +if __name__ == "__main__": + entries = [ + "music in the style of {}", + "sound in the style of {}", + "vibe in the style of {}", + "audio in the style of {}", + "groove in the style of {}", + ] + for i in range(len(STYLE_TEMPLATE)): + STYLE_TEMPLATE[i] = entries[i % len(entries)] + print(STYLE_TEMPLATE) + + train( + pretrained_model_name_or_path=MODEL_NAME, + instance_data_dir=INSTANCE_DIR, + output_dir=OUTPUT_DIR, + train_text_encoder=True, + resolution=512, + train_batch_size=1, + gradient_accumulation_steps=4, + scale_lr=True, + learning_rate_unet=1e-4, + learning_rate_text=1e-5, + learning_rate_ti=5e-4, + color_jitter=False, + lr_scheduler="linear", + lr_warmup_steps=0, + placeholder_tokens="|", + use_template="style", + save_steps=100, + max_train_steps_ti=1000, + max_train_steps_tuning=1000, + perform_inversion=True, + clip_ti_decay=True, + weight_decay_ti=0.000, + weight_decay_lora=0.001, + continue_inversion=True, + continue_inversion_lr=1e-4, + device="cuda:0", + lora_rank=1, + use_face_segmentation_condition=False, + ) diff --git a/riffusion/external/lora/train_lora.sh b/riffusion/external/lora/train_lora.sh new file mode 100755 index 0000000..b47f898 --- /dev/null +++ b/riffusion/external/lora/train_lora.sh @@ -0,0 +1,37 @@ +export MODEL_NAME="riffusion/riffusion-model-v1" +export INSTANCE_DIR="/tmp/sample_clips_xzv8p57g/images" +export OUTPUT_DIR="./lora_output_acoustic" + +lora_pti \ + --pretrained_model_name_or_path=$MODEL_NAME \ + --instance_data_dir=$INSTANCE_DIR \ + --output_dir=$OUTPUT_DIR \ + --train_text_encoder \ + --resolution=512 \ + # Started as 1 + --train_batch_size=4 \ + --gradient_accumulation_steps=4 \ + --scale_lr \ + --learning_rate_unet=1e-4 \ + --learning_rate_text=1e-5 \ + --learning_rate_ti=5e-4 \ +# --color_jitter \ + --lr_scheduler="linear" \ + --lr_warmup_steps=0 \ + --placeholder_tokens="" \ +# initializer tokens +# class prompt +# --use_template="style"\ + --save_steps=100 \ + --max_train_steps_ti=1000 \ + --max_train_steps_tuning=1000 \ + --perform_inversion=True \ + --clip_ti_decay \ + --weight_decay_ti=0.000 \ + --weight_decay_lora=0.001\ + --continue_inversion \ + --continue_inversion_lr=1e-4 \ + --device="cuda:0" \ + # 1 or 4? + --lora_rank=4 \ +# --use_face_segmentation_condition\ diff --git a/riffusion/external/lora/train_lora_dreambooth.py b/riffusion/external/lora/train_lora_dreambooth.py new file mode 100644 index 0000000..7488683 --- /dev/null +++ b/riffusion/external/lora/train_lora_dreambooth.py @@ -0,0 +1,958 @@ +# Bootstrapped from: +# https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py + +# ruff: noqa +# mypy: ignore-errors + +import argparse +import hashlib +import inspect +import itertools +import math +import os +from pathlib import Path + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import set_seed +from diffusers import ( + AutoencoderKL, + DDPMScheduler, + StableDiffusionPipeline, + UNet2DConditionModel, +) +from diffusers.optimization import get_scheduler +from lora_diffusion import ( + extract_lora_ups_down, + inject_trainable_lora, + safetensors_available, + save_lora_weight, + save_safeloras, +) +from lora_diffusion.xformers_utils import set_use_memory_efficient_attention_xformers +from PIL import Image +from torch.utils.data import Dataset +from torchvision import transforms +from tqdm.auto import tqdm +from transformers import CLIPTextModel, CLIPTokenizer + + +class DreamBoothDataset(Dataset): + """ + A dataset to prepare the instance and class images with the prompts for fine-tuning the model. + It pre-processes the images and the tokenizes prompts. + """ + + def __init__( + self, + instance_data_root, + instance_prompt, + tokenizer, + class_data_root=None, + class_prompt=None, + size=512, + center_crop=False, + color_jitter=False, + h_flip=False, + resize=False, + ): + self.size = size + self.center_crop = center_crop + self.tokenizer = tokenizer + self.resize = resize + + self.instance_data_root = Path(instance_data_root) + if not self.instance_data_root.exists(): + raise ValueError("Instance images root doesn't exists.") + + self.instance_images_path = list(Path(instance_data_root).iterdir()) + self.num_instance_images = len(self.instance_images_path) + self.instance_prompt = instance_prompt + self._length = self.num_instance_images + + if class_data_root is not None: + self.class_data_root = Path(class_data_root) + self.class_data_root.mkdir(parents=True, exist_ok=True) + self.class_images_path = list(self.class_data_root.iterdir()) + self.num_class_images = len(self.class_images_path) + self._length = max(self.num_class_images, self.num_instance_images) + self.class_prompt = class_prompt + else: + self.class_data_root = None + + img_transforms = [] + + if resize: + img_transforms.append( + transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR) + ) + if center_crop: + img_transforms.append(transforms.CenterCrop(size)) + if color_jitter: + img_transforms.append(transforms.ColorJitter(0.2, 0.1)) + if h_flip: + img_transforms.append(transforms.RandomHorizontalFlip()) + + self.image_transforms = transforms.Compose( + [*img_transforms, transforms.ToTensor(), transforms.Normalize([0.5], [0.5])] + ) + + def __len__(self): + return self._length + + def __getitem__(self, index): + example = {} + instance_image = Image.open(self.instance_images_path[index % self.num_instance_images]) + if not instance_image.mode == "RGB": + instance_image = instance_image.convert("RGB") + example["instance_images"] = self.image_transforms(instance_image) + example["instance_prompt_ids"] = self.tokenizer( + self.instance_prompt, + padding="do_not_pad", + truncation=True, + max_length=self.tokenizer.model_max_length, + ).input_ids + + if self.class_data_root: + class_image = Image.open(self.class_images_path[index % self.num_class_images]) + if not class_image.mode == "RGB": + class_image = class_image.convert("RGB") + example["class_images"] = self.image_transforms(class_image) + example["class_prompt_ids"] = self.tokenizer( + self.class_prompt, + padding="do_not_pad", + truncation=True, + max_length=self.tokenizer.model_max_length, + ).input_ids + + return example + + +class PromptDataset(Dataset): + "A simple dataset to prepare the prompts to generate class images on multiple GPUs." + + def __init__(self, prompt, num_samples): + self.prompt = prompt + self.num_samples = num_samples + + def __len__(self): + return self.num_samples + + def __getitem__(self, index): + example = {} + example["prompt"] = self.prompt + example["index"] = index + return example + + +logger = get_logger(__name__) + + +def parse_args(input_args=None): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_model_name_or_path", + type=str, + default=None, + required=True, + help="Path to pretrained model or model identifier from huggingface.co/models.", + ) + parser.add_argument( + "--pretrained_vae_name_or_path", + type=str, + default=None, + help="Path to pretrained vae or vae identifier from huggingface.co/models.", + ) + parser.add_argument( + "--revision", + type=str, + default=None, + required=False, + help="Revision of pretrained model identifier from huggingface.co/models.", + ) + parser.add_argument( + "--tokenizer_name", + type=str, + default=None, + help="Pretrained tokenizer name or path if not the same as model_name", + ) + parser.add_argument( + "--instance_data_dir", + type=str, + default=None, + required=True, + help="A folder containing the training data of instance images.", + ) + parser.add_argument( + "--class_data_dir", + type=str, + default=None, + required=False, + help="A folder containing the training data of class images.", + ) + parser.add_argument( + "--instance_prompt", + type=str, + default=None, + required=True, + help="The prompt with identifier specifying the instance", + ) + parser.add_argument( + "--class_prompt", + type=str, + default=None, + help="The prompt to specify images in the same class as provided instance images.", + ) + parser.add_argument( + "--with_prior_preservation", + default=False, + action="store_true", + help="Flag to add prior preservation loss.", + ) + parser.add_argument( + "--prior_loss_weight", + type=float, + default=1.0, + help="The weight of prior preservation loss.", + ) + parser.add_argument( + "--num_class_images", + type=int, + default=100, + help=( + "Minimal class images for prior preservation loss. If not have enough images, additional images will be" + " sampled with class_prompt." + ), + ) + parser.add_argument( + "--output_dir", + type=str, + default="text-inversion-model", + help="The output directory where the model predictions and checkpoints will be written.", + ) + parser.add_argument( + "--output_format", + type=str, + choices=["pt", "safe", "both"], + default="both", + help="The output format of the model predicitions and checkpoints.", + ) + parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") + parser.add_argument( + "--resolution", + type=int, + default=512, + help=( + "The resolution for input images, all the images in the train/validation dataset will be resized to this" + " resolution" + ), + ) + parser.add_argument( + "--center_crop", + action="store_true", + help="Whether to center crop images before resizing to resolution", + ) + parser.add_argument( + "--color_jitter", + action="store_true", + help="Whether to apply color jitter to images", + ) + parser.add_argument( + "--train_text_encoder", + action="store_true", + help="Whether to train the text encoder", + ) + parser.add_argument( + "--train_batch_size", + type=int, + default=4, + help="Batch size (per device) for the training dataloader.", + ) + parser.add_argument( + "--sample_batch_size", + type=int, + default=4, + help="Batch size (per device) for sampling images.", + ) + parser.add_argument("--num_train_epochs", type=int, default=1) + parser.add_argument( + "--max_train_steps", + type=int, + default=None, + help="Total number of training steps to perform. If provided, overrides num_train_epochs.", + ) + parser.add_argument( + "--save_steps", + type=int, + default=500, + help="Save checkpoint every X updates steps.", + ) + parser.add_argument( + "--gradient_accumulation_steps", + type=int, + default=1, + help="Number of updates steps to accumulate before performing a backward/update pass.", + ) + parser.add_argument( + "--gradient_checkpointing", + action="store_true", + help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", + ) + parser.add_argument( + "--lora_rank", + type=int, + default=4, + help="Rank of LoRA approximation.", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=None, + help="Initial learning rate (after the potential warmup period) to use.", + ) + parser.add_argument( + "--learning_rate_text", + type=float, + default=5e-6, + help="Initial learning rate for text encoder (after the potential warmup period) to use.", + ) + parser.add_argument( + "--scale_lr", + action="store_true", + default=False, + help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", + ) + parser.add_argument( + "--lr_scheduler", + type=str, + default="constant", + help=( + 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' + ' "constant", "constant_with_warmup"]' + ), + ) + parser.add_argument( + "--lr_warmup_steps", + type=int, + default=500, + help="Number of steps for the warmup in the lr scheduler.", + ) + parser.add_argument( + "--use_8bit_adam", + action="store_true", + help="Whether or not to use 8-bit Adam from bitsandbytes.", + ) + parser.add_argument( + "--adam_beta1", + type=float, + default=0.9, + help="The beta1 parameter for the Adam optimizer.", + ) + parser.add_argument( + "--adam_beta2", + type=float, + default=0.999, + help="The beta2 parameter for the Adam optimizer.", + ) + parser.add_argument( + "--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use." + ) + parser.add_argument( + "--adam_epsilon", + type=float, + default=1e-08, + help="Epsilon value for the Adam optimizer", + ) + parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") + parser.add_argument( + "--push_to_hub", + action="store_true", + help="Whether or not to push the model to the Hub.", + ) + parser.add_argument( + "--hub_token", + type=str, + default=None, + help="The token to use to push to the Model Hub.", + ) + parser.add_argument( + "--logging_dir", + type=str, + default="logs", + help=( + "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" + " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." + ), + ) + parser.add_argument( + "--mixed_precision", + type=str, + default=None, + choices=["no", "fp16", "bf16"], + help=( + "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" + " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" + " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." + ), + ) + parser.add_argument( + "--local_rank", + type=int, + default=-1, + help="For distributed training: local_rank", + ) + parser.add_argument( + "--resume_unet", + type=str, + default=None, + help=("File path for unet lora to resume training."), + ) + parser.add_argument( + "--resume_text_encoder", + type=str, + default=None, + help=("File path for text encoder lora to resume training."), + ) + parser.add_argument( + "--resize", + type=bool, + default=True, + required=False, + help="Should images be resized to --resolution before training?", + ) + parser.add_argument( + "--use_xformers", action="store_true", help="Whether or not to use xformers" + ) + + if input_args is not None: + args = parser.parse_args(input_args) + else: + args = parser.parse_args() + + env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) + if env_local_rank != -1 and env_local_rank != args.local_rank: + args.local_rank = env_local_rank + + if args.with_prior_preservation: + if args.class_data_dir is None: + raise ValueError("You must specify a data directory for class images.") + if args.class_prompt is None: + raise ValueError("You must specify prompt for class images.") + else: + if args.class_data_dir is not None: + logger.warning("You need not use --class_data_dir without --with_prior_preservation.") + if args.class_prompt is not None: + logger.warning("You need not use --class_prompt without --with_prior_preservation.") + + if not safetensors_available: + if args.output_format == "both": + print( + "Safetensors is not available - changing output format to just output PyTorch files" + ) + args.output_format = "pt" + elif args.output_format == "safe": + raise ValueError( + "Safetensors is not available - either install it, or change output_format." + ) + + return args + + +def main(args): + logging_dir = Path(args.output_dir, args.logging_dir) + + accelerator = Accelerator( + gradient_accumulation_steps=args.gradient_accumulation_steps, + mixed_precision=args.mixed_precision, + log_with="tensorboard", + logging_dir=logging_dir, + ) + + # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate + # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models. + # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate. + if ( + args.train_text_encoder + and args.gradient_accumulation_steps > 1 + and accelerator.num_processes > 1 + ): + raise ValueError( + "Gradient accumulation is not supported when training the text encoder in distributed training. " + "Please set gradient_accumulation_steps to 1. This feature will be supported in the future." + ) + + if args.seed is not None: + set_seed(args.seed) + + if args.with_prior_preservation: + class_images_dir = Path(args.class_data_dir) + if not class_images_dir.exists(): + class_images_dir.mkdir(parents=True) + cur_class_images = len(list(class_images_dir.iterdir())) + + if cur_class_images < args.num_class_images: + torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32 + pipeline = StableDiffusionPipeline.from_pretrained( + args.pretrained_model_name_or_path, + torch_dtype=torch_dtype, + safety_checker=None, + revision=args.revision, + ) + pipeline.set_progress_bar_config(disable=True) + + num_new_images = args.num_class_images - cur_class_images + logger.info(f"Number of class images to sample: {num_new_images}.") + + sample_dataset = PromptDataset(args.class_prompt, num_new_images) + sample_dataloader = torch.utils.data.DataLoader( + sample_dataset, batch_size=args.sample_batch_size + ) + + sample_dataloader = accelerator.prepare(sample_dataloader) + pipeline.to(accelerator.device) + + for example in tqdm( + sample_dataloader, + desc="Generating class images", + disable=not accelerator.is_local_main_process, + ): + images = pipeline(example["prompt"]).images + + for i, image in enumerate(images): + hash_image = hashlib.sha1(image.tobytes()).hexdigest() + image_filename = ( + class_images_dir + / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" + ) + image.save(image_filename) + + del pipeline + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + # Handle the repository creation + if accelerator.is_main_process: + + if args.output_dir is not None: + os.makedirs(args.output_dir, exist_ok=True) + + # Load the tokenizer + if args.tokenizer_name: + tokenizer = CLIPTokenizer.from_pretrained( + args.tokenizer_name, + revision=args.revision, + ) + elif args.pretrained_model_name_or_path: + tokenizer = CLIPTokenizer.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="tokenizer", + revision=args.revision, + ) + + # Load models and create wrapper for stable diffusion + text_encoder = CLIPTextModel.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="text_encoder", + revision=args.revision, + ) + vae = AutoencoderKL.from_pretrained( + args.pretrained_vae_name_or_path or args.pretrained_model_name_or_path, + subfolder=None if args.pretrained_vae_name_or_path else "vae", + revision=None if args.pretrained_vae_name_or_path else args.revision, + ) + unet = UNet2DConditionModel.