stable

stable/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Stability AI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
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+
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+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

stable/LICENSES/LICENSE_ADP.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 archinet.ai
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
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+SOFTWARE.

stable/LICENSES/LICENSE_AURALOSS.txt

@@ -0,0 +1,201 @@
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stable/LICENSES/LICENSE_DESCRIPT.txt

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+MIT License
+
+Copyright (c) 2023-present, Descript
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

stable/LICENSES/LICENSE_META.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) Meta Platforms, Inc. and affiliates.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
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+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

stable/LICENSES/LICENSE_NVIDIA.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 NVIDIA CORPORATION.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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+furnished to do so, subject to the following conditions:
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+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software. 
+
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+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

stable/LICENSES/LICENSE_XTRANSFORMERS.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 Phil Wang
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
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+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
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+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

stable/README.md

@@ -0,0 +1,157 @@
+# stable-audio-tools
+Training and inference code for audio generation models
+
+# Install
+
+The library can be installed from PyPI with:
+```bash
+$ pip install stable-audio-tools
+```
+
+To run the training scripts or inference code, you'll want to clone this repository, navigate to the root, and run:
+```bash
+$ pip install .
+```
+
+# Requirements
+Requires PyTorch 2.0 or later for Flash Attention support
+
+Development for the repo is done in Python 3.8.10
+
+# Interface
+
+A basic Gradio interface is provided to test out trained models. 
+
+For example, to create an interface for the [`stable-audio-open-1.0`](https://huggingface.co/stabilityai/stable-audio-open-1.0) model, once you've accepted the terms for the model on Hugging Face, you can run:
+```bash
+$ python3 ./run_gradio.py --pretrained-name stabilityai/stable-audio-open-1.0
+```
+
+The `run_gradio.py` script accepts the following command line arguments:
+
+- `--pretrained-name`
+  - Hugging Face repository name for a Stable Audio Tools model
+  - Will prioritize `model.safetensors` over `model.ckpt` in the repo
+  - Optional, used in place of `model-config` and `ckpt-path` when using pre-trained model checkpoints on Hugging Face
+- `--model-config`
+  - Path to the model config file for a local model
+- `--ckpt-path`
+  - Path to unwrapped model checkpoint file for a local model
+- `--pretransform-ckpt-path` 
+  - Path to an unwrapped pretransform checkpoint, replaces the pretransform in the model, useful for testing out fine-tuned decoders
+  - Optional
+- `--share`
+  - If true, a publicly shareable link will be created for the Gradio demo
+  - Optional
+- `--username` and `--password`
+  - Used together to set a login for the Gradio demo
+  - Optional
+- `--model-half`
+  - If true, the model weights to half-precision
+  - Optional
+
+# Training
+
+## Prerequisites
+Before starting your training run, you'll need a model config file, as well as a dataset config file. For more information about those, refer to the Configurations section below
+
+The training code also requires a Weights & Biases account to log the training outputs and demos. Create an account and log in with:
+```bash
+$ wandb login
+```
+
+## Start training
+To start a training run, run the `train.py` script in the repo root with:
+```bash
+$ python3 ./train.py --dataset-config /path/to/dataset/config --model-config /path/to/model/config --name harmonai_train
+```
+
+The `--name` parameter will set the project name for your Weights and Biases run.
+
+## Training wrappers and model unwrapping
+`stable-audio-tools` uses PyTorch Lightning to facilitate multi-GPU and multi-node training. 
+
+When a model is being trained, it is wrapped in a "training wrapper", which is a `pl.LightningModule` that contains all of the relevant objects needed only for training. That includes things like discriminators for autoencoders, EMA copies of models, and all of the optimizer states.
+
+The checkpoint files created during training include this training wrapper, which greatly increases the size of the checkpoint file.
+
+`unwrap_model.py` in the repo root will take in a wrapped model checkpoint and save a new checkpoint file including only the model itself.
+
+That can be run with from the repo root with:
+```bash
+$ python3 ./unwrap_model.py --model-config /path/to/model/config --ckpt-path /path/to/wrapped/ckpt --name model_unwrap
+```
+
+Unwrapped model checkpoints are required for:
+  - Inference scripts
+  - Using a model as a pretransform for another model (e.g. using an autoencoder model for latent diffusion)
+  - Fine-tuning a pre-trained model with a modified configuration (i.e. partial initialization)
+
+## Fine-tuning
+Fine-tuning a model involves continuning a training run from a pre-trained checkpoint. 
+
+To continue a training run from a wrapped model checkpoint, you can pass in the checkpoint path to `train.py` with the `--ckpt-path` flag.
+
+To start a fresh training run using a pre-trained unwrapped model, you can pass in the unwrapped checkpoint to `train.py` with the `--pretrained-ckpt-path` flag.
+
+## Additional training flags
+
+Additional optional flags for `train.py` include:
+- `--config-file`
+  - The path to the defaults.ini file in the repo root, required if running `train.py` from a directory other than the repo root
+- `--pretransform-ckpt-path`
+  - Used in various model types such as latent diffusion models to load a pre-trained autoencoder. Requires an unwrapped model checkpoint.
+- `--save-dir`
+  - The directory in which to save the model checkpoints
+- `--checkpoint-every`
+  - The number of steps between saved checkpoints.
+  - *Default*: 10000
+- `--batch-size`
+  - Number of samples per-GPU during training. Should be set as large as your GPU VRAM will allow.
+  - *Default*: 8
+- `--num-gpus`
+  - Number of GPUs per-node to use for training
+  - *Default*: 1
+- `--num-nodes`
+  - Number of GPU nodes being used for training
+  - *Default*: 1
+- `--accum-batches`
+  - Enables and sets the number of batches for gradient batch accumulation. Useful for increasing effective batch size when training on smaller GPUs.
+- `--strategy`
+  - Multi-GPU strategy for distributed training. Setting to `deepspeed` will enable DeepSpeed ZeRO Stage 2.
+  - *Default*: `ddp` if `--num_gpus` > 1, else None
+- `--precision`
+  - floating-point precision to use during training
+  - *Default*: 16
+- `--num-workers`
+  - Number of CPU workers used by the data loader
+- `--seed`
+  - RNG seed for PyTorch, helps with deterministic training
+
+# Configurations
+Training and inference code for `stable-audio-tools` is based around JSON configuration files that define model hyperparameters, training settings, and information about your training dataset.
+
+## Model config
+The model config file defines all of the information needed to load a model for training or inference. It also contains the training configuration needed to fine-tune a model or train from scratch.
+
+The following properties are defined in the top level of the model configuration:
+
+- `model_type`
+  - The type of model being defined, currently limited to one of `"autoencoder", "diffusion_uncond", "diffusion_cond", "diffusion_cond_inpaint", "diffusion_autoencoder", "lm"`.
+- `sample_size`
+  - The length of the audio provided to the model during training, in samples. For diffusion models, this is also the raw audio sample length used for inference.
+- `sample_rate`
+  - The sample rate of the audio provided to the model during training, and generated during inference, in Hz.
+- `audio_channels`
+  - The number of channels of audio provided to the model during training, and generated during inference. Defaults to 2. Set to 1 for mono.
+- `model`
+  - The specific configuration for the model being defined, varies based on `model_type`
+- `training`
+  - The training configuration for the model, varies based on `model_type`. Provides parameters for training as well as demos.
+
+## Dataset config
+`stable-audio-tools` currently supports two kinds of data sources: local directories of audio files, and WebDataset datasets stored in Amazon S3. More information can be found in [the dataset config documentation](docs/datasets.md)
+
+# Todo
+- [ ] Add troubleshooting section
+- [ ] Add contribution guidelines 

