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@@ -33,3 +33,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+example/V2A_sample-1.mp4 filter=lfs diff=lfs merge=lfs -text
+example/V2A_sample-2.mp4 filter=lfs diff=lfs merge=lfs -text
+example/V2A_sample-3.mp4 filter=lfs diff=lfs merge=lfs -text
+example/V2M_sample-1.mp4 filter=lfs diff=lfs merge=lfs -text
+example/V2M_sample-2.mp4 filter=lfs diff=lfs merge=lfs -text
+example/V2M_sample-3.mp4 filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,177 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
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+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
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+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
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+#Pipfile.lock
+
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+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+*.ckpt
+*.wav
+# *.mp4
+*.mp3
+*.jsonl
+wandb/*
+
+
+
+
+model/
+logs/
+log/
+saved_ckpt/
+wandb/
+demo_result/
+model/

LICENSE

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README.md

@@ -1,12 +1,180 @@
----
-title: AudioX Viet
-emoji: 🐢
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 5.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: AudioX-Viet
+app_file: run_gradio.py
+sdk: gradio
+sdk_version: 4.44.1
+---
+# 🎧 AudioX: Diffusion Transformer for Anything-to-Audio Generation
+
+[![arXiv](https://img.shields.io/badge/arXiv-2503.10522-brightgreen.svg?style=flat-square)](https://arxiv.org/abs/2503.10522)
+[![Project Page](https://img.shields.io/badge/GitHub.io-Project-blue?logo=Github&style=flat-square)](https://zeyuet.github.io/AudioX/)
+[![🤗 Model](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue)](https://huggingface.co/HKUSTAudio/AudioX)
+[![🤗 Demo](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue)](https://huggingface.co/spaces/Zeyue7/AudioX)
+
+---
+
+**This is the official repository for "[AudioX: Diffusion Transformer for Anything-to-Audio Generation](https://arxiv.org/pdf/2503.10522)".**
+
+
+## 📺 Demo Video
+
+https://github.com/user-attachments/assets/0d8dd927-ff0f-4b35-ab1f-b3c3915017be
+
+---
+
+
+## ✨ Abstract
+
+Audio and music generation have emerged as crucial tasks in many applications, yet existing approaches face significant limitations: they operate in isolation without unified capabilities across modalities, suffer from scarce high-quality, multi-modal training data, and struggle to effectively integrate diverse inputs. In this work, we propose AudioX, a unified Diffusion Transformer model for Anything-to-Audio and Music Generation. Unlike previous domain-specific models, AudioX can generate both general audio and music with high quality, while offering flexible natural language control and seamless processing of various modalities including text, video, image, music, and audio. Its key innovation is a multi-modal masked training strategy that masks inputs across modalities and forces the model to learn from masked inputs, yielding robust and unified cross-modal representations. To address data scarcity, we curate two comprehensive datasets: vggsound-caps with 190K audio captions based on the VGGSound dataset, and V2M-caps with 6 million music captions derived from the V2M dataset. Extensive experiments demonstrate that AudioX not only matches or outperforms state-of-the-art specialized models, but also offers remarkable versatility in handling diverse input modalities and generation tasks within a unified architecture.
+
+
+## ✨ Teaser
+
+<p align="center">
+  <img src="https://github.com/user-attachments/assets/ea723225-f9c8-4ca2-8837-2c2c08189bdd" alt="method">
+</p>
+<p style="text-align: left;">(a) Overview of AudioX, illustrating its capabilities across various tasks. (b) Radar chart comparing the performance of different methods across multiple benchmarks. AudioX demonstrates superior Inception Scores (IS) across a diverse set of datasets in audio and music generation tasks.</p>
+
+
+## ✨ Method
+
+<p align="center">
+  <img src="https://github.com/user-attachments/assets/94ea3df0-8c66-4259-b681-791ee41bada8" alt="method">
+</p>
+<p align="center">Overview of the AudioX Framework.</p>
+
+
+
+## Code
+
+
+### 🛠️ Environment Setup
+
+```bash
+git clone https://github.com/ZeyueT/AudioX.git
+cd AudioX
+conda create -n AudioX python=3.8.20
+conda activate AudioX
+pip install git+https://github.com/ZeyueT/AudioX.git
+conda install -c conda-forge ffmpeg libsndfile
+
+```
+
+## 🪄 Pretrained Checkpoints
+
+Download the pretrained model from 🤗 [AudioX on Hugging Face](https://huggingface.co/HKUSTAudio/AudioX):
+
+```bash
+mkdir -p model
+wget https://huggingface.co/HKUSTAudio/AudioX/resolve/main/model.ckpt -O model/model.ckpt
+wget https://huggingface.co/HKUSTAudio/AudioX/resolve/main/config.json -O model/config.json
+```
+
+### 🤗 Gradio Demo
+
+To launch the Gradio demo locally, run:
+
+```bash
+python3 run_gradio.py \
+    --model-config model/config.json \
+    --share
+```
+
+
+### 🎯 Prompt Configuration Examples
+
+| Task                 | `video_path`       | `text_prompt`                                 | `audio_path` |
+|:---------------------|:-------------------|:----------------------------------------------|:-------------|
+| Text-to-Audio (T2A)  | `None`             | `"Typing on a keyboard"`                      | `None`       |
+| Text-to-Music (T2M)  | `None`             | `"A music with piano and violin"`             | `None`       |
+| Video-to-Audio (V2A) | `"video_path.mp4"` | `"Generate general audio for the video"`      | `None`       |
+| Video-to-Music (V2M) | `"video_path.mp4"` | `"Generate music for the video"`              | `None`       |
+| TV-to-Audio (TV2A)   | `"video_path.mp4"` | `"Ocean waves crashing with people laughing"` | `None`       |
+| TV-to-Music (TV2M)   | `"video_path.mp4"` | `"Generate music with piano instrument"`      | `None`       |
+
+### 🖥️ Script Inference
+
+```python
+import torch
+import torchaudio
+from einops import rearrange
+from stable_audio_tools import get_pretrained_model
+from stable_audio_tools.inference.generation import generate_diffusion_cond
+from stable_audio_tools.data.utils import read_video, merge_video_audio
+from stable_audio_tools.data.utils import load_and_process_audio
+import os
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Download model
+model, model_config = get_pretrained_model("HKUSTAudio/AudioX")
+sample_rate = model_config["sample_rate"]
+sample_size = model_config["sample_size"]
+target_fps = model_config["video_fps"]
+seconds_start = 0
+seconds_total = 10
+
+model = model.to(device)
+
+# for video-to-music generation
+video_path = "example/V2M_sample-1.mp4"
+text_prompt = "Generate music for the video" 
+audio_path = None
+
+video_tensor = read_video(video_path, seek_time=0, duration=seconds_total, target_fps=target_fps)
+audio_tensor = load_and_process_audio(audio_path, sample_rate, seconds_start, seconds_total)
+
+conditioning = [{
+    "video_prompt": [video_tensor.unsqueeze(0)],        
+    "text_prompt": text_prompt,
+    "audio_prompt": audio_tensor.unsqueeze(0),
+    "seconds_start": seconds_start,
+    "seconds_total": seconds_total
+}]
+    
+# Generate stereo audio
+output = generate_diffusion_cond(
+    model,
+    steps=250,
+    cfg_scale=7,
+    conditioning=conditioning,
+    sample_size=sample_size,
+    sigma_min=0.3,
+    sigma_max=500,
+    sampler_type="dpmpp-3m-sde",
+    device=device
+)
+
+# Rearrange audio batch to a single sequence
+output = rearrange(output, "b d n -> d (b n)")
+
+# Peak normalize, clip, convert to int16, and save to file
+output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+torchaudio.save("output.wav", output, sample_rate)
+
+if video_path is not None and os.path.exists(video_path):
+    merge_video_audio(video_path, "output.wav", "output.mp4", 0, seconds_total)
+
+```
+
+
+## 🚀 Citation
+
+If you find our work useful, please consider citing:
+
+```
+@article{tian2025audiox,
+  title={AudioX: Diffusion Transformer for Anything-to-Audio Generation},
+  author={Tian, Zeyue and Jin, Yizhu and Liu, Zhaoyang and Yuan, Ruibin and Tan, Xu and Chen, Qifeng and Xue, Wei and Guo, Yike},
+  journal={arXiv preprint arXiv:2503.10522},
+  year={2025}
+}
+```
+
+## 📭 Contact
+
+If you have any comments or questions, feel free to contact Zeyue Tian([email protected]).
+
+## License
+
+Please follow [CC-BY-NC](./LICENSE).

defaults.ini

@@ -0,0 +1,56 @@
+
+[DEFAULTS]
+
+#name of the run
+name = stable_audio_tools
+
+# the batch size
+batch_size = 8 
+
+# number of GPUs to use for training
+num_gpus = 1 
+
+# number of nodes to use for training
+num_nodes = 1 
+
+# Multi-GPU strategy for PyTorch Lightning
+strategy = ""
+
+# Precision to use for training
+precision = "16-mixed"
+
+# number of CPU workers for the DataLoader
+num_workers = 8
+
+# the random seed
+seed = 42
+
+# Batches for gradient accumulation
+accum_batches = 1
+
+# Number of steps between checkpoints
+checkpoint_every = 10000                              
+                     
+# trainer checkpoint file to restart training from
+ckpt_path = ''
+
+# model checkpoint file to start a new training run from
+pretrained_ckpt_path = ''
+
+# Checkpoint path for the pretransform model if needed
+pretransform_ckpt_path = ''
+
+# configuration model specifying model hyperparameters
+model_config = ''
+
+# configuration for datasets
+dataset_config = ''
+
+# directory to save the checkpoints in
+save_dir = ''
+
+# gradient_clip_val passed into PyTorch Lightning Trainer
+gradient_clip_val = 0.0
+
+# remove the weight norm from the pretransform model
+remove_pretransform_weight_norm = ''

example/V2A_sample-1.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7bcb04e7f53461f2420078122338447a18a2baea5e14397cd7099cd97bec6260
+size 4101390

example/V2A_sample-2.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87850f3803ed2aba8928322b9eb703fc4653638e3ce4bd4a2dc179bbbe9c0542
+size 2434915

example/V2A_sample-3.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:339d929f307f01adf0ce139a3ecc19a1b95bea711cc718114099102fb2280200
+size 2772307

example/V2M_sample-1.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:489488199943d4154430e0adeeede9ae41e070a63d3a4bbb01de3247e8817a2e
+size 7634025

example/V2M_sample-2.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f225024c10bfeed527f666d5c00f5232a8822b57abdbaecc2cc105fc8a7d509f
+size 1705374

example/V2M_sample-3.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b49940836aa92b24bcb82627780081ff2cfd5e3e977a5529c64a6a5e15b96e5
+size 2654967

pyproject.toml

@@ -0,0 +1,3 @@
+[build-system]
+requires = ["setuptools"]
+build-backend = "setuptools.build_meta"

run.bat

@@ -0,0 +1,8 @@
+REM — 1) Activate your Conda environment (must use CALL) :contentReference[oaicite:0]{index=0}
+call conda activate AudioX
+
+REM — 2) Run the Gradio script
+python run_gradio.py --model-config model/config.json --share
+
+REM — 3) Exit the batch file and close the window :contentReference[oaicite:1]{index=1}
+exit /B 0

run_gradio.py

@@ -0,0 +1,32 @@
+from stable_audio_tools import get_pretrained_model
+from stable_audio_tools.interface.gradio import create_ui
+import json 
+
+import torch
+
+def main(args):
+    torch.manual_seed(42)
+
+    interface = create_ui(
+        model_config_path = args.model_config, 
+        ckpt_path=args.ckpt_path, 
+        pretrained_name=args.pretrained_name, 
+        pretransform_ckpt_path=args.pretransform_ckpt_path,
+        model_half=args.model_half
+    )
+    interface.queue()
+    interface.launch(share=args.share, auth=(args.username, args.password) if args.username is not None else None)
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser(description='Run gradio interface')
+    parser.add_argument('--pretrained-name', type=str, help='Name of pretrained model', required=False)
+    parser.add_argument('--model-config', type=str, help='Path to model config', required=False)
+    parser.add_argument('--ckpt-path', type=str, help='Path to model checkpoint', required=False)
+    parser.add_argument('--pretransform-ckpt-path', type=str, help='Optional to model pretransform checkpoint', required=False)
+    parser.add_argument('--share', action='store_true', help='Create a publicly shareable link', required=False)
+    parser.add_argument('--username', type=str, help='Gradio username', required=False)
+    parser.add_argument('--password', type=str, help='Gradio password', required=False)
+    parser.add_argument('--model-half', action='store_true', help='Whether to use half precision', required=False)
+    args = parser.parse_args()
+    main(args)

setup.py

@@ -0,0 +1,46 @@
+from setuptools import setup, find_packages
+
+setup(
+    name='AudioX',
+    version='0.1.0',
+    url='https://github.com/ZeyueT/AudioX.git',
+    author='AudioX, HKUST',
+    description='Training and inference tools for generative audio models from AudioX',
+    packages=find_packages(),  
+    install_requires=[
+        'aeiou',
+        'alias-free-torch==0.0.6',
+        'auraloss==0.4.0',
+        'descript-audio-codec==1.0.0',
+        'decord==0.6.0',
+        'einops',
+        'einops_exts',
+        'ema-pytorch==0.2.3',
+        'encodec==0.1.1',
+        'gradio==4.44.1',
+        'gradio_client==1.3.0',
+        'huggingface_hub',
+        'importlib-resources==5.12.0',
+        'k-diffusion==0.1.1',
+        'laion-clap==1.1.6',
+        'local-attention==1.8.6',
+        'pandas==2.0.2',
+        'pedalboard==0.9.14',
+        'prefigure==0.0.9',
+        'pytorch_lightning==2.4.0', 
+        'PyWavelets==1.4.1',
+        'safetensors',
+        'sentencepiece==0.1.99',
+        'torch>=2.0.1',
+        'torchaudio>=2.0.2',
+        'torchmetrics==0.11.4',
+        'tqdm',
+        'transformers',
+        'v-diffusion-pytorch==0.0.2',
+        'vector-quantize-pytorch==1.9.14',
+        'wandb',
+        'webdataset==0.2.48',
+        'x-transformers<1.27.0',
+    ],
+
+)

stable_audio_tools/__init__.py

@@ -0,0 +1,2 @@
+from .models.factory import create_model_from_config, create_model_from_config_path
+from .models.pretrained import get_pretrained_model

stable_audio_tools/data/dataset.py

@@ -0,0 +1,876 @@
+import importlib
+import numpy as np
+import io
+import os
+import posixpath
+import random
+import re
+import subprocess
+import time
+import torch
+import torchaudio
+import webdataset as wds
+
+from aeiou.core import is_silence
+from os import path
+from pedalboard.io import AudioFile
+from torchaudio import transforms as T
+from typing import Optional, Callable, List
+from torchdata.datapipes.iter import IterDataPipe, IterableWrapper
+from torchdata.datapipes.iter import Prefetcher
+
+from .utils import Stereo, Mono, PhaseFlipper, PadCrop_Normalized_T
+import json
+
+
+import os
+import datetime
+from memory_profiler import profile
+
+
+AUDIO_KEYS = ("flac", "wav", "mp3", "m4a", "ogg", "opus")
+
+# fast_scandir implementation by Scott Hawley originally in https://github.com/zqevans/audio-diffusion/blob/main/dataset/dataset.py
+
+def fast_scandir(
+    dir:str,  # top-level directory at which to begin scanning
+    ext:list,  # list of allowed file extensions,
+    #max_size = 1 * 1000 * 1000 * 1000 # Only files < 1 GB
+    ):
+    "very fast `glob` alternative. from https://stackoverflow.com/a/59803793/4259243"
+    subfolders, files = [], []
+    ext = ['.'+x if x[0]!='.' else x for x in ext]  # add starting period to extensions if needed
+    try: # hope to avoid 'permission denied' by this try
+        for f in os.scandir(dir):
+            try: # 'hope to avoid too many levels of symbolic links' error
+                if f.is_dir():
+                    subfolders.append(f.path)
+                elif f.is_file():
+                    file_ext = os.path.splitext(f.name)[1].lower()
+                    is_hidden = os.path.basename(f.path).startswith(".")
+
+                    if file_ext in ext and not is_hidden:
+                        files.append(f.path)
+            except:
+                pass 
+    except:
+        pass
+
+    for dir in list(subfolders):
+        sf, f = fast_scandir(dir, ext)
+        subfolders.extend(sf)
+        files.extend(f)
+    return subfolders, files
+
+def extract_audio_paths(jsonl_file, exts):
+    audio_paths = []
+    video_paths = []
+    text_prompts = []
+    data_types = []
+    with open(jsonl_file, 'r') as file:
+        for line in file:
+            try:
+                data = json.loads(line.strip())
+                path = data.get('path', '')
+                video_path = data.get('video_path', '')
+                text_prompt = data.get('caption', '')
+                data_type = data.get('type', None)
+                if any(path.endswith(ext) for ext in exts):
+                    audio_paths.append(path)
+                    video_paths.append(video_path)
+                    text_prompts.append(text_prompt)
+                    data_types.append(data_type)
+            except json.JSONDecodeError:
+                print(f"Error decoding JSON line: {line.strip()}")
+    return audio_paths, video_paths, text_prompts, data_types
+
+def keyword_scandir(
+    dir: str,  # top-level directory at which to begin scanning
+    ext: list,  # list of allowed file extensions
+    keywords: list,  # list of keywords to search for in the file name
+):
+    "very fast `glob` alternative. from https://stackoverflow.com/a/59803793/4259243"
+    subfolders, files = [], []
+    # make keywords case insensitive
+    keywords = [keyword.lower() for keyword in keywords]
+    # add starting period to extensions if needed
+    ext = ['.'+x if x[0] != '.' else x for x in ext]
+    banned_words = ["paxheader", "__macosx"]
+    try:  # hope to avoid 'permission denied' by this try
+        for f in os.scandir(dir):
+            try:  # 'hope to avoid too many levels of symbolic links' error
+                if f.is_dir():
+                    subfolders.append(f.path)
+                elif f.is_file():
+                    is_hidden = f.name.split("/")[-1][0] == '.'
+                    has_ext = os.path.splitext(f.name)[1].lower() in ext
+                    name_lower = f.name.lower()
+                    has_keyword = any(
+                        [keyword in name_lower for keyword in keywords])
+                    has_banned = any(
+                        [banned_word in name_lower for banned_word in banned_words])
+                    if has_ext and has_keyword and not has_banned and not is_hidden and not os.path.basename(f.path).startswith("._"):
+                        files.append(f.path)
+            except:
+                pass
+    except:
+        pass
+
+    for dir in list(subfolders):
+        sf, f = keyword_scandir(dir, ext, keywords)
+        subfolders.extend(sf)
+        files.extend(f)
+    return subfolders, files
+
+def get_audio_filenames(
+    paths: list,  # directories in which to search
+    keywords=None,
+    exts=['.wav', '.mp3', '.flac', '.ogg', '.aif', '.opus']
+):
+
+    "recursively get a list of audio filenames"
+    filenames = []
+    video_filenames = []
+    text_prompts = []
+    data_types = []
+    
+    if type(paths) is str:
+        paths = [paths]
+
+
+    if os.path.isdir(paths[0]):
+        for path in paths:               # get a list of relevant filenames
+            if keywords is not None:
+                subfolders, files = keyword_scandir(path, exts, keywords)
+            else:
+                subfolders, files = fast_scandir(path, exts)
+            filenames.extend(files)
+        return filenames
+    
+    elif os.path.isfile(paths[0]):
+        assert paths[0].endswith('.jsonl')
+        for path in paths:
+            audio_paths, video_paths, text_prompt, data_type = extract_audio_paths(path, exts)
+            filenames.extend(audio_paths)
+            video_filenames.extend(video_paths)
+            text_prompts.extend(text_prompt)
+            data_types.extend(data_type)
+            
+        return filenames, video_filenames, text_prompts, data_types
+
+
+class LocalDatasetConfig:
+    def __init__(
+        self,
+        id: str,
+        path: str,
+        video_fps: int,
+        custom_metadata_fn: Optional[Callable[[str], str]] = None
+    ):
+        self.id = id
+        self.path = path
+        self.video_fps = video_fps
+        self.custom_metadata_fn = custom_metadata_fn
+
+
+# @profile
+class SampleDataset(torch.utils.data.Dataset):
+    # @profile
+    def __init__(
+        self, 
+        configs,
+        sample_size=65536, 
+        sample_rate=48000, 
+        keywords=None, 
+        random_crop=True,
+        force_channels="stereo",
+        video_fps=5
+    ):
+        super().__init__()
+        self.filenames = []
+        self.video_filenames = []
+        self.text_prompts = []
+        self.data_types = []
+
+        self.augs = torch.nn.Sequential(
+            PhaseFlipper(),
+        )
+
+        self.root_paths = []
+
+        self.pad_crop = PadCrop_Normalized_T(sample_size, sample_rate, randomize=random_crop)
+
+        self.force_channels = force_channels
+
+        self.encoding = torch.nn.Sequential(
+            Stereo() if self.force_channels == "stereo" else torch.nn.Identity(),
+            Mono() if self.force_channels == "mono" else torch.nn.Identity(),
+        )
+
+        self.sr = sample_rate
+
+        self.custom_metadata_fns = {}
+
+        for config in configs:
+            self.video_fps = config.video_fps
+            
+            self.root_paths.append(config.path)
+            audio_files, video_files, text_prompt, data_types = get_audio_filenames(config.path, keywords)
+
+            self.filenames.extend(audio_files)
+            self.video_filenames.extend(video_files)
+            self.text_prompts.extend(text_prompt)
+            self.data_types.extend(data_types)
+            if config.custom_metadata_fn is not None:
+                self.custom_metadata_fns[config.path] = config.custom_metadata_fn
+
+        print(f'Found {len(self.filenames)} files')
+
+
+    def load_file(self, filename):
+        ext = filename.split(".")[-1]
+
+        if ext == "mp3":
+            with AudioFile(filename) as f:
+                audio = f.read(f.frames)
+                audio = torch.from_numpy(audio)
+                in_sr = f.samplerate
+        else:
+            audio, in_sr = torchaudio.load(filename, format=ext)
+
+        if in_sr != self.sr:
+            resample_tf = T.Resample(in_sr, self.sr)
+            audio = resample_tf(audio)
+
+        return audio
+
+    def __len__(self):
+        return len(self.filenames)
+
+
+    def __getitem__(self, idx):
+        audio_filename = self.filenames[idx]
+        video_filename = self.video_filenames[idx]
+        text_prompt = self.text_prompts[idx]
+        data_type = self.data_types[idx]
+        
+        try:
+            
+            start_time = time.time()
+            audio = self.load_file(audio_filename)
+
+
+            if data_type in ["text_condition-audio", "text_condition-music",
+                            "video_condition-audio", "video_condition-music",
+                            "text+video_condition-audio","text+video_condition-music"]:
+                if_audio_contition = False
+                audio_prompt = torch.zeros((2, self.sr * 10))
+            elif data_type in ["audio_condition-audio", "audio_condition-music",
+                            "uni_condition-audio", "uni_condition-music"]:
+                if_audio_contition = True
+                    
+            if if_audio_contition:
+                audio_org = audio.clamp(-1, 1)           
+                
+       
+            audio, t_start, t_end, seconds_start, seconds_total, padding_mask = self.pad_crop(audio)
+
+            if self.augs is not None:
+                audio = self.augs(audio)
+
+            audio = audio.clamp(-1, 1)
+
+            if if_audio_contition:
+                if data_type.split("-")[-1] == "audio":
+                    start_index = max(0, int((seconds_start) * self.sr))
+                    end_index = int((seconds_start+10) * self.sr)
+                    audio_prompt = audio_org[:, start_index:end_index]
+                    
+                elif data_type.split("-")[-1] == "music":
+                    if seconds_start < 10:
+                        start_index = 0
+                        end_index = int(10 * self.sr)
+                    else:
+                        start_index = max(0, int((seconds_start - 10) * self.sr))
+                        end_index = int(seconds_start * self.sr)
+                    audio_prompt = audio_org[:, start_index:end_index]
+
+            # Encode the file to assist in prediction
+            if self.encoding is not None:
+                audio = self.encoding(audio)
+
+            info = {}
+            
+            
+            info["path"] = audio_filename
+            info["video_path"] = video_filename
+            info["text_prompt"] = text_prompt
+            info["audio_prompt"] = audio_prompt            
+            info["data_type"] = data_type
+            
+            for root_path in self.root_paths:
+                if root_path in audio_filename:
+                    info["relpath"] = path.relpath(audio_filename, root_path)
+
+            info["timestamps"] = (t_start, t_end)
+            info["seconds_start"] = seconds_start
+            info["seconds_total"] = seconds_total
+            info["padding_mask"] = padding_mask
+            info["video_fps"] = self.video_fps
+            end_time = time.time()
+
+            info["load_time"] = end_time - start_time
+
+            for custom_md_path in self.custom_metadata_fns.keys():
+                if os.path.isdir(custom_md_path):
+                    if custom_md_path in audio_filename:
+                        custom_metadata_fn = self.custom_metadata_fns[custom_md_path]
+                        custom_metadata = custom_metadata_fn(info, audio)
+                        info.update(custom_metadata)
+                elif os.path.isfile(custom_md_path):
+                    custom_metadata_fn = self.custom_metadata_fns[custom_md_path]
+                    custom_metadata = custom_metadata_fn(info, audio)
+                    info.update(custom_metadata)                    
+
+                if "__reject__" in info and info["__reject__"]:
+                    return self[random.randrange(len(self))]
+
+            file_name = audio_filename.split('/')[-1]
+            
+            return (audio, info)
+        except Exception as e:
+            print(f'Couldn\'t load file {audio_filename}: {e}')
+            return self[random.randrange(len(self))]
+
+def group_by_keys(data, keys=wds.tariterators.base_plus_ext, lcase=True, suffixes=None, handler=None):
+    """Return function over iterator that groups key, value pairs into samples.
+    :param keys: function that splits the key into key and extension (base_plus_ext)
+    :param lcase: convert suffixes to lower case (Default value = True)
+    """
+    current_sample = None
+    for filesample in data:
+        assert isinstance(filesample, dict)
+        fname, value = filesample["fname"], filesample["data"]
+        prefix, suffix = keys(fname)
+        if wds.tariterators.trace:
+            print(
+                prefix,
+                suffix,
+                current_sample.keys() if isinstance(current_sample, dict) else None,
+            )
+        if prefix is None:
+            continue
+        if lcase:
+            suffix = suffix.lower()
+        if current_sample is None or prefix != current_sample["__key__"]:
+            if wds.tariterators.valid_sample(current_sample):
+                yield current_sample
+            current_sample = dict(__key__=prefix, __url__=filesample["__url__"])
+        if suffix in current_sample:
+            print(f"{fname}: duplicate file name in tar file {suffix} {current_sample.keys()}")
+        if suffixes is None or suffix in suffixes:
+            current_sample[suffix] = value
+    if wds.tariterators.valid_sample(current_sample):
+        yield current_sample
+
+wds.tariterators.group_by_keys = group_by_keys
+
+# S3 code and WDS preprocessing code based on implementation by Scott Hawley originally in https://github.com/zqevans/audio-diffusion/blob/main/dataset/dataset.py
+
+def get_s3_contents(dataset_path, s3_url_prefix=None, filter='', recursive=True, debug=False, profile=None):
+    """
+    Returns a list of full S3 paths to files in a given S3 bucket and directory path.
+    """
+    # Ensure dataset_path ends with a trailing slash
+    if dataset_path != '' and not dataset_path.endswith('/'):
+        dataset_path += '/'
+    # Use posixpath to construct the S3 URL path
+    bucket_path = posixpath.join(s3_url_prefix or '', dataset_path)
+    # Construct the `aws s3 ls` command
+    cmd = ['aws', 's3', 'ls', bucket_path]
+
+    if profile is not None:
+        cmd.extend(['--profile', profile])
+
+    if recursive:
+        # Add the --recursive flag if requested
+        cmd.append('--recursive')
+    
+    # Run the `aws s3 ls` command and capture the output
+    run_ls = subprocess.run(cmd, capture_output=True, check=True)
+    # Split the output into lines and strip whitespace from each line
+    contents = run_ls.stdout.decode('utf-8').split('\n')
+    contents = [x.strip() for x in contents if x]
+    # Remove the timestamp from lines that begin with a timestamp
+    contents = [re.sub(r'^\S+\s+\S+\s+\d+\s+', '', x)
+                if re.match(r'^\S+\s+\S+\s+\d+\s+', x) else x for x in contents]
+    # Construct a full S3 path for each file in the contents list
+    contents = [posixpath.join(s3_url_prefix or '', x)
+                for x in contents if not x.endswith('/')]
+    # Apply the filter, if specified
+    if filter:
+        contents = [x for x in contents if filter in x]
+    # Remove redundant directory names in the S3 URL
+    if recursive:
+        # Get the main directory name from the S3 URL
+        main_dir = "/".join(bucket_path.split('/')[3:])
+        # Remove the redundant directory names from each file path
+        contents = [x.replace(f'{main_dir}', '').replace(
+            '//', '/') for x in contents]
+    # Print debugging information, if requested
+    if debug:
+        print("contents = \n", contents)
+    # Return the list of S3 paths to files
+    return contents
+
+
+def get_all_s3_urls(
+    names=[],           # list of all valid [LAION AudioDataset] dataset names
+    # list of subsets you want from those datasets, e.g. ['train','valid']
+    subsets=[''],
+    s3_url_prefix=None,  # prefix for those dataset names
+    recursive=True,     # recursively list all tar files in all subdirs
+    filter_str='tar',   # only grab files with this substring
+    # print debugging info -- note: info displayed likely to change at dev's whims
+    debug=False,
+    profiles={},        # dictionary of profiles for each item in names, e.g. {'dataset1': 'profile1', 'dataset2': 'profile2'}
+):
+    "get urls of shards (tar files) for multiple datasets in one s3 bucket"
+    urls = []
+    for name in names:
+        # If s3_url_prefix is not specified, assume the full S3 path is included in each element of the names list
+        if s3_url_prefix is None:
+            contents_str = name
+        else:
+            # Construct the S3 path using the s3_url_prefix and the current name value
+            contents_str = posixpath.join(s3_url_prefix, name)
+        if debug:
+            print(f"get_all_s3_urls: {contents_str}:")
+        for subset in subsets:
+            subset_str = posixpath.join(contents_str, subset)
+            if debug:
+                print(f"subset_str = {subset_str}")
+            # Get the list of tar files in the current subset directory
+            profile = profiles.get(name, None)
+            tar_list = get_s3_contents(
+                subset_str, s3_url_prefix=None, recursive=recursive, filter=filter_str, debug=debug, profile=profile)
+            for tar in tar_list:
+                # Escape spaces and parentheses in the tar filename for use in the shell command
+                tar = tar.replace(" ", "\ ").replace(
+                    "(", "\(").replace(")", "\)")
+                # Construct the S3 path to the current tar file
+                s3_path = posixpath.join(name, subset, tar) + " -"
+                # Construct the AWS CLI command to download the current tar file
+                if s3_url_prefix is None:
+                    request_str = f"pipe:aws s3 --cli-connect-timeout 0 cp {s3_path}"
+                else:
+                    request_str = f"pipe:aws s3 --cli-connect-timeout 0 cp {posixpath.join(s3_url_prefix, s3_path)}"
+                if profiles.get(name):
+                    request_str += f" --profile {profiles.get(name)}"
+                if debug:
+                    print("request_str = ", request_str)
+                # Add the constructed URL to the list of URLs
+                urls.append(request_str)
+    return urls
+
+
+def log_and_continue(exn):
+    """Call in an exception handler to ignore any exception, isssue a warning, and continue."""
+    print(f"Handling webdataset error ({repr(exn)}). Ignoring.")
+    return True
+
+
+def is_valid_sample(sample):
+    has_json = "json" in sample
+    has_audio = "audio" in sample
+    is_silent = is_silence(sample["audio"])
+    is_rejected = "__reject__" in sample["json"] and sample["json"]["__reject__"]
+
+    return has_json and has_audio and not is_silent and not is_rejected
+
+class S3DatasetConfig:
+    def __init__(
+        self,
+        id: str,
+        s3_path: str,
+        custom_metadata_fn: Optional[Callable[[str], str]] = None,
+        profile: Optional[str] = None,
+    ):
+        self.id = id
+        self.path = s3_path
+        self.custom_metadata_fn = custom_metadata_fn
+        self.profile = profile
+        self.urls = []
+
+    def load_data_urls(self):
+        self.urls = get_all_s3_urls(
+            names=[self.path],
+            s3_url_prefix=None,
+            recursive=True,
+            profiles={self.path: self.profile} if self.profile else {},
+        )
+
+        return self.urls
+
+class LocalWebDatasetConfig:
+    def __init__(
+        self,
+        id: str,
+        path: str,
+        custom_metadata_fn: Optional[Callable[[str], str]] = None,
+        profile: Optional[str] = None,
+    ):
+        self.id = id
+        self.path = path
+        self.custom_metadata_fn = custom_metadata_fn
+        self.urls = []
+
+    def load_data_urls(self):
+
+        self.urls = fast_scandir(self.path, ["tar"])[1]
+
+        return self.urls
+
+def audio_decoder(key, value):
+    # Get file extension from key
+    ext = key.split(".")[-1]
+
+    if ext in AUDIO_KEYS:
+        return torchaudio.load(io.BytesIO(value))
+    else:
+        return None
+
+def collation_fn(samples):
+        batched = list(zip(*samples))
+        result = []
+        for b in batched:
+            if isinstance(b[0], (int, float)):
+                b = np.array(b)
+            elif isinstance(b[0], torch.Tensor):
+                b = torch.stack(b)
+            elif isinstance(b[0], np.ndarray):
+                b = np.array(b)
+            else:
+                b = b
+            result.append(b)
+        return result
+
+class WebDatasetDataLoader():
+    def __init__(
+        self,
+        datasets: List[S3DatasetConfig],
+        batch_size,
+        sample_size,
+        sample_rate=48000,
+        num_workers=8,
+        epoch_steps=1000,
+        random_crop=True,
+        force_channels="stereo",
+        augment_phase=True,
+        **data_loader_kwargs
+    ):
+
+        self.datasets = datasets
+
+        self.sample_size = sample_size
+        self.sample_rate = sample_rate
+        self.random_crop = random_crop
+        self.force_channels = force_channels
+        self.augment_phase = augment_phase
+
+        urls = [dataset.load_data_urls() for dataset in datasets]
+
+        # Flatten the list of lists of URLs
+        urls = [url for dataset_urls in urls for url in dataset_urls]
+
+        # Shuffle the urls
+        random.shuffle(urls)
+
+        self.dataset = wds.DataPipeline(
+            wds.ResampledShards(urls),
+            wds.tarfile_to_samples(handler=log_and_continue),
+            wds.decode(audio_decoder, handler=log_and_continue),
+            wds.map(self.wds_preprocess, handler=log_and_continue),
+            wds.select(is_valid_sample),
+            wds.to_tuple("audio", "json", handler=log_and_continue),
+            #wds.shuffle(bufsize=1000, initial=5000),
+            wds.batched(batch_size, partial=False, collation_fn=collation_fn),
+        ).with_epoch(epoch_steps//num_workers if num_workers > 0 else epoch_steps)
+
+        self.data_loader = wds.WebLoader(self.dataset, num_workers=num_workers, **data_loader_kwargs)
+
+    def wds_preprocess(self, sample):
+
+        found_key, rewrite_key = '', ''
+        for k, v in sample.items():  # print the all entries in dict
+            for akey in AUDIO_KEYS:
+                if k.endswith(akey):
+                    # to rename long/weird key with its simpler counterpart
+                    found_key, rewrite_key = k, akey
+                    break
+            if '' != found_key:
+                break
+        if '' == found_key:  # got no audio!
+            return None  # try returning None to tell WebDataset to skip this one
+
+        audio, in_sr = sample[found_key]
+        if in_sr != self.sample_rate:
+            resample_tf = T.Resample(in_sr, self.sample_rate)
+            audio = resample_tf(audio)
+
+        if self.sample_size is not None:
+            # Pad/crop and get the relative timestamp
+            pad_crop = PadCrop_Normalized_T(
+                self.sample_size, randomize=self.random_crop, sample_rate=self.sample_rate)
+            audio, t_start, t_end, seconds_start, seconds_total, padding_mask = pad_crop(
+                audio)
+            sample["json"]["seconds_start"] = seconds_start
+            sample["json"]["seconds_total"] = seconds_total
+            sample["json"]["padding_mask"] = padding_mask
+        else:
+            t_start, t_end = 0, 1
+
+        # Check if audio is length zero, initialize to a single zero if so
+        if audio.shape[-1] == 0:
+            audio = torch.zeros(1, 1)
+
+        # Make the audio stereo and augment by randomly inverting phase
+        augs = torch.nn.Sequential(
+            Stereo() if self.force_channels == "stereo" else torch.nn.Identity(),
+            Mono() if self.force_channels == "mono" else torch.nn.Identity(),
+            PhaseFlipper() if self.augment_phase else torch.nn.Identity()
+        )
+
+        audio = augs(audio)
+
+        sample["json"]["timestamps"] = (t_start, t_end)
+
+        if "text" in sample["json"]:
+            sample["json"]["prompt"] = sample["json"]["text"]
+
+        # Check for custom metadata functions
+        for dataset in self.datasets:
+            if dataset.custom_metadata_fn is None:
+                continue
+        
+            if dataset.path in sample["__url__"]:
+                custom_metadata = dataset.custom_metadata_fn(sample["json"], audio)
+                sample["json"].update(custom_metadata)
+
+        if found_key != rewrite_key:   # rename long/weird key with its simpler counterpart
+            del sample[found_key]
+
+        sample["audio"] = audio
+
+        # Add audio to the metadata as well for conditioning
+        sample["json"]["audio"] = audio
+        
+        return sample
+
+def create_dataloader_from_config(dataset_config, batch_size, sample_size, sample_rate, audio_channels=2, num_workers=4, video_fps=5):
+
+    dataset_type = dataset_config.get("dataset_type", None)
+
+    assert dataset_type is not None, "Dataset type must be specified in dataset config"
+
+    if audio_channels == 1:
+        force_channels = "mono"
+    else:
+        force_channels = "stereo"
+
+    if dataset_type == "audio_dir":
+
+        audio_dir_configs = dataset_config.get("datasets", None)
+
+        assert audio_dir_configs is not None, "Directory configuration must be specified in datasets[\"dataset\"]"
+
+        configs = []
+
+        for audio_dir_config in audio_dir_configs:
+            audio_dir_path = audio_dir_config.get("path", None)
+            assert audio_dir_path is not None, "Path must be set for local audio directory configuration"
+
+            custom_metadata_fn = None
+            custom_metadata_module_path = audio_dir_config.get("custom_metadata_module", None)
+
+            if custom_metadata_module_path is not None:
+                spec = importlib.util.spec_from_file_location("metadata_module", custom_metadata_module_path)
+                metadata_module = importlib.util.module_from_spec(spec)
+                spec.loader.exec_module(metadata_module)                
+
+                custom_metadata_fn = metadata_module.get_custom_metadata
+
+            configs.append(
+                LocalDatasetConfig(
+                    id=audio_dir_config["id"],
+                    path=audio_dir_path,
+                    custom_metadata_fn=custom_metadata_fn,
+                    video_fps=video_fps
+                )
+            )
+
+        train_set = SampleDataset(
+            configs,
+            sample_rate=sample_rate,
+            sample_size=sample_size,
+            random_crop=dataset_config.get("random_crop", True),
+            force_channels=force_channels,
+            video_fps=video_fps
+        )
+
+        return torch.utils.data.DataLoader(train_set, batch_size, shuffle=True,
+                                num_workers=num_workers, persistent_workers=True, pin_memory=False, drop_last=True, collate_fn=collation_fn)
+
+    elif dataset_type in ["s3", "wds"]: # Support "s3" type for backwards compatibility
+        wds_configs = []
+
+        for wds_config in dataset_config["datasets"]:
+
+            custom_metadata_fn = None
+            custom_metadata_module_path = wds_config.get("custom_metadata_module", None)
+
+            if custom_metadata_module_path is not None:
+                spec = importlib.util.spec_from_file_location("metadata_module", custom_metadata_module_path)
+                metadata_module = importlib.util.module_from_spec(spec)
+                spec.loader.exec_module(metadata_module)                
+
+                custom_metadata_fn = metadata_module.get_custom_metadata
+
+            if "s3_path" in wds_config:
+
+                wds_configs.append(
+                    S3DatasetConfig(
+                        id=wds_config["id"],
+                        s3_path=wds_config["s3_path"],
+                        custom_metadata_fn=custom_metadata_fn,
+                        profile=wds_config.get("profile", None),
+                    )
+                )
+            
+            elif "path" in wds_config:
+                    
+                    wds_configs.append(
+                        LocalWebDatasetConfig(
+                            id=wds_config["id"],
+                            path=wds_config["path"],
+                            custom_metadata_fn=custom_metadata_fn
+                        )
+                    )
+
+        return WebDatasetDataLoader(
+            wds_configs,
+            sample_rate=sample_rate,
+            sample_size=sample_size,
+            batch_size=batch_size,
+            random_crop=dataset_config.get("random_crop", True),
+            num_workers=num_workers,
+            persistent_workers=True,
+            force_channels=force_channels,
+            epoch_steps=dataset_config.get("epoch_steps", 2000)
+        ).data_loader
+        
+
+
+
+def create_dataloader_from_config_valid(dataset_config, batch_size, sample_size, sample_rate, audio_channels=2, num_workers=4):
+    
+
+    dataset_type = dataset_config.get("dataset_type", None)
+
+    assert dataset_type is not None, "Dataset type must be specified in dataset config"
+
+    if audio_channels == 1:
+        force_channels = "mono"
+    else:
+        force_channels = "stereo"
+
+    if dataset_type == "audio_dir":
+
+        audio_dir_configs = dataset_config.get("datasets", None)
+
+        assert audio_dir_configs is not None, "Directory configuration must be specified in datasets[\"dataset\"]"
+
+        configs = []
+
+        for audio_dir_config in audio_dir_configs:
+            audio_dir_path = audio_dir_config.get("path", None)
+            assert audio_dir_path is not None, "Path must be set for local audio directory configuration"
+
+            custom_metadata_fn = None
+            custom_metadata_module_path = audio_dir_config.get("custom_metadata_module", None)
+
+            if custom_metadata_module_path is not None:
+                spec = importlib.util.spec_from_file_location("metadata_module", custom_metadata_module_path)
+                metadata_module = importlib.util.module_from_spec(spec)
+                spec.loader.exec_module(metadata_module)                
+
+                custom_metadata_fn = metadata_module.get_custom_metadata
+
+            configs.append(
+                LocalDatasetConfig(
+                    id=audio_dir_config["id"],
+                    path=audio_dir_path,
+                    custom_metadata_fn=custom_metadata_fn
+                )
+            )
+
+        valid_set = SampleDataset(
+            configs,
+            sample_rate=sample_rate,
+            sample_size=sample_size,
+            random_crop=dataset_config.get("random_crop", True),
+            force_channels=force_channels
+        )
+
+
+        return torch.utils.data.DataLoader(valid_set, batch_size, shuffle=False,
+                                num_workers=num_workers, persistent_workers=True, pin_memory=True, drop_last=True, collate_fn=collation_fn)
+
+    elif dataset_type in ["s3", "wds"]: # Support "s3" type for backwards compatibility
+        wds_configs = []
+
+        for wds_config in dataset_config["datasets"]:
+
+            custom_metadata_fn = None
+            custom_metadata_module_path = wds_config.get("custom_metadata_module", None)
+
+            if custom_metadata_module_path is not None:
+                spec = importlib.util.spec_from_file_location("metadata_module", custom_metadata_module_path)
+                metadata_module = importlib.util.module_from_spec(spec)
+                spec.loader.exec_module(metadata_module)                
+
+                custom_metadata_fn = metadata_module.get_custom_metadata
+
+            if "s3_path" in wds_config:
+
+                wds_configs.append(
+                    S3DatasetConfig(
+                        id=wds_config["id"],
+                        s3_path=wds_config["s3_path"],
+                        custom_metadata_fn=custom_metadata_fn,
+                        profile=wds_config.get("profile", None),
+                    )
+                )
+            
+            elif "path" in wds_config:
+                    
+                    wds_configs.append(
+                        LocalWebDatasetConfig(
+                            id=wds_config["id"],
+                            path=wds_config["path"],
+                            custom_metadata_fn=custom_metadata_fn
+                        )
+                    )
+
+        return WebDatasetDataLoader(
+            wds_configs,
+            sample_rate=sample_rate,
+            sample_size=sample_size,
+            batch_size=batch_size,
+            random_crop=dataset_config.get("random_crop", True),
+            num_workers=num_workers,
+            persistent_workers=True,
+            force_channels=force_channels,
+            epoch_steps=dataset_config.get("epoch_steps", 2000)
+        ).data_loader
+                

stable_audio_tools/data/utils.py

@@ -0,0 +1,199 @@
+import math
+import random
+import torch
+
+from torch import nn
+from typing import Tuple
+import os
+import subprocess as sp
+from PIL import Image
+from torchvision import transforms
+from decord import VideoReader, cpu
+
+class PadCrop(nn.Module):
+    def __init__(self, n_samples, randomize=True):
+        super().__init__()
+        self.n_samples = n_samples
+        self.randomize = randomize
+
+    def __call__(self, signal):
+        n, s = signal.shape
+        start = 0 if (not self.randomize) else torch.randint(0, max(0, s - self.n_samples) + 1, []).item()
+        end = start + self.n_samples
+        output = signal.new_zeros([n, self.n_samples])
+        output[:, :min(s, self.n_samples)] = signal[:, start:end]
+        return output
+
+
+class PadCrop_Normalized_T(nn.Module):
+
+    def __init__(self, n_samples: int, sample_rate: int, randomize: bool = True):
+        super().__init__()
+        self.n_samples = n_samples
+        self.sample_rate = sample_rate
+        self.randomize = randomize
+
+    def __call__(self, source: torch.Tensor) -> Tuple[torch.Tensor, float, float, int, int, torch.Tensor]:
+        n_channels, n_samples = source.shape
+
+        # Calculate the duration of the audio in seconds
+        total_duration = n_samples // self.sample_rate
+        
+        # If the audio is shorter than the desired length, pad it
+        upper_bound = max(0, n_samples - self.n_samples)
+        
+        # If randomize is False, always start at the beginning of the audio
+        offset = 0
+        
+        if self.randomize and n_samples > self.n_samples:
+            valid_offsets = [
+                i * self.sample_rate for i in range(0, total_duration, 10) 
+                if i * self.sample_rate + self.n_samples <= n_samples and 
+                (total_duration <= 20 or total_duration - i >= 15)
+            ]
+            if valid_offsets:
+                offset = random.choice(valid_offsets)
+
+        # Calculate the start and end times of the chunk
+        t_start = offset / (upper_bound + self.n_samples)
+        t_end = (offset + self.n_samples) / (upper_bound + self.n_samples)
+
+        # Create the chunk
+        chunk = source.new_zeros([n_channels, self.n_samples])
+
+        # Copy the audio into the chunk
+        chunk[:, :min(n_samples, self.n_samples)] = source[:, offset:offset + self.n_samples]
+        
+        # Calculate the start and end times of the chunk in seconds
+        seconds_start = math.floor(offset / self.sample_rate)
+        seconds_total = math.ceil(n_samples / self.sample_rate)
+
+        # Create a mask the same length as the chunk with 1s where the audio is and 0s where it isn't
+        padding_mask = torch.zeros([self.n_samples])
+        padding_mask[:min(n_samples, self.n_samples)] = 1
+        
+        return (
+            chunk,
+            t_start,
+            t_end,
+            seconds_start,
+            seconds_total,
+            padding_mask
+        )
+
+
+class PhaseFlipper(nn.Module):
+    "Randomly invert the phase of a signal"
+    def __init__(self, p=0.5):
+        super().__init__()
+        self.p = p
+    def __call__(self, signal):
+        return -signal if (random.random() < self.p) else signal
+        
+class Mono(nn.Module):
+  def __call__(self, signal):
+    return torch.mean(signal, dim=0, keepdims=True) if len(signal.shape) > 1 else signal
+
+class Stereo(nn.Module):
+  def __call__(self, signal):
+    signal_shape = signal.shape
+    # Check if it's mono
+    if len(signal_shape) == 1: # s -> 2, s
+        signal = signal.unsqueeze(0).repeat(2, 1)
+    elif len(signal_shape) == 2:
+        if signal_shape[0] == 1: #1, s -> 2, s
+            signal = signal.repeat(2, 1)
+        elif signal_shape[0] > 2: #?, s -> 2,s
+            signal = signal[:2, :]    
+
+    return signal
+
+
+def adjust_video_duration(video_tensor, duration, target_fps):
+    current_duration = video_tensor.shape[0]
+    target_duration = duration * target_fps
+    if current_duration > target_duration:
+        video_tensor = video_tensor[:target_duration]
+    elif current_duration < target_duration:
+        last_frame = video_tensor[-1:]
+        repeat_times = target_duration - current_duration
+        video_tensor = torch.cat((video_tensor, last_frame.repeat(repeat_times, 1, 1, 1)), dim=0)
+    return video_tensor
+
+def read_video(filepath, seek_time=0., duration=-1, target_fps=2):
+    if filepath is None:
+        return torch.zeros((int(duration * target_fps), 3, 224, 224))
+    
+    ext = os.path.splitext(filepath)[1].lower()
+    if ext in ['.jpg', '.jpeg', '.png']:
+        resize_transform = transforms.Resize((224, 224))
+        image = Image.open(filepath).convert("RGB")
+        frame = transforms.ToTensor()(image).unsqueeze(0)
+        frame = resize_transform(frame)
+        target_frames = int(duration * target_fps)
+        frame = frame.repeat(int(math.ceil(target_frames / frame.shape[0])), 1, 1, 1)[:target_frames]
+        assert frame.shape[0] == target_frames, f"The shape of frame is {frame.shape}"
+        return frame
+
+    vr = VideoReader(filepath, ctx=cpu(0))
+    fps = vr.get_avg_fps()
+    total_frames = len(vr)
+
+    seek_frame = int(seek_time * fps)
+    if duration > 0:
+        total_frames_to_read = int(target_fps * duration)
+        frame_interval = int(math.ceil(fps / target_fps))
+        end_frame = min(seek_frame + total_frames_to_read * frame_interval, total_frames)
+        frame_ids = list(range(seek_frame, end_frame, frame_interval))
+    else:
+        frame_interval = int(math.ceil(fps / target_fps))
+        frame_ids = list(range(0, total_frames, frame_interval))
+
+    frames = vr.get_batch(frame_ids).asnumpy()
+    frames = torch.from_numpy(frames).permute(0, 3, 1, 2)
+
+    if frames.shape[2] != 224 or frames.shape[3] != 224:
+        resize_transform = transforms.Resize((224, 224))
+        frames = resize_transform(frames)
+
+    video_tensor = adjust_video_duration(frames, duration, target_fps)
+    assert video_tensor.shape[0] == duration * target_fps, f"The shape of video_tensor is {video_tensor.shape}"
+    return video_tensor
+
+def merge_video_audio(video_path, audio_path, output_path, start_time, duration):
+    command = [
+        'ffmpeg',
+        '-y',
+        '-ss', str(start_time),
+        '-t', str(duration),
+        '-i', video_path,
+        '-i', audio_path,
+        '-c:v', 'copy',
+        '-c:a', 'aac',
+        '-map', '0:v:0',
+        '-map', '1:a:0',
+        '-shortest',
+        '-strict', 'experimental',
+        output_path
+    ]
+    
+    try:
+        sp.run(command, check=True)
+        print(f"Successfully merged audio and video into {output_path}")
+        return output_path
+    except sp.CalledProcessError as e:
+        print(f"Error merging audio and video: {e}")
+        return None
+    
+def load_and_process_audio(audio_path, sample_rate, seconds_start, seconds_total):
+    if audio_path is None:
+        return torch.zeros((2, int(sample_rate * seconds_total)))
+    audio_tensor, sr = torchaudio.load(audio_path)
+    start_index = int(sample_rate * seconds_start)
+    target_length = int(sample_rate * seconds_total)
+    end_index = start_index + target_length
+    audio_tensor = audio_tensor[:, start_index:end_index]
+    if audio_tensor.shape[1] < target_length:
+        pad_length = target_length - audio_tensor.shape[1]
+        audio_tensor = F.pad(audio_tensor, (pad_length, 0))
+    return audio_tensor    

stable_audio_tools/inference/generation.py

@@ -0,0 +1,275 @@
+import numpy as np
+import torch 
+import typing as tp
+import math 
+from torchaudio import transforms as T
+
+from .utils import prepare_audio
+from .sampling import sample, sample_k, sample_rf
+from ..data.utils import PadCrop
+
+def generate_diffusion_uncond(
+        model,
+        steps: int = 250,
+        batch_size: int = 1,
+        sample_size: int = 2097152,
+        seed: int = -1,
+        device: str = "cuda",
+        init_audio: tp.Optional[tp.Tuple[int, torch.Tensor]] = None,
+        init_noise_level: float = 1.0,
+        return_latents = False,
+        **sampler_kwargs
+        ) -> torch.Tensor:
+    
+    # The length of the output in audio samples 
+    audio_sample_size = sample_size
+
+    # If this is latent diffusion, change sample_size instead to the downsampled latent size
+    if model.pretransform is not None:
+        sample_size = sample_size // model.pretransform.downsampling_ratio
+        
+    # Seed
+    # The user can explicitly set the seed to deterministically generate the same output. Otherwise, use a random seed.
+    seed = seed if seed != -1 else np.random.randint(0, 2**32 - 1, dtype=np.uint32)
+    # seed = 777
+    print(seed)
+    torch.manual_seed(seed)
+    # Define the initial noise immediately after setting the seed
+    noise = torch.randn([batch_size, model.io_channels, sample_size], device=device)
+
+    if init_audio is not None:
+        # The user supplied some initial audio (for inpainting or variation). Let us prepare the input audio.
+        in_sr, init_audio = init_audio
+
+        io_channels = model.io_channels
+
+        # For latent models, set the io_channels to the autoencoder's io_channels
+        if model.pretransform is not None:
+            io_channels = model.pretransform.io_channels
+
+        # Prepare the initial audio for use by the model
+        init_audio = prepare_audio(init_audio, in_sr=in_sr, target_sr=model.sample_rate, target_length=audio_sample_size, target_channels=io_channels, device=device)
+
+        # For latent models, encode the initial audio into latents
+        if model.pretransform is not None:
+            init_audio = model.pretransform.encode(init_audio)
+
+        init_audio = init_audio.repeat(batch_size, 1, 1)
+    else:
+        # The user did not supply any initial audio for inpainting or variation. Generate new output from scratch. 
+        init_audio = None
+        init_noise_level = None
+
+    # Inpainting mask
+    
+    if init_audio is not None:
+        # variations
+        sampler_kwargs["sigma_max"] = init_noise_level
+        mask = None 
+    else:
+        mask = None
+
+    # Now the generative AI part:
+
+    diff_objective = model.diffusion_objective
+
+    if diff_objective == "v":
+        # k-diffusion denoising process go!
+        sampled = sample_k(model.model, noise, init_audio, mask, steps, **sampler_kwargs, device=device)
+    elif diff_objective == "rectified_flow":
+        sampled = sample_rf(model.model, noise, init_data=init_audio, steps=steps, **sampler_kwargs, device=device)
+
+    # Denoising process done. 
+    # If this is latent diffusion, decode latents back into audio
+    if model.pretransform is not None and not return_latents:
+        sampled = model.pretransform.decode(sampled)
+
+    # Return audio
+    return sampled
+
+
+def generate_diffusion_cond(
+        model,
+        steps: int = 250,
+        cfg_scale=6,
+        conditioning: dict = None,
+        conditioning_tensors: tp.Optional[dict] = None,
+        negative_conditioning: dict = None,
+        negative_conditioning_tensors: tp.Optional[dict] = None,
+        batch_size: int = 1,
+        sample_size: int = 2097152,
+        sample_rate: int = 48000,
+        seed: int = -1,
+        device: str = "cuda",
+        init_audio: tp.Optional[tp.Tuple[int, torch.Tensor]] = None,
+        init_noise_level: float = 1.0,
+        mask_args: dict = None,
+        return_latents = False,
+        **sampler_kwargs
+        ) -> torch.Tensor: 
+    """
+    Generate audio from a prompt using a diffusion model.
+    
+    Args:
+        model: The diffusion model to use for generation.
+        steps: The number of diffusion steps to use.
+        cfg_scale: Classifier-free guidance scale 
+        conditioning: A dictionary of conditioning parameters to use for generation.
+        conditioning_tensors: A dictionary of precomputed conditioning tensors to use for generation.
+        batch_size: The batch size to use for generation.
+        sample_size: The length of the audio to generate, in samples.
+        sample_rate: The sample rate of the audio to generate (Deprecated, now pulled from the model directly)
+        seed: The random seed to use for generation, or -1 to use a random seed.
+        device: The device to use for generation.
+        init_audio: A tuple of (sample_rate, audio) to use as the initial audio for generation.
+        init_noise_level: The noise level to use when generating from an initial audio sample.
+        return_latents: Whether to return the latents used for generation instead of the decoded audio.
+        **sampler_kwargs: Additional keyword arguments to pass to the sampler.    
+    """
+
+    # The length of the output in audio samples 
+    audio_sample_size = sample_size
+
+    # If this is latent diffusion, change sample_size instead to the downsampled latent size
+    if model.pretransform is not None:
+        sample_size = sample_size // model.pretransform.downsampling_ratio
+        
+    # Seed
+    # The user can explicitly set the seed to deterministically generate the same output. Otherwise, use a random seed.
+    seed = seed if seed != -1 else np.random.randint(0, 2**32 - 1, dtype=np.uint32)
+    # seed = 777
+    # print(seed)
+    torch.manual_seed(seed)
+    # Define the initial noise immediately after setting the seed
+    noise = torch.randn([batch_size, model.io_channels, sample_size], device=device)
+
+    torch.backends.cuda.matmul.allow_tf32 = False
+    torch.backends.cudnn.allow_tf32 = False
+    torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
+    torch.backends.cudnn.benchmark = False
+
+    # Conditioning
+    assert conditioning is not None or conditioning_tensors is not None, "Must provide either conditioning or conditioning_tensors"
+    if conditioning_tensors is None:
+        conditioning_tensors = model.conditioner(conditioning, device)
+    conditioning_inputs = model.get_conditioning_inputs(conditioning_tensors)
+
+    if negative_conditioning is not None or negative_conditioning_tensors is not None:
+        
+        if negative_conditioning_tensors is None:
+            negative_conditioning_tensors = model.conditioner(negative_conditioning, device)
+
+        negative_conditioning_tensors = model.get_conditioning_inputs(negative_conditioning_tensors, negative=True)
+    else:
+        negative_conditioning_tensors = {}
+
+    if init_audio is not None:
+        # The user supplied some initial audio (for inpainting or variation). Let us prepare the input audio.
+        in_sr, init_audio = init_audio
+
+        io_channels = model.io_channels
+
+        # For latent models, set the io_channels to the autoencoder's io_channels
+        if model.pretransform is not None:
+            io_channels = model.pretransform.io_channels
+
+        # Prepare the initial audio for use by the model
+        init_audio = prepare_audio(init_audio, in_sr=in_sr, target_sr=model.sample_rate, target_length=audio_sample_size, target_channels=io_channels, device=device)
+
+        # For latent models, encode the initial audio into latents
+        if model.pretransform is not None:
+            init_audio = model.pretransform.encode(init_audio)
+
+        init_audio = init_audio.repeat(batch_size, 1, 1)
+    else:
+        # The user did not supply any initial audio for inpainting or variation. Generate new output from scratch. 
+        init_audio = None
+        init_noise_level = None
+        mask_args = None
+
+    # Inpainting mask
+    if init_audio is not None and mask_args is not None:
+        # Cut and paste init_audio according to cropfrom, pastefrom, pasteto
+        # This is helpful for forward and reverse outpainting
+        cropfrom = math.floor(mask_args["cropfrom"]/100.0 * sample_size)
+        pastefrom = math.floor(mask_args["pastefrom"]/100.0 * sample_size)
+        pasteto = math.ceil(mask_args["pasteto"]/100.0 * sample_size)
+        assert pastefrom < pasteto, "Paste From should be less than Paste To"
+        croplen = pasteto - pastefrom
+        if cropfrom + croplen > sample_size:
+            croplen = sample_size - cropfrom 
+        cropto = cropfrom + croplen
+        pasteto = pastefrom + croplen
+        cutpaste = init_audio.new_zeros(init_audio.shape)
+        cutpaste[:, :, pastefrom:pasteto] = init_audio[:,:,cropfrom:cropto]
+        #print(cropfrom, cropto, pastefrom, pasteto)
+        init_audio = cutpaste
+        # Build a soft mask (list of floats 0 to 1, the size of the latent) from the given args
+        mask = build_mask(sample_size, mask_args)
+        mask = mask.to(device)
+    elif init_audio is not None and mask_args is None:
+        # variations
+        sampler_kwargs["sigma_max"] = init_noise_level
+        mask = None 
+    else:
+        mask = None
+
+    model_dtype = next(model.model.parameters()).dtype
+    noise = noise.type(model_dtype)
+    conditioning_inputs = {k: v.type(model_dtype) if v is not None else v for k, v in conditioning_inputs.items()}
+    # Now the generative AI part:
+    # k-diffusion denoising process go!
+
+    diff_objective = model.diffusion_objective
+
+    if diff_objective == "v":    
+        # k-diffusion denoising process go!
+        sampled = sample_k(model.model, noise, init_audio, mask, steps, **sampler_kwargs, **conditioning_inputs, **negative_conditioning_tensors, cfg_scale=cfg_scale, batch_cfg=True, rescale_cfg=True, device=device)
+
+    elif diff_objective == "rectified_flow":
+
+        if "sigma_min" in sampler_kwargs:
+            del sampler_kwargs["sigma_min"]
+
+        if "sampler_type" in sampler_kwargs:
+            del sampler_kwargs["sampler_type"]
+
+        sampled = sample_rf(model.model, noise, init_data=init_audio, steps=steps, **sampler_kwargs, **conditioning_inputs, **negative_conditioning_tensors, cfg_scale=cfg_scale, batch_cfg=True, rescale_cfg=True, device=device)
+
+    # v-diffusion: 
+    del noise
+    del conditioning_tensors
+    del conditioning_inputs
+    torch.cuda.empty_cache()
+    # Denoising process done. 
+    # If this is latent diffusion, decode latents back into audio
+
+    if model.pretransform is not None and not return_latents:
+        #cast sampled latents to pretransform dtype
+        sampled = sampled.to(next(model.pretransform.parameters()).dtype)
+        sampled = model.pretransform.decode(sampled)
+
+    return sampled
+
+# builds a softmask given the parameters
+# returns array of values 0 to 1, size sample_size, where 0 means noise / fresh generation, 1 means keep the input audio, 
+# and anything between is a mixture of old/new
+# ideally 0.5 is half/half mixture but i haven't figured this out yet
+def build_mask(sample_size, mask_args):
+    maskstart = math.floor(mask_args["maskstart"]/100.0 * sample_size)
+    maskend = math.ceil(mask_args["maskend"]/100.0 * sample_size)
+    softnessL = round(mask_args["softnessL"]/100.0 * sample_size)
+    softnessR = round(mask_args["softnessR"]/100.0 * sample_size)
+    marination = mask_args["marination"]
+    # use hann windows for softening the transition (i don't know if this is correct)
+    hannL = torch.hann_window(softnessL*2, periodic=False)[:softnessL]
+    hannR = torch.hann_window(softnessR*2, periodic=False)[softnessR:]
+    # build the mask. 
+    mask = torch.zeros((sample_size))
+    mask[maskstart:maskend] = 1
+    mask[maskstart:maskstart+softnessL] = hannL
+    mask[maskend-softnessR:maskend] = hannR
+    # marination finishes the inpainting early in the denoising schedule, and lets audio get changed in the final rounds
+    if marination > 0:        
+        mask = mask * (1-marination) 
+    return mask