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="unet", + revision=args.revision, + ) + unet.requires_grad_(False) + unet_lora_params, _ = inject_trainable_lora(unet, r=args.lora_rank, loras=args.resume_unet) + + for _up, _down in extract_lora_ups_down(unet): + print("Before training: Unet First Layer lora up", _up.weight.data) + print("Before training: Unet First Layer lora down", _down.weight.data) + break + + vae.requires_grad_(False) + text_encoder.requires_grad_(False) + + if args.train_text_encoder: + text_encoder_lora_params, _ = inject_trainable_lora( + text_encoder, + target_replace_module=["CLIPAttention"], + r=args.lora_rank, + ) + for _up, _down in extract_lora_ups_down( + text_encoder, target_replace_module=["CLIPAttention"] + ): + print("Before training: text encoder First Layer lora up", _up.weight.data) + print("Before training: text encoder First Layer lora down", _down.weight.data) + break + + if args.use_xformers: + set_use_memory_efficient_attention_xformers(unet, True) + set_use_memory_efficient_attention_xformers(vae, True) + + if args.gradient_checkpointing: + unet.enable_gradient_checkpointing() + if args.train_text_encoder: + text_encoder.gradient_checkpointing_enable() + + if args.scale_lr: + args.learning_rate = ( + args.learning_rate + * args.gradient_accumulation_steps + * args.train_batch_size + * accelerator.num_processes + ) + + # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs + if args.use_8bit_adam: + try: + import bitsandbytes as bnb + except ImportError: + raise ImportError( + "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`." + ) + + optimizer_class = bnb.optim.AdamW8bit + else: + optimizer_class = torch.optim.AdamW + + text_lr = args.learning_rate if args.learning_rate_text is None else args.learning_rate_text + + params_to_optimize = ( + [ + {"params": itertools.chain(*unet_lora_params), "lr": args.learning_rate}, + { + "params": itertools.chain(*text_encoder_lora_params), + "lr": text_lr, + }, + ] + if args.train_text_encoder + else itertools.chain(*unet_lora_params) + ) + optimizer = optimizer_class( + params_to_optimize, + lr=args.learning_rate, + betas=(args.adam_beta1, args.adam_beta2), + weight_decay=args.adam_weight_decay, + eps=args.adam_epsilon, + ) + + noise_scheduler = DDPMScheduler.from_config( + args.pretrained_model_name_or_path, subfolder="scheduler" + ) + + train_dataset = DreamBoothDataset( + instance_data_root=args.instance_data_dir, + instance_prompt=args.instance_prompt, + class_data_root=args.class_data_dir if args.with_prior_preservation else None, + class_prompt=args.class_prompt, + tokenizer=tokenizer, + size=args.resolution, + center_crop=args.center_crop, + color_jitter=args.color_jitter, + resize=args.resize, + ) + + def collate_fn(examples): + input_ids = [example["instance_prompt_ids"] for example in examples] + pixel_values = [example["instance_images"] for example in examples] + + # Concat class and instance examples for prior preservation. + # We do this to avoid doing two forward passes. + if args.with_prior_preservation: + input_ids += [example["class_prompt_ids"] for example in examples] + pixel_values += [example["class_images"] for example in examples] + + pixel_values = torch.stack(pixel_values) + pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() + + input_ids = tokenizer.pad( + {"input_ids": input_ids}, + padding="max_length", + max_length=tokenizer.model_max_length, + return_tensors="pt", + ).input_ids + + batch = { + "input_ids": input_ids, + "pixel_values": pixel_values, + } + return batch + + train_dataloader = torch.utils.data.DataLoader( + train_dataset, + batch_size=args.train_batch_size, + shuffle=True, + collate_fn=collate_fn, + num_workers=1, + ) + + # Scheduler and math around the number of training steps. + overrode_max_train_steps = False + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + if args.max_train_steps is None: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + overrode_max_train_steps = True + + lr_scheduler = get_scheduler( + args.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps, + num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, + ) + + if args.train_text_encoder: + ( + unet, + text_encoder, + optimizer, + train_dataloader, + lr_scheduler, + ) = accelerator.prepare(unet, text_encoder, optimizer, train_dataloader, lr_scheduler) + else: + unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + unet, optimizer, train_dataloader, lr_scheduler + ) + + weight_dtype = torch.float32 + if accelerator.mixed_precision == "fp16": + weight_dtype = torch.float16 + elif accelerator.mixed_precision == "bf16": + weight_dtype = torch.bfloat16 + + # Move text_encode and vae to gpu. + # For mixed precision training we cast the text_encoder and vae weights to half-precision + # as these models are only used for inference, keeping weights in full precision is not required. + vae.to(accelerator.device, dtype=weight_dtype) + if not args.train_text_encoder: + text_encoder.to(accelerator.device, dtype=weight_dtype) + + # We need to recalculate our total training steps as the size of the training dataloader may have changed. + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + if overrode_max_train_steps: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + # Afterwards we recalculate our number of training epochs + args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + + # We need to initialize the trackers we use, and also store our configuration. + # The trackers initializes automatically on the main process. + if accelerator.is_main_process: + accelerator.init_trackers("dreambooth", config=vars(args)) + + # Train! + total_batch_size = ( + args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps + ) + + print("***** Running training *****") + print(f" Num examples = {len(train_dataset)}") + print(f" Num batches each epoch = {len(train_dataloader)}") + print(f" Num Epochs = {args.num_train_epochs}") + print(f" Instantaneous batch size per device = {args.train_batch_size}") + print( + f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}" + ) + print(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") + print(f" Total optimization steps = {args.max_train_steps}") + # Only show the progress bar once on each machine. + progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) + progress_bar.set_description("Steps") + global_step = 0 + last_save = 0 + + for epoch in range(args.num_train_epochs): + unet.train() + if args.train_text_encoder: + text_encoder.train() + + for step, batch in enumerate(train_dataloader): + # Convert images to latent space + latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample() + latents = latents * 0.18215 + + # Sample noise that we'll add to the latents + noise = torch.randn_like(latents) + bsz = latents.shape[0] + # Sample a random timestep for each image + timesteps = torch.randint( + 0, + noise_scheduler.config.num_train_timesteps, + (bsz,), + device=latents.device, + ) + timesteps = timesteps.long() + + # Add noise to the latents according to the noise magnitude at each timestep + # (this is the forward diffusion process) + noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) + + # Get the text embedding for conditioning + encoder_hidden_states = text_encoder(batch["input_ids"])[0] + + # Predict the noise residual + model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample + + # Get the target for loss depending on the prediction type + if noise_scheduler.config.prediction_type == "epsilon": + target = noise + elif noise_scheduler.config.prediction_type == "v_prediction": + target = noise_scheduler.get_velocity(latents, noise, timesteps) + else: + raise ValueError( + f"Unknown prediction type {noise_scheduler.config.prediction_type}" + ) + + if args.with_prior_preservation: + # Chunk the noise and model_pred into two parts and compute the loss on each part separately. + model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) + target, target_prior = torch.chunk(target, 2, dim=0) + + # Compute instance loss + loss = ( + F.mse_loss(model_pred.float(), target.float(), reduction="none") + .mean([1, 2, 3]) + .mean() + ) + + # Compute prior loss + prior_loss = F.mse_loss( + model_pred_prior.float(), target_prior.float(), reduction="mean" + ) + + # Add the prior loss to the instance loss. + loss = loss + args.prior_loss_weight * prior_loss + else: + loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") + + accelerator.backward(loss) + if accelerator.sync_gradients: + params_to_clip = ( + itertools.chain(unet.parameters(), text_encoder.parameters()) + if args.train_text_encoder + else unet.parameters() + ) + accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) + optimizer.step() + lr_scheduler.step() + progress_bar.update(1) + optimizer.zero_grad() + + global_step += 1 + + # Checks if the accelerator has performed an optimization step behind the scenes + if accelerator.sync_gradients: + if args.save_steps and global_step - last_save >= args.save_steps: + if accelerator.is_main_process: + # newer versions of accelerate allow the 'keep_fp32_wrapper' arg. without passing + # it, the models will be unwrapped, and when they are then used for further training, + # we will crash. pass this, but only to newer versions of accelerate. fixes + # https://github.com/huggingface/diffusers/issues/1566 + accepts_keep_fp32_wrapper = "keep_fp32_wrapper" in set( + inspect.signature(accelerator.unwrap_model).parameters.keys() + ) + extra_args = ( + {"keep_fp32_wrapper": True} if accepts_keep_fp32_wrapper else {} + ) + pipeline = StableDiffusionPipeline.from_pretrained( + args.pretrained_model_name_or_path, + unet=accelerator.unwrap_model(unet, **extra_args), + text_encoder=accelerator.unwrap_model(text_encoder, **extra_args), + revision=args.revision, + ) + + filename_unet = f"{args.output_dir}/lora_weight_e{epoch}_s{global_step}.pt" + filename_text_encoder = ( + f"{args.output_dir}/lora_weight_e{epoch}_s{global_step}.text_encoder.pt" + ) + print(f"save weights {filename_unet}, {filename_text_encoder}") + save_lora_weight(pipeline.unet, filename_unet) + if args.train_text_encoder: + save_lora_weight( + pipeline.text_encoder, + filename_text_encoder, + target_replace_module=["CLIPAttention"], + ) + + for _up, _down in extract_lora_ups_down(pipeline.unet): + print( + "First Unet Layer's Up Weight is now : ", + _up.weight.data, + ) + print( + "First Unet Layer's Down Weight is now : ", + _down.weight.data, + ) + break + if args.train_text_encoder: + for _up, _down in extract_lora_ups_down( + pipeline.text_encoder, + target_replace_module=["CLIPAttention"], + ): + print( + "First Text Encoder Layer's Up Weight is now : ", + _up.weight.data, + ) + print( + "First Text Encoder Layer's Down Weight is now : ", + _down.weight.data, + ) + break + + last_save = global_step + + logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} + progress_bar.set_postfix(**logs) + accelerator.log(logs, step=global_step) + + if global_step >= args.max_train_steps: + break + + accelerator.wait_for_everyone() + + # Create the pipeline using using the trained modules and save it. + if accelerator.is_main_process: + pipeline = StableDiffusionPipeline.from_pretrained( + args.pretrained_model_name_or_path, + unet=accelerator.unwrap_model(unet), + text_encoder=accelerator.unwrap_model(text_encoder), + revision=args.revision, + ) + + print("\n\nLora TRAINING DONE!\n\n") + + if args.output_format == "pt" or args.output_format == "both": + save_lora_weight(pipeline.unet, args.output_dir + "/lora_weight.pt") + if args.train_text_encoder: + save_lora_weight( + pipeline.text_encoder, + args.output_dir + "/lora_weight.text_encoder.pt", + target_replace_module=["CLIPAttention"], + ) + + if args.output_format == "safe" or args.output_format == "both": + loras = {} + loras["unet"] = (pipeline.unet, {"CrossAttention", "Attention", "GEGLU"}) + if args.train_text_encoder: + loras["text_encoder"] = (pipeline.text_encoder, {"CLIPAttention"}) + + save_safeloras(loras, args.output_dir + "/lora_weight.safetensors") + + if args.push_to_hub: + repo.push_to_hub( + commit_message="End of training", + blocking=False, + auto_lfs_prune=True, + ) + + accelerator.end_training() + + +if __name__ == "__main__": + args = parse_args() + main(args) diff --git a/riffusion/external/prompt_weighting.py b/riffusion/external/prompt_weighting.py new file mode 100644 index 0000000..160b7a9 --- /dev/null +++ b/riffusion/external/prompt_weighting.py @@ -0,0 +1,372 @@ +""" +This code is taken from the diffusers community pipeline: + + https://github.com/huggingface/diffusers/blob/f242eba4fdc5b76dc40d3a9c01ba49b2c74b9796/examples/community/lpw_stable_diffusion.py + +License: Apache 2.0 +""" +# ruff: noqa +# mypy: ignore-errors + +import logging +import re +import typing as T + +import torch + +from diffusers import StableDiffusionPipeline + + +logger = logging.getLogger(__name__) + + +re_attention = re.compile( + r""" +\\\(| +\\\)| +\\\[| +\\]| +\\\\| +\\| +\(| +\[| +:([+-]?[.\d]+)\)| +\)| +]| +[^\\()\[\]:]+| +: +""", + re.X, +) + + +def parse_prompt_attention(text): + """ + Parses a string with attention tokens and returns a list of pairs: text and its associated weight. + Accepted tokens are: + (abc) - increases attention to abc by a multiplier of 1.1 + (abc:3.12) - increases attention to abc by a multiplier of 3.12 + [abc] - decreases attention to abc by a multiplier of 1.1 + \( - literal character '(' + \[ - literal character '[' + \) - literal character ')' + \] - literal character ']' + \\ - literal character '\' + anything else - just text + >>> parse_prompt_attention('normal text') + [['normal text', 1.0]] + >>> parse_prompt_attention('an (important) word') + [['an ', 1.0], ['important', 1.1], [' word', 1.0]] + >>> parse_prompt_attention('(unbalanced') + [['unbalanced', 1.1]] + >>> parse_prompt_attention('\(literal\]') + [['(literal]', 1.0]] + >>> parse_prompt_attention('(unnecessary)(parens)') + [['unnecessaryparens', 1.1]] + >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).') + [['a ', 1.0], + ['house', 1.5730000000000004], + [' ', 1.1], + ['on', 1.0], + [' a ', 1.1], + ['hill', 0.55], + [', sun, ', 1.1], + ['sky', 1.4641000000000006], + ['.', 1.1]] + """ + + res = [] + round_brackets = [] + square_brackets = [] + + round_bracket_multiplier = 1.1 + square_bracket_multiplier = 1 / 1.1 + + def multiply_range(start_position, multiplier): + for p in range(start_position, len(res)): + res[p][1] *= multiplier + + for m in re_attention.finditer(text): + text = m.group(0) + weight = m.group(1) + + if text.startswith("\\"): + res.append([text[1:], 1.0]) + elif text == "(": + round_brackets.append(len(res)) + elif text == "[": + square_brackets.append(len(res)) + elif weight is not None and len(round_brackets) > 0: + multiply_range(round_brackets.pop(), float(weight)) + elif text == ")" and len(round_brackets) > 0: + multiply_range(round_brackets.pop(), round_bracket_multiplier) + elif text == "]" and len(square_brackets) > 0: + multiply_range(square_brackets.pop(), square_bracket_multiplier) + else: + res.append([text, 1.0]) + + for pos in round_brackets: + multiply_range(pos, round_bracket_multiplier) + + for pos in square_brackets: + multiply_range(pos, square_bracket_multiplier) + + if len(res) == 0: + res = [["", 1.0]] + + # merge runs of identical weights + i = 0 + while i + 1 < len(res): + if res[i][1] == res[i + 1][1]: + res[i][0] += res[i + 1][0] + res.pop(i + 1) + else: + i += 1 + + return res + + +def get_prompts_with_weights(pipe: StableDiffusionPipeline, prompt: T.List[str], max_length: int): + r""" + Tokenize a list of prompts and return its tokens with weights of each token. + No padding, starting or ending token is included. + """ + tokens = [] + weights = [] + truncated = False + for text in prompt: + texts_and_weights = parse_prompt_attention(text) + text_token = [] + text_weight = [] + for word, weight in texts_and_weights: + # tokenize and discard the starting and the ending token + token = pipe.tokenizer(word).input_ids[1:-1] + text_token += token + # copy the weight by length of token + text_weight += [weight] * len(token) + # stop if the text is too long (longer than truncation limit) + if len(text_token) > max_length: + truncated = True + break + # truncate + if len(text_token) > max_length: + truncated = True + text_token = text_token[:max_length] + text_weight = text_weight[:max_length] + tokens.append(text_token) + weights.append(text_weight) + if truncated: + logger.warning( + "Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples" + ) + return tokens, weights + + +def pad_tokens_and_weights( + tokens, weights, max_length, bos, eos, no_boseos_middle=True, chunk_length=77 +): + r""" + Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length. + """ + max_embeddings_multiples = (max_length - 2) // (chunk_length - 2) + weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length + for i in range(len(tokens)): + tokens[i] = [bos] + tokens[i] + [eos] * (max_length - 1 - len(tokens[i])) + if no_boseos_middle: + weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i])) + else: + w = [] + if len(weights[i]) == 0: + w = [1.0] * weights_length + else: + for j in range(max_embeddings_multiples): + w.append(1.0) # weight for starting token in this chunk + w += weights[i][ + j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2)) + ] + w.append(1.0) # weight for ending token in this chunk + w += [1.0] * (weights_length - len(w)) + weights[i] = w[:] + + return tokens, weights + + +def get_unweighted_text_embeddings( + pipe: StableDiffusionPipeline, + text_input: torch.Tensor, + chunk_length: int, + no_boseos_middle: T.Optional[bool] = True, +) -> torch.FloatTensor: + """ + When the length of tokens is a multiple of the capacity of the text encoder, + it should be split into chunks and sent to the text encoder individually. + """ + max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2) + if max_embeddings_multiples > 1: + text_embeddings = [] + for i in range(max_embeddings_multiples): + # extract the i-th chunk + text_input_chunk = text_input[ + :, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2 + ].clone() + + # cover the head and the tail by the starting and the ending tokens + text_input_chunk[:, 0] = text_input[0, 0] + text_input_chunk[:, -1] = text_input[0, -1] + text_embedding = pipe.text_encoder(text_input_chunk)[0] + + if no_boseos_middle: + if i == 0: + # discard the ending token + text_embedding = text_embedding[:, :-1] + elif i == max_embeddings_multiples - 1: + # discard the starting token + text_embedding = text_embedding[:, 1:] + else: + # discard both starting and ending tokens + text_embedding = text_embedding[:, 1:-1] + + text_embeddings.append(text_embedding) + text_embeddings = torch.concat(text_embeddings, axis=1) + else: + text_embeddings = pipe.text_encoder(text_input)[0] + return text_embeddings + + +def get_weighted_text_embeddings( + pipe: StableDiffusionPipeline, + prompt: T.Union[str, T.List[str]], + uncond_prompt: T.Optional[T.Union[str, T.List[str]]] = None, + max_embeddings_multiples: T.Optional[int] = 3, + no_boseos_middle: T.Optional[bool] = False, + skip_parsing: T.Optional[bool] = False, + skip_weighting: T.Optional[bool] = False, + **kwargs, +) -> T.Tuple[torch.FloatTensor, T.Optional[torch.FloatTensor]]: + r""" + Prompts can be assigned with local weights using brackets. For example, + prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful', + and the embedding tokens corresponding to the words get multiplied by a constant, 1.1. + Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean. + Args: + pipe (`StableDiffusionPipeline`): + Pipe to provide access to the tokenizer and the text encoder. + prompt (`str` or `T.List[str]`): + The prompt or prompts to guide the image generation. + uncond_prompt (`str` or `T.List[str]`): + The unconditional prompt or prompts for guide the image generation. If unconditional prompt + is provided, the embeddings of prompt and uncond_prompt are concatenated. + max_embeddings_multiples (`int`, *optional*, defaults to `3`): + The max multiple length of prompt embeddings compared to the max output length of text encoder. + no_boseos_middle (`bool`, *optional*, defaults to `False`): + If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and + ending token in each of the chunk in the middle. + skip_parsing (`bool`, *optional*, defaults to `False`): + Skip the parsing of brackets. + skip_weighting (`bool`, *optional*, defaults to `False`): + Skip the weighting. When the parsing is skipped, it is forced True. + """ + max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2 + if isinstance(prompt, str): + prompt = [prompt] + + if not skip_parsing: + prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2) + + if uncond_prompt is not None: + if isinstance(uncond_prompt, str): + uncond_prompt = [uncond_prompt] + uncond_tokens, uncond_weights = get_prompts_with_weights( + pipe, uncond_prompt, max_length - 2 + ) + else: + prompt_tokens = [ + token[1:-1] + for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids + ] + prompt_weights = [[1.0] * len(token) for token in prompt_tokens] + if uncond_prompt is not None: + if isinstance(uncond_prompt, str): + uncond_prompt = [uncond_prompt] + uncond_tokens = [ + token[1:-1] + for token in pipe.tokenizer( + uncond_prompt, max_length=max_length, truncation=True + ).input_ids + ] + uncond_weights = [[1.0] * len(token) for token in uncond_tokens] + + # round up the longest length of tokens to a multiple of (model_max_length - 2) + max_length = max([len(token) for token in prompt_tokens]) + if uncond_prompt is not None: + max_length = max(max_length, max([len(token) for token in uncond_tokens])) + + max_embeddings_multiples = min( + max_embeddings_multiples, + (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1, + ) + max_embeddings_multiples = max(1, max_embeddings_multiples) + max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2 + + # pad the length of tokens and weights + bos = pipe.tokenizer.bos_token_id + eos = pipe.tokenizer.eos_token_id + prompt_tokens, prompt_weights = pad_tokens_and_weights( + prompt_tokens, + prompt_weights, + max_length, + bos, + eos, + no_boseos_middle=no_boseos_middle, + chunk_length=pipe.tokenizer.model_max_length, + ) + prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=pipe.device) + if uncond_prompt is not None: + uncond_tokens, uncond_weights = pad_tokens_and_weights( + uncond_tokens, + uncond_weights, + max_length, + bos, + eos, + no_boseos_middle=no_boseos_middle, + chunk_length=pipe.tokenizer.model_max_length, + ) + uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device=pipe.device) + + # get the embeddings + text_embeddings = get_unweighted_text_embeddings( + pipe, + prompt_tokens, + pipe.tokenizer.model_max_length, + no_boseos_middle=no_boseos_middle, + ) + prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=pipe.device) + if uncond_prompt is not None: + uncond_embeddings = get_unweighted_text_embeddings( + pipe, + uncond_tokens, + pipe.tokenizer.model_max_length, + no_boseos_middle=no_boseos_middle, + ) + uncond_weights = torch.tensor( + uncond_weights, dtype=uncond_embeddings.dtype, device=pipe.device + ) + + # assign weights to the prompts and normalize in the sense of mean + # TODO: should we normalize by chunk or in a whole (current implementation)? + if (not skip_parsing) and (not skip_weighting): + previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype) + text_embeddings *= prompt_weights.unsqueeze(-1) + current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype) + text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1) + if uncond_prompt is not None: + previous_mean = ( + uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype) + ) + uncond_embeddings *= uncond_weights.unsqueeze(-1) + current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype) + uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1) + + if uncond_prompt is not None: + return text_embeddings, uncond_embeddings + return text_embeddings, None diff --git a/riffusion/riffusion_pipeline.py b/riffusion/riffusion_pipeline.py new file mode 100644 index 0000000..9256a94 --- /dev/null +++ b/riffusion/riffusion_pipeline.py @@ -0,0 +1,477 @@ +""" +Riffusion inference pipeline. +""" +from __future__ import annotations + +import dataclasses +import functools +import inspect +import typing as T + +import numpy as np +import torch +from diffusers.models import AutoencoderKL, UNet2DConditionModel +from diffusers.pipeline_utils import DiffusionPipeline +from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker +from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler +from diffusers.utils import logging +from huggingface_hub import hf_hub_download +from PIL import Image +from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer + +from riffusion.datatypes import InferenceInput +from riffusion.external.prompt_weighting import get_weighted_text_embeddings +from riffusion.util import torch_util + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +class RiffusionPipeline(DiffusionPipeline): + """ + Diffusers pipeline for doing a controlled img2img interpolation for audio generation. + + # TODO(hayk): Document more + + Part of this code was adapted from the non-img2img interpolation pipeline at: + + https://github.com/huggingface/diffusers/blob/main/examples/community/interpolate_stable_diffusion.py + + Check the documentation for DiffusionPipeline for full information. + """ + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + unet: UNet2DConditionModel, + scheduler: T.Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], + safety_checker: StableDiffusionSafetyChecker, + feature_extractor: CLIPFeatureExtractor, + ): + super().__init__() + self.register_modules( + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + unet=unet, + scheduler=scheduler, + safety_checker=safety_checker, + feature_extractor=feature_extractor, + ) + + @classmethod + def load_checkpoint( + cls, + checkpoint: str, + use_traced_unet: bool = True, + channels_last: bool = False, + dtype: torch.dtype = torch.float16, + device: str = "cuda", + local_files_only: bool = False, + low_cpu_mem_usage: bool = False, + cache_dir: T.Optional[str] = None, + ) -> RiffusionPipeline: + """ + Load the riffusion model pipeline. + + Args: + checkpoint: Model checkpoint on disk in diffusers format + use_traced_unet: Whether to use the traced unet for speedups + device: Device to load the model on + channels_last: Whether to use channels_last memory format + local_files_only: Don't download, only use local files + low_cpu_mem_usage: Attempt to use less memory on CPU + """ + device = torch_util.check_device(device) + + if device == "cpu" or device.lower().startswith("mps"): + print(f"WARNING: Falling back to float32 on {device}, float16 is unsupported") + dtype = torch.float32 + + pipeline = RiffusionPipeline.from_pretrained( + checkpoint, + revision="main", + torch_dtype=dtype, + # Disable the NSFW filter, causes incorrect false positives + # TODO(hayk): Disable the "you have passed a non-standard module" warning from this. + safety_checker=lambda images, **kwargs: (images, False), + low_cpu_mem_usage=low_cpu_mem_usage, + local_files_only=local_files_only, + cache_dir=cache_dir, + ).to(device) + + if channels_last: + pipeline.unet.to(memory_format=torch.channels_last) + + # Optionally load a traced unet + if checkpoint == "riffusion/riffusion-model-v1" and use_traced_unet: + traced_unet = cls.load_traced_unet( + checkpoint=checkpoint, + subfolder="unet_traced", + filename="unet_traced.pt", + in_channels=pipeline.unet.in_channels, + dtype=dtype, + device=device, + local_files_only=local_files_only, + cache_dir=cache_dir, + ) + + if traced_unet is not None: + pipeline.unet = traced_unet + + model = pipeline.to(device) + + return model + + @staticmethod + def load_traced_unet( + checkpoint: str, + subfolder: str, + filename: str, + in_channels: int, + dtype: torch.dtype, + device: str = "cuda", + local_files_only=False, + cache_dir: T.Optional[str] = None, + ) -> T.Optional[torch.nn.Module]: + """ + Load a traced unet from the huggingface hub. This can improve performance. + """ + if device == "cpu" or device.lower().startswith("mps"): + print("WARNING: Traced UNet only available for CUDA, skipping") + return None + + # Download and load the traced unet + unet_file = hf_hub_download( + checkpoint, + subfolder=subfolder, + filename=filename, + local_files_only=local_files_only, + cache_dir=cache_dir, + ) + unet_traced = torch.jit.load(unet_file) + + # Wrap it in a torch module + class TracedUNet(torch.nn.Module): + @dataclasses.dataclass + class UNet2DConditionOutput: + sample: torch.FloatTensor + + def __init__(self): + super().__init__() + self.in_channels = device + self.device = device + self.dtype = dtype + + def forward(self, latent_model_input, t, encoder_hidden_states): + sample = unet_traced(latent_model_input, t, encoder_hidden_states)[0] + return self.UNet2DConditionOutput(sample=sample) + + return TracedUNet() + + @property + def device(self) -> str: + return str(self.vae.device) + + @functools.lru_cache() + def embed_text(self, text) -> torch.FloatTensor: + """ + Takes in text and turns it into text embeddings. + """ + text_input = self.tokenizer( + text, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + with torch.no_grad(): + embed = self.text_encoder(text_input.input_ids.to(self.device))[0] + return embed + + @functools.lru_cache() + def embed_text_weighted(self, text) -> torch.FloatTensor: + """ + Get text embedding with weights. + """ + return get_weighted_text_embeddings( + pipe=self, + prompt=text, + uncond_prompt=None, + max_embeddings_multiples=3, + no_boseos_middle=False, + skip_parsing=False, + skip_weighting=False, + )[0] + + @torch.no_grad() + def riffuse( + self, + inputs: InferenceInput, + init_image: Image.Image, + mask_image: T.Optional[Image.Image] = None, + use_reweighting: bool = True, + ) -> Image.Image: + """ + Runs inference using interpolation with both img2img and text conditioning. + + Args: + inputs: Parameter dataclass + init_image: Image used for conditioning + mask_image: White pixels in the mask will be replaced by noise and therefore repainted, + while black pixels will be preserved. It will be converted to a single + channel (luminance) before use. + use_reweighting: Use prompt reweighting + """ + alpha = inputs.alpha + start = inputs.start + end = inputs.end + + guidance_scale = start.guidance * (1.0 - alpha) + end.guidance * alpha + + # TODO(hayk): Always generate the seed on CPU? + if self.device.lower().startswith("mps"): + generator_start = torch.Generator(device="cpu").manual_seed(start.seed) + generator_end = torch.Generator(device="cpu").manual_seed(end.seed) + else: + generator_start = torch.Generator(device=self.device).manual_seed(start.seed) + generator_end = torch.Generator(device=self.device).manual_seed(end.seed) + + # Text encodings + if use_reweighting: + embed_start = self.embed_text_weighted(start.prompt) + embed_end = self.embed_text_weighted(end.prompt) + else: + embed_start = self.embed_text(start.prompt) + embed_end = self.embed_text(end.prompt) + + text_embedding = embed_start + alpha * (embed_end - embed_start) + + # Image latents + init_image_torch = preprocess_image(init_image).to( + device=self.device, dtype=embed_start.dtype + ) + init_latent_dist = self.vae.encode(init_image_torch).latent_dist + # TODO(hayk): Probably this seed should just be 0 always? Make it 100% symmetric. The + # result is so close no matter the seed that it doesn't really add variety. + if self.device.lower().startswith("mps"): + generator = torch.Generator(device="cpu").manual_seed(start.seed) + else: + generator = torch.Generator(device=self.device).manual_seed(start.seed) + + init_latents = init_latent_dist.sample(generator=generator) + init_latents = 0.18215 * init_latents + + # Prepare mask latent + mask: T.Optional[torch.Tensor] = None + if mask_image: + vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + mask = preprocess_mask(mask_image, scale_factor=vae_scale_factor).to( + device=self.device, dtype=embed_start.dtype + ) + + outputs = self.interpolate_img2img( + text_embeddings=text_embedding, + init_latents=init_latents, + mask=mask, + generator_a=generator_start, + generator_b=generator_end, + interpolate_alpha=alpha, + strength_a=start.denoising, + strength_b=end.denoising, + num_inference_steps=inputs.num_inference_steps, + guidance_scale=guidance_scale, + ) + + return outputs["images"][0] + + @torch.no_grad() + def interpolate_img2img( + self, + text_embeddings: torch.Tensor, + init_latents: torch.Tensor, + generator_a: torch.Generator, + generator_b: torch.Generator, + interpolate_alpha: float, + mask: T.Optional[torch.Tensor] = None, + strength_a: float = 0.8, + strength_b: float = 0.8, + num_inference_steps: int = 50, + guidance_scale: float = 7.5, + negative_prompt: T.Optional[T.Union[str, T.List[str]]] = None, + num_images_per_prompt: int = 1, + eta: T.Optional[float] = 0.0, + output_type: T.Optional[str] = "pil", + **kwargs, + ): + """ + TODO + """ + batch_size = text_embeddings.shape[0] + + # set timesteps + self.scheduler.set_timesteps(num_inference_steps) + + # duplicate text embeddings for each generation per prompt, using mps friendly method + bs_embed, seq_len, _ = text_embeddings.shape + text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1) + text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance: + if negative_prompt is None: + uncond_tokens = [""] + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError("The length of `negative_prompt` should be equal to batch_size.") + else: + uncond_tokens = negative_prompt + + # max_length = text_input_ids.shape[-1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] + + # duplicate unconditional embeddings for each generation per prompt + uncond_embeddings = uncond_embeddings.repeat_interleave( + batch_size * num_images_per_prompt, dim=0 + ) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) + + latents_dtype = text_embeddings.dtype + + strength = (1 - interpolate_alpha) * strength_a + interpolate_alpha * strength_b + + # get the original timestep using init_timestep + offset = self.scheduler.config.get("steps_offset", 0) + init_timestep = int(num_inference_steps * strength) + offset + init_timestep = min(init_timestep, num_inference_steps) + + timesteps = self.scheduler.timesteps[-init_timestep] + timesteps = torch.tensor( + [timesteps] * batch_size * num_images_per_prompt, device=self.device + ) + + # add noise to latents using the timesteps + noise_a = torch.randn( + init_latents.shape, generator=generator_a, device=self.device, dtype=latents_dtype + ) + noise_b = torch.randn( + init_latents.shape, generator=generator_b, device=self.device, dtype=latents_dtype + ) + noise = torch_util.slerp(interpolate_alpha, noise_a, noise_b) + init_latents_orig = init_latents + init_latents = self.scheduler.add_noise(init_latents, noise, timesteps) + + # prepare extra kwargs for the scheduler step, since not all schedulers have the same args + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + latents = init_latents.clone() + + t_start = max(num_inference_steps - init_timestep + offset, 0) + + # Some schedulers like PNDM have timesteps as arrays + # It's more optimized to move all timesteps to correct device beforehand + timesteps = self.scheduler.timesteps[t_start:].to(self.device) + + for i, t in enumerate(self.progress_bar(timesteps)): + # expand the latents if we are doing classifier free guidance + latent_model_input = ( + torch.cat([latents] * 2) if do_classifier_free_guidance else latents + ) + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + # predict the noise residual + noise_pred = self.unet( + latent_model_input, t, encoder_hidden_states=text_embeddings + ).sample + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + if mask is not None: + init_latents_proper = self.scheduler.add_noise( + init_latents_orig, noise, torch.tensor([t]) + ) + # import ipdb; ipdb.set_trace() + latents = (init_latents_proper * mask) + (latents * (1 - mask)) + + latents = 1.0 / 0.18215 * latents + image = self.vae.decode(latents).sample + + image = (image / 2 + 0.5).clamp(0, 1) + image = image.cpu().permute(0, 2, 3, 1).numpy() + + if output_type == "pil": + image = self.numpy_to_pil(image) + + return dict(images=image, latents=latents, nsfw_content_detected=False) + + +def preprocess_image(image: Image.Image) -> torch.Tensor: + """ + Preprocess an image for the model. + """ + w, h = image.size + w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + image = image.resize((w, h), resample=Image.LANCZOS) + + image_np = np.array(image).astype(np.float32) / 255.0 + image_np = image_np[None].transpose(0, 3, 1, 2) + + image_torch = torch.from_numpy(image_np) + + return 2.0 * image_torch - 1.0 + + +def preprocess_mask(mask: Image.Image, scale_factor: int = 8) -> torch.Tensor: + """ + Preprocess a mask for the model. + """ + # Convert to grayscale + mask = mask.convert("L") + + # Resize to integer multiple of 32 + w, h = mask.size + w, h = map(lambda x: x - x % 32, (w, h)) + mask = mask.resize((w // scale_factor, h // scale_factor), resample=Image.NEAREST) + + # Convert to numpy array and rescale + mask_np = np.array(mask).astype(np.float32) / 255.0 + + # Tile and transpose + mask_np = np.tile(mask_np, (4, 1, 1)) + mask_np = mask_np[None].transpose(0, 1, 2, 3) # what does this step do? + + # Invert to repaint white and keep black + mask_np = 1 - mask_np # repaint white, keep black + + return torch.from_numpy(mask_np) diff --git a/riffusion/server.py b/riffusion/server.py new file mode 100644 index 0000000..aad1ca0 --- /dev/null +++ b/riffusion/server.py @@ -0,0 +1,189 @@ +""" +Flask server that serves the riffusion model as an API. +""" + +import dataclasses +import io +import json +import logging +import time +import typing as T +from pathlib import Path + +import dacite +import flask +import PIL +from flask_cors import CORS + +from riffusion.datatypes import InferenceInput, InferenceOutput +from riffusion.riffusion_pipeline import RiffusionPipeline +from riffusion.spectrogram_image_converter import SpectrogramImageConverter +from riffusion.spectrogram_params import SpectrogramParams +from riffusion.util import base64_util + +# Flask app with CORS +app = flask.Flask(__name__) +CORS(app) + +# Log at the INFO level to both stdout and disk +logging.basicConfig(level=logging.INFO) +logging.getLogger().addHandler(logging.FileHandler("server.log")) + +# Global variable for the model pipeline +PIPELINE: T.Optional[RiffusionPipeline] = None + +# Where built-in seed images are stored +SEED_IMAGES_DIR = Path(Path(__file__).resolve().parent.parent, "seed_images") + + +def run_app( + *, + checkpoint: str = "riffusion/riffusion-model-v1", + no_traced_unet: bool = False, + device: str = "cuda", + host: str = "127.0.0.1", + port: int = 3013, + debug: bool = False, + ssl_certificate: T.Optional[str] = None, + ssl_key: T.Optional[str] = None, +): + """ + Run a flask API that serves the given riffusion model checkpoint. + """ + # Initialize the model + global PIPELINE + PIPELINE = RiffusionPipeline.load_checkpoint( + checkpoint=checkpoint, + use_traced_unet=not no_traced_unet, + device=device, + ) + + args = dict( + debug=debug, + threaded=False, + host=host, + port=port, + ) + + if ssl_certificate: + assert ssl_key is not None + args["ssl_context"] = (ssl_certificate, ssl_key) + + app.run(**args) # type: ignore + + +@app.route("/run_inference/", methods=["POST"]) +def run_inference(): + """ + Execute the riffusion model as an API. + + Inputs: + Serialized JSON of the InferenceInput dataclass + + Returns: + Serialized JSON of the InferenceOutput dataclass + """ + start_time = time.time() + + # Parse the payload as JSON + json_data = json.loads(flask.request.data) + + # Log the request + logging.info(json_data) + + # Parse an InferenceInput dataclass from the payload + try: + inputs = dacite.from_dict(InferenceInput, json_data) + except dacite.exceptions.WrongTypeError as exception: + logging.info(json_data) + return str(exception), 400 + except dacite.exceptions.MissingValueError as exception: + logging.info(json_data) + return str(exception), 400 + + response = compute_request( + inputs=inputs, + seed_images_dir=SEED_IMAGES_DIR, + pipeline=PIPELINE, + ) + + # Log the total time + logging.info(f"Request took {time.time() - start_time:.2f} s") + + return response + + +def compute_request( + inputs: InferenceInput, + pipeline: RiffusionPipeline, + seed_images_dir: str, +) -> T.Union[str, T.Tuple[str, int]]: + """ + Does all the heavy lifting of the request. + + Args: + inputs: The input dataclass + pipeline: The riffusion model pipeline + seed_images_dir: The directory where seed images are stored + """ + # Load the seed image by ID + init_image_path = Path(seed_images_dir, f"{inputs.seed_image_id}.png") + + if not init_image_path.is_file(): + return f"Invalid seed image: {inputs.seed_image_id}", 400 + init_image = PIL.Image.open(str(init_image_path)).convert("RGB") + + # Load the mask image by ID + mask_image: T.Optional[PIL.Image.Image] = None + if inputs.mask_image_id: + mask_image_path = Path(seed_images_dir, f"{inputs.mask_image_id}.png") + if not mask_image_path.is_file(): + return f"Invalid mask image: {inputs.mask_image_id}", 400 + mask_image = PIL.Image.open(str(mask_image_path)).convert("RGB") + + # Execute the model to get the spectrogram image + image = pipeline.riffuse( + inputs, + init_image=init_image, + mask_image=mask_image, + ) + + # TODO(hayk): Change the frequency range to [20, 20k] once the model is retrained + params = SpectrogramParams( + min_frequency=0, + max_frequency=10000, + ) + + # Reconstruct audio from the image + # TODO(hayk): It may help performance a bit to cache this object + converter = SpectrogramImageConverter(params=params, device=str(pipeline.device)) + + segment = converter.audio_from_spectrogram_image( + image, + apply_filters=True, + ) + + # Export audio to MP3 bytes + mp3_bytes = io.BytesIO() + segment.export(mp3_bytes, format="mp3") + mp3_bytes.seek(0) + + # Export image to JPEG bytes + image_bytes = io.BytesIO() + image.save(image_bytes, exif=image.getexif(), format="JPEG") + image_bytes.seek(0) + + # Assemble the output dataclass + output = InferenceOutput( + image="data:image/jpeg;base64," + base64_util.encode(image_bytes), + audio="data:audio/mpeg;base64," + base64_util.encode(mp3_bytes), + duration_s=segment.duration_seconds, + ) + + return json.dumps(dataclasses.asdict(output)) + + +if __name__ == "__main__": + import argh + + argh.dispatch_command(run_app) diff --git a/riffusion/spectrogram_converter.py b/riffusion/spectrogram_converter.py new file mode 100644 index 0000000..9fbfc65 --- /dev/null +++ b/riffusion/spectrogram_converter.py @@ -0,0 +1,204 @@ +import warnings + +import numpy as np +import pydub +import torch +import torchaudio + +from riffusion.spectrogram_params import SpectrogramParams +from riffusion.util import audio_util, torch_util + + +class SpectrogramConverter: + """ + Convert between audio segments and spectrogram tensors using torchaudio. + + In this class a "spectrogram" is defined as a (batch, time, frequency) tensor with float values + that represent the amplitude of the frequency at that time bucket (in the frequency domain). + Frequencies are given in the perceptul Mel scale defined by the params. A more specific term + used in some functions is "mel amplitudes". + + The spectrogram computed from `spectrogram_from_audio` is complex valued, but it only + returns the amplitude, because the phase is chaotic and hard to learn. The function + `audio_from_spectrogram` is an approximate inverse of `spectrogram_from_audio`, which + approximates the phase information using the Griffin-Lim algorithm. + + Each channel in the audio is treated independently, and the spectrogram has a batch dimension + equal to the number of channels in the input audio segment. + + Both the Griffin Lim algorithm and the Mel scaling process are lossy. + + For more information, see https://pytorch.org/audio/stable/transforms.html + """ + + def __init__(self, params: SpectrogramParams, device: str = "cuda"): + self.p = params + + self.device = torch_util.check_device(device) + + if device.lower().startswith("mps"): + warnings.warn( + "WARNING: MPS does not support audio operations, falling back to CPU for them", + stacklevel=2, + ) + self.device = "cpu" + + # https://pytorch.org/audio/stable/generated/torchaudio.transforms.Spectrogram.html + self.spectrogram_func = torchaudio.transforms.Spectrogram( + n_fft=params.n_fft, + hop_length=params.hop_length, + win_length=params.win_length, + pad=0, + window_fn=torch.hann_window, + power=None, + normalized=False, + wkwargs=None, + center=True, + pad_mode="reflect", + onesided=True, + ).to(self.device) + + # https://pytorch.org/audio/stable/generated/torchaudio.transforms.GriffinLim.html + self.inverse_spectrogram_func = torchaudio.transforms.GriffinLim( + n_fft=params.n_fft, + n_iter=params.num_griffin_lim_iters, + win_length=params.win_length, + hop_length=params.hop_length, + window_fn=torch.hann_window, + power=1.0, + wkwargs=None, + momentum=0.99, + length=None, + rand_init=True, + ).to(self.device) + + # https://pytorch.org/audio/stable/generated/torchaudio.transforms.MelScale.html + self.mel_scaler = torchaudio.transforms.MelScale( + n_mels=params.num_frequencies, + sample_rate=params.sample_rate, + f_min=params.min_frequency, + f_max=params.max_frequency, + n_stft=params.n_fft // 2 + 1, + norm=params.mel_scale_norm, + mel_scale=params.mel_scale_type, + ).to(self.device) + + # https://pytorch.org/audio/stable/generated/torchaudio.transforms.InverseMelScale.html + self.inverse_mel_scaler = torchaudio.transforms.InverseMelScale( + n_stft=params.n_fft // 2 + 1, + n_mels=params.num_frequencies, + sample_rate=params.sample_rate, + f_min=params.min_frequency, + f_max=params.max_frequency, + max_iter=params.max_mel_iters, + tolerance_loss=1e-5, + tolerance_change=1e-8, + sgdargs=None, + norm=params.mel_scale_norm, + mel_scale=params.mel_scale_type, + ).to(self.device) + + def spectrogram_from_audio( + self, + audio: pydub.AudioSegment, + ) -> np.ndarray: + """ + Compute a spectrogram from an audio segment. + + Args: + audio: Audio segment which must match the sample rate of the params + + Returns: + spectrogram: (channel, frequency, time) + """ + assert int(audio.frame_rate) == self.p.sample_rate, "Audio sample rate must match params" + + # Get the samples as a numpy array in (batch, samples) shape + waveform = np.array([c.get_array_of_samples() for c in audio.split_to_mono()]) + + # Convert to floats if necessary + if waveform.dtype != np.float32: + waveform = waveform.astype(np.float32) + + waveform_tensor = torch.from_numpy(waveform).to(self.device) + amplitudes_mel = self.mel_amplitudes_from_waveform(waveform_tensor) + return amplitudes_mel.cpu().numpy() + + def audio_from_spectrogram( + self, + spectrogram: np.ndarray, + apply_filters: bool = True, + ) -> pydub.AudioSegment: + """ + Reconstruct an audio segment from a spectrogram. + + Args: + spectrogram: (batch, frequency, time) + apply_filters: Post-process with normalization and compression + + Returns: + audio: Audio segment with channels equal to the batch dimension + """ + # Move to device + amplitudes_mel = torch.from_numpy(spectrogram).to(self.device) + + # Reconstruct the waveform + waveform = self.waveform_from_mel_amplitudes(amplitudes_mel) + + # Convert to audio segment + segment = audio_util.audio_from_waveform( + samples=waveform.cpu().numpy(), + sample_rate=self.p.sample_rate, + # Normalize the waveform to the range [-1, 1] + normalize=True, + ) + + # Optionally apply post-processing filters + if apply_filters: + segment = audio_util.apply_filters( + segment, + compression=False, + ) + + return segment + + def mel_amplitudes_from_waveform( + self, + waveform: torch.Tensor, + ) -> torch.Tensor: + """ + Torch-only function to compute Mel-scale amplitudes from a waveform. + + Args: + waveform: (batch, samples) + + Returns: + amplitudes_mel: (batch, frequency, time) + """ + # Compute the complex-valued spectrogram + spectrogram_complex = self.spectrogram_func(waveform) + + # Take the magnitude + amplitudes = torch.abs(spectrogram_complex) + + # Convert to mel scale + return self.mel_scaler(amplitudes) + + def waveform_from_mel_amplitudes( + self, + amplitudes_mel: torch.Tensor, + ) -> torch.Tensor: + """ + Torch-only function to approximately reconstruct a waveform from Mel-scale amplitudes. + + Args: + amplitudes_mel: (batch, frequency, time) + + Returns: + waveform: (batch, samples) + """ + # Convert from mel scale to linear + amplitudes_linear = self.inverse_mel_scaler(amplitudes_mel) + + # Run the approximate algorithm to compute the phase and recover the waveform + return self.inverse_spectrogram_func(amplitudes_linear) diff --git a/riffusion/spectrogram_image_converter.py b/riffusion/spectrogram_image_converter.py new file mode 100644 index 0000000..fea7729 --- /dev/null +++ b/riffusion/spectrogram_image_converter.py @@ -0,0 +1,91 @@ +import numpy as np +import pydub +from PIL import Image + +from riffusion.spectrogram_converter import SpectrogramConverter +from riffusion.spectrogram_params import SpectrogramParams +from riffusion.util import image_util + + +class SpectrogramImageConverter: + """ + Convert between spectrogram images and audio segments. + + This is a wrapper around SpectrogramConverter that additionally converts from spectrograms + to images and back. The real audio processing lives in SpectrogramConverter. + """ + + def __init__(self, params: SpectrogramParams, device: str = "cuda"): + self.p = params + self.device = device + self.converter = SpectrogramConverter(params=params, device=device) + + def spectrogram_image_from_audio( + self, + segment: pydub.AudioSegment, + ) -> Image.Image: + """ + Compute a spectrogram image from an audio segment. + + Args: + segment: Audio segment to convert + + Returns: + Spectrogram image (in pillow format) + """ + assert int(segment.frame_rate) == self.p.sample_rate, "Sample rate mismatch" + + if self.p.stereo: + if segment.channels == 1: + print("WARNING: Mono audio but stereo=True, cloning channel") + segment = segment.set_channels(2) + elif segment.channels > 2: + print("WARNING: Multi channel audio, reducing to stereo") + segment = segment.set_channels(2) + else: + if segment.channels > 1: + print("WARNING: Stereo audio but stereo=False, setting to mono") + segment = segment.set_channels(1) + + spectrogram = self.converter.spectrogram_from_audio(segment) + + image = image_util.image_from_spectrogram( + spectrogram, + power=self.p.power_for_image, + ) + + # Store conversion params in exif metadata of the image + exif_data = self.p.to_exif() + exif_data[SpectrogramParams.ExifTags.MAX_VALUE.value] = float(np.max(spectrogram)) + exif = image.getexif() + exif.update(exif_data.items()) + + return image + + def audio_from_spectrogram_image( + self, + image: Image.Image, + apply_filters: bool = True, + max_value: float = 30e6, + ) -> pydub.AudioSegment: + """ + Reconstruct an audio segment from a spectrogram image. + + Args: + image: Spectrogram image (in pillow format) + apply_filters: Apply post-processing to improve the reconstructed audio + max_value: Scaled max amplitude of the spectrogram. Shouldn't matter. + """ + spectrogram = image_util.spectrogram_from_image( + image, + max_value=max_value, + power=self.p.power_for_image, + stereo=self.p.stereo, + ) + + segment = self.converter.audio_from_spectrogram( + spectrogram, + apply_filters=apply_filters, + ) + + return segment diff --git a/riffusion/spectrogram_params.py b/riffusion/spectrogram_params.py new file mode 100644 index 0000000..03346ca --- /dev/null +++ b/riffusion/spectrogram_params.py @@ -0,0 +1,115 @@ +from __future__ import annotations + +import typing as T +from dataclasses import dataclass +from enum import Enum + + +@dataclass(frozen=True) +class SpectrogramParams: + """ + Parameters for the conversion from audio to spectrograms to images and back. + + Includes helpers to convert to and from EXIF tags, allowing these parameters to be stored + within spectrogram images. + + To understand what these parameters do and to customize them, read `spectrogram_converter.py` + and the linked torchaudio documentation. + """ + + # Whether the audio is stereo or mono + stereo: bool = False + + # FFT parameters + sample_rate: int = 44100 + step_size_ms: int = 10 + window_duration_ms: int = 100 + padded_duration_ms: int = 400 + + # Mel scale parameters + num_frequencies: int = 512 + # TODO(hayk): Set these to [20, 20000] for newer models + min_frequency: int = 0 + max_frequency: int = 10000 + mel_scale_norm: T.Optional[str] = None + mel_scale_type: str = "htk" + max_mel_iters: int = 200 + + # Griffin Lim parameters + num_griffin_lim_iters: int = 32 + + # Image parameterization + power_for_image: float = 0.25 + + class ExifTags(Enum): + """ + Custom EXIF tags for the spectrogram image. + """ + + SAMPLE_RATE = 11000 + STEREO = 11005 + STEP_SIZE_MS = 11010 + WINDOW_DURATION_MS = 11020 + PADDED_DURATION_MS = 11030 + + NUM_FREQUENCIES = 11040 + MIN_FREQUENCY = 11050 + MAX_FREQUENCY = 11060 + + POWER_FOR_IMAGE = 11070 + MAX_VALUE = 11080 + + @property + def n_fft(self) -> int: + """ + The number of samples in each STFT window, with padding. + """ + return int(self.padded_duration_ms / 1000.0 * self.sample_rate) + + @property + def win_length(self) -> int: + """ + The number of samples in each STFT window. + """ + return int(self.window_duration_ms / 1000.0 * self.sample_rate) + + @property + def hop_length(self) -> int: + """ + The number of samples between each STFT window. + """ + return int(self.step_size_ms / 1000.0 * self.sample_rate) + + def to_exif(self) -> T.Dict[int, T.Any]: + """ + Return a dictionary of EXIF tags for the current values. + """ + return { + self.ExifTags.SAMPLE_RATE.value: self.sample_rate, + self.ExifTags.STEREO.value: self.stereo, + self.ExifTags.STEP_SIZE_MS.value: self.step_size_ms, + self.ExifTags.WINDOW_DURATION_MS.value: self.window_duration_ms, + self.ExifTags.PADDED_DURATION_MS.value: self.padded_duration_ms, + self.ExifTags.NUM_FREQUENCIES.value: self.num_frequencies, + self.ExifTags.MIN_FREQUENCY.value: self.min_frequency, + self.ExifTags.MAX_FREQUENCY.value: self.max_frequency, + self.ExifTags.POWER_FOR_IMAGE.value: float(self.power_for_image), + } + + @classmethod + def from_exif(cls, exif: T.Mapping[int, T.Any]) -> SpectrogramParams: + """ + Create a SpectrogramParams object from the EXIF tags of the given image. + """ + # TODO(hayk): Handle missing tags + return cls( + sample_rate=exif[cls.ExifTags.SAMPLE_RATE.value], + stereo=bool(exif[cls.ExifTags.STEREO.value]), + step_size_ms=exif[cls.ExifTags.STEP_SIZE_MS.value], + window_duration_ms=exif[cls.ExifTags.WINDOW_DURATION_MS.value], + padded_duration_ms=exif[cls.ExifTags.PADDED_DURATION_MS.value], + num_frequencies=exif[cls.ExifTags.NUM_FREQUENCIES.value], + min_frequency=exif[cls.ExifTags.MIN_FREQUENCY.value], + max_frequency=exif[cls.ExifTags.MAX_FREQUENCY.value], + power_for_image=exif[cls.ExifTags.POWER_FOR_IMAGE.value], + ) diff --git a/riffusion/streamlit/README.md b/riffusion/streamlit/README.md new file mode 100644 index 0000000..8bc102f --- /dev/null +++ b/riffusion/streamlit/README.md @@ -0,0 +1,3 @@ +# streamlit + +This package is an interactive streamlit app for riffusion. diff --git a/riffusion/streamlit/__init__.py b/riffusion/streamlit/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/riffusion/streamlit/pages/audio_to_audio.py b/riffusion/streamlit/pages/audio_to_audio.py new file mode 100644 index 0000000..16f7950 --- /dev/null +++ b/riffusion/streamlit/pages/audio_to_audio.py @@ -0,0 +1,404 @@ +import io +import typing as T +from pathlib import Path + +import numpy as np +import pydub +import streamlit as st +from PIL import Image + +from riffusion.datatypes import InferenceInput, PromptInput +from riffusion.spectrogram_params import SpectrogramParams +from riffusion.streamlit import util as streamlit_util +from riffusion.streamlit.pages.interpolation import get_prompt_inputs, run_interpolation +from riffusion.util import audio_util + + +def render_audio_to_audio() -> None: + st.set_page_config(layout="wide", page_icon="🎸") + + st.subheader(":wave: Audio to Audio") + st.write( + """ + Modify existing audio from a text prompt or interpolate between two. + """ + ) + + with st.expander("Help", False): + st.write( + """ + This tool allows you to upload an audio file of arbitrary length and modify it with + a text prompt. It does this by sweeping over the audio in overlapping clips, doing + img2img style transfer with riffusion, then stitching the clips back together with + cross fading to eliminate seams. + + Try a denoising strength of 0.4 for light modification and 0.55 for more heavy + modification. The best specific denoising depends on how different the prompt is + from the source audio. You can play with the seed to get infinite variations. + Currently the same seed is used for all clips along the track. + + If the Interpolation check box is enabled, supports entering two sets of prompt, + seed, and denoising value and smoothly blends between them along the selected + duration of the audio. This is a great way to create a transition. + """ + ) + + device = streamlit_util.select_device(st.sidebar) + extension = streamlit_util.select_audio_extension(st.sidebar) + + use_magic_mix = st.sidebar.checkbox("Use Magic Mix", False) + lora_path = st.sidebar.text_input("Lora Path", "") + lora_scale = st.sidebar.number_input("Lora Scale", value=1.0) + + with