stable/build/lib/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/build/lib/stable_audio_tools/data/dataset.py

@@ -0,0 +1,654 @@
+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 .utils import Stereo, Mono, PhaseFlipper, PadCrop_Normalized_T
+
+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 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 = []
+    if type(paths) is str:
+        paths = [paths]
+    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
+
+class LocalDatasetConfig:
+    def __init__(
+        self,
+        id: str,
+        path: str,
+        custom_metadata_fn: Optional[Callable[[str], str]] = None
+    ):
+        self.id = id
+        self.path = path
+        self.custom_metadata_fn = custom_metadata_fn
+
+class SampleDataset(torch.utils.data.Dataset):
+    def __init__(
+        self, 
+        configs,
+        sample_size=65536, 
+        sample_rate=48000, 
+        keywords=None, 
+        random_crop=True,
+        force_channels="stereo"
+    ):
+        super().__init__()
+        self.filenames = []
+
+        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.root_paths.append(config.path)
+            self.filenames.extend(get_audio_filenames(config.path, keywords))
+            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]
+        try:
+            start_time = time.time()
+            audio = self.load_file(audio_filename)
+
+            audio, t_start, t_end, seconds_start, seconds_total, padding_mask = self.pad_crop(audio)
+
+            # Run augmentations on this sample (including random crop)
+            if self.augs is not None:
+                audio = self.augs(audio)
+
+            audio = audio.clamp(-1, 1)
+
+            # Encode the file to assist in prediction
+            if self.encoding is not None:
+                audio = self.encoding(audio)
+
+            info = {}
+
+            info["path"] = audio_filename
+
+            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
+
+            end_time = time.time()
+
+            info["load_time"] = end_time - start_time
+
+            for custom_md_path in self.custom_metadata_fns.keys():
+                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)
+
+                if "__reject__" in info and info["__reject__"]:
+                    return self[random.randrange(len(self))]
+
+            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):
+
+    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
+                )
+            )
+
+        train_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(train_set, batch_size, shuffle=True,
+                                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/build/lib/stable_audio_tools/data/utils.py

@@ -0,0 +1,96 @@
+import math
+import random
+import torch
+
+from torch import nn
+from typing import Tuple
+
+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]:
+        
+        n_channels, n_samples = source.shape
+        
+        # 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):
+            offset = random.randint(0, upper_bound)
+
+        # 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

stable/build/lib/stable_audio_tools/inference/generation.py

@@ -0,0 +1,274 @@
+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)
+    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)
+    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: 
+    #sampled = sample(model.model, noise, steps, 0, **conditioning_tensors, embedding_scale=cfg_scale)
+    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 audio
+    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) 
+    #print(mask)
+    return mask

stable/build/lib/stable_audio_tools/inference/sampling.py

@@ -0,0 +1,232 @@
+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
+
+
+    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/build/lib/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/build/lib/stable_audio_tools/interface/gradio.py