stable_audio_tools/inference/sampling.py

@@ -0,0 +1,235 @@
+import torch
+import math
+from tqdm import trange, tqdm
+
+import k_diffusion as K
+    
+# Define the noise schedule and sampling loop
+def get_alphas_sigmas(t):
+    """Returns the scaling factors for the clean image (alpha) and for the
+    noise (sigma), given a timestep."""
+    return torch.cos(t * math.pi / 2), torch.sin(t * math.pi / 2)
+
+def alpha_sigma_to_t(alpha, sigma):
+    """Returns a timestep, given the scaling factors for the clean image and for
+    the noise."""
+    return torch.atan2(sigma, alpha) / math.pi * 2
+
+def t_to_alpha_sigma(t):
+    """Returns the scaling factors for the clean image and for the noise, given
+    a timestep."""
+    return torch.cos(t * math.pi / 2), torch.sin(t * math.pi / 2)
+
+
+@torch.no_grad()
+def sample_discrete_euler(model, x, steps, sigma_max=1, **extra_args):
+    """Draws samples from a model given starting noise. Euler method"""
+
+    # Make tensor of ones to broadcast the single t values
+    ts = x.new_ones([x.shape[0]])
+
+    # Create the noise schedule
+    t = torch.linspace(sigma_max, 0, steps + 1)
+
+    #alphas, sigmas = 1-t, t
+
+    for t_curr, t_prev in tqdm(zip(t[:-1], t[1:])):
+            # Broadcast the current timestep to the correct shape
+            t_curr_tensor = t_curr * torch.ones(
+                (x.shape[0],), dtype=x.dtype, device=x.device
+            )
+            dt = t_prev - t_curr  # we solve backwards in our formulation
+            x = x + dt * model(x, t_curr_tensor, **extra_args) #.denoise(x, denoiser, t_curr_tensor, cond, uc)
+
+    # If we are on the last timestep, output the denoised image
+    return x
+
+@torch.no_grad()
+def sample(model, x, steps, eta, **extra_args):
+    """Draws samples from a model given starting noise. v-diffusion"""
+    ts = x.new_ones([x.shape[0]])
+
+    # Create the noise schedule
+    t = torch.linspace(1, 0, steps + 1)[:-1]
+
+    alphas, sigmas = get_alphas_sigmas(t)
+
+    # The sampling loop
+    for i in trange(steps):
+
+        # Get the model output (v, the predicted velocity)
+        with torch.cuda.amp.autocast():
+            v = model(x, ts * t[i], **extra_args).float()
+
+        # Predict the noise and the denoised image
+        pred = x * alphas[i] - v * sigmas[i]
+        eps = x * sigmas[i] + v * alphas[i]
+
+        # If we are not on the last timestep, compute the noisy image for the
+        # next timestep.
+        if i < steps - 1:
+            # If eta > 0, adjust the scaling factor for the predicted noise
+            # downward according to the amount of additional noise to add
+            ddim_sigma = eta * (sigmas[i + 1]**2 / sigmas[i]**2).sqrt() * \
+                (1 - alphas[i]**2 / alphas[i + 1]**2).sqrt()
+            adjusted_sigma = (sigmas[i + 1]**2 - ddim_sigma**2).sqrt()
+
+            # Recombine the predicted noise and predicted denoised image in the
+            # correct proportions for the next step
+            x = pred * alphas[i + 1] + eps * adjusted_sigma
+
+            # Add the correct amount of fresh noise
+            if eta:
+                x += torch.randn_like(x) * ddim_sigma
+
+    # If we are on the last timestep, output the denoised image
+    return pred
+
+# Soft mask inpainting is just shrinking hard (binary) mask inpainting
+# Given a float-valued soft mask (values between 0 and 1), get the binary mask for this particular step
+def get_bmask(i, steps, mask):
+    strength = (i+1)/(steps)
+    # convert to binary mask
+    bmask = torch.where(mask<=strength,1,0)
+    return bmask
+
+def make_cond_model_fn(model, cond_fn):
+    def cond_model_fn(x, sigma, **kwargs):
+        with torch.enable_grad():
+            x = x.detach().requires_grad_()
+            denoised = model(x, sigma, **kwargs)
+            cond_grad = cond_fn(x, sigma, denoised=denoised, **kwargs).detach()
+            cond_denoised = denoised.detach() + cond_grad * K.utils.append_dims(sigma**2, x.ndim)
+        return cond_denoised
+    return cond_model_fn
+
+# Uses k-diffusion from https://github.com/crowsonkb/k-diffusion
+# init_data is init_audio as latents (if this is latent diffusion)
+# For sampling, set both init_data and mask to None
+# For variations, set init_data 
+# For inpainting, set both init_data & mask 
+def sample_k(
+        model_fn, 
+        noise, 
+        init_data=None,
+        mask=None,
+        steps=100, 
+        sampler_type="dpmpp-2m-sde", 
+        sigma_min=0.5, 
+        sigma_max=50, 
+        rho=1.0, device="cuda", 
+        callback=None, 
+        cond_fn=None,
+        **extra_args
+    ):
+
+    denoiser = K.external.VDenoiser(model_fn)
+
+    if cond_fn is not None:
+        denoiser = make_cond_model_fn(denoiser, cond_fn)
+
+    # Make the list of sigmas. Sigma values are scalars related to the amount of noise each denoising step has
+    sigmas = K.sampling.get_sigmas_polyexponential(steps, sigma_min, sigma_max, rho, device=device)
+    # Scale the initial noise by sigma 
+    noise = noise * sigmas[0]
+
+    wrapped_callback = callback
+
+
+    if mask is None and init_data is not None:
+        # VARIATION (no inpainting)
+        # set the initial latent to the init_data, and noise it with initial sigma
+
+        x = init_data + noise
+        
+    elif mask is not None and init_data is not None:
+        # INPAINTING
+        bmask = get_bmask(0, steps, mask)
+        # initial noising
+        input_noised = init_data + noise
+        # set the initial latent to a mix of init_data and noise, based on step 0's binary mask
+        x = input_noised * bmask + noise * (1-bmask)
+        # define the inpainting callback function (Note: side effects, it mutates x)
+        # See https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py#L596C13-L596C105
+        # callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        # This is called immediately after `denoised = model(x, sigmas[i] * s_in, **extra_args)`
+        def inpainting_callback(args):
+            i = args["i"]
+            x = args["x"]
+            sigma = args["sigma"]
+            #denoised = args["denoised"]
+            # noise the init_data input with this step's appropriate amount of noise
+            input_noised = init_data + torch.randn_like(init_data) * sigma
+            # shrinking hard mask
+            bmask = get_bmask(i, steps, mask)
+            # mix input_noise with x, using binary mask
+            new_x = input_noised * bmask + x * (1-bmask)
+            # mutate x
+            x[:,:,:] = new_x[:,:,:]
+        # wrap together the inpainting callback and the user-submitted callback. 
+        if callback is None: 
+            wrapped_callback = inpainting_callback
+        else:
+            wrapped_callback = lambda args: (inpainting_callback(args), callback(args))
+    else:
+        # SAMPLING
+        # set the initial latent to noise
+        x = noise
+    # x = noise
+
+    with torch.cuda.amp.autocast():
+        if sampler_type == "k-heun":
+            return K.sampling.sample_heun(denoiser, x, sigmas, disable=False, callback=wrapped_callback, extra_args=extra_args)
+        elif sampler_type == "k-lms":
+            return K.sampling.sample_lms(denoiser, x, sigmas, disable=False, callback=wrapped_callback, extra_args=extra_args)
+        elif sampler_type == "k-dpmpp-2s-ancestral":
+            return K.sampling.sample_dpmpp_2s_ancestral(denoiser, x, sigmas, disable=False, callback=wrapped_callback, extra_args=extra_args)
+        elif sampler_type == "k-dpm-2":
+            return K.sampling.sample_dpm_2(denoiser, x, sigmas, disable=False, callback=wrapped_callback, extra_args=extra_args)
+        elif sampler_type == "k-dpm-fast":
+            return K.sampling.sample_dpm_fast(denoiser, x, sigma_min, sigma_max, steps, disable=False, callback=wrapped_callback, extra_args=extra_args)
+        elif sampler_type == "k-dpm-adaptive":
+            return K.sampling.sample_dpm_adaptive(denoiser, x, sigma_min, sigma_max, rtol=0.01, atol=0.01, disable=False, callback=wrapped_callback, extra_args=extra_args)
+        elif sampler_type == "dpmpp-2m-sde":
+            return K.sampling.sample_dpmpp_2m_sde(denoiser, x, sigmas, disable=False, callback=wrapped_callback, extra_args=extra_args)
+        elif sampler_type == "dpmpp-3m-sde":
+            return K.sampling.sample_dpmpp_3m_sde(denoiser, x, sigmas, disable=False, callback=wrapped_callback, extra_args=extra_args)
+
+# Uses discrete Euler sampling for rectified flow models
+# init_data is init_audio as latents (if this is latent diffusion)
+# For sampling, set both init_data and mask to None
+# For variations, set init_data 
+# For inpainting, set both init_data & mask 
+def sample_rf(
+        model_fn, 
+        noise, 
+        init_data=None,
+        steps=100, 
+        sigma_max=1,
+        device="cuda", 
+        callback=None, 
+        cond_fn=None,
+        **extra_args
+    ):
+
+    if sigma_max > 1:
+        sigma_max = 1
+
+    if cond_fn is not None:
+        denoiser = make_cond_model_fn(denoiser, cond_fn)
+
+    wrapped_callback = callback
+
+    if init_data is not None:
+        # VARIATION (no inpainting)
+        # Interpolate the init data and the noise for init audio
+        x = init_data * (1 - sigma_max) + noise * sigma_max
+    else:
+        # SAMPLING
+        # set the initial latent to noise
+        x = noise
+
+    with torch.cuda.amp.autocast():
+        # TODO: Add callback support
+        #return sample_discrete_euler(model_fn, x, steps, sigma_max, callback=wrapped_callback, **extra_args)
+        return sample_discrete_euler(model_fn, x, steps, sigma_max, **extra_args)

stable_audio_tools/inference/utils.py

@@ -0,0 +1,35 @@
+from ..data.utils import PadCrop
+
+from torchaudio import transforms as T
+
+def set_audio_channels(audio, target_channels):
+    if target_channels == 1:
+        # Convert to mono
+        audio = audio.mean(1, keepdim=True)
+    elif target_channels == 2:
+        # Convert to stereo
+        if audio.shape[1] == 1:
+            audio = audio.repeat(1, 2, 1)
+        elif audio.shape[1] > 2:
+            audio = audio[:, :2, :]
+    return audio
+
+def prepare_audio(audio, in_sr, target_sr, target_length, target_channels, device):
+    
+    audio = audio.to(device)
+
+    if in_sr != target_sr:
+        resample_tf = T.Resample(in_sr, target_sr).to(device)
+        audio = resample_tf(audio)
+
+    audio = PadCrop(target_length, randomize=False)(audio)
+
+    # Add batch dimension
+    if audio.dim() == 1:
+        audio = audio.unsqueeze(0).unsqueeze(0)
+    elif audio.dim() == 2:
+        audio = audio.unsqueeze(0)
+
+    audio = set_audio_channels(audio, target_channels)
+
+    return audio

stable_audio_tools/interface/gradio.py

@@ -0,0 +1,495 @@
+import gc
+import platform
+import os
+import subprocess as sp
+import gradio as gr
+import json 
+import torch
+import torchaudio
+
+from aeiou.viz import audio_spectrogram_image
+from einops import rearrange
+from safetensors.torch import load_file
+from torch.nn import functional as F
+from torchaudio import transforms as T
+
+from ..inference.generation import generate_diffusion_cond, generate_diffusion_uncond
+from ..models.factory import create_model_from_config
+from ..models.pretrained import get_pretrained_model
+from ..models.utils import load_ckpt_state_dict
+from ..inference.utils import prepare_audio
+from ..training.utils import copy_state_dict
+from ..data.utils import read_video, merge_video_audio
+
+
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+import warnings
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+device = torch.device("cpu")
+
+os.environ['TMPDIR'] = './tmp'
+
+current_model_name = None
+current_model = None
+current_sample_rate = None
+current_sample_size = None
+
+
+
+def load_model(model_name, model_config=None, model_ckpt_path=None, pretrained_name=None, pretransform_ckpt_path=None, device="cuda", model_half=False):
+    global model_configurations
+    
+    if pretrained_name is not None:
+        print(f"Loading pretrained model {pretrained_name}")
+        model, model_config = get_pretrained_model(pretrained_name)
+    elif model_config is not None and model_ckpt_path is not None:
+        print(f"Creating model from config")
+        model = create_model_from_config(model_config)
+        print(f"Loading model checkpoint from {model_ckpt_path}")
+        copy_state_dict(model, load_ckpt_state_dict(model_ckpt_path))
+    sample_rate = model_config["sample_rate"]
+    sample_size = model_config["sample_size"]
+    if pretransform_ckpt_path is not None:
+        print(f"Loading pretransform checkpoint from {pretransform_ckpt_path}")
+        model.pretransform.load_state_dict(load_ckpt_state_dict(pretransform_ckpt_path), strict=False)
+        print(f"Done loading pretransform")
+    model.to(device).eval().requires_grad_(False)
+    if model_half:
+        model.to(torch.float16)
+    print(f"Done loading model")
+    return model, model_config, sample_rate, sample_size
+
+def load_and_process_audio(audio_path, sample_rate, seconds_start, seconds_total):
+    if audio_path is None:
+        return torch.zeros((2, int(sample_rate * seconds_total)))
+    audio_tensor, sr = torchaudio.load(audio_path)
+    start_index = int(sample_rate * seconds_start)
+    target_length = int(sample_rate * seconds_total)
+    end_index = start_index + target_length
+    audio_tensor = audio_tensor[:, start_index:end_index]
+    if audio_tensor.shape[1] < target_length:
+        pad_length = target_length - audio_tensor.shape[1]
+        audio_tensor = F.pad(audio_tensor, (pad_length, 0))
+    return audio_tensor
+
+def generate_cond(
+        prompt,
+        negative_prompt=None,
+        video_file=None,
+        video_path=None,
+        audio_prompt_file=None,
+        audio_prompt_path=None,
+        seconds_start=0,
+        seconds_total=10,
+        cfg_scale=6.0,
+        steps=250,
+        preview_every=None,
+        seed=-1,
+        sampler_type="dpmpp-3m-sde",
+        sigma_min=0.03,
+        sigma_max=1000,
+        cfg_rescale=0.0,
+        use_init=False,
+        init_audio=None,
+        init_noise_level=1.0,
+        mask_cropfrom=None,
+        mask_pastefrom=None,
+        mask_pasteto=None,
+        mask_maskstart=None,
+        mask_maskend=None,
+        mask_softnessL=None,
+        mask_softnessR=None,
+        mask_marination=None,
+        batch_size=1
+    ):
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gc.collect()
+    print(f"Prompt: {prompt}")
+    preview_images = []
+    if preview_every == 0:
+        preview_every = None
+
+    try:
+        has_mps = platform.system() == "Darwin" and torch.backends.mps.is_available()
+    except Exception:
+        has_mps = False
+    if has_mps:
+        device = torch.device("mps")
+    elif torch.cuda.is_available():
+        device = torch.device("cuda")
+    else:
+        device = torch.device("cpu")
+    model_name = 'default'
+    cfg = model_configurations[model_name]
+    model_config_path = cfg.get("model_config")
+    ckpt_path = cfg.get("ckpt_path")
+    pretrained_name = cfg.get("pretrained_name")
+    pretransform_ckpt_path = cfg.get("pretransform_ckpt_path")
+    model_type = cfg.get("model_type", "diffusion_cond")
+    if model_config_path:
+        with open(model_config_path) as f:
+            model_config = json.load(f)
+    else:
+        model_config = None
+    target_fps = model_config.get("video_fps", 5)
+    global current_model_name, current_model, current_sample_rate, current_sample_size
+    if current_model is None or model_name != current_model_name:
+        current_model, model_config, sample_rate, sample_size = load_model(
+            model_name=model_name,
+            model_config=model_config,
+            model_ckpt_path=ckpt_path,
+            pretrained_name=pretrained_name,
+            pretransform_ckpt_path=pretransform_ckpt_path,
+            device=device,
+            model_half=False
+        )
+        current_model_name = model_name
+        model = current_model
+        current_sample_rate = sample_rate
+        current_sample_size = sample_size
+    else:
+        model = current_model
+        sample_rate = current_sample_rate
+        sample_size = current_sample_size
+    if video_file is not None:
+        video_path = video_file.name
+    elif video_path:
+        video_path = video_path.strip()
+    else:
+        video_path = None
+        
+    if audio_prompt_file is not None:
+        print(f'audio_prompt_file: {audio_prompt_file}')
+        audio_path = audio_prompt_file.name
+    elif audio_prompt_path:
+        audio_path = audio_prompt_path.strip()
+    else:
+        audio_path = None
+    
+    Video_tensors = read_video(video_path, seek_time=seconds_start, duration=seconds_total, target_fps=target_fps)        
+    audio_tensor = load_and_process_audio(audio_path, sample_rate, seconds_start, seconds_total)
+    
+    audio_tensor = audio_tensor.to(device)
+    seconds_input = sample_size / sample_rate
+    print(f'video_path: {video_path}')
+    
+    if not prompt:
+        prompt = ""
+        
+    conditioning = [{
+        "video_prompt": [Video_tensors.unsqueeze(0)],        
+        "text_prompt": prompt,
+        "audio_prompt": audio_tensor.unsqueeze(0),
+        "seconds_start": seconds_start,
+        "seconds_total": seconds_input
+    }] * batch_size
+    if negative_prompt:
+        negative_conditioning = [{
+            "video_prompt": [Video_tensors.unsqueeze(0)],        
+            "text_prompt": negative_prompt,
+            "audio_prompt": audio_tensor.unsqueeze(0),
+            "seconds_start": seconds_start,
+            "seconds_total": seconds_total
+        }] * batch_size
+    else:
+        negative_conditioning = None
+    try:
+        device = next(model.parameters()).device 
+    except Exception as e:
+        device = next(current_model.parameters()).device
+    seed = int(seed)
+    if not use_init:
+        init_audio = None
+    input_sample_size = sample_size
+    if init_audio is not None:
+        in_sr, init_audio = init_audio
+        init_audio = torch.from_numpy(init_audio).float().div(32767)
+        if init_audio.dim() == 1:
+            init_audio = init_audio.unsqueeze(0)
+        elif init_audio.dim() == 2:
+            init_audio = init_audio.transpose(0, 1)
+        if in_sr != sample_rate:
+            resample_tf = T.Resample(in_sr, sample_rate).to(init_audio.device)
+            init_audio = resample_tf(init_audio)
+        audio_length = init_audio.shape[-1]
+        if audio_length > sample_size:
+            input_sample_size = audio_length + (model.min_input_length - (audio_length % model.min_input_length)) % model.min_input_length
+        init_audio = (sample_rate, init_audio)
+    def progress_callback(callback_info):
+        nonlocal preview_images
+        denoised = callback_info["denoised"]
+        current_step = callback_info["i"]
+        sigma = callback_info["sigma"]
+        if (current_step - 1) % preview_every == 0:
+            if model.pretransform is not None:
+                denoised = model.pretransform.decode(denoised)
+            denoised = rearrange(denoised, "b d n -> d (b n)")
+            denoised = denoised.clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+            audio_spectrogram = audio_spectrogram_image(denoised, sample_rate=sample_rate)
+            preview_images.append((audio_spectrogram, f"Step {current_step} sigma={sigma:.3f})"))
+    if mask_cropfrom is not None: 
+        mask_args = {
+            "cropfrom": mask_cropfrom,
+            "pastefrom": mask_pastefrom,
+            "pasteto": mask_pasteto,
+            "maskstart": mask_maskstart,
+            "maskend": mask_maskend,
+            "softnessL": mask_softnessL,
+            "softnessR": mask_softnessR,
+            "marination": mask_marination,
+        }
+    else:
+        mask_args = None 
+    if model_type == "diffusion_cond":
+        audio = generate_diffusion_cond(
+            model, 
+            conditioning=conditioning,
+            negative_conditioning=negative_conditioning,
+            steps=steps,
+            cfg_scale=cfg_scale,
+            batch_size=batch_size,
+            sample_size=input_sample_size,
+            sample_rate=sample_rate,
+            seed=seed,
+            device=device,
+            sampler_type=sampler_type,
+            sigma_min=sigma_min,
+            sigma_max=sigma_max,
+            init_audio=init_audio,
+            init_noise_level=init_noise_level,
+            mask_args=mask_args,
+            callback=progress_callback if preview_every is not None else None,
+            scale_phi=cfg_rescale
+        )
+    elif model_type == "diffusion_uncond":
+        audio = generate_diffusion_uncond(
+            model, 
+            steps=steps,
+            batch_size=batch_size,
+            sample_size=input_sample_size,
+            seed=seed,
+            device=device,
+            sampler_type=sampler_type,
+            sigma_min=sigma_min,
+            sigma_max=sigma_max,
+            init_audio=init_audio,
+            init_noise_level=init_noise_level,
+            callback=progress_callback if preview_every is not None else None
+        )
+    else:
+        raise ValueError(f"Unsupported model type: {model_type}")
+    audio = rearrange(audio, "b d n -> d (b n)")
+    audio = audio.to(torch.float32).div(torch.max(torch.abs(audio))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+    file_name = os.path.basename(video_path) if video_path else "output"
+    output_dir = f"demo_result"
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    output_video_path = f"{output_dir}/{file_name}"
+    torchaudio.save(f"{output_dir}/output.wav", audio, sample_rate)
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    if video_path:
+        merge_video_audio(video_path, f"{output_dir}/output.wav", output_video_path, seconds_start, seconds_total)
+    audio_spectrogram = audio_spectrogram_image(audio, sample_rate=sample_rate)
+    del video_path
+    torch.cuda.empty_cache()
+    gc.collect()
+    return (output_video_path, f"{output_dir}/output.wav")
+
+def toggle_custom_model(selected_model):
+    return gr.Row.update(visible=(selected_model == "Custom Model"))
+
+def create_sampling_ui(model_config_map, inpainting=False):
+    with gr.Blocks() as demo:
+        gr.Markdown(
+            """
+            # 🎧AudioX: Diffusion Transformer for Anything-to-Audio Generation  
+            **[Project Page](https://zeyuet.github.io/AudioX/) · [Huggingface](https://huggingface.co/Zeyue7/AudioX) · [GitHub](https://github.com/ZeyueT/AudioX)**
+            """
+        )
+
+        with gr.Tab("Generation"):
+
+            with gr.Row():
+                with gr.Column():
+                    prompt = gr.Textbox(show_label=False, placeholder="Enter your prompt")
+                    negative_prompt = gr.Textbox(show_label=False, placeholder="Negative prompt", visible=False)
+                    video_path = gr.Textbox(label="Video Path", placeholder="Enter video file path")
+                    video_file = gr.File(label="Upload Video File")
+                    audio_prompt_file = gr.File(label="Upload Audio Prompt File", visible=False)
+                    audio_prompt_path = gr.Textbox(label="Audio Prompt Path", placeholder="Enter audio file path", visible=False)
+            with gr.Row():
+                with gr.Column(scale=6):
+                    with gr.Accordion("Video Params", open=False):                
+                        seconds_start_slider = gr.Slider(minimum=0, maximum=512, step=1, value=0, label="Video Seconds Start")
+                        seconds_total_slider = gr.Slider(minimum=0, maximum=10, step=1, value=10, label="Seconds Total", interactive=False)
+            with gr.Row():
+                with gr.Column(scale=4):
+                    with gr.Accordion("Sampler Params", open=False):
+                        steps_slider = gr.Slider(minimum=1, maximum=500, step=1, value=100, label="Steps")
+                        preview_every_slider = gr.Slider(minimum=0, maximum=100, step=1, value=0, label="Preview Every")
+                        cfg_scale_slider = gr.Slider(minimum=0.0, maximum=25.0, step=0.1, value=7.0, label="CFG Scale")
+                        seed_textbox = gr.Textbox(label="Seed (set to -1 for random seed)", value="-1")
+                        sampler_type_dropdown = gr.Dropdown(
+                            ["dpmpp-2m-sde", "dpmpp-3m-sde", "k-heun", "k-lms", "k-dpmpp-2s-ancestral", "k-dpm-2", "k-dpm-fast"],
+                            label="Sampler Type",
+                            value="dpmpp-3m-sde"
+                        )
+                        sigma_min_slider = gr.Slider(minimum=0.0, maximum=2.0, step=0.01, value=0.03, label="Sigma Min")
+                        sigma_max_slider = gr.Slider(minimum=0.0, maximum=1000.0, step=0.1, value=500, label="Sigma Max")
+                        cfg_rescale_slider = gr.Slider(minimum=0.0, maximum=1, step=0.01, value=0.0, label="CFG Rescale Amount")
+            with gr.Row():
+                with gr.Column(scale=4):
+                    with gr.Accordion("Init Audio", open=False, visible=False):
+                        init_audio_checkbox = gr.Checkbox(label="Use Init Audio")
+                        init_audio_input = gr.Audio(label="Init Audio")
+                        init_noise_level_slider = gr.Slider(minimum=0.1, maximum=100.0, step=0.01, value=0.1, label="Init Noise Level")
+            gr.Markdown("## Examples")
+            with gr.Accordion("Click to show examples", open=False):
+                with gr.Row():
+                    gr.Markdown("**📝 Task: Text-to-Audio**")                
+                    with gr.Column(scale=1.2):
+                        gr.Markdown("Prompt: *Typing on a keyboard*")
+                        ex1 = gr.Button("Load Example")
+                    with gr.Column(scale=1.2):
+                        gr.Markdown("Prompt: *Ocean waves crashing*")
+                        ex2 = gr.Button("Load Example")
+                    with gr.Column(scale=1.2):
+                        gr.Markdown("Prompt: *Footsteps in snow*")
+                        ex3 = gr.Button("Load Example")
+                with gr.Row():
+                    gr.Markdown("**🎶 Task: Text-to-Music**")                
+                    with gr.Column(scale=1.2):
+                        gr.Markdown("Prompt: *An orchestral music piece for a fantasy world.*")
+                        ex4 = gr.Button("Load Example")
+                    with gr.Column(scale=1.2):
+                        gr.Markdown("Prompt: *Produce upbeat electronic music for a dance party*")
+                        ex5 = gr.Button("Load Example")
+                    with gr.Column(scale=1.2):
+                        gr.Markdown("Prompt: *A dreamy lo-fi beat with vinyl crackle*")
+                        ex6 = gr.Button("Load Example")
+                with gr.Row():
+                    gr.Markdown("**🎬 Task: Video-to-Audio**\nPrompt: *Generate general audio for the video*")
+                    with gr.Column(scale=1.2):
+                        gr.Video("example/V2A_sample-1.mp4")
+                        ex7 = gr.Button("Load Example")
+                    with gr.Column(scale=1.2):
+                        gr.Video("example/V2A_sample-2.mp4")
+                        ex8 = gr.Button("Load Example")
+                    with gr.Column(scale=1.2):
+                        gr.Video("example/V2A_sample-3.mp4")
+                        ex9 = gr.Button("Load Example")
+                with gr.Row():
+                    gr.Markdown("**🎵 Task: Video-to-Music**\nPrompt: *Generate music for the video*")                
+                    with gr.Column(scale=1.2):
+                        gr.Video("example/V2M_sample-1.mp4")
+                        ex10 = gr.Button("Load Example")
+                    with gr.Column(scale=1.2):
+                        gr.Video("example/V2M_sample-2.mp4")
+                        ex11 = gr.Button("Load Example")
+                    with gr.Column(scale=1.2):
+                        gr.Video("example/V2M_sample-3.mp4")
+                        ex12 = gr.Button("Load Example")
+            with gr.Row():
+                generate_button = gr.Button("Generate", variant='primary', scale=1)
+            with gr.Row():
+                with gr.Column(scale=6):
+                    video_output = gr.Video(label="Output Video", interactive=False)
+                    audio_output = gr.Audio(label="Output Audio", interactive=False)
+                    send_to_init_button = gr.Button("Send to Init Audio", scale=1, visible=False)
+            send_to_init_button.click(
+                fn=lambda audio: audio,
+                inputs=[audio_output],
+                outputs=[init_audio_input]
+            )
+            inputs = [
+                prompt, 
+                negative_prompt,
+                video_file,
+                video_path,
+                audio_prompt_file,
+                audio_prompt_path,
+                seconds_start_slider, 
+                seconds_total_slider, 
+                cfg_scale_slider, 
+                steps_slider, 
+                preview_every_slider, 
+                seed_textbox, 
+                sampler_type_dropdown, 
+                sigma_min_slider, 
+                sigma_max_slider,
+                cfg_rescale_slider,
+                init_audio_checkbox,
+                init_audio_input,
+                init_noise_level_slider
+            ]
+            generate_button.click(
+                fn=generate_cond, 
+                inputs=inputs,
+                outputs=[
+                    video_output,
+                    audio_output
+                ], 
+                api_name="generate"
+            ) 
+            ex1.click(lambda: ["Typing on a keyboard", None, None, None, None, None, 0, 10, 7.0, 100, 0, "1225575558", "dpmpp-3m-sde", 0.03, 500, 0.0, False, None, 0.1], inputs=[], outputs=inputs)
+            ex2.click(lambda: ["Ocean waves crashing", None, None, None, None, None, 0, 10, 7.0, 100, 0, "3615819170", "dpmpp-3m-sde", 0.03, 500, 0.0, False, None, 0.1], inputs=[], outputs=inputs)
+            ex3.click(lambda: ["Footsteps in snow", None, None, None, None, None, 0, 10, 7.0, 100, 0, "1703896811", "dpmpp-3m-sde", 0.03, 500, 0.0, False, None, 0.1], inputs=[], outputs=inputs)
+            ex4.click(lambda: ["An orchestral music piece for a fantasy world.", None, None, None, None, None, 0, 10, 7.0, 100, 0, "1561898939", "dpmpp-3m-sde", 0.03, 500, 0.0, False, None, 0.1], inputs=[], outputs=inputs)
+            ex5.click(lambda: ["Produce upbeat electronic music for a dance party", None, None, None, None, None, 0, 10, 7.0, 100, 0, "406022999", "dpmpp-3m-sde", 0.03, 500, 0.0, False, None, 0.1], inputs=[], outputs=inputs)
+            ex6.click(lambda: ["A dreamy lo-fi beat with vinyl crackle", None, None, None, None, None, 0, 10, 7.0, 100, 0, "807934770", "dpmpp-3m-sde", 0.03, 500, 0.0, False, None, 0.1], inputs=[], outputs=inputs)
+            ex7.click(lambda: ["Generate general audio for the video", None, None, "example/V2A_sample-1.mp4", None, None, 0, 10, 7.0, 100, 0, "3737819478", "dpmpp-3m-sde", 0.03, 500, 0.0, False, None, 0.1], inputs=[], outputs=inputs)
+            ex8.click(lambda: ["Generate general audio for the video", None, None, "example/V2A_sample-2.mp4", None, None, 0, 10, 7.0, 100, 0, "1900718499", "dpmpp-3m-sde", 0.03, 500, 0.0, False, None, 0.1], inputs=[], outputs=inputs)
+            ex9.click(lambda: ["Generate general audio for the video", None, None, "example/V2A_sample-3.mp4", None, None, 0, 10, 7.0, 100, 0, "2289822202", "dpmpp-3m-sde", 0.03, 500, 0.0, False, None, 0.1], inputs=[], outputs=inputs)
+            ex10.click(lambda: ["Generate music for the video", None, None, "example/V2M_sample-1.mp4", None, None, 0, 10, 7.0, 100, 0, "3498087420", "dpmpp-3m-sde", 0.03, 500, 0.0, False, None, 0.1], inputs=[], outputs=inputs)
+            ex11.click(lambda: ["Generate music for the video", None, None, "example/V2M_sample-2.mp4", None, None, 0, 10, 7.0, 100, 0, "3753837734", "dpmpp-3m-sde", 0.03, 500, 0.0, False, None, 0.1], inputs=[], outputs=inputs)
+            ex12.click(lambda: ["Generate music for the video", None, None, "example/V2M_sample-3.mp4", None, None, 0, 10, 7.0, 100, 0, "3510832996", "dpmpp-3m-sde", 0.03, 500, 0.0, False, None, 0.1], inputs=[], outputs=inputs)
+        return demo
+
+def create_txt2audio_ui(model_config_map):
+    with gr.Blocks(css=".gradio-container { max-width: 1120px; margin: auto; }") as ui:
+        with gr.Tab("Generation"):
+            create_sampling_ui(model_config_map)
+    return ui
+
+def toggle_custom_model(selected_model):
+    return gr.Row.update(visible=(selected_model == "Custom Model"))
+
+def create_ui(model_config_path=None, ckpt_path=None, pretrained_name=None, pretransform_ckpt_path=None, model_half=False):
+    global model_configurations
+    global device
+
+    try:
+        has_mps = platform.system() == "Darwin" and torch.backends.mps.is_available()
+    except Exception:
+        has_mps = False
+
+    if has_mps:
+        device = torch.device("mps")
+    elif torch.cuda.is_available():
+        device = torch.device("cuda")
+    else:
+        device = torch.device("cpu")
+
+    print("Using device:", device)
+
+    model_configurations = {
+        "default": {
+            "model_config": "./model/config.json",
+            "ckpt_path": "./model/model.ckpt"
+        }
+    }
+    ui = create_txt2audio_ui(model_configurations)
+    return ui
+
+if __name__ == "__main__":
+    ui = create_ui(
+        model_config_path='./model/config.json',
+        share=True
+    )
+    ui.launch()

stable_audio_tools/models/__init__.py

@@ -0,0 +1 @@
+from .factory import create_model_from_config, create_model_from_config_path

stable_audio_tools/models/adp.py

@@ -0,0 +1,1588 @@
+# Copied and modified from https://github.com/archinetai/audio-diffusion-pytorch/blob/v0.0.94/audio_diffusion_pytorch/modules.py under MIT License
+# License can be found in LICENSES/LICENSE_ADP.txt
+
+import math
+from inspect import isfunction
+from math import ceil, floor, log, pi, log2
+from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, TypeVar, Union
+from packaging import version
+
+import torch
+import torch.nn as nn
+from einops import rearrange, reduce, repeat
+from einops.layers.torch import Rearrange
+from einops_exts import rearrange_many
+from torch import Tensor, einsum
+from torch.backends.cuda import sdp_kernel
+from torch.nn import functional as F
+from dac.nn.layers import Snake1d
+
+"""
+Utils
+"""
+
+
+class ConditionedSequential(nn.Module):
+    def __init__(self, *modules):
+        super().__init__()
+        self.module_list = nn.ModuleList(*modules)
+
+    def forward(self, x: Tensor, mapping: Optional[Tensor] = None):
+        for module in self.module_list:
+            x = module(x, mapping)
+        return x
+
+T = TypeVar("T")
+
+def default(val: Optional[T], d: Union[Callable[..., T], T]) -> T:
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+def exists(val: Optional[T]) -> T:
+    return val is not None
+
+def closest_power_2(x: float) -> int:
+    exponent = log2(x)
+    distance_fn = lambda z: abs(x - 2 ** z)  # noqa
+    exponent_closest = min((floor(exponent), ceil(exponent)), key=distance_fn)
+    return 2 ** int(exponent_closest)
+
+def group_dict_by_prefix(prefix: str, d: Dict) -> Tuple[Dict, Dict]:
+    return_dicts: Tuple[Dict, Dict] = ({}, {})
+    for key in d.keys():
+        no_prefix = int(not key.startswith(prefix))
+        return_dicts[no_prefix][key] = d[key]
+    return return_dicts
+
+def groupby(prefix: str, d: Dict, keep_prefix: bool = False) -> Tuple[Dict, Dict]:
+    kwargs_with_prefix, kwargs = group_dict_by_prefix(prefix, d)
+    if keep_prefix:
+        return kwargs_with_prefix, kwargs
+    kwargs_no_prefix = {k[len(prefix) :]: v for k, v in kwargs_with_prefix.items()}
+    return kwargs_no_prefix, kwargs
+
+"""
+Convolutional Blocks
+"""
+import typing as tp
+
+# Copied from https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/modules/conv.py under MIT License
+# License available in LICENSES/LICENSE_META.txt
+
+def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
+                                 padding_total: int = 0) -> int:
+    """See `pad_for_conv1d`."""
+    length = x.shape[-1]
+    n_frames = (length - kernel_size + padding_total) / stride + 1
+    ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
+    return ideal_length - length
+
+
+def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
+    """Pad for a convolution to make sure that the last window is full.
+    Extra padding is added at the end. This is required to ensure that we can rebuild
+    an output of the same length, as otherwise, even with padding, some time steps
+    might get removed.
+    For instance, with total padding = 4, kernel size = 4, stride = 2:
+        0 0 1 2 3 4 5 0 0   # (0s are padding)
+        1   2   3           # (output frames of a convolution, last 0 is never used)
+        0 0 1 2 3 4 5 0     # (output of tr. conv., but pos. 5 is going to get removed as padding)
+            1 2 3 4         # once you removed padding, we are missing one time step !
+    """
+    extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
+    return F.pad(x, (0, extra_padding))
+
+
+def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'constant', value: float = 0.):
+    """Tiny wrapper around F.pad, just to allow for reflect padding on small input.
+    If this is the case, we insert extra 0 padding to the right before the reflection happen.
+    """
+    length = x.shape[-1]
+    padding_left, padding_right = paddings
+    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
+    if mode == 'reflect':
+        max_pad = max(padding_left, padding_right)
+        extra_pad = 0
+        if length <= max_pad:
+            extra_pad = max_pad - length + 1
+            x = F.pad(x, (0, extra_pad))
+        padded = F.pad(x, paddings, mode, value)
+        end = padded.shape[-1] - extra_pad
+        return padded[..., :end]
+    else:
+        return F.pad(x, paddings, mode, value)
+
+
+def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
+    """Remove padding from x, handling properly zero padding. Only for 1d!"""
+    padding_left, padding_right = paddings
+    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
+    assert (padding_left + padding_right) <= x.shape[-1]
+    end = x.shape[-1] - padding_right
+    return x[..., padding_left: end]
+
+
+class Conv1d(nn.Conv1d):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+    
+    def forward(self, x: Tensor, causal=False) -> Tensor:
+        kernel_size = self.kernel_size[0]
+        stride = self.stride[0]
+        dilation = self.dilation[0]
+        kernel_size = (kernel_size - 1) * dilation + 1  # effective kernel size with dilations
+        padding_total = kernel_size - stride
+        extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
+        if causal:
+            # Left padding for causal
+            x = pad1d(x, (padding_total, extra_padding))
+        else:
+            # Asymmetric padding required for odd strides
+            padding_right = padding_total // 2
+            padding_left = padding_total - padding_right
+            x = pad1d(x, (padding_left, padding_right + extra_padding))
+        return super().forward(x)
+        
+class ConvTranspose1d(nn.ConvTranspose1d):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, x: Tensor, causal=False) -> Tensor:
+        kernel_size = self.kernel_size[0]
+        stride = self.stride[0]
+        padding_total = kernel_size - stride
+
+        y = super().forward(x)
+
+        # We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
+        # removed at the very end, when keeping only the right length for the output,
+        # as removing it here would require also passing the length at the matching layer
+        # in the encoder.
+        if causal:
+            padding_right = ceil(padding_total)
+            padding_left = padding_total - padding_right
+            y = unpad1d(y, (padding_left, padding_right))
+        else:
+            # Asymmetric padding required for odd strides
+            padding_right = padding_total // 2
+            padding_left = padding_total - padding_right
+            y = unpad1d(y, (padding_left, padding_right))
+        return y
+    
+
+def Downsample1d(
+    in_channels: int, out_channels: int, factor: int, kernel_multiplier: int = 2
+) -> nn.Module:
+    assert kernel_multiplier % 2 == 0, "Kernel multiplier must be even"
+
+    return Conv1d(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        kernel_size=factor * kernel_multiplier + 1,
+        stride=factor
+    )
+
+
+def Upsample1d(
+    in_channels: int, out_channels: int, factor: int, use_nearest: bool = False
+) -> nn.Module:
+
+    if factor == 1:
+        return Conv1d(
+            in_channels=in_channels, out_channels=out_channels, kernel_size=3
+        )
+
+    if use_nearest:
+        return nn.Sequential(
+            nn.Upsample(scale_factor=factor, mode="nearest"),
+            Conv1d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=3
+            ),
+        )
+    else:
+        return ConvTranspose1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=factor * 2,
+            stride=factor
+        )
+
+
+class ConvBlock1d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        *,
+        kernel_size: int = 3,
+        stride: int = 1,
+        dilation: int = 1,
+        num_groups: int = 8,
+        use_norm: bool = True,
+        use_snake: bool = False
+    ) -> None:
+        super().__init__()
+
+        self.groupnorm = (
+            nn.GroupNorm(num_groups=num_groups, num_channels=in_channels)
+            if use_norm
+            else nn.Identity()
+        )
+
+        if use_snake:
+            self.activation = Snake1d(in_channels)
+        else:
+            self.activation = nn.SiLU() 
+
+        self.project = Conv1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            dilation=dilation,
+        )
+
+    def forward(
+        self, x: Tensor, scale_shift: Optional[Tuple[Tensor, Tensor]] = None, causal=False
+    ) -> Tensor:
+        x = self.groupnorm(x)
+        if exists(scale_shift):
+            scale, shift = scale_shift
+            x = x * (scale + 1) + shift
+        x = self.activation(x)
+        return self.project(x, causal=causal)
+
+
+class MappingToScaleShift(nn.Module):
+    def __init__(
+        self,
+        features: int,
+        channels: int,
+    ):
+        super().__init__()
+
+        self.to_scale_shift = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(in_features=features, out_features=channels * 2),
+        )
+
+    def forward(self, mapping: Tensor) -> Tuple[Tensor, Tensor]:
+        scale_shift = self.to_scale_shift(mapping)
+        scale_shift = rearrange(scale_shift, "b c -> b c 1")
+        scale, shift = scale_shift.chunk(2, dim=1)
+        return scale, shift
+
+
+class ResnetBlock1d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        *,
+        kernel_size: int = 3,
+        stride: int = 1,
+        dilation: int = 1,
+        use_norm: bool = True,
+        use_snake: bool = False,
+        num_groups: int = 8,
+        context_mapping_features: Optional[int] = None,
+    ) -> None:
+        super().__init__()
+
+        self.use_mapping = exists(context_mapping_features)
+
+        self.block1 = ConvBlock1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            dilation=dilation,
+            use_norm=use_norm,
+            num_groups=num_groups,
+            use_snake=use_snake
+        )
+
+        if self.use_mapping:
+            assert exists(context_mapping_features)
+            self.to_scale_shift = MappingToScaleShift(
+                features=context_mapping_features, channels=out_channels
+            )
+
+        self.block2 = ConvBlock1d(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            use_norm=use_norm,
+            num_groups=num_groups,
+            use_snake=use_snake
+        )
+
+        self.to_out = (
+            Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=1)
+            if in_channels != out_channels
+            else nn.Identity()
+        )
+
+    def forward(self, x: Tensor, mapping: Optional[Tensor] = None, causal=False) -> Tensor:
+        assert_message = "context mapping required if context_mapping_features > 0"
+        assert not (self.use_mapping ^ exists(mapping)), assert_message
+
+        h = self.block1(x, causal=causal)
+
+        scale_shift = None
+        if self.use_mapping:
+            scale_shift = self.to_scale_shift(mapping)
+
+        h = self.block2(h, scale_shift=scale_shift, causal=causal)
+
+        return h + self.to_out(x)
+
+
+class Patcher(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        patch_size: int,
+        context_mapping_features: Optional[int] = None,
+        use_snake: bool = False,
+    ):
+        super().__init__()
+        assert_message = f"out_channels must be divisible by patch_size ({patch_size})"
+        assert out_channels % patch_size == 0, assert_message
+        self.patch_size = patch_size
+
+        self.block = ResnetBlock1d(
+            in_channels=in_channels,
+            out_channels=out_channels // patch_size,
+            num_groups=1,
+            context_mapping_features=context_mapping_features,
+            use_snake=use_snake
+        )
+
+    def forward(self, x: Tensor, mapping: Optional[Tensor] = None, causal=False) -> Tensor:
+        x = self.block(x, mapping, causal=causal)
+        x = rearrange(x, "b c (l p) -> b (c p) l", p=self.patch_size)
+        return x
+
+
+class Unpatcher(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        patch_size: int,
+        context_mapping_features: Optional[int] = None,
+        use_snake: bool = False
+    ):
+        super().__init__()
+        assert_message = f"in_channels must be divisible by patch_size ({patch_size})"
+        assert in_channels % patch_size == 0, assert_message
+        self.patch_size = patch_size
+
+        self.block = ResnetBlock1d(
+            in_channels=in_channels // patch_size,
+            out_channels=out_channels,
+            num_groups=1,
+            context_mapping_features=context_mapping_features,
+            use_snake=use_snake
+        )
+
+    def forward(self, x: Tensor, mapping: Optional[Tensor] = None, causal=False) -> Tensor:
+        x = rearrange(x, " b (c p) l -> b c (l p) ", p=self.patch_size)
+        x = self.block(x, mapping, causal=causal)
+        return x
+
+
+"""
+Attention Components
+"""
+def FeedForward(features: int, multiplier: int) -> nn.Module:
+    mid_features = features * multiplier
+    return nn.Sequential(
+        nn.Linear(in_features=features, out_features=mid_features),
+        nn.GELU(),
+        nn.Linear(in_features=mid_features, out_features=features),
+    )
+
+def add_mask(sim: Tensor, mask: Tensor) -> Tensor:
+    b, ndim = sim.shape[0], mask.ndim
+    if ndim == 3:
+        mask = rearrange(mask, "b n m -> b 1 n m")
+    if ndim == 2:
+        mask = repeat(mask, "n m -> b 1 n m", b=b)
+    max_neg_value = -torch.finfo(sim.dtype).max
+    sim = sim.masked_fill(~mask, max_neg_value)
+    return sim
+
+def causal_mask(q: Tensor, k: Tensor) -> Tensor:
+    b, i, j, device = q.shape[0], q.shape[-2], k.shape[-2], q.device
+    mask = ~torch.ones((i, j), dtype=torch.bool, device=device).triu(j - i + 1)
+    mask = repeat(mask, "n m -> b n m", b=b)
+    return mask
+
+class AttentionBase(nn.Module):
+    def __init__(
+        self,
+        features: int,
+        *,
+        head_features: int,
+        num_heads: int,
+        out_features: Optional[int] = None,
+    ):
+        super().__init__()
+        self.scale = head_features**-0.5
+        self.num_heads = num_heads
+        mid_features = head_features * num_heads
+        out_features = default(out_features, features)
+
+        self.to_out = nn.Linear(
+            in_features=mid_features, out_features=out_features
+        )
+
+        self.use_flash = torch.cuda.is_available() and version.parse(torch.__version__) >= version.parse('2.0.0')
+
+        if not self.use_flash:
+            return
+
+        device_properties = torch.cuda.get_device_properties(torch.device('cuda'))
+
+        if device_properties.major == 8 and device_properties.minor == 0:
+            # Use flash attention for A100 GPUs
+            self.sdp_kernel_config = (True, False, False)
+        else:
+            # Don't use flash attention for other GPUs
+            self.sdp_kernel_config = (False, True, True)
+
+    def forward(
+        self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None, is_causal: bool = False
+    ) -> Tensor:
+        # Split heads
+        q, k, v = rearrange_many((q, k, v), "b n (h d) -> b h n d", h=self.num_heads)
+
+        if not self.use_flash:
+            if is_causal and not mask:
+                # Mask out future tokens for causal attention
+                mask = causal_mask(q, k)
+
+            # Compute similarity matrix and add eventual mask
+            sim = einsum("... n d, ... m d -> ... n m", q, k) * self.scale
+            sim = add_mask(sim, mask) if exists(mask) else sim
+
+            # Get attention matrix with softmax
+            attn = sim.softmax(dim=-1, dtype=torch.float32)
+
+            # Compute values
+            out = einsum("... n m, ... m d -> ... n d", attn, v)
+        else:
+            with sdp_kernel(*self.sdp_kernel_config):
+                out = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, is_causal=is_causal)
+
+        out = rearrange(out, "b h n d -> b n (h d)")
+        return self.to_out(out)
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        features: int,
+        *,
+        head_features: int,
+        num_heads: int,
+        out_features: Optional[int] = None,
+        context_features: Optional[int] = None,
+        causal: bool = False,
+    ):
+        super().__init__()
+        self.context_features = context_features
+        self.causal = causal
+        mid_features = head_features * num_heads
+        context_features = default(context_features, features)
+
+        self.norm = nn.LayerNorm(features)
+        self.norm_context = nn.LayerNorm(context_features)
+        self.to_q = nn.Linear(
+            in_features=features, out_features=mid_features, bias=False
+        )
+        self.to_kv = nn.Linear(
+            in_features=context_features, out_features=mid_features * 2, bias=False
+        )
+        self.attention = AttentionBase(
+            features,
+            num_heads=num_heads,
+            head_features=head_features,
+            out_features=out_features,
+        )
+
+    def forward(
+        self,
+        x: Tensor, # [b, n, c]
+        context: Optional[Tensor] = None, # [b, m, d]
+        context_mask: Optional[Tensor] = None,  # [b, m], false is masked,
+        causal: Optional[bool] = False,
+    ) -> Tensor:
+        assert_message = "You must provide a context when using context_features"
+        assert not self.context_features or exists(context), assert_message
+        # Use context if provided
+        context = default(context, x)
+        # Normalize then compute q from input and k,v from context
+        x, context = self.norm(x), self.norm_context(context)
+
+        q, k, v = (self.to_q(x), *torch.chunk(self.to_kv(context), chunks=2, dim=-1))
+
+        if exists(context_mask):
+            # Mask out cross-attention for padding tokens
+            mask = repeat(context_mask, "b m -> b m d", d=v.shape[-1])
+            k, v = k * mask, v * mask
+
+        # Compute and return attention
+        return self.attention(q, k, v, is_causal=self.causal or causal)
+
+
+def FeedForward(features: int, multiplier: int) -> nn.Module:
+    mid_features = features * multiplier
+    return nn.Sequential(
+        nn.Linear(in_features=features, out_features=mid_features),
+        nn.GELU(),
+        nn.Linear(in_features=mid_features, out_features=features),
+    )
+
+"""
+Transformer Blocks
+"""
+
+
+class TransformerBlock(nn.Module):
+    def __init__(
+        self,
+        features: int,
+        num_heads: int,
+        head_features: int,
+        multiplier: int,
+        context_features: Optional[int] = None,
+    ):
+        super().__init__()
+
+        self.use_cross_attention = exists(context_features) and context_features > 0
+
+        self.attention = Attention(
+            features=features,
+            num_heads=num_heads,
+            head_features=head_features
+        )
+
+        if self.use_cross_attention:
+            self.cross_attention = Attention(
+                features=features,
+                num_heads=num_heads,
+                head_features=head_features,
+                context_features=context_features
+            )
+
+        self.feed_forward = FeedForward(features=features, multiplier=multiplier)
+
+    def forward(self, x: Tensor, *, context: Optional[Tensor] = None, context_mask: Optional[Tensor] = None, causal: Optional[bool] = False) -> Tensor:
+        x = self.attention(x, causal=causal) + x
+        if self.use_cross_attention:
+            x = self.cross_attention(x, context=context, context_mask=context_mask) + x
+        x = self.feed_forward(x) + x
+        return x
+
+
+"""
+Transformers
+"""
+
+
+class Transformer1d(nn.Module):
+    def __init__(
+        self,
+        num_layers: int,
+        channels: int,
+        num_heads: int,
+        head_features: int,
+        multiplier: int,
+        context_features: Optional[int] = None,
+    ):
+        super().__init__()
+
+        self.to_in = nn.Sequential(
+            nn.GroupNorm(num_groups=32, num_channels=channels, eps=1e-6, affine=True),
+            Conv1d(
+                in_channels=channels,
+                out_channels=channels,
+                kernel_size=1,
+            ),
+            Rearrange("b c t -> b t c"),
+        )
+
+        self.blocks = nn.ModuleList(
+            [
+                TransformerBlock(
+                    features=channels,
+                    head_features=head_features,
+                    num_heads=num_heads,
+                    multiplier=multiplier,
+                    context_features=context_features,
+                )
+                for i in range(num_layers)
+            ]
+        )
+
+        self.to_out = nn.Sequential(
+            Rearrange("b t c -> b c t"),
+            Conv1d(
+                in_channels=channels,
+                out_channels=channels,
+                kernel_size=1,
+            ),
+        )
+
+    def forward(self, x: Tensor, *, context: Optional[Tensor] = None, context_mask: Optional[Tensor] = None, causal=False) -> Tensor:
+        x = self.to_in(x)
+        for block in self.blocks:
+            x = block(x, context=context, context_mask=context_mask, causal=causal)
+        x = self.to_out(x)
+        return x
+
+
+"""
+Time Embeddings
+"""
+
+
+class SinusoidalEmbedding(nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x: Tensor) -> Tensor:
+        device, half_dim = x.device, self.dim // 2
+        emb = torch.tensor(log(10000) / (half_dim - 1), device=device)
+        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
+        emb = rearrange(x, "i -> i 1") * rearrange(emb, "j -> 1 j")
+        return torch.cat((emb.sin(), emb.cos()), dim=-1)
+
+
+class LearnedPositionalEmbedding(nn.Module):
+    """Used for continuous time"""
+
+    def __init__(self, dim: int):
+        super().__init__()
+        assert (dim % 2) == 0
+        half_dim = dim // 2
+        self.weights = nn.Parameter(torch.randn(half_dim))
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = rearrange(x, "b -> b 1")
+        freqs = x * rearrange(self.weights, "d -> 1 d") * 2 * pi
+        fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1)
+        fouriered = torch.cat((x, fouriered), dim=-1)
+        return fouriered
+
+
+def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module:
+    return nn.Sequential(
+        LearnedPositionalEmbedding(dim),
+        nn.Linear(in_features=dim + 1, out_features=out_features),
+    )
+
+
+"""
+Encoder/Decoder Components
+"""
+
+
+class DownsampleBlock1d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        *,
+        factor: int,
+        num_groups: int,
+        num_layers: int,
+        kernel_multiplier: int = 2,
+        use_pre_downsample: bool = True,
+        use_skip: bool = False,
+        use_snake: bool = False,
+        extract_channels: int = 0,
+        context_channels: int = 0,
+        num_transformer_blocks: int = 0,
+        attention_heads: Optional[int] = None,
+        attention_features: Optional[int] = None,
+        attention_multiplier: Optional[int] = None,
+        context_mapping_features: Optional[int] = None,
+        context_embedding_features: Optional[int] = None,
+    ):
+        super().__init__()
+        self.use_pre_downsample = use_pre_downsample
+        self.use_skip = use_skip
+        self.use_transformer = num_transformer_blocks > 0
+        self.use_extract = extract_channels > 0
+        self.use_context = context_channels > 0
+
+        channels = out_channels if use_pre_downsample else in_channels
+
+        self.downsample = Downsample1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            factor=factor,
+            kernel_multiplier=kernel_multiplier,
+        )
+
+        self.blocks = nn.ModuleList(
+            [
+                ResnetBlock1d(
+                    in_channels=channels + context_channels if i == 0 else channels,
+                    out_channels=channels,
+                    num_groups=num_groups,
+                    context_mapping_features=context_mapping_features,
+                    use_snake=use_snake
+                )
+                for i in range(num_layers)
+            ]
+        )
+
+        if self.use_transformer:
+            assert (
+                (exists(attention_heads) or exists(attention_features))
+                and exists(attention_multiplier)
+            )
+
+            if attention_features is None and attention_heads is not None:
+                attention_features = channels // attention_heads
+
+            if attention_heads is None and attention_features is not None:
+                attention_heads = channels // attention_features
+
+            self.transformer = Transformer1d(
+                num_layers=num_transformer_blocks,
+                channels=channels,
+                num_heads=attention_heads,
+                head_features=attention_features,
+                multiplier=attention_multiplier,
+                context_features=context_embedding_features
+            )
+
+        if self.use_extract:
+            num_extract_groups = min(num_groups, extract_channels)
+            self.to_extracted = ResnetBlock1d(
+                in_channels=out_channels,
+                out_channels=extract_channels,
+                num_groups=num_extract_groups,
+                use_snake=use_snake
+            )
+
+    def forward(
+        self,
+        x: Tensor,
+        *,
+        mapping: Optional[Tensor] = None,
+        channels: Optional[Tensor] = None,
+        embedding: Optional[Tensor] = None,
+        embedding_mask: Optional[Tensor] = None,
+        causal: Optional[bool] = False
+    ) -> Union[Tuple[Tensor, List[Tensor]], Tensor]:
+
+        if self.use_pre_downsample:
+            x = self.downsample(x)
+
+        if self.use_context and exists(channels):
+            x = torch.cat([x, channels], dim=1)
+
+        skips = []
+        for block in self.blocks:
+            x = block(x, mapping=mapping, causal=causal)
+            skips += [x] if self.use_skip else []
+
+        if self.use_transformer:
+            x = self.transformer(x, context=embedding, context_mask=embedding_mask, causal=causal)
+            skips += [x] if self.use_skip else []
+
+        if not self.use_pre_downsample:
+            x = self.downsample(x)
+
+        if self.use_extract:
+            extracted = self.to_extracted(x)
+            return x, extracted
+
+        return (x, skips) if self.use_skip else x
+
+
+class UpsampleBlock1d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        *,
+        factor: int,
+        num_layers: int,
+        num_groups: int,
+        use_nearest: bool = False,
+        use_pre_upsample: bool = False,
+        use_skip: bool = False,
+        use_snake: bool = False,
+        skip_channels: int = 0,
+        use_skip_scale: bool = False,
+        extract_channels: int = 0,
+        num_transformer_blocks: int = 0,
+        attention_heads: Optional[int] = None,
+        attention_features: Optional[int] = None,
+        attention_multiplier: Optional[int] = None,
+        context_mapping_features: Optional[int] = None,
+        context_embedding_features: Optional[int] = None,
+    ):
+        super().__init__()
+
+        self.use_extract = extract_channels > 0
+        self.use_pre_upsample = use_pre_upsample
+        self.use_transformer = num_transformer_blocks > 0
+        self.use_skip = use_skip
+        self.skip_scale = 2 ** -0.5 if use_skip_scale else 1.0
+
+        channels = out_channels if use_pre_upsample else in_channels
+
+        self.blocks = nn.ModuleList(
+            [
+                ResnetBlock1d(
+                    in_channels=channels + skip_channels,
+                    out_channels=channels,
+                    num_groups=num_groups,
+                    context_mapping_features=context_mapping_features,
+                    use_snake=use_snake
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        if self.use_transformer:
+            assert (
+                (exists(attention_heads) or exists(attention_features))
+                and exists(attention_multiplier)
+            )
+
+            if attention_features is None and attention_heads is not None:
+                attention_features = channels // attention_heads
+
+            if attention_heads is None and attention_features is not None:
+                attention_heads = channels // attention_features
+
+            self.transformer = Transformer1d(
+                num_layers=num_transformer_blocks,
+                channels=channels,
+                num_heads=attention_heads,
+                head_features=attention_features,
+                multiplier=attention_multiplier,
+                context_features=context_embedding_features,
+            )
+
+        self.upsample = Upsample1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            factor=factor,
+            use_nearest=use_nearest,
+        )
+
+        if self.use_extract:
+            num_extract_groups = min(num_groups, extract_channels)
+            self.to_extracted = ResnetBlock1d(
+                in_channels=out_channels,
+                out_channels=extract_channels,
+                num_groups=num_extract_groups,
+                use_snake=use_snake
+            )
+
+    def add_skip(self, x: Tensor, skip: Tensor) -> Tensor:
+        return torch.cat([x, skip * self.skip_scale], dim=1)
+
+    def forward(
+        self,
+        x: Tensor,
+        *,
+        skips: Optional[List[Tensor]] = None,
+        mapping: Optional[Tensor] = None,
+        embedding: Optional[Tensor] = None,
+        embedding_mask: Optional[Tensor] = None,
+        causal: Optional[bool] = False
+    ) -> Union[Tuple[Tensor, Tensor], Tensor]:
+
+        if self.use_pre_upsample:
+            x = self.upsample(x)
+
+        for block in self.blocks:
+            x = self.add_skip(x, skip=skips.pop()) if exists(skips) else x
+            x = block(x, mapping=mapping, causal=causal)
+
+        if self.use_transformer:
+            x = self.transformer(x, context=embedding, context_mask=embedding_mask, causal=causal)
+
+        if not self.use_pre_upsample:
+            x = self.upsample(x)
+
+        if self.use_extract:
+            extracted = self.to_extracted(x)
+            return x, extracted
+
+        return x
+
+
+class BottleneckBlock1d(nn.Module):
+    def __init__(
+        self,
+        channels: int,
+        *,
+        num_groups: int,
+        num_transformer_blocks: int = 0,
+        attention_heads: Optional[int] = None,
+        attention_features: Optional[int] = None,
+        attention_multiplier: Optional[int] = None,
+        context_mapping_features: Optional[int] = None,
+        context_embedding_features: Optional[int] = None,
+        use_snake: bool = False,
+    ):
+        super().__init__()
+        self.use_transformer = num_transformer_blocks > 0
+
+        self.pre_block = ResnetBlock1d(
+            in_channels=channels,
+            out_channels=channels,
+            num_groups=num_groups,
+            context_mapping_features=context_mapping_features,
+            use_snake=use_snake
+        )
+
+        if self.use_transformer:
+            assert (
+                (exists(attention_heads) or exists(attention_features))
+                and exists(attention_multiplier)
+            )
+
+            if attention_features is None and attention_heads is not None:
+                attention_features = channels // attention_heads
+
+            if attention_heads is None and attention_features is not None:
+                attention_heads = channels // attention_features
+
+            self.transformer = Transformer1d(
+                num_layers=num_transformer_blocks,
+                channels=channels,
+                num_heads=attention_heads,
+                head_features=attention_features,
+                multiplier=attention_multiplier,
+                context_features=context_embedding_features,
+            )
+
+        self.post_block = ResnetBlock1d(
+            in_channels=channels,
+            out_channels=channels,
+            num_groups=num_groups,
+            context_mapping_features=context_mapping_features,
+            use_snake=use_snake
+        )
+
+    def forward(
+        self,
+        x: Tensor,
+        *,
+        mapping: Optional[Tensor] = None,
+        embedding: Optional[Tensor] = None,
+        embedding_mask: Optional[Tensor] = None,
+        causal: Optional[bool] = False
+    ) -> Tensor:
+        x = self.pre_block(x, mapping=mapping, causal=causal)
+        if self.use_transformer:
+            x = self.transformer(x, context=embedding, context_mask=embedding_mask, causal=causal)
+        x = self.post_block(x, mapping=mapping, causal=causal)
+        return x
+
+
+"""
+UNet
+"""
+
+
+class UNet1d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        channels: int,
+        multipliers: Sequence[int],
+        factors: Sequence[int],
+        num_blocks: Sequence[int],
+        attentions: Sequence[int],
+        patch_size: int = 1,
+        resnet_groups: int = 8,
+        use_context_time: bool = True,
+        kernel_multiplier_downsample: int = 2,
+        use_nearest_upsample: bool = False,
+        use_skip_scale: bool = True,
+        use_snake: bool = False,
+        use_stft: bool = False,
+        use_stft_context: bool = False,
+        out_channels: Optional[int] = None,
+        context_features: Optional[int] = None,
+        context_features_multiplier: int = 4,
+        context_channels: Optional[Sequence[int]] = None,
+        context_embedding_features: Optional[int] = None,
+        **kwargs,
+    ):
+        super().__init__()
+        out_channels = default(out_channels, in_channels)
+        context_channels = list(default(context_channels, []))
+        num_layers = len(multipliers) - 1
+        use_context_features = exists(context_features)
+        use_context_channels = len(context_channels) > 0
+        context_mapping_features = None
+
+        attention_kwargs, kwargs = groupby("attention_", kwargs, keep_prefix=True)
+
+        self.num_layers = num_layers
+        self.use_context_time = use_context_time
+        self.use_context_features = use_context_features
+        self.use_context_channels = use_context_channels
+        self.use_stft = use_stft
+        self.use_stft_context = use_stft_context
+
+        self.context_features = context_features
+        context_channels_pad_length = num_layers + 1 - len(context_channels)
+        context_channels = context_channels + [0] * context_channels_pad_length
+        self.context_channels = context_channels
+        self.context_embedding_features = context_embedding_features
+
+        if use_context_channels:
+            has_context = [c > 0 for c in context_channels]
+            self.has_context = has_context
+            self.channels_ids = [sum(has_context[:i]) for i in range(len(has_context))]
+
+        assert (
+            len(factors) == num_layers
+            and len(attentions) >= num_layers
+            and len(num_blocks) == num_layers
+        )
+
+        if use_context_time or use_context_features:
+            context_mapping_features = channels * context_features_multiplier
+
+            self.to_mapping = nn.Sequential(
+                nn.Linear(context_mapping_features, context_mapping_features),
+                nn.GELU(),
+                nn.Linear(context_mapping_features, context_mapping_features),
+                nn.GELU(),
+            )
+
+        if use_context_time:
+            assert exists(context_mapping_features)
+            self.to_time = nn.Sequential(
+                TimePositionalEmbedding(
+                    dim=channels, out_features=context_mapping_features
+                ),
+                nn.GELU(),
+            )
+
+        if use_context_features:
+            assert exists(context_features) and exists(context_mapping_features)
+            self.to_features = nn.Sequential(
+                nn.Linear(
+                    in_features=context_features, out_features=context_mapping_features
+                ),
+                nn.GELU(),
+            )
+
+        if use_stft:
+            stft_kwargs, kwargs = groupby("stft_", kwargs)
+            assert "num_fft" in stft_kwargs, "stft_num_fft required if use_stft=True"
+            stft_channels = (stft_kwargs["num_fft"] // 2 + 1) * 2
+            in_channels *= stft_channels
+            out_channels *= stft_channels
+            context_channels[0] *= stft_channels if use_stft_context else 1
+            assert exists(in_channels) and exists(out_channels)
+            self.stft = STFT(**stft_kwargs)
+
+        assert not kwargs, f"Unknown arguments: {', '.join(list(kwargs.keys()))}"
+
+        self.to_in = Patcher(
+            in_channels=in_channels + context_channels[0],
+            out_channels=channels * multipliers[0],
+            patch_size=patch_size,
+            context_mapping_features=context_mapping_features,
+            use_snake=use_snake
+        )
+
+        self.downsamples = nn.ModuleList(
+            [
+                DownsampleBlock1d(
+                    in_channels=channels * multipliers[i],
+                    out_channels=channels * multipliers[i + 1],
+                    context_mapping_features=context_mapping_features,
+                    context_channels=context_channels[i + 1],
+                    context_embedding_features=context_embedding_features,
+                    num_layers=num_blocks[i],
+                    factor=factors[i],
+                    kernel_multiplier=kernel_multiplier_downsample,
+                    num_groups=resnet_groups,
+                    use_pre_downsample=True,
+                    use_skip=True,
+                    use_snake=use_snake,
+                    num_transformer_blocks=attentions[i],
+                    **attention_kwargs,
+                )
+                for i in range(num_layers)
+            ]
+        )
+
+        self.bottleneck = BottleneckBlock1d(
+            channels=channels * multipliers[-1],
+            context_mapping_features=context_mapping_features,
+            context_embedding_features=context_embedding_features,
+            num_groups=resnet_groups,
+            num_transformer_blocks=attentions[-1],
+            use_snake=use_snake,
+            **attention_kwargs,
+        )
+
+        self.upsamples = nn.ModuleList(
+            [
+                UpsampleBlock1d(
+                    in_channels=channels * multipliers[i + 1],
+                    out_channels=channels * multipliers[i],
+                    context_mapping_features=context_mapping_features,
+                    context_embedding_features=context_embedding_features,
+                    num_layers=num_blocks[i] + (1 if attentions[i] else 0),
+                    factor=factors[i],
+                    use_nearest=use_nearest_upsample,
+                    num_groups=resnet_groups,
+                    use_skip_scale=use_skip_scale,
+                    use_pre_upsample=False,
+                    use_skip=True,
+                    use_snake=use_snake,
+                    skip_channels=channels * multipliers[i + 1],
+                    num_transformer_blocks=attentions[i],
+                    **attention_kwargs,
+                )
+                for i in reversed(range(num_layers))
+            ]
+        )
+
+        self.to_out = Unpatcher(
+            in_channels=channels * multipliers[0],
+            out_channels=out_channels,
+            patch_size=patch_size,
+            context_mapping_features=context_mapping_features,
+            use_snake=use_snake
+        )
+
+    def get_channels(
+        self, channels_list: Optional[Sequence[Tensor]] = None, layer: int = 0
+    ) -> Optional[Tensor]:
+        """Gets context channels at `layer` and checks that shape is correct"""
+        use_context_channels = self.use_context_channels and self.has_context[layer]
+        if not use_context_channels:
+            return None
+        assert exists(channels_list), "Missing context"
+        # Get channels index (skipping zero channel contexts)
+        channels_id = self.channels_ids[layer]
+        # Get channels
+        channels = channels_list[channels_id]
+        message = f"Missing context for layer {layer} at index {channels_id}"
+        assert exists(channels), message
+        # Check channels
+        num_channels = self.context_channels[layer]
+        message = f"Expected context with {num_channels} channels at idx {channels_id}"
+        assert channels.shape[1] == num_channels, message
+        # STFT channels if requested
+        channels = self.stft.encode1d(channels) if self.use_stft_context else channels  # type: ignore # noqa
+        return channels
+
+    def get_mapping(
+        self, time: Optional[Tensor] = None, features: Optional[Tensor] = None
+    ) -> Optional[Tensor]:
+        """Combines context time features and features into mapping"""
+        items, mapping = [], None
+        # Compute time features
+        if self.use_context_time:
+            assert_message = "use_context_time=True but no time features provided"
+            assert exists(time), assert_message
+            items += [self.to_time(time)]
+        # Compute features
+        if self.use_context_features:
+            assert_message = "context_features exists but no features provided"
+            assert exists(features), assert_message
+            items += [self.to_features(features)]
+        # Compute joint mapping
+        if self.use_context_time or self.use_context_features:
+            mapping = reduce(torch.stack(items), "n b m -> b m", "sum")
+            mapping = self.to_mapping(mapping)
+        return mapping
+
+    def forward(
+        self,
+        x: Tensor,
+        time: Optional[Tensor] = None,
+        *,
+        features: Optional[Tensor] = None,
+        channels_list: Optional[Sequence[Tensor]] = None,
+        embedding: Optional[Tensor] = None,
+        embedding_mask: Optional[Tensor] = None,
+        causal: Optional[bool] = False,
+    ) -> Tensor:
+        channels = self.get_channels(channels_list, layer=0)
+        # Apply stft if required
+        x = self.stft.encode1d(x) if self.use_stft else x  # type: ignore
+        # Concat context channels at layer 0 if provided
+        x = torch.cat([x, channels], dim=1) if exists(channels) else x
+        # Compute mapping from time and features
+        mapping = self.get_mapping(time, features)
+        x = self.to_in(x, mapping, causal=causal)
+        skips_list = [x]
+
+        for i, downsample in enumerate(self.downsamples):
+            channels = self.get_channels(channels_list, layer=i + 1)
+            x, skips = downsample(
+                x, mapping=mapping, channels=channels, embedding=embedding, embedding_mask=embedding_mask, causal=causal
+            )
+            skips_list += [skips]
+
+        x = self.bottleneck(x, mapping=mapping, embedding=embedding, embedding_mask=embedding_mask, causal=causal)
+
+        for i, upsample in enumerate(self.upsamples):
+            skips = skips_list.pop()
+            x = upsample(x, skips=skips, mapping=mapping, embedding=embedding, embedding_mask=embedding_mask, causal=causal)
+
+        x += skips_list.pop()
+        x = self.to_out(x, mapping, causal=causal)
+        x = self.stft.decode1d(x) if self.use_stft else x
+
+        return x
+
+
+""" Conditioning Modules """
+
+
+class FixedEmbedding(nn.Module):
+    def __init__(self, max_length: int, features: int):
+        super().__init__()
+        self.max_length = max_length
+        self.embedding = nn.Embedding(max_length, features)
+
+    def forward(self, x: Tensor) -> Tensor:
+        batch_size, length, device = *x.shape[0:2], x.device
+        assert_message = "Input sequence length must be <= max_length"
+        assert length <= self.max_length, assert_message
+        position = torch.arange(length, device=device)
+        fixed_embedding = self.embedding(position)
+        fixed_embedding = repeat(fixed_embedding, "n d -> b n d", b=batch_size)
+        return fixed_embedding
+
+
+def rand_bool(shape: Any, proba: float, device: Any = None) -> Tensor:
+    if proba == 1:
+        return torch.ones(shape, device=device, dtype=torch.bool)
+    elif proba == 0:
+        return torch.zeros(shape, device=device, dtype=torch.bool)
+    else:
+        return torch.bernoulli(torch.full(shape, proba, device=device)).to(torch.bool)
+
+
+class UNetCFG1d(UNet1d):
+
+    """UNet1d with Classifier-Free Guidance"""
+
+    def __init__(
+        self,
+        context_embedding_max_length: int,
+        context_embedding_features: int,
+        use_xattn_time: bool = False,
+        **kwargs,
+    ):
+        super().__init__(
+            context_embedding_features=context_embedding_features, **kwargs
+        )
+
+        self.use_xattn_time = use_xattn_time
+
+        if use_xattn_time:
+            assert exists(context_embedding_features)
+            self.to_time_embedding = nn.Sequential(
+                TimePositionalEmbedding(
+                    dim=kwargs["channels"], out_features=context_embedding_features
+                ),
+                nn.GELU(),
+            )
+
+            context_embedding_max_length += 1   # Add one for time embedding
+
+        self.fixed_embedding = FixedEmbedding(
+            max_length=context_embedding_max_length, features=context_embedding_features
+        )
+
+    def forward(  # type: ignore
+        self,
+        x: Tensor,
+        time: Tensor,
+        *,
+        embedding: Tensor,
+        embedding_mask: Optional[Tensor] = None,
+        embedding_scale: float = 1.0,
+        embedding_mask_proba: float = 0.0,
+        batch_cfg: bool = False,
+        rescale_cfg: bool = False,
+        scale_phi: float = 0.4,
+        negative_embedding: Optional[Tensor] = None,
+        negative_embedding_mask: Optional[Tensor] = None,
+        **kwargs,
+    ) -> Tensor:
+        b, device = embedding.shape[0], embedding.device
+
+        if self.use_xattn_time:
+            embedding = torch.cat([embedding, self.to_time_embedding(time).unsqueeze(1)], dim=1)
+
+            if embedding_mask is not None:
+                embedding_mask = torch.cat([embedding_mask, torch.ones((b, 1), device=device)], dim=1)
+
+        fixed_embedding = self.fixed_embedding(embedding)
+
+        if embedding_mask_proba > 0.0:
+            # Randomly mask embedding
+            batch_mask = rand_bool(
+                shape=(b, 1, 1), proba=embedding_mask_proba, device=device
+            )
+            embedding = torch.where(batch_mask, fixed_embedding, embedding)
+
+        if embedding_scale != 1.0:
+            if batch_cfg:
+                batch_x = torch.cat([x, x], dim=0)
+                batch_time = torch.cat([time, time], dim=0)
+
+                if negative_embedding is not None:
+                    if negative_embedding_mask is not None:
+                        negative_embedding_mask = negative_embedding_mask.to(torch.bool).unsqueeze(2)
+
+                        negative_embedding = torch.where(negative_embedding_mask, negative_embedding, fixed_embedding)
+                    
+                    batch_embed = torch.cat([embedding, negative_embedding], dim=0)
+
+                else:
+                    batch_embed = torch.cat([embedding, fixed_embedding], dim=0)
+
+                batch_mask = None
+                if embedding_mask is not None:
+                    batch_mask = torch.cat([embedding_mask, embedding_mask], dim=0)
+
+                batch_features = None
+                features = kwargs.pop("features", None)
+                if self.use_context_features:
+                    batch_features = torch.cat([features, features], dim=0)
+
+                batch_channels = None
+                channels_list = kwargs.pop("channels_list", None)
+                if self.use_context_channels:
+                    batch_channels = []
+                    for channels in channels_list:
+                        batch_channels += [torch.cat([channels, channels], dim=0)]
+
+                # Compute both normal and fixed embedding outputs
+                batch_out = super().forward(batch_x, batch_time, embedding=batch_embed, embedding_mask=batch_mask, features=batch_features, channels_list=batch_channels, **kwargs)
+                out, out_masked = batch_out.chunk(2, dim=0)
+           
+            else:
+                # Compute both normal and fixed embedding outputs
+                out = super().forward(x, time, embedding=embedding, embedding_mask=embedding_mask, **kwargs)
+                out_masked = super().forward(x, time, embedding=fixed_embedding, embedding_mask=embedding_mask, **kwargs)
+
+            out_cfg = out_masked + (out - out_masked) * embedding_scale
+
+            if rescale_cfg:
+
+                out_std = out.std(dim=1, keepdim=True)
+                out_cfg_std = out_cfg.std(dim=1, keepdim=True)
+
+                return scale_phi * (out_cfg * (out_std/out_cfg_std)) + (1-scale_phi) * out_cfg
+
+            else:
+
+                return out_cfg
+                
+        else:
+            return super().forward(x, time, embedding=embedding, embedding_mask=embedding_mask, **kwargs)
+
+
+class UNetNCCA1d(UNet1d):
+
+    """UNet1d with Noise Channel Conditioning Augmentation"""
+
+    def __init__(self, context_features: int, **kwargs):
+        super().__init__(context_features=context_features, **kwargs)
+        self.embedder = NumberEmbedder(features=context_features)
+
+    def expand(self, x: Any, shape: Tuple[int, ...]) -> Tensor:
+        x = x if torch.is_tensor(x) else torch.tensor(x)
+        return x.expand(shape)
+
+    def forward(  # type: ignore
+        self,
+        x: Tensor,
+        time: Tensor,
+        *,
+        channels_list: Sequence[Tensor],
+        channels_augmentation: Union[
+            bool, Sequence[bool], Sequence[Sequence[bool]], Tensor
+        ] = False,
+        channels_scale: Union[
+            float, Sequence[float], Sequence[Sequence[float]], Tensor
+        ] = 0,
+        **kwargs,
+    ) -> Tensor:
+        b, n = x.shape[0], len(channels_list)
+        channels_augmentation = self.expand(channels_augmentation, shape=(b, n)).to(x)
+        channels_scale = self.expand(channels_scale, shape=(b, n)).to(x)
+
+        # Augmentation (for each channel list item)
+        for i in range(n):
+            scale = channels_scale[:, i] * channels_augmentation[:, i]
+            scale = rearrange(scale, "b -> b 1 1")
+            item = channels_list[i]
+            channels_list[i] = torch.randn_like(item) * scale + item * (1 - scale)  # type: ignore # noqa
+
+        # Scale embedding (sum reduction if more than one channel list item)
+        channels_scale_emb = self.embedder(channels_scale)
+        channels_scale_emb = reduce(channels_scale_emb, "b n d -> b d", "sum")
+
+        return super().forward(
+            x=x,
+            time=time,
+            channels_list=channels_list,
+            features=channels_scale_emb,
+            **kwargs,
+        )
+
+
+class UNetAll1d(UNetCFG1d, UNetNCCA1d):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, *args, **kwargs):  # type: ignore
+        return UNetCFG1d.forward(self, *args, **kwargs)
+
+
+def XUNet1d(type: str = "base", **kwargs) -> UNet1d:
+    if type == "base":
+        return UNet1d(**kwargs)
+    elif type == "all":
+        return UNetAll1d(**kwargs)
+    elif type == "cfg":
+        return UNetCFG1d(**kwargs)
+    elif type == "ncca":
+        return UNetNCCA1d(**kwargs)
+    else:
+        raise ValueError(f"Unknown XUNet1d type: {type}")
+
+class NumberEmbedder(nn.Module):
+    def __init__(
+        self,
+        features: int,
+        dim: int = 256,
+    ):
+        super().__init__()
+        self.features = features
+        self.embedding = TimePositionalEmbedding(dim=dim, out_features=features)
+
+    def forward(self, x: Union[List[float], Tensor]) -> Tensor:
+        if not torch.is_tensor(x):
+            device = next(self.embedding.parameters()).device
+            x = torch.tensor(x, device=device)
+        assert isinstance(x, Tensor)
+        shape = x.shape
+        x = rearrange(x, "... -> (...)")
+        embedding = self.embedding(x)
+        x = embedding.view(*shape, self.features)
+        return x  # type: ignore
+
+
+"""
+Audio Transforms
+"""
+
+
+class STFT(nn.Module):
+    """Helper for torch stft and istft"""
+
+    def __init__(
+        self,
+        num_fft: int = 1023,
+        hop_length: int = 256,
+        window_length: Optional[int] = None,
+        length: Optional[int] = None,
+        use_complex: bool = False,
+    ):
+        super().__init__()
+        self.num_fft = num_fft
+        self.hop_length = default(hop_length, floor(num_fft // 4))
+        self.window_length = default(window_length, num_fft)
+        self.length = length
+        self.register_buffer("window", torch.hann_window(self.window_length))
+        self.use_complex = use_complex
+
+    def encode(self, wave: Tensor) -> Tuple[Tensor, Tensor]:
+        b = wave.shape[0]
+        wave = rearrange(wave, "b c t -> (b c) t")
+
+        stft = torch.stft(
+            wave,
+            n_fft=self.num_fft,
+            hop_length=self.hop_length,
+            win_length=self.window_length,
+            window=self.window,  # type: ignore
+            return_complex=True,
+            normalized=True,
+        )
+
+        if self.use_complex:
+            # Returns real and imaginary
+            stft_a, stft_b = stft.real, stft.imag
+        else:
+            # Returns magnitude and phase matrices
+            magnitude, phase = torch.abs(stft), torch.angle(stft)
+            stft_a, stft_b = magnitude, phase
+
+        return rearrange_many((stft_a, stft_b), "(b c) f l -> b c f l", b=b)
+
+    def decode(self, stft_a: Tensor, stft_b: Tensor) -> Tensor:
+        b, l = stft_a.shape[0], stft_a.shape[-1]  # noqa
+        length = closest_power_2(l * self.hop_length)
+
+        stft_a, stft_b = rearrange_many((stft_a, stft_b), "b c f l -> (b c) f l")
+
+        if self.use_complex:
+            real, imag = stft_a, stft_b
+        else:
+            magnitude, phase = stft_a, stft_b
+            real, imag = magnitude * torch.cos(phase), magnitude * torch.sin(phase)
+
+        stft = torch.stack([real, imag], dim=-1)
+
+        wave = torch.istft(
+            stft,
+            n_fft=self.num_fft,
+            hop_length=self.hop_length,
+            win_length=self.window_length,
+            window=self.window,  # type: ignore
+            length=default(self.length, length),
+            normalized=True,
+        )
+
+        return rearrange(wave, "(b c) t -> b c t", b=b)
+
+    def encode1d(
+        self, wave: Tensor, stacked: bool = True
+    ) -> Union[Tensor, Tuple[Tensor, Tensor]]:
+        stft_a, stft_b = self.encode(wave)
+        stft_a, stft_b = rearrange_many((stft_a, stft_b), "b c f l -> b (c f) l")
+        return torch.cat((stft_a, stft_b), dim=1) if stacked else (stft_a, stft_b)
+
+    def decode1d(self, stft_pair: Tensor) -> Tensor:
+        f = self.num_fft // 2 + 1
+        stft_a, stft_b = stft_pair.chunk(chunks=2, dim=1)
+        stft_a, stft_b = rearrange_many((stft_a, stft_b), "b (c f) l -> b c f l", f=f)
+        return self.decode(stft_a, stft_b)