st.sidebar: + num_inference_steps = T.cast( + int, + st.number_input( + "Steps per sample", value=50, help="Number of denoising steps per model run" + ), + ) + + guidance = st.number_input( + "Guidance", + value=7.0, + help="How much the model listens to the text prompt", + ) + + scheduler = st.selectbox( + "Scheduler", + options=streamlit_util.SCHEDULER_OPTIONS, + index=0, + help="Which diffusion scheduler to use", + ) + assert scheduler is not None + + audio_file = st.file_uploader( + "Upload audio", + type=streamlit_util.AUDIO_EXTENSIONS, + label_visibility="collapsed", + ) + + if not audio_file: + st.info("Upload audio to get started") + return + + st.write("#### Original") + st.audio(audio_file) + + segment = streamlit_util.load_audio_file(audio_file) + + # TODO(hayk): Fix + if segment.frame_rate != 44100: + st.warning("Audio must be 44100Hz. Converting") + segment = segment.set_frame_rate(44100) + st.write(f"Duration: {segment.duration_seconds:.2f}s, Sample Rate: {segment.frame_rate}Hz") + + clip_p = get_clip_params() + start_time_s = clip_p["start_time_s"] + clip_duration_s = clip_p["clip_duration_s"] + overlap_duration_s = clip_p["overlap_duration_s"] + + duration_s = min(clip_p["duration_s"], segment.duration_seconds - start_time_s) + increment_s = clip_duration_s - overlap_duration_s + clip_start_times = start_time_s + np.arange(0, duration_s - clip_duration_s, increment_s) + + write_clip_details( + clip_start_times=clip_start_times, + clip_duration_s=clip_duration_s, + overlap_duration_s=overlap_duration_s, + ) + + interpolate = st.checkbox( + "Interpolate between two endpoints", + value=False, + help="Interpolate between two prompts, seeds, or denoising values along the" + "duration of the segment", + ) + + counter = streamlit_util.StreamlitCounter() + + with st.form("audio to audio form"): + if interpolate: + left, right = st.columns(2) + + with left: + st.write("##### Prompt A") + prompt_input_a = PromptInput(guidance=guidance, **get_prompt_inputs(key="a")) + + with right: + st.write("##### Prompt B") + prompt_input_b = PromptInput(guidance=guidance, **get_prompt_inputs(key="b")) + elif use_magic_mix: + prompt = st.text_input("Prompt", key="prompt_a") + + row = st.columns(4) + + seed = T.cast( + int, + row[0].number_input( + "Seed", + value=42, + key="seed_a", + ), + ) + prompt_input_a = PromptInput( + prompt=prompt, + seed=seed, + guidance=guidance, + ) + magic_mix_kmin = row[1].number_input("Kmin", value=0.3) + magic_mix_kmax = row[2].number_input("Kmax", value=0.5) + magic_mix_mix_factor = row[3].number_input("Mix Factor", value=0.5) + else: + prompt_input_a = PromptInput( + guidance=guidance, + **get_prompt_inputs(key="a", include_negative_prompt=True, cols=True), + ) + + st.form_submit_button("Riff", type="primary", on_click=counter.increment) + + show_clip_details = st.sidebar.checkbox("Show Clip Details", True) + show_difference = st.sidebar.checkbox("Show Difference", False) + + clip_segments = slice_audio_into_clips( + segment=segment, + clip_start_times=clip_start_times, + clip_duration_s=clip_duration_s, + ) + + if not prompt_input_a.prompt: + st.info("Enter a prompt") + return + + if counter.value == 0: + return + + params = SpectrogramParams() + + if interpolate: + # TODO(hayk): Make not linspace + alphas = list(np.linspace(0, 1, len(clip_segments))) + alphas_str = ", ".join([f"{alpha:.2f}" for alpha in alphas]) + st.write(f"**Alphas** : [{alphas_str}]") + + result_images: T.List[Image.Image] = [] + result_segments: T.List[pydub.AudioSegment] = [] + for i, clip_segment in enumerate(clip_segments): + st.write(f"### Clip {i} at {clip_start_times[i]:.2f}s") + + audio_bytes = io.BytesIO() + clip_segment.export(audio_bytes, format="wav") + + init_image = streamlit_util.spectrogram_image_from_audio( + clip_segment, + params=params, + device=device, + ) + + # TODO(hayk): Roll this into spectrogram_image_from_audio? + init_image_resized = scale_image_to_32_stride(init_image) + + progress_callback = None + if show_clip_details: + left, right = st.columns(2) + + left.write("##### Source Clip") + left.image(init_image, use_column_width=False) + left.audio(audio_bytes) + + right.write("##### Riffed Clip") + empty_bin = right.empty() + with empty_bin.container(): + st.info("Riffing...") + progress = st.progress(0.0) + progress_callback = progress.progress + + if interpolate: + assert use_magic_mix is False, "Cannot use magic mix and interpolate together" + inputs = InferenceInput( + alpha=float(alphas[i]), + num_inference_steps=num_inference_steps, + seed_image_id="og_beat", + start=prompt_input_a, + end=prompt_input_b, + ) + + image, audio_bytes = run_interpolation( + inputs=inputs, + init_image=init_image_resized, + device=device, + ) + elif use_magic_mix: + assert not prompt_input_a.negative_prompt, "No negative prompt with magic mix" + image = streamlit_util.run_img2img_magic_mix( + prompt=prompt_input_a.prompt, + init_image=init_image_resized, + num_inference_steps=num_inference_steps, + guidance_scale=guidance, + seed=prompt_input_a.seed, + kmin=magic_mix_kmin, + kmax=magic_mix_kmax, + mix_factor=magic_mix_mix_factor, + device=device, + scheduler=scheduler, + ) + else: + image = streamlit_util.run_img2img( + prompt=prompt_input_a.prompt, + init_image=init_image_resized, + denoising_strength=prompt_input_a.denoising, + num_inference_steps=num_inference_steps, + guidance_scale=guidance, + negative_prompt=prompt_input_a.negative_prompt, + seed=prompt_input_a.seed, + progress_callback=progress_callback, + device=device, + scheduler=scheduler, + lora_path=lora_path, + lora_scale=lora_scale, + ) + + # Resize back to original size + image = image.resize(init_image.size, Image.BICUBIC) + + result_images.append(image) + + if show_clip_details: + empty_bin.empty() + right.image(image, use_column_width=False) + + riffed_segment = streamlit_util.audio_segment_from_spectrogram_image( + image=image, + params=params, + device=device, + ) + result_segments.append(riffed_segment) + + audio_bytes = io.BytesIO() + riffed_segment.export(audio_bytes, format="wav") + + if show_clip_details: + right.audio(audio_bytes) + + if show_clip_details and show_difference: + diff_np = np.maximum( + 0, np.asarray(init_image).astype(np.float32) - np.asarray(image).astype(np.float32) + ) + diff_image = Image.fromarray(255 - diff_np.astype(np.uint8)) + diff_segment = streamlit_util.audio_segment_from_spectrogram_image( + image=diff_image, + params=params, + device=device, + ) + + audio_bytes = io.BytesIO() + diff_segment.export(audio_bytes, format=extension) + st.audio(audio_bytes) + + # Combine clips with a crossfade based on overlap + combined_segment = audio_util.stitch_segments(result_segments, crossfade_s=overlap_duration_s) + + st.write(f"#### Final Audio ({combined_segment.duration_seconds}s)") + + input_name = Path(audio_file.name).stem + output_name = f"{input_name}_{prompt_input_a.prompt.replace(' ', '_')}" + streamlit_util.display_and_download_audio(combined_segment, output_name, extension=extension) + + +def get_clip_params(advanced: bool = False) -> T.Dict[str, T.Any]: + """ + Render the parameters of slicing audio into clips. + """ + p: T.Dict[str, T.Any] = {} + + cols = st.columns(4) + + p["start_time_s"] = cols[0].number_input( + "Start Time [s]", + min_value=0.0, + value=0.0, + ) + p["duration_s"] = cols[1].number_input( + "Duration [s]", + min_value=0.0, + value=15.0, + ) + + if advanced: + p["clip_duration_s"] = cols[2].number_input( + "Clip Duration [s]", + min_value=3.0, + max_value=10.0, + value=5.0, + ) + else: + p["clip_duration_s"] = 5.0 + + if advanced: + p["overlap_duration_s"] = cols[3].number_input( + "Overlap Duration [s]", + min_value=0.0, + max_value=10.0, + value=0.2, + ) + else: + p["overlap_duration_s"] = 0.2 + + return p + + +def write_clip_details( + clip_start_times: np.ndarray, clip_duration_s: float, overlap_duration_s: float +): + """ + Write details of the clips to be sliced from an audio segment. + """ + clip_details_text = ( + f"Slicing {len(clip_start_times)} clips of duration {clip_duration_s}s " + f"with overlap {overlap_duration_s}s" + ) + + with st.expander(clip_details_text): + st.dataframe( + { + "Start Time [s]": clip_start_times, + "End Time [s]": clip_start_times + clip_duration_s, + "Duration [s]": clip_duration_s, + } + ) + + +def slice_audio_into_clips( + segment: pydub.AudioSegment, clip_start_times: T.Sequence[float], clip_duration_s: float +) -> T.List[pydub.AudioSegment]: + """ + Slice an audio segment into a list of clips of a given duration at the given start times. + """ + clip_segments: T.List[pydub.AudioSegment] = [] + for i, clip_start_time_s in enumerate(clip_start_times): + clip_start_time_ms = int(clip_start_time_s * 1000) + clip_duration_ms = int(clip_duration_s * 1000) + clip_segment = segment[clip_start_time_ms : clip_start_time_ms + clip_duration_ms] + + # TODO(hayk): I don't think this is working properly + if i == len(clip_start_times) - 1: + silence_ms = clip_duration_ms - int(clip_segment.duration_seconds * 1000) + if silence_ms > 0: + clip_segment = clip_segment.append(pydub.AudioSegment.silent(duration=silence_ms)) + + clip_segments.append(clip_segment) + + return clip_segments + + +def scale_image_to_32_stride(image: Image.Image) -> Image.Image: + """ + Scale an image to a size that is a multiple of 32. + """ + closest_width = int(np.ceil(image.width / 32) * 32) + closest_height = int(np.ceil(image.height / 32) * 32) + return image.resize((closest_width, closest_height), Image.BICUBIC) + + +if __name__ == "__main__": + render_audio_to_audio() diff --git a/riffusion/streamlit/pages/image_to_audio.py b/riffusion/streamlit/pages/image_to_audio.py new file mode 100644 index 0000000..d6ba406 --- /dev/null +++ b/riffusion/streamlit/pages/image_to_audio.py @@ -0,0 +1,74 @@ +import dataclasses +from pathlib import Path + +import streamlit as st +from PIL import Image + +from riffusion.spectrogram_params import SpectrogramParams +from riffusion.streamlit import util as streamlit_util +from riffusion.util.image_util import exif_from_image + + +def render_image_to_audio() -> None: + st.set_page_config(layout="wide", page_icon="🎸") + + st.subheader(":musical_keyboard: Image to Audio") + st.write( + """ + Reconstruct audio from spectrogram images. + """ + ) + + with st.expander("Help", False): + st.write( + """ + This tool takes an existing spectrogram image and reconstructs it into an audio + waveform. It also displays the EXIF metadata stored inside the image, which can + contain the parameters used to create the spectrogram image. If no EXIF is contained, + assumes default parameters. + """ + ) + + device = streamlit_util.select_device(st.sidebar) + extension = streamlit_util.select_audio_extension(st.sidebar) + + image_file = st.file_uploader( + "Upload a file", + type=streamlit_util.IMAGE_EXTENSIONS, + label_visibility="collapsed", + ) + if not image_file: + st.info("Upload an image file to get started") + return + + image = Image.open(image_file) + st.image(image) + + with st.expander("Image metadata", expanded=False): + exif = exif_from_image(image) + st.json(exif) + + try: + params = SpectrogramParams.from_exif(exif=image.getexif()) + except KeyError: + st.info("Could not find spectrogram parameters in exif data. Using defaults.") + params = SpectrogramParams() + + with st.expander("Spectrogram Parameters", expanded=False): + st.json(dataclasses.asdict(params)) + + segment = streamlit_util.audio_segment_from_spectrogram_image( + image=image.copy(), + params=params, + device=device, + ) + + streamlit_util.display_and_download_audio( + segment, + name=Path(image_file.name).stem, + extension=extension, + ) + + +if __name__ == "__main__": + render_image_to_audio() diff --git a/riffusion/streamlit/pages/interpolation.py b/riffusion/streamlit/pages/interpolation.py new file mode 100644 index 0000000..d58e3dc --- /dev/null +++ b/riffusion/streamlit/pages/interpolation.py @@ -0,0 +1,273 @@ +import dataclasses +import io +import typing as T +from pathlib import Path + +import numpy as np +import pydub +import streamlit as st +from PIL import Image + +from riffusion.datatypes import InferenceInput, PromptInput +from riffusion.spectrogram_params import SpectrogramParams +from riffusion.streamlit import util as streamlit_util + + +def render_interpolation() -> None: + st.set_page_config(layout="wide", page_icon="🎸") + + st.subheader(":performing_arts: Interpolation") + st.write( + """ + Interpolate between prompts in the latent space. + """ + ) + + with st.expander("Help", False): + st.write( + """ + This tool allows specifying two endpoints and generating a long-form interpolation + between them that traverses the latent space. The interpolation is generated by + the method described at https://www.riffusion.com/about. A seed image is used to + set the beat and tempo of the generated audio, and can be set in the sidebar. + Usually the seed is changed or the prompt, but not both at once. You can browse + infinite variations of the same prompt by changing the seed. + + For example, try going from "church bells" to "jazz" with 10 steps and 0.75 denoising. + This will generate a 50 second clip at 5 seconds per step. Then play with the seeds + or denoising to get different variations. + """ + ) + + # Sidebar params + + device = streamlit_util.select_device(st.sidebar) + extension = streamlit_util.select_audio_extension(st.sidebar) + + num_interpolation_steps = T.cast( + int, + st.sidebar.number_input( + "Interpolation steps", + value=4, + min_value=1, + max_value=20, + help="Number of model generations between the two prompts. Controls the duration.", + ), + ) + + num_inference_steps = T.cast( + int, + st.sidebar.number_input( + "Steps per sample", value=50, help="Number of denoising steps per model run" + ), + ) + + guidance = st.sidebar.number_input( + "Guidance", + value=7.0, + help="How much the model listens to the text prompt", + ) + + init_image_name = st.sidebar.selectbox( + "Seed image", + # TODO(hayk): Read from directory + options=["og_beat", "agile", "marim", "motorway", "vibes", "custom"], + index=0, + help="Which seed image to use for img2img. Custom allows uploading your own.", + ) + assert init_image_name is not None + if init_image_name == "custom": + init_image_file = st.sidebar.file_uploader( + "Upload a custom seed image", + type=streamlit_util.IMAGE_EXTENSIONS, + label_visibility="collapsed", + ) + if init_image_file: + st.sidebar.image(init_image_file) + + show_individual_outputs = st.sidebar.checkbox( + "Show individual outputs", + value=False, + help="Show each model output", + ) + show_images = st.sidebar.checkbox( + "Show individual images", + value=False, + help="Show each generated image", + ) + + # Prompt inputs A and B in two columns + + with st.form(key="interpolation_form"): + left, right = st.columns(2) + + with left: + st.write("##### Prompt A") + prompt_input_a = PromptInput(guidance=guidance, **get_prompt_inputs(key="a")) + + with right: + st.write("##### Prompt B") + prompt_input_b = PromptInput(guidance=guidance, **get_prompt_inputs(key="b")) + + st.form_submit_button("Generate", type="primary") + + if not prompt_input_a.prompt or not prompt_input_b.prompt: + st.info("Enter both prompts to interpolate between them") + return + + alphas = list(np.linspace(0, 1, num_interpolation_steps)) + alphas_str = ", ".join([f"{alpha:.2f}" for alpha in alphas]) + st.write(f"**Alphas** : [{alphas_str}]") + + # TODO(hayk): Apply scaling to alphas like this + # T_shifted = T * 2 - 1 + # T_sample = (np.abs(T_shifted)**t_scale_power * np.sign(T_shifted) + 1) / 2 + # T_sample = T_sample * (t_end - t_start) + t_start + + if init_image_name == "custom": + if not init_image_file: + st.info("Upload a custom seed image") + return + init_image = Image.open(init_image_file).convert("RGB") + else: + init_image_path = ( + Path(__file__).parent.parent.parent.parent / "seed_images" / f"{init_image_name}.png" + ) + init_image = Image.open(str(init_image_path)).convert("RGB") + + # TODO(hayk): Move this code into a shared place and add to riffusion.cli + image_list: T.List[Image.Image] = [] + audio_bytes_list: T.List[io.BytesIO] = [] + for i, alpha in enumerate(alphas): + inputs = InferenceInput( + alpha=float(alpha), + num_inference_steps=num_inference_steps, + seed_image_id="og_beat", + start=prompt_input_a, + end=prompt_input_b, + ) + + if i == 0: + with st.expander("Example input JSON", expanded=False): + st.json(dataclasses.asdict(inputs)) + + image, audio_bytes = run_interpolation( + inputs=inputs, + init_image=init_image, + device=device, + extension=extension, + ) + + if show_individual_outputs: + st.write(f"#### ({i + 1} / {len(alphas)}) Alpha={alpha:.2f}") + if show_images: + st.image(image) + st.audio(audio_bytes) + + image_list.append(image) + audio_bytes_list.append(audio_bytes) + + st.write("#### Final Output") + + # TODO(hayk): Concatenate with overlap and better blending like in audio to audio + audio_segments = [pydub.AudioSegment.from_file(audio_bytes) for audio_bytes in audio_bytes_list] + concat_segment = audio_segments[0] + for segment in audio_segments[1:]: + concat_segment = concat_segment.append(segment, crossfade=0) + + audio_bytes = io.BytesIO() + concat_segment.export(audio_bytes, format=extension) + audio_bytes.seek(0) + + st.write(f"Duration: {concat_segment.duration_seconds:.3f} seconds") + st.audio(audio_bytes) + + output_name = ( + f"{prompt_input_a.prompt.replace(' ', '_')}_" + f"{prompt_input_b.prompt.replace(' ', '_')}.