@@ -0,0 +1,700 @@
+import gc
+import platform
+
+import numpy as np
+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
+
+model = None
+sample_rate = 32000
+sample_size = 1920000
+
+def load_model(model_config=None, model_ckpt_path=None, pretrained_name=None, pretransform_ckpt_path=None, device="cuda", model_half=False):
+    global model, sample_rate, sample_size
+    
+    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}")
+        # Load checkpoint
+        copy_state_dict(model, load_ckpt_state_dict(model_ckpt_path))
+        #model.load_state_dict(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
+
+def generate_cond(
+        prompt,
+        negative_prompt=None,
+        seconds_start=0,
+        seconds_total=30,
+        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}")
+
+    global preview_images
+    preview_images = []
+    if preview_every == 0:
+        preview_every = None
+
+    # Return fake stereo audio
+    conditioning = [{"prompt": prompt, "seconds_start": seconds_start, "seconds_total": seconds_total}] * batch_size
+
+    if negative_prompt:
+        negative_conditioning = [{"prompt": negative_prompt, "seconds_start": seconds_start, "seconds_total": seconds_total}] * batch_size
+    else:
+        negative_conditioning = None
+        
+    #Get the device from the model
+    device = next(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
+        # Turn into torch tensor, converting from int16 to float32
+        init_audio = torch.from_numpy(init_audio).float().div(32767)
+        
+        if init_audio.dim() == 1:
+            init_audio = init_audio.unsqueeze(0) # [1, n]
+        elif init_audio.dim() == 2:
+            init_audio = init_audio.transpose(0, 1) # [n, 2] -> [2, n]
+
+        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):
+        global 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 inpainting, send mask args
+    # This will definitely change in the future
+    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 
+
+    # Do the audio generation
+    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
+    )
+
+    # Convert to WAV file
+    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()
+    torchaudio.save("output.wav", audio, sample_rate)
+
+    # Let's look at a nice spectrogram too
+    audio_spectrogram = audio_spectrogram_image(audio, sample_rate=sample_rate)
+
+    return ("output.wav", [audio_spectrogram, *preview_images])
+
+def generate_uncond(
+        steps=250,
+        seed=-1,
+        sampler_type="dpmpp-3m-sde",
+        sigma_min=0.03,
+        sigma_max=1000,
+        use_init=False,
+        init_audio=None,
+        init_noise_level=1.0,
+        batch_size=1,
+        preview_every=None
+        ):
+
+    global preview_images
+
+    preview_images = []
+
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gc.collect()
+
+    #Get the device from the model
+    device = next(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
+        # Turn into torch tensor, converting from int16 to float32
+        init_audio = torch.from_numpy(init_audio).float().div(32767)
+        
+        if init_audio.dim() == 1:
+            init_audio = init_audio.unsqueeze(0) # [1, n]
+        elif init_audio.dim() == 2:
+            init_audio = init_audio.transpose(0, 1) # [n, 2] -> [2, n]
+
+        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):
+        global 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})"))
+
+    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
+    )
+
+    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()
+
+    torchaudio.save("output.wav", audio, sample_rate)
+
+    audio_spectrogram = audio_spectrogram_image(audio, sample_rate=sample_rate)
+
+    return ("output.wav", [audio_spectrogram, *preview_images])
+
+def generate_lm(
+        temperature=1.0,
+        top_p=0.95,
+        top_k=0,    
+        batch_size=1,
+        ):
+    
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gc.collect()
+
+    #Get the device from the model
+    device = next(model.parameters()).device
+
+    audio = model.generate_audio(
+        batch_size=batch_size,
+        max_gen_len = sample_size//model.pretransform.downsampling_ratio,
+        conditioning=None,
+        temp=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        use_cache=True
+    )
+
+    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()
+
+    torchaudio.save("output.wav", audio, sample_rate)
+
+    audio_spectrogram = audio_spectrogram_image(audio, sample_rate=sample_rate)
+
+    return ("output.wav", [audio_spectrogram])
+
+
+def create_uncond_sampling_ui(model_config):   
+    generate_button = gr.Button("Generate", variant='primary', scale=1)
+    
+    with gr.Row(equal_height=False):
+        with gr.Column():            
+            with gr.Row():
+                # Steps slider
+                steps_slider = gr.Slider(minimum=1, maximum=500, step=1, value=100, label="Steps")
+
+            with gr.Accordion("Sampler params", open=False):
+            
+                # Seed
+                seed_textbox = gr.Textbox(label="Seed (set to -1 for random seed)", value="-1")
+
+            # Sampler params
+                with gr.Row():
+                    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")
+
+            with gr.Accordion("Init audio", open=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=0.0, maximum=100.0, step=0.01, value=0.1, label="Init noise level")
+
+        with gr.Column():
+            audio_output = gr.Audio(label="Output audio", interactive=False)
+            audio_spectrogram_output = gr.Gallery(label="Output spectrogram", show_label=False)
+            send_to_init_button = gr.Button("Send to init audio", scale=1)
+            send_to_init_button.click(fn=lambda audio: audio, inputs=[audio_output], outputs=[init_audio_input])
+    
+    generate_button.click(fn=generate_uncond, 
+        inputs=[
+            steps_slider, 
+            seed_textbox, 
+            sampler_type_dropdown, 
+            sigma_min_slider, 
+            sigma_max_slider,
+            init_audio_checkbox,
+            init_audio_input,
+            init_noise_level_slider,
+        ], 
+        outputs=[
+            audio_output, 
+            audio_spectrogram_output
+        ], 
+        api_name="generate")
+
+def create_sampling_ui(model_config, inpainting=False):
+    with gr.Row():
+        with gr.Column(scale=6):
+            prompt = gr.Textbox(show_label=False, placeholder="Prompt")
+            negative_prompt = gr.Textbox(show_label=False, placeholder="Negative prompt")
+        generate_button = gr.Button("Generate", variant='primary', scale=1)
+    
+    model_conditioning_config = model_config["model"].get("conditioning", None)
+
+    has_seconds_start = False
+    has_seconds_total = False
+
+    if model_conditioning_config is not None:
+        for conditioning_config in model_conditioning_config["configs"]:
+            if conditioning_config["id"] == "seconds_start":
+                has_seconds_start = True
+            if conditioning_config["id"] == "seconds_total":
+                has_seconds_total = True
+
+    with gr.Row(equal_height=False):
+        with gr.Column():
+            with gr.Row(visible = has_seconds_start or has_seconds_total):
+                # Timing controls
+                seconds_start_slider = gr.Slider(minimum=0, maximum=512, step=1, value=0, label="Seconds start", visible=has_seconds_start)
+                seconds_total_slider = gr.Slider(minimum=0, maximum=512, step=1, value=sample_size//sample_rate, label="Seconds total", visible=has_seconds_total)
+            
+            with gr.Row():
+                # Steps slider
+                steps_slider = gr.Slider(minimum=1, maximum=500, step=1, value=100, label="Steps")
+
+                # Preview Every slider
+                preview_every_slider = gr.Slider(minimum=0, maximum=100, step=1, value=0, label="Preview Every")
+
+                # CFG scale 
+                cfg_scale_slider = gr.Slider(minimum=0.0, maximum=25.0, step=0.1, value=7.0, label="CFG scale")
+
+            with gr.Accordion("Sampler params", open=False):
+            
+                # Seed
+                seed_textbox = gr.Textbox(label="Seed (set to -1 for random seed)", value="-1")
+
+                # Sampler params
+                with gr.Row():
+                    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")
+
+            if inpainting: 
+                # Inpainting Tab
+                with gr.Accordion("Inpainting", open=False):
+                    sigma_max_slider.maximum=1000
+                    
+                    init_audio_checkbox = gr.Checkbox(label="Do inpainting")
+                    init_audio_input = gr.Audio(label="Init audio")
+                    init_noise_level_slider = gr.Slider(minimum=0.1, maximum=100.0, step=0.1, value=80, label="Init audio noise level", visible=False) # hide this
+
+                    mask_cropfrom_slider = gr.Slider(minimum=0.0, maximum=100.0, step=0.1, value=0, label="Crop From %")
+                    mask_pastefrom_slider = gr.Slider(minimum=0.0, maximum=100.0, step=0.1, value=0, label="Paste From %")