stable_audio_tools/models/autoencoders.py

@@ -0,0 +1,794 @@
+import torch
+import math
+import numpy as np
+
+from torch import nn
+from torch.nn import functional as F
+from torchaudio import transforms as T
+from alias_free_torch import Activation1d
+from dac.nn.layers import WNConv1d, WNConvTranspose1d
+from typing import Literal, Dict, Any
+
+from ..inference.sampling import sample
+from ..inference.utils import prepare_audio
+from .blocks import SnakeBeta
+from .bottleneck import Bottleneck, DiscreteBottleneck
+from .diffusion import ConditionedDiffusionModel, DAU1DCondWrapper, UNet1DCondWrapper, DiTWrapper
+from .factory import create_pretransform_from_config, create_bottleneck_from_config
+from .pretransforms import Pretransform
+
+def checkpoint(function, *args, **kwargs):
+    kwargs.setdefault("use_reentrant", False)
+    return torch.utils.checkpoint.checkpoint(function, *args, **kwargs)
+
+def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module:
+    if activation == "elu":
+        act = nn.ELU()
+    elif activation == "snake":
+        act = SnakeBeta(channels)
+    elif activation == "none":
+        act = nn.Identity()
+    else:
+        raise ValueError(f"Unknown activation {activation}")
+    
+    if antialias:
+        act = Activation1d(act)
+    
+    return act
+
+class ResidualUnit(nn.Module):
+    def __init__(self, in_channels, out_channels, dilation, use_snake=False, antialias_activation=False):
+        super().__init__()
+        
+        self.dilation = dilation
+
+        padding = (dilation * (7-1)) // 2
+
+        self.layers = nn.Sequential(
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
+            WNConv1d(in_channels=in_channels, out_channels=out_channels,
+                      kernel_size=7, dilation=dilation, padding=padding),
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
+            WNConv1d(in_channels=out_channels, out_channels=out_channels,
+                      kernel_size=1)
+        )
+
+    def forward(self, x):
+        res = x
+        
+        #x = checkpoint(self.layers, x)
+        x = self.layers(x)
+
+        return x + res
+
+class EncoderBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False):
+        super().__init__()
+
+        self.layers = nn.Sequential(
+            ResidualUnit(in_channels=in_channels,
+                         out_channels=in_channels, dilation=1, use_snake=use_snake),
+            ResidualUnit(in_channels=in_channels,
+                         out_channels=in_channels, dilation=3, use_snake=use_snake),
+            ResidualUnit(in_channels=in_channels,
+                         out_channels=in_channels, dilation=9, use_snake=use_snake),
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
+            WNConv1d(in_channels=in_channels, out_channels=out_channels,
+                      kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)),
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+class DecoderBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False, use_nearest_upsample=False):
+        super().__init__()
+
+        if use_nearest_upsample:
+            upsample_layer = nn.Sequential(
+                nn.Upsample(scale_factor=stride, mode="nearest"),
+                WNConv1d(in_channels=in_channels,
+                        out_channels=out_channels, 
+                        kernel_size=2*stride,
+                        stride=1,
+                        bias=False,
+                        padding='same')
+            )
+        else:
+            upsample_layer = WNConvTranspose1d(in_channels=in_channels,
+                               out_channels=out_channels,
+                               kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2))
+
+        self.layers = nn.Sequential(
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
+            upsample_layer,
+            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+                         dilation=1, use_snake=use_snake),
+            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+                         dilation=3, use_snake=use_snake),
+            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+                         dilation=9, use_snake=use_snake),
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+class OobleckEncoder(nn.Module):
+    def __init__(self, 
+                 in_channels=2, 
+                 channels=128, 
+                 latent_dim=32, 
+                 c_mults = [1, 2, 4, 8], 
+                 strides = [2, 4, 8, 8],
+                 use_snake=False,
+                 antialias_activation=False
+        ):
+        super().__init__()
+          
+        c_mults = [1] + c_mults
+
+        self.depth = len(c_mults)
+
+        layers = [
+            WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3)
+        ]
+        
+        for i in range(self.depth-1):
+            layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)]
+
+        layers += [
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[-1] * channels),
+            WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1)
+        ]
+
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class OobleckDecoder(nn.Module):
+    def __init__(self, 
+                 out_channels=2, 
+                 channels=128, 
+                 latent_dim=32, 
+                 c_mults = [1, 2, 4, 8], 
+                 strides = [2, 4, 8, 8],
+                 use_snake=False,
+                 antialias_activation=False,
+                 use_nearest_upsample=False,
+                 final_tanh=True):
+        super().__init__()
+
+        c_mults = [1] + c_mults
+        
+        self.depth = len(c_mults)
+
+        layers = [
+            WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3),
+        ]
+        
+        for i in range(self.depth-1, 0, -1):
+            layers += [DecoderBlock(
+                in_channels=c_mults[i]*channels, 
+                out_channels=c_mults[i-1]*channels, 
+                stride=strides[i-1], 
+                use_snake=use_snake, 
+                antialias_activation=antialias_activation,
+                use_nearest_upsample=use_nearest_upsample
+                )
+            ]
+
+        layers += [
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[0] * channels),
+            WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False),
+            nn.Tanh() if final_tanh else nn.Identity()
+        ]
+
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class DACEncoderWrapper(nn.Module):
+    def __init__(self, in_channels=1, **kwargs):
+        super().__init__()
+
+        from dac.model.dac import Encoder as DACEncoder
+
+        latent_dim = kwargs.pop("latent_dim", None)
+
+        encoder_out_dim = kwargs["d_model"] * (2 ** len(kwargs["strides"]))
+        self.encoder = DACEncoder(d_latent=encoder_out_dim, **kwargs)
+        self.latent_dim = latent_dim
+
+        # Latent-dim support was added to DAC after this was first written, and implemented differently, so this is for backwards compatibility
+        self.proj_out = nn.Conv1d(self.encoder.enc_dim, latent_dim, kernel_size=1) if latent_dim is not None else nn.Identity()
+
+        if in_channels != 1:
+            self.encoder.block[0] = WNConv1d(in_channels, kwargs.get("d_model", 64), kernel_size=7, padding=3)
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.proj_out(x)
+        return x
+
+class DACDecoderWrapper(nn.Module):
+    def __init__(self, latent_dim, out_channels=1, **kwargs):
+        super().__init__()
+
+        from dac.model.dac import Decoder as DACDecoder
+
+        self.decoder = DACDecoder(**kwargs, input_channel = latent_dim, d_out=out_channels)
+
+        self.latent_dim = latent_dim
+
+    def forward(self, x):
+        return self.decoder(x)
+
+class AudioAutoencoder(nn.Module):
+    def __init__(
+        self,
+        encoder,
+        decoder,
+        latent_dim,
+        downsampling_ratio,
+        sample_rate,
+        io_channels=2,
+        bottleneck: Bottleneck = None,
+        pretransform: Pretransform = None,
+        in_channels = None,
+        out_channels = None,
+        soft_clip = False
+    ):
+        super().__init__()
+
+        self.downsampling_ratio = downsampling_ratio
+        self.sample_rate = sample_rate
+
+        self.latent_dim = latent_dim
+        self.io_channels = io_channels
+        self.in_channels = io_channels
+        self.out_channels = io_channels
+
+        self.min_length = self.downsampling_ratio
+
+        if in_channels is not None:
+            self.in_channels = in_channels
+
+        if out_channels is not None:
+            self.out_channels = out_channels
+
+        self.bottleneck = bottleneck
+
+        self.encoder = encoder
+
+        self.decoder = decoder
+
+        self.pretransform = pretransform
+
+        self.soft_clip = soft_clip
+ 
+        self.is_discrete = self.bottleneck is not None and self.bottleneck.is_discrete
+
+    def encode(self, audio, return_info=False, skip_pretransform=False, iterate_batch=False, **kwargs):
+
+        info = {}
+
+        if self.pretransform is not None and not skip_pretransform:
+            if self.pretransform.enable_grad:
+                if iterate_batch:
+                    audios = []
+                    for i in range(audio.shape[0]):
+                        audios.append(self.pretransform.encode(audio[i:i+1]))
+                    audio = torch.cat(audios, dim=0)
+                else:
+                    audio = self.pretransform.encode(audio)
+            else:
+                with torch.no_grad():
+                    if iterate_batch:
+                        audios = []
+                        for i in range(audio.shape[0]):
+                            audios.append(self.pretransform.encode(audio[i:i+1]))
+                        audio = torch.cat(audios, dim=0)
+                    else:
+                        audio = self.pretransform.encode(audio)
+
+        if self.encoder is not None:
+            if iterate_batch:
+                latents = []
+                for i in range(audio.shape[0]):
+                    latents.append(self.encoder(audio[i:i+1]))
+                latents = torch.cat(latents, dim=0)
+            else:
+                latents = self.encoder(audio)
+        else:
+            latents = audio
+
+        if self.bottleneck is not None:
+            # TODO: Add iterate batch logic, needs to merge the info dicts
+            latents, bottleneck_info = self.bottleneck.encode(latents, return_info=True, **kwargs)
+
+            info.update(bottleneck_info)
+        
+        if return_info:
+            return latents, info
+
+        return latents
+
+    def decode(self, latents, iterate_batch=False, **kwargs):
+
+        if self.bottleneck is not None:
+            if iterate_batch:
+                decoded = []
+                for i in range(latents.shape[0]):
+                    decoded.append(self.bottleneck.decode(latents[i:i+1]))
+                latents = torch.cat(decoded, dim=0)
+            else:
+                latents = self.bottleneck.decode(latents)
+
+        if iterate_batch:
+            decoded = []
+            for i in range(latents.shape[0]):
+                decoded.append(self.decoder(latents[i:i+1]))
+            decoded = torch.cat(decoded, dim=0)
+        else:
+            decoded = self.decoder(latents, **kwargs)
+
+        if self.pretransform is not None:
+            if self.pretransform.enable_grad:
+                if iterate_batch:
+                    decodeds = []
+                    for i in range(decoded.shape[0]):
+                        decodeds.append(self.pretransform.decode(decoded[i:i+1]))
+                    decoded = torch.cat(decodeds, dim=0)
+                else:
+                    decoded = self.pretransform.decode(decoded)
+            else:
+                with torch.no_grad():
+                    if iterate_batch:
+                        decodeds = []
+                        for i in range(latents.shape[0]):
+                            decodeds.append(self.pretransform.decode(decoded[i:i+1]))
+                        decoded = torch.cat(decodeds, dim=0)
+                    else:
+                        decoded = self.pretransform.decode(decoded)
+
+        if self.soft_clip:
+            decoded = torch.tanh(decoded)
+        
+        return decoded
+          
+    def decode_tokens(self, tokens, **kwargs):
+        '''
+        Decode discrete tokens to audio
+        Only works with discrete autoencoders
+        '''
+
+        assert isinstance(self.bottleneck, DiscreteBottleneck), "decode_tokens only works with discrete autoencoders"
+
+        latents = self.bottleneck.decode_tokens(tokens, **kwargs)
+
+        return self.decode(latents, **kwargs)
+        
+    
+    def preprocess_audio_for_encoder(self, audio, in_sr):
+        '''
+        Preprocess single audio tensor (Channels x Length) to be compatible with the encoder.
+        If the model is mono, stereo audio will be converted to mono.
+        Audio will be silence-padded to be a multiple of the model's downsampling ratio.
+        Audio will be resampled to the model's sample rate. 
+        The output will have batch size 1 and be shape (1 x Channels x Length)
+        '''
+        return self.preprocess_audio_list_for_encoder([audio], [in_sr])
+
+    def preprocess_audio_list_for_encoder(self, audio_list, in_sr_list):
+        '''
+        Preprocess a [list] of audio (Channels x Length) into a batch tensor to be compatable with the encoder. 
+        The audio in that list can be of different lengths and channels. 
+        in_sr can be an integer or list. If it's an integer it will be assumed it is the input sample_rate for every audio.
+        All audio will be resampled to the model's sample rate. 
+        Audio will be silence-padded to the longest length, and further padded to be a multiple of the model's downsampling ratio. 
+        If the model is mono, all audio will be converted to mono. 
+        The output will be a tensor of shape (Batch x Channels x Length)
+        '''
+        batch_size = len(audio_list)
+        if isinstance(in_sr_list, int):
+            in_sr_list = [in_sr_list]*batch_size
+        assert len(in_sr_list) == batch_size, "list of sample rates must be the same length of audio_list"
+        new_audio = []
+        max_length = 0
+        # resample & find the max length
+        for i in range(batch_size):
+            audio = audio_list[i]
+            in_sr = in_sr_list[i]
+            if len(audio.shape) == 3 and audio.shape[0] == 1:
+                # batchsize 1 was given by accident. Just squeeze it.
+                audio = audio.squeeze(0)
+            elif len(audio.shape) == 1:
+                # Mono signal, channel dimension is missing, unsqueeze it in
+                audio = audio.unsqueeze(0)
+            assert len(audio.shape)==2, "Audio should be shape (Channels x Length) with no batch dimension" 
+            # Resample audio
+            if in_sr != self.sample_rate:
+                resample_tf = T.Resample(in_sr, self.sample_rate).to(audio.device)
+                audio = resample_tf(audio)
+            new_audio.append(audio)
+            if audio.shape[-1] > max_length:
+                max_length = audio.shape[-1]
+        # Pad every audio to the same length, multiple of model's downsampling ratio
+        padded_audio_length = max_length + (self.min_length - (max_length % self.min_length)) % self.min_length
+        for i in range(batch_size):
+            # Pad it & if necessary, mixdown/duplicate stereo/mono channels to support model
+            new_audio[i] = prepare_audio(new_audio[i], in_sr=in_sr, target_sr=in_sr, target_length=padded_audio_length, 
+                target_channels=self.in_channels, device=new_audio[i].device).squeeze(0)
+        # convert to tensor 
+        return torch.stack(new_audio) 
+
+    def encode_audio(self, audio, chunked=False, overlap=32, chunk_size=128, **kwargs):
+        '''
+        Encode audios into latents. Audios should already be preprocesed by preprocess_audio_for_encoder.
+        If chunked is True, split the audio into chunks of a given maximum size chunk_size, with given overlap.
+        Overlap and chunk_size params are both measured in number of latents (not audio samples) 
+        # and therefore you likely could use the same values with decode_audio. 
+        A overlap of zero will cause discontinuity artefacts. Overlap should be => receptive field size. 
+        Every autoencoder will have a different receptive field size, and thus ideal overlap.
+        You can determine it empirically by diffing unchunked vs chunked output and looking at maximum diff.
+        The final chunk may have a longer overlap in order to keep chunk_size consistent for all chunks.
+        Smaller chunk_size uses less memory, but more compute.
+        The chunk_size vs memory tradeoff isn't linear, and possibly depends on the GPU and CUDA version
+        For example, on a A6000 chunk_size 128 is overall faster than 256 and 512 even though it has more chunks
+        '''
+        if not chunked:
+            # default behavior. Encode the entire audio in parallel
+            return self.encode(audio, **kwargs)
+        else:
+            # CHUNKED ENCODING
+            # samples_per_latent is just the downsampling ratio (which is also the upsampling ratio)
+            samples_per_latent = self.downsampling_ratio
+            total_size = audio.shape[2] # in samples
+            batch_size = audio.shape[0]
+            chunk_size *= samples_per_latent # converting metric in latents to samples
+            overlap *= samples_per_latent # converting metric in latents to samples
+            hop_size = chunk_size - overlap
+            chunks = []
+            for i in range(0, total_size - chunk_size + 1, hop_size):
+                chunk = audio[:,:,i:i+chunk_size]
+                chunks.append(chunk)
+            if i+chunk_size != total_size:
+                # Final chunk
+                chunk = audio[:,:,-chunk_size:]
+                chunks.append(chunk)
+            chunks = torch.stack(chunks)
+            num_chunks = chunks.shape[0]
+            # Note: y_size might be a different value from the latent length used in diffusion training
+            # because we can encode audio of varying lengths
+            # However, the audio should've been padded to a multiple of samples_per_latent by now.
+            y_size = total_size // samples_per_latent
+            # Create an empty latent, we will populate it with chunks as we encode them
+            y_final = torch.zeros((batch_size,self.latent_dim,y_size)).to(audio.device)
+            for i in range(num_chunks):
+                x_chunk = chunks[i,:]
+                # encode the chunk
+                y_chunk = self.encode(x_chunk)
+                # figure out where to put the audio along the time domain
+                if i == num_chunks-1:
+                    # final chunk always goes at the end
+                    t_end = y_size
+                    t_start = t_end - y_chunk.shape[2]
+                else:
+                    t_start = i * hop_size // samples_per_latent
+                    t_end = t_start + chunk_size // samples_per_latent
+                #  remove the edges of the overlaps
+                ol = overlap//samples_per_latent//2
+                chunk_start = 0
+                chunk_end = y_chunk.shape[2]
+                if i > 0:
+                    # no overlap for the start of the first chunk
+                    t_start += ol
+                    chunk_start += ol
+                if i < num_chunks-1:
+                    # no overlap for the end of the last chunk
+                    t_end -= ol
+                    chunk_end -= ol
+                # paste the chunked audio into our y_final output audio
+                y_final[:,:,t_start:t_end] = y_chunk[:,:,chunk_start:chunk_end]
+            return y_final
+    
+    def decode_audio(self, latents, chunked=False, overlap=32, chunk_size=128, **kwargs):
+        '''
+        Decode latents to audio. 
+        If chunked is True, split the latents into chunks of a given maximum size chunk_size, with given overlap, both of which are measured in number of latents. 
+        A overlap of zero will cause discontinuity artefacts. Overlap should be => receptive field size. 
+        Every autoencoder will have a different receptive field size, and thus ideal overlap.
+        You can determine it empirically by diffing unchunked vs chunked audio and looking at maximum diff.
+        The final chunk may have a longer overlap in order to keep chunk_size consistent for all chunks.
+        Smaller chunk_size uses less memory, but more compute.
+        The chunk_size vs memory tradeoff isn't linear, and possibly depends on the GPU and CUDA version
+        For example, on a A6000 chunk_size 128 is overall faster than 256 and 512 even though it has more chunks
+        '''
+        if not chunked:
+            # default behavior. Decode the entire latent in parallel
+            return self.decode(latents, **kwargs)
+        else:
+            # chunked decoding
+            hop_size = chunk_size - overlap
+            total_size = latents.shape[2]
+            batch_size = latents.shape[0]
+            chunks = []
+            for i in range(0, total_size - chunk_size + 1, hop_size):
+                chunk = latents[:,:,i:i+chunk_size]
+                chunks.append(chunk)
+            if i+chunk_size != total_size:
+                # Final chunk
+                chunk = latents[:,:,-chunk_size:]
+                chunks.append(chunk)
+            chunks = torch.stack(chunks)
+            num_chunks = chunks.shape[0]
+            # samples_per_latent is just the downsampling ratio
+            samples_per_latent = self.downsampling_ratio
+            # Create an empty waveform, we will populate it with chunks as decode them
+            y_size = total_size * samples_per_latent
+            y_final = torch.zeros((batch_size,self.out_channels,y_size)).to(latents.device)
+            for i in range(num_chunks):
+                x_chunk = chunks[i,:]
+                # decode the chunk
+                y_chunk = self.decode(x_chunk)
+                # figure out where to put the audio along the time domain
+                if i == num_chunks-1:
+                    # final chunk always goes at the end
+                    t_end = y_size
+                    t_start = t_end - y_chunk.shape[2]
+                else:
+                    t_start = i * hop_size * samples_per_latent
+                    t_end = t_start + chunk_size * samples_per_latent
+                #  remove the edges of the overlaps
+                ol = (overlap//2) * samples_per_latent
+                chunk_start = 0
+                chunk_end = y_chunk.shape[2]
+                if i > 0:
+                    # no overlap for the start of the first chunk
+                    t_start += ol
+                    chunk_start += ol
+                if i < num_chunks-1:
+                    # no overlap for the end of the last chunk
+                    t_end -= ol
+                    chunk_end -= ol
+                # paste the chunked audio into our y_final output audio
+                y_final[:,:,t_start:t_end] = y_chunk[:,:,chunk_start:chunk_end]
+            return y_final
+
+    
+class DiffusionAutoencoder(AudioAutoencoder):
+    def __init__(
+        self,
+        diffusion: ConditionedDiffusionModel,
+        diffusion_downsampling_ratio,
+        *args,
+        **kwargs
+    ):
+        super().__init__(*args, **kwargs)
+
+        self.diffusion = diffusion
+
+        self.min_length = self.downsampling_ratio * diffusion_downsampling_ratio
+
+        if self.encoder is not None:
+            # Shrink the initial encoder parameters to avoid saturated latents
+            with torch.no_grad():
+                for param in self.encoder.parameters():
+                    param *= 0.5
+
+    def decode(self, latents, steps=100):
+
+        upsampled_length = latents.shape[2] * self.downsampling_ratio
+
+        if self.bottleneck is not None:
+            latents = self.bottleneck.decode(latents)
+
+        if self.decoder is not None:
+            latents = self.decode(latents)
+    
+        # Upsample latents to match diffusion length
+        if latents.shape[2] != upsampled_length:
+            latents = F.interpolate(latents, size=upsampled_length, mode='nearest')
+
+        noise = torch.randn(latents.shape[0], self.io_channels, upsampled_length, device=latents.device)
+        decoded = sample(self.diffusion, noise, steps, 0, input_concat_cond=latents)
+
+        if self.pretransform is not None:
+            if self.pretransform.enable_grad:
+                decoded = self.pretransform.decode(decoded)
+            else:
+                with torch.no_grad():
+                    decoded = self.pretransform.decode(decoded)
+
+        return decoded
+        
+# AE factories
+
+def create_encoder_from_config(encoder_config: Dict[str, Any]):
+    encoder_type = encoder_config.get("type", None)
+    assert encoder_type is not None, "Encoder type must be specified"
+
+    if encoder_type == "oobleck":
+        encoder = OobleckEncoder(
+            **encoder_config["config"]
+        )
+    
+    elif encoder_type == "seanet":
+        from encodec.modules import SEANetEncoder
+        seanet_encoder_config = encoder_config["config"]
+
+        #SEANet encoder expects strides in reverse order
+        seanet_encoder_config["ratios"] = list(reversed(seanet_encoder_config.get("ratios", [2, 2, 2, 2, 2])))
+        encoder = SEANetEncoder(
+            **seanet_encoder_config
+        )
+    elif encoder_type == "dac":
+        dac_config = encoder_config["config"]
+
+        encoder = DACEncoderWrapper(**dac_config)
+    elif encoder_type == "local_attn":
+        from .local_attention import TransformerEncoder1D
+
+        local_attn_config = encoder_config["config"]
+
+        encoder = TransformerEncoder1D(
+            **local_attn_config
+        )
+    else:
+        raise ValueError(f"Unknown encoder type {encoder_type}")
+    
+    requires_grad = encoder_config.get("requires_grad", True)
+    if not requires_grad:
+        for param in encoder.parameters():
+            param.requires_grad = False
+
+    return encoder
+
+def create_decoder_from_config(decoder_config: Dict[str, Any]):
+    decoder_type = decoder_config.get("type", None)
+    assert decoder_type is not None, "Decoder type must be specified"
+
+    if decoder_type == "oobleck":
+        decoder = OobleckDecoder(
+            **decoder_config["config"]
+        )
+    elif decoder_type == "seanet":
+        from encodec.modules import SEANetDecoder
+
+        decoder = SEANetDecoder(
+            **decoder_config["config"]
+        )
+    elif decoder_type == "dac":
+        dac_config = decoder_config["config"]
+
+        decoder = DACDecoderWrapper(**dac_config)
+    elif decoder_type == "local_attn":
+        from .local_attention import TransformerDecoder1D
+
+        local_attn_config = decoder_config["config"]
+
+        decoder = TransformerDecoder1D(
+            **local_attn_config
+        )
+    else:
+        raise ValueError(f"Unknown decoder type {decoder_type}")
+    
+    requires_grad = decoder_config.get("requires_grad", True)
+    if not requires_grad:
+        for param in decoder.parameters():
+            param.requires_grad = False
+
+    return decoder
+
+def create_autoencoder_from_config(config: Dict[str, Any]):
+    
+    ae_config = config["model"]
+
+    encoder = create_encoder_from_config(ae_config["encoder"])
+    decoder = create_decoder_from_config(ae_config["decoder"])
+
+    bottleneck = ae_config.get("bottleneck", None)
+
+    latent_dim = ae_config.get("latent_dim", None)
+    assert latent_dim is not None, "latent_dim must be specified in model config"
+    downsampling_ratio = ae_config.get("downsampling_ratio", None)
+    assert downsampling_ratio is not None, "downsampling_ratio must be specified in model config"
+    io_channels = ae_config.get("io_channels", None)
+    assert io_channels is not None, "io_channels must be specified in model config"
+    sample_rate = config.get("sample_rate", None)
+    assert sample_rate is not None, "sample_rate must be specified in model config"
+
+    in_channels = ae_config.get("in_channels", None)
+    out_channels = ae_config.get("out_channels", None)
+
+    pretransform = ae_config.get("pretransform", None)
+
+    if pretransform is not None:
+        pretransform = create_pretransform_from_config(pretransform, sample_rate)
+
+    if bottleneck is not None:
+        bottleneck = create_bottleneck_from_config(bottleneck)
+
+    soft_clip = ae_config["decoder"].get("soft_clip", False)
+
+    return AudioAutoencoder(
+        encoder,
+        decoder,
+        io_channels=io_channels,
+        latent_dim=latent_dim,
+        downsampling_ratio=downsampling_ratio,
+        sample_rate=sample_rate,
+        bottleneck=bottleneck,
+        pretransform=pretransform,
+        in_channels=in_channels,
+        out_channels=out_channels,
+        soft_clip=soft_clip
+    )
+
+def create_diffAE_from_config(config: Dict[str, Any]):
+    
+    diffae_config = config["model"]
+
+    if "encoder" in diffae_config:
+        encoder = create_encoder_from_config(diffae_config["encoder"])
+    else:
+        encoder = None
+
+    if "decoder" in diffae_config:
+        decoder = create_decoder_from_config(diffae_config["decoder"])
+    else:
+        decoder = None
+
+    diffusion_model_type = diffae_config["diffusion"]["type"]
+
+    if diffusion_model_type == "DAU1d":
+        diffusion = DAU1DCondWrapper(**diffae_config["diffusion"]["config"])
+    elif diffusion_model_type == "adp_1d":
+        diffusion = UNet1DCondWrapper(**diffae_config["diffusion"]["config"])
+    elif diffusion_model_type == "dit":
+        diffusion = DiTWrapper(**diffae_config["diffusion"]["config"])
+
+    latent_dim = diffae_config.get("latent_dim", None)
+    assert latent_dim is not None, "latent_dim must be specified in model config"
+    downsampling_ratio = diffae_config.get("downsampling_ratio", None)
+    assert downsampling_ratio is not None, "downsampling_ratio must be specified in model config"
+    io_channels = diffae_config.get("io_channels", None)
+    assert io_channels is not None, "io_channels must be specified in model config"
+    sample_rate = config.get("sample_rate", None)
+    assert sample_rate is not None, "sample_rate must be specified in model config"
+
+    bottleneck = diffae_config.get("bottleneck", None)
+
+    pretransform = diffae_config.get("pretransform", None)
+
+    if pretransform is not None:
+        pretransform = create_pretransform_from_config(pretransform, sample_rate)
+
+    if bottleneck is not None:
+        bottleneck = create_bottleneck_from_config(bottleneck)
+
+    diffusion_downsampling_ratio = None,
+
+    if diffusion_model_type == "DAU1d":
+        diffusion_downsampling_ratio = np.prod(diffae_config["diffusion"]["config"]["strides"])
+    elif diffusion_model_type == "adp_1d":
+        diffusion_downsampling_ratio = np.prod(diffae_config["diffusion"]["config"]["factors"])
+    elif diffusion_model_type == "dit":
+        diffusion_downsampling_ratio = 1
+
+    return DiffusionAutoencoder(
+        encoder=encoder,
+        decoder=decoder,
+        diffusion=diffusion,
+        io_channels=io_channels,
+        sample_rate=sample_rate,
+        latent_dim=latent_dim,
+        downsampling_ratio=downsampling_ratio,
+        diffusion_downsampling_ratio=diffusion_downsampling_ratio,
+        bottleneck=bottleneck,
+        pretransform=pretransform
+    )

stable_audio_tools/models/blocks.py

@@ -0,0 +1,339 @@
+from functools import reduce
+import math
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.backends.cuda import sdp_kernel
+from packaging import version
+
+from dac.nn.layers import Snake1d
+
+class ResidualBlock(nn.Module):
+    def __init__(self, main, skip=None):
+        super().__init__()
+        self.main = nn.Sequential(*main)
+        self.skip = skip if skip else nn.Identity()
+
+    def forward(self, input):
+        return self.main(input) + self.skip(input)
+
+class ResConvBlock(ResidualBlock):
+    def __init__(self, c_in, c_mid, c_out, is_last=False, kernel_size=5, conv_bias=True, use_snake=False):
+        skip = None if c_in == c_out else nn.Conv1d(c_in, c_out, 1, bias=False)
+        super().__init__([
+            nn.Conv1d(c_in, c_mid, kernel_size, padding=kernel_size//2, bias=conv_bias),
+            nn.GroupNorm(1, c_mid),
+            Snake1d(c_mid) if use_snake else nn.GELU(),
+            nn.Conv1d(c_mid, c_out, kernel_size, padding=kernel_size//2, bias=conv_bias),
+            nn.GroupNorm(1, c_out) if not is_last else nn.Identity(),
+            (Snake1d(c_out) if use_snake else nn.GELU()) if not is_last else nn.Identity(),
+        ], skip)
+
+class SelfAttention1d(nn.Module):
+    def __init__(self, c_in, n_head=1, dropout_rate=0.):
+        super().__init__()
+        assert c_in % n_head == 0
+        self.norm = nn.GroupNorm(1, c_in)
+        self.n_head = n_head
+        self.qkv_proj = nn.Conv1d(c_in, c_in * 3, 1)
+        self.out_proj = nn.Conv1d(c_in, c_in, 1)
+        self.dropout = nn.Dropout(dropout_rate, inplace=True)
+
+        self.use_flash = torch.cuda.is_available() and version.parse(torch.__version__) >= version.parse('2.0.0')
+
+        if not self.use_flash:
+            return
+
+        device_properties = torch.cuda.get_device_properties(torch.device('cuda'))
+
+        if device_properties.major == 8 and device_properties.minor == 0:
+            # Use flash attention for A100 GPUs
+            self.sdp_kernel_config = (True, False, False)
+        else:
+            # Don't use flash attention for other GPUs
+            self.sdp_kernel_config = (False, True, True)
+
+    def forward(self, input):
+        n, c, s = input.shape
+        qkv = self.qkv_proj(self.norm(input))
+        qkv = qkv.view(
+            [n, self.n_head * 3, c // self.n_head, s]).transpose(2, 3)
+        q, k, v = qkv.chunk(3, dim=1)
+        scale = k.shape[3]**-0.25
+
+        if self.use_flash:
+            with sdp_kernel(*self.sdp_kernel_config):
+                y = F.scaled_dot_product_attention(q, k, v, is_causal=False).contiguous().view([n, c, s])
+        else:
+            att = ((q * scale) @ (k.transpose(2, 3) * scale)).softmax(3)
+            y = (att @ v).transpose(2, 3).contiguous().view([n, c, s])
+
+
+        return input + self.dropout(self.out_proj(y))
+
+class SkipBlock(nn.Module):
+    def __init__(self, *main):
+        super().__init__()
+        self.main = nn.Sequential(*main)
+
+    def forward(self, input):
+        return torch.cat([self.main(input), input], dim=1)
+
+class FourierFeatures(nn.Module):
+    def __init__(self, in_features, out_features, std=1.):
+        super().__init__()
+        assert out_features % 2 == 0
+        self.weight = nn.Parameter(torch.randn(
+            [out_features // 2, in_features]) * std)
+
+    def forward(self, input):
+        f = 2 * math.pi * input @ self.weight.T
+        return torch.cat([f.cos(), f.sin()], dim=-1)
+
+def expand_to_planes(input, shape):
+    return input[..., None].repeat([1, 1, shape[2]])
+
+_kernels = {
+    'linear':
+        [1 / 8, 3 / 8, 3 / 8, 1 / 8],
+    'cubic': 
+        [-0.01171875, -0.03515625, 0.11328125, 0.43359375,
+        0.43359375, 0.11328125, -0.03515625, -0.01171875],
+    'lanczos3': 
+        [0.003689131001010537, 0.015056144446134567, -0.03399861603975296,
+        -0.066637322306633, 0.13550527393817902, 0.44638532400131226,
+        0.44638532400131226, 0.13550527393817902, -0.066637322306633,
+        -0.03399861603975296, 0.015056144446134567, 0.003689131001010537]
+}
+
+class Downsample1d(nn.Module):
+    def __init__(self, kernel='linear', pad_mode='reflect', channels_last=False):
+        super().__init__()
+        self.pad_mode = pad_mode
+        kernel_1d = torch.tensor(_kernels[kernel])
+        self.pad = kernel_1d.shape[0] // 2 - 1
+        self.register_buffer('kernel', kernel_1d)
+        self.channels_last = channels_last
+    
+    def forward(self, x):
+        if self.channels_last:
+            x = x.permute(0, 2, 1)
+        x = F.pad(x, (self.pad,) * 2, self.pad_mode)
+        weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0]])
+        indices = torch.arange(x.shape[1], device=x.device)
+        weight[indices, indices] = self.kernel.to(weight)
+        x = F.conv1d(x, weight, stride=2)
+        if self.channels_last:
+            x = x.permute(0, 2, 1)
+        return x
+
+
+class Upsample1d(nn.Module):
+    def __init__(self, kernel='linear', pad_mode='reflect', channels_last=False):
+        super().__init__()
+        self.pad_mode = pad_mode
+        kernel_1d = torch.tensor(_kernels[kernel]) * 2
+        self.pad = kernel_1d.shape[0] // 2 - 1
+        self.register_buffer('kernel', kernel_1d)
+        self.channels_last = channels_last
+    
+    def forward(self, x):
+        if self.channels_last:
+            x = x.permute(0, 2, 1)
+        x = F.pad(x, ((self.pad + 1) // 2,) * 2, self.pad_mode)
+        weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0]])
+        indices = torch.arange(x.shape[1], device=x.device)
+        weight[indices, indices] = self.kernel.to(weight)
+        x = F.conv_transpose1d(x, weight, stride=2, padding=self.pad * 2 + 1)
+        if self.channels_last:
+            x = x.permute(0, 2, 1)
+        return x
+        
+def Downsample1d_2(
+    in_channels: int, out_channels: int, factor: int, kernel_multiplier: int = 2
+) -> nn.Module:
+    assert kernel_multiplier % 2 == 0, "Kernel multiplier must be even"
+
+    return nn.Conv1d(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        kernel_size=factor * kernel_multiplier + 1,
+        stride=factor,
+        padding=factor * (kernel_multiplier // 2),
+    )
+
+
+def Upsample1d_2(
+    in_channels: int, out_channels: int, factor: int, use_nearest: bool = False
+) -> nn.Module:
+
+    if factor == 1:
+        return nn.Conv1d(
+            in_channels=in_channels, out_channels=out_channels, kernel_size=3, padding=1
+        )
+
+    if use_nearest:
+        return nn.Sequential(
+            nn.Upsample(scale_factor=factor, mode="nearest"),
+            nn.Conv1d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=3,
+                padding=1,
+            ),
+        )
+    else:
+        return nn.ConvTranspose1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=factor * 2,
+            stride=factor,
+            padding=factor // 2 + factor % 2,
+            output_padding=factor % 2,
+        )
+
+def zero_init(layer):
+    nn.init.zeros_(layer.weight)
+    if layer.bias is not None:
+        nn.init.zeros_(layer.bias)
+    return layer
+
+def rms_norm(x, scale, eps):
+    dtype = reduce(torch.promote_types, (x.dtype, scale.dtype, torch.float32))
+    mean_sq = torch.mean(x.to(dtype)**2, dim=-1, keepdim=True)
+    scale = scale.to(dtype) * torch.rsqrt(mean_sq + eps)
+    return x * scale.to(x.dtype)
+
+#rms_norm = torch.compile(rms_norm)
+
+class AdaRMSNorm(nn.Module):
+    def __init__(self, features, cond_features, eps=1e-6):
+        super().__init__()
+        self.eps = eps
+        self.linear = zero_init(nn.Linear(cond_features, features, bias=False))
+  
+    def extra_repr(self):
+        return f"eps={self.eps},"
+
+    def forward(self, x, cond):
+        return rms_norm(x, self.linear(cond)[:, None, :] + 1, self.eps)
+    
+def normalize(x, eps=1e-4):
+    dim = list(range(1, x.ndim))
+    n = torch.linalg.vector_norm(x, dim=dim, keepdim=True)
+    alpha = np.sqrt(n.numel() / x.numel())
+    return x / torch.add(eps, n, alpha=alpha)
+
+class ForcedWNConv1d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1):
+        super().__init__()
+        self.weight = nn.Parameter(torch.randn([out_channels, in_channels, kernel_size]))
+
+    def forward(self, x):
+        if self.training:
+            with torch.no_grad():
+                self.weight.copy_(normalize(self.weight))
+        
+        fan_in = self.weight[0].numel()
+
+        w = normalize(self.weight) / math.sqrt(fan_in)
+
+        return F.conv1d(x, w, padding='same')
+        
+# Kernels
+
+use_compile = True
+
+def compile(function, *args, **kwargs):
+    if not use_compile:
+        return function
+    try:
+        return torch.compile(function, *args, **kwargs)
+    except RuntimeError:
+        return function
+
+
+@compile
+def linear_geglu(x, weight, bias=None):
+    x = x @ weight.mT
+    if bias is not None:
+        x = x + bias
+    x, gate = x.chunk(2, dim=-1)
+    return x * F.gelu(gate)
+
+
+@compile
+def rms_norm(x, scale, eps):
+    dtype = reduce(torch.promote_types, (x.dtype, scale.dtype, torch.float32))
+    mean_sq = torch.mean(x.to(dtype)**2, dim=-1, keepdim=True)
+    scale = scale.to(dtype) * torch.rsqrt(mean_sq + eps)
+    return x * scale.to(x.dtype)
+
+# Layers
+
+class LinearGEGLU(nn.Linear):
+    def __init__(self, in_features, out_features, bias=True):
+        super().__init__(in_features, out_features * 2, bias=bias)
+        self.out_features = out_features
+
+    def forward(self, x):
+        return linear_geglu(x, self.weight, self.bias)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, shape, fix_scale = False, eps=1e-6):
+        super().__init__()
+        self.eps = eps
+
+        if fix_scale:
+            self.register_buffer("scale", torch.ones(shape))
+        else:
+            self.scale = nn.Parameter(torch.ones(shape))
+
+    def extra_repr(self):
+        return f"shape={tuple(self.scale.shape)}, eps={self.eps}"
+
+    def forward(self, x):
+        return rms_norm(x, self.scale, self.eps)    
+
+def snake_beta(x, alpha, beta):
+    return x + (1.0 / (beta + 0.000000001)) * pow(torch.sin(x * alpha), 2)
+
+# try:
+#     snake_beta = torch.compile(snake_beta)
+# except RuntimeError:
+#     pass
+
+# Adapted from https://github.com/NVIDIA/BigVGAN/blob/main/activations.py under MIT license
+# License available in LICENSES/LICENSE_NVIDIA.txt
+class SnakeBeta(nn.Module):
+
+    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True):
+        super(SnakeBeta, self).__init__()
+        self.in_features = in_features
+
+        # initialize alpha
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale: # log scale alphas initialized to zeros
+            self.alpha = nn.Parameter(torch.zeros(in_features) * alpha)
+            self.beta = nn.Parameter(torch.zeros(in_features) * alpha)
+        else: # linear scale alphas initialized to ones
+            self.alpha = nn.Parameter(torch.ones(in_features) * alpha)
+            self.beta = nn.Parameter(torch.ones(in_features) * alpha)
+
+        self.alpha.requires_grad = alpha_trainable
+        self.beta.requires_grad = alpha_trainable
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
+        beta = self.beta.unsqueeze(0).unsqueeze(-1)
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+            beta = torch.exp(beta)
+        x = snake_beta(x, alpha, beta)
+
+        return x