{extension}" + ) + st.download_button( + output_name, + data=audio_bytes, + file_name=output_name, + mime=f"audio/{extension}", + ) + + +def get_prompt_inputs( + key: str, + include_negative_prompt: bool = False, + cols: bool = False, +) -> T.Dict[str, T.Any]: + """ + Compute prompt inputs from widgets. + """ + p: T.Dict[str, T.Any] = {} + + # Optionally use columns + left, right = T.cast(T.Any, st.columns(2) if cols else (st, st)) + + visibility = "visible" if cols else "collapsed" + p["prompt"] = left.text_input("Prompt", label_visibility=visibility, key=f"prompt_{key}") + + if include_negative_prompt: + p["negative_prompt"] = right.text_input("Negative Prompt", key=f"negative_prompt_{key}") + + p["seed"] = T.cast( + int, + left.number_input( + "Seed", + value=42, + key=f"seed_{key}", + help="Integer used to generate a random result. Vary this to explore alternatives.", + ), + ) + + p["denoising"] = right.number_input( + "Denoising", + value=0.5, + key=f"denoising_{key}", + help="How much to modify the seed image", + ) + + return p + + +@st.experimental_memo +def run_interpolation( + inputs: InferenceInput, init_image: Image.Image, device: str = "cuda", extension: str = "mp3" +) -> T.Tuple[Image.Image, io.BytesIO]: + """ + Cached function for riffusion interpolation. + """ + pipeline = streamlit_util.load_riffusion_checkpoint( + device=device, + # No trace so we can have variable width + no_traced_unet=True, + ) + + image = pipeline.riffuse( + inputs, + init_image=init_image, + mask_image=None, + ) + + # TODO(hayk): Change the frequency range to [20, 20k] once the model is retrained + params = SpectrogramParams( + min_frequency=0, + max_frequency=10000, + ) + + # Reconstruct from image to audio + audio_bytes = streamlit_util.audio_bytes_from_spectrogram_image( + image=image, + params=params, + device=device, + output_format=extension, + ) + + return image, audio_bytes + + +if __name__ == "__main__": + render_interpolation() diff --git a/riffusion/streamlit/pages/sample_clips.py b/riffusion/streamlit/pages/sample_clips.py new file mode 100644 index 0000000..9acf3c1 --- /dev/null +++ b/riffusion/streamlit/pages/sample_clips.py @@ -0,0 +1,131 @@ +import tempfile +import typing as T +from pathlib import Path + +import numpy as np +import pydub +import streamlit as st + +from riffusion.spectrogram_params import SpectrogramParams +from riffusion.streamlit import util as streamlit_util + + +def render_sample_clips() -> None: + st.set_page_config(layout="wide", page_icon="🎸") + + st.subheader(":paperclip: Sample Clips") + st.write( + """ + Export short clips from an audio file. + """ + ) + + with st.expander("Help", False): + st.write( + """ + This tool simply allows uploading an audio file and randomly sampling short clips + from it. It's useful for generating a large number of short clips from a single + audio file. Outputs can be saved to a given directory with a given audio extension. + """ + ) + + audio_file = st.file_uploader( + "Upload a file", + type=streamlit_util.AUDIO_EXTENSIONS, + label_visibility="collapsed", + ) + if not audio_file: + st.info("Upload an audio file to get started") + return + + st.audio(audio_file) + + segment = pydub.AudioSegment.from_file(audio_file) + st.write( + " \n".join( + [ + f"**Duration**: {segment.duration_seconds:.3f} seconds", + f"**Channels**: {segment.channels}", + f"**Sample rate**: {segment.frame_rate} Hz", + f"**Sample width**: {segment.sample_width} bytes", + ] + ) + ) + + device = streamlit_util.select_device(st.sidebar) + extension = streamlit_util.select_audio_extension(st.sidebar) + save_to_disk = st.sidebar.checkbox("Save to Disk", False) + export_as_mono = st.sidebar.checkbox("Export as Mono", False) + compute_spectrograms = st.sidebar.checkbox("Compute Spectrograms", False) + + row = st.columns(4) + num_clips = T.cast(int, row[0].number_input("Number of Clips", value=3)) + duration_ms = T.cast(int, row[1].number_input("Duration (ms)", value=5000)) + seed = T.cast(int, row[2].number_input("Seed", value=42)) + + counter = streamlit_util.StreamlitCounter() + st.button("Sample Clips", type="primary", on_click=counter.increment) + if counter.value == 0: + return + + # Optionally pick an output directory + if save_to_disk: + output_dir = tempfile.mkdtemp(prefix="sample_clips_") + output_path = Path(output_dir) + output_path.mkdir(parents=True, exist_ok=True) + st.info(f"Output directory: `{output_dir}`") + + if compute_spectrograms: + images_dir = output_path / "images" + images_dir.mkdir(parents=True, exist_ok=True) + + if seed >= 0: + np.random.seed(seed) + + if export_as_mono and segment.channels > 1: + segment = segment.set_channels(1) + + if save_to_disk: + st.info(f"Writing {num_clips} clip(s) to `{str(output_path)}`") + + # TODO(hayk): Share code with riffusion.cli.sample_clips. + segment_duration_ms = int(segment.duration_seconds * 1000) + for i in range(num_clips): + clip_start_ms = np.random.randint(0, segment_duration_ms - duration_ms) + clip = segment[clip_start_ms : clip_start_ms + duration_ms] + + clip_name = f"clip_{i}_start_{clip_start_ms}_ms_duration_{duration_ms}_ms" + + st.write(f"#### Clip {i + 1} / {num_clips} -- `{clip_name}`") + + streamlit_util.display_and_download_audio( + clip, + name=clip_name, + extension=extension, + ) + + if save_to_disk: + clip_path = output_path / f"{clip_name}.{extension}" + clip.export(clip_path, format=extension) + + if compute_spectrograms: + params = SpectrogramParams() + + image = streamlit_util.spectrogram_image_from_audio( + clip, + params=params, + device=device, + ) + + st.image(image) + + if save_to_disk: + image_path = images_dir / f"{clip_name}.jpeg" + image.save(image_path) + + if save_to_disk: + st.info(f"Wrote {num_clips} clip(s) to `{str(output_path)}`") + + +if __name__ == "__main__": + render_sample_clips() diff --git a/riffusion/streamlit/pages/split_audio.py b/riffusion/streamlit/pages/split_audio.py new file mode 100644 index 0000000..69720a5 --- /dev/null +++ b/riffusion/streamlit/pages/split_audio.py @@ -0,0 +1,105 @@ +import typing as T +from pathlib import Path + +import pydub +import streamlit as st + +from riffusion.audio_splitter import split_audio +from riffusion.streamlit import util as streamlit_util +from riffusion.util import audio_util + + +def render_split_audio() -> None: + st.set_page_config(layout="wide", page_icon="🎸") + + st.subheader(":scissors: Audio Splitter") + st.write( + """ + Split audio into individual instrument stems. + """ + ) + + with st.expander("Help", False): + st.write( + """ + This tool allows uploading an audio file of arbitrary length and splits it into + stems of vocals, drums, bass, and other. It does this using a deep network that + sweeps over the audio in clips, extracts the stems, and then cross fades the clips + back together to construct the full length stems. It's particularly useful in + combination with audio_to_audio, for example to split and preserve vocals while + modifying the rest of the track with a prompt. Or, to pull out drums to add later + in a DAW. + """ + ) + + device = streamlit_util.select_device(st.sidebar) + + extension_options = ["mp3", "wav", "m4a", "ogg", "flac", "webm"] + extension = st.sidebar.selectbox( + "Output format", + options=extension_options, + index=extension_options.index("mp3"), + ) + assert extension is not None + + audio_file = st.file_uploader( + "Upload audio", + type=extension_options, + label_visibility="collapsed", + ) + + stem_options = ["Vocals", "Drums", "Bass", "Guitar", "Piano", "Other"] + recombine = st.sidebar.multiselect( + "Recombine", + options=stem_options, + default=[], + help="Recombine these stems at the end", + ) + + if not audio_file: + st.info("Upload audio to get started") + return + + st.write("#### Original") + st.audio(audio_file) + + counter = streamlit_util.StreamlitCounter() + st.button("Split", type="primary", on_click=counter.increment) + if counter.value == 0: + return + + segment = streamlit_util.load_audio_file(audio_file) + + # Split + stems = split_audio_cached(segment, device=device) + + input_name = Path(audio_file.name).stem + + # Display each + for name in stem_options: + stem = stems[name.lower()] + st.write(f"#### Stem: {name}") + + output_name = f"{input_name}_{name.lower()}" + streamlit_util.display_and_download_audio(stem, output_name, extension=extension) + + if recombine: + recombine_lower = [r.lower() for r in recombine] + segments = [s for name, s in stems.items() if name in recombine_lower] + recombined = audio_util.overlay_segments(segments) + + # Display + st.write(f"#### Recombined: {', '.join(recombine)}") + output_name = f"{input_name}_{'_'.join(recombine_lower)}" + streamlit_util.display_and_download_audio(recombined, output_name, extension=extension) + + +@st.cache +def split_audio_cached( + segment: pydub.AudioSegment, device: str = "cuda" +) -> T.Dict[str, pydub.AudioSegment]: + return split_audio(segment, device=device) + + +if __name__ == "__main__": + render_split_audio() diff --git a/riffusion/streamlit/pages/text_to_audio.py b/riffusion/streamlit/pages/text_to_audio.py new file mode 100644 index 0000000..ec4ce07 --- /dev/null +++ b/riffusion/streamlit/pages/text_to_audio.py @@ -0,0 +1,118 @@ +import typing as T + +import streamlit as st + +from riffusion.spectrogram_params import SpectrogramParams +from riffusion.streamlit import util as streamlit_util + + +def render_text_to_audio() -> None: + st.set_page_config(layout="wide", page_icon="🎸") + + st.subheader(":pencil2: Text to Audio") + st.write( + """ + Generate audio from text prompts. + """ + ) + + with st.expander("Help", False): + st.write( + """ + This tool runs riffusion in the simplest text to image form to generate an audio + clip from a text prompt. There is no seed image or interpolation here. This mode + allows more diversity and creativity than when using a seed image, but it also + leads to having less control. Play with the seed to get infinite variations. + """ + ) + + device = streamlit_util.select_device(st.sidebar) + extension = streamlit_util.select_audio_extension(st.sidebar) + + lora_path = st.sidebar.text_input("Lora Path", "") + lora_scale = st.sidebar.number_input("Lora Scale", value=1.0) + + with st.form("Inputs"): + prompt = st.text_input("Prompt") + negative_prompt = st.text_input("Negative prompt") + + row = st.columns(4) + num_clips = T.cast( + int, + row[0].number_input( + "Number of clips", + value=1, + min_value=1, + max_value=25, + help="How many outputs to generate (seed gets incremented)", + ), + ) + starting_seed = T.cast( + int, + row[1].number_input( + "Seed", + value=42, + help="Change this to generate different variations", + ), + ) + + st.form_submit_button("Riff", type="primary") + + with st.sidebar: + num_inference_steps = T.cast(int, st.number_input("Inference steps", value=50)) + width = T.cast(int, st.number_input("Width", value=512)) + guidance = st.number_input( + "Guidance", value=7.0, help="How much the model listens to the text prompt" + ) + scheduler = st.selectbox( + "Scheduler", + options=streamlit_util.SCHEDULER_OPTIONS, + index=0, + help="Which diffusion scheduler to use", + ) + assert scheduler is not None + + if not prompt: + st.info("Enter a prompt") + return + + # TODO(hayk): Change the frequency range to [20, 20k] once the model is retrained + params = SpectrogramParams( + min_frequency=0, + max_frequency=10000, + ) + + seed = starting_seed + for i in range(1, num_clips + 1): + st.write(f"#### Riff {i} / {num_clips} - Seed {seed}") + + image = streamlit_util.run_txt2img( + prompt=prompt, + num_inference_steps=num_inference_steps, + guidance=guidance, + negative_prompt=negative_prompt, + seed=seed, + width=width, + height=512, + device=device, + scheduler=scheduler, + lora_path=lora_path, + lora_scale=lora_scale, + ) + st.image(image) + + segment = streamlit_util.audio_segment_from_spectrogram_image( + image=image, + params=params, + device=device, + ) + + streamlit_util.display_and_download_audio( + segment, name=f"{prompt.replace(' ', '_')}_{seed}", extension=extension + ) + + seed += 1 + + +if __name__ == "__main__": + render_text_to_audio() diff --git a/riffusion/streamlit/pages/text_to_audio_batch.py b/riffusion/streamlit/pages/text_to_audio_batch.py new file mode 100644 index 0000000..8763011 --- /dev/null +++ b/riffusion/streamlit/pages/text_to_audio_batch.py @@ -0,0 +1,147 @@ +import json +import typing as T +from pathlib import Path + +import streamlit as st + +from riffusion.spectrogram_params import SpectrogramParams +from riffusion.streamlit import util as streamlit_util + +# Example input json file to process in batch +EXAMPLE_INPUT = """ +{ + "params": { + "seed": 42, + "num_inference_steps": 50, + "guidance": 7.0, + "width": 512, + }, + "entries": [ + { + "prompt": "Church bells" + }, + { + "prompt": "electronic beats", + "negative_prompt": "drums" + }, + { + "prompt": "classical violin concerto" + } + ] +} +""" + + +def render_text_to_audio_batch() -> None: + st.set_page_config(layout="wide", page_icon="🎸") + + st.subheader(":scroll: Text to Audio Batch") + st.write( + """ + Generate audio in batch from a JSON file of text prompts. + """ + ) + + with st.expander("Help", False): + st.write( + """ + This tool is a batch form of text_to_audio, where the inputs are read in from a JSON + file. The input file contains a global params block and a list of entries with positive + and negative prompts. It's useful for automating a larger set of generations. See the + example inputs below for the format of the file. + """ + ) + + device = streamlit_util.select_device(st.sidebar) + + # Upload a JSON file + json_file = st.file_uploader( + "JSON file", + type=["json"], + label_visibility="collapsed", + ) + + # Handle the null case + if json_file is None: + st.info("Upload a JSON file containing params and prompts") + with st.expander("Example inputs.json", expanded=False): + st.code(EXAMPLE_INPUT) + return + + # Read in and print it + data = json.loads(json_file.read()) + with st.expander("Input Data", expanded=False): + st.json(data) + + params = data["params"] + entries = data["entries"] + + show_images = st.sidebar.checkbox("Show Images", False) + + # Optionally specify an output directory + output_dir = st.sidebar.text_input("Output Directory", "") + output_path: T.Optional[Path] = None + if output_dir: + output_path = Path(output_dir) + output_path.mkdir(parents=True, exist_ok=True) + + for i, entry in enumerate(entries): + st.write(f"#### Entry {i + 1} / {len(entries)}") + + negative_prompt = entry.get("negative_prompt", None) + + st.write(f"**Prompt**: {entry['prompt']} \n" + f"**Negative prompt**: {negative_prompt}") + + image = streamlit_util.run_txt2img( + prompt=entry["prompt"], + negative_prompt=negative_prompt, + seed=params.get("seed", 42), + num_inference_steps=params.get("num_inference_steps", 50), + guidance=params.get("guidance", 7.0), + width=params.get("width", 512), + height=512, + device=device, + ) + + if show_images: + st.image(image) + + # TODO(hayk): Change the frequency range to [20, 20k] once the model is retrained + p_spectrogram = SpectrogramParams( + min_frequency=0, + max_frequency=10000, + ) + + output_format = "mp3" + audio_bytes = streamlit_util.audio_bytes_from_spectrogram_image( + image=image, + params=p_spectrogram, + device=device, + output_format=output_format, + ) + st.audio(audio_bytes) + + if output_path: + prompt_slug = entry["prompt"].replace(" ", "_") + negative_prompt_slug = entry.get("negative_prompt", "").replace(" ", "_") + + image_path = output_path / f"image_{i}_{prompt_slug}_neg_{negative_prompt_slug}.jpg" + image.save(image_path, format="JPEG") + entry["image_path"] = str(image_path) + + audio_path = ( + output_path / f"audio_{i}_{prompt_slug}_neg_{negative_prompt_slug}.{output_format}" + ) + audio_path.write_bytes(audio_bytes.getbuffer()) + entry["audio_path"] = str(audio_path) + + if output_path: + output_json_path = output_path / "index.json" + output_json_path.write_text(json.dumps(data, indent=4)) + st.info(f"Output written to {str(output_path)}") + else: + st.info("Enter output directory in sidebar to save to disk") + + +if __name__ == "__main__": + render_text_to_audio_batch() diff --git a/riffusion/streamlit/playground.py b/riffusion/streamlit/playground.py new file mode 100644 index 0000000..f705803 --- /dev/null +++ b/riffusion/streamlit/playground.py @@ -0,0 +1,43 @@ +import streamlit as st + + +def render_main(): + st.set_page_config(layout="wide", page_icon="🎸") + + st.title(":guitar: Riffusion Playground") + + left, right = st.columns(2) + + with left: + create_link(":pencil2: Text to Audio", "/text_to_audio") + st.write("Generate audio clips from text prompts.") + + create_link(":wave: Audio to Audio", "/audio_to_audio") + st.write("Upload audio and modify with text prompt (interpolation supported).") + + create_link(":performing_arts: Interpolation", "/interpolation") + st.write("Interpolate between prompts in the latent space.") + + create_link(":scissors: Audio Splitter", "/split_audio") + st.write("Split audio into stems like vocals, bass, drums, guitar, etc.") + + with right: + create_link(":scroll: Text to Audio Batch", "/text_to_audio_batch") + st.write("Generate audio in batch from a JSON file of text prompts.") + + create_link(":paperclip: Sample Clips", "/sample_clips") + st.write("Export short clips from an audio file.") + + create_link(":musical_keyboard: Image to Audio", "/image_to_audio") + st.write("Reconstruct audio from spectrogram images.") + + +def create_link(name: str, url: str) -> None: + st.markdown( + f"### {name}", + unsafe_allow_html=True, + ) + + +if __name__ == "__main__": + render_main() diff --git a/riffusion/streamlit/util.py b/riffusion/streamlit/util.py new file mode 100644 index 0000000..0eab7c8 --- /dev/null +++ b/riffusion/streamlit/util.py @@ -0,0 +1,457 @@ +""" +Streamlit utilities (mostly cached wrappers around riffusion code). +""" +import io +import threading +import typing as T +from pathlib import Path + +import pydub +import streamlit as st +import torch +from diffusers import DiffusionPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionPipeline +from PIL import Image + +from riffusion.audio_splitter import AudioSplitter +from riffusion.riffusion_pipeline import RiffusionPipeline +from riffusion.spectrogram_image_converter import SpectrogramImageConverter +from riffusion.spectrogram_params import SpectrogramParams + +# TODO(hayk): Add URL params + +AUDIO_EXTENSIONS = ["mp3", "wav", "flac", "webm", "m4a", "ogg"] +IMAGE_EXTENSIONS = ["png", "jpg", "jpeg"] + +SCHEDULER_OPTIONS = [ + "PNDMScheduler", + "DDIMScheduler", + "LMSDiscreteScheduler", + "EulerDiscreteScheduler", + "EulerAncestralDiscreteScheduler", + "DPMSolverMultistepScheduler", +] + + +@st.experimental_singleton +def load_riffusion_checkpoint( + checkpoint: str = "riffusion/riffusion-model-v1", + no_traced_unet: bool = False, + device: str = "cuda", +) -> RiffusionPipeline: + """ + Load the riffusion pipeline. + """ + return RiffusionPipeline.load_checkpoint( + checkpoint=checkpoint, + use_traced_unet=not no_traced_unet, + device=device, + ) + + +@st.experimental_singleton +def load_stable_diffusion_pipeline( + checkpoint: str = "riffusion/riffusion-model-v1", + device: str = "cuda", + dtype: torch.dtype = torch.float16, + scheduler: str = SCHEDULER_OPTIONS[0], + lora_path: T.Optional[str] = None, + lora_scale: float = 1.0, +) -> StableDiffusionPipeline: + """ + Load the riffusion pipeline. + + TODO(hayk): Merge this into RiffusionPipeline to just load one model. + """ + if device == "cpu" or device.lower().startswith("mps"): + print(f"WARNING: Falling back to float32 on {device}, float16 is unsupported") + dtype = torch.float32 + + pipeline = StableDiffusionPipeline.from_pretrained( + checkpoint, + revision="main", + torch_dtype=dtype, + safety_checker=lambda images, **kwargs: (images, False), + ).to(device) + + pipeline.scheduler = get_scheduler(scheduler, config=pipeline.scheduler.config) + + if lora_path: + if not Path(lora_path).is_file() or Path(lora_path).is_dir(): + raise RuntimeError("Bad lora path") + + from lora_diffusion import patch_pipe, tune_lora_scale + + patch_pipe( + pipeline, + lora_path, + patch_text=True, + patch_ti=True, + patch_unet=True, + ) + tune_lora_scale(pipeline.unet, lora_scale) + + return