+                    mask_pasteto_slider = gr.Slider(minimum=0.0, maximum=100.0, step=0.1, value=100, label="Paste To %")
+
+                    mask_maskstart_slider = gr.Slider(minimum=0.0, maximum=100.0, step=0.1, value=50, label="Mask Start %")
+                    mask_maskend_slider = gr.Slider(minimum=0.0, maximum=100.0, step=0.1, value=100, label="Mask End %")
+                    mask_softnessL_slider = gr.Slider(minimum=0.0, maximum=100.0, step=0.1, value=0, label="Softmask Left Crossfade Length %")
+                    mask_softnessR_slider = gr.Slider(minimum=0.0, maximum=100.0, step=0.1, value=0, label="Softmask Right Crossfade Length %")
+                    mask_marination_slider = gr.Slider(minimum=0.0, maximum=1, step=0.0001, value=0, label="Marination level", visible=False) # still working on the usefulness of this 
+
+                    inputs = [prompt, 
+                        negative_prompt,
+                        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,
+                        mask_cropfrom_slider,
+                        mask_pastefrom_slider,
+                        mask_pasteto_slider,
+                        mask_maskstart_slider,
+                        mask_maskend_slider,
+                        mask_softnessL_slider,
+                        mask_softnessR_slider,
+                        mask_marination_slider
+                    ]
+            else:
+                # Default generation tab
+                with gr.Accordion("Init audio", open=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")
+
+                    inputs = [prompt, 
+                        negative_prompt,
+                        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
+                    ]
+
+        with gr.Column():
+            audio_output = gr.Audio(label="Output audio", interactive=False)
+            audio_spectrogram_output = gr.Gallery(label="Output spectrogram", show_label=False)
+            send_to_init_button = gr.Button("Send to init audio", scale=1)
+            send_to_init_button.click(fn=lambda audio: audio, inputs=[audio_output], outputs=[init_audio_input])
+    
+    generate_button.click(fn=generate_cond, 
+        inputs=inputs,
+        outputs=[
+            audio_output, 
+            audio_spectrogram_output
+        ], 
+        api_name="generate")
+
+
+def create_txt2audio_ui(model_config):
+    with gr.Blocks() as ui:
+        with gr.Tab("Generation"):
+            create_sampling_ui(model_config) 
+        with gr.Tab("Inpainting"):
+            create_sampling_ui(model_config, inpainting=True)    
+    return ui
+
+def create_diffusion_uncond_ui(model_config):
+    with gr.Blocks() as ui:
+        create_uncond_sampling_ui(model_config)
+    
+    return ui
+
+def autoencoder_process(audio, latent_noise, n_quantizers):
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gc.collect()
+
+    #Get the device from the model
+    device = next(model.parameters()).device
+
+    in_sr, audio = audio
+
+    audio = torch.from_numpy(audio).float().div(32767).to(device)
+
+    if audio.dim() == 1:
+        audio = audio.unsqueeze(0)
+    else:
+        audio = audio.transpose(0, 1)
+
+    audio = model.preprocess_audio_for_encoder(audio, in_sr)
+    # Note: If you need to do chunked encoding, to reduce VRAM, 
+    # then add these arguments to encode_audio and decode_audio: chunked=True, overlap=32, chunk_size=128
+    # To turn it off, do chunked=False
+    # Optimal overlap and chunk_size values will depend on the model. 
+    # See encode_audio & decode_audio in autoencoders.py for more info
+    # Get dtype of model
+    dtype = next(model.parameters()).dtype
+
+    audio = audio.to(dtype)
+
+    if n_quantizers > 0:
+        latents = model.encode_audio(audio, chunked=False, n_quantizers=n_quantizers)
+    else:
+        latents = model.encode_audio(audio, chunked=False)
+
+    if latent_noise > 0:
+        latents = latents + torch.randn_like(latents) * latent_noise
+
+    audio = model.decode_audio(latents, chunked=False)
+
+    audio = rearrange(audio, "b d n -> d (b n)")
+
+    audio = audio.to(torch.float32).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+
+    torchaudio.save("output.wav", audio, sample_rate)
+
+    return "output.wav"
+
+def create_autoencoder_ui(model_config):
+
+    is_dac_rvq = "model" in model_config and "bottleneck" in model_config["model"] and model_config["model"]["bottleneck"]["type"] in ["dac_rvq","dac_rvq_vae"]
+
+    if is_dac_rvq:
+        n_quantizers = model_config["model"]["bottleneck"]["config"]["n_codebooks"]
+    else:
+        n_quantizers = 0
+
+    with gr.Blocks() as ui:
+        input_audio = gr.Audio(label="Input audio")
+        output_audio = gr.Audio(label="Output audio", interactive=False)
+        n_quantizers_slider = gr.Slider(minimum=1, maximum=n_quantizers, step=1, value=n_quantizers, label="# quantizers", visible=is_dac_rvq)
+        latent_noise_slider = gr.Slider(minimum=0.0, maximum=10.0, step=0.001, value=0.0, label="Add latent noise")
+        process_button = gr.Button("Process", variant='primary', scale=1)
+        process_button.click(fn=autoencoder_process, inputs=[input_audio, latent_noise_slider, n_quantizers_slider], outputs=output_audio, api_name="process")
+
+    return ui
+
+def diffusion_prior_process(audio, steps, sampler_type, sigma_min, sigma_max):
+
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gc.collect()
+
+    #Get the device from the model
+    device = next(model.parameters()).device
+
+    in_sr, audio = audio
+
+    audio = torch.from_numpy(audio).float().div(32767).to(device)
+    
+    if audio.dim() == 1:
+        audio = audio.unsqueeze(0) # [1, n]
+    elif audio.dim() == 2:
+        audio = audio.transpose(0, 1) # [n, 2] -> [2, n]
+
+    audio = audio.unsqueeze(0)
+
+    audio = model.stereoize(audio, in_sr, steps, sampler_kwargs={"sampler_type": sampler_type, "sigma_min": sigma_min, "sigma_max": sigma_max})
+
+    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()
+
+    torchaudio.save("output.wav", audio, sample_rate)
+
+    return "output.wav"
+
+def create_diffusion_prior_ui(model_config):
+    with gr.Blocks() as ui:
+        input_audio = gr.Audio(label="Input audio")
+        output_audio = gr.Audio(label="Output audio", interactive=False)
+        # Sampler params
+        with gr.Row():
+            steps_slider = gr.Slider(minimum=1, maximum=500, step=1, value=100, label="Steps")
+            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")
+        process_button = gr.Button("Process", variant='primary', scale=1)
+        process_button.click(fn=diffusion_prior_process, inputs=[input_audio, steps_slider, sampler_type_dropdown, sigma_min_slider, sigma_max_slider], outputs=output_audio, api_name="process")    
+
+    return ui
+
+def create_lm_ui(model_config):
+    with gr.Blocks() as ui:
+        output_audio = gr.Audio(label="Output audio", interactive=False)
+        audio_spectrogram_output = gr.Gallery(label="Output spectrogram", show_label=False)
+
+        # Sampling params
+        with gr.Row():
+            temperature_slider = gr.Slider(minimum=0, maximum=5, step=0.01, value=1.0, label="Temperature")
+            top_p_slider = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.95, label="Top p")
+            top_k_slider = gr.Slider(minimum=0, maximum=100, step=1, value=0, label="Top k")
+
+        generate_button = gr.Button("Generate", variant='primary', scale=1)
+        generate_button.click(
+            fn=generate_lm, 
+            inputs=[
+                temperature_slider, 
+                top_p_slider, 
+                top_k_slider
+            ], 
+            outputs=[output_audio, audio_spectrogram_output],
+            api_name="generate"
+        )
+
+    return ui
+
+def create_ui(model_config_path=None, ckpt_path=None, pretrained_name=None, pretransform_ckpt_path=None, model_half=False):
+
+    assert (pretrained_name is not None) ^ (model_config_path is not None and ckpt_path is not None), "Must specify either pretrained name or provide a model config and checkpoint, but not both"
+
+    if model_config_path is not None:
+        # Load config from json file
+        with open(model_config_path) as f:
+            model_config = json.load(f)
+    else:
+        model_config = None
+
+    try:
+        has_mps = platform.system() == "Darwin" and torch.backends.mps.is_available()
+    except Exception:
+        # In case this version of Torch doesn't even have `torch.backends.mps`...
+        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_config = load_model(model_config, ckpt_path, pretrained_name=pretrained_name, pretransform_ckpt_path=pretransform_ckpt_path, model_half=model_half, device=device)
+    
+    model_type = model_config["model_type"]
+
+    if model_type == "diffusion_cond":
+        ui = create_txt2audio_ui(model_config)
+    elif model_type == "diffusion_uncond":
+        ui = create_diffusion_uncond_ui(model_config)
+    elif model_type == "autoencoder" or model_type == "diffusion_autoencoder":
+        ui = create_autoencoder_ui(model_config)
+    elif model_type == "diffusion_prior":
+        ui = create_diffusion_prior_ui(model_config)
+    elif model_type == "lm":
+        ui = create_lm_ui(model_config)
+        
+    return ui