stable_audio_tools/models/bottleneck.py

@@ -0,0 +1,355 @@
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from einops import rearrange
+from vector_quantize_pytorch import ResidualVQ, FSQ
+from dac.nn.quantize import ResidualVectorQuantize as DACResidualVQ
+
+class Bottleneck(nn.Module):
+    def __init__(self, is_discrete: bool = False):
+        super().__init__()
+
+        self.is_discrete = is_discrete
+
+    def encode(self, x, return_info=False, **kwargs):
+        raise NotImplementedError
+
+    def decode(self, x):
+        raise NotImplementedError
+
+class DiscreteBottleneck(Bottleneck):
+    def __init__(self, num_quantizers, codebook_size, tokens_id):
+        super().__init__(is_discrete=True)
+
+        self.num_quantizers = num_quantizers
+        self.codebook_size = codebook_size
+        self.tokens_id = tokens_id
+
+    def decode_tokens(self, codes, **kwargs):
+        raise NotImplementedError
+    
+class TanhBottleneck(Bottleneck):
+    def __init__(self):
+        super().__init__(is_discrete=False)
+        self.tanh = nn.Tanh()
+
+    def encode(self, x, return_info=False):
+        info = {}
+
+        x = torch.tanh(x)
+
+        if return_info:
+            return x, info
+        else:
+            return x
+
+    def decode(self, x):
+        return x
+
+def vae_sample(mean, scale):
+        stdev = nn.functional.softplus(scale) + 1e-4
+        var = stdev * stdev
+        logvar = torch.log(var)
+        latents = torch.randn_like(mean) * stdev + mean
+
+        kl = (mean * mean + var - logvar - 1).sum(1).mean()
+
+        return latents, kl
+
+class VAEBottleneck(Bottleneck):
+    def __init__(self):
+        super().__init__(is_discrete=False)
+
+    def encode(self, x, return_info=False, **kwargs):
+        info = {}
+
+        mean, scale = x.chunk(2, dim=1)
+
+        x, kl = vae_sample(mean, scale)
+
+        info["kl"] = kl
+
+        if return_info:
+            return x, info
+        else:
+            return x
+
+    def decode(self, x):
+        return x
+
+def compute_mean_kernel(x, y):
+        kernel_input = (x[:, None] - y[None]).pow(2).mean(2) / x.shape[-1]
+        return torch.exp(-kernel_input).mean()
+
+def compute_mmd(latents):
+    latents_reshaped = latents.permute(0, 2, 1).reshape(-1, latents.shape[1])
+    noise = torch.randn_like(latents_reshaped)
+
+    latents_kernel = compute_mean_kernel(latents_reshaped, latents_reshaped)
+    noise_kernel = compute_mean_kernel(noise, noise)
+    latents_noise_kernel = compute_mean_kernel(latents_reshaped, noise)
+    
+    mmd = latents_kernel + noise_kernel - 2 * latents_noise_kernel
+    return mmd.mean()
+
+class WassersteinBottleneck(Bottleneck):
+    def __init__(self, noise_augment_dim: int = 0, bypass_mmd: bool = False):
+        super().__init__(is_discrete=False)
+
+        self.noise_augment_dim = noise_augment_dim
+        self.bypass_mmd = bypass_mmd
+    
+    def encode(self, x, return_info=False):
+        info = {}
+
+        if self.training and return_info:
+            if self.bypass_mmd:
+                mmd = torch.tensor(0.0)
+            else:
+                mmd = compute_mmd(x)
+                
+            info["mmd"] = mmd
+        
+        if return_info:
+            return x, info
+        
+        return x
+
+    def decode(self, x):
+
+        if self.noise_augment_dim > 0:
+            noise = torch.randn(x.shape[0], self.noise_augment_dim,
+                                x.shape[-1]).type_as(x)
+            x = torch.cat([x, noise], dim=1)
+
+        return x
+
+class L2Bottleneck(Bottleneck):
+    def __init__(self):
+        super().__init__(is_discrete=False)
+    
+    def encode(self, x, return_info=False):
+        info = {}
+
+        x = F.normalize(x, dim=1)
+
+        if return_info:
+            return x, info
+        else:
+            return x
+        
+    def decode(self, x):
+        return F.normalize(x, dim=1)
+        
+class RVQBottleneck(DiscreteBottleneck):
+    def __init__(self, **quantizer_kwargs):
+        super().__init__(num_quantizers = quantizer_kwargs["num_quantizers"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "quantizer_indices")
+        self.quantizer = ResidualVQ(**quantizer_kwargs)
+        self.num_quantizers = quantizer_kwargs["num_quantizers"]
+
+    def encode(self, x, return_info=False, **kwargs):
+        info = {}
+
+        x = rearrange(x, "b c n -> b n c")
+        x, indices, loss = self.quantizer(x)
+        x = rearrange(x, "b n c -> b c n")
+
+        info["quantizer_indices"] = indices
+        info["quantizer_loss"] = loss.mean()
+
+        if return_info:
+            return x, info
+        else:
+            return x
+        
+    def decode(self, x):
+        return x
+    
+    def decode_tokens(self, codes, **kwargs):
+        latents = self.quantizer.get_outputs_from_indices(codes)
+
+        return self.decode(latents, **kwargs)
+    
+class RVQVAEBottleneck(DiscreteBottleneck):
+    def __init__(self, **quantizer_kwargs):
+        super().__init__(num_quantizers = quantizer_kwargs["num_quantizers"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "quantizer_indices")
+        self.quantizer = ResidualVQ(**quantizer_kwargs)
+        self.num_quantizers = quantizer_kwargs["num_quantizers"]
+
+    def encode(self, x, return_info=False):
+        info = {}
+
+        x, kl = vae_sample(*x.chunk(2, dim=1))
+
+        info["kl"] = kl
+
+        x = rearrange(x, "b c n -> b n c")
+        x, indices, loss = self.quantizer(x)
+        x = rearrange(x, "b n c -> b c n")
+
+        info["quantizer_indices"] = indices
+        info["quantizer_loss"] = loss.mean()
+
+        if return_info:
+            return x, info
+        else:
+            return x
+        
+    def decode(self, x):
+        return x
+    
+    def decode_tokens(self, codes, **kwargs):
+        latents = self.quantizer.get_outputs_from_indices(codes)
+
+        return self.decode(latents, **kwargs)
+
+class DACRVQBottleneck(DiscreteBottleneck):
+    def __init__(self, quantize_on_decode=False, noise_augment_dim=0, **quantizer_kwargs):
+        super().__init__(num_quantizers = quantizer_kwargs["n_codebooks"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "codes")
+        self.quantizer = DACResidualVQ(**quantizer_kwargs)
+        self.num_quantizers = quantizer_kwargs["n_codebooks"]
+        self.quantize_on_decode = quantize_on_decode
+        self.noise_augment_dim = noise_augment_dim
+
+    def encode(self, x, return_info=False, **kwargs):
+        info = {}
+
+        info["pre_quantizer"] = x
+
+        if self.quantize_on_decode:
+            return x, info if return_info else x
+
+        z, codes, latents, commitment_loss, codebook_loss = self.quantizer(x, **kwargs)
+
+        output = {
+            "z": z,
+            "codes": codes,
+            "latents": latents,
+            "vq/commitment_loss": commitment_loss,
+            "vq/codebook_loss": codebook_loss,
+        }
+
+        output["vq/commitment_loss"] /= self.num_quantizers
+        output["vq/codebook_loss"] /= self.num_quantizers
+
+        info.update(output)
+
+        if return_info:
+            return output["z"], info
+        
+        return output["z"]
+    
+    def decode(self, x):
+
+        if self.quantize_on_decode:
+            x = self.quantizer(x)[0]
+
+        if self.noise_augment_dim > 0:
+            noise = torch.randn(x.shape[0], self.noise_augment_dim,
+                                x.shape[-1]).type_as(x)
+            x = torch.cat([x, noise], dim=1)
+
+        return x
+    
+    def decode_tokens(self, codes, **kwargs):
+        latents, _, _ = self.quantizer.from_codes(codes)
+
+        return self.decode(latents, **kwargs)
+
+class DACRVQVAEBottleneck(DiscreteBottleneck):
+    def __init__(self, quantize_on_decode=False, **quantizer_kwargs):
+        super().__init__(num_quantizers = quantizer_kwargs["n_codebooks"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "codes")
+        self.quantizer = DACResidualVQ(**quantizer_kwargs)
+        self.num_quantizers = quantizer_kwargs["n_codebooks"]
+        self.quantize_on_decode = quantize_on_decode
+
+    def encode(self, x, return_info=False, n_quantizers: int = None):
+        info = {}
+
+        mean, scale = x.chunk(2, dim=1)
+
+        x, kl = vae_sample(mean, scale)
+
+        info["pre_quantizer"] = x
+        info["kl"] = kl
+
+        if self.quantize_on_decode:
+            return x, info if return_info else x
+
+        z, codes, latents, commitment_loss, codebook_loss = self.quantizer(x, n_quantizers=n_quantizers)
+
+        output = {
+            "z": z,
+            "codes": codes,
+            "latents": latents,
+            "vq/commitment_loss": commitment_loss,
+            "vq/codebook_loss": codebook_loss,
+        }
+
+        output["vq/commitment_loss"] /= self.num_quantizers
+        output["vq/codebook_loss"] /= self.num_quantizers
+
+        info.update(output)
+
+        if return_info:
+            return output["z"], info
+        
+        return output["z"]
+    
+    def decode(self, x):
+
+        if self.quantize_on_decode:
+            x = self.quantizer(x)[0]
+
+        return x
+
+    def decode_tokens(self, codes, **kwargs):
+        latents, _, _ = self.quantizer.from_codes(codes)
+
+        return self.decode(latents, **kwargs)
+    
+class FSQBottleneck(DiscreteBottleneck):
+    def __init__(self, noise_augment_dim=0, **kwargs):
+        super().__init__(num_quantizers = kwargs.get("num_codebooks", 1), codebook_size = np.prod(kwargs["levels"]), tokens_id = "quantizer_indices")
+
+        self.noise_augment_dim = noise_augment_dim
+
+        self.quantizer = FSQ(**kwargs, allowed_dtypes=[torch.float16, torch.float32, torch.float64])
+
+    def encode(self, x, return_info=False):
+        info = {}
+
+        orig_dtype = x.dtype
+        x = x.float()
+
+        x = rearrange(x, "b c n -> b n c")
+        x, indices = self.quantizer(x)
+        x = rearrange(x, "b n c -> b c n")
+
+        x = x.to(orig_dtype)
+
+        # Reorder indices to match the expected format
+        indices = rearrange(indices, "b n q -> b q n")
+
+        info["quantizer_indices"] = indices
+
+        if return_info:
+            return x, info
+        else:
+            return x
+        
+    def decode(self, x):
+
+        if self.noise_augment_dim > 0:
+            noise = torch.randn(x.shape[0], self.noise_augment_dim,
+                                x.shape[-1]).type_as(x)
+            x = torch.cat([x, noise], dim=1)
+
+        return x
+    
+    def decode_tokens(self, tokens, **kwargs):
+        latents = self.quantizer.indices_to_codes(tokens)
+
+        return self.decode(latents, **kwargs)

stable_audio_tools/models/codebook_patterns.py

@@ -0,0 +1,545 @@
+# Copied from https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/modules/codebooks_patterns.py under MIT License
+# License available in LICENSES/LICENSE_META.txt
+
+from collections import namedtuple
+from dataclasses import dataclass
+from functools import lru_cache
+import logging
+import typing as tp
+
+from abc import ABC, abstractmethod
+import torch
+
+LayoutCoord = namedtuple('LayoutCoord', ['t', 'q'])  # (timestep, codebook index)
+PatternLayout = tp.List[tp.List[LayoutCoord]]  # Sequence of coordinates
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class Pattern:
+    """Base implementation of a pattern over a sequence with multiple codebooks.
+
+    The codebook pattern consists in a layout, defining for each sequence step
+    the list of coordinates of each codebook timestep in the resulting interleaved sequence.
+    The first item of the pattern is always an empty list in order to properly insert a special token
+    to start with. For convenience, we also keep track of ``n_q`` the number of codebooks used for the pattern
+    and ``timesteps`` the number of timesteps corresponding to the original sequence.
+
+    The pattern provides convenient methods to build and revert interleaved sequences from it:
+    ``build_pattern_sequence`` maps a given a dense input tensor of multi-codebook sequence from [B, K, T]
+        to the interleaved sequence of shape [B, K, S] applying the pattern, with B being the batch size,
+        K being the number of codebooks, T the number of original timesteps and S the number of sequence steps
+        for the output sequence. The unfilled positions are replaced with a special token and the built sequence
+        is returned along with a mask indicating valid tokens.
+    ``revert_pattern_sequence`` maps back an interleaved sequence of shape [B, K, S] to the original alignment
+        of codebooks across timesteps to an output tensor of shape [B, K, T], using again a special token and a mask
+        to fill and specify invalid positions if needed.
+    See the dedicated methods for more details.
+    """
+    # Pattern layout, for each sequence step, we have a list of coordinates
+    # corresponding to the original codebook timestep and position.
+    # The first list is always an empty list in order to properly insert
+    # a special token to start with.
+    layout: PatternLayout
+    timesteps: int
+    n_q: int
+
+    def __post_init__(self):
+        assert len(self.layout) > 0
+        self._validate_layout()
+        self._build_reverted_sequence_scatter_indexes = lru_cache(100)(self._build_reverted_sequence_scatter_indexes)
+        self._build_pattern_sequence_scatter_indexes = lru_cache(100)(self._build_pattern_sequence_scatter_indexes)
+        logger.info("New pattern, time steps: %d, sequence steps: %d", self.timesteps, len(self.layout))
+
+    def _validate_layout(self):
+        """Runs checks on the layout to ensure a valid pattern is defined.
+        A pattern is considered invalid if:
+            - Multiple timesteps for a same codebook are defined in the same sequence step
+            - The timesteps for a given codebook are not in ascending order as we advance in the sequence
+              (this would mean that we have future timesteps before past timesteps).
+        """
+        q_timesteps = {q: 0 for q in range(self.n_q)}
+        for s, seq_coords in enumerate(self.layout):
+            if len(seq_coords) > 0:
+                qs = set()
+                for coord in seq_coords:
+                    qs.add(coord.q)
+                    last_q_timestep = q_timesteps[coord.q]
+                    assert coord.t >= last_q_timestep, \
+                        f"Past timesteps are found in the sequence for codebook = {coord.q} at step {s}"
+                    q_timesteps[coord.q] = coord.t
+                # each sequence step contains at max 1 coordinate per codebook
+                assert len(qs) == len(seq_coords), \
+                    f"Multiple entries for a same codebook are found at step {s}"
+
+    @property
+    def num_sequence_steps(self):
+        return len(self.layout) - 1
+
+    @property
+    def max_delay(self):
+        max_t_in_seq_coords = 0
+        for seq_coords in self.layout[1:]:
+            for coords in seq_coords:
+                max_t_in_seq_coords = max(max_t_in_seq_coords, coords.t + 1)
+        return max_t_in_seq_coords - self.timesteps
+
+    @property
+    def valid_layout(self):
+        valid_step = len(self.layout) - self.max_delay
+        return self.layout[:valid_step]
+
+    def starts_with_special_token(self):
+        return self.layout[0] == []
+
+    def get_sequence_coords_with_timestep(self, t: int, q: tp.Optional[int] = None):
+        """Get codebook coordinates in the layout that corresponds to the specified timestep t
+        and optionally to the codebook q. Coordinates are returned as a tuple with the sequence step
+        and the actual codebook coordinates.
+        """
+        assert t <= self.timesteps, "provided timesteps is greater than the pattern's number of timesteps"
+        if q is not None:
+            assert q <= self.n_q, "provided number of codebooks is greater than the pattern's number of codebooks"
+        coords = []
+        for s, seq_codes in enumerate(self.layout):
+            for code in seq_codes:
+                if code.t == t and (q is None or code.q == q):
+                    coords.append((s, code))
+        return coords
+
+    def get_steps_with_timestep(self, t: int, q: tp.Optional[int] = None) -> tp.List[int]:
+        return [step for step, coords in self.get_sequence_coords_with_timestep(t, q)]
+
+    def get_first_step_with_timesteps(self, t: int, q: tp.Optional[int] = None) -> tp.Optional[int]:
+        steps_with_timesteps = self.get_steps_with_timestep(t, q)
+        return steps_with_timesteps[0] if len(steps_with_timesteps) > 0 else None
+
+    def _build_pattern_sequence_scatter_indexes(self, timesteps: int, n_q: int, keep_only_valid_steps: bool,
+                                                device: tp.Union[torch.device, str] = 'cpu'):
+        """Build scatter indexes corresponding to the pattern, up to the provided sequence_steps.
+
+        Args:
+            timesteps (int): Maximum number of timesteps steps to consider.
+            keep_only_valid_steps (bool): Restrict the pattern layout to match only valid steps.
+            device (torch.device or str): Device for created tensors.
+        Returns:
+            indexes (torch.Tensor): Indexes corresponding to the sequence, of shape [K, S].
+            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes, of shape [K, S].
+        """
+        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
+        assert timesteps <= self.timesteps, "invalid number of timesteps used to build the sequence from the pattern"
+        # use the proper layout based on whether we limit ourselves to valid steps only or not,
+        # note that using the valid_layout will result in a truncated sequence up to the valid steps
+        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
+        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
+        indexes = torch.zeros(n_q, len(ref_layout), dtype=torch.long).numpy()
+        mask = torch.zeros(n_q, len(ref_layout), dtype=torch.bool).numpy()
+        # fill indexes with last sequence step value that will correspond to our special token
+        # the last value is n_q * timesteps as we have flattened z and append special token as the last token
+        # which will correspond to the index: n_q * timesteps
+        indexes[:] = n_q * timesteps
+        # iterate over the pattern and fill scattered indexes and mask
+        for s, sequence_coords in enumerate(ref_layout):
+            for coords in sequence_coords:
+                if coords.t < timesteps:
+                    indexes[coords.q, s] = coords.t + coords.q * timesteps
+                    mask[coords.q, s] = 1
+        indexes = torch.from_numpy(indexes).to(device)
+        mask = torch.from_numpy(mask).to(device)
+        return indexes, mask
+
+    def build_pattern_sequence(self, z: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
+        """Build sequence corresponding to the pattern from the input tensor z.
+        The sequence is built using up to sequence_steps if specified, and non-pattern
+        coordinates are filled with the special token.
+
+        Args:
+            z (torch.Tensor): Input tensor of multi-codebooks sequence, of shape [B, K, T].
+            special_token (int): Special token used to fill non-pattern coordinates in the new sequence.
+            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
+                Steps that are beyond valid steps will be replaced by the special_token in that case.
+        Returns:
+            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, S] with S
+                corresponding either to the sequence_steps if provided, otherwise to the length of the pattern.
+            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, S].
+            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, S].
+        """
+        B, K, T = z.shape
+        indexes, mask = self._build_pattern_sequence_scatter_indexes(
+            T, K, keep_only_valid_steps=keep_only_valid_steps, device=str(z.device)
+        )
+        z = z.view(B, -1)
+        # we append the special token as the last index of our flattened z tensor
+        z = torch.cat([z, torch.zeros_like(z[:, :1]) + special_token], dim=1)
+        values = z[:, indexes.view(-1)]
+        values = values.view(B, K, indexes.shape[-1])
+        return values, indexes, mask
+
+    def _build_reverted_sequence_scatter_indexes(self, sequence_steps: int, n_q: int,
+                                                 keep_only_valid_steps: bool = False,
+                                                 is_model_output: bool = False,
+                                                 device: tp.Union[torch.device, str] = 'cpu'):
+        """Builds scatter indexes required to retrieve the original multi-codebook sequence
+        from interleaving pattern.
+
+        Args:
+            sequence_steps (int): Sequence steps.
+            n_q (int): Number of codebooks.
+            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
+                Steps that are beyond valid steps will be replaced by the special_token in that case.
+            is_model_output (bool): Whether to keep the sequence item corresponding to initial special token or not.
+            device (torch.device or str): Device for created tensors.
+        Returns:
+            indexes (torch.Tensor): Indexes for reconstructing the output, of shape [K, T].
+            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
+        """
+        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
+        # TODO(jade): Do we want to further truncate to only valid timesteps here as well?
+        timesteps = self.timesteps
+        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
+        assert sequence_steps <= len(ref_layout), \
+            f"sequence to revert is longer than the defined pattern: {sequence_steps} > {len(ref_layout)}"
+
+        # ensure we take the appropriate indexes to keep the model output from the first special token as well
+        if is_model_output and self.starts_with_special_token():
+            ref_layout = ref_layout[1:]
+
+        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
+        indexes = torch.zeros(n_q, timesteps, dtype=torch.long).numpy()
+        mask = torch.zeros(n_q, timesteps, dtype=torch.bool).numpy()
+        # fill indexes with last sequence step value that will correspond to our special token
+        indexes[:] = n_q * sequence_steps
+        for s, sequence_codes in enumerate(ref_layout):
+            if s < sequence_steps:
+                for code in sequence_codes:
+                    if code.t < timesteps:
+                        indexes[code.q, code.t] = s + code.q * sequence_steps
+                        mask[code.q, code.t] = 1
+        indexes = torch.from_numpy(indexes).to(device)
+        mask = torch.from_numpy(mask).to(device)
+        return indexes, mask
+
+    def revert_pattern_sequence(self, s: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
+        """Revert a sequence built from the pattern back to the original multi-codebook sequence without interleaving.
+        The sequence is reverted using up to timesteps if specified, and non-pattern coordinates
+        are filled with the special token.
+
+        Args:
+            s (torch.Tensor): Interleaved sequence tensor obtained from the pattern, of shape [B, K, S].
+            special_token (int or float): Special token used to fill non-pattern coordinates in the new sequence.
+        Returns:
+            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, T] with T
+                corresponding either to the timesteps if provided, or the total timesteps in pattern otherwise.
+            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, T].
+            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
+        """
+        B, K, S = s.shape
+        indexes, mask = self._build_reverted_sequence_scatter_indexes(
+            S, K, keep_only_valid_steps, is_model_output=False, device=str(s.device)
+        )
+        s = s.view(B, -1)
+        # we append the special token as the last index of our flattened z tensor
+        s = torch.cat([s, torch.zeros_like(s[:, :1]) + special_token], dim=1)
+        values = s[:, indexes.view(-1)]
+        values = values.view(B, K, indexes.shape[-1])
+        return values, indexes, mask
+
+    def revert_pattern_logits(self, logits: torch.Tensor, special_token: float, keep_only_valid_steps: bool = False):
+        """Revert model logits obtained on a sequence built from the pattern
+        back to a tensor matching the original sequence.
+
+        This method is similar to ``revert_pattern_sequence`` with the following specificities:
+        1. It is designed to work with the extra cardinality dimension
+        2. We return the logits for the first sequence item that matches the special_token and
+        which matching target in the original sequence is the first item of the sequence,
+        while we skip the last logits as there is no matching target
+        """
+        B, card, K, S = logits.shape
+        indexes, mask = self._build_reverted_sequence_scatter_indexes(
+            S, K, keep_only_valid_steps, is_model_output=True, device=logits.device
+        )
+        logits = logits.reshape(B, card, -1)
+        # we append the special token as the last index of our flattened z tensor
+        logits = torch.cat([logits, torch.zeros_like(logits[:, :, :1]) + special_token], dim=-1)  # [B, card, K x S]
+        values = logits[:, :, indexes.view(-1)]
+        values = values.view(B, card, K, indexes.shape[-1])
+        return values, indexes, mask
+
+
+class CodebooksPatternProvider(ABC):
+    """Abstraction around providing pattern for interleaving codebooks.
+
+    The CodebooksPatternProvider abstraction allows to implement various strategies to
+    define interleaving pattern of sequences composed of multiple codebooks. For a given
+    number of codebooks `n_q`, the pattern provider can generate a specified pattern
+    corresponding to a sequence of `T` timesteps with `n_q` parallel codebooks. This pattern
+    can be used to construct a new sequence from the original codes respecting the specified
+    pattern. The pattern is defined as a list of list of code coordinates, code coordinate
+    being a tuple with the original timestep and codebook to build the new sequence.
+    Note that all patterns must start with an empty list that is then used to insert a first
+    sequence step of special tokens in the newly generated sequence.
+
+    Args:
+        n_q (int): number of codebooks.
+        cached (bool): if True, patterns for a given length are cached. In general
+            that should be true for efficiency reason to avoid synchronization points.
+    """
+    def __init__(self, n_q: int, cached: bool = True):
+        assert n_q > 0
+        self.n_q = n_q
+        self.get_pattern = lru_cache(100)(self.get_pattern)  # type: ignore
+
+    @abstractmethod
+    def get_pattern(self, timesteps: int) -> Pattern:
+        """Builds pattern with specific interleaving between codebooks.
+
+        Args:
+            timesteps (int): Total number of timesteps.
+        """
+        raise NotImplementedError()
+
+
+class DelayedPatternProvider(CodebooksPatternProvider):
+    """Provider for delayed pattern across delayed codebooks.
+    Codebooks are delayed in the sequence and sequence steps will contain codebooks
+    from different timesteps.
+
+    Example:
+        Taking timesteps=4 and n_q=3, delays=None, the multi-codebook sequence:
+        [[1, 2, 3, 4],
+        [1, 2, 3, 4],
+        [1, 2, 3, 4]]
+        The resulting sequence obtained from the returned pattern is:
+        [[S, 1, 2, 3, 4],
+        [S, S, 1, 2, 3],
+        [S, S, S, 1, 2]]
+        (with S being a special token)
+
+    Args:
+        n_q (int): Number of codebooks.
+        delays (list of int, optional): Delay for each of the codebooks.
+            If delays not defined, each codebook is delayed by 1 compared to the previous one.
+        flatten_first (int): Flatten the first N timesteps.
+        empty_initial (int): Prepend with N empty list of coordinates.
+    """
+    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None,
+                 flatten_first: int = 0, empty_initial: int = 0):
+        super().__init__(n_q)
+        if delays is None:
+            delays = list(range(n_q))
+        self.delays = delays
+        self.flatten_first = flatten_first
+        self.empty_initial = empty_initial
+        assert len(self.delays) == self.n_q
+        assert sorted(self.delays) == self.delays
+
+    def get_pattern(self, timesteps: int) -> Pattern:
+        omit_special_token = self.empty_initial < 0
+        out: PatternLayout = [] if omit_special_token else [[]]
+        max_delay = max(self.delays)
+        if self.empty_initial:
+            out += [[] for _ in range(self.empty_initial)]
+        if self.flatten_first:
+            for t in range(min(timesteps, self.flatten_first)):
+                for q in range(self.n_q):
+                    out.append([LayoutCoord(t, q)])
+        for t in range(self.flatten_first, timesteps + max_delay):
+            v = []
+            for q, delay in enumerate(self.delays):
+                t_for_q = t - delay
+                if t_for_q >= self.flatten_first:
+                    v.append(LayoutCoord(t_for_q, q))
+            out.append(v)
+        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
+
+
+class ParallelPatternProvider(DelayedPatternProvider):
+    """Provider for parallel pattern across codebooks.
+    This pattern provider is a special case of the delayed pattern with actually no delay,
+    hence delays=repeat(0, n_q).
+
+    Args:
+        n_q (int): Number of codebooks.
+        empty_initial (int): Prepend with N empty list of coordinates.
+    """
+    def __init__(self, n_q: int, empty_initial: int = 0):
+        super().__init__(n_q, [0] * n_q, empty_initial=empty_initial)
+
+
+class UnrolledPatternProvider(CodebooksPatternProvider):
+    """Provider for unrolling codebooks pattern.
+    This pattern provider enables to represent the codebook flattened completely or only to some extend
+    while also specifying a given delay between the flattened codebooks representation, allowing to
+    unroll the codebooks in the sequence.
+
+    Example:
+        1. Flattening of the codebooks.
+        By default, the pattern provider will fully flatten the codebooks such as flattening=range(n_q),
+        taking n_q = 3 and timesteps = 4:
+        [[1, 2, 3, 4],
+         [1, 2, 3, 4],
+         [1, 2, 3, 4]]
+        will result into:
+        [[S, S, 1, S, S, 2, S, S, 3, S, S, 4],
+         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
+         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
+        2. Partial flattening of the codebooks. The ``flattening`` parameter allows to specify the inner step
+        for each of the codebook, allowing to define which codebook to flatten (or keep in parallel), for example
+        taking n_q = 3, timesteps = 4 and flattening = [0, 1, 1]:
+        [[1, 2, 3, 4],
+         [1, 2, 3, 4],
+         [1, 2, 3, 4]]
+        will result into:
+        [[S, 1, S, S, 2, S, S, 3, S, S, 4, S],
+         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
+         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
+        3. Flattening with delay. The ``delay`` parameter allows to further unroll the sequence of codebooks
+        allowing to specify the delay per codebook. Note that the delay between codebooks flattened to the
+        same inner timestep should be coherent. For example, taking n_q = 3, timesteps = 4, flattening = [0, 1, 1]
+        and delays = [0, 3, 3]:
+        [[1, 2, 3, 4],
+         [1, 2, 3, 4],
+         [1, 2, 3, 4]]
+        will result into:
+        [[S, S, S, 1, S, 2, S, 3, S, 4],
+         [S, S, S, 1, S, 2, S, 3, S, 4],
+         [1, 2, 3, S, 4, S, 5, S, 6, S]]
+
+    Args:
+        n_q (int): Number of codebooks.
+        flattening (list of int, optional): Flattening schema over the codebooks. If not defined,
+            the codebooks will be flattened to 1 codebook per step, meaning that the sequence will
+            have n_q extra steps for each timestep.
+        delays (list of int, optional): Delay for each of the codebooks. If not defined,
+            no delay is added and therefore will default to [0] * ``n_q``.
+            Note that two codebooks that will be flattened to the same inner step
+            should have the same delay, otherwise the pattern is considered as invalid.
+    """
+    FlattenedCodebook = namedtuple('FlattenedCodebook', ['codebooks', 'delay'])
+
+    def __init__(self, n_q: int, flattening: tp.Optional[tp.List[int]] = None,
+                 delays: tp.Optional[tp.List[int]] = None):
+        super().__init__(n_q)
+        if flattening is None:
+            flattening = list(range(n_q))
+        if delays is None:
+            delays = [0] * n_q
+        assert len(flattening) == n_q
+        assert len(delays) == n_q
+        assert sorted(flattening) == flattening
+        assert sorted(delays) == delays
+        self._flattened_codebooks = self._build_flattened_codebooks(delays, flattening)
+        self.max_delay = max(delays)
+
+    def _build_flattened_codebooks(self, delays: tp.List[int], flattening: tp.List[int]):
+        """Build a flattened codebooks representation as a dictionary of inner step
+        and the actual codebook indices corresponding to the flattened codebook. For convenience, we
+        also store the delay associated to the flattened codebook to avoid maintaining an extra mapping.
+        """
+        flattened_codebooks: dict = {}
+        for q, (inner_step, delay) in enumerate(zip(flattening, delays)):
+            if inner_step not in flattened_codebooks:
+                flat_codebook = UnrolledPatternProvider.FlattenedCodebook(codebooks=[q], delay=delay)
+            else:
+                flat_codebook = flattened_codebooks[inner_step]
+                assert flat_codebook.delay == delay, (
+                    "Delay and flattening between codebooks is inconsistent: ",
+                    "two codebooks flattened to the same position should have the same delay."
+                )
+                flat_codebook.codebooks.append(q)
+            flattened_codebooks[inner_step] = flat_codebook
+        return flattened_codebooks
+
+    @property
+    def _num_inner_steps(self):
+        """Number of inner steps to unroll between timesteps in order to flatten the codebooks.
+        """
+        return max([inner_step for inner_step in self._flattened_codebooks.keys()]) + 1
+
+    def num_virtual_steps(self, timesteps: int) -> int:
+        return timesteps * self._num_inner_steps + 1
+
+    def get_pattern(self, timesteps: int) -> Pattern:
+        """Builds pattern for delay across codebooks.
+
+        Args:
+            timesteps (int): Total number of timesteps.
+        """
+        # the PatternLayout is built as a tuple of sequence position and list of coordinates
+        # so that it can be reordered properly given the required delay between codebooks of given timesteps
+        indexed_out: list = [(-1, [])]
+        max_timesteps = timesteps + self.max_delay
+        for t in range(max_timesteps):
+            # for each timestep, we unroll the flattened codebooks,
+            # emitting the sequence step with the corresponding delay
+            for step in range(self._num_inner_steps):
+                if step in self._flattened_codebooks:
+                    # we have codebooks at this virtual step to emit
+                    step_codebooks = self._flattened_codebooks[step]
+                    t_for_q = t + step_codebooks.delay
+                    coords = [LayoutCoord(t, q) for q in step_codebooks.codebooks]
+                    if t_for_q < max_timesteps and t < max_timesteps:
+                        indexed_out.append((t_for_q, coords))
+                else:
+                    # there is no codebook in this virtual step so we emit an empty list
+                    indexed_out.append((t, []))
+        out = [coords for _, coords in sorted(indexed_out)]
+        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
+
+
+class CoarseFirstPattern(CodebooksPatternProvider):
+    """First generates all the codebooks #1 (e.g. coarser), then the remaining ones,
+    potentially with delays.
+
+    ..Warning:: You must always generate the full training duration at test time, for instance,
+        30 seconds, as otherwise, the fine codebooks will start being generated in an unexpected
+        location. This is due to the non causality of the remaining codebooks with respect to
+        the first ones.
+
+    Args:
+        n_q (int): Number of codebooks.
+        delays (list of int, optional): Delay for each of the codebooks.
+            If delays not defined, each codebook is delayed by 1 compared to the previous one.
+    """
+    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None):
+        super().__init__(n_q)
+        if delays is None:
+            delays = [0] * (n_q - 1)
+        self.delays = delays
+        assert len(self.delays) == self.n_q - 1
+        assert sorted(self.delays) == self.delays
+
+    def get_pattern(self, timesteps: int) -> Pattern:
+        out: PatternLayout = [[]]
+        for t in range(timesteps):
+            out.append([LayoutCoord(t, 0)])
+        max_delay = max(self.delays)
+        for t in range(timesteps + max_delay):
+            v = []
+            for q, delay in enumerate(self.delays):
+                t_for_q = t - delay
+                if t_for_q >= 0:
+                    v.append(LayoutCoord(t_for_q, q + 1))
+            out.append(v)
+        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
+
+
+class MusicLMPattern(CodebooksPatternProvider):
+    """Almost MusicLM style pattern. This is equivalent to full flattening
+    but in a different order.
+
+    Args:
+        n_q (int): Number of codebooks.
+        group_by (int): Number of codebooks to group together.
+    """
+    def __init__(self, n_q: int, group_by: int = 2):
+        super().__init__(n_q)
+        self.group_by = group_by
+
+    def get_pattern(self, timesteps: int) -> Pattern:
+        out: PatternLayout = [[]]
+        for offset in range(0, self.n_q, self.group_by):
+            for t in range(timesteps):
+                for q in range(offset, offset + self.group_by):
+                    out.append([LayoutCoord(t, q)])
+        return Pattern(out, n_q=self.n_q, timesteps=timesteps)

stable_audio_tools/models/conditioners.py

@@ -0,0 +1,710 @@
+#Heavily influenced by https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/modules/conditioners.py
+
+import torch
+import logging, warnings
+import string
+import typing as tp
+import gc
+
+from .adp import NumberEmbedder
+from ..inference.utils import set_audio_channels
+from .factory import create_pretransform_from_config
+from .pretransforms import Pretransform
+from .utils import load_ckpt_state_dict
+
+from torch import nn
+from transformers import AutoProcessor, CLIPVisionModelWithProjection
+import einops
+from .temptransformer import SA_Transformer
+from torchvision import transforms
+import torch
+import einops
+import torchvision.transforms as transforms
+
+
+class Conditioner(nn.Module):
+    def __init__(
+            self,
+            dim: int,
+            output_dim: int,
+            project_out: bool = False
+            ):
+        
+        super().__init__()
+
+        self.dim = dim
+        self.output_dim = output_dim
+        self.proj_out = nn.Linear(dim, output_dim) if (dim != output_dim or project_out) else nn.Identity()
+
+    def forward(self, x: tp.Any) -> tp.Any:
+        raise NotImplementedError()
+    
+class IntConditioner(Conditioner):
+    def __init__(self, 
+                output_dim: int,
+                min_val: int=0,
+                max_val: int=512
+                ):
+        super().__init__(output_dim, output_dim)
+
+        self.min_val = min_val
+        self.max_val = max_val
+        self.int_embedder = nn.Embedding(max_val - min_val + 1, output_dim).requires_grad_(True)
+
+    def forward(self, ints: tp.List[int], device=None) -> tp.Any:
+            
+            #self.int_embedder.to(device)
+    
+            ints = torch.tensor(ints).to(device)
+            ints = ints.clamp(self.min_val, self.max_val)
+    
+            int_embeds = self.int_embedder(ints).unsqueeze(1)
+    
+            return [int_embeds, torch.ones(int_embeds.shape[0], 1).to(device)]
+
+class NumberConditioner(Conditioner):
+    '''
+        Conditioner that takes a list of floats, normalizes them for a given range, and returns a list of embeddings
+    '''
+    def __init__(self, 
+                output_dim: int,
+                min_val: float=0,
+                max_val: float=1
+                ):
+        super().__init__(output_dim, output_dim)
+
+        self.min_val = min_val
+        self.max_val = max_val
+
+        self.embedder = NumberEmbedder(features=output_dim)
+
+    def forward(self, floats: tp.List[float], device=None) -> tp.Any:
+    
+            # Cast the inputs to floats
+            floats = [float(x) for x in floats]
+
+            floats = torch.tensor(floats).to(device)
+
+            floats = floats.clamp(self.min_val, self.max_val)
+    
+            normalized_floats = (floats - self.min_val) / (self.max_val - self.min_val)
+
+            # Cast floats to same type as embedder
+            embedder_dtype = next(self.embedder.parameters()).dtype
+            normalized_floats = normalized_floats.to(embedder_dtype)
+
+            float_embeds = self.embedder(normalized_floats).unsqueeze(1)
+    
+            return [float_embeds, torch.ones(float_embeds.shape[0], 1).to(device)]
+
+class CLAPTextConditioner(Conditioner):
+    def __init__(self, 
+                 output_dim: int, 
+                 clap_ckpt_path,
+                 use_text_features = False,
+                 feature_layer_ix: int = -1,
+                 audio_model_type="HTSAT-base", 
+                 enable_fusion=True,
+                 project_out: bool = False,
+                 finetune: bool = False):
+        super().__init__(768 if use_text_features else 512, output_dim, project_out=project_out)
+
+        self.use_text_features = use_text_features
+        self.feature_layer_ix = feature_layer_ix
+        self.finetune = finetune
+
+        # Suppress logging from transformers
+        previous_level = logging.root.manager.disable
+        logging.disable(logging.ERROR)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            try:
+                import laion_clap
+                from laion_clap.clap_module.factory import load_state_dict as clap_load_state_dict
+                
+                model = laion_clap.CLAP_Module(enable_fusion=enable_fusion, amodel=audio_model_type, device='cpu')
+
+                if self.finetune:
+                    self.model = model
+                else: 
+                    self.__dict__["model"] = model
+
+                state_dict = clap_load_state_dict(clap_ckpt_path)
+                self.model.model.load_state_dict(state_dict, strict=False)
+
+                if self.finetune:
+                    self.model.model.text_branch.requires_grad_(True)
+                    self.model.model.text_branch.train()
+                else:
+                    self.model.model.text_branch.requires_grad_(False)
+                    self.model.model.text_branch.eval()
+
+            finally:
+                logging.disable(previous_level)
+
+        del self.model.model.audio_branch
+
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def get_clap_features(self, prompts, layer_ix=-2, device: tp.Any = "cuda"):
+        prompt_tokens = self.model.tokenizer(prompts)
+        attention_mask = prompt_tokens["attention_mask"].to(device=device, non_blocking=True)
+        prompt_features = self.model.model.text_branch(
+            input_ids=prompt_tokens["input_ids"].to(device=device, non_blocking=True),
+            attention_mask=attention_mask,
+            output_hidden_states=True
+        )["hidden_states"][layer_ix]
+
+        return prompt_features, attention_mask
+
+    def forward(self, texts: tp.List[str], device: tp.Any = "cuda") -> tp.Any:
+        self.model.to(device)
+
+        if self.use_text_features:
+            if len(texts) == 1:
+                text_features, text_attention_mask = self.get_clap_features([texts[0], ""], layer_ix=self.feature_layer_ix, device=device)
+                text_features = text_features[:1, ...]
+                text_attention_mask = text_attention_mask[:1, ...]
+            else:
+                text_features, text_attention_mask = self.get_clap_features(texts, layer_ix=self.feature_layer_ix, device=device)
+            return [self.proj_out(text_features), text_attention_mask]
+
+        # Fix for CLAP bug when only one text is passed
+        if len(texts) == 1:
+            text_embedding = self.model.get_text_embedding([texts[0], ""], use_tensor=True)[:1, ...]
+        else:
+            text_embedding = self.model.get_text_embedding(texts, use_tensor=True)
+
+        text_embedding = text_embedding.unsqueeze(1).to(device)
+
+        return [self.proj_out(text_embedding), torch.ones(text_embedding.shape[0], 1).to(device)]
+
+class CLAPAudioConditioner(Conditioner):
+    def __init__(self, 
+                 output_dim: int, 
+                 clap_ckpt_path,
+                 audio_model_type="HTSAT-base", 
+                 enable_fusion=True,
+                 project_out: bool = False):
+        super().__init__(512, output_dim, project_out=project_out)
+
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+        # Suppress logging from transformers
+        previous_level = logging.root.manager.disable
+        logging.disable(logging.ERROR)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            try:
+                import laion_clap
+                from laion_clap.clap_module.factory import load_state_dict as clap_load_state_dict
+                
+                model = laion_clap.CLAP_Module(enable_fusion=enable_fusion, amodel=audio_model_type, device='cpu')
+
+                if self.finetune:
+                    self.model = model
+                else: 
+                    self.__dict__["model"] = model
+
+                state_dict = clap_load_state_dict(clap_ckpt_path)
+                self.model.model.load_state_dict(state_dict, strict=False)
+
+                if self.finetune:
+                    self.model.model.audio_branch.requires_grad_(True)
+                    self.model.model.audio_branch.train()
+                else:
+                    self.model.model.audio_branch.requires_grad_(False)
+                    self.model.model.audio_branch.eval()
+
+            finally:
+                logging.disable(previous_level)
+
+        del self.model.model.text_branch
+
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def forward(self, audios: tp.Union[torch.Tensor, tp.List[torch.Tensor], tp.Tuple[torch.Tensor]] , device: tp.Any = "cuda") -> tp.Any:
+
+        self.model.to(device)
+
+        if isinstance(audios, list) or isinstance(audios, tuple):
+            audios = torch.cat(audios, dim=0)
+
+        # Convert to mono
+        mono_audios = audios.mean(dim=1)
+
+        with torch.cuda.amp.autocast(enabled=False):
+            audio_embedding = self.model.get_audio_embedding_from_data(mono_audios.float(), use_tensor=True)
+
+        audio_embedding = audio_embedding.unsqueeze(1).to(device)
+
+        return [self.proj_out(audio_embedding), torch.ones(audio_embedding.shape[0], 1).to(device)]
+
+
+class CLIPConditioner(Conditioner):
+    CLIP_MODELS = ["clip-vit-base-patch32"]
+
+    def __init__(
+            self,
+            output_dim: int,
+            clip_model_name: str = "clip-vit-base-patch32",
+            video_fps: int = 5,
+            out_features: str = 128,
+            enable_grad: bool = False,
+            in_features: int = 5000,
+            project_out: bool = False,
+    ):
+        assert clip_model_name in self.CLIP_MODELS, f"Unknown clip model name: {clip_model_name}"
+        super().__init__(dim = 768, output_dim=output_dim, project_out=project_out)
+        
+        sa_depth=4
+        num_heads=16
+        dim_head=64
+        hidden_scale=4
+        duration = 10
+        
+        self.clip_model_name=clip_model_name
+                
+        if self.clip_model_name=='clip-vit-base-patch32':
+            out_features = 128
+            temporal_dim=768
+            
+            self.empty_visual_feat = nn.Parameter(torch.zeros(1, out_features, temporal_dim), requires_grad=True)
+            nn.init.constant_(self.empty_visual_feat, 0)
+
+            in_features = 50*video_fps*duration
+
+            self.visual_encoder_model = CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-base-patch32')
+            self.proj = nn.Linear(in_features=in_features, out_features=out_features)
+            
+            self.in_features = in_features
+            self.out_features = out_features
+
+            self.Temp_transformer = SA_Transformer(temporal_dim, sa_depth, num_heads, dim_head, temporal_dim*hidden_scale, 0.)
+            self.Temp_pos_embedding = nn.Parameter(torch.randn(1, duration*video_fps, temporal_dim))
+
+            clip_mean = [0.48145466, 0.4578275, 0.40821073]
+            clip_std = [0.26862954, 0.26130258, 0.27577711]
+            self.preprocess_CLIP = transforms.Compose([
+                transforms.Normalize(mean=clip_mean, std=clip_std)
+            ])
+
+    def process_video_with_custom_preprocessing(self, video_tensor):
+        video_tensor = video_tensor / 255.0 
+        video_tensor = self.preprocess_CLIP(video_tensor)
+        return video_tensor
+
+    def init_first_from_ckpt(self, path):
+        model = torch.load(path, map_location="cpu")
+        if "state_dict" in list(model.keys()):
+            model = model["state_dict"]
+        # Remove: module prefix
+        new_model = {}
+        for key in model.keys():
+            new_key = key.replace("module.","")
+            new_model[new_key] = model[key]
+        missing, unexpected = self.visual_encoder_model.load_state_dict(new_model, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+        if len(unexpected) > 0:
+            print(f"Unexpected Keys: {unexpected}")
+
+    def forward(self, Video_tensors: tp.List[torch.Tensor], device: tp.Union[torch.device, str]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+        visual_encoder_model = self.visual_encoder_model.eval().to(device)
+        proj = self.proj.to(device)
+        
+        original_videos = torch.cat(Video_tensors, dim=0).to(device)
+        batch_size, time_length, _, _, _ = original_videos.size()
+        is_zero = torch.all(original_videos == 0, dim=(1,2,3,4))
+        Video_tensors = original_videos
+        Video_tensors = einops.rearrange(Video_tensors, 'b t c h w -> (b t) c h w')
+
+        video_cond_pixel_values = self.process_video_with_custom_preprocessing(video_tensor=Video_tensors.to(device)).to(device)
+        if self.clip_model_name=='clip-vit-base-patch32':
+            with torch.no_grad():
+                outputs = visual_encoder_model(pixel_values=video_cond_pixel_values)
+            video_hidden = outputs.last_hidden_state
+
+            video_hidden = einops.rearrange(video_hidden, '(b t) q h -> (b q) t h',b=batch_size,t=time_length)
+            video_hidden += self.Temp_pos_embedding
+            video_hidden = self.Temp_transformer(video_hidden)
+            video_hidden = einops.rearrange(video_hidden, '(b q) t h -> b (t q) h',b=batch_size,t=time_length)
+
+        video_hidden = proj(video_hidden.view(-1, self.in_features))
+        video_hidden = video_hidden.view(batch_size, self.out_features, -1)
+
+        empty_visual_feat = self.empty_visual_feat.expand(batch_size, -1, -1)
+        is_zero_expanded = is_zero.view(batch_size, 1, 1)
+        video_hidden = torch.where(is_zero_expanded, empty_visual_feat, video_hidden)
+                
+        return video_hidden, torch.ones(video_hidden.shape[0], 1).to(device)
+
+
+    
+class T5Conditioner(Conditioner):
+
+    T5_MODELS = ["t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b",
+              "google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large",
+              "google/flan-t5-xl", "google/flan-t5-xxl"]
+    
+    T5_MODEL_DIMS = {
+        "t5-small": 512,
+        "t5-base": 768,
+        "t5-large": 1024,
+        "t5-3b": 1024,
+        "t5-11b": 1024,
+        "t5-xl": 2048,
+        "t5-xxl": 4096,
+        "google/flan-t5-small": 512,
+        "google/flan-t5-base": 768,
+        "google/flan-t5-large": 1024,
+        "google/flan-t5-3b": 1024,
+        "google/flan-t5-11b": 1024,
+        "google/flan-t5-xl": 2048,
+        "google/flan-t5-xxl": 4096,
+    }
+
+    def __init__(
+            self,
+            output_dim: int,
+            t5_model_name: str = "t5-base",
+            max_length: str = 128,
+            enable_grad: bool = False,
+            project_out: bool = False,
+    ):
+        assert t5_model_name in self.T5_MODELS, f"Unknown T5 model name: {t5_model_name}"
+        super().__init__(self.T5_MODEL_DIMS[t5_model_name], output_dim, project_out=project_out)
+        
+        from transformers import T5EncoderModel, AutoTokenizer
+
+        self.max_length = max_length
+        self.enable_grad = enable_grad
+        # Suppress logging from transformers
+        previous_level = logging.root.manager.disable
+        logging.disable(logging.ERROR)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            try:
+                self.tokenizer = AutoTokenizer.from_pretrained(t5_model_name)
+                model = T5EncoderModel.from_pretrained(t5_model_name).train(enable_grad).requires_grad_(enable_grad).to(torch.float16)
+            finally:
+                logging.disable(previous_level)
+            
+        if self.enable_grad:
+            self.model = model
+        else: 
+            self.__dict__["model"] = model
+
+    def forward(self, texts: tp.List[str], device: tp.Union[torch.device, str]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+        
+        self.model.to(device)
+        self.proj_out.to(device)
+
+        encoded = self.tokenizer(
+            texts,
+            truncation=True,
+            max_length=self.max_length,
+            padding="max_length",
+            return_tensors="pt",
+        )
+
+        input_ids = encoded["input_ids"].to(device)
+        attention_mask = encoded["attention_mask"].to(device).to(torch.bool)
+
+        self.model.eval()
+            
+        with torch.cuda.amp.autocast(dtype=torch.float16), torch.set_grad_enabled(self.enable_grad):
+            embeddings = self.model(
+                input_ids=input_ids, attention_mask=attention_mask
+            )["last_hidden_state"]    
+            
+        embeddings = self.proj_out(embeddings.float())
+        embeddings = embeddings * attention_mask.unsqueeze(-1).float()
+
+        return embeddings, attention_mask    
+    
+class PhonemeConditioner(Conditioner):
+    """
+    A conditioner that turns text into phonemes and embeds them using a lookup table
+    Only works for English text
+
+    Args:
+        output_dim: the dimension of the output embeddings
+        max_length: the maximum number of phonemes to embed
+        project_out: whether to add another linear projection to the output embeddings
+    """
+
+    def __init__(
+            self,
+            output_dim: int,
+            max_length: int = 1024,
+            project_out: bool = False,
+    ):
+        super().__init__(output_dim, output_dim, project_out=project_out)
+        
+        from g2p_en import G2p
+        self.max_length = max_length
+        self.g2p = G2p()
+        # Reserving 0 for padding, 1 for ignored
+        self.phoneme_embedder = nn.Embedding(len(self.g2p.phonemes) + 2, output_dim)
+
+    def forward(self, texts: tp.List[str], device: tp.Union[torch.device, str]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+        
+        self.phoneme_embedder.to(device)
+        self.proj_out.to(device)
+
+        batch_phonemes = [self.g2p(text) for text in texts] # shape [batch_size, length]
+        phoneme_ignore = [" ", *string.punctuation]
+        # Remove ignored phonemes and cut to max length
+        batch_phonemes = [[p if p not in phoneme_ignore else "_" for p in phonemes] for phonemes in batch_phonemes]
+
+        # Convert to ids
+        phoneme_ids = [[self.g2p.p2idx[p] + 2 if p in self.g2p.p2idx else 1 for p in phonemes] for phonemes in batch_phonemes]
+
+        #Pad to match longest and make a mask tensor for the padding
+        longest = max([len(ids) for ids in phoneme_ids])
+        phoneme_ids = [ids + [0] * (longest - len(ids)) for ids in phoneme_ids]        
+        phoneme_ids = torch.tensor(phoneme_ids).to(device)
+
+        # Convert to embeddings
+        phoneme_embeds = self.phoneme_embedder(phoneme_ids)
+        phoneme_embeds = self.proj_out(phoneme_embeds)
+
+        return phoneme_embeds, torch.ones(phoneme_embeds.shape[0], phoneme_embeds.shape[1]).to(device)
+
+
+
+class TokenizerLUTConditioner(Conditioner):
+    """
+    A conditioner that embeds text using a lookup table on a pretrained tokenizer's vocabulary
+
+    Args:
+        tokenizer_name: the name of the tokenizer from the Hugging Face transformers library
+        output_dim: the dimension of the output embeddings
+        max_length: the maximum length of the text to embed
+        project_out: whether to add another linear projection to the output embeddings
+    """
+
+    def __init__(
+            self,
+            tokenizer_name: str, # Name of a tokenizer from the Hugging Face transformers library
+            output_dim: int,
+            max_length: int = 1024,
+            project_out: bool = False,
+    ):
+        super().__init__(output_dim, output_dim, project_out=project_out)
+        
+        from transformers import AutoTokenizer
+
+         # Suppress logging from transformers
+        previous_level = logging.root.manager.disable
+        logging.disable(logging.ERROR)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            try:
+                self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+            finally:
+                logging.disable(previous_level)
+
+        self.max_length = max_length
+
+        self.token_embedder = nn.Embedding(len(self.tokenizer), output_dim)
+
+    def forward(self, texts: tp.List[str], device: tp.Union[torch.device, str]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+        self.proj_out.to(device)
+
+        encoded = self.tokenizer(
+            texts,
+            truncation=True,
+            max_length=self.max_length,
+            padding="max_length",
+            return_tensors="pt",
+        )
+
+        input_ids = encoded["input_ids"].to(device)
+        attention_mask = encoded["attention_mask"].to(device).to(torch.bool)
+    
+        embeddings = self.token_embedder(input_ids)
+            
+        embeddings = self.proj_out(embeddings)
+
+        embeddings = embeddings * attention_mask.unsqueeze(-1).float()
+
+        return embeddings, attention_mask
+
+class PretransformConditioner(Conditioner):
+    """
+    A conditioner that uses a pretransform's encoder for conditioning
+
+    Args:
+        pretransform: an instantiated pretransform to use for conditioning
+        output_dim: the dimension of the output embeddings
+    """
+    def __init__(self, pretransform: Pretransform, output_dim: int):
+        super().__init__(pretransform.encoded_channels, output_dim)
+
+        self.pretransform = pretransform
+
+    def forward(self, audio: tp.Union[torch.Tensor, tp.List[torch.Tensor], tp.Tuple[torch.Tensor]], device: tp.Union[torch.device, str]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+
+        self.pretransform.to(device)
+        self.proj_out.to(device)
+
+        if isinstance(audio, list) or isinstance(audio, tuple):
+            audio = torch.cat(audio, dim=0)
+
+        # Convert audio to pretransform input channels
+        audio = set_audio_channels(audio, self.pretransform.io_channels)
+        
+        latents = self.pretransform.encode(audio)
+        latents = self.proj_out(latents)
+
+        return [latents, torch.ones(latents.shape[0], latents.shape[2]).to(latents.device)]
+
+
+class AudioAutoencoderConditioner(Conditioner):
+    """
+    A conditioner that uses a pretransform's encoder for conditioning
+
+    Args:
+        pretransform: an instantiated pretransform to use for conditioning
+        output_dim: the dimension of the output embeddings
+    """
+    def __init__(self, pretransform: Pretransform, output_dim: int):
+        super().__init__(pretransform.encoded_channels, output_dim)
+
+        self.pretransform = pretransform        
+        self.empty_audio_feat = nn.Parameter(torch.zeros(1, 215, self.proj_out.out_features), requires_grad=True)
+        nn.init.constant_(self.empty_audio_feat, 0)
+
+    def forward(self, audio: tp.Union[torch.Tensor, tp.List[torch.Tensor], tp.Tuple[torch.Tensor]], device: tp.Union[torch.device, str]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+
+        self.pretransform.to(device)
+        self.proj_out.to(device)
+
+        if isinstance(audio, list) or isinstance(audio, tuple):
+            original_audios = torch.cat(audio, dim=0).to(device)
+            is_zero = torch.all(original_audios == 0, dim=(1,2))  
+            audio = original_audios
+
+        # Convert audio to pretransform input channels
+        audio = set_audio_channels(audio, self.pretransform.io_channels)
+
+        latents = self.pretransform.encode(audio)
+        latents = latents.permute(0, 2, 1)
+        latents = self.proj_out(latents)
+
+        empty_audio_feat = self.empty_audio_feat.expand(latents.shape[0], -1, -1)
+        is_zero_expanded = is_zero.view(latents.shape[0], 1, 1)
+        latents = torch.where(is_zero_expanded, empty_audio_feat, latents)        
+        return [latents, torch.ones(latents.shape[0], latents.shape[2]).to(latents.device)]
+    
+
+class MultiConditioner(nn.Module):
+    """
+    A module that applies multiple conditioners to an input dictionary based on the keys
+
+    Args:
+        conditioners: a dictionary of conditioners with keys corresponding to the keys of the conditioning input dictionary (e.g. "prompt")
+        default_keys: a dictionary of default keys to use if the key is not in the input dictionary (e.g. {"prompt_t5": "prompt"})
+    """
+    def __init__(self, conditioners: tp.Dict[str, Conditioner], default_keys: tp.Dict[str, str] = {}):
+        super().__init__()
+
+        self.conditioners = nn.ModuleDict(conditioners)
+        self.default_keys = default_keys
+
+    def forward(self, batch_metadata: tp.List[tp.Dict[str, tp.Any]], device: tp.Union[torch.device, str]) -> tp.Dict[str, tp.Any]:
+        output = {}
+
+        for key, conditioner in self.conditioners.items():
+            condition_key = key
+
+            conditioner_inputs = []
+
+            for x in batch_metadata:
+
+                if condition_key not in x:
+                    if condition_key in self.default_keys:
+                        condition_key = self.default_keys[condition_key]
+                    else:
+                        raise ValueError(f"Conditioner key {condition_key} not found in batch metadata")
+
+                if isinstance(x[condition_key], list) or isinstance(x[condition_key], tuple) and len(x[condition_key]) == 1:
+                    conditioner_input = x[condition_key][0]
+
+                else:
+                    conditioner_input = x[condition_key]
+
+                conditioner_inputs.append(conditioner_input)
+            
+            output[key] = conditioner(conditioner_inputs, device)
+
+        return output
+    
+def create_multi_conditioner_from_conditioning_config(config: tp.Dict[str, tp.Any]) -> MultiConditioner:
+    """
+    Create a MultiConditioner from a conditioning config dictionary
+
+    Args:
+        config: the conditioning config dictionary
+        device: the device to put the conditioners on
+    """
+    conditioners = {}
+    cond_dim = config["cond_dim"]
+    
+    default_keys = config.get("default_keys", {})
+
+    for conditioner_info in config["configs"]:
+        id = conditioner_info["id"]
+
+        conditioner_type = conditioner_info["type"]
+
+        conditioner_config = {"output_dim": cond_dim}
+        
+        conditioner_config.update(conditioner_info["config"])
+
+        if conditioner_type == "t5":
+            conditioners[id] = T5Conditioner(**conditioner_config)
+        elif conditioner_type == "clip":
+            conditioners[id] = CLIPConditioner(**conditioner_config)
+        elif conditioner_type == "clap_text":
+            conditioners[id] = CLAPTextConditioner(**conditioner_config)
+        elif conditioner_type == "clap_audio":
+            conditioners[id] = CLAPAudioConditioner(**conditioner_config)
+        elif conditioner_type == "int":
+            conditioners[id] = IntConditioner(**conditioner_config)
+        elif conditioner_type == "number":
+            conditioners[id] = NumberConditioner(**conditioner_config)
+        elif conditioner_type == "phoneme":
+            conditioners[id] = PhonemeConditioner(**conditioner_config)
+        elif conditioner_type == "lut":
+            conditioners[id] = TokenizerLUTConditioner(**conditioner_config)
+        elif conditioner_type == "pretransform":
+            sample_rate = conditioner_config.pop("sample_rate", None)
+            assert sample_rate is not None, "Sample rate must be specified for pretransform conditioners"
+
+            pretransform = create_pretransform_from_config(conditioner_config.pop("pretransform_config"), sample_rate=sample_rate)
+
+            if conditioner_config.get("pretransform_ckpt_path", None) is not None:
+                pretransform.load_state_dict(load_ckpt_state_dict(conditioner_config.pop("pretransform_ckpt_path")))
+
+            conditioners[id] = PretransformConditioner(pretransform, **conditioner_config)
+            
+        elif conditioner_type == "audio_autoencoder":
+            sample_rate = conditioner_config.pop("sample_rate", None)
+            assert sample_rate is not None, "Sample rate must be specified for pretransform conditioners"
+
+            pretransform = create_pretransform_from_config(conditioner_config.pop("pretransform_config"), sample_rate=sample_rate)
+
+            if conditioner_config.get("pretransform_ckpt_path", None) is not None:
+                pretransform.load_state_dict(load_ckpt_state_dict(conditioner_config.pop("pretransform_ckpt_path")))
+
+            conditioners[id] = AudioAutoencoderConditioner(pretransform, **conditioner_config)            
+        else:
+            raise ValueError(f"Unknown conditioner type: {conditioner_type}")
+
+    return MultiConditioner(conditioners, default_keys=default_keys)