pipeline + + +def get_scheduler(scheduler: str, config: T.Any) -> T.Any: + """ + Construct a denoising scheduler from a string. + """ + if scheduler == "PNDMScheduler": + from diffusers import PNDMScheduler + + return PNDMScheduler.from_config(config) + elif scheduler == "DPMSolverMultistepScheduler": + from diffusers import DPMSolverMultistepScheduler + + return DPMSolverMultistepScheduler.from_config(config) + elif scheduler == "DDIMScheduler": + from diffusers import DDIMScheduler + + return DDIMScheduler.from_config(config) + elif scheduler == "LMSDiscreteScheduler": + from diffusers import LMSDiscreteScheduler + + return LMSDiscreteScheduler.from_config(config) + elif scheduler == "EulerDiscreteScheduler": + from diffusers import EulerDiscreteScheduler + + return EulerDiscreteScheduler.from_config(config) + elif scheduler == "EulerAncestralDiscreteScheduler": + from diffusers import EulerAncestralDiscreteScheduler + + return EulerAncestralDiscreteScheduler.from_config(config) + else: + raise ValueError(f"Unknown scheduler {scheduler}") + + +@st.experimental_singleton +def pipeline_lock() -> threading.Lock: + """ + Singleton lock used to prevent concurrent access to any model pipeline. + """ + return threading.Lock() + + +@st.experimental_singleton +def load_stable_diffusion_img2img_pipeline( + checkpoint: str = "riffusion/riffusion-model-v1", + device: str = "cuda", + dtype: torch.dtype = torch.float16, + scheduler: str = SCHEDULER_OPTIONS[0], + lora_path: T.Optional[str] = None, + lora_scale: float = 1.0, +) -> StableDiffusionImg2ImgPipeline: + """ + Load the image to image pipeline. + + TODO(hayk): Merge this into RiffusionPipeline to just load one model. + """ + if device == "cpu" or device.lower().startswith("mps"): + print(f"WARNING: Falling back to float32 on {device}, float16 is unsupported") + dtype = torch.float32 + + pipeline = StableDiffusionImg2ImgPipeline.from_pretrained( + checkpoint, + revision="main", + torch_dtype=dtype, + safety_checker=lambda images, **kwargs: (images, False), + ).to(device) + + pipeline.scheduler = get_scheduler(scheduler, config=pipeline.scheduler.config) + + # TODO reduce duplication + if lora_path: + if not Path(lora_path).is_file() or Path(lora_path).is_dir(): + raise RuntimeError("Bad lora path") + + from lora_diffusion import patch_pipe, tune_lora_scale + + patch_pipe( + pipeline, + lora_path, + patch_text=True, + patch_ti=True, + patch_unet=True, + ) + tune_lora_scale(pipeline.unet, lora_scale) + + return pipeline + + +@st.experimental_memo +def run_txt2img( + prompt: str, + num_inference_steps: int, + guidance: float, + negative_prompt: str, + seed: int, + width: int, + height: int, + device: str = "cuda", + scheduler: str = SCHEDULER_OPTIONS[0], + lora_path: T.Optional[str] = None, + lora_scale: float = 1.0, +) -> Image.Image: + """ + Run the text to image pipeline with caching. + """ + with pipeline_lock(): + pipeline = load_stable_diffusion_pipeline( + device=device, + scheduler=scheduler, + lora_path=lora_path, + lora_scale=lora_scale, + ) + + generator_device = "cpu" if device.lower().startswith("mps") else device + generator = torch.Generator(device=generator_device).manual_seed(seed) + + output = pipeline( + prompt=prompt, + num_inference_steps=num_inference_steps, + guidance_scale=guidance, + negative_prompt=negative_prompt or None, + generator=generator, + width=width, + height=height, + ) + + return output["images"][0] + + +@st.experimental_singleton +def spectrogram_image_converter( + params: SpectrogramParams, + device: str = "cuda", +) -> SpectrogramImageConverter: + return SpectrogramImageConverter(params=params, device=device) + + +@st.cache +def spectrogram_image_from_audio( + segment: pydub.AudioSegment, + params: SpectrogramParams, + device: str = "cuda", +) -> Image.Image: + converter = spectrogram_image_converter(params=params, device=device) + return converter.spectrogram_image_from_audio(segment) + + +@st.experimental_memo +def audio_segment_from_spectrogram_image( + image: Image.Image, + params: SpectrogramParams, + device: str = "cuda", +) -> pydub.AudioSegment: + converter = spectrogram_image_converter(params=params, device=device) + return converter.audio_from_spectrogram_image(image) + + +@st.experimental_memo +def audio_bytes_from_spectrogram_image( + image: Image.Image, + params: SpectrogramParams, + device: str = "cuda", + output_format: str = "mp3", +) -> io.BytesIO: + segment = audio_segment_from_spectrogram_image(image=image, params=params, device=device) + + audio_bytes = io.BytesIO() + segment.export(audio_bytes, format=output_format) + + return audio_bytes + + +def select_device(container: T.Any = st.sidebar) -> str: + """ + Dropdown to select a torch device, with an intelligent default. + """ + default_device = "cpu" + if torch.cuda.is_available(): + default_device = "cuda" + elif torch.backends.mps.is_available(): + default_device = "mps" + + device_options = ["cuda", "cpu", "mps"] + device = st.sidebar.selectbox( + "Device", + options=device_options, + index=device_options.index(default_device), + help="Which compute device to use. CUDA is recommended.", + ) + assert device is not None + + return device + + +def select_audio_extension(container: T.Any = st.sidebar) -> str: + """ + Dropdown to select an audio extension, with an intelligent default. + """ + default = "mp3" if pydub.AudioSegment.ffmpeg else "wav" + extension = container.selectbox( + "Output format", + options=AUDIO_EXTENSIONS, + index=AUDIO_EXTENSIONS.index(default), + ) + assert extension is not None + return extension + + +def select_scheduler(container: T.Any = st.sidebar) -> str: + """ + Dropdown to select a scheduler. + """ + scheduler = st.sidebar.selectbox( + "Scheduler", + options=SCHEDULER_OPTIONS, + index=0, + help="Which diffusion scheduler to use", + ) + assert scheduler is not None + return scheduler + + +@st.experimental_memo +def load_audio_file(audio_file: io.BytesIO) -> pydub.AudioSegment: + return pydub.AudioSegment.from_file(audio_file) + + +@st.experimental_singleton +def get_audio_splitter(device: str = "cuda"): + return AudioSplitter(device=device) + + +@st.experimental_singleton +def load_magic_mix_pipeline(device: str = "cuda", scheduler: str = SCHEDULER_OPTIONS[0]): + pipeline = DiffusionPipeline.from_pretrained( + "riffusion/riffusion-model-v1", + custom_pipeline="magic_mix", + ).to(device) + + pipeline.scheduler = get_scheduler(scheduler, pipeline.scheduler.config) + + return pipeline + + +@st.cache +def run_img2img_magic_mix( + prompt: str, + init_image: Image.Image, + num_inference_steps: int, + guidance_scale: float, + seed: int, + kmin: float, + kmax: float, + mix_factor: float, + device: str = "cuda", + scheduler: str = SCHEDULER_OPTIONS[0], +): + """ + Run the magic mix pipeline for img2img. + """ + with pipeline_lock(): + pipeline = load_magic_mix_pipeline( + device=device, + scheduler=scheduler, + ) + + return pipeline( + init_image, + prompt=prompt, + kmin=kmin, + kmax=kmax, + mix_factor=mix_factor, + seed=seed, + guidance_scale=guidance_scale, + steps=num_inference_steps, + ) + + +@st.cache +def run_img2img( + prompt: str, + init_image: Image.Image, + denoising_strength: float, + num_inference_steps: int, + guidance_scale: float, + seed: int, + negative_prompt: T.Optional[str] = None, + device: str = "cuda", + scheduler: str = SCHEDULER_OPTIONS[0], + progress_callback: T.Optional[T.Callable[[float], T.Any]] = None, + lora_path: T.Optional[str] = None, + lora_scale: float = 1.0, +) -> Image.Image: + with pipeline_lock(): + pipeline = load_stable_diffusion_img2img_pipeline( + device=device, + scheduler=scheduler, + lora_path=lora_path, + lora_scale=lora_scale, + ) + + generator_device = "cpu" if device.lower().startswith("mps") else device + generator = torch.Generator(device=generator_device).manual_seed(seed) + + num_expected_steps = max(int(num_inference_steps * denoising_strength), 1) + + def callback(step: int, tensor: torch.Tensor, foo: T.Any) -> None: + if progress_callback is not None: + progress_callback(step / num_expected_steps) + + result = pipeline( + prompt=prompt, + image=init_image, + strength=denoising_strength, + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + negative_prompt=negative_prompt or None, + num_images_per_prompt=1, + generator=generator, + callback=callback, + callback_steps=1, + ) + + return result.images[0] + + +class StreamlitCounter: + """ + Simple counter stored in streamlit session state. + """ + + def __init__(self, key="_counter"): + self.key = key + if not st.session_state.get(self.key): + st.session_state[self.key] = 0 + + def increment(self): + st.session_state[self.key] += 1 + + @property + def value(self): + return st.session_state[self.key] + + +def display_and_download_audio( + segment: pydub.AudioSegment, + name: str, + extension: str = "mp3", +) -> None: + """ + Display the given audio segment and provide a button to download it with + a proper file name, since st.audio doesn't support that. + """ + mime_type = f"audio/{extension}" + audio_bytes = io.BytesIO() + segment.export(audio_bytes, format=extension) + st.audio(audio_bytes, format=mime_type) + + st.download_button( + f"{name}.{extension}", + data=audio_bytes, + file_name=f"{name}.{extension}", + mime=mime_type, + ) diff --git a/riffusion/util/__init__.py b/riffusion/util/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/riffusion/util/audio_util.py b/riffusion/util/audio_util.py new file mode 100644 index 0000000..999a557 --- /dev/null +++ b/riffusion/util/audio_util.py @@ -0,0 +1,99 @@ +""" +Audio utility functions. +""" + +import io +import typing as T + +import numpy as np +import pydub +from scipy.io import wavfile + + +def audio_from_waveform( + samples: np.ndarray, sample_rate: int, normalize: bool = False +) -> pydub.AudioSegment: + """ + Convert a numpy array of samples of a waveform to an audio segment. + + Args: + samples: (channels, samples) array + """ + # Normalize volume to fit in int16 + if normalize: + samples *= np.iinfo(np.int16).max / np.max(np.abs(samples)) + + # Transpose and convert to int16 + samples = samples.transpose(1, 0) + samples = samples.astype(np.int16) + + # Write to the bytes of a WAV file + wav_bytes = io.BytesIO() + wavfile.write(wav_bytes, sample_rate, samples) + wav_bytes.seek(0) + + # Read into pydub + return pydub.AudioSegment.from_wav(wav_bytes) + + +def apply_filters(segment: pydub.AudioSegment, compression: bool = False) -> pydub.AudioSegment: + """ + Apply post-processing filters to the audio segment to compress it and + keep at a -10 dBFS level. + """ + # TODO(hayk): Come up with a principled strategy for these filters and experiment end-to-end. + # TODO(hayk): Is this going to make audio unbalanced between sequential clips? + + if compression: + segment = pydub.effects.normalize( + segment, + headroom=0.1, + ) + + segment = segment.apply_gain(-10 - segment.dBFS) + + # TODO(hayk): This is quite slow, ~1.7 seconds on a beefy CPU + segment = pydub.effects.compress_dynamic_range( + segment, + threshold=-20.0, + ratio=4.0, + attack=5.0, + release=50.0, + ) + + desired_db = -12 + segment = segment.apply_gain(desired_db - segment.dBFS) + + segment = pydub.effects.normalize( + segment, + headroom=0.1, + ) + + return segment + + +def stitch_segments( + segments: T.Sequence[pydub.AudioSegment], crossfade_s: float +) -> pydub.AudioSegment: + """ + Stitch together a sequence of audio segments with a crossfade between each segment. + """ + crossfade_ms = int(crossfade_s * 1000) + combined_segment = segments[0] + for segment in segments[1:]: + combined_segment = combined_segment.append(segment, crossfade=crossfade_ms) + return combined_segment + + +def overlay_segments(segments: T.Sequence[pydub.AudioSegment]) -> pydub.AudioSegment: + """ + Overlay a sequence of audio segments on top of each other. + """ + assert len(segments) > 0 + output: pydub.AudioSegment = None + for segment in segments: + if output is None: + output = segment + else: + output = output.overlay(segment) + return output diff --git a/riffusion/util/base64_util.py b/riffusion/util/base64_util.py new file mode 100644 index 0000000..84b455e --- /dev/null +++ b/riffusion/util/base64_util.py @@ -0,0 +1,9 @@ +import base64 +import io + + +def encode(buffer: io.BytesIO) -> str: + """ + Encode the given buffer as base64. + """ + return base64.encodebytes(buffer.getvalue()).decode("ascii") diff --git a/riffusion/util/fft_util.py b/riffusion/util/fft_util.py new file mode 100644 index 0000000..f7c682f --- /dev/null +++ b/riffusion/util/fft_util.py @@ -0,0 +1,60 @@ +""" +FFT tools to analyze frequency content of audio segments. This is not code for +dealing with spectrogram images, but for analysis of waveforms. +""" +import struct +import typing as T + +import numpy as np +import plotly.graph_objects as go +import pydub +from scipy.fft import rfft, rfftfreq + + +def plot_ffts( + segments: T.Dict[str, pydub.AudioSegment], + title: str = "FFT", + min_frequency: float = 20, + max_frequency: float = 20000, +) -> None: + """ + Plot an FFT analysis of the given audio segments. + """ + ffts = {name: compute_fft(seg) for name, seg in segments.items()} + + fig = go.Figure( + data=[go.Scatter(x=data[0], y=data[1], name=name) for name, data in ffts.items()], + layout={"title": title}, + ) + fig.update_xaxes( + range=[np.log(min_frequency) / np.log(10), np.log(max_frequency) / np.log(10)], + type="log", + title="Frequency", + ) + fig.update_yaxes(title="Value") + fig.show() + + +def compute_fft(sound: pydub.AudioSegment) -> T.Tuple[np.ndarray, np.ndarray]: + """ + Compute the FFT of the given audio segment as a mono signal. + + Returns: + frequencies: FFT computed frequencies + amplitudes: Amplitudes of each frequency + """ + # Convert to mono if needed. + if sound.channels > 1: + sound = sound.set_channels(1) + + sample_rate = sound.frame_rate + + num_samples = int(sound.frame_count()) + samples = struct.unpack(f"{num_samples * sound.channels}h", sound.raw_data) + + fft_values = rfft(samples) + amplitudes = np.abs(fft_values) + + frequencies = rfftfreq(n=num_samples, d=1 / sample_rate) + + return frequencies, amplitudes diff --git a/riffusion/util/image_util.py b/riffusion/util/image_util.py new file mode 100644 index 0000000..bdec25e --- /dev/null +++ b/riffusion/util/image_util.py @@ -0,0 +1,122 @@ +""" +Module for converting between spectrograms tensors and spectrogram images, as well as +general helpers for operating on pillow images. +""" +import typing as T + +import numpy as np +from PIL import Image + +from riffusion.spectrogram_params import SpectrogramParams + + +def image_from_spectrogram(spectrogram: np.ndarray, power: float = 0.25) -> Image.Image: + """ + Compute a spectrogram image from a spectrogram magnitude array. + + This is the inverse of spectrogram_from_image, except for discretization error from + quantizing to uint8. + + Args: + spectrogram: (channels, frequency, time) + power: A power curve to apply to the spectrogram to preserve contrast + + Returns: + image: (frequency, time, channels) + """ + # Rescale to 0-1 + max_value = np.max(spectrogram) + data = spectrogram / max_value + + # Apply the power curve + data = np.power(data, power) + + # Rescale to 0-255 + data = data * 255 + + # Invert + data = 255 - data + + # Convert to uint8 + data = data.astype(np.uint8) + + # Munge channels into a PIL image + if data.shape[0] == 1: + # TODO(hayk): Do we want to write single channel to disk instead? + image = Image.fromarray(data[0], mode="L").convert("RGB") + elif data.shape[0] == 2: + data = np.array([np.zeros_like(data[0]), data[0], data[1]]).transpose(1, 2, 0) + image = Image.fromarray(data, mode="RGB") + else: + raise NotImplementedError(f"Unsupported number of channels: {data.shape[0]}") + + # Flip Y + image = image.transpose(Image.Transpose.FLIP_TOP_BOTTOM) + + return image + + +def spectrogram_from_image( + image: Image.Image, + power: float = 0.25, + stereo: bool = False, + max_value: float = 30e6, +) -> np.ndarray: + """ + Compute a spectrogram magnitude array from a spectrogram image. + + This is the inverse of image_from_spectrogram, except for discretization error from + quantizing to uint8. + + Args: + image: (frequency, time, channels) + power: The power curve applied to the spectrogram + stereo: Whether the spectrogram encodes stereo data + max_value: The max value of the original spectrogram. In practice doesn't matter. + + Returns: + spectrogram: (channels, frequency, time) + """ + # Convert to RGB if single channel + if image.mode in ("P", "L"): + image = image.convert("RGB") + + # Flip Y + image = image.transpose(Image.Transpose.FLIP_TOP_BOTTOM) + + # Munge channels into a numpy array of (channels, frequency, time) + data = np.array(image).transpose(2, 0, 1) + if stereo: + # Take the G and B channels as done in image_from_spectrogram + data = data[[1, 2], :, :] + else: + data = data[0:1, :, :] + + # Convert to floats + data = data.astype(np.float32) + + # Invert + data = 255 - data + + # Rescale to 0-1 + data = data / 255 + + # Reverse the power curve + data = np.power(data, 1 / power) + + # Rescale to max value + data = data * max_value + + return data + + +def exif_from_image(pil_image: Image.Image) -> T.Dict[str, T.Any]: + """ + Get the EXIF data from a PIL image as a dict. + """ + exif = pil_image.getexif() + + if exif is None or len(exif) == 0: + return {} + + return {SpectrogramParams.ExifTags(key).name: val for key, val in exif.items()} diff --git a/riffusion/util/torch_util.py b/riffusion/util/torch_util.py new file mode 100644 index 0000000..953ef5d --- /dev/null +++ b/riffusion/util/torch_util.py @@ -0,0 +1,48 @@ +import warnings + +import numpy as np +import torch + + +def check_device(device: str, backup: str = "cpu") -> str: + """ + Check that the device is valid and available. If not, + """ + cuda_not_found = device.lower().startswith("cuda") and not torch.cuda.is_available() + mps_not_found = device.lower().startswith("mps") and not torch.backends.mps.is_available() + + if cuda_not_found or mps_not_found: + warnings.warn(f"WARNING: {device} is not available, using {backup} instead.", stacklevel=3) + return backup + + return device + + +def slerp( + t: float, v0: torch.Tensor, v1: torch.Tensor, dot_threshold: float = 0.9995 +) -> torch.Tensor: + """ + Helper function to spherically interpolate two arrays v1 v2. + """ + if not isinstance(v0, np.ndarray): + inputs_are_torch = True + input_device = v0.device + v0 = v0.cpu().numpy() + v1 = v1.cpu().numpy() + + dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1))) + if np.abs(dot) > dot_threshold: + v2 = (1 - t) * v0 + t * v1 + else: + theta_0 = np.arccos(dot) + sin_theta_0 = np.sin(theta_0) + theta_t = theta_0 * t + sin_theta_t = np.sin(theta_t) + s0 = np.sin(theta_0 - theta_t) / sin_theta_0 + s1 = sin_theta_t / sin_theta_0 + v2 = s0 * v0 + s1 * v1 + + if inputs_are_torch: + v2 = torch.from_numpy(v2).to(input_device) + + return v2 diff --git a/seed_images/agile.png b/seed_images/agile.png new file mode 100644 index 0000000000000000000000000000000000000000..5ff31cd76b38df78c9adc075d0b4bc2a956715b9 GIT binary patch literal 135636 zcmW(+dpwi<`@grAHmPRlp{K*f?HJNl&yy0fMjJvhhshxgWgQ72%%MW6nd3?