stable/build/lib/stable_audio_tools/models/__init__.py

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

stable/build/lib/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/build/lib/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]))
+                decoded = 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/build/lib/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/build/lib/stable_audio_tools/models/bottleneck.py

@@ -0,0 +1,326 @@
+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):
+        super().__init__(is_discrete=False)
+
+        self.noise_augment_dim = noise_augment_dim
+    
+    def encode(self, x, return_info=False):
+        info = {}
+
+        if self.training and return_info:
+            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, **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, **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]
+
+        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, dim, levels):
+        super().__init__(num_quantizers = 1, codebook_size = levels ** dim, tokens_id = "quantizer_indices")
+        self.quantizer = FSQ(levels=[levels] * dim)
+
+    def encode(self, x, return_info=False):
+        info = {}
+
+        x = rearrange(x, "b c n -> b n c")
+        x, indices = self.quantizer(x)
+        x = rearrange(x, "b n c -> b c n")
+
+        info["quantizer_indices"] = indices
+
+        if return_info:
+            return x, info
+        else:
+            return x
+        
+    def decode(self, x):
+        return x
+    
+    def decode_tokens(self, tokens, **kwargs):
+        latents = self.quantizer.indices_to_codes(tokens)
+
+        return self.decode(latents, **kwargs)

stable/build/lib/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/build/lib/stable_audio_tools/models/conditioners.py