stable_audio_tools/models/diffusion.py

@@ -0,0 +1,704 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+from functools import partial
+import numpy as np
+import typing as tp
+
+from .blocks import ResConvBlock, FourierFeatures, Upsample1d, Upsample1d_2, Downsample1d, Downsample1d_2, SelfAttention1d, SkipBlock, expand_to_planes
+from .conditioners import MultiConditioner, create_multi_conditioner_from_conditioning_config
+from .dit import DiffusionTransformer
+from .factory import create_pretransform_from_config
+from .pretransforms import Pretransform
+from ..inference.generation import generate_diffusion_cond
+
+from .adp import UNetCFG1d, UNet1d
+
+from time import time
+
+class Profiler:
+
+    def __init__(self):
+        self.ticks = [[time(), None]]
+
+    def tick(self, msg):
+        self.ticks.append([time(), msg])
+
+    def __repr__(self):
+        rep = 80 * "=" + "\n"
+        for i in range(1, len(self.ticks)):
+            msg = self.ticks[i][1]
+            ellapsed = self.ticks[i][0] - self.ticks[i - 1][0]
+            rep += msg + f": {ellapsed*1000:.2f}ms\n"
+        rep += 80 * "=" + "\n\n\n"
+        return rep
+
+class DiffusionModel(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, x, t, **kwargs):
+        raise NotImplementedError()
+
+class DiffusionModelWrapper(nn.Module):
+    def __init__(
+                self,
+                model: DiffusionModel,
+                io_channels,
+                sample_size,
+                sample_rate,
+                min_input_length,
+                pretransform: tp.Optional[Pretransform] = None,
+    ):
+        super().__init__()
+        self.io_channels = io_channels
+        self.sample_size = sample_size
+        self.sample_rate = sample_rate
+        self.min_input_length = min_input_length
+
+        self.model = model
+
+        if pretransform is not None:
+            self.pretransform = pretransform
+        else:
+            self.pretransform = None
+
+    def forward(self, x, t, **kwargs):
+        return self.model(x, t, **kwargs)
+
+class ConditionedDiffusionModel(nn.Module):
+    def __init__(self,
+                *args,
+                supports_cross_attention: bool = False,
+                supports_input_concat: bool = False,
+                supports_global_cond: bool = False,
+                supports_prepend_cond: bool = False,
+                **kwargs):
+        super().__init__(*args, **kwargs)
+        self.supports_cross_attention = supports_cross_attention
+        self.supports_input_concat = supports_input_concat
+        self.supports_global_cond = supports_global_cond
+        self.supports_prepend_cond = supports_prepend_cond
+
+    def forward(self,
+                x: torch.Tensor,
+                t: torch.Tensor,
+                cross_attn_cond: torch.Tensor = None,
+                cross_attn_mask: torch.Tensor = None,
+                input_concat_cond: torch.Tensor = None,
+                global_embed: torch.Tensor = None,
+                prepend_cond: torch.Tensor = None,
+                prepend_cond_mask: torch.Tensor = None,
+                cfg_scale: float = 1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = False,
+                rescale_cfg: bool = False,
+                **kwargs):
+        raise NotImplementedError()
+
+class ConditionedDiffusionModelWrapper(nn.Module):
+    """
+    A diffusion model that takes in conditioning
+    """
+    def __init__(
+            self,
+            model: ConditionedDiffusionModel,
+            conditioner: MultiConditioner,
+            io_channels,
+            sample_rate,
+            min_input_length: int,
+            diffusion_objective: tp.Literal["v", "rectified_flow"] = "v",
+            pretransform: tp.Optional[Pretransform] = None,
+            cross_attn_cond_ids: tp.List[str] = [],
+            global_cond_ids: tp.List[str] = [],
+            input_concat_ids: tp.List[str] = [],
+            prepend_cond_ids: tp.List[str] = [],
+            ):
+        super().__init__()
+
+        self.model = model
+        self.conditioner = conditioner
+        self.io_channels = io_channels
+        self.sample_rate = sample_rate
+        self.diffusion_objective = diffusion_objective
+        self.pretransform = pretransform
+        self.cross_attn_cond_ids = cross_attn_cond_ids # ['prompt', 'seconds_start', 'seconds_total']
+        self.global_cond_ids = global_cond_ids # ['seconds_start', 'seconds_total']
+        self.input_concat_ids = input_concat_ids
+        self.prepend_cond_ids = prepend_cond_ids
+        self.min_input_length = min_input_length
+
+    def get_conditioning_inputs(self, conditioning_tensors: tp.Dict[torch.Tensor, tp.Any], negative=False):
+        cross_attention_input = None
+        cross_attention_masks = None
+        global_cond = None
+        input_concat_cond = None
+        prepend_cond = None
+        prepend_cond_mask = None
+
+        if len(self.cross_attn_cond_ids) > 0:
+            # Concatenate all cross-attention inputs over the sequence dimension
+            # Assumes that the cross-attention inputs are of shape (batch, seq, channels)
+            cross_attention_input = []
+            cross_attention_masks = []
+
+            for key in self.cross_attn_cond_ids:
+                cross_attn_in, cross_attn_mask = conditioning_tensors[key]
+
+                # Add sequence dimension if it's not there
+                if len(cross_attn_in.shape) == 2:
+                    cross_attn_in = cross_attn_in.unsqueeze(1)
+                    cross_attn_mask = cross_attn_mask.unsqueeze(1)
+
+                cross_attention_input.append(cross_attn_in)
+                cross_attention_masks.append(cross_attn_mask)
+
+            cross_attention_input = torch.cat(cross_attention_input, dim=1) # [1, 130, 768] (text feature:128)
+            cross_attention_masks = torch.cat(cross_attention_masks, dim=1)
+
+        if len(self.global_cond_ids) > 0:
+            # Concatenate all global conditioning inputs over the channel dimension
+            # Assumes that the global conditioning inputs are of shape (batch, channels)
+            global_conds = []
+            for key in self.global_cond_ids:
+                
+                global_cond_input = conditioning_tensors[key][0]
+
+                global_conds.append(global_cond_input)
+
+            # Concatenate over the channel dimension
+            global_cond = torch.cat(global_conds, dim=-1)
+
+            if len(global_cond.shape) == 3:
+                global_cond = global_cond.squeeze(1)
+
+        if len(self.input_concat_ids) > 0: # False
+            # Concatenate all input concat conditioning inputs over the channel dimension
+            # Assumes that the input concat conditioning inputs are of shape (batch, channels, seq)
+            input_concat_cond = torch.cat([conditioning_tensors[key][0] for key in self.input_concat_ids], dim=1)
+
+        if len(self.prepend_cond_ids) > 0: # False
+            # Concatenate all prepend conditioning inputs over the sequence dimension
+            # Assumes that the prepend conditioning inputs are of shape (batch, seq, channels)
+            prepend_conds = []
+            prepend_cond_masks = []
+
+            for key in self.prepend_cond_ids:
+                prepend_cond_input, prepend_cond_mask = conditioning_tensors[key]
+                prepend_conds.append(prepend_cond_input)
+                prepend_cond_masks.append(prepend_cond_mask)
+
+            prepend_cond = torch.cat(prepend_conds, dim=1)
+            prepend_cond_mask = torch.cat(prepend_cond_masks, dim=1)
+
+        if negative: # False
+            return {
+                "negative_cross_attn_cond": cross_attention_input,
+                "negative_cross_attn_mask": cross_attention_masks,
+                "negative_global_cond": global_cond,
+                "negative_input_concat_cond": input_concat_cond
+            }
+        else:
+            return {
+                "cross_attn_cond": cross_attention_input,
+                "cross_attn_mask": cross_attention_masks,
+                "global_cond": global_cond,
+                "input_concat_cond": input_concat_cond,
+                "prepend_cond": prepend_cond,
+                "prepend_cond_mask": prepend_cond_mask
+            }
+
+    def forward(self, x: torch.Tensor, t: torch.Tensor, cond: tp.Dict[str, tp.Any], **kwargs):
+        return self.model(x, t, **self.get_conditioning_inputs(cond), **kwargs)
+
+    def generate(self, *args, **kwargs):
+        return generate_diffusion_cond(self, *args, **kwargs)
+
+class UNetCFG1DWrapper(ConditionedDiffusionModel):
+    def __init__(
+        self,
+        *args,
+        **kwargs
+    ):
+        super().__init__(supports_cross_attention=True, supports_global_cond=True, supports_input_concat=True)
+
+        self.model = UNetCFG1d(*args, **kwargs)
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self,
+                x,
+                t,
+                cross_attn_cond=None,
+                cross_attn_mask=None,
+                input_concat_cond=None,
+                global_cond=None,
+                cfg_scale=1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = False,
+                rescale_cfg: bool = False,
+                negative_cross_attn_cond=None,
+                negative_cross_attn_mask=None,
+                negative_global_cond=None,
+                negative_input_concat_cond=None,
+                prepend_cond=None,
+                prepend_cond_mask=None,
+                **kwargs):
+        p = Profiler()
+
+        p.tick("start")
+
+        channels_list = None
+        if input_concat_cond is not None:
+            channels_list = [input_concat_cond]
+
+        outputs = self.model(
+            x,
+            t,
+            embedding=cross_attn_cond,
+            embedding_mask=cross_attn_mask,
+            features=global_cond,
+            channels_list=channels_list,
+            embedding_scale=cfg_scale,
+            embedding_mask_proba=cfg_dropout_prob,
+            batch_cfg=batch_cfg,
+            rescale_cfg=rescale_cfg,
+            negative_embedding=negative_cross_attn_cond,
+            negative_embedding_mask=negative_cross_attn_mask,
+            **kwargs)
+
+        p.tick("UNetCFG1D forward")
+
+        #print(f"Profiler: {p}")
+        return outputs
+
+class UNet1DCondWrapper(ConditionedDiffusionModel):
+    def __init__(
+        self,
+        *args,
+        **kwargs
+    ):
+        super().__init__(supports_cross_attention=False, supports_global_cond=True, supports_input_concat=True)
+
+        self.model = UNet1d(*args, **kwargs)
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self,
+                x,
+                t,
+                input_concat_cond=None,
+                global_cond=None,
+                cross_attn_cond=None,
+                cross_attn_mask=None,
+                prepend_cond=None,
+                prepend_cond_mask=None,
+                cfg_scale=1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = False,
+                rescale_cfg: bool = False,
+                negative_cross_attn_cond=None,
+                negative_cross_attn_mask=None,
+                negative_global_cond=None,
+                negative_input_concat_cond=None,
+                **kwargs):
+
+        channels_list = None
+        if input_concat_cond is not None:
+
+            # Interpolate input_concat_cond to the same length as x
+            if input_concat_cond.shape[2] != x.shape[2]:
+                input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest')
+
+            channels_list = [input_concat_cond]
+
+        outputs = self.model(
+            x,
+            t,
+            features=global_cond,
+            channels_list=channels_list,
+            **kwargs)
+
+        return outputs
+
+class UNet1DUncondWrapper(DiffusionModel):
+    def __init__(
+        self,
+        in_channels,
+        *args,
+        **kwargs
+    ):
+        super().__init__()
+
+        self.model = UNet1d(in_channels=in_channels, *args, **kwargs)
+
+        self.io_channels = in_channels
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self, x, t, **kwargs):
+        return self.model(x, t, **kwargs)
+
+class DAU1DCondWrapper(ConditionedDiffusionModel):
+    def __init__(
+        self,
+        *args,
+        **kwargs
+    ):
+        super().__init__(supports_cross_attention=False, supports_global_cond=False, supports_input_concat=True)
+
+        self.model = DiffusionAttnUnet1D(*args, **kwargs)
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self,
+                x,
+                t,
+                input_concat_cond=None,
+                cross_attn_cond=None,
+                cross_attn_mask=None,
+                global_cond=None,
+                cfg_scale=1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = False,
+                rescale_cfg: bool = False,
+                negative_cross_attn_cond=None,
+                negative_cross_attn_mask=None,
+                negative_global_cond=None,
+                negative_input_concat_cond=None,
+                prepend_cond=None,
+                **kwargs):
+
+        return self.model(x, t, cond = input_concat_cond)
+
+class DiffusionAttnUnet1D(nn.Module):
+    def __init__(
+        self,
+        io_channels = 2,
+        depth=14,
+        n_attn_layers = 6,
+        channels = [128, 128, 256, 256] + [512] * 10,
+        cond_dim = 0,
+        cond_noise_aug = False,
+        kernel_size = 5,
+        learned_resample = False,
+        strides = [2] * 13,
+        conv_bias = True,
+        use_snake = False
+    ):
+        super().__init__()
+
+        self.cond_noise_aug = cond_noise_aug
+
+        self.io_channels = io_channels
+
+        if self.cond_noise_aug:
+            self.rng = torch.quasirandom.SobolEngine(1, scramble=True)
+
+        self.timestep_embed = FourierFeatures(1, 16)
+
+        attn_layer = depth - n_attn_layers
+
+        strides = [1] + strides
+
+        block = nn.Identity()
+
+        conv_block = partial(ResConvBlock, kernel_size=kernel_size, conv_bias = conv_bias, use_snake=use_snake)
+
+        for i in range(depth, 0, -1):
+            c = channels[i - 1]
+            stride = strides[i-1]
+            if stride > 2 and not learned_resample:
+                raise ValueError("Must have stride 2 without learned resampling")
+
+            if i > 1:
+                c_prev = channels[i - 2]
+                add_attn = i >= attn_layer and n_attn_layers > 0
+                block = SkipBlock(
+                    Downsample1d_2(c_prev, c_prev, stride) if (learned_resample or stride == 1) else Downsample1d("cubic"),
+                    conv_block(c_prev, c, c),
+                    SelfAttention1d(
+                        c, c // 32) if add_attn else nn.Identity(),
+                    conv_block(c, c, c),
+                    SelfAttention1d(
+                        c, c // 32) if add_attn else nn.Identity(),
+                    conv_block(c, c, c),
+                    SelfAttention1d(
+                        c, c // 32) if add_attn else nn.Identity(),
+                    block,
+                    conv_block(c * 2 if i != depth else c, c, c),
+                    SelfAttention1d(
+                        c, c // 32) if add_attn else nn.Identity(),
+                    conv_block(c, c, c),
+                    SelfAttention1d(
+                        c, c // 32) if add_attn else nn.Identity(),
+                    conv_block(c, c, c_prev),
+                    SelfAttention1d(c_prev, c_prev //
+                                    32) if add_attn else nn.Identity(),
+                    Upsample1d_2(c_prev, c_prev, stride) if learned_resample else Upsample1d(kernel="cubic")
+                )
+            else:
+                cond_embed_dim = 16 if not self.cond_noise_aug else 32
+                block = nn.Sequential(
+                    conv_block((io_channels + cond_dim) + cond_embed_dim, c, c),
+                    conv_block(c, c, c),
+                    conv_block(c, c, c),
+                    block,
+                    conv_block(c * 2, c, c),
+                    conv_block(c, c, c),
+                    conv_block(c, c, io_channels, is_last=True),
+                )
+        self.net = block
+
+        with torch.no_grad():
+            for param in self.net.parameters():
+                param *= 0.5
+
+    def forward(self, x, t, cond=None, cond_aug_scale=None):
+
+        timestep_embed = expand_to_planes(self.timestep_embed(t[:, None]), x.shape)
+
+        inputs = [x, timestep_embed]
+
+        if cond is not None:
+            if cond.shape[2] != x.shape[2]:
+                cond = F.interpolate(cond, (x.shape[2], ), mode='linear', align_corners=False)
+
+            if self.cond_noise_aug:
+                # Get a random number between 0 and 1, uniformly sampled
+                if cond_aug_scale is None:
+                    aug_level = self.rng.draw(cond.shape[0])[:, 0].to(cond)
+                else:
+                    aug_level = torch.tensor([cond_aug_scale]).repeat([cond.shape[0]]).to(cond)
+
+                # Add noise to the conditioning signal
+                cond = cond + torch.randn_like(cond) * aug_level[:, None, None]
+
+                # Get embedding for noise cond level, reusing timestamp_embed
+                aug_level_embed = expand_to_planes(self.timestep_embed(aug_level[:, None]), x.shape)
+
+                inputs.append(aug_level_embed)
+
+            inputs.append(cond)
+
+        outputs = self.net(torch.cat(inputs, dim=1))
+
+        return outputs
+
+class DiTWrapper(ConditionedDiffusionModel):
+    def __init__(
+        self,
+        *args,
+        **kwargs
+    ):
+        super().__init__(supports_cross_attention=True, supports_global_cond=False, supports_input_concat=False)
+
+        self.model = DiffusionTransformer(*args, **kwargs)
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self,
+                x,
+                t,
+                cross_attn_cond=None,
+                cross_attn_mask=None,
+                negative_cross_attn_cond=None,
+                negative_cross_attn_mask=None,
+                input_concat_cond=None,
+                negative_input_concat_cond=None,
+                global_cond=None,
+                negative_global_cond=None,
+                prepend_cond=None,
+                prepend_cond_mask=None,
+                cfg_scale=1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = True,
+                rescale_cfg: bool = False,
+                scale_phi: float = 0.0,
+                **kwargs):
+
+        assert batch_cfg, "batch_cfg must be True for DiTWrapper"
+        #assert negative_input_concat_cond is None, "negative_input_concat_cond is not supported for DiTWrapper"
+
+        return self.model(
+            x,
+            t,
+            cross_attn_cond=cross_attn_cond,
+            cross_attn_cond_mask=cross_attn_mask,
+            negative_cross_attn_cond=negative_cross_attn_cond,
+            negative_cross_attn_mask=negative_cross_attn_mask,
+            input_concat_cond=input_concat_cond,
+            prepend_cond=prepend_cond,
+            prepend_cond_mask=prepend_cond_mask,
+            cfg_scale=cfg_scale,
+            cfg_dropout_prob=cfg_dropout_prob,
+            scale_phi=scale_phi,
+            global_embed=global_cond,
+            **kwargs)
+
+class DiTUncondWrapper(DiffusionModel):
+    def __init__(
+        self,
+        in_channels,
+        *args,
+        **kwargs
+    ):
+        super().__init__()
+
+        self.model = DiffusionTransformer(io_channels=in_channels, *args, **kwargs)
+
+        self.io_channels = in_channels
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self, x, t, **kwargs):
+        return self.model(x, t, **kwargs)
+
+def create_diffusion_uncond_from_config(config: tp.Dict[str, tp.Any]):
+    diffusion_uncond_config = config["model"]
+
+    model_type = diffusion_uncond_config.get('type', None)
+
+    diffusion_config = diffusion_uncond_config.get('config', {})
+
+    assert model_type is not None, "Must specify model type in config"
+
+    pretransform = diffusion_uncond_config.get("pretransform", None)
+
+    sample_size = config.get("sample_size", None)
+    assert sample_size is not None, "Must specify sample size in config"
+
+    sample_rate = config.get("sample_rate", None)
+    assert sample_rate is not None, "Must specify sample rate in config"
+
+    if pretransform is not None:
+        pretransform = create_pretransform_from_config(pretransform, sample_rate)
+        min_input_length = pretransform.downsampling_ratio
+    else:
+        min_input_length = 1
+
+    if model_type == 'DAU1d':
+
+        model = DiffusionAttnUnet1D(
+            **diffusion_config
+        )
+    
+    elif model_type == "adp_uncond_1d":
+
+        model = UNet1DUncondWrapper(
+            **diffusion_config
+        )
+
+    elif model_type == "dit":
+        model = DiTUncondWrapper(
+            **diffusion_config
+        )
+
+    else:
+        raise NotImplementedError(f'Unknown model type: {model_type}')
+
+    return DiffusionModelWrapper(model,
+                                io_channels=model.io_channels,
+                                sample_size=sample_size,
+                                sample_rate=sample_rate,
+                                pretransform=pretransform,
+                                min_input_length=min_input_length)
+
+def create_diffusion_cond_from_config(config: tp.Dict[str, tp.Any]):
+
+    model_config = config["model"]
+
+    model_type = config["model_type"]
+
+    diffusion_config = model_config.get('diffusion', None)
+    assert diffusion_config is not None, "Must specify diffusion config"
+
+    diffusion_model_type = diffusion_config.get('type', None)
+    assert diffusion_model_type is not None, "Must specify diffusion model type"
+
+    diffusion_model_config = diffusion_config.get('config', None)
+    if diffusion_model_config.get('video_fps', None) is not None:
+        diffusion_model_config.pop('video_fps')
+    assert diffusion_model_config is not None, "Must specify diffusion model config"
+
+    if diffusion_model_type == 'adp_cfg_1d':
+        diffusion_model = UNetCFG1DWrapper(**diffusion_model_config)
+    elif diffusion_model_type == 'adp_1d':
+        diffusion_model = UNet1DCondWrapper(**diffusion_model_config)
+    elif diffusion_model_type == 'dit':
+        diffusion_model = DiTWrapper(**diffusion_model_config)
+
+    io_channels = model_config.get('io_channels', None)
+    assert io_channels is not None, "Must specify io_channels in model config"
+
+    sample_rate = config.get('sample_rate', None)
+    assert sample_rate is not None, "Must specify sample_rate in config"
+
+    diffusion_objective = diffusion_config.get('diffusion_objective', 'v')
+
+    conditioning_config = model_config.get('conditioning', None)
+
+    conditioner = None
+    if conditioning_config is not None:
+        conditioner = create_multi_conditioner_from_conditioning_config(conditioning_config)
+
+    cross_attention_ids = diffusion_config.get('cross_attention_cond_ids', [])
+    global_cond_ids = diffusion_config.get('global_cond_ids', [])
+    input_concat_ids = diffusion_config.get('input_concat_ids', [])
+    prepend_cond_ids = diffusion_config.get('prepend_cond_ids', [])
+
+    pretransform = model_config.get("pretransform", None)
+
+    if pretransform is not None:
+        pretransform = create_pretransform_from_config(pretransform, sample_rate)
+        min_input_length = pretransform.downsampling_ratio
+    else:
+        min_input_length = 1
+
+    if diffusion_model_type == "adp_cfg_1d" or diffusion_model_type == "adp_1d":
+        min_input_length *= np.prod(diffusion_model_config["factors"])
+    elif diffusion_model_type == "dit":
+        min_input_length *= diffusion_model.model.patch_size
+
+    # Get the proper wrapper class
+
+    extra_kwargs = {}
+
+    if model_type == "diffusion_cond" or model_type == "diffusion_cond_inpaint":
+        wrapper_fn = ConditionedDiffusionModelWrapper
+
+        extra_kwargs["diffusion_objective"] = diffusion_objective
+
+    elif model_type == "diffusion_prior":
+        prior_type = model_config.get("prior_type", None)
+        assert prior_type is not None, "Must specify prior_type in diffusion prior model config"
+
+        if prior_type == "mono_stereo":
+            from .diffusion_prior import MonoToStereoDiffusionPrior
+            wrapper_fn = MonoToStereoDiffusionPrior
+            
+    return wrapper_fn(
+        diffusion_model,
+        conditioner,
+        min_input_length=min_input_length,
+        sample_rate=sample_rate,
+        cross_attn_cond_ids=cross_attention_ids,
+        global_cond_ids=global_cond_ids,
+        input_concat_ids=input_concat_ids,
+        prepend_cond_ids=prepend_cond_ids,
+        pretransform=pretransform,
+        io_channels=io_channels,
+        **extra_kwargs
+    )

stable_audio_tools/models/discriminators.py

@@ -0,0 +1,546 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from functools import reduce
+import typing as tp
+from einops import rearrange
+from audiotools import AudioSignal, STFTParams
+from dac.model.discriminator import WNConv1d, WNConv2d
+
+def get_hinge_losses(score_real, score_fake):
+    gen_loss = -score_fake.mean()
+    dis_loss = torch.relu(1 - score_real).mean() + torch.relu(1 + score_fake).mean()
+    return dis_loss, gen_loss
+
+class EncodecDiscriminator(nn.Module):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+
+        from encodec.msstftd import MultiScaleSTFTDiscriminator
+
+        self.discriminators = MultiScaleSTFTDiscriminator(*args, **kwargs)
+
+    def forward(self, x):
+        logits, features = self.discriminators(x)
+        return logits, features
+
+    def loss(self, x, y):
+        feature_matching_distance = 0.
+        logits_true, feature_true = self.forward(x)
+        logits_fake, feature_fake = self.forward(y)
+
+        dis_loss = torch.tensor(0.)
+        adv_loss = torch.tensor(0.)
+
+        for i, (scale_true, scale_fake) in enumerate(zip(feature_true, feature_fake)):
+
+            feature_matching_distance = feature_matching_distance + sum(
+                map(
+                    lambda x, y: abs(x - y).mean(),
+                    scale_true,
+                    scale_fake,
+                )) / len(scale_true)
+
+            _dis, _adv = get_hinge_losses(
+                logits_true[i],
+                logits_fake[i],
+            )
+
+            dis_loss = dis_loss + _dis
+            adv_loss = adv_loss + _adv
+
+        return dis_loss, adv_loss, feature_matching_distance
+
+# Discriminators from oobleck
+
+IndividualDiscriminatorOut = tp.Tuple[torch.Tensor, tp.Sequence[torch.Tensor]]
+
+TensorDict = tp.Dict[str, torch.Tensor]
+
+class SharedDiscriminatorConvNet(nn.Module):
+
+    def __init__(
+        self,
+        in_size: int,
+        convolution: tp.Union[nn.Conv1d, nn.Conv2d],
+        out_size: int = 1,
+        capacity: int = 32,
+        n_layers: int = 4,
+        kernel_size: int = 15,
+        stride: int = 4,
+        activation: tp.Callable[[], nn.Module] = lambda: nn.SiLU(),
+        normalization: tp.Callable[[nn.Module], nn.Module] = torch.nn.utils.weight_norm,
+    ) -> None:
+        super().__init__()
+        channels = [in_size]
+        channels += list(capacity * 2**np.arange(n_layers))
+
+        if isinstance(stride, int):
+            stride = n_layers * [stride]
+
+        net = []
+        for i in range(n_layers):
+            if isinstance(kernel_size, int):
+                pad = kernel_size // 2
+                s = stride[i]
+            else:
+                pad = kernel_size[0] // 2
+                s = (stride[i], 1)
+
+            net.append(
+                normalization(
+                    convolution(
+                        channels[i],
+                        channels[i + 1],
+                        kernel_size,
+                        stride=s,
+                        padding=pad,
+                    )))
+            net.append(activation())
+
+        net.append(convolution(channels[-1], out_size, 1))
+
+        self.net = nn.ModuleList(net)
+
+    def forward(self, x) -> IndividualDiscriminatorOut:
+        features = []
+        for layer in self.net:
+            x = layer(x)
+            if isinstance(layer, nn.modules.conv._ConvNd):
+                features.append(x)
+        score = x.reshape(x.shape[0], -1).mean(-1)
+        return score, features
+
+
+class MultiScaleDiscriminator(nn.Module):
+
+    def __init__(self,
+                in_channels: int,
+                n_scales: int,
+                **conv_kwargs) -> None:
+        super().__init__()
+        layers = []
+        for _ in range(n_scales):
+            layers.append(SharedDiscriminatorConvNet(in_channels, nn.Conv1d, **conv_kwargs))
+        self.layers = nn.ModuleList(layers)
+
+    def forward(self, x: torch.Tensor) -> IndividualDiscriminatorOut:
+        score = 0
+        features = []
+        for layer in self.layers:
+            s, f = layer(x)
+            score = score + s
+            features.extend(f)
+            x = nn.functional.avg_pool1d(x, 2)
+        return score, features
+
+class MultiPeriodDiscriminator(nn.Module):
+
+    def __init__(self,
+                 in_channels: int,
+                 periods: tp.Sequence[int],
+                 **conv_kwargs) -> None:
+        super().__init__()
+        layers = []
+        self.periods = periods
+
+        for _ in periods:
+            layers.append(SharedDiscriminatorConvNet(in_channels, nn.Conv2d, **conv_kwargs))
+
+        self.layers = nn.ModuleList(layers)
+
+    def forward(self, x: torch.Tensor) -> IndividualDiscriminatorOut:
+        score = 0
+        features = []
+        for layer, n in zip(self.layers, self.periods):
+            s, f = layer(self.fold(x, n))
+            score = score + s
+            features.extend(f)
+        return score, features
+
+    def fold(self, x: torch.Tensor, n: int) -> torch.Tensor:
+        pad = (n - (x.shape[-1] % n)) % n
+        x = nn.functional.pad(x, (0, pad))
+        return x.reshape(*x.shape[:2], -1, n)
+
+
+class MultiDiscriminator(nn.Module):
+    """
+    Individual discriminators should take a single tensor as input (NxB C T) and
+    return a tuple composed of a score tensor (NxB) and a Sequence of Features
+    Sequence[NxB C' T'].
+    """
+
+    def __init__(self, discriminator_list: tp.Sequence[nn.Module],
+                 keys: tp.Sequence[str]) -> None:
+        super().__init__()
+        self.discriminators = nn.ModuleList(discriminator_list)
+        self.keys = keys
+
+    def unpack_tensor_to_dict(self, features: torch.Tensor) -> TensorDict:
+        features = features.chunk(len(self.keys), 0)
+        return {k: features[i] for i, k in enumerate(self.keys)}
+
+    @staticmethod
+    def concat_dicts(dict_a, dict_b):
+        out_dict = {}
+        keys = set(list(dict_a.keys()) + list(dict_b.keys()))
+        for k in keys:
+            out_dict[k] = []
+            if k in dict_a:
+                if isinstance(dict_a[k], list):
+                    out_dict[k].extend(dict_a[k])
+                else:
+                    out_dict[k].append(dict_a[k])
+            if k in dict_b:
+                if isinstance(dict_b[k], list):
+                    out_dict[k].extend(dict_b[k])
+                else:
+                    out_dict[k].append(dict_b[k])
+        return out_dict
+
+    @staticmethod
+    def sum_dicts(dict_a, dict_b):
+        out_dict = {}
+        keys = set(list(dict_a.keys()) + list(dict_b.keys()))
+        for k in keys:
+            out_dict[k] = 0.
+            if k in dict_a:
+                out_dict[k] = out_dict[k] + dict_a[k]
+            if k in dict_b:
+                out_dict[k] = out_dict[k] + dict_b[k]
+        return out_dict
+
+    def forward(self, inputs: TensorDict) -> TensorDict:
+        discriminator_input = torch.cat([inputs[k] for k in self.keys], 0)
+        all_scores = []
+        all_features = []
+
+        for discriminator in self.discriminators:
+            score, features = discriminator(discriminator_input)
+            scores = self.unpack_tensor_to_dict(score)
+            scores = {f"score_{k}": scores[k] for k in scores.keys()}
+            all_scores.append(scores)
+
+            features = map(self.unpack_tensor_to_dict, features)
+            features = reduce(self.concat_dicts, features)
+            features = {f"features_{k}": features[k] for k in features.keys()}
+            all_features.append(features)
+
+        all_scores = reduce(self.sum_dicts, all_scores)
+        all_features = reduce(self.concat_dicts, all_features)
+
+        inputs.update(all_scores)
+        inputs.update(all_features)
+
+        return inputs
+    
+class OobleckDiscriminator(nn.Module):
+
+    def __init__(
+            self,
+            in_channels=1,
+            ):
+        super().__init__()
+
+        multi_scale_discriminator = MultiScaleDiscriminator(
+            in_channels=in_channels,
+            n_scales=3,
+        )
+
+        multi_period_discriminator = MultiPeriodDiscriminator(
+            in_channels=in_channels,
+            periods=[2, 3, 5, 7, 11]
+        )
+
+        # multi_resolution_discriminator = MultiScaleSTFTDiscriminator(
+        #     filters=32,
+        #     in_channels = in_channels,
+        #     out_channels = 1,
+        #     n_ffts = [2048, 1024, 512, 256, 128],
+        #     hop_lengths = [512, 256, 128, 64, 32],
+        #     win_lengths = [2048, 1024, 512, 256, 128]
+        # )
+
+        self.multi_discriminator = MultiDiscriminator(
+            [multi_scale_discriminator, multi_period_discriminator], #, multi_resolution_discriminator],
+            ["reals", "fakes"]
+        )
+
+    def loss(self, reals, fakes):
+        inputs = {
+            "reals": reals,
+            "fakes": fakes,
+        }
+
+        inputs = self.multi_discriminator(inputs)
+
+        scores_real = inputs["score_reals"]
+        scores_fake = inputs["score_fakes"]
+
+        features_real = inputs["features_reals"]
+        features_fake = inputs["features_fakes"]
+
+        dis_loss, gen_loss = get_hinge_losses(scores_real, scores_fake)
+         
+        feature_matching_distance = torch.tensor(0.)
+
+        for _, (scale_real, scale_fake) in enumerate(zip(features_real, features_fake)):
+
+            feature_matching_distance = feature_matching_distance + sum(
+                map(
+                    lambda real, fake: abs(real - fake).mean(),
+                    scale_real,
+                    scale_fake,
+                )) / len(scale_real)
+            
+        return dis_loss, gen_loss, feature_matching_distance
+    
+
+## Discriminators from Descript Audio Codec repo
+## Copied and modified under MIT license, see LICENSES/LICENSE_DESCRIPT.txt
+class MPD(nn.Module):
+    def __init__(self, period, channels=1):
+        super().__init__()
+
+        self.period = period
+        self.convs = nn.ModuleList(
+            [
+                WNConv2d(channels, 32, (5, 1), (3, 1), padding=(2, 0)),
+                WNConv2d(32, 128, (5, 1), (3, 1), padding=(2, 0)),
+                WNConv2d(128, 512, (5, 1), (3, 1), padding=(2, 0)),
+                WNConv2d(512, 1024, (5, 1), (3, 1), padding=(2, 0)),
+                WNConv2d(1024, 1024, (5, 1), 1, padding=(2, 0)),
+            ]
+        )
+        self.conv_post = WNConv2d(
+            1024, 1, kernel_size=(3, 1), padding=(1, 0), act=False
+        )
+
+    def pad_to_period(self, x):
+        t = x.shape[-1]
+        x = F.pad(x, (0, self.period - t % self.period), mode="reflect")
+        return x
+
+    def forward(self, x):
+        fmap = []
+
+        x = self.pad_to_period(x)
+        x = rearrange(x, "b c (l p) -> b c l p", p=self.period)
+
+        for layer in self.convs:
+            x = layer(x)
+            fmap.append(x)
+
+        x = self.conv_post(x)
+        fmap.append(x)
+
+        return fmap
+
+
+class MSD(nn.Module):
+    def __init__(self, rate: int = 1, sample_rate: int = 44100, channels=1):
+        super().__init__()
+
+        self.convs = nn.ModuleList(
+            [
+                WNConv1d(channels, 16, 15, 1, padding=7),
+                WNConv1d(16, 64, 41, 4, groups=4, padding=20),
+                WNConv1d(64, 256, 41, 4, groups=16, padding=20),
+                WNConv1d(256, 1024, 41, 4, groups=64, padding=20),
+                WNConv1d(1024, 1024, 41, 4, groups=256, padding=20),
+                WNConv1d(1024, 1024, 5, 1, padding=2),
+            ]
+        )
+        self.conv_post = WNConv1d(1024, 1, 3, 1, padding=1, act=False)
+        self.sample_rate = sample_rate
+        self.rate = rate
+
+    def forward(self, x):
+        x = AudioSignal(x, self.sample_rate)
+        x.resample(self.sample_rate // self.rate)
+        x = x.audio_data
+
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+
+        return fmap
+
+
+BANDS = [(0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)]
+
+
+class MRD(nn.Module):
+    def __init__(
+        self,
+        window_length: int,
+        hop_factor: float = 0.25,
+        sample_rate: int = 44100,
+        bands: list = BANDS,
+        channels: int = 1
+    ):
+        """Complex multi-band spectrogram discriminator.
+        Parameters
+        ----------
+        window_length : int
+            Window length of STFT.
+        hop_factor : float, optional
+            Hop factor of the STFT, defaults to ``0.25 * window_length``.
+        sample_rate : int, optional
+            Sampling rate of audio in Hz, by default 44100
+        bands : list, optional
+            Bands to run discriminator over.
+        """
+        super().__init__()
+
+        self.window_length = window_length
+        self.hop_factor = hop_factor
+        self.sample_rate = sample_rate
+        self.stft_params = STFTParams(
+            window_length=window_length,
+            hop_length=int(window_length * hop_factor),
+            match_stride=True,
+        )
+
+        self.channels = channels
+
+        n_fft = window_length // 2 + 1
+        bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
+        self.bands = bands
+
+        ch = 32
+        convs = lambda: nn.ModuleList(
+            [
+                WNConv2d(2, ch, (3, 9), (1, 1), padding=(1, 4)),
+                WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
+                WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
+                WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
+                WNConv2d(ch, ch, (3, 3), (1, 1), padding=(1, 1)),
+            ]
+        )
+        self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
+        self.conv_post = WNConv2d(ch, 1, (3, 3), (1, 1), padding=(1, 1), act=False)
+
+    def spectrogram(self, x):
+        x = AudioSignal(x, self.sample_rate, stft_params=self.stft_params)
+        x = torch.view_as_real(x.stft())
+        x = rearrange(x, "b ch f t c -> (b ch) c t f", ch=self.channels)
+        # Split into bands
+        x_bands = [x[..., b[0] : b[1]] for b in self.bands]
+        return x_bands
+
+    def forward(self, x):
+        x_bands = self.spectrogram(x)
+        fmap = []
+
+        x = []
+        for band, stack in zip(x_bands, self.band_convs):
+            for layer in stack:
+                band = layer(band)
+                fmap.append(band)
+            x.append(band)
+
+        x = torch.cat(x, dim=-1)
+        x = self.conv_post(x)
+        fmap.append(x)
+
+        return fmap
+
+
+class DACDiscriminator(nn.Module):
+    def __init__(
+        self,
+        channels: int = 1,
+        rates: list = [],
+        periods: list = [2, 3, 5, 7, 11],
+        fft_sizes: list = [2048, 1024, 512],
+        sample_rate: int = 44100,
+        bands: list = BANDS,
+    ):
+        """Discriminator that combines multiple discriminators.
+
+        Parameters
+        ----------
+        rates : list, optional
+            sampling rates (in Hz) to run MSD at, by default []
+            If empty, MSD is not used.
+        periods : list, optional
+            periods (of samples) to run MPD at, by default [2, 3, 5, 7, 11]
+        fft_sizes : list, optional
+            Window sizes of the FFT to run MRD at, by default [2048, 1024, 512]
+        sample_rate : int, optional
+            Sampling rate of audio in Hz, by default 44100
+        bands : list, optional
+            Bands to run MRD at, by default `BANDS`
+        """
+        super().__init__()
+        discs = []
+        discs += [MPD(p, channels=channels) for p in periods]
+        discs += [MSD(r, sample_rate=sample_rate, channels=channels) for r in rates]
+        discs += [MRD(f, sample_rate=sample_rate, bands=bands, channels=channels) for f in fft_sizes]
+        self.discriminators = nn.ModuleList(discs)
+
+    def preprocess(self, y):
+        # Remove DC offset
+        y = y - y.mean(dim=-1, keepdims=True)
+        # Peak normalize the volume of input audio
+        y = 0.8 * y / (y.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
+        return y
+
+    def forward(self, x):
+        x = self.preprocess(x)
+        fmaps = [d(x) for d in self.discriminators]
+        return fmaps
+
+class DACGANLoss(nn.Module):
+    """
+    Computes a discriminator loss, given a discriminator on
+    generated waveforms/spectrograms compared to ground truth
+    waveforms/spectrograms. Computes the loss for both the
+    discriminator and the generator in separate functions.
+    """
+
+    def __init__(self, **discriminator_kwargs):
+        super().__init__()
+        self.discriminator = DACDiscriminator(**discriminator_kwargs)
+
+    def forward(self, fake, real):
+        d_fake = self.discriminator(fake)
+        d_real = self.discriminator(real)
+        return d_fake, d_real
+
+    def discriminator_loss(self, fake, real):
+        d_fake, d_real = self.forward(fake.clone().detach(), real)
+
+        loss_d = 0
+        for x_fake, x_real in zip(d_fake, d_real):
+            loss_d += torch.mean(x_fake[-1] ** 2)
+            loss_d += torch.mean((1 - x_real[-1]) ** 2)
+        return loss_d
+
+    def generator_loss(self, fake, real):
+        d_fake, d_real = self.forward(fake, real)
+
+        loss_g = 0
+        for x_fake in d_fake:
+            loss_g += torch.mean((1 - x_fake[-1]) ** 2)
+
+        loss_feature = 0
+
+        for i in range(len(d_fake)):
+            for j in range(len(d_fake[i]) - 1):
+                loss_feature += F.l1_loss(d_fake[i][j], d_real[i][j].detach())
+        return loss_g, loss_feature
+    
+    def loss(self, fake, real):
+        gen_loss, feature_distance = self.generator_loss(fake, real)
+        dis_loss = self.discriminator_loss(fake, real)
+
+        return dis_loss, gen_loss, feature_distance

stable_audio_tools/models/dit.py

@@ -0,0 +1,379 @@
+import typing as tp
+
+import torch
+
+from einops import rearrange
+from torch import nn
+from torch.nn import functional as F
+from x_transformers import ContinuousTransformerWrapper, Encoder
+
+from .blocks import FourierFeatures
+from .transformer import ContinuousTransformer
+
+class DiffusionTransformer(nn.Module):
+    def __init__(self, 
+        io_channels=32, 
+        patch_size=1,
+        embed_dim=768,
+        cond_token_dim=0,
+        project_cond_tokens=True,
+        global_cond_dim=0,
+        project_global_cond=True,
+        input_concat_dim=0,
+        prepend_cond_dim=0,
+        depth=12,
+        num_heads=8,
+        transformer_type: tp.Literal["x-transformers", "continuous_transformer"] = "x-transformers",
+        global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
+        **kwargs):
+
+        super().__init__()
+
+        self.cond_token_dim = cond_token_dim
+
+        # Timestep embeddings
+        timestep_features_dim = 256
+
+        self.timestep_features = FourierFeatures(1, timestep_features_dim)
+
+        self.to_timestep_embed = nn.Sequential(
+            nn.Linear(timestep_features_dim, embed_dim, bias=True),
+            nn.SiLU(),
+            nn.Linear(embed_dim, embed_dim, bias=True),
+        )
+
+        if cond_token_dim > 0:
+            # Conditioning tokens
+            cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim
+            self.to_cond_embed = nn.Sequential(
+                nn.Linear(cond_token_dim, cond_embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(cond_embed_dim, cond_embed_dim, bias=False)
+            )
+        else:
+            cond_embed_dim = 0
+
+        if global_cond_dim > 0:
+            # Global conditioning
+            global_embed_dim = global_cond_dim if not project_global_cond else embed_dim
+            self.to_global_embed = nn.Sequential(
+                nn.Linear(global_cond_dim, global_embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(global_embed_dim, global_embed_dim, bias=False)
+            )
+
+        if prepend_cond_dim > 0:
+            # Prepend conditioning
+            self.to_prepend_embed = nn.Sequential(
+                nn.Linear(prepend_cond_dim, embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(embed_dim, embed_dim, bias=False)
+            )
+
+        self.input_concat_dim = input_concat_dim
+
+        dim_in = io_channels + self.input_concat_dim
+
+        self.patch_size = patch_size
+
+        # Transformer
+
+        self.transformer_type = transformer_type
+        
+        self.global_cond_type = global_cond_type
+
+        if self.transformer_type == "x-transformers":
+            self.transformer = ContinuousTransformerWrapper(
+                dim_in=dim_in * patch_size,
+                dim_out=io_channels * patch_size,
+                max_seq_len=0, #Not relevant without absolute positional embeds
+                attn_layers = Encoder(
+                    dim=embed_dim,
+                    depth=depth,
+                    heads=num_heads,
+                    attn_flash = True,
+                    cross_attend = cond_token_dim > 0,
+                    dim_context=None if cond_embed_dim == 0 else cond_embed_dim,
+                    zero_init_branch_output=True,
+                    use_abs_pos_emb = False,
+                    rotary_pos_emb=True,
+                    ff_swish = True,
+                    ff_glu = True,
+                    **kwargs
+                )
+            )
+
+        elif self.transformer_type == "continuous_transformer":
+
+            global_dim = None
+
+            if self.global_cond_type == "adaLN":
+                # The global conditioning is projected to the embed_dim already at this point
+                global_dim = embed_dim
+
+            self.transformer = ContinuousTransformer(
+                dim=embed_dim,
+                depth=depth,
+                dim_heads=embed_dim // num_heads,
+                dim_in=dim_in * patch_size,
+                dim_out=io_channels * patch_size,
+                cross_attend = cond_token_dim > 0,
+                cond_token_dim = cond_embed_dim,
+                global_cond_dim=global_dim,
+                **kwargs
+            )
+             
+        else:
+            raise ValueError(f"Unknown transformer type: {self.transformer_type}")
+
+        self.preprocess_conv = nn.Conv1d(dim_in, dim_in, 1, bias=False)
+        nn.init.zeros_(self.preprocess_conv.weight)
+        self.postprocess_conv = nn.Conv1d(io_channels, io_channels, 1, bias=False)
+        nn.init.zeros_(self.postprocess_conv.weight)
+
+    def _forward(
+        self, 
+        x, 
+        t, 
+        mask=None,
+        cross_attn_cond=None,
+        cross_attn_cond_mask=None,
+        input_concat_cond=None,
+        global_embed=None,
+        prepend_cond=None,
+        prepend_cond_mask=None,
+        return_info=False,
+        **kwargs):
+
+        if cross_attn_cond is not None:
+            cross_attn_cond = self.to_cond_embed(cross_attn_cond) # MLP endecoder, shape: [1, 130, 768]
+
+        if global_embed is not None:
+            # Project the global conditioning to the embedding dimension
+            global_embed = self.to_global_embed(global_embed)
+
+        prepend_inputs = None
+        prepend_mask = None
+        prepend_length = 0
+        if prepend_cond is not None:
+            # Project the prepend conditioning to the embedding dimension
+            prepend_cond = self.to_prepend_embed(prepend_cond)
+            
+            prepend_inputs = prepend_cond
+            if prepend_cond_mask is not None:
+                prepend_mask = prepend_cond_mask
+
+        if input_concat_cond is not None:
+
+            # Interpolate input_concat_cond to the same length as x
+            if input_concat_cond.shape[2] != x.shape[2]:
+                input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest')
+
+            x = torch.cat([x, input_concat_cond], dim=1)
+
+        # Get the batch of timestep embeddings
+        timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None])) # (b, embed_dim)
+
+        # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists
+        if global_embed is not None:
+            global_embed = global_embed + timestep_embed
+        else:
+            global_embed = timestep_embed
+
+        # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer
+        if self.global_cond_type == "prepend": # True
+            if prepend_inputs is None: # True
+                # Prepend inputs are just the global embed, and the mask is all ones
+                prepend_inputs = global_embed.unsqueeze(1) # [1, 1, 1536]
+                prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)
+            else:
+                # Prepend inputs are the prepend conditioning + the global embed
+                prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1)
+                prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1)
+
+            prepend_length = prepend_inputs.shape[1] # 1
+
+        x = self.preprocess_conv(x) + x # [1, 64, 1024]
+
+        x = rearrange(x, "b c t -> b t c") # [1, 1024, 64]
+
+        extra_args = {}
+
+        if self.global_cond_type == "adaLN": # 'prepend'
+            extra_args["global_cond"] = global_embed
+
+        if self.patch_size > 1: # self.patch_size==1
+            x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size)
+
+        if self.transformer_type == "x-transformers":
+            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs)
+        elif self.transformer_type == "continuous_transformer":
+
+            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs)
+
+            if return_info:
+                output, info = output
+        elif self.transformer_type == "mm_transformer":
+            output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask, **extra_args, **kwargs)
+
+        output = rearrange(output, "b t c -> b c t")[:,:,prepend_length:]
+
+        if self.patch_size > 1:
+            output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size)
+
+        output = self.postprocess_conv(output) + output
+
+        if return_info:
+            return output, info
+
+        return output
+
+    def forward(
+        self, 
+        x, 
+        t, 
+        cross_attn_cond=None,
+        cross_attn_cond_mask=None,
+        negative_cross_attn_cond=None,
+        negative_cross_attn_mask=None,
+        input_concat_cond=None,
+        global_embed=None,
+        negative_global_embed=None,
+        prepend_cond=None,
+        prepend_cond_mask=None,
+        cfg_scale=1.0,
+        cfg_dropout_prob=0.0,
+        causal=False,
+        scale_phi=0.0,
+        mask=None,
+        return_info=False,
+        **kwargs):
+
+        assert causal == False, "Causal mode is not supported for DiffusionTransformer"
+
+        if cross_attn_cond_mask is not None:
+            cross_attn_cond_mask = cross_attn_cond_mask.bool()
+
+            cross_attn_cond_mask = None # Temporarily disabling conditioning masks due to kernel issue for flash attention
+
+        if prepend_cond_mask is not None:
+            prepend_cond_mask = prepend_cond_mask.bool()
+
+        # CFG dropout
+        if cfg_dropout_prob > 0.0:
+            if cross_attn_cond is not None:
+                null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)
+                dropout_mask = torch.bernoulli(torch.full((cross_attn_cond.shape[0], 1, 1), cfg_dropout_prob, device=cross_attn_cond.device)).to(torch.bool)
+                cross_attn_cond = torch.where(dropout_mask, null_embed, cross_attn_cond)
+
+            if prepend_cond is not None:
+                null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)
+                dropout_mask = torch.bernoulli(torch.full((prepend_cond.shape[0], 1, 1), cfg_dropout_prob, device=prepend_cond.device)).to(torch.bool)
+                prepend_cond = torch.where(dropout_mask, null_embed, prepend_cond)
+
+
+        if cfg_scale != 1.0 and (cross_attn_cond is not None or prepend_cond is not None):
+            # Classifier-free guidance
+            # Concatenate conditioned and unconditioned inputs on the batch dimension            
+            batch_inputs = torch.cat([x, x], dim=0)
+            batch_timestep = torch.cat([t, t], dim=0)
+
+            if global_embed is not None:
+                batch_global_cond = torch.cat([global_embed, global_embed], dim=0)
+            else:
+                batch_global_cond = None
+
+            if input_concat_cond is not None:
+                batch_input_concat_cond = torch.cat([input_concat_cond, input_concat_cond], dim=0)
+            else:
+                batch_input_concat_cond = None
+
+            batch_cond = None
+            batch_cond_masks = None
+            
+            # Handle CFG for cross-attention conditioning
+            if cross_attn_cond is not None:
+
+                null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)
+
+                # For negative cross-attention conditioning, replace the null embed with the negative cross-attention conditioning
+                if negative_cross_attn_cond is not None:
+
+                    # If there's a negative cross-attention mask, set the masked tokens to the null embed
+                    if negative_cross_attn_mask is not None:
+                        negative_cross_attn_mask = negative_cross_attn_mask.to(torch.bool).unsqueeze(2)
+
+                        negative_cross_attn_cond = torch.where(negative_cross_attn_mask, negative_cross_attn_cond, null_embed)
+                    
+                    batch_cond = torch.cat([cross_attn_cond, negative_cross_attn_cond], dim=0)
+
+                else:
+                    batch_cond = torch.cat([cross_attn_cond, null_embed], dim=0)
+
+                if cross_attn_cond_mask is not None:
+                    batch_cond_masks = torch.cat([cross_attn_cond_mask, cross_attn_cond_mask], dim=0)
+               
+            batch_prepend_cond = None
+            batch_prepend_cond_mask = None
+
+            if prepend_cond is not None:
+
+                null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)
+
+                batch_prepend_cond = torch.cat([prepend_cond, null_embed], dim=0)
+                           
+                if prepend_cond_mask is not None:
+                    batch_prepend_cond_mask = torch.cat([prepend_cond_mask, prepend_cond_mask], dim=0)
+         
+
+            if mask is not None:
+                batch_masks = torch.cat([mask, mask], dim=0)
+            else:
+                batch_masks = None
+            
+            batch_output = self._forward(
+                batch_inputs, 
+                batch_timestep, 
+                cross_attn_cond=batch_cond, 
+                cross_attn_cond_mask=batch_cond_masks, 
+                mask = batch_masks, 
+                input_concat_cond=batch_input_concat_cond, 
+                global_embed = batch_global_cond,
+                prepend_cond = batch_prepend_cond,
+                prepend_cond_mask = batch_prepend_cond_mask,
+                return_info = return_info,
+                **kwargs)
+
+            if return_info:
+                batch_output, info = batch_output
+
+            cond_output, uncond_output = torch.chunk(batch_output, 2, dim=0)
+            cfg_output = uncond_output + (cond_output - uncond_output) * cfg_scale
+
+            # CFG Rescale
+            if scale_phi != 0.0:
+                cond_out_std = cond_output.std(dim=1, keepdim=True)
+                out_cfg_std = cfg_output.std(dim=1, keepdim=True)
+                output = scale_phi * (cfg_output * (cond_out_std/out_cfg_std)) + (1-scale_phi) * cfg_output
+            else:
+                output = cfg_output
+            
+            if return_info:
+                return output, info
+
+            return output
+            
+        else:
+            return self._forward(
+                x,
+                t,
+                cross_attn_cond=cross_attn_cond, 
+                cross_attn_cond_mask=cross_attn_cond_mask, 
+                input_concat_cond=input_concat_cond, 
+                global_embed=global_embed, 
+                prepend_cond=prepend_cond, 
+                prepend_cond_mask=prepend_cond_mask,
+                mask=mask,
+                return_info=return_info,
+                **kwargs
+            )

stable_audio_tools/models/factory.py

@@ -0,0 +1,153 @@
+import json
+
+def create_model_from_config(model_config):
+    model_type = model_config.get('model_type', None)
+
+    assert model_type is not None, 'model_type must be specified in model config'
+
+    if model_type == 'autoencoder':
+        from .autoencoders import create_autoencoder_from_config
+        return create_autoencoder_from_config(model_config)
+    elif model_type == 'diffusion_uncond':
+        from .diffusion import create_diffusion_uncond_from_config
+        return create_diffusion_uncond_from_config(model_config)
+    elif model_type == 'diffusion_cond' or model_type == 'diffusion_cond_inpaint' or model_type == "diffusion_prior":
+        from .diffusion import create_diffusion_cond_from_config
+        return create_diffusion_cond_from_config(model_config)
+    elif model_type == 'diffusion_autoencoder':
+        from .autoencoders import create_diffAE_from_config
+        return create_diffAE_from_config(model_config)
+    elif model_type == 'lm':
+        from .lm import create_audio_lm_from_config
+        return create_audio_lm_from_config(model_config)
+    else:
+        raise NotImplementedError(f'Unknown model type: {model_type}')
+
+def create_model_from_config_path(model_config_path):
+    with open(model_config_path) as f:
+        model_config = json.load(f)
+    
+    return create_model_from_config(model_config)
+
+def create_pretransform_from_config(pretransform_config, sample_rate):
+    pretransform_type = pretransform_config.get('type', None)
+
+    assert pretransform_type is not None, 'type must be specified in pretransform config'
+
+    if pretransform_type == 'autoencoder':
+        from .autoencoders import create_autoencoder_from_config
+        from .pretransforms import AutoencoderPretransform
+
+        # Create fake top-level config to pass sample rate to autoencoder constructor
+        # This is a bit of a hack but it keeps us from re-defining the sample rate in the config
+        autoencoder_config = {"sample_rate": sample_rate, "model": pretransform_config["config"]}
+        autoencoder = create_autoencoder_from_config(autoencoder_config)
+
+        scale = pretransform_config.get("scale", 1.0)
+        model_half = pretransform_config.get("model_half", False)
+        iterate_batch = pretransform_config.get("iterate_batch", False)
+        chunked = pretransform_config.get("chunked", False)
+
+        pretransform = AutoencoderPretransform(autoencoder, scale=scale, model_half=model_half, iterate_batch=iterate_batch, chunked=chunked)
+    elif pretransform_type == 'wavelet':
+        from .pretransforms import WaveletPretransform
+
+        wavelet_config = pretransform_config["config"]
+        channels = wavelet_config["channels"]
+        levels = wavelet_config["levels"]
+        wavelet = wavelet_config["wavelet"]
+
+        pretransform = WaveletPretransform(channels, levels, wavelet)
+    elif pretransform_type == 'pqmf':
+        from .pretransforms import PQMFPretransform
+        pqmf_config = pretransform_config["config"]
+        pretransform = PQMFPretransform(**pqmf_config)
+    elif pretransform_type == 'dac_pretrained':
+        from .pretransforms import PretrainedDACPretransform
+        pretrained_dac_config = pretransform_config["config"]
+        pretransform = PretrainedDACPretransform(**pretrained_dac_config)
+    elif pretransform_type == "audiocraft_pretrained":
+        from .pretransforms import AudiocraftCompressionPretransform
+
+        audiocraft_config = pretransform_config["config"]
+        pretransform = AudiocraftCompressionPretransform(**audiocraft_config)
+    else:
+        raise NotImplementedError(f'Unknown pretransform type: {pretransform_type}')
+    
+    enable_grad = pretransform_config.get('enable_grad', False)
+    pretransform.enable_grad = enable_grad
+
+    pretransform.eval().requires_grad_(pretransform.enable_grad)
+
+    return pretransform
+
+def create_bottleneck_from_config(bottleneck_config):
+    bottleneck_type = bottleneck_config.get('type', None)
+
+    assert bottleneck_type is not None, 'type must be specified in bottleneck config'
+
+    if bottleneck_type == 'tanh':
+        from .bottleneck import TanhBottleneck
+        bottleneck = TanhBottleneck()
+    elif bottleneck_type == 'vae':
+        from .bottleneck import VAEBottleneck
+        bottleneck = VAEBottleneck()
+    elif bottleneck_type == 'rvq':
+        from .bottleneck import RVQBottleneck
+
+        quantizer_params = {
+            "dim": 128,
+            "codebook_size": 1024,
+            "num_quantizers": 8,
+            "decay": 0.99,
+            "kmeans_init": True,
+            "kmeans_iters": 50,
+            "threshold_ema_dead_code": 2,
+        }
+
+        quantizer_params.update(bottleneck_config["config"])
+
+        bottleneck = RVQBottleneck(**quantizer_params)
+    elif bottleneck_type == "dac_rvq":
+        from .bottleneck import DACRVQBottleneck
+
+        bottleneck = DACRVQBottleneck(**bottleneck_config["config"])
+    
+    elif bottleneck_type == 'rvq_vae':
+        from .bottleneck import RVQVAEBottleneck
+
+        quantizer_params = {
+            "dim": 128,
+            "codebook_size": 1024,
+            "num_quantizers": 8,
+            "decay": 0.99,
+            "kmeans_init": True,
+            "kmeans_iters": 50,
+            "threshold_ema_dead_code": 2,
+        }
+
+        quantizer_params.update(bottleneck_config["config"])
+
+        bottleneck = RVQVAEBottleneck(**quantizer_params)
+        
+    elif bottleneck_type == 'dac_rvq_vae':
+        from .bottleneck import DACRVQVAEBottleneck
+        bottleneck = DACRVQVAEBottleneck(**bottleneck_config["config"])
+    elif bottleneck_type == 'l2_norm':
+        from .bottleneck import L2Bottleneck
+        bottleneck = L2Bottleneck()
+    elif bottleneck_type == "wasserstein":
+        from .bottleneck import WassersteinBottleneck
+        bottleneck = WassersteinBottleneck(**bottleneck_config.get("config", {}))
+    elif bottleneck_type == "fsq":
+        from .bottleneck import FSQBottleneck
+        bottleneck = FSQBottleneck(**bottleneck_config["config"])
+    else:
+        raise NotImplementedError(f'Unknown bottleneck type: {bottleneck_type}')
+    
+    requires_grad = bottleneck_config.get('requires_grad', True)
+    if not requires_grad:
+        for param in bottleneck.parameters():
+            param.requires_grad = False
+
+    return bottleneck