$Ic{cB zQ)qNh8OzFPrO-j`LF_59hlSt1zr9}fcE9%dWA}aUx~|XreZAjTmZ$svHJTeV0RUJ- zCK0^=0IEKP0&w*^@J8o~iTVR^hU6a&0BdLdcR)5CV((UeseR7L@0|DXb0@+8Pogt= zUsTwUbEl&mgC3cw7j^!(=ooYE*y*VMU-dqH`b0F^asQvvI2-k{Rf4zQIbvA!(Wv8R z&K*B}3IO2jZZmW9hF;mWZQEp9N#s5&u~ce4=-MM)37jg0|0ohzJ_MlJ+GL`mx4r@F zNkAd!+GxB#T$?h}S;u`ZvRU39Lj7+=!hm@H!<)4ykn<_N=ALttlaV~%0E#i2yc>rx zFQ3^sF5=#xN*&slelICzXlbg2mcf?4^1KTI10}XCg32&xBn+Okt34&DJ!S8zR@oI# zx?t^-`Y^HW(HPUb%RiSE#pf4)zo}^aZ&6y>UaE5Ux8}`t{h0ki7c}m(SL`{ImdMTs z;^A=Q4&}0P)ZuhU&#$E)f-TQysoN^E%`v1dHycXysOhg5A#P{#AQj%zcmj#mnblOQ9qTD^V{k-t7g*)V>w-pTW 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0000000..4a84d76 --- /dev/null +++ b/test/audio_to_image_test.py @@ -0,0 +1,99 @@ +import typing as T + +import numpy as np +from PIL import Image + +from riffusion.cli import audio_to_image +from riffusion.spectrogram_params import SpectrogramParams + +from .test_case import TestCase + + +class AudioToImageTest(TestCase): + """ + Test riffusion.cli audio-to-image + """ + + @classmethod + def default_params(cls) -> T.Dict: + return dict( + step_size_ms=10, + num_frequencies=512, + # TODO(hayk): Change these to [20, 20000] once a model is updated + min_frequency=0, + max_frequency=10000, + stereo=False, + device=cls.DEVICE, + ) + + def test_audio_to_image(self) -> None: + """ + Test audio-to-image with default params. + """ + params = self.default_params() + self.helper_test_with_params(params) + + def test_stereo(self) -> None: + """ + Test audio-to-image with stereo=True. + """ + params = self.default_params() + params["stereo"] = True + self.helper_test_with_params(params) + + def helper_test_with_params(self, params: T.Dict) -> None: + audio_path = ( + self.TEST_DATA_PATH + / "tired_traveler" + / "clips" + / "clip_2_start_103694_ms_duration_5678_ms.wav" + ) + output_dir = self.get_tmp_dir("audio_to_image_") + + if params["stereo"]: + stem = f"{audio_path.stem}_stereo" + else: + stem = audio_path.stem + + image_path = output_dir / f"{stem}.png" + + audio_to_image(audio=str(audio_path), image=str(image_path), **params) + + # Check that the image exists + self.assertTrue(image_path.exists()) + + pil_image = Image.open(image_path) + + # Check the image mode + self.assertEqual(pil_image.mode, "RGB") + + # Check the image dimensions + duration_ms = 5678 + self.assertTrue(str(duration_ms) in audio_path.name) + expected_image_width = round(duration_ms / params["step_size_ms"]) + self.assertEqual(pil_image.width, expected_image_width) + self.assertEqual(pil_image.height, params["num_frequencies"]) + + # Get channels as numpy arrays + channels = [np.array(pil_image.getchannel(i)) for i in range(len(pil_image.getbands()))] + self.assertEqual(len(channels), 3) + + if params["stereo"]: + # Check that the first channel is zero + self.assertTrue(np.all(channels[0] == 0)) + else: + # Check that all channels are the same + self.assertTrue(np.all(channels[0] == channels[1])) + self.assertTrue(np.all(channels[0] == channels[2])) + + # Check that the image has exif data + exif = pil_image.getexif() + self.assertIsNotNone(exif) + params_from_exif = SpectrogramParams.from_exif(exif) + expected_params = SpectrogramParams( + stereo=params["stereo"], + step_size_ms=params["step_size_ms"], + num_frequencies=params["num_frequencies"], + max_frequency=params["max_frequency"], + ) + self.assertTrue(params_from_exif == expected_params) diff --git a/test/image_to_audio_test.py b/test/image_to_audio_test.py new file mode 100644 index 0000000..7ae1424 --- /dev/null +++ b/test/image_to_audio_test.py @@ -0,0 +1,71 @@ +from pathlib import Path + +import pydub + +from riffusion.cli import image_to_audio + +from .test_case import TestCase + + +class ImageToAudioTest(TestCase): + """ + Test riffusion.cli image-to-audio + """ + + def test_image_to_audio_mono(self) -> None: + self.helper_image_to_audio( + song_dir=self.TEST_DATA_PATH / "tired_traveler", + clip_name="clip_2_start_103694_ms_duration_5678_ms", + stereo=False, + ) + + def test_image_to_audio_stereo(self) -> None: + self.helper_image_to_audio( + song_dir=self.TEST_DATA_PATH / "tired_traveler", + clip_name="clip_2_start_103694_ms_duration_5678_ms", + stereo=True, + ) + + def helper_image_to_audio(self, song_dir: Path, clip_name: str, stereo: bool) -> None: + if stereo: + image_stem = clip_name + "_stereo" + else: + image_stem = clip_name + + image_path = song_dir / "images" / f"{image_stem}.png" + output_dir = self.get_tmp_dir("image_to_audio_") + audio_path = output_dir / f"{image_path.stem}.wav" + + image_to_audio( + image=str(image_path), + audio=str(audio_path), + device=self.DEVICE, + ) + + # Check that the audio exists + self.assertTrue(audio_path.exists()) + + # Load the reconstructed audio and the original clip + segment = pydub.AudioSegment.from_file(str(audio_path)) + expected_segment = pydub.AudioSegment.from_file( + str(song_dir / "clips" / f"{clip_name}.wav") + ) + + # Check sample rate + self.assertEqual(segment.frame_rate, expected_segment.frame_rate) + + # Check duration + actual_duration_ms = round(segment.duration_seconds * 1000) + expected_duration_ms = round(expected_segment.duration_seconds * 1000) + self.assertTrue(abs(actual_duration_ms - expected_duration_ms) < 10) + + # Check the number of channels + self.assertEqual(expected_segment.channels, 2) + if stereo: + self.assertEqual(segment.channels, 2) + else: + self.assertEqual(segment.channels, 1) + + +if __name__ == "__main__": + TestCase.main() diff --git a/test/image_util_test.py b/test/image_util_test.py new file mode 100644 index 0000000..00f5a9f --- /dev/null +++ b/test/image_util_test.py @@ -0,0 +1,65 @@ +import numpy as np +import pydub + +from riffusion.spectrogram_converter import SpectrogramConverter +from riffusion.spectrogram_params import SpectrogramParams +from riffusion.util import image_util + +from .test_case import TestCase + + +class ImageUtilTest(TestCase): + """ + Test riffusion.util.image_util + """ + + def test_spectrogram_to_image_round_trip(self) -> None: + audio_path = ( + self.TEST_DATA_PATH + / "tired_traveler" + / "clips" + / "clip_2_start_103694_ms_duration_5678_ms.wav" + ) + + # Load up the audio file + segment = pydub.AudioSegment.from_file(audio_path) + + # Convert to mono + segment = segment.set_channels(1) + + # Compute a spectrogram with default params + params = SpectrogramParams(sample_rate=segment.frame_rate) + converter = SpectrogramConverter(params=params, device=self.DEVICE) + spectrogram = converter.spectrogram_from_audio(segment) + + # Compute the image from the spectrogram + image = image_util.image_from_spectrogram( + spectrogram=spectrogram, + power=params.power_for_image, + ) + + # Save the max value + max_value = np.max(spectrogram) + + # Compute the spectrogram from the image + spectrogram_reversed = image_util.spectrogram_from_image( + image=image, + max_value=max_value, + power=params.power_for_image, + stereo=params.stereo, + ) + + # Check the shapes + self.assertEqual(spectrogram.shape, spectrogram_reversed.shape) + + # Check the max values + self.assertEqual(np.max(spectrogram), np.max(spectrogram_reversed)) + + # Check the median values + self.assertTrue( + np.allclose(np.median(spectrogram), np.median(spectrogram_reversed), rtol=0.05) + ) + + # Make sure all values are somewhat similar, but allow for discretization error + # TODO(hayk): Investigate error more closely + self.assertTrue(np.allclose(spectrogram, spectrogram_reversed, rtol=0.15)) diff --git a/test/linter_test.py b/test/linter_test.py new file mode 100644 index 0000000..65456b9 --- /dev/null +++ b/test/linter_test.py @@ -0,0 +1,24 @@ +import subprocess +from pathlib import Path + +from .test_case import TestCase + + +class LinterTest(TestCase): + """ + Test that ruff, black, and mypy run cleanly. + """ + + HOME = Path(__file__).parent.parent + + def test_ruff(self) -> None: + code = subprocess.check_call(["ruff", str(self.HOME)]) + self.assertEqual(code, 0) + + def test_black(self) -> None: + code = subprocess.check_call(["black", "--check", str(self.HOME)]) + self.assertEqual(code, 0) + + def test_mypy(self) -> None: + code = subprocess.check_call(["mypy", str(self.HOME)]) + self.assertEqual(code, 0) diff --git a/test/print_exif_test.py b/test/print_exif_test.py new file mode 100644 index 0000000..ad32b00 --- /dev/null +++ b/test/print_exif_test.py @@ -0,0 +1,32 @@ +import contextlib +import io + +from riffusion.cli import print_exif + +from .test_case import TestCase + + +class PrintExifTest(TestCase): + """ + Test riffusion.cli print-exif + """ + + def test_print_exif(self) -> None: + """ + Test print-exif. + """ + image_path = ( + self.TEST_DATA_PATH + / "tired_traveler" + / "images" + / "clip_2_start_103694_ms_duration_5678_ms.png" + ) + + # Redirect stdout + stdout = io.StringIO() + with contextlib.redirect_stdout(stdout): + print_exif(image=str(image_path)) + + # Check that a couple of values are printed + self.assertTrue("NUM_FREQUENCIES = 512" in stdout.getvalue()) + self.assertTrue("SAMPLE_RATE = 44100" in stdout.getvalue()) diff --git a/test/sample_clips_test.py b/test/sample_clips_test.py new file mode 100644 index 0000000..4b3080a --- /dev/null +++ b/test/sample_clips_test.py @@ -0,0 +1,88 @@ +import typing as T + +import pydub + +from riffusion.cli import sample_clips + +from .test_case import TestCase + + +class SampleClipsTest(TestCase): + """ + Test riffusion.cli sample-clips + """ + + @staticmethod + def default_params() -> T.Dict: + return dict( + num_clips=3, + duration_ms=5678, + mono=False, + extension="wav", + seed=42, + ) + + def test_sample_clips(self) -> None: + """ + Test sample-clips with default params. + """ + params = self.default_params() + self.helper_test_with_params(params) + + def test_mono(self) -> None: + """ + Test sample-clips with mono=True. + """ + params = self.default_params() + params["mono"] = True + params["num_clips"] = 1 + self.helper_test_with_params(params) + + def test_mp3(self) -> None: + """ + Test sample-clips with extension=mp3. + """ + if pydub.AudioSegment.converter is None: + self.skipTest("skipping, ffmpeg not found") + + params = self.default_params() + params["extension"] = "mp3" + params["num_clips"] = 1 + self.helper_test_with_params(params) + + def helper_test_with_params(self, params: T.Dict) -> None: + """ + Test sample-clips with the given params. + """ + audio_path = self.TEST_DATA_PATH / "tired_traveler" / "tired_traveler.mp3" + output_dir = self.get_tmp_dir("sample_clips_") + + sample_clips( + audio=str(audio_path), + output_dir=str(output_dir), + **params, + ) + + # For each file in output dir + counter = 0 + for clip_path in output_dir.iterdir(): + # Check that it has the right extension + self.assertEqual(clip_path.suffix, f".{params['extension']}") + + # Check that it has the right duration + segment = pydub.AudioSegment.from_file(clip_path) + self.assertEqual(round(segment.duration_seconds * 1000), params["duration_ms"]) + + # Check that it has the right number of channels + if params["mono"]: + self.assertEqual(segment.channels, 1) + else: + self.assertEqual(segment.channels, 2) + + counter += 1 + + self.assertEqual(counter, params["num_clips"]) + + +if __name__ == "__main__": + TestCase.main() diff --git a/test/spectrogram_converter_test.py b/test/spectrogram_converter_test.py new file mode 100644 index 0000000..4368b43 --- /dev/null +++ b/test/spectrogram_converter_test.py @@ -0,0 +1,86 @@ +import dataclasses +import typing as T + +import pydub + +from riffusion.spectrogram_converter import SpectrogramConverter +from riffusion.spectrogram_params import SpectrogramParams +from riffusion.util import fft_util + +from .test_case import TestCase + + +class SpectrogramConverterTest(TestCase): + """ + Test going from audio to spectrogram to audio, without converting to + an image, to check quality loss of the reconstruction. + + This test allows comparing multiple sets of spectrogram params by listening to output audio + and by plotting their FFTs. + """ + + # TODO(hayk): Do an ablation of Griffin Lim and how much loss that introduces. + + def test_round_trip(self) -> None: + audio_path = ( + self.TEST_DATA_PATH + / "tired_traveler" + / "clips" + / "clip_2_start_103694_ms_duration_5678_ms.wav" + ) + output_dir = self.get_tmp_dir(prefix="spectrogram_round_trip_test_") + + # Load up the audio file + segment = pydub.AudioSegment.from_file(audio_path) + + # Convert to mono if desired + use_stereo = False + if use_stereo: + assert segment.channels == 2 + else: + segment = segment.set_channels(1) + + # Define named sets of parameters + param_sets: T.Dict[str, SpectrogramParams] = {} + + param_sets["default"] = SpectrogramParams( + sample_rate=segment.frame_rate, + stereo=use_stereo, + step_size_ms=10, + min_frequency=20, + max_frequency=20000, + num_frequencies=512, + ) + + if self.DEBUG: + param_sets["freq_0_to_10k"] = dataclasses.replace( + param_sets["default"], + min_frequency=0, + max_frequency=10000, + ) + + segments: T.Dict[str, pydub.AudioSegment] = { + "original": segment, + } + for name, params in param_sets.items(): + converter = SpectrogramConverter(params=params, device=self.DEVICE) + spectrogram = converter.spectrogram_from_audio(segment) + segments[name] = converter.audio_from_spectrogram(spectrogram, apply_filters=True) + + # Save segments to disk + for name, segment in segments.items(): + audio_out = output_dir / f"{name}.wav" + segment.export(audio_out, format="wav") + print(f"Saved {audio_out}") + + # Check params + self.assertEqual(segments["default"].channels, 2 if use_stereo else 1) + self.assertEqual(segments["original"].channels, segments["default"].channels) + self.assertEqual(segments["original"].frame_rate, segments["default"].frame_rate) + self.assertEqual(segments["original"].sample_width, segments["default"].sample_width) + + # TODO(hayk): Test something more rigorous about the quality of the reconstruction. + + # If debugging, load up a browser tab plotting the FFTs + if self.DEBUG: + fft_util.plot_ffts(segments) diff --git a/test/spectrogram_image_converter_test.py b/test/spectrogram_image_converter_test.py new file mode 100644 index 0000000..48e4d0c --- /dev/null +++ b/test/spectrogram_image_converter_test.py @@ -0,0 +1,97 @@ +import dataclasses +import typing as T + +import pydub +from PIL import Image + +from riffusion.spectrogram_image_converter import SpectrogramImageConverter +from riffusion.spectrogram_params import SpectrogramParams +from riffusion.util import fft_util + +from .test_case import TestCase + + +class SpectrogramImageConverterTest(TestCase): + """ + Test going from audio to spectrogram images to audio, testing the quality loss of the + end-to-end pipeline. + + This test allows comparing multiple sets of spectrogram params by listening to output audio + and by plotting their FFTs. + + See spectrogram_converter_test.py for a similar test that does not convert to images. + """ + + def test_round_trip(self) -> None: + audio_path = ( + self.TEST_DATA_PATH + / "tired_traveler" + / "clips" + / "clip_2_start_103694_ms_duration_5678_ms.wav" + ) + output_dir = self.get_tmp_dir(prefix="spectrogram_image_round_trip_test_") + + # Load up the audio file + segment = pydub.AudioSegment.from_file(audio_path) + + # Convert to mono if desired + use_stereo = False + if use_stereo: + assert segment.channels == 2 + else: + segment = segment.set_channels(1) + + # Define named sets of parameters + param_sets: T.Dict[str, SpectrogramParams] = {} + + param_sets["default"] = SpectrogramParams( + sample_rate=segment.frame_rate, + stereo=use_stereo, + step_size_ms=10, + min_frequency=20, + max_frequency=20000, + num_frequencies=512, + ) + + if self.DEBUG: + param_sets["freq_0_to_10k"] = dataclasses.replace( + param_sets["default"], + min_frequency=0, + max_frequency=10000, + ) + + segments: T.Dict[str, pydub.AudioSegment] = { + "original": segment, + } + images: T.Dict[str, Image.Image] = {} + for name, params in param_sets.items(): + converter = SpectrogramImageConverter(params=params, device=self.DEVICE) + images[name] = converter.spectrogram_image_from_audio(segment) + segments[name] = converter.audio_from_spectrogram_image( + image=images[name], + apply_filters=True, + ) + + # Save images to disk + for name, image in images.items(): + image_out = output_dir / f"{name}.png" + image.save(image_out, exif=image.getexif(), format="PNG") + print(f"Saved {image_out}") + + # Save segments to disk + for name, segment in segments.items(): + audio_out = output_dir / f"{name}.wav" + segment.export(audio_out, format="wav") + print(f"Saved {audio_out}") + + # Check params + self.assertEqual(segments["default"].channels, 2 if use_stereo else 1) + self.assertEqual(segments["original"].channels, segments["default"].channels) + self.assertEqual(segments["original"].frame_rate, segments["default"].frame_rate) + self.assertEqual(segments["original"].sample_width, segments["default"].sample_width) + + # TODO(hayk): Test something more rigorous about the quality of the reconstruction. + + # If debugging, load up a browser tab plotting the FFTs + if self.DEBUG: + fft_util.plot_ffts(segments) diff --git a/test/test_case.py b/test/test_case.py new file mode 100644 index 0000000..92bff45 --- /dev/null +++ b/test/test_case.py @@ -0,0 +1,48 @@ +import os +import shutil +import tempfile +import typing as T +import unittest +import warnings +from pathlib import Path + + +class TestCase(unittest.TestCase): + """ + Base class for tests. + """ + + # Where checked-in test data is stored + TEST_DATA_PATH = Path(__file__).resolve().parent / "test_data" + + # Whether to run tests in debug mode (e.g. don't clean up temporary directories, show plots) + DEBUG = bool(os.environ.get("RIFFUSION_TEST_DEBUG")) + + # Which torch device to use for tests + DEVICE = os.environ.get("RIFFUSION_TEST_DEVICE", "cuda") + + @staticmethod + def main(*args: T.Any, **kwargs: T.Any) -> None: + """ + Run the tests. + """ + unittest.main(*args, **kwargs) + + @classmethod + def setUpClass(cls): + warnings.filterwarnings("ignore", category=ResourceWarning) + + def get_tmp_dir(self, prefix: str) -> Path: + """ + Create a temporary directory. + """ + tmp_dir = tempfile.mkdtemp(prefix=prefix) + + # Clean up the temporary directory if not debugging + if not self.DEBUG: + self.addCleanup(lambda: shutil.rmtree(tmp_dir, ignore_errors=True)) + + dir_path = Path(tmp_dir) + assert dir_path.is_dir() + + return dir_path diff --git a/test/test_data/README.md 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