@@ -0,0 +1,561 @@
+#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 ..training.utils import copy_state_dict
+from .utils import load_ckpt_state_dict
+
+from torch import nn
+
+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 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 = T5Tokenizer.from_pretrained(t5_model_name, model_max_length = max_length)
+                # model = T5EncoderModel.from_pretrained(t5_model_name, max_length=max_length).train(enable_grad).requires_grad_(enable_grad)
+                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) and 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 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")
+
+                #Unwrap the condition info if it's a single-element list or tuple, this is to support collation functions that wrap everything in a list
+                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 == "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)
+        else:
+            raise ValueError(f"Unknown conditioner type: {conditioner_type}")
+
+    return MultiConditioner(conditioners, default_keys=default_keys)

stable/build/lib/stable_audio_tools/models/diffusion.py

@@ -0,0 +1,701 @@
+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
+        self.global_cond_ids = global_cond_ids
+        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[str, 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)
+            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:
+            # 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:
+            # 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:
+            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)
+    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/build/lib/stable_audio_tools/models/diffusion_prior.py

@@ -0,0 +1,79 @@
+from enum import Enum
+import typing as tp
+
+from .diffusion import ConditionedDiffusionModelWrapper
+from ..inference.generation import generate_diffusion_cond
+from ..inference.utils import prepare_audio
+
+import torch
+from torch.nn import functional as F
+from torchaudio import transforms as T
+
+# Define prior types enum
+class PriorType(Enum):
+    MonoToStereo = 1
+
+class DiffusionPrior(ConditionedDiffusionModelWrapper):
+    def __init__(self, *args, prior_type: PriorType=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.prior_type = prior_type  
+
+class MonoToStereoDiffusionPrior(DiffusionPrior):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, prior_type=PriorType.MonoToStereo, **kwargs)
+
+    def stereoize(
+        self, 
+        audio: torch.Tensor, # (batch, channels, time)
+        in_sr: int,
+        steps: int,
+        sampler_kwargs: dict = {},
+    ):
+        """
+        Generate stereo audio from mono audio using a pre-trained diffusion prior
+
+        Args:
+            audio: The mono audio to convert to stereo
+            in_sr: The sample rate of the input audio
+            steps: The number of diffusion steps to run
+            sampler_kwargs: Keyword arguments to pass to the diffusion sampler
+        """
+
+        device = audio.device
+
+        sample_rate = self.sample_rate
+
+        # Resample input audio if necessary
+        if in_sr != sample_rate:
+            resample_tf = T.Resample(in_sr, sample_rate).to(audio.device)
+            audio = resample_tf(audio)
+
+        audio_length = audio.shape[-1]
+
+        # Pad input audio to be compatible with the model
+        min_length = self.min_input_length
+        padded_input_length = audio_length + (min_length - (audio_length % min_length)) % min_length
+
+        # Pad input audio to be compatible with the model
+        if padded_input_length > audio_length:
+            audio = F.pad(audio, (0, padded_input_length - audio_length))
+
+        # Make audio mono, duplicate to stereo
+        dual_mono = audio.mean(1, keepdim=True).repeat(1, 2, 1)
+
+        if self.pretransform is not None:
+            dual_mono = self.pretransform.encode(dual_mono)
+
+        conditioning = {"source": [dual_mono]}
+
+        stereo_audio = generate_diffusion_cond(
+            self, 
+            conditioning_tensors=conditioning,
+            steps=steps,
+            sample_size=padded_input_length,
+            sample_rate=sample_rate,
+            device=device,
+            **sampler_kwargs,
+        ) 
+
+        return stereo_audio

stable/build/lib/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/build/lib/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)
+
+        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":
+            if prepend_inputs is None:
+                # Prepend inputs are just the global embed, and the mask is all ones
+                prepend_inputs = global_embed.unsqueeze(1)
+                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]
+
+        x = self.preprocess_conv(x) + x
+
+        x = rearrange(x, "b c t -> b t c")
+
+        extra_args = {}
+
+        if self.global_cond_type == "adaLN":
+            extra_args["global_cond"] = global_embed
+
+        if 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/build/lib/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/build/lib/stable_audio_tools/models/lm.py

@@ -0,0 +1,541 @@
+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/build/lib/stable_audio_tools/models/lm_backbone.py