stable_audio_tools/models/lm.py

@@ -0,0 +1,542 @@
+from dataclasses import dataclass
+import torch
+from tqdm.auto import trange
+import typing as tp
+from einops import rearrange
+from torch import nn
+
+from .conditioners import MultiConditioner, create_multi_conditioner_from_conditioning_config
+from .factory import create_pretransform_from_config
+from .lm_backbone import AudioLMBackbone, XTransformersAudioLMBackbone, ContinuousTransformerAudioLMBackbone
+from .pretransforms import Pretransform, AutoencoderPretransform, PretrainedDACPretransform, AudiocraftCompressionPretransform
+from .utils import multinomial, sample_top_k, sample_top_p
+
+from .codebook_patterns import (
+    CodebooksPatternProvider,
+    DelayedPatternProvider,
+    MusicLMPattern,
+    ParallelPatternProvider,
+    UnrolledPatternProvider
+)
+
+# Copied and modified from https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/models/lm.py under MIT license
+# License can be found in LICENSES/LICENSE_META.txt
+
+@dataclass
+class LMOutput:
+    # The logits are already re-aligned with the input codes
+    # hence no extra shift is required, e.g. when computing CE
+    logits: torch.Tensor  # [B, K, T, card]
+    mask: torch.Tensor  # [B, K, T]
+
+# Wrapper for a multi-codebook language model
+# Handles patterns and quantizer heads
+class AudioLanguageModel(nn.Module):
+    def __init__(
+            self, 
+            pattern_provider: CodebooksPatternProvider, 
+            backbone: AudioLMBackbone,
+            num_quantizers: int,
+            codebook_size: int
+        ):
+        super().__init__()
+
+        self.pattern_provider = pattern_provider
+        self.backbone = backbone
+        self.num_quantizers = num_quantizers
+        self.codebook_size = codebook_size
+
+        self.masked_token_id = codebook_size
+
+        # Per-quantizer embedders
+        # Add one for the mask embed
+        self.embeds = nn.ModuleList([nn.Embedding(codebook_size + 1, backbone.embed_dim) for _ in range(num_quantizers)])
+
+        # Per-quantizer output heads
+        self.quantizer_heads = nn.ModuleList([
+            nn.Linear(backbone.embed_dim, codebook_size) for _ in range(num_quantizers)
+        ])
+
+    def forward(self,
+            sequence: torch.Tensor, #[batch, seq_len, 
+            prepend_cond=None, #[batch, seq, channels]
+            prepend_cond_mask=None,
+            cross_attn_cond=None, #[batch, seq, channels],
+            **kwargs
+        ):
+        
+        
+        batch, num_quantizers, seq_len = sequence.shape
+
+        assert num_quantizers == self.num_quantizers, "Number of quantizers in sequence must match number of quantizers in model"
+
+        backbone_input = sum([self.embeds[i](sequence[:, i]) for i in range(num_quantizers)]) # [batch, seq_len, embed_dim]
+
+        dtype = next(self.parameters()).dtype
+
+        if cross_attn_cond is not None:
+            cross_attn_cond = cross_attn_cond.to(dtype)
+
+        if prepend_cond is not None:
+            prepend_cond = prepend_cond.to(dtype)
+
+            if prepend_cond_mask is not None:
+                prepend_cond_mask = prepend_cond_mask.to(dtype)
+            
+        backbone_input = backbone_input.to(dtype)
+
+        output = self.backbone(
+            backbone_input,
+            cross_attn_cond=cross_attn_cond,
+            prepend_cond=prepend_cond,
+            prepend_cond_mask=prepend_cond_mask,
+            **kwargs
+        ) # [batch, seq_len, embed_dim]
+
+        # Run output through quantizer heads
+        logits = torch.stack([self.quantizer_heads[i](output) for i in range(num_quantizers)], dim=1) # [batch, num_quantizers, seq_len, codebook_size]
+
+        return logits
+    
+    def compute_logits(
+            self, 
+            codes, #[batch, num_quantizers, seq_len]
+            **kwargs):
+        """
+        Compute logits for a batch of codes, optionally conditioning on cross-attention and prepend conditioning
+        Handles translation between input sequence and pattern-shifted sequence
+        Only used during training
+        """
+        
+        batch, _, seq_len = codes.shape
+
+        pattern = self.pattern_provider.get_pattern(seq_len)
+
+        # Apply the token pattern to the codes, shifting the codes as needed and masking out invalid steps
+        shifted_codes, _, _ = pattern.build_pattern_sequence(
+            codes,
+            self.masked_token_id,
+            keep_only_valid_steps=True
+        )
+
+        # Run the model to get logits for each quantizer [batch, num_quantizers, seq_len, codebook_size]
+        logits = self(shifted_codes, **kwargs)
+
+        # Rearrange logits to prepare to revert pattern
+        logits = rearrange(logits, "b n s c -> b c n s")
+
+        # Revert sequence logits back to original sequence length, removing masked steps
+        logits, _, logits_mask = pattern.revert_pattern_logits(
+            logits, float('nan'), keep_only_valid_steps=True
+        )
+
+        logits = rearrange(logits, "b c n t -> b n t c")
+
+        logits_mask = logits_mask[None, :, :].expand(batch, -1, -1) # [batch, num_quantizers, seq_len]
+
+        return LMOutput(logits=logits, mask=logits_mask)
+
+# Conditioning and generation wrapper for a multi-codebook language model
+# Handles conditioning, CFG, generation, and encoding/decoding
+class AudioLanguageModelWrapper(nn.Module):
+    def __init__(
+            self, 
+            pretransform: Pretransform,
+            lm: AudioLanguageModel,
+            sample_rate: int,
+            min_input_length: int,
+            conditioner: MultiConditioner = None,
+            cross_attn_cond_ids: tp.List[str] = [],
+            prepend_cond_ids: tp.List[str] = [],
+            global_cond_ids: tp.List[str] = []
+        ):
+        super().__init__()
+        
+        assert pretransform.is_discrete, "Pretransform must be discrete"
+        self.pretransform = pretransform
+
+        self.pretransform.requires_grad_(False)
+        self.pretransform.eval()
+
+        if isinstance(self.pretransform, AutoencoderPretransform):
+            self.num_quantizers = self.pretransform.model.bottleneck.num_quantizers
+            self.codebook_size = self.pretransform.model.bottleneck.codebook_size
+        elif isinstance(self.pretransform, PretrainedDACPretransform):
+            self.num_quantizers = self.pretransform.model.num_quantizers
+            self.codebook_size = self.pretransform.model.codebook_size
+        elif isinstance(self.pretransform, AudiocraftCompressionPretransform):
+            self.num_quantizers = self.pretransform.num_quantizers
+            self.codebook_size = self.pretransform.codebook_size
+        else:
+            raise NotImplementedError(f"Unrecognized pretransform type {type(self.pretransform)}")
+
+        self.conditioner = conditioner
+
+        self.lm = lm
+
+        self.sample_rate = sample_rate
+        self.min_input_length = min_input_length
+
+        self.cross_attn_cond_ids = cross_attn_cond_ids
+        self.prepend_cond_ids = prepend_cond_ids
+        self.global_cond_ids = global_cond_ids
+    
+    def get_conditioning_inputs(self, cond: tp.Dict[str, tp.Any], negative=False):
+        cross_attention_input = None
+        prepend_cond = None
+        prepend_cond_mask = None
+        global_cond = None
+
+        if len(self.cross_attn_cond_ids) > 0:
+            # Concatenate all cross-attention inputs over the sequence dimension
+            # Assumes that the cross-attention inputs are of shape (batch, seq, channels)
+            cross_attention_input = torch.cat([cond[key][0] for key in self.cross_attn_cond_ids], dim=1)
+
+        if len(self.prepend_cond_ids) > 0:
+            # Concatenate all prepend conditioning inputs over the sequence dimension
+            # Assumes that the prepend conditioning inputs are of shape (batch, seq, channels)
+            prepend_cond = torch.cat([cond[key][0] for key in self.prepend_cond_ids], dim=1)
+            prepend_cond_mask = torch.cat([cond[key][1] for key in self.prepend_cond_ids], dim=1)
+
+        if len(self.global_cond_ids) > 0:
+            # Concatenate all global conditioning inputs over the channel dimension
+            # Assumes that the global conditioning inputs are of shape (batch, channels)
+            global_cond = torch.cat([cond[key][0] for key in self.global_cond_ids], dim=-1)
+            if len(global_cond.shape) == 3:
+                global_cond = global_cond.squeeze(1)
+
+        if negative:
+            return {
+                "negative_cross_attn_cond": cross_attention_input,
+                "negative_prepend_cond": prepend_cond,
+                "negative_prepend_cond_mask": prepend_cond_mask,
+                "negative_global_cond": global_cond
+            }
+        else:
+            return {
+                "cross_attn_cond": cross_attention_input,
+                "prepend_cond": prepend_cond,
+                "prepend_cond_mask": prepend_cond_mask,
+                "global_cond": global_cond
+            }
+        
+    def compute_logits(
+            self, 
+            codes, 
+            condition_tensors=None, 
+            cfg_dropout_prob=0.0,
+            **kwargs
+        ):
+        """
+        Compute logits for a batch of codes, and translates from conditioning inputs to model inputs
+        Handles CFG dropout
+        """
+
+        if condition_tensors is None:
+            condition_tensors = {}
+
+        conditioning_inputs = self.get_conditioning_inputs(condition_tensors)
+
+        cross_attn_cond = conditioning_inputs["cross_attn_cond"]
+        prepend_cond = conditioning_inputs["prepend_cond"]
+        prepend_cond_mask = conditioning_inputs["prepend_cond_mask"]
+        global_cond = conditioning_inputs["global_cond"]
+
+        if cfg_dropout_prob > 0.0:
+            if cross_attn_cond is not None:
+                null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)
+                dropout_mask = torch.bernoulli(torch.full((cross_attn_cond.shape[0], 1, 1), cfg_dropout_prob, device=cross_attn_cond.device)).to(torch.bool)
+                cross_attn_cond = torch.where(dropout_mask, null_embed, cross_attn_cond)
+        
+            if prepend_cond is not None:
+                null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)
+                dropout_mask = torch.bernoulli(torch.full((prepend_cond.shape[0], 1, 1), cfg_dropout_prob, device=prepend_cond.device)).to(torch.bool)
+                prepend_cond = torch.where(dropout_mask, null_embed, prepend_cond)
+
+            if global_cond is not None:
+                null_embed = torch.zeros_like(global_cond, device=global_cond.device)
+                dropout_mask = torch.bernoulli(torch.full((global_cond.shape[0], 1), cfg_dropout_prob, device=global_cond.device)).to(torch.bool)
+                global_cond = torch.where(dropout_mask, null_embed, global_cond)
+
+        return self.lm.compute_logits(codes, cross_attn_cond=cross_attn_cond, prepend_cond=prepend_cond, prepend_cond_mask=prepend_cond_mask, global_cond=global_cond, **kwargs)
+
+    def _sample_next_token(
+            self, 
+            sequence, #[batch, num_quantizers, seq_len]
+            conditioning_tensors=None, 
+            cross_attn_use_cfg=True,
+            prepend_use_cfg=True,
+            global_use_cfg=True,
+            cfg_scale=1.0,
+            top_k=250,
+            top_p=0.0,
+            temp=1.0,
+            **kwargs
+        ):
+        """
+        Sample the next token for a batch of codes, and translates from conditioning inputs to model inputs
+        Handles CFG inference
+        """
+
+        if conditioning_tensors is None:
+            conditioning_tensors = {}
+
+        conditioning_inputs = self.get_conditioning_inputs(conditioning_tensors)
+
+        cross_attn_cond = conditioning_inputs["cross_attn_cond"]
+        prepend_cond = conditioning_inputs["prepend_cond"]
+        prepend_cond_mask = conditioning_inputs["prepend_cond_mask"]
+        global_cond = conditioning_inputs["global_cond"]
+
+        if cfg_scale != 1.0:
+            
+            # Batch size is doubled to account for negative samples
+            sequence = torch.cat([sequence, sequence], dim=0)
+
+            if cross_attn_cond is not None and cross_attn_use_cfg:
+                null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)
+
+                cross_attn_cond = torch.cat([cross_attn_cond, null_embed], dim=0)
+            
+            if prepend_cond is not None and prepend_use_cfg:
+                null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)
+
+                prepend_cond = torch.cat([prepend_cond, null_embed], dim=0) 
+
+                if prepend_cond_mask is not None:
+                    prepend_cond_mask = torch.cat([prepend_cond_mask, prepend_cond_mask], dim=0)
+
+            if global_cond is not None and global_use_cfg:
+                null_embed = torch.zeros_like(global_cond, device=global_cond.device)
+
+                global_cond = torch.cat([global_cond, null_embed], dim=0)
+
+        logits = self.lm(sequence, cross_attn_cond=cross_attn_cond, prepend_cond=prepend_cond, prepend_cond_mask=prepend_cond_mask, global_cond=global_cond, **kwargs)
+
+        if cfg_scale != 1.0:
+            cond_logits, uncond_logits = logits.chunk(2, dim=0)
+
+            logits = uncond_logits + (cond_logits - uncond_logits) * cfg_scale
+
+        logits = rearrange(logits, "b n s c -> b n c s") # [batch, num_quantizers, codebook_size, seq_len]
+        
+        # Grab the logits for the last step
+        logits = logits[:, :, :, -1] # [batch, num_quantizers, codebook_size]
+
+        # Apply top-k or top-p sampling
+
+        if temp > 0:
+            probs = torch.softmax(logits / temp, dim=-1)
+
+            if top_p > 0.0:
+                next_token = sample_top_p(probs, p=top_p)
+            elif top_k > 0:
+                next_token = sample_top_k(probs, k=top_k)
+            else:
+                next_token = multinomial(probs, num_samples=1)
+
+        else:
+            next_token = torch.argmax(logits, dim=-1, keepdim=True) # [batch, num_quantizers, 1]
+
+        return next_token
+
+    @torch.no_grad()
+    def generate(
+        self,
+        max_gen_len: int = 256,
+        batch_size: tp.Optional[int] = None,
+        init_data: tp.Optional[torch.Tensor] = None,
+        conditioning: tp.Optional[tp.Dict[str, tp.Any]] = None,
+        conditioning_tensors: tp.Optional[tp.Dict[str, tp.Any]] = None,
+        callback: tp.Optional[tp.Callable[[int, int], None]] = None,
+        use_cache: bool = True,
+        cfg_scale: float = 1.0,
+        **kwargs
+    ):
+        device = next(self.parameters()).device
+
+        if conditioning_tensors is None and conditioning is not None:
+            # Convert conditioning inputs to conditioning tensors
+            conditioning_tensors = self.conditioner(conditioning, device)
+
+        # Check that batch size is consistent across inputs
+        possible_batch_sizes = []
+
+        if batch_size is not None:
+            possible_batch_sizes.append(batch_size)
+        elif init_data is not None:
+            possible_batch_sizes.append(init_data.shape[0])
+        elif conditioning_tensors is not None:
+            # Assume that the first conditioning tensor has the batch dimension
+            possible_batch_sizes.append(conditioning_tensors[list(conditioning_tensors.keys())[0]][0].shape[0])
+        else:
+            possible_batch_sizes.append(1)
+
+        assert [x == possible_batch_sizes[0] for x in possible_batch_sizes], "Batch size must be consistent across inputs"
+
+        batch_size = possible_batch_sizes[0]
+        
+        if init_data is None:
+            # Initialize with zeros
+            assert batch_size > 0
+            init_data = torch.zeros((batch_size, self.num_quantizers, 0), device=device, dtype=torch.long)
+
+        batch_size, num_quantizers, seq_len = init_data.shape
+
+        start_offset = seq_len
+        assert start_offset < max_gen_len, "init data longer than max gen length"
+
+        pattern = self.lm.pattern_provider.get_pattern(max_gen_len)
+
+        unknown_token = -1
+
+        # Initialize the generated codes with the init data, padded with unknown tokens
+        gen_codes = torch.full((batch_size, num_quantizers, max_gen_len), unknown_token, device=device, dtype=torch.long)
+        gen_codes[:, :, :start_offset] = init_data # [batch, num_quantizers, max_gen_len]
+
+        gen_sequence, _, mask = pattern.build_pattern_sequence(gen_codes, self.lm.masked_token_id) # [batch, num_quantizers, gen_sequence_len]
+
+        start_offset_sequence = pattern.get_first_step_with_timesteps(start_offset)
+        assert start_offset_sequence is not None
+
+        # Generation
+        prev_offset = 0
+        gen_sequence_len = gen_sequence.shape[-1]
+
+        # Reset generation cache
+        if use_cache and self.lm.backbone.use_generation_cache:
+            self.lm.backbone.reset_generation_cache(max_gen_len, batch_size if cfg_scale == 1.0 else batch_size * 2)
+
+        for offset in trange(start_offset_sequence, gen_sequence_len):
+
+            # Get the full sequence up to the current offset
+            curr_sequence = gen_sequence[..., prev_offset:offset]
+
+            next_token = self._sample_next_token(
+                curr_sequence,
+                conditioning_tensors=conditioning_tensors,
+                use_cache=use_cache,
+                cfg_scale=cfg_scale,
+                **kwargs
+            )
+
+            valid_mask = mask[..., offset:offset+1].expand(batch_size, -1, -1)
+            next_token[~valid_mask] = self.lm.masked_token_id
+
+            # Update the generated sequence with the next token
+            gen_sequence[..., offset:offset+1] = torch.where(
+                gen_sequence[..., offset:offset+1] == unknown_token,
+                next_token, 
+                gen_sequence[..., offset:offset+1]
+            )
+
+            if use_cache and self.lm.backbone.use_generation_cache:
+                # Only update the offset if caching is being used
+                prev_offset = offset
+
+                self.lm.backbone.update_generation_cache(offset)
+
+            if callback is not None:
+                # Callback to report progress
+                # Pass in the offset relative to the start of the sequence, and the length of the current sequence
+                callback(1 + offset - start_offset_sequence, gen_sequence_len - start_offset_sequence)
+
+        assert not (gen_sequence == unknown_token).any(), "Unknown tokens in generated sequence"
+
+        out_codes, _, out_mask = pattern.revert_pattern_sequence(gen_sequence, special_token=unknown_token)
+
+        # sanity checks over the returned codes and corresponding masks
+        assert (out_codes[..., :max_gen_len] != unknown_token).all()
+        assert (out_mask[..., :max_gen_len] == 1).all()
+
+        #out_codes = out_codes[..., 0:max_gen_len]
+
+        return out_codes
+    
+
+    def generate_audio(
+        self,
+        **kwargs
+    ):
+        """
+        Generate audio from a batch of codes
+        """
+
+        codes = self.generate(**kwargs)
+
+        audio = self.pretransform.decode_tokens(codes)
+
+        return audio
+
+
+def create_audio_lm_from_config(config):
+    model_config = config.get('model', None)
+    assert model_config is not None, 'model config must be specified in config'
+
+    sample_rate = config.get('sample_rate', None)
+    assert sample_rate is not None, "Must specify sample_rate in config"
+    
+    lm_config = model_config.get('lm', None)
+    assert lm_config is not None, 'lm config must be specified in model config'
+
+    codebook_pattern = lm_config.get("codebook_pattern", "delay")
+
+    pattern_providers = {
+        'parallel': ParallelPatternProvider,
+        'delay': DelayedPatternProvider,
+        'unroll': UnrolledPatternProvider,
+        'musiclm': MusicLMPattern,
+    }
+
+    pretransform_config = model_config.get("pretransform", None)
+    
+    pretransform = create_pretransform_from_config(pretransform_config, sample_rate)
+
+    assert pretransform.is_discrete, "Pretransform must be discrete"
+
+    min_input_length = pretransform.downsampling_ratio
+
+    pattern_provider = pattern_providers[codebook_pattern](n_q=pretransform.num_quantizers)
+
+    conditioning_config = model_config.get('conditioning', None)
+
+    conditioner = None
+    if conditioning_config is not None:
+        conditioner = create_multi_conditioner_from_conditioning_config(conditioning_config)
+
+    cross_attn_cond_ids = lm_config.get('cross_attention_cond_ids', [])
+    prepend_cond_ids = lm_config.get('prepend_cond_ids', [])
+    global_cond_ids = lm_config.get('global_cond_ids', [])
+
+    lm_type = lm_config.get("type", None)
+    lm_model_config = lm_config.get("config", None)
+
+    assert lm_type is not None, "Must specify lm type in lm config"
+    assert lm_model_config is not None, "Must specify lm model config in lm config"
+
+    if lm_type == "x-transformers":
+        backbone = XTransformersAudioLMBackbone(**lm_model_config)
+    elif lm_type == "continuous_transformer":
+        backbone = ContinuousTransformerAudioLMBackbone(**lm_model_config)
+    else:
+        raise NotImplementedError(f"Unrecognized lm type {lm_type}")
+
+    lm = AudioLanguageModel(
+        pattern_provider=pattern_provider,
+        backbone=backbone,
+        num_quantizers=pretransform.num_quantizers,
+        codebook_size=pretransform.codebook_size
+    )
+
+    model = AudioLanguageModelWrapper(
+        pretransform=pretransform,
+        lm=lm,
+        conditioner=conditioner,
+        sample_rate=sample_rate,
+        min_input_length=min_input_length,
+        cross_attn_cond_ids=cross_attn_cond_ids,
+        prepend_cond_ids=prepend_cond_ids,
+        global_cond_ids=global_cond_ids
+    )
+
+    return model

stable_audio_tools/models/local_attention.py

@@ -0,0 +1,278 @@
+import torch
+
+from einops import rearrange
+from torch import nn
+
+from .blocks import AdaRMSNorm
+from .transformer import Attention, FeedForward, RotaryEmbedding, LayerNorm
+
+def checkpoint(function, *args, **kwargs):
+    kwargs.setdefault("use_reentrant", False)
+    return torch.utils.checkpoint.checkpoint(function, *args, **kwargs)
+
+# Adapted from https://github.com/lucidrains/local-attention/blob/master/local_attention/transformer.py
+class ContinuousLocalTransformer(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth,
+        dim_in = None,
+        dim_out = None,
+        causal = False,
+        local_attn_window_size = 64,
+        heads = 8,
+        ff_mult = 2,
+        cond_dim = 0,
+        cross_attn_cond_dim = 0,
+        **kwargs
+    ):
+        super().__init__()
+        
+        dim_head = dim//heads
+
+        self.layers = nn.ModuleList([])
+
+        self.project_in = nn.Linear(dim_in, dim) if dim_in is not None else nn.Identity()
+
+        self.project_out = nn.Linear(dim, dim_out) if dim_out is not None else nn.Identity()
+
+        self.local_attn_window_size = local_attn_window_size
+
+        self.cond_dim = cond_dim
+
+        self.cross_attn_cond_dim = cross_attn_cond_dim
+
+        self.rotary_pos_emb = RotaryEmbedding(max(dim_head // 2, 32))
+       
+        for _ in range(depth):
+
+            self.layers.append(nn.ModuleList([
+                AdaRMSNorm(dim, cond_dim, eps=1e-8) if cond_dim > 0 else LayerNorm(dim),
+                Attention(
+                    dim=dim,
+                    dim_heads=dim_head,
+                    causal=causal,
+                    zero_init_output=True,
+                    natten_kernel_size=local_attn_window_size,
+                ),
+                Attention(
+                    dim=dim,
+                    dim_heads=dim_head,
+                    dim_context = cross_attn_cond_dim,
+                    zero_init_output=True
+                ) if self.cross_attn_cond_dim > 0 else nn.Identity(),
+                AdaRMSNorm(dim, cond_dim, eps=1e-8) if cond_dim > 0 else LayerNorm(dim),
+                FeedForward(dim = dim, mult = ff_mult, no_bias=True)
+            ]))
+
+    def forward(self, x, mask = None, cond = None, cross_attn_cond = None, cross_attn_cond_mask = None, prepend_cond = None):
+ 
+        x = checkpoint(self.project_in, x)
+
+        if prepend_cond is not None:
+            x = torch.cat([prepend_cond, x], dim=1)
+
+        pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1])
+
+        for attn_norm, attn, xattn, ff_norm, ff in self.layers:
+
+            residual = x
+            if cond is not None:
+                x = checkpoint(attn_norm, x, cond)
+            else:
+                x = checkpoint(attn_norm, x)
+
+            x = checkpoint(attn, x, mask = mask, rotary_pos_emb=pos_emb) + residual
+
+            if cross_attn_cond is not None:
+                x = checkpoint(xattn, x, context=cross_attn_cond, context_mask=cross_attn_cond_mask) + x
+
+            residual = x
+
+            if cond is not None:
+                x = checkpoint(ff_norm, x, cond)
+            else:
+                x = checkpoint(ff_norm, x)
+
+            x = checkpoint(ff, x) + residual
+
+        return checkpoint(self.project_out, x)
+
+class TransformerDownsampleBlock1D(nn.Module):
+    def __init__(
+        self, 
+        in_channels,
+        embed_dim = 768,
+        depth = 3,
+        heads = 12,
+        downsample_ratio = 2,
+        local_attn_window_size = 64,
+        **kwargs
+    ):
+        super().__init__()
+
+        self.downsample_ratio = downsample_ratio
+
+        self.transformer = ContinuousLocalTransformer(
+            dim=embed_dim,
+            depth=depth,
+            heads=heads,
+            local_attn_window_size=local_attn_window_size,
+            **kwargs
+        )
+
+        self.project_in = nn.Linear(in_channels, embed_dim, bias=False) if in_channels != embed_dim else nn.Identity()
+
+        self.project_down = nn.Linear(embed_dim * self.downsample_ratio, embed_dim, bias=False)
+        
+    
+    def forward(self, x):
+
+        x = checkpoint(self.project_in, x)
+
+        # Compute
+        x = self.transformer(x)
+
+        # Trade sequence length for channels
+        x = rearrange(x, "b (n r) c -> b n (c r)", r=self.downsample_ratio)
+
+        # Project back to embed dim
+        x = checkpoint(self.project_down, x)
+
+        return x
+
+class TransformerUpsampleBlock1D(nn.Module):
+    def __init__(
+        self, 
+        in_channels,
+        embed_dim,
+        depth = 3,
+        heads = 12,
+        upsample_ratio = 2,
+        local_attn_window_size = 64,
+        **kwargs
+    ):
+        super().__init__()
+
+        self.upsample_ratio = upsample_ratio
+
+        self.transformer = ContinuousLocalTransformer(
+            dim=embed_dim,
+            depth=depth,
+            heads=heads,
+            local_attn_window_size = local_attn_window_size,
+            **kwargs
+        )
+
+        self.project_in = nn.Linear(in_channels, embed_dim, bias=False) if in_channels != embed_dim else nn.Identity()
+
+        self.project_up = nn.Linear(embed_dim, embed_dim * self.upsample_ratio, bias=False)
+        
+    def forward(self, x):
+
+        # Project to embed dim
+        x = checkpoint(self.project_in, x)
+
+        # Project to increase channel dim
+        x = checkpoint(self.project_up, x)
+
+        # Trade channels for sequence length
+        x = rearrange(x, "b n (c r) -> b (n r) c", r=self.upsample_ratio)
+
+        # Compute
+        x = self.transformer(x)        
+
+        return x
+   
+
+class TransformerEncoder1D(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        embed_dims = [96, 192, 384, 768],
+        heads = [12, 12, 12, 12],
+        depths = [3, 3, 3, 3],
+        ratios = [2, 2, 2, 2],
+        local_attn_window_size = 64,
+        **kwargs
+    ):
+        super().__init__()
+        
+        layers = []
+       
+        for layer in range(len(depths)):
+            prev_dim = embed_dims[layer - 1] if layer > 0 else embed_dims[0]
+
+            layers.append(
+                TransformerDownsampleBlock1D(
+                    in_channels = prev_dim,
+                    embed_dim = embed_dims[layer],
+                    heads = heads[layer],
+                    depth = depths[layer],
+                    downsample_ratio = ratios[layer],
+                    local_attn_window_size = local_attn_window_size,
+                    **kwargs
+                )
+            )
+        
+        self.layers = nn.Sequential(*layers)
+
+        self.project_in = nn.Linear(in_channels, embed_dims[0], bias=False)
+        self.project_out = nn.Linear(embed_dims[-1], out_channels, bias=False)
+
+    def forward(self, x):
+        x = rearrange(x, "b c n -> b n c")
+        x = checkpoint(self.project_in, x)
+        x = self.layers(x)
+        x = checkpoint(self.project_out, x)
+        x = rearrange(x, "b n c -> b c n")
+
+        return x
+
+
+class TransformerDecoder1D(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        embed_dims = [768, 384, 192, 96],
+        heads = [12, 12, 12, 12],
+        depths = [3, 3, 3, 3],
+        ratios = [2, 2, 2, 2],
+        local_attn_window_size = 64,
+        **kwargs
+    ):
+
+        super().__init__()
+
+        layers = []
+       
+        for layer in range(len(depths)):
+            prev_dim = embed_dims[layer - 1] if layer > 0 else embed_dims[0]
+
+            layers.append(
+                TransformerUpsampleBlock1D(
+                    in_channels = prev_dim,
+                    embed_dim = embed_dims[layer],
+                    heads = heads[layer],
+                    depth = depths[layer],
+                    upsample_ratio = ratios[layer],
+                    local_attn_window_size = local_attn_window_size,
+                    **kwargs
+                )
+            )
+        
+        self.layers = nn.Sequential(*layers)
+
+        self.project_in = nn.Linear(in_channels, embed_dims[0], bias=False)
+        self.project_out = nn.Linear(embed_dims[-1], out_channels, bias=False)
+
+    def forward(self, x):
+        x = rearrange(x, "b c n -> b n c")
+        x = checkpoint(self.project_in, x)
+        x = self.layers(x)
+        x = checkpoint(self.project_out, x)
+        x = rearrange(x, "b n c -> b c n")
+        return x

stable_audio_tools/models/pqmf.py

@@ -0,0 +1,393 @@
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from scipy.optimize import fmin
+from scipy.signal import firwin, kaiser, kaiser_beta, kaiserord
+
+class PQMF(nn.Module):
+    """
+    Pseudo Quadrature Mirror Filter (PQMF) for multiband signal decomposition and reconstruction.
+    Uses polyphase representation which is computationally more efficient for real-time. 
+    
+    Parameters:
+    - attenuation (int): Desired attenuation of the rejected frequency bands, usually between 80 and 120 dB.
+    - num_bands (int): Number of desired frequency bands. It must be a power of 2.
+    """
+
+    def __init__(self, attenuation, num_bands):
+        super(PQMF, self).__init__()
+        
+        # Ensure num_bands is a power of 2 
+        is_power_of_2 = (math.log2(num_bands) == int(math.log2(num_bands)))
+        assert is_power_of_2, "'num_bands' must be a power of 2."
+        
+        # Create the prototype filter
+        prototype_filter = design_prototype_filter(attenuation, num_bands)
+        filter_bank = generate_modulated_filter_bank(prototype_filter, num_bands)
+        padded_filter_bank = pad_to_nearest_power_of_two(filter_bank)
+        
+        # Register filters and settings
+        self.register_buffer("filter_bank", padded_filter_bank)
+        self.register_buffer("prototype", prototype_filter)
+        self.num_bands = num_bands
+
+    def forward(self, signal):
+        """Decompose the signal into multiple frequency bands."""
+        # If signal is not a pytorch tensor of Batch x Channels x Length, convert it 
+        signal = prepare_signal_dimensions(signal)
+        # The signal length must be a multiple of num_bands. Pad it with zeros.
+        signal = pad_signal(signal, self.num_bands)
+        # run it
+        signal = polyphase_analysis(signal, self.filter_bank)
+        return apply_alias_cancellation(signal)
+
+    def inverse(self, bands):
+        """Reconstruct the original signal from the frequency bands."""
+        bands = apply_alias_cancellation(bands)
+        return polyphase_synthesis(bands, self.filter_bank)
+
+
+def prepare_signal_dimensions(signal):
+    """
+    Rearrange signal into Batch x Channels x Length. 
+    
+    Parameters
+    ----------
+    signal : torch.Tensor or numpy.ndarray
+        The input signal.
+        
+    Returns
+    -------
+    torch.Tensor
+        Preprocessed signal tensor.
+    """
+    # Convert numpy to torch tensor
+    if isinstance(signal, np.ndarray):
+        signal = torch.from_numpy(signal)
+        
+    # Ensure tensor
+    if not isinstance(signal, torch.Tensor):
+        raise ValueError("Input should be either a numpy array or a PyTorch tensor.")
+    
+    # Modify dimension of signal to Batch x Channels x Length
+    if signal.dim() == 1:
+        # This is just a mono signal. Unsqueeze to 1 x 1 x Length
+        signal = signal.unsqueeze(0).unsqueeze(0)
+    elif signal.dim() == 2:
+        # This is a multi-channel signal (e.g. stereo)
+        # Rearrange so that larger dimension (Length) is last
+        if signal.shape[0] > signal.shape[1]:
+            signal = signal.T
+        # Unsqueeze to 1 x Channels x Length 
+        signal = signal.unsqueeze(0)
+    return signal
+    
+def pad_signal(signal, num_bands):
+    """
+    Pads the signal to make its length divisible by the given number of bands.
+
+    Parameters
+    ----------
+    signal : torch.Tensor
+        The input signal tensor, where the last dimension represents the signal length.
+
+    num_bands : int
+        The number of bands by which the signal length should be divisible.
+
+    Returns
+    -------
+    torch.Tensor
+        The padded signal tensor. If the original signal length was already divisible
+        by num_bands, returns the original signal unchanged.
+    """
+    remainder = signal.shape[-1] % num_bands
+    if remainder > 0:
+        padding_size = num_bands - remainder
+        signal = nn.functional.pad(signal, (0, padding_size))
+    return signal
+
+def generate_modulated_filter_bank(prototype_filter, num_bands):
+    """
+    Generate a QMF bank of cosine modulated filters based on a given prototype filter. 
+    
+    Parameters
+    ----------
+    prototype_filter : torch.Tensor
+        The prototype filter used as the basis for modulation.
+    num_bands : int
+        The number of desired subbands or filters.
+    
+    Returns
+    -------
+    torch.Tensor
+        A bank of cosine modulated filters.
+    """
+    
+    # Initialize indices for modulation.
+    subband_indices = torch.arange(num_bands).reshape(-1, 1)
+    
+    # Calculate the length of the prototype filter.
+    filter_length = prototype_filter.shape[-1]
+    
+    # Generate symmetric time indices centered around zero.
+    time_indices = torch.arange(-(filter_length // 2), (filter_length // 2) + 1)
+    
+    # Calculate phase offsets to ensure orthogonality between subbands.
+    phase_offsets = (-1)**subband_indices * np.pi / 4
+    
+    # Compute the cosine modulation function.
+    modulation = torch.cos(
+        (2 * subband_indices + 1) * np.pi / (2 * num_bands) * time_indices + phase_offsets
+    )
+    
+    # Apply modulation to the prototype filter.
+    modulated_filters = 2 * prototype_filter * modulation
+    
+    return modulated_filters
+
+
+def design_kaiser_lowpass(angular_cutoff, attenuation, filter_length=None):
+    """
+    Design a lowpass filter using the Kaiser window.
+    
+    Parameters
+    ----------
+    angular_cutoff : float
+        The angular frequency cutoff of the filter.
+    attenuation : float
+        The desired stopband attenuation in decibels (dB).
+    filter_length : int, optional
+        Desired length of the filter. If not provided, it's computed based on the given specs.
+    
+    Returns
+    -------
+    ndarray
+        The designed lowpass filter coefficients.
+    """
+    
+    estimated_length, beta = kaiserord(attenuation, angular_cutoff / np.pi)
+    
+    # Ensure the estimated length is odd.
+    estimated_length = 2 * (estimated_length // 2) + 1
+    
+    if filter_length is None:
+        filter_length = estimated_length
+    
+    return firwin(filter_length, angular_cutoff, window=('kaiser', beta), scale=False, nyq=np.pi)
+
+
+def evaluate_filter_objective(angular_cutoff, attenuation, num_bands, filter_length):
+    """
+    Evaluate the filter's objective value based on the criteria from https://ieeexplore.ieee.org/document/681427
+    
+    Parameters
+    ----------
+    angular_cutoff : float
+        Angular frequency cutoff of the filter.
+    attenuation : float
+        Desired stopband attenuation in dB.
+    num_bands : int
+        Number of bands for the multiband filter system.
+    filter_length : int, optional
+        Desired length of the filter.
+    
+    Returns
+    -------
+    float
+        The computed objective (loss) value for the given filter specs.
+    """
+    
+    filter_coeffs = design_kaiser_lowpass(angular_cutoff, attenuation, filter_length)
+    convolved_filter = np.convolve(filter_coeffs, filter_coeffs[::-1], "full")
+    
+    return np.max(np.abs(convolved_filter[convolved_filter.shape[-1] // 2::2 * num_bands][1:]))
+
+
+def design_prototype_filter(attenuation, num_bands, filter_length=None):
+    """
+    Design the optimal prototype filter for a multiband system given the desired specs.
+    
+    Parameters
+    ----------
+    attenuation : float
+        The desired stopband attenuation in dB.
+    num_bands : int
+        Number of bands for the multiband filter system.
+    filter_length : int, optional
+        Desired length of the filter. If not provided, it's computed based on the given specs.
+    
+    Returns
+    -------
+    ndarray
+        The optimal prototype filter coefficients.
+    """
+    
+    optimal_angular_cutoff = fmin(lambda angular_cutoff: evaluate_filter_objective(angular_cutoff, attenuation, num_bands, filter_length), 
+                                  1 / num_bands, disp=0)[0]
+    
+    prototype_filter = design_kaiser_lowpass(optimal_angular_cutoff, attenuation, filter_length)
+    return torch.tensor(prototype_filter, dtype=torch.float32)
+
+def pad_to_nearest_power_of_two(x):
+    """
+    Pads the input tensor 'x' on both sides such that its last dimension 
+    becomes the nearest larger power of two.
+    
+    Parameters:
+    -----------
+    x : torch.Tensor
+        The input tensor to be padded.
+        
+    Returns:
+    --------
+    torch.Tensor
+        The padded tensor.
+    """
+    current_length = x.shape[-1]
+    target_length = 2**math.ceil(math.log2(current_length))
+    
+    total_padding = target_length - current_length
+    left_padding = total_padding // 2
+    right_padding = total_padding - left_padding
+    
+    return nn.functional.pad(x, (left_padding, right_padding))
+
+def apply_alias_cancellation(x):
+    """
+    Applies alias cancellation by inverting the sign of every 
+    second element of every second row, starting from the second 
+    row's first element in a tensor.
+    
+    This operation helps ensure that the aliasing introduced in 
+    each band during the decomposition will be counteracted during 
+    the reconstruction.
+    
+    Parameters:
+    -----------
+    x : torch.Tensor
+        The input tensor.
+        
+    Returns:
+    --------
+    torch.Tensor
+        Tensor with specific elements' sign inverted for alias cancellation.
+    """
+    
+    # Create a mask of the same shape as 'x', initialized with all ones
+    mask = torch.ones_like(x)
+    
+    # Update specific elements in the mask to -1 to perform inversion
+    mask[..., 1::2, ::2] = -1
+    
+    # Apply the mask to the input tensor 'x'
+    return x * mask
+
+def ensure_odd_length(tensor):
+    """
+    Pads the last dimension of a tensor to ensure its size is odd.
+    
+    Parameters:
+    -----------
+    tensor : torch.Tensor
+        Input tensor whose last dimension might need padding.
+        
+    Returns:
+    --------
+    torch.Tensor
+        The original tensor if its last dimension was already odd, 
+        or the padded tensor with an odd-sized last dimension.
+    """
+    
+    last_dim_size = tensor.shape[-1]
+    
+    if last_dim_size % 2 == 0:
+        tensor = nn.functional.pad(tensor, (0, 1))
+    
+    return tensor
+
+def polyphase_analysis(signal, filter_bank):
+    """
+    Applies the polyphase method to efficiently analyze the signal using a filter bank.
+
+    Parameters:
+    -----------
+    signal : torch.Tensor
+        Input signal tensor with shape (Batch x Channels x Length).
+    
+    filter_bank : torch.Tensor
+        Filter bank tensor with shape (Bands x Length).
+    
+    Returns:
+    --------
+    torch.Tensor
+        Signal split into sub-bands. (Batch x Channels x Bands x Length)
+    """
+    
+    num_bands = filter_bank.shape[0]
+    num_channels = signal.shape[1]
+    
+    # Rearrange signal for polyphase processing. 
+    # Also combine Batch x Channel into one dimension for now. 
+    #signal = rearrange(signal, "b c (t n) -> b (c n) t", n=num_bands)
+    signal = rearrange(signal, "b c (t n) -> (b c) n t", n=num_bands)
+    
+    # Rearrange the filter bank for matching signal shape
+    filter_bank = rearrange(filter_bank, "c (t n) -> c n t", n=num_bands)
+    
+    # Apply convolution with appropriate padding to maintain spatial dimensions
+    padding = filter_bank.shape[-1] // 2
+    filtered_signal = nn.functional.conv1d(signal, filter_bank, padding=padding)
+    
+    # Truncate the last dimension post-convolution to adjust the output shape
+    filtered_signal = filtered_signal[..., :-1]
+    # Rearrange the first dimension back into Batch x Channels
+    filtered_signal = rearrange(filtered_signal, "(b c) n t -> b c n t", c=num_channels)
+
+    return filtered_signal
+
+def polyphase_synthesis(signal, filter_bank):
+    """
+    Polyphase Inverse: Apply polyphase filter bank synthesis to reconstruct a signal. 
+    
+    Parameters
+    ----------
+    signal : torch.Tensor
+        Decomposed signal to be reconstructed (shape: Batch x Channels x Bands x Length).
+    
+    filter_bank : torch.Tensor
+        Analysis filter bank (shape: Bands x Length).
+    
+    should_rearrange : bool, optional
+        Flag to determine if the filters should be rearranged for polyphase synthesis. Default is True.
+
+    Returns
+    -------
+    torch.Tensor
+        Reconstructed signal (shape: Batch x Channels X Length)
+    """
+    
+    num_bands = filter_bank.shape[0]
+    num_channels = signal.shape[1]
+
+    # Rearrange the filter bank 
+    filter_bank = filter_bank.flip(-1)
+    filter_bank = rearrange(filter_bank, "c (t n) -> n c t", n=num_bands)
+
+    # Combine Batch x Channels into one dimension for now.
+    signal = rearrange(signal, "b c n t -> (b c) n t")
+
+    # Apply convolution with appropriate padding
+    padding_amount = filter_bank.shape[-1] // 2 + 1
+    reconstructed_signal = nn.functional.conv1d(signal, filter_bank, padding=int(padding_amount))
+    
+    # Scale the result
+    reconstructed_signal = reconstructed_signal[..., :-1] * num_bands
+
+    # Reorganize the output and truncate
+    reconstructed_signal = reconstructed_signal.flip(1)
+    reconstructed_signal = rearrange(reconstructed_signal, "(b c) n t -> b c (t n)", c=num_channels, n=num_bands)
+    reconstructed_signal = reconstructed_signal[..., 2 * filter_bank.shape[1]:]
+    
+    return reconstructed_signal

stable_audio_tools/models/pretrained.py

@@ -0,0 +1,25 @@
+import json
+
+from .factory import create_model_from_config
+from .utils import load_ckpt_state_dict
+
+from huggingface_hub import hf_hub_download
+
+def get_pretrained_model(name: str):
+    
+    model_config_path = hf_hub_download(name, filename="config.json", repo_type='model')
+
+    with open(model_config_path) as f:
+        model_config = json.load(f)
+
+    model = create_model_from_config(model_config)
+
+    # Try to download the model.safetensors file first, if it doesn't exist, download the model.ckpt file
+    try:
+        model_ckpt_path = hf_hub_download(name, filename="model.safetensors", repo_type='model')
+    except Exception as e:
+        model_ckpt_path = hf_hub_download(name, filename="model.ckpt", repo_type='model')
+
+    model.load_state_dict(load_ckpt_state_dict(model_ckpt_path))
+
+    return model, model_config

stable_audio_tools/models/pretransforms.py

@@ -0,0 +1,258 @@
+import torch
+from einops import rearrange
+from torch import nn
+
+class Pretransform(nn.Module):
+    def __init__(self, enable_grad, io_channels, is_discrete):
+        super().__init__()
+
+        self.is_discrete = is_discrete
+        self.io_channels = io_channels
+        self.encoded_channels = None
+        self.downsampling_ratio = None
+
+        self.enable_grad = enable_grad
+
+    def encode(self, x):
+        raise NotImplementedError
+
+    def decode(self, z):
+        raise NotImplementedError
+    
+    def tokenize(self, x):
+        raise NotImplementedError
+    
+    def decode_tokens(self, tokens):
+        raise NotImplementedError
+
+class AutoencoderPretransform(Pretransform):
+    def __init__(self, model, scale=1.0, model_half=False, iterate_batch=False, chunked=False):
+        super().__init__(enable_grad=False, io_channels=model.io_channels, is_discrete=model.bottleneck is not None and model.bottleneck.is_discrete)
+        self.model = model
+        self.model.requires_grad_(False).eval()
+        self.scale=scale
+        self.downsampling_ratio = model.downsampling_ratio
+        self.io_channels = model.io_channels
+        self.sample_rate = model.sample_rate
+        
+        self.model_half = model_half
+        self.iterate_batch = iterate_batch
+
+        self.encoded_channels = model.latent_dim
+
+        self.chunked = chunked
+        self.num_quantizers = model.bottleneck.num_quantizers if model.bottleneck is not None and model.bottleneck.is_discrete else None
+        self.codebook_size = model.bottleneck.codebook_size if model.bottleneck is not None and model.bottleneck.is_discrete else None
+
+        if self.model_half:
+            self.model.half()
+    
+    def encode(self, x, **kwargs):
+        
+        if self.model_half:
+            x = x.half()
+            self.model.to(torch.float16)
+
+        encoded = self.model.encode_audio(x, chunked=self.chunked, iterate_batch=self.iterate_batch, **kwargs)
+
+        if self.model_half:
+            encoded = encoded.float()
+
+        return encoded / self.scale
+
+    def decode(self, z, **kwargs):
+        z = z * self.scale
+
+        if self.model_half:
+            z = z.half()
+            self.model.to(torch.float16)
+
+        decoded = self.model.decode_audio(z, chunked=self.chunked, iterate_batch=self.iterate_batch, **kwargs)
+
+        if self.model_half:
+            decoded = decoded.float()
+
+        return decoded
+    
+    def tokenize(self, x, **kwargs):
+        assert self.model.is_discrete, "Cannot tokenize with a continuous model"
+
+        _, info = self.model.encode(x, return_info = True, **kwargs)
+
+        return info[self.model.bottleneck.tokens_id]
+    
+    def decode_tokens(self, tokens, **kwargs):
+        assert self.model.is_discrete, "Cannot decode tokens with a continuous model"
+
+        return self.model.decode_tokens(tokens, **kwargs)
+    
+    def load_state_dict(self, state_dict, strict=True):
+        self.model.load_state_dict(state_dict, strict=strict)
+
+class WaveletPretransform(Pretransform):
+    def __init__(self, channels, levels, wavelet):
+        super().__init__(enable_grad=False, io_channels=channels, is_discrete=False)
+
+        from .wavelets import WaveletEncode1d, WaveletDecode1d
+
+        self.encoder = WaveletEncode1d(channels, levels, wavelet)
+        self.decoder = WaveletDecode1d(channels, levels, wavelet)
+
+        self.downsampling_ratio = 2 ** levels
+        self.io_channels = channels
+        self.encoded_channels = channels * self.downsampling_ratio
+    
+    def encode(self, x):
+        return self.encoder(x)
+    
+    def decode(self, z):
+        return self.decoder(z)
+    
+class PQMFPretransform(Pretransform):
+    def __init__(self, attenuation=100, num_bands=16):
+        # TODO: Fix PQMF to take in in-channels
+        super().__init__(enable_grad=False, io_channels=1, is_discrete=False)
+        from .pqmf import PQMF
+        self.pqmf = PQMF(attenuation, num_bands)
+
+
+    def encode(self, x):
+        # x is (Batch x Channels x Time)
+        x = self.pqmf.forward(x)
+        # pqmf.forward returns (Batch x Channels x Bands x Time)
+        # but Pretransform needs Batch x Channels x Time
+        # so concatenate channels and bands into one axis
+        return rearrange(x, "b c n t -> b (c n) t")
+
+    def decode(self, x):
+        # x is (Batch x (Channels Bands) x Time), convert back to (Batch x Channels x Bands x Time) 
+        x = rearrange(x, "b (c n) t -> b c n t", n=self.pqmf.num_bands)
+        # returns (Batch x Channels x Time) 
+        return self.pqmf.inverse(x)
+        
+class PretrainedDACPretransform(Pretransform):
+    def __init__(self, model_type="44khz", model_bitrate="8kbps", scale=1.0, quantize_on_decode: bool = True, chunked=True):
+        super().__init__(enable_grad=False, io_channels=1, is_discrete=True)
+        
+        import dac
+        
+        model_path = dac.utils.download(model_type=model_type, model_bitrate=model_bitrate)
+        
+        self.model = dac.DAC.load(model_path)
+
+        self.quantize_on_decode = quantize_on_decode
+
+        if model_type == "44khz":
+            self.downsampling_ratio = 512
+        else:
+            self.downsampling_ratio = 320
+
+        self.io_channels = 1
+
+        self.scale = scale
+
+        self.chunked = chunked
+
+        self.encoded_channels = self.model.latent_dim
+
+        self.num_quantizers = self.model.n_codebooks
+
+        self.codebook_size = self.model.codebook_size
+
+    def encode(self, x):
+
+        latents = self.model.encoder(x)
+
+        if self.quantize_on_decode:
+            output = latents
+        else:
+            z, _, _, _, _ = self.model.quantizer(latents, n_quantizers=self.model.n_codebooks)
+            output = z
+        
+        if self.scale != 1.0:
+            output = output / self.scale
+        
+        return output
+
+    def decode(self, z):
+        
+        if self.scale != 1.0:
+            z = z * self.scale
+
+        if self.quantize_on_decode:
+            z, _, _, _, _ = self.model.quantizer(z, n_quantizers=self.model.n_codebooks)
+
+        return self.model.decode(z)
+
+    def tokenize(self, x):
+        return self.model.encode(x)[1]
+    
+    def decode_tokens(self, tokens):
+        latents = self.model.quantizer.from_codes(tokens)
+        return self.model.decode(latents)
+    
+class AudiocraftCompressionPretransform(Pretransform):
+    def __init__(self, model_type="facebook/encodec_32khz", scale=1.0, quantize_on_decode: bool = True):
+        super().__init__(enable_grad=False, io_channels=1, is_discrete=True)
+        
+        try:
+            from audiocraft.models import CompressionModel
+        except ImportError:
+            raise ImportError("Audiocraft is not installed. Please install audiocraft to use Audiocraft models.")
+               
+        self.model = CompressionModel.get_pretrained(model_type)
+
+        self.quantize_on_decode = quantize_on_decode
+
+        self.downsampling_ratio = round(self.model.sample_rate / self.model.frame_rate)
+
+        self.sample_rate = self.model.sample_rate
+
+        self.io_channels = self.model.channels
+
+        self.scale = scale
+
+        #self.encoded_channels = self.model.latent_dim
+
+        self.num_quantizers = self.model.num_codebooks
+
+        self.codebook_size = self.model.cardinality
+
+        self.model.to(torch.float16).eval().requires_grad_(False)
+
+    def encode(self, x):
+
+        assert False, "Audiocraft compression models do not support continuous encoding"
+
+        # latents = self.model.encoder(x)
+
+        # if self.quantize_on_decode:
+        #     output = latents
+        # else:
+        #     z, _, _, _, _ = self.model.quantizer(latents, n_quantizers=self.model.n_codebooks)
+        #     output = z
+        
+        # if self.scale != 1.0:
+        #     output = output / self.scale
+        
+        # return output
+
+    def decode(self, z):
+        
+        assert False, "Audiocraft compression models do not support continuous decoding"
+
+        # if self.scale != 1.0:
+        #     z = z * self.scale
+
+        # if self.quantize_on_decode:
+        #     z, _, _, _, _ = self.model.quantizer(z, n_quantizers=self.model.n_codebooks)
+
+        # return self.model.decode(z)
+
+    def tokenize(self, x):
+        with torch.cuda.amp.autocast(enabled=False):
+            return self.model.encode(x.to(torch.float16))[0]
+    
+    def decode_tokens(self, tokens):
+        with torch.cuda.amp.autocast(enabled=False):
+            return self.model.decode(tokens)

stable_audio_tools/models/temptransformer.py

@@ -0,0 +1,190 @@
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange
+
+class Residual(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+    def forward(self, x, **kwargs):
+        return self.fn(x, **kwargs) + x
+
+class SA_PreNorm(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.norm = nn.LayerNorm(dim)
+        self.fn = fn
+    def forward(self, x, **kwargs):
+        return self.fn(self.norm(x), **kwargs)
+
+class SA_FeedForward(nn.Module):
+    def __init__(self, dim, hidden_dim, dropout = 0.):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(dim, hidden_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim, dim),
+            nn.Dropout(dropout)
+        )
+    def forward(self, x):
+        return self.net(x)
+
+class SA_Attention(nn.Module):
+    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        project_out = not (heads == 1 and dim_head == dim)
+
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, dim),
+            nn.Dropout(dropout)
+        ) if project_out else nn.Identity()
+
+    def forward(self, x):
+        b, n, _, h = *x.shape, self.heads
+        qkv = self.to_qkv(x).chunk(3, dim = -1)
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
+
+        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
+
+        attn = dots.softmax(dim=-1)
+
+        out = einsum('b h i j, b h j d -> b h i d', attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        out =  self.to_out(out)
+        return out
+
+
+class ReAttention(nn.Module):
+    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
+
+        self.reattn_weights = nn.Parameter(torch.randn(heads, heads))
+
+        self.reattn_norm = nn.Sequential(
+            Rearrange('b h i j -> b i j h'),
+            nn.LayerNorm(heads),
+            Rearrange('b i j h -> b h i j')
+        )
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, dim),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x):
+        b, n, _, h = *x.shape, self.heads
+        qkv = self.to_qkv(x).chunk(3, dim = -1)
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
+
+        # attention
+
+        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
+        attn = dots.softmax(dim=-1)
+
+        # re-attention
+
+        attn = einsum('b h i j, h g -> b g i j', attn, self.reattn_weights)
+        attn = self.reattn_norm(attn)
+
+        # aggregate and out
+
+        out = einsum('b h i j, b h j d -> b h i d', attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        out =  self.to_out(out)
+        return out
+    
+class LeFF(nn.Module):
+    
+    def __init__(self, dim = 192, scale = 4, depth_kernel = 3):
+        super().__init__()
+        
+        scale_dim = dim*scale
+        self.up_proj = nn.Sequential(nn.Linear(dim, scale_dim),
+                                    Rearrange('b n c -> b c n'),
+                                    nn.BatchNorm1d(scale_dim),
+                                    nn.GELU(),
+                                    Rearrange('b c (h w) -> b c h w', h=14, w=14)
+                                    )
+        
+        self.depth_conv =  nn.Sequential(nn.Conv2d(scale_dim, scale_dim, kernel_size=depth_kernel, padding=1, groups=scale_dim, bias=False),
+                          nn.BatchNorm2d(scale_dim),
+                          nn.GELU(),
+                          Rearrange('b c h w -> b (h w) c', h=14, w=14)
+                          )
+        
+        self.down_proj = nn.Sequential(nn.Linear(scale_dim, dim),
+                                    Rearrange('b n c -> b c n'),
+                                    nn.BatchNorm1d(dim),
+                                    nn.GELU(),
+                                    Rearrange('b c n -> b n c')
+                                    )
+        
+    def forward(self, x):
+        x = self.up_proj(x)
+        x = self.depth_conv(x)
+        x = self.down_proj(x)
+        return x
+    
+    
+class LCAttention(nn.Module):
+    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        project_out = not (heads == 1 and dim_head == dim)
+
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, dim),
+            nn.Dropout(dropout)
+        ) if project_out else nn.Identity()
+
+    def forward(self, x):
+        b, n, _, h = *x.shape, self.heads
+        qkv = self.to_qkv(x).chunk(3, dim = -1)
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
+        q = q[:, :, -1, :].unsqueeze(2) # Only Lth element use as query
+
+        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
+
+        attn = dots.softmax(dim=-1)
+
+        out = einsum('b h i j, b h j d -> b h i d', attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        out =  self.to_out(out)
+        return out
+
+class SA_Transformer(nn.Module):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+        self.norm = nn.LayerNorm(dim)
+        for _ in range(depth):
+            self.layers.append(nn.ModuleList([
+                SA_PreNorm(dim, SA_Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
+                SA_PreNorm(dim, SA_FeedForward(dim, mlp_dim, dropout = dropout))
+            ]))
+
+    def forward(self, x):
+        for attn, ff in self.layers:
+            x = attn(x) + x
+            x = ff(x) + x
+        return self.norm(x)

stable_audio_tools/models/transformer.py

@@ -0,0 +1,812 @@
+from functools import reduce, partial
+from packaging import version
+
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from torch.cuda.amp import autocast
+from typing import Callable, Literal
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
+        
+try:
+    from flash_attn import flash_attn_func, flash_attn_kvpacked_func
+except ImportError as e:
+    print(e)
+    print('flash_attn not installed, disabling Flash Attention')
+    flash_attn_kvpacked_func = None
+    flash_attn_func = None
+
+try:
+    import natten
+except ImportError:
+    natten = None
+
+def checkpoint(function, *args, **kwargs):
+    kwargs.setdefault("use_reentrant", False)
+    return torch.utils.checkpoint.checkpoint(function, *args, **kwargs)
+
+
+# Copied and modified from https://github.com/lucidrains/x-transformers/blob/main/x_transformers/attend.py under MIT License
+# License can be found in LICENSES/LICENSE_XTRANSFORMERS.txt
+
+def create_causal_mask(i, j, device):
+    return torch.ones((i, j), device = device, dtype = torch.bool).triu(j - i + 1)
+
+def or_reduce(masks):
+    head, *body = masks
+    for rest in body:
+        head = head | rest
+    return head
+
+# positional embeddings
+
+class AbsolutePositionalEmbedding(nn.Module):
+    def __init__(self, dim, max_seq_len):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.max_seq_len = max_seq_len
+        self.emb = nn.Embedding(max_seq_len, dim)
+
+    def forward(self, x, pos = None, seq_start_pos = None):
+        seq_len, device = x.shape[1], x.device
+        assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}'
+
+        if pos is None:
+            pos = torch.arange(seq_len, device = device)
+
+        if seq_start_pos is not None:
+            pos = (pos - seq_start_pos[..., None]).clamp(min = 0)
+
+        pos_emb = self.emb(pos)
+        pos_emb = pos_emb * self.scale
+        return pos_emb
+
+class ScaledSinusoidalEmbedding(nn.Module):
+    def __init__(self, dim, theta = 10000):
+        super().__init__()
+        assert (dim % 2) == 0, 'dimension must be divisible by 2'
+        self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5)
+
+        half_dim = dim // 2
+        freq_seq = torch.arange(half_dim).float() / half_dim
+        inv_freq = theta ** -freq_seq
+        self.register_buffer('inv_freq', inv_freq, persistent = False)
+
+    def forward(self, x, pos = None, seq_start_pos = None):
+        seq_len, device = x.shape[1], x.device
+
+        if pos is None:
+            pos = torch.arange(seq_len, device = device)
+
+        if seq_start_pos is not None:
+            pos = pos - seq_start_pos[..., None]
+
+        emb = einsum('i, j -> i j', pos, self.inv_freq)
+        emb = torch.cat((emb.sin(), emb.cos()), dim = -1)
+        return emb * self.scale
+    
+class RotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim,
+        use_xpos = False,
+        scale_base = 512,
+        interpolation_factor = 1.,
+        base = 10000,
+        base_rescale_factor = 1.
+    ):
+        super().__init__()
+        # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
+        # has some connection to NTK literature
+        # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+        base *= base_rescale_factor ** (dim / (dim - 2))
+
+        inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer('inv_freq', inv_freq)
+
+        assert interpolation_factor >= 1.
+        self.interpolation_factor = interpolation_factor
+
+        if not use_xpos:
+            self.register_buffer('scale', None)
+            return
+
+        scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
+
+        self.scale_base = scale_base
+        self.register_buffer('scale', scale)
+
+    def forward_from_seq_len(self, seq_len):
+        device = self.inv_freq.device
+
+        t = torch.arange(seq_len, device = device)
+        return self.forward(t)
+
+    @autocast(enabled = False)
+    def forward(self, t):
+        device = self.inv_freq.device
+
+        t = t.to(torch.float32)
+
+        t = t / self.interpolation_factor
+
+        freqs = torch.einsum('i , j -> i j', t, self.inv_freq)
+        freqs = torch.cat((freqs, freqs), dim = -1)
+
+        if self.scale is None:
+            return freqs, 1.
+
+        power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base
+        scale = self.scale ** rearrange(power, 'n -> n 1')
+        scale = torch.cat((scale, scale), dim = -1)
+
+        return freqs, scale
+
+def rotate_half(x):
+    x = rearrange(x, '... (j d) -> ... j d', j = 2)
+    x1, x2 = x.unbind(dim = -2)
+    return torch.cat((-x2, x1), dim = -1)
+
+@autocast(enabled = False)
+def apply_rotary_pos_emb(t, freqs, scale = 1):
+    out_dtype = t.dtype
+
+    # cast to float32 if necessary for numerical stability
+    dtype = reduce(torch.promote_types, (t.dtype, freqs.dtype, torch.float32))
+    rot_dim, seq_len = freqs.shape[-1], t.shape[-2]
+    freqs, t = freqs.to(dtype), t.to(dtype)
+    freqs = freqs[-seq_len:, :]
+
+    if t.ndim == 4 and freqs.ndim == 3:
+        freqs = rearrange(freqs, 'b n d -> b 1 n d')
+
+    # partial rotary embeddings, Wang et al. GPT-J
+    t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:]
+    t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)
+
+    t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype)
+
+    return torch.cat((t, t_unrotated), dim = -1)
+
+# norms
+class LayerNorm(nn.Module):
+    def __init__(self, dim, bias=False, fix_scale=False):
+        """
+        bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less
+        """
+        super().__init__()
+
+        if fix_scale:
+            self.register_buffer("gamma", torch.ones(dim))
+        else:
+            self.gamma = nn.Parameter(torch.ones(dim))
+
+        if bias:
+            self.beta = nn.Parameter(torch.zeros(dim))
+        else:
+            self.register_buffer("beta", torch.zeros(dim))
+
+
+    def forward(self, x):
+        return F.layer_norm(x, x.shape[-1:], weight=self.gamma, bias=self.beta)
+
+# feedforward
+
+class GLU(nn.Module):
+    def __init__(
+        self,
+        dim_in,
+        dim_out,
+        activation: Callable,
+        use_conv = False,
+        conv_kernel_size = 3,
+    ):
+        super().__init__()
+        self.act = activation
+        self.proj = nn.Linear(dim_in, dim_out * 2) if not use_conv else nn.Conv1d(dim_in, dim_out * 2, conv_kernel_size, padding = (conv_kernel_size // 2))
+        self.use_conv = use_conv
+
+    def forward(self, x):
+        if self.use_conv:
+            x = rearrange(x, 'b n d -> b d n')
+            x = self.proj(x)
+            x = rearrange(x, 'b d n -> b n d')
+        else:
+            x = self.proj(x)
+
+        x, gate = x.chunk(2, dim = -1)
+        return x * self.act(gate)
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim,
+        dim_out = None,
+        mult = 4,
+        no_bias = False,
+        glu = True,
+        use_conv = False,
+        conv_kernel_size = 3,
+        zero_init_output = True,
+    ):
+        super().__init__()
+        inner_dim = int(dim * mult)
+
+        # Default to SwiGLU
+
+        activation = nn.SiLU()
+
+        dim_out = dim if dim_out is None else dim_out
+
+        if glu:
+            linear_in = GLU(dim, inner_dim, activation)
+        else:
+            linear_in = nn.Sequential(
+                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+                nn.Linear(dim, inner_dim, bias = not no_bias) if not use_conv else nn.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias),
+                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+                activation
+            )
+
+        linear_out = nn.Linear(inner_dim, dim_out, bias = not no_bias) if not use_conv else nn.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias)
+
+        # init last linear layer to 0
+        if zero_init_output:
+            nn.init.zeros_(linear_out.weight)
+            if not no_bias:
+                nn.init.zeros_(linear_out.bias)
+
+
+        self.ff = nn.Sequential(
+            linear_in,
+            Rearrange('b d n -> b n d') if use_conv else nn.Identity(),
+            linear_out,
+            Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+        )
+
+    def forward(self, x):
+        return self.ff(x)
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        dim_heads = 64,
+        dim_context = None,
+        causal = False,
+        zero_init_output=True,
+        qk_norm: Literal['l2', 'ln', 'none'] = 'none',
+        natten_kernel_size = None
+    ):
+        super().__init__()
+        self.dim = dim
+        self.dim_heads = dim_heads
+        self.causal = causal
+
+        dim_kv = dim_context if dim_context is not None else dim
+        
+        self.num_heads = dim // dim_heads
+        self.kv_heads = dim_kv // dim_heads
+
+        if dim_context is not None:
+            self.to_q = nn.Linear(dim, dim, bias=False)
+            self.to_kv = nn.Linear(dim_kv, dim_kv * 2, bias=False)
+        else:
+            self.to_qkv = nn.Linear(dim, dim * 3, bias=False)
+
+        self.to_out = nn.Linear(dim, dim, bias=False)
+
+        if zero_init_output:
+            nn.init.zeros_(self.to_out.weight)
+
+        self.qk_norm = qk_norm
+
+        if self.qk_norm == "ln":
+            self.q_norm = nn.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6)
+            self.k_norm = nn.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6)
+
+        # Using 1d neighborhood attention
+        self.natten_kernel_size = natten_kernel_size
+        if natten_kernel_size is not None:
+            return
+
+        self.use_pt_flash = torch.cuda.is_available() and version.parse(torch.__version__) >= version.parse('2.0.0')
+
+        self.use_fa_flash = torch.cuda.is_available() and flash_attn_func is not None
+
+        self.sdp_kwargs = dict(
+            enable_flash = True,
+            enable_math = True,
+            enable_mem_efficient = True
+        )
+
+    def flash_attn(
+            self,
+            q, 
+            k, 
+            v,
+            mask = None,
+            causal = None
+    ):
+        batch, heads, q_len, _, k_len, device = *q.shape, k.shape[-2], q.device
+        kv_heads = k.shape[1]
+        # Recommended for multi-query single-key-value attention by Tri Dao
+        # kv shape torch.Size([1, 512, 64]) -> torch.Size([1, 8, 512, 64])
+
+        if heads != kv_heads:
+            # Repeat interleave kv_heads to match q_heads
+            heads_per_kv_head = heads // kv_heads
+            k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
+
+        if k.ndim == 3:
+            k = rearrange(k, 'b ... -> b 1 ...').expand_as(q)
+
+        if v.ndim == 3:
+            v = rearrange(v, 'b ... -> b 1 ...').expand_as(q)
+
+        causal = self.causal if causal is None else causal
+
+        if q_len == 1 and causal:
+            causal = False
+        
+        if mask is not None:
+            assert mask.ndim == 4
+            mask = mask.expand(batch, heads, q_len, k_len)
+
+        # handle kv cache - this should be bypassable in updated flash attention 2
+
+        if k_len > q_len and causal:
+            causal_mask = self.create_causal_mask(q_len, k_len, device = device)
+            if mask is None:
+                mask = ~causal_mask
+            else:
+                mask = mask & ~causal_mask
+            causal = False
+
+        # manually handle causal mask, if another mask was given
+
+        row_is_entirely_masked = None
+
+        if mask is not None and causal:
+            causal_mask = self.create_causal_mask(q_len, k_len, device = device)
+            mask = mask & ~causal_mask
+
+            # protect against an entire row being masked out
+
+            row_is_entirely_masked = ~mask.any(dim = -1)
+            mask[..., 0] = mask[..., 0] | row_is_entirely_masked
+
+            causal = False
+        
+        with torch.backends.cuda.sdp_kernel(**self.sdp_kwargs):
+            out = F.scaled_dot_product_attention(
+                q, k, v,
+                attn_mask = mask,
+                is_causal = causal
+            )
+
+        # for a row that is entirely masked out, should zero out the output of that row token
+
+        if row_is_entirely_masked is not None:
+            out = out.masked_fill(row_is_entirely_masked[..., None], 0.)
+
+        return out
+
+    def forward(
+        self,
+        x,
+        context = None,
+        mask = None,
+        context_mask = None,
+        rotary_pos_emb = None,
+        causal = None
+    ):
+        h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None
+
+        kv_input = context if has_context else x
+
+        if hasattr(self, 'to_q'):
+            # Use separate linear projections for q and k/v
+            q = self.to_q(x)
+            q = rearrange(q, 'b n (h d) -> b h n d', h = h) # [B, 24, 1025, 64]
+
+            k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+            k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v))
+        else:
+            # Use fused linear projection
+            q, k, v = self.to_qkv(x).chunk(3, dim=-1)
+            q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
+        
+        # Normalize q and k for cosine sim attention
+        if self.qk_norm == "l2":
+            q = F.normalize(q, dim=-1)
+            k = F.normalize(k, dim=-1)
+        elif self.qk_norm == "ln":
+            q = self.q_norm(q)
+            k = self.k_norm(k)
+
+        if rotary_pos_emb is not None and not has_context:
+            freqs, _ = rotary_pos_emb
+
+            q_dtype = q.dtype
+            k_dtype = k.dtype
+
+            q = q.to(torch.float32)
+            k = k.to(torch.float32)
+            freqs = freqs.to(torch.float32)
+
+            q = apply_rotary_pos_emb(q, freqs)
+            k = apply_rotary_pos_emb(k, freqs)
+
+            q = q.to(q_dtype)
+            k = k.to(k_dtype)
+        
+        input_mask = context_mask 
+
+        if input_mask is None and not has_context:
+            input_mask = mask
+
+        # determine masking
+        masks = []
+        final_attn_mask = None # The mask that will be applied to the attention matrix, taking all masks into account
+
+        if input_mask is not None:
+            input_mask = rearrange(input_mask, 'b j -> b 1 1 j')
+            masks.append(~input_mask)
+
+        # Other masks will be added here later
+
+        if len(masks) > 0:
+            final_attn_mask = ~or_reduce(masks)
+
+        n, device = q.shape[-2], q.device
+
+        causal = self.causal if causal is None else causal
+
+        if n == 1 and causal:
+            causal = False
+
+        if self.natten_kernel_size is not None:
+            if natten is None:
+                raise ImportError('natten not installed, please install natten to use neighborhood attention')
+            
+            dtype_in = q.dtype
+            q, k, v = map(lambda t: t.to(torch.float32), (q, k, v))
+
+            attn = natten.functional.natten1dqk(q, k, kernel_size = self.natten_kernel_size, dilation=1)
+
+            if final_attn_mask is not None:
+                attn = attn.masked_fill(final_attn_mask, -torch.finfo(attn.dtype).max)
+
+            attn = F.softmax(attn, dim=-1, dtype=torch.float32)
+
+            out = natten.functional.natten1dav(attn, v, kernel_size = self.natten_kernel_size, dilation=1).to(dtype_in)
+
+        # Prioritize Flash Attention 2
+        elif self.use_fa_flash:
+            assert final_attn_mask is None, 'masking not yet supported for Flash Attention 2'
+            # Flash Attention 2 requires FP16 inputs
+            fa_dtype_in = q.dtype
+            q, k, v = map(lambda t: rearrange(t, 'b h n d -> b n h d').to(torch.float16), (q, k, v))
+            
+            out = flash_attn_func(q, k, v, causal = causal)
+            
+            out = rearrange(out.to(fa_dtype_in), 'b n h d -> b h n d')
+
+        # Fall back to PyTorch implementation
+        elif self.use_pt_flash:
+            out = self.flash_attn(q, k, v, causal = causal, mask = final_attn_mask)
+
+        else:
+            # Fall back to custom implementation
+
+            if h != kv_h:
+                # Repeat interleave kv_heads to match q_heads
+                heads_per_kv_head = h // kv_h
+                k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
+
+            scale = 1. / (q.shape[-1] ** 0.5)
+
+            kv_einsum_eq = 'b j d' if k.ndim == 3 else 'b h j d'
+
+            dots = einsum(f'b h i d, {kv_einsum_eq} -> b h i j', q, k) * scale
+            
+            i, j, dtype = *dots.shape[-2:], dots.dtype
+
+            mask_value = -torch.finfo(dots.dtype).max
+
+            if final_attn_mask is not None:
+                dots = dots.masked_fill(~final_attn_mask, mask_value)
+
+            if causal:
+                causal_mask = self.create_causal_mask(i, j, device = device)
+                dots = dots.masked_fill(causal_mask, mask_value)
+
+            attn = F.softmax(dots, dim=-1, dtype=torch.float32)
+            attn = attn.type(dtype)
+
+            out = einsum(f'b h i j, {kv_einsum_eq} -> b h i d', attn, v)
+
+        # merge heads
+        out = rearrange(out, ' b h n d -> b n (h d)')
+
+        # Communicate between heads
+        out = self.to_out(out)
+
+        if mask is not None:
+            mask = rearrange(mask, 'b n -> b n 1')
+            out = out.masked_fill(~mask, 0.)
+
+        return out
+
+
+class ConformerModule(nn.Module):
+    def __init__(
+        self,
+        dim,
+        norm_kwargs = {},
+    ):     
+
+        super().__init__()
+
+        self.dim = dim
+        
+        self.in_norm = LayerNorm(dim, **norm_kwargs)
+        self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
+        self.glu = GLU(dim, dim, nn.SiLU())
+        self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False)
+        self.mid_norm = LayerNorm(dim, **norm_kwargs) # This is a batch norm in the original but I don't like batch norm
+        self.swish = nn.SiLU()
+        self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
+
+    def forward(self, x):
+        x = self.in_norm(x)
+        x = rearrange(x, 'b n d -> b d n')
+        x = self.pointwise_conv(x)
+        x = rearrange(x, 'b d n -> b n d')
+        x = self.glu(x)
+        x = rearrange(x, 'b n d -> b d n')
+        x = self.depthwise_conv(x)
+        x = rearrange(x, 'b d n -> b n d')
+        x = self.mid_norm(x)
+        x = self.swish(x)
+        x = rearrange(x, 'b n d -> b d n')
+        x = self.pointwise_conv_2(x)
+        x = rearrange(x, 'b d n -> b n d')
+
+        return x
+
+class TransformerBlock(nn.Module):
+    def __init__(
+            self,
+            dim,
+            dim_heads = 64,
+            cross_attend = False,
+            dim_context = None,
+            global_cond_dim = None,
+            causal = False,
+            zero_init_branch_outputs = True,
+            conformer = False,
+            layer_ix = -1,
+            remove_norms = False,
+            attn_kwargs = {},
+            ff_kwargs = {},
+            norm_kwargs = {}
+    ):
+        
+        super().__init__()
+        self.dim = dim
+        self.dim_heads = dim_heads
+        self.cross_attend = cross_attend
+        self.dim_context = dim_context
+        self.causal = causal
+
+        self.pre_norm = LayerNorm(dim, **norm_kwargs) if not remove_norms else nn.Identity()
+
+        self.self_attn = Attention(
+            dim,
+            dim_heads = dim_heads,
+            causal = causal,
+            zero_init_output=zero_init_branch_outputs,
+            **attn_kwargs
+        )
+
+        if cross_attend: 
+            self.cross_attend_norm = LayerNorm(dim, **norm_kwargs) if not remove_norms else nn.Identity()
+            self.cross_attn = Attention(
+                dim,
+                dim_heads = dim_heads,
+                dim_context=dim_context,
+                causal = causal,
+                zero_init_output=zero_init_branch_outputs,
+                **attn_kwargs
+            )
+        
+        self.ff_norm = LayerNorm(dim, **norm_kwargs) if not remove_norms else nn.Identity()
+        self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, **ff_kwargs)
+
+        self.layer_ix = layer_ix
+
+        self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None
+
+        self.global_cond_dim = global_cond_dim
+
+        if global_cond_dim is not None:
+            self.to_scale_shift_gate = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(global_cond_dim, dim * 6, bias=False)
+            )
+
+            nn.init.zeros_(self.to_scale_shift_gate[1].weight)
+
+    def forward(
+        self,
+        x,
+        context = None,
+        global_cond=None,
+        mask = None,
+        context_mask = None,
+        rotary_pos_emb = None,
+        adapter=None
+    ):
+
+        if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None: # False
+            
+            scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1)
+
+            # self-attention with adaLN
+            residual = x
+            x = self.pre_norm(x)
+            x = x * (1 + scale_self) + shift_self
+            
+            x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb)
+            x = x * torch.sigmoid(1 - gate_self)
+            x = x + residual
+
+            if context is not None:
+                
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
+                
+            if self.conformer is not None:
+                x = x + self.conformer(x)
+
+            # feedforward with adaLN
+            residual = x
+            x = self.ff_norm(x)
+            x = x * (1 + scale_ff) + shift_ff
+            x = self.ff(x)
+            x = x * torch.sigmoid(1 - gate_ff)
+            x = x + residual
+
+        else:
+            x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb)
+
+            if context is not None:
+
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
+
+            if self.conformer is not None:
+                x = x + self.conformer(x)
+
+            x = x + self.ff(self.ff_norm(x))
+
+        return x
+        
+class ContinuousTransformer(nn.Module):
+    def __init__(
+        self,
+        dim,
+        depth,
+        *,
+        dim_in = None,
+        dim_out = None,
+        dim_heads = 64,
+        cross_attend=False,
+        cond_token_dim=None,
+        global_cond_dim=None,
+        causal=False,
+        rotary_pos_emb=True,
+        zero_init_branch_outputs=True,
+        conformer=False,
+        use_sinusoidal_emb=False,
+        use_abs_pos_emb=False,
+        abs_pos_emb_max_length=10000,
+        **kwargs
+        ):
+
+        super().__init__()
+
+        self.dim = dim
+        self.depth = depth
+        self.causal = causal
+        self.layers = nn.ModuleList([])
+
+        self.project_in = nn.Linear(dim_in, dim, bias=False) if dim_in is not None else nn.Identity()
+        self.project_out = nn.Linear(dim, dim_out, bias=False) if dim_out is not None else nn.Identity()
+
+        if rotary_pos_emb:
+            self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32))
+        else:
+            self.rotary_pos_emb = None
+
+        self.use_sinusoidal_emb = use_sinusoidal_emb
+        if use_sinusoidal_emb:
+            self.pos_emb = ScaledSinusoidalEmbedding(dim)
+
+        self.use_abs_pos_emb = use_abs_pos_emb
+        if use_abs_pos_emb:
+            self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length)
+
+        for i in range(depth):
+            self.layers.append(
+                TransformerBlock(
+                    dim,
+                    dim_heads = dim_heads,
+                    cross_attend = cross_attend,
+                    dim_context = cond_token_dim,
+                    global_cond_dim = global_cond_dim,
+                    causal = causal,
+                    zero_init_branch_outputs = zero_init_branch_outputs,
+                    conformer=conformer,
+                    layer_ix=i,
+                    **kwargs
+                )
+            )
+        
+    def forward(
+        self,
+        x,
+        mask = None,
+        prepend_embeds = None,
+        prepend_mask = None,
+        global_cond = None,
+        return_info = False,
+        **kwargs
+    ):
+        batch, seq, device = *x.shape[:2], x.device
+
+        info = {
+            "hidden_states": [],
+        }
+
+        x = self.project_in(x)
+
+        if prepend_embeds is not None:
+            prepend_length, prepend_dim = prepend_embeds.shape[1:]
+
+            assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension'
+
+            x = torch.cat((prepend_embeds, x), dim = -2)
+
+            if prepend_mask is not None or mask is not None:
+                mask = mask if mask is not None else torch.ones((batch, seq), device = device, dtype = torch.bool)
+                prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length), device = device, dtype = torch.bool)
+
+                mask = torch.cat((prepend_mask, mask), dim = -1)
+
+        # Attention layers
+        if self.rotary_pos_emb is not None:
+            rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1])
+        else:
+            rotary_pos_emb = None
+
+        if self.use_sinusoidal_emb or self.use_abs_pos_emb:
+            x = x + self.pos_emb(x)
+
+        # Iterate over the transformer layers
+        for index, layer in enumerate(self.layers):
+            x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
+
+            if return_info:
+                info["hidden_states"].append(x)
+
+        x = self.project_out(x)
+
+        if return_info:
+            return x, info
+        
+        return x

stable_audio_tools/models/utils.py

@@ -0,0 +1,92 @@
+import torch
+from safetensors.torch import load_file
+
+from torch.nn.utils import remove_weight_norm
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
+
+
+def load_ckpt_state_dict(ckpt_path):
+    if ckpt_path.endswith(".safetensors"):
+        state_dict = load_file(ckpt_path)
+    else:
+        state_dict = torch.load(ckpt_path, map_location="cpu")["state_dict"]
+    
+    return state_dict
+
+def remove_weight_norm_from_model(model):
+    for module in model.modules():
+        if hasattr(module, "weight"):
+            print(f"Removing weight norm from {module}")
+            remove_weight_norm(module)
+
+    return model
+
+# Sampling functions copied from https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/utils/utils.py under MIT license
+# License can be found in LICENSES/LICENSE_META.txt
+
+def multinomial(input: torch.Tensor, num_samples: int, replacement=False, *, generator=None):
+    """torch.multinomial with arbitrary number of dimensions, and number of candidates on the last dimension.
+
+    Args:
+        input (torch.Tensor): The input tensor containing probabilities.
+        num_samples (int): Number of samples to draw.
+        replacement (bool): Whether to draw with replacement or not.
+    Keywords args:
+        generator (torch.Generator): A pseudorandom number generator for sampling.
+    Returns:
+        torch.Tensor: Last dimension contains num_samples indices
+            sampled from the multinomial probability distribution
+            located in the last dimension of tensor input.
+    """
+
+    if num_samples == 1:
+        q = torch.empty_like(input).exponential_(1, generator=generator)
+        return torch.argmax(input / q, dim=-1, keepdim=True).to(torch.int64)
+
+    input_ = input.reshape(-1, input.shape[-1])
+    output_ = torch.multinomial(input_, num_samples=num_samples, replacement=replacement, generator=generator)
+    output = output_.reshape(*list(input.shape[:-1]), -1)
+    return output
+
+
+def sample_top_k(probs: torch.Tensor, k: int) -> torch.Tensor:
+    """Sample next token from top K values along the last dimension of the input probs tensor.
+
+    Args:
+        probs (torch.Tensor): Input probabilities with token candidates on the last dimension.
+        k (int): The k in “top-k”.
+    Returns:
+        torch.Tensor: Sampled tokens.
+    """
+    top_k_value, _ = torch.topk(probs, k, dim=-1)
+    min_value_top_k = top_k_value[..., [-1]]
+    probs *= (probs >= min_value_top_k).float()
+    probs.div_(probs.sum(dim=-1, keepdim=True))
+    next_token = multinomial(probs, num_samples=1)
+    return next_token
+
+
+def sample_top_p(probs: torch.Tensor, p: float) -> torch.Tensor:
+    """Sample next token from top P probabilities along the last dimension of the input probs tensor.
+
+    Args:
+        probs (torch.Tensor): Input probabilities with token candidates on the last dimension.
+        p (int): The p in “top-p”.
+    Returns:
+        torch.Tensor: Sampled tokens.
+    """
+    probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
+    probs_sum = torch.cumsum(probs_sort, dim=-1)
+    mask = probs_sum - probs_sort > p
+    probs_sort *= (~mask).float()
+    probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
+    next_token = multinomial(probs_sort, num_samples=1)
+    next_token = torch.gather(probs_idx, -1, next_token)
+    return next_token
+
+def next_power_of_two(n):
+    return 2 ** (n - 1).bit_length()
+
+def next_multiple_of_64(n):
+    return ((n + 63) // 64) * 64

stable_audio_tools/models/wavelets.py

@@ -0,0 +1,82 @@
+"""The 1D discrete wavelet transform for PyTorch."""
+
+from einops import rearrange
+import pywt
+import torch
+from torch import nn
+from torch.nn import functional as F
+from typing import Literal
+
+
+def get_filter_bank(wavelet):
+    filt = torch.tensor(pywt.Wavelet(wavelet).filter_bank)
+    if wavelet.startswith("bior") and torch.all(filt[:, 0] == 0):
+        filt = filt[:, 1:]
+    return filt
+
+class WaveletEncode1d(nn.Module):
+    def __init__(self, 
+                 channels, 
+                 levels,
+                 wavelet: Literal["bior2.2", "bior2.4", "bior2.6", "bior2.8", "bior4.4", "bior6.8"] = "bior4.4"):
+        super().__init__()
+        self.wavelet = wavelet
+        self.channels = channels
+        self.levels = levels
+        filt = get_filter_bank(wavelet)
+        assert filt.shape[-1] % 2 == 1
+        kernel = filt[:2, None]
+        kernel = torch.flip(kernel, dims=(-1,))
+        index_i = torch.repeat_interleave(torch.arange(2), channels)
+        index_j = torch.tile(torch.arange(channels), (2,))
+        kernel_final = torch.zeros(channels * 2, channels, filt.shape[-1])
+        kernel_final[index_i * channels + index_j, index_j] = kernel[index_i, 0]
+        self.register_buffer("kernel", kernel_final)
+
+    def forward(self, x):
+        for i in range(self.levels):
+            low, rest = x[:, : self.channels], x[:, self.channels :]
+            pad = self.kernel.shape[-1] // 2
+            low = F.pad(low, (pad, pad), "reflect")
+            low = F.conv1d(low, self.kernel, stride=2)
+            rest = rearrange(
+                rest, "n (c c2) (l l2) -> n (c l2 c2) l", l2=2, c2=self.channels
+            )
+            x = torch.cat([low, rest], dim=1)
+        return x
+
+
+class WaveletDecode1d(nn.Module):
+    def __init__(self, 
+                 channels, 
+                 levels,
+                 wavelet: Literal["bior2.2", "bior2.4", "bior2.6", "bior2.8", "bior4.4", "bior6.8"] = "bior4.4"):
+        super().__init__()
+        self.wavelet = wavelet
+        self.channels = channels
+        self.levels = levels
+        filt = get_filter_bank(wavelet)
+        assert filt.shape[-1] % 2 == 1
+        kernel = filt[2:, None]
+        index_i = torch.repeat_interleave(torch.arange(2), channels)
+        index_j = torch.tile(torch.arange(channels), (2,))
+        kernel_final = torch.zeros(channels * 2, channels, filt.shape[-1])
+        kernel_final[index_i * channels + index_j, index_j] = kernel[index_i, 0]
+        self.register_buffer("kernel", kernel_final)
+
+    def forward(self, x):
+        for i in range(self.levels):
+            low, rest = x[:, : self.channels * 2], x[:, self.channels * 2 :]
+            pad = self.kernel.shape[-1] // 2 + 2
+            low = rearrange(low, "n (l2 c) l -> n c (l l2)", l2=2)
+            low = F.pad(low, (pad, pad), "reflect")
+            low = rearrange(low, "n c (l l2) -> n (l2 c) l", l2=2)
+            low = F.conv_transpose1d(
+                low, self.kernel, stride=2, padding=self.kernel.shape[-1] // 2
+            )
+            low = low[..., pad - 1 : -pad]
+            rest = rearrange(
+                rest, "n (c l2 c2) l -> n (c c2) (l l2)", l2=2, c2=self.channels
+            )
+            x = torch.cat([low, rest], dim=1)
+        return x

stable_audio_tools/training/__init__.py

@@ -0,0 +1 @@
+from .factory import create_training_wrapper_from_config, create_demo_callback_from_config

stable_audio_tools/training/autoencoders.py

@@ -0,0 +1,476 @@
+import torch
+import torchaudio
+import wandb
+from einops import rearrange
+from safetensors.torch import save_file, save_model
+from ema_pytorch import EMA
+from .losses.auraloss import SumAndDifferenceSTFTLoss, MultiResolutionSTFTLoss
+import pytorch_lightning as pl
+from ..models.autoencoders import AudioAutoencoder
+from ..models.discriminators import EncodecDiscriminator, OobleckDiscriminator, DACGANLoss
+from ..models.bottleneck import VAEBottleneck, RVQBottleneck, DACRVQBottleneck, DACRVQVAEBottleneck, RVQVAEBottleneck, WassersteinBottleneck
+from .losses import MultiLoss, AuralossLoss, ValueLoss, L1Loss
+from .utils import create_optimizer_from_config, create_scheduler_from_config
+
+
+from pytorch_lightning.utilities.rank_zero import rank_zero_only
+from aeiou.viz import pca_point_cloud, audio_spectrogram_image, tokens_spectrogram_image
+
+class AutoencoderTrainingWrapper(pl.LightningModule):
+    def __init__(
+            self, 
+            autoencoder: AudioAutoencoder,
+            lr: float = 1e-4,
+            warmup_steps: int = 0,
+            encoder_freeze_on_warmup: bool = False,
+            sample_rate=48000,
+            loss_config: dict = None,
+            optimizer_configs: dict = None,
+            use_ema: bool = True,
+            ema_copy = None,
+            force_input_mono = False,
+            latent_mask_ratio = 0.0,
+            teacher_model: AudioAutoencoder = None
+    ):
+        super().__init__()
+
+        self.automatic_optimization = False
+
+        self.autoencoder = autoencoder
+
+        self.warmed_up = False
+        self.warmup_steps = warmup_steps
+        self.encoder_freeze_on_warmup = encoder_freeze_on_warmup
+        self.lr = lr
+
+        self.force_input_mono = force_input_mono
+
+        self.teacher_model = teacher_model
+
+        if optimizer_configs is None:
+            optimizer_configs ={
+                "autoencoder": {
+                    "optimizer": {
+                        "type": "AdamW",
+                        "config": {
+                            "lr": lr,
+                            "betas": (.8, .99)
+                        }
+                    }
+                },
+                "discriminator": {
+                    "optimizer": {
+                        "type": "AdamW",
+                        "config": {
+                            "lr": lr,
+                            "betas": (.8, .99)
+                        }
+                    }
+                }
+
+            } 
+            
+        self.optimizer_configs = optimizer_configs
+
+        if loss_config is None:
+            scales = [2048, 1024, 512, 256, 128, 64, 32]
+            hop_sizes = []
+            win_lengths = []
+            overlap = 0.75
+            for s in scales:
+                hop_sizes.append(int(s * (1 - overlap)))
+                win_lengths.append(s)
+        
+            loss_config = {
+                "discriminator": {
+                    "type": "encodec",
+                    "config": {
+                        "n_ffts": scales,
+                        "hop_lengths": hop_sizes,
+                        "win_lengths": win_lengths,
+                        "filters": 32
+                    },
+                    "weights": {
+                        "adversarial": 0.1,
+                        "feature_matching": 5.0,
+                    }
+                },
+                "spectral": {
+                    "type": "mrstft",
+                    "config": {
+                        "fft_sizes": scales,
+                        "hop_sizes": hop_sizes,
+                        "win_lengths": win_lengths,
+                        "perceptual_weighting": True
+                    },
+                    "weights": {
+                        "mrstft": 1.0,
+                    }
+                },
+                "time": {
+                    "type": "l1",
+                    "config": {},
+                    "weights": {
+                        "l1": 0.0,
+                    }
+                }
+            }
+        
+        self.loss_config = loss_config
+       
+        # Spectral reconstruction loss
+
+        stft_loss_args = loss_config['spectral']['config']
+
+        if self.autoencoder.out_channels == 2:
+            self.sdstft = SumAndDifferenceSTFTLoss(sample_rate=sample_rate, **stft_loss_args)
+            self.lrstft = MultiResolutionSTFTLoss(sample_rate=sample_rate, **stft_loss_args)
+        else:
+            self.sdstft = MultiResolutionSTFTLoss(sample_rate=sample_rate, **stft_loss_args)
+
+        # Discriminator
+
+        if loss_config['discriminator']['type'] == 'oobleck':
+            self.discriminator = OobleckDiscriminator(**loss_config['discriminator']['config'])
+        elif loss_config['discriminator']['type'] == 'encodec':
+            self.discriminator = EncodecDiscriminator(in_channels=self.autoencoder.out_channels, **loss_config['discriminator']['config'])
+        elif loss_config['discriminator']['type'] == 'dac':
+            self.discriminator = DACGANLoss(channels=self.autoencoder.out_channels, sample_rate=sample_rate, **loss_config['discriminator']['config'])
+
+        self.gen_loss_modules = []
+
+        # Adversarial and feature matching losses
+        self.gen_loss_modules += [
+            ValueLoss(key='loss_adv', weight=self.loss_config['discriminator']['weights']['adversarial'], name='loss_adv'),
+            ValueLoss(key='feature_matching_distance', weight=self.loss_config['discriminator']['weights']['feature_matching'], name='feature_matching'),
+        ]
+
+        if self.teacher_model is not None:
+            # Distillation losses
+
+            stft_loss_weight = self.loss_config['spectral']['weights']['mrstft'] * 0.25
+            self.gen_loss_modules += [
+                AuralossLoss(self.sdstft, 'reals', 'decoded', name='mrstft_loss', weight=stft_loss_weight), # Reconstruction loss
+                AuralossLoss(self.sdstft, 'decoded', 'teacher_decoded', name='mrstft_loss_distill', weight=stft_loss_weight), # Distilled model's decoder is compatible with teacher's decoder
+                AuralossLoss(self.sdstft, 'reals', 'own_latents_teacher_decoded', name='mrstft_loss_own_latents_teacher', weight=stft_loss_weight), # Distilled model's encoder is compatible with teacher's decoder
+                AuralossLoss(self.sdstft, 'reals', 'teacher_latents_own_decoded', name='mrstft_loss_teacher_latents_own', weight=stft_loss_weight) # Teacher's encoder is compatible with distilled model's decoder
+            ]
+
+        else:
+
+            # Reconstruction loss
+            self.gen_loss_modules += [
+                AuralossLoss(self.sdstft, 'reals', 'decoded', name='mrstft_loss', weight=self.loss_config['spectral']['weights']['mrstft']),
+            ]
+
+            if self.autoencoder.out_channels == 2:
+
+                # Add left and right channel reconstruction losses in addition to the sum and difference
+                self.gen_loss_modules += [
+                    AuralossLoss(self.lrstft, 'reals_left', 'decoded_left', name='stft_loss_left', weight=self.loss_config['spectral']['weights']['mrstft']/2),
+                    AuralossLoss(self.lrstft, 'reals_right', 'decoded_right', name='stft_loss_right', weight=self.loss_config['spectral']['weights']['mrstft']/2),
+                ]
+
+            self.gen_loss_modules += [
+                AuralossLoss(self.sdstft, 'reals', 'decoded', name='mrstft_loss', weight=self.loss_config['spectral']['weights']['mrstft']),
+            ]
+
+        if self.loss_config['time']['weights']['l1'] > 0.0:
+            self.gen_loss_modules.append(L1Loss(key_a='reals', key_b='decoded', weight=self.loss_config['time']['weights']['l1'], name='l1_time_loss'))
+
+        if self.autoencoder.bottleneck is not None:
+            self.gen_loss_modules += create_loss_modules_from_bottleneck(self.autoencoder.bottleneck, self.loss_config)
+
+        self.losses_gen = MultiLoss(self.gen_loss_modules)
+
+        self.disc_loss_modules = [
+            ValueLoss(key='loss_dis', weight=1.0, name='discriminator_loss'),
+        ]
+
+        self.losses_disc = MultiLoss(self.disc_loss_modules)
+
+        # Set up EMA for model weights
+        self.autoencoder_ema = None
+        
+        self.use_ema = use_ema
+
+        if self.use_ema:
+            self.autoencoder_ema = EMA(
+                self.autoencoder,
+                ema_model=ema_copy,
+                beta=0.9999,
+                power=3/4,
+                update_every=1,
+                update_after_step=1
+            )
+
+        self.latent_mask_ratio = latent_mask_ratio
+
+    def configure_optimizers(self):
+
+        opt_gen = create_optimizer_from_config(self.optimizer_configs['autoencoder']['optimizer'], self.autoencoder.parameters())
+        opt_disc = create_optimizer_from_config(self.optimizer_configs['discriminator']['optimizer'], self.discriminator.parameters())
+
+        if "scheduler" in self.optimizer_configs['autoencoder'] and "scheduler" in self.optimizer_configs['discriminator']:
+            sched_gen = create_scheduler_from_config(self.optimizer_configs['autoencoder']['scheduler'], opt_gen)
+            sched_disc = create_scheduler_from_config(self.optimizer_configs['discriminator']['scheduler'], opt_disc)
+            return [opt_gen, opt_disc], [sched_gen, sched_disc]
+
+        return [opt_gen, opt_disc]
+  
+    def training_step(self, batch, batch_idx):
+        reals, _ = batch
+
+        # Remove extra dimension added by WebDataset
+        if reals.ndim == 4 and reals.shape[0] == 1:
+            reals = reals[0]
+
+        if self.global_step >= self.warmup_steps:
+            self.warmed_up = True
+
+        loss_info = {}
+
+        loss_info["reals"] = reals
+
+        encoder_input = reals
+
+        if self.force_input_mono and encoder_input.shape[1] > 1:
+            encoder_input = encoder_input.mean(dim=1, keepdim=True)
+
+        loss_info["encoder_input"] = encoder_input
+
+        data_std = encoder_input.std()
+
+        if self.warmed_up and self.encoder_freeze_on_warmup:
+            with torch.no_grad():
+                latents, encoder_info = self.autoencoder.encode(encoder_input, return_info=True)
+        else:
+            latents, encoder_info = self.autoencoder.encode(encoder_input, return_info=True)
+
+        loss_info["latents"] = latents
+
+        loss_info.update(encoder_info)
+
+        # Encode with teacher model for distillation
+        if self.teacher_model is not None:
+            with torch.no_grad():
+                teacher_latents = self.teacher_model.encode(encoder_input, return_info=False)
+                loss_info['teacher_latents'] = teacher_latents
+
+        if self.latent_mask_ratio > 0.0:
+            mask = torch.rand_like(latents) < self.latent_mask_ratio
+            latents = torch.where(mask, torch.zeros_like(latents), latents)
+
+        decoded = self.autoencoder.decode(latents)
+
+        loss_info["decoded"] = decoded
+
+        if self.autoencoder.out_channels == 2:
+            loss_info["decoded_left"] = decoded[:, 0:1, :]
+            loss_info["decoded_right"] = decoded[:, 1:2, :]
+            loss_info["reals_left"] = reals[:, 0:1, :]
+            loss_info["reals_right"] = reals[:, 1:2, :]
+
+        # Distillation
+        if self.teacher_model is not None:
+            with torch.no_grad():
+                teacher_decoded = self.teacher_model.decode(teacher_latents)
+                own_latents_teacher_decoded = self.teacher_model.decode(latents) #Distilled model's latents decoded by teacher
+                teacher_latents_own_decoded = self.autoencoder.decode(teacher_latents) #Teacher's latents decoded by distilled model
+
+                loss_info['teacher_decoded'] = teacher_decoded
+                loss_info['own_latents_teacher_decoded'] = own_latents_teacher_decoded
+                loss_info['teacher_latents_own_decoded'] = teacher_latents_own_decoded
+
+       
+        if self.warmed_up:
+            loss_dis, loss_adv, feature_matching_distance = self.discriminator.loss(reals, decoded)
+        else:
+            loss_dis = torch.tensor(0.).to(reals)
+            loss_adv = torch.tensor(0.).to(reals)
+            feature_matching_distance = torch.tensor(0.).to(reals)
+
+        loss_info["loss_dis"] = loss_dis
+        loss_info["loss_adv"] = loss_adv
+        loss_info["feature_matching_distance"] = feature_matching_distance
+
+        opt_gen, opt_disc = self.optimizers()
+
+        lr_schedulers = self.lr_schedulers()
+
+        sched_gen = None
+        sched_disc = None
+
+        if lr_schedulers is not None:
+            sched_gen, sched_disc = lr_schedulers
+
+        # Train the discriminator
+        if self.global_step % 2 and self.warmed_up:
+            loss, losses = self.losses_disc(loss_info)
+
+            log_dict = {
+                'train/disc_lr': opt_disc.param_groups[0]['lr']
+            }
+
+            opt_disc.zero_grad()
+            self.manual_backward(loss)
+            opt_disc.step()
+
+            if sched_disc is not None:
+                # sched step every step
+                sched_disc.step()
+
+        # Train the generator 
+        else:
+
+            loss, losses = self.losses_gen(loss_info)
+
+            if self.use_ema:
+                self.autoencoder_ema.update()
+
+            opt_gen.zero_grad()
+            self.manual_backward(loss)
+            opt_gen.step()
+
+            if sched_gen is not None:
+                # scheduler step every step
+                sched_gen.step()
+
+            log_dict = {
+                'train/loss': loss.detach(),
+                'train/latent_std': latents.std().detach(),
+                'train/data_std': data_std.detach(),
+                'train/gen_lr': opt_gen.param_groups[0]['lr']
+            }
+
+        for loss_name, loss_value in losses.items():
+            log_dict[f'train/{loss_name}'] = loss_value.detach()
+
+        self.log_dict(log_dict, prog_bar=True, on_step=True)
+
+        return loss
+    
+    def export_model(self, path, use_safetensors=False):
+        if self.autoencoder_ema is not None:
+            model = self.autoencoder_ema.ema_model
+        else:
+            model = self.autoencoder
+            
+        if use_safetensors:
+            save_model(model, path)
+        else:
+            torch.save({"state_dict": model.state_dict()}, path)
+        
+
+class AutoencoderDemoCallback(pl.Callback):
+    def __init__(
+        self, 
+        demo_dl, 
+        demo_every=2000,
+        sample_size=65536,
+        sample_rate=48000
+    ):
+        super().__init__()
+        self.demo_every = demo_every
+        self.demo_samples = sample_size
+        self.demo_dl = iter(demo_dl)
+        self.sample_rate = sample_rate
+        self.last_demo_step = -1
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_end(self, trainer, module, outputs, batch, batch_idx): 
+        if (trainer.global_step - 1) % self.demo_every != 0 or self.last_demo_step == trainer.global_step:
+            return
+        
+        self.last_demo_step = trainer.global_step
+
+        module.eval()
+
+        try:
+            demo_reals, _ = next(self.demo_dl)
+
+            # Remove extra dimension added by WebDataset
+            if demo_reals.ndim == 4 and demo_reals.shape[0] == 1:
+                demo_reals = demo_reals[0]
+
+            encoder_input = demo_reals
+            
+            encoder_input = encoder_input.to(module.device)
+
+            if module.force_input_mono:
+                encoder_input = encoder_input.mean(dim=1, keepdim=True)
+
+            demo_reals = demo_reals.to(module.device)
+
+            with torch.no_grad():
+                if module.use_ema:
+
+                    latents = module.autoencoder_ema.ema_model.encode(encoder_input)
+
+                    fakes = module.autoencoder_ema.ema_model.decode(latents)
+                else:
+                    latents = module.autoencoder.encode(encoder_input)
+
+                    fakes = module.autoencoder.decode(latents)
+
+            #Interleave reals and fakes
+            reals_fakes = rearrange([demo_reals, fakes], 'i b d n -> (b i) d n')
+
+            # Put the demos together
+            reals_fakes = rearrange(reals_fakes, 'b d n -> d (b n)')
+
+            log_dict = {}
+            
+            filename = f'recon_{trainer.global_step:08}.wav'
+            reals_fakes = reals_fakes.to(torch.float32).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+            torchaudio.save(filename, reals_fakes, self.sample_rate)
+
+            log_dict[f'recon'] = wandb.Audio(filename,
+                                                sample_rate=self.sample_rate,
+                                                caption=f'Reconstructed')
+            
+            log_dict[f'embeddings_3dpca'] = pca_point_cloud(latents)
+            log_dict[f'embeddings_spec'] = wandb.Image(tokens_spectrogram_image(latents))
+
+            log_dict[f'recon_melspec_left'] = wandb.Image(audio_spectrogram_image(reals_fakes))
+
+            trainer.logger.experiment.log(log_dict)
+        except Exception as e:
+            print(f'{type(e).__name__}: {e}')
+            raise e
+        finally:
+            module.train()
+
+def create_loss_modules_from_bottleneck(bottleneck, loss_config):
+    losses = []
+    
+    if isinstance(bottleneck, VAEBottleneck) or isinstance(bottleneck, DACRVQVAEBottleneck) or isinstance(bottleneck, RVQVAEBottleneck):
+        try:
+            kl_weight = loss_config['bottleneck']['weights']['kl']
+        except:
+            kl_weight = 1e-6
+
+        kl_loss = ValueLoss(key='kl', weight=kl_weight, name='kl_loss')
+        losses.append(kl_loss)
+
+    if isinstance(bottleneck, RVQBottleneck) or isinstance(bottleneck, RVQVAEBottleneck):
+        quantizer_loss = ValueLoss(key='quantizer_loss', weight=1.0, name='quantizer_loss')
+        losses.append(quantizer_loss)
+
+    if isinstance(bottleneck, DACRVQBottleneck) or isinstance(bottleneck, DACRVQVAEBottleneck):
+        codebook_loss = ValueLoss(key='vq/codebook_loss', weight=1.0, name='codebook_loss')
+        commitment_loss = ValueLoss(key='vq/commitment_loss', weight=0.25, name='commitment_loss')
+        losses.append(codebook_loss)
+        losses.append(commitment_loss)
+
+    if isinstance(bottleneck, WassersteinBottleneck):
+        try:
+            mmd_weight = loss_config['bottleneck']['weights']['mmd']
+        except:
+            mmd_weight = 100
+
+        mmd_loss = ValueLoss(key='mmd', weight=mmd_weight, name='mmd_loss')
+        losses.append(mmd_loss)
+    
+    return losses