@@ -0,0 +1,159 @@
+from torch import nn
+from x_transformers import ContinuousTransformerWrapper, Decoder
+
+from .transformer import ContinuousTransformer
+
+# Interface for backbone of a language model
+# Handles conditioning and cross-attention
+# Does not have to deal with patterns or quantizer heads
+class AudioLMBackbone(nn.Module):
+    def __init__(self, embed_dim: int, use_generation_cache=False, **kwargs):
+        super().__init__()
+
+        self.embed_dim = embed_dim
+        self.use_generation_cache = use_generation_cache
+
+    def forward(
+        self, 
+        x, 
+        cross_attn_cond=None, 
+        prepend_cond=None, 
+        prepend_cond_mask=None,
+        global_cond=None,
+        use_cache=False,
+        **kwargs
+        ):
+        raise NotImplementedError
+    
+    def reset_generation_cache(
+        self,
+        max_seq_len, 
+        batch_size,
+        dtype=None
+    ):
+        pass
+
+    def update_generation_cache(
+        self,
+        seqlen_offset
+    ):
+        pass
+
+class XTransformersAudioLMBackbone(AudioLMBackbone):
+    def __init__(self,
+                 embed_dim: int,
+                 cross_attn_cond_dim: int = 0,
+                 prepend_cond_dim: int = 0,
+                 **kwargs):
+        super().__init__(embed_dim=embed_dim)
+
+        # Embeddings are done in the AudioLanguageModel, so we use the continuous-input transformer
+        self.model = ContinuousTransformerWrapper(
+            dim_in=embed_dim,
+            dim_out=embed_dim,
+            max_seq_len=0, #Not relevant without absolute positional embeds,
+            attn_layers=Decoder(
+                dim=embed_dim,
+                attn_flash = True,
+                cross_attend = cross_attn_cond_dim > 0,
+                zero_init_branch_output=True,
+                use_abs_pos_emb = False,
+                rotary_pos_emb=True,
+                ff_swish = True,
+                ff_glu = True,
+                **kwargs
+            )
+        )
+
+        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)
+            )
+
+        if cross_attn_cond_dim > 0:
+            # Cross-attention conditioning
+            self.to_cross_attn_embed = nn.Sequential(
+                nn.Linear(cross_attn_cond_dim, embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(embed_dim, embed_dim, bias=False)
+            )
+
+    def forward(self, x, mask=None, prepend_cond=None, prepend_cond_mask=None, cross_attn_cond=None, global_cond=None, use_cache=False):
+
+        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_length = prepend_cond.shape[1]
+
+            if prepend_cond_mask is not None:
+                # Cast mask to bool
+                prepend_cond_mask = prepend_cond_mask.bool()
+
+        if cross_attn_cond is not None:
+            # Project the cross-attention conditioning to the embedding dimension
+            cross_attn_cond = self.to_cross_attn_embed(cross_attn_cond)
+
+        return self.model(x, mask=mask, context=cross_attn_cond, prepend_embeds=prepend_cond, prepend_mask=prepend_cond_mask)[:, prepend_length:, :]
+    
+class ContinuousTransformerAudioLMBackbone(AudioLMBackbone):
+    def __init__(self,
+                 embed_dim: int,
+                 cross_attn_cond_dim: int = 0,
+                 prepend_cond_dim: int = 0,
+                 project_cross_attn_cond: bool = False,
+                 **kwargs):
+        super().__init__(embed_dim=embed_dim)
+
+        # Embeddings are done in the AudioLanguageModel, so we use the continuous-input transformer
+        self.model = ContinuousTransformer(
+            dim=embed_dim,
+            dim_in=embed_dim,
+            dim_out=embed_dim,
+            cross_attend = cross_attn_cond_dim > 0,
+            cond_token_dim = embed_dim if project_cross_attn_cond else cross_attn_cond_dim,
+            causal=True,
+            **kwargs
+        )
+
+        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)
+            )
+
+        if cross_attn_cond_dim > 0 and project_cross_attn_cond:
+            # Cross-attention conditioning
+            self.to_cross_attn_embed = nn.Sequential(
+                nn.Linear(cross_attn_cond_dim, embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(embed_dim, embed_dim, bias=False)
+            )
+        else:
+            self.to_cross_attn_embed = nn.Identity()
+
+    def forward(self, x, mask=None, prepend_cond=None, prepend_cond_mask=None, cross_attn_cond=None, global_cond=None, use_cache=False):
+
+        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_length = prepend_cond.shape[1]
+
+            if prepend_cond_mask is not None:
+                # Cast mask to bool
+                prepend_cond_mask = prepend_cond_mask.bool()
+
+        if cross_attn_cond is not None:
+            # Cast cross_attn_cond to same dtype as self.to_cross_attn_embed
+            cross_attn_cond = cross_attn_cond.to(self.to_cross_attn_embed[0].weight.dtype)
+
+            # Project the cross-attention conditioning to the embedding dimension
+            cross_attn_cond = self.to_cross_attn_embed(cross_attn_cond)
+
+        return self.model(x, mask=mask, context=cross_attn_cond, prepend_embeds=prepend_cond, prepend_mask=prepend_cond_mask)[:, prepend_length:, :]

stable/build/lib/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/build/lib/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/build/lib/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="model_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/build/lib/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/build/lib/stable_audio_tools/models/transformer.py

@@ -0,0 +1,805 @@
+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
+
+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 = False,
+        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
+
+        # 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)
+
+            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:
+            q = F.normalize(q, dim=-1)
+            k = F.normalize(k, dim=-1)
+
+        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
+        
+        # with autocast(enabled = False):
+        #     out_dtype = out.dtype
+        #     out = out.to(torch.float32)
+        #     out = self.to_out(out).to(out_dtype)
+        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)
+            #nn.init.zeros_(self.to_scale_shift_gate_self[1].bias)
+
+    def forward(
+        self,
+        x,
+        context = None,
+        global_cond=None,
+        mask = None,
+        context_mask = None,
+        rotary_pos_emb = None
+    ):
+        if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:
+            
+            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 layer in self.layers:
+            #x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
+            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/build/lib/stable_audio_tools/models/utils.py

@@ -0,0 +1,89 @@
+import torch
+from safetensors.torch import load_file
+
+from torch.nn.utils import remove_weight_norm
+
+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/build/lib/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/build/lib/stable_audio_tools/training/__init__.py

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

stable/build/lib/stable_audio_tools/training/autoencoders.py

@@ -0,0 +1,477 @@
+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
+
+        # Optionally mask out some latents for noise resistance
+        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/build/lib/stable_audio_tools/training/diffusion.py

@@ -0,0 +1,1505 @@
+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 = False,
+            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
+
+        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)
+            
+        # 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):
+                    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")
+
+                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 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/build/lib/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/build/lib/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()