stable_audio_tools/training/diffusion.py

@@ -0,0 +1,1656 @@
+import pytorch_lightning as pl
+import sys, gc
+import random
+import torch
+import torchaudio
+import typing as tp
+import wandb
+
+from aeiou.viz import pca_point_cloud, audio_spectrogram_image, tokens_spectrogram_image
+import auraloss
+from ema_pytorch import EMA
+from einops import rearrange
+from safetensors.torch import save_file
+from torch import optim
+from torch.nn import functional as F
+from pytorch_lightning.utilities.rank_zero import rank_zero_only
+
+from ..inference.sampling import get_alphas_sigmas, sample, sample_discrete_euler
+from ..models.diffusion import DiffusionModelWrapper, ConditionedDiffusionModelWrapper
+from ..models.autoencoders import DiffusionAutoencoder
+from ..models.diffusion_prior import PriorType
+from .autoencoders import create_loss_modules_from_bottleneck
+from .losses import AuralossLoss, MSELoss, MultiLoss
+from .utils import create_optimizer_from_config, create_scheduler_from_config
+
+from time import time
+
+
+class Profiler:
+
+    def __init__(self):
+        self.ticks = [[time(), None]]
+
+    def tick(self, msg):
+        self.ticks.append([time(), msg])
+
+    def __repr__(self):
+        rep = 80 * "=" + "\n"
+        for i in range(1, len(self.ticks)):
+            msg = self.ticks[i][1]
+            ellapsed = self.ticks[i][0] - self.ticks[i - 1][0]
+            rep += msg + f": {ellapsed*1000:.2f}ms\n"
+        rep += 80 * "=" + "\n\n\n"
+        return rep
+
+class DiffusionUncondTrainingWrapper(pl.LightningModule):
+    '''
+    Wrapper for training an unconditional audio diffusion model (like Dance Diffusion).
+    '''
+    def __init__(
+            self,
+            model: DiffusionModelWrapper,
+            lr: float = 1e-4,
+            pre_encoded: bool = False
+    ):
+        super().__init__()
+
+        self.diffusion = model
+        
+        self.diffusion_ema = EMA(
+            self.diffusion.model,
+            beta=0.9999,
+            power=3/4,
+            update_every=1,
+            update_after_step=1
+        )
+
+        self.lr = lr
+
+        self.rng = torch.quasirandom.SobolEngine(1, scramble=True)
+
+        loss_modules = [
+            MSELoss("v",
+                     "targets",
+                     weight=1.0,
+                     name="mse_loss"
+                )
+        ]
+
+        self.losses = MultiLoss(loss_modules)
+
+        self.pre_encoded = pre_encoded
+
+    def configure_optimizers(self):
+        return optim.Adam([*self.diffusion.parameters()], lr=self.lr)
+
+    def training_step(self, batch, batch_idx):
+        reals = batch[0]
+
+        if reals.ndim == 4 and reals.shape[0] == 1:
+            reals = reals[0]
+        
+        diffusion_input = reals
+
+        loss_info = {}
+
+        if not self.pre_encoded:
+            loss_info["audio_reals"] = diffusion_input
+
+        if self.diffusion.pretransform is not None:
+            if not self.pre_encoded:
+                with torch.set_grad_enabled(self.diffusion.pretransform.enable_grad):
+                    diffusion_input = self.diffusion.pretransform.encode(diffusion_input)
+            else:            
+                # Apply scale to pre-encoded latents if needed, as the pretransform encode function will not be run
+                if hasattr(self.diffusion.pretransform, "scale") and self.diffusion.pretransform.scale != 1.0:
+                    diffusion_input = diffusion_input / self.diffusion.pretransform.scale
+
+        loss_info["reals"] = diffusion_input
+
+        # Draw uniformly distributed continuous timesteps
+        t = self.rng.draw(reals.shape[0])[:, 0].to(self.device)
+
+        # Calculate the noise schedule parameters for those timesteps
+        alphas, sigmas = get_alphas_sigmas(t)
+
+        # Combine the ground truth data and the noise
+        alphas = alphas[:, None, None]
+        sigmas = sigmas[:, None, None]
+        noise = torch.randn_like(diffusion_input)
+        noised_inputs = diffusion_input * alphas + noise * sigmas
+        targets = noise * alphas - diffusion_input * sigmas
+
+        with torch.cuda.amp.autocast():
+            v = self.diffusion(noised_inputs, t)
+
+            loss_info.update({
+                "v": v,
+                "targets": targets
+            })
+
+            loss, losses = self.losses(loss_info)
+
+        log_dict = {
+            'train/loss': loss.detach(),
+            'train/std_data': diffusion_input.std(),
+        }
+
+        for loss_name, loss_value in losses.items():
+            log_dict[f"train/{loss_name}"] = loss_value.detach()
+
+        self.log_dict(log_dict, prog_bar=True, on_step=True)
+        return loss
+    
+    def on_before_zero_grad(self, *args, **kwargs):
+        self.diffusion_ema.update()
+
+    def export_model(self, path, use_safetensors=False):
+
+        self.diffusion.model = self.diffusion_ema.ema_model
+        
+        if use_safetensors:
+            save_file(self.diffusion.state_dict(), path)
+        else:
+            torch.save({"state_dict": self.diffusion.state_dict()}, path)
+
+class DiffusionUncondDemoCallback(pl.Callback):
+    def __init__(self, 
+                 demo_every=2000,
+                 num_demos=8,
+                 demo_steps=250,
+                 sample_rate=48000
+    ):
+        super().__init__()
+
+        self.demo_every = demo_every
+        self.num_demos = num_demos
+        self.demo_steps = demo_steps
+        self.sample_rate = sample_rate
+        self.last_demo_step = -1
+    
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_end(self, trainer, module, outputs, batch, batch_idx):        
+
+        if (trainer.global_step - 1) % self.demo_every != 0 or self.last_demo_step == trainer.global_step:
+            return
+        
+        self.last_demo_step = trainer.global_step
+
+        demo_samples = module.diffusion.sample_size
+
+        if module.diffusion.pretransform is not None:
+            demo_samples = demo_samples // module.diffusion.pretransform.downsampling_ratio
+
+        noise = torch.randn([self.num_demos, module.diffusion.io_channels, demo_samples]).to(module.device)
+
+        try:
+            with torch.cuda.amp.autocast():
+                fakes = sample(module.diffusion_ema, noise, self.demo_steps, 0)
+
+                if module.diffusion.pretransform is not None:
+                    fakes = module.diffusion.pretransform.decode(fakes)
+
+            # Put the demos together
+            fakes = rearrange(fakes, 'b d n -> d (b n)')
+
+            log_dict = {}
+            
+            filename = f'demo_{trainer.global_step:08}.wav'
+            fakes = fakes.to(torch.float32).div(torch.max(torch.abs(fakes))).mul(32767).to(torch.int16).cpu()
+            torchaudio.save(filename, fakes, self.sample_rate)
+
+            log_dict[f'demo'] = wandb.Audio(filename,
+                                                sample_rate=self.sample_rate,
+                                                caption=f'Reconstructed')
+        
+            log_dict[f'demo_melspec_left'] = wandb.Image(audio_spectrogram_image(fakes))
+
+            trainer.logger.experiment.log(log_dict)
+
+            del fakes
+            
+        except Exception as e:
+            print(f'{type(e).__name__}: {e}')
+        finally:
+            gc.collect()
+            torch.cuda.empty_cache()
+
+class DiffusionCondTrainingWrapper(pl.LightningModule):
+    '''
+    Wrapper for training a conditional audio diffusion model.
+    '''
+    def __init__(
+            self,
+            model: ConditionedDiffusionModelWrapper,
+            lr: float = None,
+            mask_padding: bool = False,
+            mask_padding_dropout: float = 0.0,
+            use_ema: bool = True,
+            log_loss_info: bool = True,
+            optimizer_configs: dict = None,
+            pre_encoded: bool = False,
+            cfg_dropout_prob = 0.1,
+            timestep_sampler: tp.Literal["uniform", "logit_normal"] = "uniform",
+    ):
+        super().__init__()
+
+        self.diffusion = model
+
+        if use_ema:
+            self.diffusion_ema = EMA(
+                self.diffusion.model,
+                beta=0.9999,
+                power=3/4,
+                update_every=1,
+                update_after_step=1,
+                include_online_model=False
+            )
+        else:
+            self.diffusion_ema = None
+
+        self.mask_padding = mask_padding
+        self.mask_padding_dropout = mask_padding_dropout
+
+        self.cfg_dropout_prob = cfg_dropout_prob
+
+        self.rng = torch.quasirandom.SobolEngine(1, scramble=True)
+
+        self.timestep_sampler = timestep_sampler
+
+        self.diffusion_objective = model.diffusion_objective
+
+        if 'av_loss' in optimizer_configs and optimizer_configs['av_loss']['if_add_av_loss']:
+            av_align_weight = optimizer_configs['av_loss']['config']['weight']
+            self.loss_modules = [
+                MSELoss("output", 
+                    "targets", 
+                    weight=1.0 - av_align_weight, 
+                    mask_key="padding_mask" if self.mask_padding else None, 
+                    name="mse_loss"
+                )  
+            ]     
+        else:       
+            self.loss_modules = [
+                MSELoss("output", 
+                    "targets", 
+                    weight=1.0, 
+                    mask_key="padding_mask" if self.mask_padding else None, 
+                    name="mse_loss"
+                )
+            ]
+
+
+        self.losses = MultiLoss(self.loss_modules)
+
+        self.log_loss_info = log_loss_info
+
+        assert lr is not None or optimizer_configs is not None, "Must specify either lr or optimizer_configs in training config"
+
+        if optimizer_configs is None:
+            optimizer_configs = {
+                "diffusion": {
+                    "optimizer": {
+                        "type": "Adam",
+                        "config": {
+                            "lr": lr
+                        }
+                    }
+                }
+            }
+        else:
+            if lr is not None:
+                print(f"WARNING: learning_rate and optimizer_configs both specified in config. Ignoring learning_rate and using optimizer_configs.")
+
+        self.optimizer_configs = optimizer_configs
+
+        self.pre_encoded = pre_encoded
+
+    def configure_optimizers(self):
+        diffusion_opt_config = self.optimizer_configs['diffusion']
+        opt_diff = create_optimizer_from_config(diffusion_opt_config['optimizer'], self.diffusion.parameters())
+
+        if "scheduler" in diffusion_opt_config:
+            sched_diff = create_scheduler_from_config(diffusion_opt_config['scheduler'], opt_diff)
+            sched_diff_config = {
+                "scheduler": sched_diff,
+                "interval": "step"
+            }
+            return [opt_diff], [sched_diff_config]
+
+        return [opt_diff]
+
+    def training_step(self, batch, batch_idx):
+
+
+        reals, metadata = batch
+
+        p = Profiler()
+
+        if reals.ndim == 4 and reals.shape[0] == 1:
+            reals = reals[0]
+
+        loss_info = {}
+
+        diffusion_input = reals
+        if not self.pre_encoded:
+            loss_info["audio_reals"] = diffusion_input
+
+        p.tick("setup")
+
+        with torch.cuda.amp.autocast():
+            conditioning = self.diffusion.conditioner(metadata, self.device)
+
+        use_padding_mask = self.mask_padding and random.random() > self.mask_padding_dropout
+
+        # Create batch tensor of attention masks from the "mask" field of the metadata array
+        if use_padding_mask:
+            padding_masks = torch.stack([md["padding_mask"][0] for md in metadata], dim=0).to(self.device)
+
+        p.tick("conditioning")
+
+        if self.diffusion.pretransform is not None:
+            self.diffusion.pretransform.to(self.device)
+
+            if not self.pre_encoded:
+                with torch.cuda.amp.autocast() and torch.set_grad_enabled(self.diffusion.pretransform.enable_grad):
+                    self.diffusion.pretransform.train(self.diffusion.pretransform.enable_grad)
+
+                    diffusion_input = self.diffusion.pretransform.encode(diffusion_input)
+                    p.tick("pretransform")
+
+                    # If mask_padding is on, interpolate the padding masks to the size of the pretransformed input
+                    if use_padding_mask:
+                        padding_masks = F.interpolate(padding_masks.unsqueeze(1).float(), size=diffusion_input.shape[2], mode="nearest").squeeze(1).bool()
+            else:
+                # Apply scale to pre-encoded latents if needed, as the pretransform encode function will not be run
+                if hasattr(self.diffusion.pretransform, "scale") and self.diffusion.pretransform.scale != 1.0:
+                    diffusion_input = diffusion_input / self.diffusion.pretransform.scale
+
+        if self.timestep_sampler == "uniform":
+            # Draw uniformly distributed continuous timesteps
+            t = self.rng.draw(reals.shape[0])[:, 0].to(self.device) # [0.1360, 0.5232]
+        elif self.timestep_sampler == "logit_normal":
+            t = torch.sigmoid(torch.randn(reals.shape[0], device=self.device))
+
+        # Calculate the noise schedule parameters for those timesteps
+        if self.diffusion_objective == "v":
+            alphas, sigmas = get_alphas_sigmas(t)
+        elif self.diffusion_objective == "rectified_flow":
+            alphas, sigmas = 1-t, t
+
+        # Combine the ground truth data and the noise
+        alphas = alphas[:, None, None]
+        sigmas = sigmas[:, None, None]
+        noise = torch.randn_like(diffusion_input)
+        noised_inputs = diffusion_input * alphas + noise * sigmas
+
+        if self.diffusion_objective == "v":
+            targets = noise * alphas - diffusion_input * sigmas
+        elif self.diffusion_objective == "rectified_flow":
+            targets = noise - diffusion_input
+
+        p.tick("noise")
+
+        extra_args = {}
+
+        if use_padding_mask:
+            extra_args["mask"] = padding_masks
+
+        with torch.cuda.amp.autocast():
+            p.tick("amp")
+            output = self.diffusion(noised_inputs, t, cond=conditioning, cfg_dropout_prob = self.cfg_dropout_prob, **extra_args)
+            p.tick("diffusion")
+
+            loss_info.update({
+                "output": output,
+                "targets": targets,
+                "padding_mask": padding_masks if use_padding_mask else None,
+            })
+
+            loss, losses = self.losses(loss_info)
+
+            p.tick("loss")
+
+            if self.log_loss_info:
+                # Loss debugging logs
+                num_loss_buckets = 10
+                bucket_size = 1 / num_loss_buckets
+                loss_all = F.mse_loss(output, targets, reduction="none")
+
+                sigmas = rearrange(self.all_gather(sigmas), "b c n -> (b) c n").squeeze()
+
+                # gather loss_all across all GPUs
+                loss_all = rearrange(self.all_gather(loss_all), "b c n -> (b) c n")
+
+                # Bucket loss values based on corresponding sigma values, bucketing sigma values by bucket_size
+                loss_all = torch.stack([loss_all[(sigmas >= i) & (sigmas < i + bucket_size)].mean() for i in torch.arange(0, 1, bucket_size).to(self.device)])
+
+                # Log bucketed losses with corresponding sigma bucket values, if it's not NaN
+                debug_log_dict = {
+                    f"model/loss_all_{i/num_loss_buckets:.1f}": loss_all[i].detach() for i in range(num_loss_buckets) if not torch.isnan(loss_all[i])
+                }
+
+                self.log_dict(debug_log_dict)
+
+
+        log_dict = {
+            'train/loss': loss.detach(),
+            'train/std_data': diffusion_input.std(),
+            'train/lr': self.trainer.optimizers[0].param_groups[0]['lr']
+        }
+
+        for loss_name, loss_value in losses.items():
+            log_dict[f"train/{loss_name}"] = loss_value.detach()
+
+        self.log_dict(log_dict, prog_bar=True, on_step=True)
+        p.tick("log")
+        #print(f"Profiler: {p}")
+        return loss
+
+    def validation_step(self, batch, batch_idx):
+        reals, metadata = batch
+
+        p = Profiler()
+
+        if reals.ndim == 4 and reals.shape[0] == 1:
+            reals = reals[0]
+
+        loss_info = {}
+
+        diffusion_input = reals
+
+        if not self.pre_encoded:
+            loss_info["audio_reals"] = diffusion_input
+
+        p.tick("setup")
+        with torch.cuda.amp.autocast():
+            conditioning = self.diffusion.conditioner(metadata, self.device)
+            
+        # If mask_padding is on, randomly drop the padding masks to allow for learning silence padding
+        use_padding_mask = self.mask_padding and random.random() > self.mask_padding_dropout
+
+        # Create batch tensor of attention masks from the "mask" field of the metadata array
+        if use_padding_mask:
+            padding_masks = torch.stack([md["padding_mask"][0] for md in metadata], dim=0).to(self.device) # Shape (batch_size, sequence_length)
+
+        p.tick("conditioning")
+
+        if self.diffusion.pretransform is not None:
+            self.diffusion.pretransform.to(self.device)
+
+            if not self.pre_encoded:
+                with torch.cuda.amp.autocast() and torch.set_grad_enabled(self.diffusion.pretransform.enable_grad):
+                    self.diffusion.pretransform.train(self.diffusion.pretransform.enable_grad)
+                    
+                    diffusion_input = self.diffusion.pretransform.encode(diffusion_input)
+                    p.tick("pretransform")
+
+                    # If mask_padding is on, interpolate the padding masks to the size of the pretransformed input
+                    if use_padding_mask:
+                        padding_masks = F.interpolate(padding_masks.unsqueeze(1).float(), size=diffusion_input.shape[2], mode="nearest").squeeze(1).bool()
+            else:            
+                # Apply scale to pre-encoded latents if needed, as the pretransform encode function will not be run
+                if hasattr(self.diffusion.pretransform, "scale") and self.diffusion.pretransform.scale != 1.0:
+                    diffusion_input = diffusion_input / self.diffusion.pretransform.scale
+
+        if self.timestep_sampler == "uniform":
+            # Draw uniformly distributed continuous timesteps
+            t = self.rng.draw(reals.shape[0])[:, 0].to(self.device)
+        elif self.timestep_sampler == "logit_normal":
+            t = torch.sigmoid(torch.randn(reals.shape[0], device=self.device))
+            
+        # Calculate the noise schedule parameters for those timesteps
+        if self.diffusion_objective == "v":
+            alphas, sigmas = get_alphas_sigmas(t)
+        elif self.diffusion_objective == "rectified_flow":
+            alphas, sigmas = 1-t, t
+
+        # Combine the ground truth data and the noise
+        alphas = alphas[:, None, None]
+        sigmas = sigmas[:, None, None]
+        noise = torch.randn_like(diffusion_input)
+        noised_inputs = diffusion_input * alphas + noise * sigmas
+
+        if self.diffusion_objective == "v":
+            targets = noise * alphas - diffusion_input * sigmas
+        elif self.diffusion_objective == "rectified_flow":
+            targets = noise - diffusion_input
+
+        p.tick("noise")
+
+        extra_args = {}
+
+        if use_padding_mask:
+            extra_args["mask"] = padding_masks
+
+        with torch.cuda.amp.autocast():
+            p.tick("amp")
+            
+            output = self.diffusion(noised_inputs, t, cond=conditioning, cfg_dropout_prob = self.cfg_dropout_prob, **extra_args)
+            p.tick("diffusion")
+
+            loss_info.update({
+                "output": output,
+                "targets": targets,
+                "padding_mask": padding_masks if use_padding_mask else None,
+            })
+
+            loss, losses = self.losses(loss_info)
+
+            p.tick("loss")
+
+            if self.log_loss_info:
+                # Loss debugging logs
+                num_loss_buckets = 10
+                bucket_size = 1 / num_loss_buckets
+                loss_all = F.mse_loss(output, targets, reduction="none")
+                # loss_all = F.binary_cross_entropy_with_logits(output, targets, reduction="none")
+                
+
+                sigmas = rearrange(self.all_gather(sigmas), "b c n -> (b) c n").squeeze()
+                # sigmas = rearrange(self.all_gather(sigmas), "w b c n -> (w b) c n").squeeze()
+
+                # gather loss_all across all GPUs
+                loss_all = rearrange(self.all_gather(loss_all), "b c n -> (b) c n")
+                # loss_all = rearrange(self.all_gather(loss_all), "w b c n -> (w b) c n")
+
+                # Bucket loss values based on corresponding sigma values, bucketing sigma values by bucket_size
+                loss_all = torch.stack([loss_all[(sigmas >= i) & (sigmas < i + bucket_size)].mean() for i in torch.arange(0, 1, bucket_size).to(self.device)])
+
+                # Log bucketed losses with corresponding sigma bucket values, if it's not NaN
+                debug_log_dict = {
+                    f"model/loss_all_{i/num_loss_buckets:.1f}": loss_all[i].detach() for i in range(num_loss_buckets) if not torch.isnan(loss_all[i])
+                }
+
+                self.log_dict(debug_log_dict)
+
+
+        log_dict = {
+            'valid/loss': loss.detach(),
+            'valid/std_data': diffusion_input.std(),
+            'valid/lr': self.trainer.optimizers[0].param_groups[0]['lr']
+        }
+
+
+        for loss_name, loss_value in losses.items():
+            log_dict[f"valid/{loss_name}"] = loss_value.detach()
+
+        self.log_dict(log_dict, prog_bar=True, on_step=True)
+        # self.log('val_loss', val_loss, on_epoch=True, on_step=True)
+
+        p.tick("log")
+        #print(f"Profiler: {p}")
+        return loss
+    
+    def on_before_zero_grad(self, *args, **kwargs):
+        if self.diffusion_ema is not None:
+            self.diffusion_ema.update()
+
+    def export_model(self, path, use_safetensors=False):
+        if self.diffusion_ema is not None:
+            self.diffusion.model = self.diffusion_ema.ema_model
+        
+        if use_safetensors:
+            save_file(self.diffusion.state_dict(), path)
+        else:
+            torch.save({"state_dict": self.diffusion.state_dict()}, path)
+
+class DiffusionCondDemoCallback(pl.Callback):
+    def __init__(self, 
+                 demo_every=2000,
+                 num_demos=8,
+                 sample_size=65536,
+                 demo_steps=250,
+                 sample_rate=48000,
+                 demo_conditioning: tp.Optional[tp.Dict[str, tp.Any]] = {},
+                 demo_cfg_scales: tp.Optional[tp.List[int]] = [3, 5, 7],
+                 demo_cond_from_batch: bool = False,
+                 display_audio_cond: bool = False
+    ):
+        super().__init__()
+
+        self.demo_every = demo_every
+        self.num_demos = num_demos
+        self.demo_samples = sample_size
+        self.demo_steps = demo_steps
+        self.sample_rate = sample_rate
+        self.last_demo_step = -1
+        self.demo_conditioning = demo_conditioning
+        self.demo_cfg_scales = demo_cfg_scales
+
+        # If true, the callback will use the metadata from the batch to generate the demo conditioning
+        self.demo_cond_from_batch = demo_cond_from_batch
+
+        # If true, the callback will display the audio conditioning
+        self.display_audio_cond = display_audio_cond
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_end(self, trainer, module: DiffusionCondTrainingWrapper, outputs, batch, batch_idx):        
+
+        if (trainer.global_step - 1) % self.demo_every != 0 or self.last_demo_step == trainer.global_step:
+            return
+
+        module.eval()
+
+        print(f"Generating demo")
+        self.last_demo_step = trainer.global_step
+
+        demo_samples = self.demo_samples
+
+        demo_cond = self.demo_conditioning
+
+        if self.demo_cond_from_batch:
+            # Get metadata from the batch
+            demo_cond = batch[1][:self.num_demos]
+
+        if module.diffusion.pretransform is not None:
+            demo_samples = demo_samples // module.diffusion.pretransform.downsampling_ratio
+
+        noise = torch.randn([self.num_demos, module.diffusion.io_channels, demo_samples]).to(module.device)
+
+        try:
+            print("Getting conditioning")
+            with torch.cuda.amp.autocast():
+                conditioning = module.diffusion.conditioner(demo_cond, module.device)
+
+
+            cond_inputs = module.diffusion.get_conditioning_inputs(conditioning) 
+
+            log_dict = {}
+
+            if self.display_audio_cond:
+                audio_inputs = torch.cat([cond["audio"] for cond in demo_cond], dim=0)
+                audio_inputs = rearrange(audio_inputs, 'b d n -> d (b n)')
+
+                filename = f'demo_audio_cond_{trainer.global_step:08}.wav'
+                audio_inputs = audio_inputs.to(torch.float32).mul(32767).to(torch.int16).cpu()
+                torchaudio.save(filename, audio_inputs, self.sample_rate)
+                log_dict[f'demo_audio_cond'] = wandb.Audio(filename, sample_rate=self.sample_rate, caption="Audio conditioning")
+                log_dict[f"demo_audio_cond_melspec_left"] = wandb.Image(audio_spectrogram_image(audio_inputs))
+                trainer.logger.experiment.log(log_dict)
+
+            for cfg_scale in self.demo_cfg_scales:
+
+                print(f"Generating demo for cfg scale {cfg_scale}")
+                
+                with torch.cuda.amp.autocast():
+                    model = module.diffusion_ema.model if module.diffusion_ema is not None else module.diffusion.model
+
+                    if module.diffusion_objective == "v":
+                        fakes = sample(model, noise, self.demo_steps, 0, **cond_inputs, cfg_scale=cfg_scale, batch_cfg=True)
+                    elif module.diffusion_objective == "rectified_flow":
+                        fakes = sample_discrete_euler(model, noise, self.demo_steps, **cond_inputs, cfg_scale=cfg_scale, batch_cfg=True)
+                    
+                    if module.diffusion.pretransform is not None:
+                        fakes = module.diffusion.pretransform.decode(fakes)
+
+                # Put the demos together
+                fakes = rearrange(fakes, 'b d n -> d (b n)')
+
+                log_dict = {}
+                
+                filename = f'demo_cfg_{cfg_scale}_{trainer.global_step:08}.wav'
+                fakes = fakes.div(torch.max(torch.abs(fakes))).mul(32767).to(torch.int16).cpu()
+                torchaudio.save(filename, fakes, self.sample_rate)
+
+                log_dict[f'demo_cfg_{cfg_scale}'] = wandb.Audio(filename,
+                                                    sample_rate=self.sample_rate,
+                                                    caption=f'Reconstructed')
+            
+                log_dict[f'demo_melspec_left_cfg_{cfg_scale}'] = wandb.Image(audio_spectrogram_image(fakes))
+
+                trainer.logger.experiment.log(log_dict)
+            
+            del fakes
+
+        except Exception as e:
+            raise e
+        finally:
+            gc.collect()
+            torch.cuda.empty_cache()
+            module.train()
+
+class DiffusionCondInpaintTrainingWrapper(pl.LightningModule):
+    '''
+    Wrapper for training a conditional audio diffusion model.
+    '''
+    def __init__(
+            self,
+            model: ConditionedDiffusionModelWrapper,
+            lr: float = 1e-4,
+            max_mask_segments = 10,
+            log_loss_info: bool = False,
+            optimizer_configs: dict = None,
+            use_ema: bool = True,
+            pre_encoded: bool = False,
+            cfg_dropout_prob = 0.1,
+            timestep_sampler: tp.Literal["uniform", "logit_normal"] = "uniform",
+    ):
+        super().__init__()
+
+        self.diffusion = model
+        
+        self.use_ema = use_ema
+
+        if self.use_ema:
+            self.diffusion_ema = EMA(
+                self.diffusion.model,
+                beta=0.9999,
+                power=3/4,
+                update_every=1,
+                update_after_step=1,
+                include_online_model=False
+            )
+        else:
+            self.diffusion_ema = None
+
+        self.cfg_dropout_prob = cfg_dropout_prob
+
+        self.lr = lr
+        self.max_mask_segments = max_mask_segments
+
+        self.rng = torch.quasirandom.SobolEngine(1, scramble=True)
+        
+        self.timestep_sampler = timestep_sampler
+
+        self.diffusion_objective = model.diffusion_objective
+
+        self.loss_modules = [
+            MSELoss("output", 
+                   "targets", 
+                   weight=1.0, 
+                   name="mse_loss"
+            )
+        ]
+
+        self.losses = MultiLoss(self.loss_modules)
+
+        self.log_loss_info = log_loss_info
+
+        assert lr is not None or optimizer_configs is not None, "Must specify either lr or optimizer_configs in training config"
+
+        if optimizer_configs is None:
+            optimizer_configs = {
+                "diffusion": {
+                    "optimizer": {
+                        "type": "Adam",
+                        "config": {
+                            "lr": lr
+                        }
+                    }
+                }
+            }
+        else:
+            if lr is not None:
+                print(f"WARNING: learning_rate and optimizer_configs both specified in config. Ignoring learning_rate and using optimizer_configs.")
+
+        self.optimizer_configs = optimizer_configs
+
+        self.pre_encoded = pre_encoded
+
+    def configure_optimizers(self):
+        diffusion_opt_config = self.optimizer_configs['diffusion']
+        opt_diff = create_optimizer_from_config(diffusion_opt_config['optimizer'], self.diffusion.parameters())
+
+        if "scheduler" in diffusion_opt_config:
+            sched_diff = create_scheduler_from_config(diffusion_opt_config['scheduler'], opt_diff)
+            sched_diff_config = {
+                "scheduler": sched_diff,
+                "interval": "step"
+            }
+            return [opt_diff], [sched_diff_config]
+
+        return [opt_diff]
+
+    def random_mask(self, sequence, max_mask_length):
+        b, _, sequence_length = sequence.size()
+
+        # Create a mask tensor for each batch element
+        masks = []
+
+        for i in range(b):
+            mask_type = random.randint(0, 2)
+
+            if mask_type == 0:  # Random mask with multiple segments
+                num_segments = random.randint(1, self.max_mask_segments)
+                max_segment_length = max_mask_length // num_segments
+
+                segment_lengths = random.sample(range(1, max_segment_length + 1), num_segments)
+               
+                mask = torch.ones((1, 1, sequence_length))
+                for length in segment_lengths:
+                    mask_start = random.randint(0, sequence_length - length)
+                    mask[:, :, mask_start:mask_start + length] = 0
+
+            elif mask_type == 1:  # Full mask
+                mask = torch.zeros((1, 1, sequence_length))
+
+            elif mask_type == 2:  # Causal mask
+                mask = torch.ones((1, 1, sequence_length))
+                mask_length = random.randint(1, max_mask_length)
+                mask[:, :, -mask_length:] = 0
+
+            mask = mask.to(sequence.device)
+            masks.append(mask)
+
+        # Concatenate the mask tensors into a single tensor
+        mask = torch.cat(masks, dim=0).to(sequence.device)
+
+        # Apply the mask to the sequence tensor for each batch element
+        masked_sequence = sequence * mask
+
+        return masked_sequence, mask
+
+    def training_step(self, batch, batch_idx):
+        reals, metadata = batch
+
+        p = Profiler()
+
+        if reals.ndim == 4 and reals.shape[0] == 1:
+            reals = reals[0]
+
+        loss_info = {}
+
+        diffusion_input = reals
+
+        if not self.pre_encoded:
+            loss_info["audio_reals"] = diffusion_input
+
+        p.tick("setup")
+
+        with torch.cuda.amp.autocast():
+            conditioning = self.diffusion.conditioner(metadata, self.device)
+
+        p.tick("conditioning")
+
+        if self.diffusion.pretransform is not None:
+            self.diffusion.pretransform.to(self.device)
+
+            if not self.pre_encoded:
+                with torch.cuda.amp.autocast() and torch.set_grad_enabled(self.diffusion.pretransform.enable_grad):
+                    diffusion_input = self.diffusion.pretransform.encode(diffusion_input)
+                    p.tick("pretransform")
+
+                    # If mask_padding is on, interpolate the padding masks to the size of the pretransformed input
+                    # if use_padding_mask:
+                    #     padding_masks = F.interpolate(padding_masks.unsqueeze(1).float(), size=diffusion_input.shape[2], mode="nearest").squeeze(1).bool()
+            else:            
+                # Apply scale to pre-encoded latents if needed, as the pretransform encode function will not be run
+                if hasattr(self.diffusion.pretransform, "scale") and self.diffusion.pretransform.scale != 1.0:
+                    diffusion_input = diffusion_input / self.diffusion.pretransform.scale
+
+        # Max mask size is the full sequence length
+        max_mask_length = diffusion_input.shape[2]
+
+        # Create a mask of random length for a random slice of the input
+        masked_input, mask = self.random_mask(diffusion_input, max_mask_length)
+
+        conditioning['inpaint_mask'] = [mask]
+        conditioning['inpaint_masked_input'] = [masked_input]
+
+        if self.timestep_sampler == "uniform":
+            # Draw uniformly distributed continuous timesteps
+            t = self.rng.draw(reals.shape[0])[:, 0].to(self.device)
+        elif self.timestep_sampler == "logit_normal":
+            t = torch.sigmoid(torch.randn(reals.shape[0], device=self.device))
+            
+        # Calculate the noise schedule parameters for those timesteps
+        if self.diffusion_objective == "v":
+            alphas, sigmas = get_alphas_sigmas(t)
+        elif self.diffusion_objective == "rectified_flow":
+            alphas, sigmas = 1-t, t
+
+        # Combine the ground truth data and the noise
+        alphas = alphas[:, None, None]
+        sigmas = sigmas[:, None, None]
+        noise = torch.randn_like(diffusion_input)
+        noised_inputs = diffusion_input * alphas + noise * sigmas
+
+        if self.diffusion_objective == "v":
+            targets = noise * alphas - diffusion_input * sigmas
+        elif self.diffusion_objective == "rectified_flow":
+            targets = noise - diffusion_input
+
+        p.tick("noise")
+
+        extra_args = {}
+
+        with torch.cuda.amp.autocast():
+            p.tick("amp")
+            output = self.diffusion(noised_inputs, t, cond=conditioning, cfg_dropout_prob = self.cfg_dropout_prob, **extra_args)
+            p.tick("diffusion")
+
+            loss_info.update({
+                "output": output,
+                "targets": targets,
+            })
+
+            loss, losses = self.losses(loss_info)
+
+            if self.log_loss_info:
+                # Loss debugging logs
+                num_loss_buckets = 10
+                bucket_size = 1 / num_loss_buckets
+                loss_all = F.mse_loss(output, targets, reduction="none")
+
+                sigmas = rearrange(self.all_gather(sigmas), "w b c n -> (w b) c n").squeeze()
+
+                # gather loss_all across all GPUs
+                loss_all = rearrange(self.all_gather(loss_all), "w b c n -> (w b) c n")
+
+                # Bucket loss values based on corresponding sigma values, bucketing sigma values by bucket_size
+                loss_all = torch.stack([loss_all[(sigmas >= i) & (sigmas < i + bucket_size)].mean() for i in torch.arange(0, 1, bucket_size).to(self.device)])
+
+                # Log bucketed losses with corresponding sigma bucket values, if it's not NaN
+                debug_log_dict = {
+                    f"model/loss_all_{i/num_loss_buckets:.1f}": loss_all[i].detach() for i in range(num_loss_buckets) if not torch.isnan(loss_all[i])
+                }
+
+                self.log_dict(debug_log_dict)
+
+        log_dict = {
+            'train/loss': loss.detach(),
+            'train/std_data': diffusion_input.std(),
+            'train/lr': self.trainer.optimizers[0].param_groups[0]['lr']
+        }
+
+        for loss_name, loss_value in losses.items():
+            log_dict[f"train/{loss_name}"] = loss_value.detach()
+
+        self.log_dict(log_dict, prog_bar=True, on_step=True)
+        p.tick("log")
+        #print(f"Profiler: {p}")
+        return loss
+    
+    def on_before_zero_grad(self, *args, **kwargs):
+        if self.diffusion_ema is not None:
+            self.diffusion_ema.update()
+
+    def export_model(self, path, use_safetensors=False):
+        if self.diffusion_ema is not None:
+            self.diffusion.model = self.diffusion_ema.ema_model
+        
+        if use_safetensors:
+            save_file(self.diffusion.state_dict(), path)
+        else:
+            torch.save({"state_dict": self.diffusion.state_dict()}, path)
+
+class DiffusionCondInpaintDemoCallback(pl.Callback):
+    def __init__(
+        self, 
+        demo_dl, 
+        demo_every=2000,
+        demo_steps=250,
+        sample_size=65536,
+        sample_rate=48000,
+        demo_cfg_scales: tp.Optional[tp.List[int]] = [3, 5, 7]
+    ):
+        super().__init__()
+        self.demo_every = demo_every
+        self.demo_steps = demo_steps
+        self.demo_samples = sample_size
+        self.demo_dl = iter(demo_dl)
+        self.sample_rate = sample_rate
+        self.demo_cfg_scales = demo_cfg_scales
+        self.last_demo_step = -1
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_end(self, trainer, module: DiffusionCondTrainingWrapper, outputs, batch, batch_idx): 
+        if (trainer.global_step - 1) % self.demo_every != 0 or self.last_demo_step == trainer.global_step:
+            return
+        
+        self.last_demo_step = trainer.global_step
+
+        try:
+            log_dict = {}
+
+            demo_reals, metadata = next(self.demo_dl)
+
+            # Remove extra dimension added by WebDataset
+            if demo_reals.ndim == 4 and demo_reals.shape[0] == 1:
+                demo_reals = demo_reals[0]
+
+            demo_reals = demo_reals.to(module.device)
+
+            if not module.pre_encoded:
+                # Log the real audio
+                log_dict[f'demo_reals_melspec_left'] = wandb.Image(audio_spectrogram_image(rearrange(demo_reals, "b d n -> d (b n)").mul(32767).to(torch.int16).cpu()))
+                # log_dict[f'demo_reals'] = wandb.Audio(rearrange(demo_reals, "b d n -> d (b n)").mul(32767).to(torch.int16).cpu(), sample_rate=self.sample_rate, caption="demo reals")
+
+                if module.diffusion.pretransform is not None:
+                    module.diffusion.pretransform.to(module.device)
+                    with torch.cuda.amp.autocast():
+                        demo_reals = module.diffusion.pretransform.encode(demo_reals)
+
+            demo_samples = demo_reals.shape[2]
+
+            # Get conditioning
+            conditioning = module.diffusion.conditioner(metadata, module.device)
+
+            masked_input, mask = module.random_mask(demo_reals, demo_reals.shape[2])
+
+            conditioning['inpaint_mask'] = [mask]
+            conditioning['inpaint_masked_input'] = [masked_input]
+
+            if module.diffusion.pretransform is not None:
+                log_dict[f'demo_masked_input'] = wandb.Image(tokens_spectrogram_image(masked_input.cpu()))
+            else:
+                log_dict[f'demo_masked_input'] = wandb.Image(audio_spectrogram_image(rearrange(masked_input, "b c t -> c (b t)").mul(32767).to(torch.int16).cpu()))
+
+            cond_inputs = module.diffusion.get_conditioning_inputs(conditioning)
+
+            noise = torch.randn([demo_reals.shape[0], module.diffusion.io_channels, demo_samples]).to(module.device)
+
+            trainer.logger.experiment.log(log_dict)
+
+            for cfg_scale in self.demo_cfg_scales:
+                model = module.diffusion_ema.model if module.diffusion_ema is not None else module.diffusion.model
+                print(f"Generating demo for cfg scale {cfg_scale}")
+
+                if module.diffusion_objective == "v":
+                    fakes = sample(model, noise, self.demo_steps, 0, **cond_inputs, cfg_scale=cfg_scale, batch_cfg=True)
+                elif module.diffusion_objective == "rectified_flow":
+                    fakes = sample_discrete_euler(model, noise, self.demo_steps, **cond_inputs, cfg_scale=cfg_scale, batch_cfg=True)
+
+                if module.diffusion.pretransform is not None:
+                    with torch.cuda.amp.autocast():
+                        fakes = module.diffusion.pretransform.decode(fakes)
+
+                # Put the demos together
+                fakes = rearrange(fakes, 'b d n -> d (b n)')
+
+                log_dict = {}
+                
+                filename = f'demo_cfg_{cfg_scale}_{trainer.global_step:08}.wav'
+                fakes = fakes.to(torch.float32).div(torch.max(torch.abs(fakes))).mul(32767).to(torch.int16).cpu()
+                torchaudio.save(filename, fakes, self.sample_rate)
+
+                log_dict[f'demo_cfg_{cfg_scale}'] = wandb.Audio(filename,
+                                                    sample_rate=self.sample_rate,
+                                                    caption=f'Reconstructed')
+            
+                log_dict[f'demo_melspec_left_cfg_{cfg_scale}'] = wandb.Image(audio_spectrogram_image(fakes))
+
+                trainer.logger.experiment.log(log_dict)
+        except Exception as e:
+            print(f'{type(e).__name__}: {e}')
+            raise e
+
+class DiffusionAutoencoderTrainingWrapper(pl.LightningModule):
+    '''
+    Wrapper for training a diffusion autoencoder
+    '''
+    def __init__(
+            self,
+            model: DiffusionAutoencoder,
+            lr: float = 1e-4,
+            ema_copy = None,
+            use_reconstruction_loss: bool = False
+    ):
+        super().__init__()
+
+        self.diffae = model
+        
+        self.diffae_ema = EMA(
+            self.diffae,
+            ema_model=ema_copy,
+            beta=0.9999,
+            power=3/4,
+            update_every=1,
+            update_after_step=1,
+            include_online_model=False
+        )
+
+        self.lr = lr
+
+        self.rng = torch.quasirandom.SobolEngine(1, scramble=True)
+
+        loss_modules = [
+            MSELoss("v",
+                    "targets",
+                    weight=1.0,
+                    name="mse_loss"
+            )
+        ]
+
+        if model.bottleneck is not None:
+            # TODO: Use loss config for configurable bottleneck weights and reconstruction losses
+            loss_modules += create_loss_modules_from_bottleneck(model.bottleneck, {})
+
+        self.use_reconstruction_loss = use_reconstruction_loss
+
+        if use_reconstruction_loss:
+            scales = [2048, 1024, 512, 256, 128, 64, 32]
+            hop_sizes = []
+            win_lengths = []
+            overlap = 0.75
+            for s in scales:
+                hop_sizes.append(int(s * (1 - overlap)))
+                win_lengths.append(s)
+
+            sample_rate = model.sample_rate
+
+            stft_loss_args = {
+                "fft_sizes": scales,
+                "hop_sizes": hop_sizes,
+                "win_lengths": win_lengths,
+                "perceptual_weighting": True
+            }
+
+            out_channels = model.out_channels
+
+            if model.pretransform is not None:
+                out_channels = model.pretransform.io_channels
+
+            if out_channels == 2:
+                self.sdstft = auraloss.freq.SumAndDifferenceSTFTLoss(sample_rate=sample_rate, **stft_loss_args)
+            else:
+                self.sdstft = auraloss.freq.MultiResolutionSTFTLoss(sample_rate=sample_rate, **stft_loss_args)
+
+            loss_modules.append(
+                AuralossLoss(self.sdstft, 'audio_reals', 'audio_pred', name='mrstft_loss', weight=0.1), # Reconstruction loss
+            )
+
+        self.losses = MultiLoss(loss_modules)
+
+    def configure_optimizers(self):
+        return optim.Adam([*self.diffae.parameters()], lr=self.lr)
+
+    def training_step(self, batch, batch_idx):
+        reals = batch[0]
+
+        if reals.ndim == 4 and reals.shape[0] == 1:
+            reals = reals[0]
+
+        loss_info = {}
+
+        loss_info["audio_reals"] = reals
+        
+        if self.diffae.pretransform is not None:
+            with torch.no_grad():
+                reals = self.diffae.pretransform.encode(reals)
+
+        loss_info["reals"] = reals
+
+        #Encode reals, skipping the pretransform since it was already applied
+        latents, encoder_info = self.diffae.encode(reals, return_info=True, skip_pretransform=True)
+
+        loss_info["latents"] = latents
+        loss_info.update(encoder_info)
+
+        if self.diffae.decoder is not None:
+            latents = self.diffae.decoder(latents)
+        
+        # Upsample latents to match diffusion length
+        if latents.shape[2] != reals.shape[2]:
+            latents = F.interpolate(latents, size=reals.shape[2], mode='nearest')
+
+        loss_info["latents_upsampled"] = latents
+
+        # Draw uniformly distributed continuous timesteps
+        t = self.rng.draw(reals.shape[0])[:, 0].to(self.device)
+
+        # Calculate the noise schedule parameters for those timesteps
+        alphas, sigmas = get_alphas_sigmas(t)
+
+        # Combine the ground truth data and the noise
+        alphas = alphas[:, None, None]
+        sigmas = sigmas[:, None, None]
+        noise = torch.randn_like(reals)
+        noised_reals = reals * alphas + noise * sigmas
+        targets = noise * alphas - reals * sigmas
+
+        with torch.cuda.amp.autocast():
+            v = self.diffae.diffusion(noised_reals, t, input_concat_cond=latents)
+            
+            loss_info.update({
+                "v": v,
+                "targets": targets
+            })
+
+            if self.use_reconstruction_loss:
+                pred = noised_reals * alphas - v * sigmas
+
+                loss_info["pred"] = pred
+
+                if self.diffae.pretransform is not None:
+                    pred = self.diffae.pretransform.decode(pred)
+                    loss_info["audio_pred"] = pred
+
+            loss, losses = self.losses(loss_info)
+
+        log_dict = {
+            'train/loss': loss.detach(),
+            'train/std_data': reals.std(),
+            'train/latent_std': latents.std(),
+        }
+
+        for loss_name, loss_value in losses.items():
+            log_dict[f"train/{loss_name}"] = loss_value.detach()
+
+        self.log_dict(log_dict, prog_bar=True, on_step=True)
+        return loss
+    
+    def on_before_zero_grad(self, *args, **kwargs):
+        self.diffae_ema.update()
+
+    def export_model(self, path, use_safetensors=False):
+
+        model = self.diffae_ema.ema_model
+        
+        if use_safetensors:
+            save_file(model.state_dict(), path)
+        else:
+            torch.save({"state_dict": model.state_dict()}, path)
+
+class DiffusionAutoencoderDemoCallback(pl.Callback):
+    def __init__(
+        self, 
+        demo_dl, 
+        demo_every=2000,
+        demo_steps=250,
+        sample_size=65536,
+        sample_rate=48000
+    ):
+        super().__init__()
+        self.demo_every = demo_every
+        self.demo_steps = demo_steps
+        self.demo_samples = sample_size
+        self.demo_dl = iter(demo_dl)
+        self.sample_rate = sample_rate
+        self.last_demo_step = -1
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_end(self, trainer, module: DiffusionAutoencoderTrainingWrapper, outputs, batch, batch_idx): 
+        if (trainer.global_step - 1) % self.demo_every != 0 or self.last_demo_step == trainer.global_step:
+            return
+        
+        self.last_demo_step = trainer.global_step
+
+        demo_reals, _ = next(self.demo_dl)
+
+        # Remove extra dimension added by WebDataset
+        if demo_reals.ndim == 4 and demo_reals.shape[0] == 1:
+            demo_reals = demo_reals[0]
+
+        encoder_input = demo_reals
+        
+        encoder_input = encoder_input.to(module.device)
+
+        demo_reals = demo_reals.to(module.device)
+
+        with torch.no_grad() and torch.cuda.amp.autocast():
+            latents = module.diffae_ema.ema_model.encode(encoder_input).float()
+            fakes = module.diffae_ema.ema_model.decode(latents, steps=self.demo_steps)
+
+        #Interleave reals and fakes
+        reals_fakes = rearrange([demo_reals, fakes], 'i b d n -> (b i) d n')
+
+        # Put the demos together
+        reals_fakes = rearrange(reals_fakes, 'b d n -> d (b n)')
+
+        log_dict = {}
+        
+        filename = f'recon_{trainer.global_step:08}.wav'
+        reals_fakes = reals_fakes.to(torch.float32).div(torch.max(torch.abs(reals_fakes))).mul(32767).to(torch.int16).cpu()
+        torchaudio.save(filename, reals_fakes, self.sample_rate)
+
+        log_dict[f'recon'] = wandb.Audio(filename,
+                                            sample_rate=self.sample_rate,
+                                            caption=f'Reconstructed')
+
+        log_dict[f'embeddings_3dpca'] = pca_point_cloud(latents)
+        log_dict[f'embeddings_spec'] = wandb.Image(tokens_spectrogram_image(latents))
+
+        log_dict[f'recon_melspec_left'] = wandb.Image(audio_spectrogram_image(reals_fakes))
+
+        if module.diffae_ema.ema_model.pretransform is not None:
+            with torch.no_grad() and torch.cuda.amp.autocast():
+                initial_latents = module.diffae_ema.ema_model.pretransform.encode(encoder_input)
+                first_stage_fakes = module.diffae_ema.ema_model.pretransform.decode(initial_latents)
+                first_stage_fakes = rearrange(first_stage_fakes, 'b d n -> d (b n)')
+                first_stage_fakes = first_stage_fakes.to(torch.float32).mul(32767).to(torch.int16).cpu()
+                first_stage_filename = f'first_stage_{trainer.global_step:08}.wav'
+                torchaudio.save(first_stage_filename, first_stage_fakes, self.sample_rate)
+
+                log_dict[f'first_stage_latents'] = wandb.Image(tokens_spectrogram_image(initial_latents))
+
+                log_dict[f'first_stage'] = wandb.Audio(first_stage_filename,
+                                            sample_rate=self.sample_rate,
+                                            caption=f'First Stage Reconstructed')
+                
+                log_dict[f'first_stage_melspec_left'] = wandb.Image(audio_spectrogram_image(first_stage_fakes))
+                
+
+        trainer.logger.experiment.log(log_dict)
+
+def create_source_mixture(reals, num_sources=2):
+    # Create a fake mixture source by mixing elements from the training batch together with random offsets
+    source = torch.zeros_like(reals)
+    for i in range(reals.shape[0]):
+        sources_added = 0
+        
+        js = list(range(reals.shape[0]))
+        random.shuffle(js)
+        for j in js:
+            if i == j or (i != j and sources_added < num_sources):
+                # Randomly offset the mixed element between 0 and the length of the source
+                seq_len = reals.shape[2]
+                offset = random.randint(0, seq_len-1)
+                source[i, :, offset:] += reals[j, :, :-offset]
+                if i == j:
+                    # If this is the real one, shift the reals as well to ensure alignment
+                    new_reals = torch.zeros_like(reals[i])
+                    new_reals[:, offset:] = reals[i, :, :-offset]
+                    reals[i] = new_reals
+                sources_added += 1
+
+    return source
+
+class DiffusionPriorTrainingWrapper(pl.LightningModule):
+    '''
+    Wrapper for training a diffusion prior for inverse problems
+    Prior types:
+        mono_stereo: The prior is conditioned on a mono version of the audio to generate a stereo version
+    '''
+    def __init__(
+            self,
+            model: ConditionedDiffusionModelWrapper,
+            lr: float = 1e-4,
+            ema_copy = None,
+            prior_type: PriorType = PriorType.MonoToStereo,
+            use_reconstruction_loss: bool = False,
+            log_loss_info: bool = False,
+    ):
+        super().__init__()
+
+        self.diffusion = model
+        
+        self.diffusion_ema = EMA(
+            self.diffusion,
+            ema_model=ema_copy,
+            beta=0.9999,
+            power=3/4,
+            update_every=1,
+            update_after_step=1,
+            include_online_model=False
+        )
+
+        self.lr = lr
+
+        self.rng = torch.quasirandom.SobolEngine(1, scramble=True)
+
+        self.log_loss_info = log_loss_info
+
+        loss_modules = [
+            MSELoss("v",
+                    "targets",
+                    weight=1.0,
+                    name="mse_loss"
+            )
+        ]
+
+        self.use_reconstruction_loss = use_reconstruction_loss
+
+        if use_reconstruction_loss:
+            scales = [2048, 1024, 512, 256, 128, 64, 32]
+            hop_sizes = []
+            win_lengths = []
+            overlap = 0.75
+            for s in scales:
+                hop_sizes.append(int(s * (1 - overlap)))
+                win_lengths.append(s)
+
+            sample_rate = model.sample_rate
+
+            stft_loss_args = {
+                "fft_sizes": scales,
+                "hop_sizes": hop_sizes,
+                "win_lengths": win_lengths,
+                "perceptual_weighting": True
+            }
+
+            out_channels = model.io_channels
+
+            self.audio_out_channels = out_channels
+
+            if model.pretransform is not None:
+                out_channels = model.pretransform.io_channels
+
+            if self.audio_out_channels == 2:
+                self.sdstft = auraloss.freq.SumAndDifferenceSTFTLoss(sample_rate=sample_rate, **stft_loss_args)
+                self.lrstft = auraloss.freq.MultiResolutionSTFTLoss(sample_rate=sample_rate, **stft_loss_args)
+
+                # Add left and right channel reconstruction losses in addition to the sum and difference
+                self.loss_modules += [
+                    AuralossLoss(self.lrstft, 'audio_reals_left', 'pred_left', name='stft_loss_left', weight=0.05),
+                    AuralossLoss(self.lrstft, 'audio_reals_right', 'pred_right', name='stft_loss_right', weight=0.05),
+                ]
+
+            else:
+                self.sdstft = auraloss.freq.MultiResolutionSTFTLoss(sample_rate=sample_rate, **stft_loss_args)
+
+            self.loss_modules.append(
+                AuralossLoss(self.sdstft, 'audio_reals', 'audio_pred', name='mrstft_loss', weight=0.1), # Reconstruction loss
+            )
+
+        self.losses = MultiLoss(loss_modules)
+
+        self.prior_type = prior_type
+
+    def configure_optimizers(self):
+        return optim.Adam([*self.diffusion.parameters()], lr=self.lr)
+
+    def training_step(self, batch, batch_idx):
+        reals, metadata = batch
+
+        if reals.ndim == 4 and reals.shape[0] == 1:
+            reals = reals[0]
+
+        loss_info = {}
+
+        loss_info["audio_reals"] = reals
+
+        if self.prior_type == PriorType.MonoToStereo:
+            source = reals.mean(dim=1, keepdim=True).repeat(1, reals.shape[1], 1).to(self.device)
+            loss_info["audio_reals_mono"] = source
+        else:
+            raise ValueError(f"Unknown prior type {self.prior_type}")
+        
+        if self.diffusion.pretransform is not None:
+            with torch.no_grad():
+                reals = self.diffusion.pretransform.encode(reals)
+
+                if self.prior_type in [PriorType.MonoToStereo]:
+                    source = self.diffusion.pretransform.encode(source)
+
+        if self.diffusion.conditioner is not None:
+            with torch.cuda.amp.autocast():
+                conditioning = self.diffusion.conditioner(metadata, self.device)
+        else:
+            conditioning = {}
+
+        loss_info["reals"] = reals
+
+        # Draw uniformly distributed continuous timesteps
+        t = self.rng.draw(reals.shape[0])[:, 0].to(self.device)
+
+        # Calculate the noise schedule parameters for those timesteps
+        alphas, sigmas = get_alphas_sigmas(t)
+
+        # Combine the ground truth data and the noise
+        alphas = alphas[:, None, None]
+        sigmas = sigmas[:, None, None]
+        noise = torch.randn_like(reals)
+        noised_reals = reals * alphas + noise * sigmas
+        targets = noise * alphas - reals * sigmas
+
+        with torch.cuda.amp.autocast():
+            
+            conditioning['source'] = [source]
+
+            v = self.diffusion(noised_reals, t, cond=conditioning, cfg_dropout_prob = 0.1)
+            
+            loss_info.update({
+                "v": v,
+                "targets": targets
+            })
+
+            if self.use_reconstruction_loss:
+                pred = noised_reals * alphas - v * sigmas
+
+                loss_info["pred"] = pred
+
+                if self.diffusion.pretransform is not None:
+                    pred = self.diffusion.pretransform.decode(pred)
+                    loss_info["audio_pred"] = pred
+
+                if self.audio_out_channels == 2:
+                    loss_info["pred_left"] = pred[:, 0:1, :]
+                    loss_info["pred_right"] = pred[:, 1:2, :]
+                    loss_info["audio_reals_left"] = loss_info["audio_reals"][:, 0:1, :]
+                    loss_info["audio_reals_right"] = loss_info["audio_reals"][:, 1:2, :]
+
+            loss, losses = self.losses(loss_info)
+
+            if self.log_loss_info:
+                # Loss debugging logs
+                num_loss_buckets = 10
+                bucket_size = 1 / num_loss_buckets
+                loss_all = F.mse_loss(v, targets, reduction="none")
+
+                sigmas = rearrange(self.all_gather(sigmas), "w b c n -> (w b) c n").squeeze()
+
+                # gather loss_all across all GPUs
+                loss_all = rearrange(self.all_gather(loss_all), "w b c n -> (w b) c n")
+
+                # Bucket loss values based on corresponding sigma values, bucketing sigma values by bucket_size
+                loss_all = torch.stack([loss_all[(sigmas >= i) & (sigmas < i + bucket_size)].mean() for i in torch.arange(0, 1, bucket_size).to(self.device)])
+
+                # Log bucketed losses with corresponding sigma bucket values, if it's not NaN
+                debug_log_dict = {
+                    f"model/loss_all_{i/num_loss_buckets:.1f}": loss_all[i].detach() for i in range(num_loss_buckets) if not torch.isnan(loss_all[i])
+                }
+
+                self.log_dict(debug_log_dict)
+
+        log_dict = {
+            'train/loss': loss.detach(),
+            'train/std_data': reals.std()
+        }
+
+        for loss_name, loss_value in losses.items():
+            log_dict[f"train/{loss_name}"] = loss_value.detach()
+
+        self.log_dict(log_dict, prog_bar=True, on_step=True)
+        return loss
+    
+    def on_before_zero_grad(self, *args, **kwargs):
+        self.diffusion_ema.update()
+
+    def export_model(self, path, use_safetensors=False):
+
+        #model = self.diffusion_ema.ema_model
+        model = self.diffusion
+        
+        if use_safetensors:
+            save_file(model.state_dict(), path)
+        else:
+            torch.save({"state_dict": model.state_dict()}, path)
+
+class DiffusionPriorDemoCallback(pl.Callback):
+    def __init__(
+        self, 
+        demo_dl, 
+        demo_every=2000,
+        demo_steps=250,
+        sample_size=65536,
+        sample_rate=48000
+    ):
+        super().__init__()
+        self.demo_every = demo_every
+        self.demo_steps = demo_steps
+        self.demo_samples = sample_size
+        self.demo_dl = iter(demo_dl)
+        self.sample_rate = sample_rate
+        self.last_demo_step = -1
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_end(self, trainer, module: DiffusionAutoencoderTrainingWrapper, outputs, batch, batch_idx): 
+        if (trainer.global_step - 1) % self.demo_every != 0 or self.last_demo_step == trainer.global_step:
+            return
+        
+        self.last_demo_step = trainer.global_step
+
+        demo_reals, metadata = next(self.demo_dl)
+
+        # Remove extra dimension added by WebDataset
+        if demo_reals.ndim == 4 and demo_reals.shape[0] == 1:
+            demo_reals = demo_reals[0]
+
+        demo_reals = demo_reals.to(module.device)
+
+        encoder_input = demo_reals
+
+        if module.diffusion.conditioner is not None:
+            with torch.cuda.amp.autocast():
+                conditioning_tensors = module.diffusion.conditioner(metadata, module.device)
+
+        else:
+            conditioning_tensors = {}
+
+               
+        with torch.no_grad() and torch.cuda.amp.autocast():
+            if module.prior_type == PriorType.MonoToStereo and encoder_input.shape[1] > 1:
+                source = encoder_input.mean(dim=1, keepdim=True).repeat(1, encoder_input.shape[1], 1).to(module.device)
+
+            if module.diffusion.pretransform is not None:
+                encoder_input = module.diffusion.pretransform.encode(encoder_input)
+                source_input = module.diffusion.pretransform.encode(source)
+            else:
+                source_input = source
+
+            conditioning_tensors['source'] = [source_input]
+
+            fakes = sample(module.diffusion_ema.model, torch.randn_like(encoder_input), self.demo_steps, 0, cond=conditioning_tensors)
+
+            if module.diffusion.pretransform is not None:
+                fakes = module.diffusion.pretransform.decode(fakes)
+
+        #Interleave reals and fakes
+        reals_fakes = rearrange([demo_reals, fakes], 'i b d n -> (b i) d n')
+
+        # Put the demos together
+        reals_fakes = rearrange(reals_fakes, 'b d n -> d (b n)')
+
+        log_dict = {}
+        
+        filename = f'recon_{trainer.global_step:08}.wav'
+        reals_fakes = reals_fakes.to(torch.float32).div(torch.max(torch.abs(reals_fakes))).mul(32767).to(torch.int16).cpu()
+        torchaudio.save(filename, reals_fakes, self.sample_rate)
+
+        log_dict[f'recon'] = wandb.Audio(filename,
+                                            sample_rate=self.sample_rate,
+                                            caption=f'Reconstructed')
+
+        log_dict[f'recon_melspec_left'] = wandb.Image(audio_spectrogram_image(reals_fakes))   
+
+        #Log the source
+        filename = f'source_{trainer.global_step:08}.wav'
+        source = rearrange(source, 'b d n -> d (b n)')
+        source = source.to(torch.float32).mul(32767).to(torch.int16).cpu()
+        torchaudio.save(filename, source, self.sample_rate)
+
+        log_dict[f'source'] = wandb.Audio(filename,
+                                            sample_rate=self.sample_rate,
+                                            caption=f'Source')
+
+        log_dict[f'source_melspec_left'] = wandb.Image(audio_spectrogram_image(source))
+
+        trainer.logger.experiment.log(log_dict)

stable_audio_tools/training/factory.py

@@ -0,0 +1,240 @@
+import torch
+from torch.nn import Parameter
+from ..models.factory import create_model_from_config
+
+def create_training_wrapper_from_config(model_config, model):
+    model_type = model_config.get('model_type', None)
+    assert model_type is not None, 'model_type must be specified in model config'
+
+    training_config = model_config.get('training', None)
+    assert training_config is not None, 'training config must be specified in model config'
+
+    if model_type == 'autoencoder':
+        from .autoencoders import AutoencoderTrainingWrapper
+
+        ema_copy = None
+
+        if training_config.get("use_ema", False):
+            ema_copy = create_model_from_config(model_config)
+            ema_copy = create_model_from_config(model_config) # I don't know why this needs to be called twice but it broke when I called it once
+            # Copy each weight to the ema copy
+            for name, param in model.state_dict().items():
+                if isinstance(param, Parameter):
+                    # backwards compatibility for serialized parameters
+                    param = param.data
+                ema_copy.state_dict()[name].copy_(param)
+
+        use_ema = training_config.get("use_ema", False)
+
+        latent_mask_ratio = training_config.get("latent_mask_ratio", 0.0)
+
+        teacher_model = training_config.get("teacher_model", None)
+        if teacher_model is not None:
+            teacher_model = create_model_from_config(teacher_model)
+            teacher_model = teacher_model.eval().requires_grad_(False)
+
+            teacher_model_ckpt = training_config.get("teacher_model_ckpt", None)
+            if teacher_model_ckpt is not None:
+                teacher_model.load_state_dict(torch.load(teacher_model_ckpt)["state_dict"])
+            else:
+                raise ValueError("teacher_model_ckpt must be specified if teacher_model is specified")
+
+        return AutoencoderTrainingWrapper(
+            model, 
+            lr=training_config["learning_rate"],
+            warmup_steps=training_config.get("warmup_steps", 0), 
+            encoder_freeze_on_warmup=training_config.get("encoder_freeze_on_warmup", False),
+            sample_rate=model_config["sample_rate"],
+            loss_config=training_config.get("loss_configs", None),
+            optimizer_configs=training_config.get("optimizer_configs", None),
+            use_ema=use_ema,
+            ema_copy=ema_copy if use_ema else None,
+            force_input_mono=training_config.get("force_input_mono", False),
+            latent_mask_ratio=latent_mask_ratio,
+            teacher_model=teacher_model
+        )
+    elif model_type == 'diffusion_uncond':
+        from .diffusion import DiffusionUncondTrainingWrapper
+        return DiffusionUncondTrainingWrapper(
+            model, 
+            lr=training_config["learning_rate"],
+            pre_encoded=training_config.get("pre_encoded", False),
+        )
+    elif model_type == 'diffusion_cond':
+        from .diffusion import DiffusionCondTrainingWrapper
+        return DiffusionCondTrainingWrapper(
+            model, 
+            lr=training_config.get("learning_rate", None),
+            mask_padding=training_config.get("mask_padding", False),
+            mask_padding_dropout=training_config.get("mask_padding_dropout", 0.0),
+            use_ema = training_config.get("use_ema", True),
+            log_loss_info=training_config.get("log_loss_info", False),
+            optimizer_configs=training_config.get("optimizer_configs", None),
+            pre_encoded=training_config.get("pre_encoded", False),
+            cfg_dropout_prob = training_config.get("cfg_dropout_prob", 0.1),
+            timestep_sampler = training_config.get("timestep_sampler", "uniform")
+        )
+    elif model_type == 'diffusion_prior':
+        from .diffusion import DiffusionPriorTrainingWrapper
+        from ..models.diffusion_prior import PriorType
+
+        ema_copy = create_model_from_config(model_config)
+        
+        # Copy each weight to the ema copy
+        for name, param in model.state_dict().items():
+            if isinstance(param, Parameter):
+                # backwards compatibility for serialized parameters
+                param = param.data
+            ema_copy.state_dict()[name].copy_(param)
+
+        prior_type = training_config.get("prior_type", "mono_stereo")
+
+        if prior_type == "mono_stereo":
+            prior_type_enum = PriorType.MonoToStereo
+        else:
+            raise ValueError(f"Unknown prior type: {prior_type}")
+
+        return DiffusionPriorTrainingWrapper(
+            model, 
+            lr=training_config["learning_rate"],
+            ema_copy=ema_copy,
+            prior_type=prior_type_enum,
+            log_loss_info=training_config.get("log_loss_info", False),
+            use_reconstruction_loss=training_config.get("use_reconstruction_loss", False),
+        )
+    elif model_type == 'diffusion_cond_inpaint':
+        from .diffusion import DiffusionCondInpaintTrainingWrapper
+        return DiffusionCondInpaintTrainingWrapper(
+            model, 
+            lr=training_config.get("learning_rate", None),
+            max_mask_segments = training_config.get("max_mask_segments", 10),
+            log_loss_info=training_config.get("log_loss_info", False),
+            optimizer_configs=training_config.get("optimizer_configs", None),
+            use_ema=training_config.get("use_ema", True),
+            pre_encoded=training_config.get("pre_encoded", False),
+            cfg_dropout_prob = training_config.get("cfg_dropout_prob", 0.1),
+            timestep_sampler = training_config.get("timestep_sampler", "uniform")
+        )
+    elif model_type == 'diffusion_autoencoder':
+        from .diffusion import DiffusionAutoencoderTrainingWrapper
+
+        ema_copy = create_model_from_config(model_config)
+        
+        # Copy each weight to the ema copy
+        for name, param in model.state_dict().items():
+            if isinstance(param, Parameter):
+                # backwards compatibility for serialized parameters
+                param = param.data
+            ema_copy.state_dict()[name].copy_(param)
+
+        return DiffusionAutoencoderTrainingWrapper(
+            model,
+            ema_copy=ema_copy,
+            lr=training_config["learning_rate"],
+            use_reconstruction_loss=training_config.get("use_reconstruction_loss", False)
+        )
+    elif model_type == 'lm':
+        from .lm import AudioLanguageModelTrainingWrapper
+
+        ema_copy = create_model_from_config(model_config)
+
+        for name, param in model.state_dict().items():
+            if isinstance(param, Parameter):
+                # backwards compatibility for serialized parameters
+                param = param.data
+            ema_copy.state_dict()[name].copy_(param)
+
+        return AudioLanguageModelTrainingWrapper(
+            model,
+            ema_copy=ema_copy,
+            lr=training_config.get("learning_rate", None),
+            use_ema=training_config.get("use_ema", False),
+            optimizer_configs=training_config.get("optimizer_configs", None),
+            pre_encoded=training_config.get("pre_encoded", False),
+        )
+
+    else:
+        raise NotImplementedError(f'Unknown model type: {model_type}')
+
+def create_demo_callback_from_config(model_config, **kwargs):
+    model_type = model_config.get('model_type', None)
+    assert model_type is not None, 'model_type must be specified in model config'
+
+    training_config = model_config.get('training', None)
+    assert training_config is not None, 'training config must be specified in model config'
+
+    demo_config = training_config.get("demo", {})
+
+    if model_type == 'autoencoder':
+        from .autoencoders import AutoencoderDemoCallback
+        return AutoencoderDemoCallback(
+            demo_every=demo_config.get("demo_every", 2000), 
+            sample_size=model_config["sample_size"], 
+            sample_rate=model_config["sample_rate"],
+            **kwargs
+        )
+    elif model_type == 'diffusion_uncond':
+        from .diffusion import DiffusionUncondDemoCallback
+        return DiffusionUncondDemoCallback(
+            demo_every=demo_config.get("demo_every", 2000), 
+            demo_steps=demo_config.get("demo_steps", 250), 
+            sample_rate=model_config["sample_rate"]
+        )
+    elif model_type == "diffusion_autoencoder":
+        from .diffusion import DiffusionAutoencoderDemoCallback
+        return DiffusionAutoencoderDemoCallback(
+            demo_every=demo_config.get("demo_every", 2000), 
+            demo_steps=demo_config.get("demo_steps", 250),
+            sample_size=model_config["sample_size"],
+            sample_rate=model_config["sample_rate"],
+            **kwargs
+        )
+    elif model_type == "diffusion_prior":
+        from .diffusion import DiffusionPriorDemoCallback
+        return DiffusionPriorDemoCallback(
+            demo_every=demo_config.get("demo_every", 2000), 
+            demo_steps=demo_config.get("demo_steps", 250),
+            sample_size=model_config["sample_size"],
+            sample_rate=model_config["sample_rate"],
+            **kwargs
+        )
+    elif model_type == "diffusion_cond":
+        from .diffusion import DiffusionCondDemoCallback
+
+        return DiffusionCondDemoCallback(
+            demo_every=demo_config.get("demo_every", 2000), 
+            sample_size=model_config["sample_size"],
+            sample_rate=model_config["sample_rate"],
+            demo_steps=demo_config.get("demo_steps", 250), 
+            num_demos=demo_config["num_demos"],
+            demo_cfg_scales=demo_config["demo_cfg_scales"],
+            demo_conditioning=demo_config.get("demo_cond", {}),
+            demo_cond_from_batch=demo_config.get("demo_cond_from_batch", False),
+            display_audio_cond=demo_config.get("display_audio_cond", False),
+        )
+    elif model_type == "diffusion_cond_inpaint":
+        from .diffusion import DiffusionCondInpaintDemoCallback
+
+        return DiffusionCondInpaintDemoCallback(
+            demo_every=demo_config.get("demo_every", 2000), 
+            sample_size=model_config["sample_size"],
+            sample_rate=model_config["sample_rate"],
+            demo_steps=demo_config.get("demo_steps", 250),
+            demo_cfg_scales=demo_config["demo_cfg_scales"],
+            **kwargs
+        )
+    
+    elif model_type == "lm":
+        from .lm import AudioLanguageModelDemoCallback
+
+        return AudioLanguageModelDemoCallback(
+            demo_every=demo_config.get("demo_every", 2000), 
+            sample_size=model_config["sample_size"],
+            sample_rate=model_config["sample_rate"],
+            demo_cfg_scales=demo_config.get("demo_cfg_scales", [1]),
+            demo_conditioning=demo_config.get("demo_cond", None),
+            num_demos=demo_config.get("num_demos", 8),
+            **kwargs
+        )
+    else:
+        raise NotImplementedError(f'Unknown model type: {model_type}')

stable_audio_tools/training/lm.py

@@ -0,0 +1,267 @@
+import pytorch_lightning as pl
+import sys, gc
+import random
+import torch
+import torchaudio
+import typing as tp
+import wandb
+
+from aeiou.viz import pca_point_cloud, audio_spectrogram_image, tokens_spectrogram_image
+from ema_pytorch import EMA
+from einops import rearrange
+from safetensors.torch import save_file
+from torch import optim
+from torch.nn import functional as F
+from pytorch_lightning.utilities.rank_zero import rank_zero_only
+
+from ..models.lm import AudioLanguageModelWrapper
+from .utils import create_optimizer_from_config, create_scheduler_from_config
+
+class AudioLanguageModelTrainingWrapper(pl.LightningModule):
+    def __init__(
+            self, 
+            model: AudioLanguageModelWrapper, 
+            lr = 1e-4, 
+            use_ema=False, 
+            ema_copy=None,
+            optimizer_configs: dict = None,
+            pre_encoded=False
+        ):
+        super().__init__()
+
+        self.model = model
+
+        self.model.pretransform.requires_grad_(False)
+
+        self.model_ema = None
+        if use_ema:
+            self.model_ema = EMA(self.model, ema_model=ema_copy, beta=0.99, update_every=10)
+
+        assert lr is not None or optimizer_configs is not None, "Must specify either lr or optimizer_configs in training config"
+
+        if optimizer_configs is None:
+            optimizer_configs = {
+                "lm": {
+                    "optimizer": {
+                        "type": "AdamW",
+                        "config": {
+                            "lr": lr,
+                            "betas": (0.9, 0.95),
+                            "weight_decay": 0.1
+                        }
+                    }
+                }
+            }
+        else:
+            if lr is not None:
+                print(f"WARNING: learning_rate and optimizer_configs both specified in config. Ignoring learning_rate and using optimizer_configs.")
+
+        self.optimizer_configs = optimizer_configs
+
+        self.pre_encoded = pre_encoded
+
+    def configure_optimizers(self):
+        lm_opt_config = self.optimizer_configs['lm']
+        opt_lm = create_optimizer_from_config(lm_opt_config['optimizer'], self.model.parameters())
+
+        if "scheduler" in lm_opt_config:
+            sched_lm = create_scheduler_from_config(lm_opt_config['scheduler'], opt_lm)
+            sched_lm_config = {
+                "scheduler": sched_lm,
+                "interval": "step"
+            }
+            return [opt_lm], [sched_lm_config]
+
+        return [opt_lm]
+        
+    # Copied and modified from https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/solvers/musicgen.py under MIT license
+    # License can be found in LICENSES/LICENSE_META.txt
+
+    def _compute_cross_entropy(
+        self, logits: torch.Tensor, targets: torch.Tensor, mask: torch.Tensor
+    ) -> tp.Tuple[torch.Tensor, tp.List[torch.Tensor]]:
+        """Compute cross entropy between multi-codebook targets and model's logits.
+        The cross entropy is computed per codebook to provide codebook-level cross entropy.
+        Valid timesteps for each of the codebook are pulled from the mask, where invalid
+        timesteps are set to 0.
+
+        Args:
+            logits (torch.Tensor): Model's logits of shape [B, K, T, card].
+            targets (torch.Tensor): Target codes, of shape [B, K, T].
+            mask (torch.Tensor): Mask for valid target codes, of shape [B, K, T].
+        Returns:
+            ce (torch.Tensor): Cross entropy averaged over the codebooks
+            ce_per_codebook (list of torch.Tensor): Cross entropy per codebook (detached).
+        """
+        B, K, T = targets.shape
+        assert logits.shape[:-1] == targets.shape
+        assert mask.shape == targets.shape
+        ce = torch.zeros([], device=targets.device)
+        ce_per_codebook: tp.List[torch.Tensor] = []
+        for k in range(K):
+            logits_k = logits[:, k, ...].contiguous().view(-1, logits.size(-1))  # [B x T, card]
+            targets_k = targets[:, k, ...].contiguous().view(-1)  # [B x T]
+            mask_k = mask[:, k, ...].contiguous().view(-1)  # [B x T]
+            ce_targets = targets_k[mask_k]
+            ce_logits = logits_k[mask_k]
+            q_ce = F.cross_entropy(ce_logits, ce_targets)
+            ce += q_ce
+            ce_per_codebook.append(q_ce.detach())
+        # average cross entropy across codebooks
+        ce = ce / K
+        return ce, ce_per_codebook
+
+    def training_step(self, batch, batch_idx):
+        reals, metadata = batch
+
+        if reals.ndim == 4 and reals.shape[0] == 1:
+            reals = reals[0]
+
+        if not self.pre_encoded:
+            codes = self.model.pretransform.tokenize(reals)
+        else:
+            codes = reals
+
+        padding_masks = []
+        for md in metadata:
+            if md["padding_mask"].ndim == 1:
+                padding_masks.append(md["padding_mask"])
+            else:
+                padding_masks.append(md["padding_mask"][0])
+            
+        padding_masks = torch.stack(padding_masks, dim=0).to(self.device) # Shape (batch_size, sequence_length)
+
+        # Interpolate padding masks to the same length as the codes
+        padding_masks = F.interpolate(padding_masks.unsqueeze(1).float(), size=codes.shape[2], mode='nearest').bool()
+    
+        condition_tensors = None
+
+        # If the model is conditioned, get the conditioning tensors
+        if self.model.conditioner is not None:
+            condition_tensors = self.model.conditioner(metadata, self.device)
+
+        lm_output = self.model.compute_logits(codes, condition_tensors=condition_tensors, cfg_dropout_prob=0.1)
+
+        logits = lm_output.logits # [b, k, t, c]
+        logits_mask = lm_output.mask # [b, k, t]
+
+        logits_mask = logits_mask & padding_masks
+
+        cross_entropy, cross_entropy_per_codebook = self._compute_cross_entropy(logits, codes, logits_mask)
+
+        loss = cross_entropy
+
+        log_dict = {
+            'train/loss': loss.detach(),
+            'train/cross_entropy': cross_entropy.detach(),
+            'train/perplexity': torch.exp(cross_entropy).detach(),
+            'train/lr': self.trainer.optimizers[0].param_groups[0]['lr']
+        }
+
+        for k, ce_q in enumerate(cross_entropy_per_codebook):
+            log_dict[f'cross_entropy_q{k + 1}'] = ce_q
+            log_dict[f'perplexity_q{k + 1}'] = torch.exp(ce_q)
+
+        self.log_dict(log_dict, prog_bar=True, on_step=True)
+        return loss
+
+    def on_before_zero_grad(self, *args, **kwargs):
+        if self.model_ema is not None:
+            self.model_ema.update()
+
+    def export_model(self, path, use_safetensors=False):
+        
+        model = self.model_ema.ema_model if self.model_ema is not None else self.model
+
+        if use_safetensors:
+            save_file(model.state_dict(), path)
+        else:
+            torch.save({"state_dict": model.state_dict()}, path)
+        
+
+class AudioLanguageModelDemoCallback(pl.Callback):
+    def __init__(self, 
+                 demo_every=2000,
+                 num_demos=8,
+                 sample_size=65536,
+                 sample_rate=48000,
+                 demo_conditioning: tp.Optional[tp.Dict[str, tp.Any]] = None,
+                 demo_cfg_scales: tp.Optional[tp.List[int]] = [3, 5, 7],
+                 **kwargs
+    ):
+        super().__init__()
+
+        self.demo_every = demo_every
+        self.num_demos = num_demos
+        self.demo_samples = sample_size
+        self.sample_rate = sample_rate
+        self.last_demo_step = -1
+        self.demo_conditioning = demo_conditioning
+        self.demo_cfg_scales = demo_cfg_scales
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_end(self, trainer, module: AudioLanguageModelTrainingWrapper, outputs, batch, batch_idx):        
+
+        if (trainer.global_step - 1) % self.demo_every != 0 or self.last_demo_step == trainer.global_step:
+            return
+
+        module.eval()
+
+        print(f"Generating demo")
+        self.last_demo_step = trainer.global_step
+
+        demo_length_tokens = self.demo_samples // module.model.pretransform.downsampling_ratio
+
+        #demo_reals = batch[0][:self.num_demos]
+
+        # if demo_reals.ndim == 4 and demo_reals.shape[0] == 1:
+        #     demo_reals = demo_reals[0]
+
+        #demo_reals_tokens = module.model.pretransform.tokenize(demo_reals)
+
+        ##Limit to first 50 tokens
+        #demo_reals_tokens = demo_reals_tokens[:, :, :50]
+
+        try:
+            print("Getting conditioning")
+
+            for cfg_scale in self.demo_cfg_scales:
+
+                model = module.model # module.model_ema.ema_model if module.model_ema is not None else module.model
+
+                print(f"Generating demo for cfg scale {cfg_scale}")
+                fakes = model.generate_audio(
+                    batch_size=self.num_demos,
+                    max_gen_len=demo_length_tokens, 
+                    conditioning=self.demo_conditioning, 
+                    #init_data = demo_reals_tokens,
+                    cfg_scale=cfg_scale,
+                    temp=1.0,
+                    top_p=0.95
+                )
+
+                # Put the demos together
+                fakes = rearrange(fakes, 'b d n -> d (b n)')
+
+                log_dict = {}
+                
+                filename = f'demo_cfg_{cfg_scale}_{trainer.global_step:08}.wav'
+                fakes = fakes / fakes.abs().max()
+                fakes = fakes.type(torch.float32).clamp(-1, 1).mul(32767).type(torch.int16).cpu()
+                torchaudio.save(filename, fakes, self.sample_rate)
+
+                log_dict[f'demo_cfg_{cfg_scale}'] = wandb.Audio(filename,
+                                                    sample_rate=self.sample_rate,
+                                                    caption=f'Reconstructed')
+            
+                log_dict[f'demo_melspec_left_cfg_{cfg_scale}'] = wandb.Image(audio_spectrogram_image(fakes))
+
+                trainer.logger.experiment.log(log_dict)
+
+        except Exception as e:
+            raise e
+        finally:
+            gc.collect()
+            torch.cuda.empty_cache()
+            module.train()