stable/build/lib/stable_audio_tools/training/losses/__init__.py

@@ -0,0 +1 @@
+from .losses import *

stable/build/lib/stable_audio_tools/training/losses/auraloss.py

@@ -0,0 +1,607 @@
+# Copied and modified from https://github.com/csteinmetz1/auraloss/blob/main/auraloss/freq.py under Apache License 2.0
+# You can find the license at LICENSES/LICENSE_AURALOSS.txt
+
+import torch
+import numpy as np
+from typing import List, Any
+import scipy.signal
+
+def apply_reduction(losses, reduction="none"):
+    """Apply reduction to collection of losses."""
+    if reduction == "mean":
+        losses = losses.mean()
+    elif reduction == "sum":
+        losses = losses.sum()
+    return losses
+
+def get_window(win_type: str, win_length: int):
+    """Return a window function.
+
+    Args:
+        win_type (str): Window type. Can either be one of the window function provided in PyTorch
+            ['hann_window', 'bartlett_window', 'blackman_window', 'hamming_window', 'kaiser_window']
+            or any of the windows provided by [SciPy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.windows.get_window.html).
+        win_length (int): Window length
+
+    Returns:
+        win: The window as a 1D torch tensor
+    """
+
+    try:
+        win = getattr(torch, win_type)(win_length)
+    except:
+        win = torch.from_numpy(scipy.signal.windows.get_window(win_type, win_length))
+
+    return win
+
+class SumAndDifference(torch.nn.Module):
+    """Sum and difference signal extraction module."""
+
+    def __init__(self):
+        """Initialize sum and difference extraction module."""
+        super(SumAndDifference, self).__init__()
+
+    def forward(self, x):
+        """Calculate forward propagation.
+
+        Args:
+            x (Tensor): Predicted signal (B, #channels, #samples).
+        Returns:
+            Tensor: Sum signal.
+            Tensor: Difference signal.
+        """
+        if not (x.size(1) == 2):  # inputs must be stereo
+            raise ValueError(f"Input must be stereo: {x.size(1)} channel(s).")
+
+        sum_sig = self.sum(x).unsqueeze(1)
+        diff_sig = self.diff(x).unsqueeze(1)
+
+        return sum_sig, diff_sig
+
+    @staticmethod
+    def sum(x):
+        return x[:, 0, :] + x[:, 1, :]
+
+    @staticmethod
+    def diff(x):
+        return x[:, 0, :] - x[:, 1, :]
+
+
+class FIRFilter(torch.nn.Module):
+    """FIR pre-emphasis filtering module.
+
+    Args:
+        filter_type (str): Shape of the desired FIR filter ("hp", "fd", "aw"). Default: "hp"
+        coef (float): Coefficient value for the filter tap (only applicable for "hp" and "fd"). Default: 0.85
+        ntaps (int): Number of FIR filter taps for constructing A-weighting filters. Default: 101
+        plot (bool): Plot the magnitude respond of the filter. Default: False
+
+    Based upon the perceptual loss pre-empahsis filters proposed by
+    [Wright & Välimäki, 2019](https://arxiv.org/abs/1911.08922).
+
+    A-weighting filter - "aw"
+    First-order highpass - "hp"
+    Folded differentiator - "fd"
+
+    Note that the default coefficeint value of 0.85 is optimized for
+    a sampling rate of 44.1 kHz, considering adjusting this value at differnt sampling rates.
+    """
+
+    def __init__(self, filter_type="hp", coef=0.85, fs=44100, ntaps=101, plot=False):
+        """Initilize FIR pre-emphasis filtering module."""
+        super(FIRFilter, self).__init__()
+        self.filter_type = filter_type
+        self.coef = coef
+        self.fs = fs
+        self.ntaps = ntaps
+        self.plot = plot
+
+        import scipy.signal
+
+        if ntaps % 2 == 0:
+            raise ValueError(f"ntaps must be odd (ntaps={ntaps}).")
+
+        if filter_type == "hp":
+            self.fir = torch.nn.Conv1d(1, 1, kernel_size=3, bias=False, padding=1)
+            self.fir.weight.requires_grad = False
+            self.fir.weight.data = torch.tensor([1, -coef, 0]).view(1, 1, -1)
+        elif filter_type == "fd":
+            self.fir = torch.nn.Conv1d(1, 1, kernel_size=3, bias=False, padding=1)
+            self.fir.weight.requires_grad = False
+            self.fir.weight.data = torch.tensor([1, 0, -coef]).view(1, 1, -1)
+        elif filter_type == "aw":
+            # Definition of analog A-weighting filter according to IEC/CD 1672.
+            f1 = 20.598997
+            f2 = 107.65265
+            f3 = 737.86223
+            f4 = 12194.217
+            A1000 = 1.9997
+
+            NUMs = [(2 * np.pi * f4) ** 2 * (10 ** (A1000 / 20)), 0, 0, 0, 0]
+            DENs = np.polymul(
+                [1, 4 * np.pi * f4, (2 * np.pi * f4) ** 2],
+                [1, 4 * np.pi * f1, (2 * np.pi * f1) ** 2],
+            )
+            DENs = np.polymul(
+                np.polymul(DENs, [1, 2 * np.pi * f3]), [1, 2 * np.pi * f2]
+            )
+
+            # convert analog filter to digital filter
+            b, a = scipy.signal.bilinear(NUMs, DENs, fs=fs)
+
+            # compute the digital filter frequency response
+            w_iir, h_iir = scipy.signal.freqz(b, a, worN=512, fs=fs)
+
+            # then we fit to 101 tap FIR filter with least squares
+            taps = scipy.signal.firls(ntaps, w_iir, abs(h_iir), fs=fs)
+
+            # now implement this digital FIR filter as a Conv1d layer
+            self.fir = torch.nn.Conv1d(
+                1, 1, kernel_size=ntaps, bias=False, padding=ntaps // 2
+            )
+            self.fir.weight.requires_grad = False
+            self.fir.weight.data = torch.tensor(taps.astype("float32")).view(1, 1, -1)
+
+            if plot:
+                from .plotting import compare_filters
+                compare_filters(b, a, taps, fs=fs)
+
+    def forward(self, input, target):
+        """Calculate forward propagation.
+        Args:
+            input (Tensor): Predicted signal (B, #channels, #samples).
+            target (Tensor): Groundtruth signal (B, #channels, #samples).
+        Returns:
+            Tensor: Filtered signal.
+        """
+        input = torch.nn.functional.conv1d(
+            input, self.fir.weight.data, padding=self.ntaps // 2
+        )
+        target = torch.nn.functional.conv1d(
+            target, self.fir.weight.data, padding=self.ntaps // 2
+        )
+        return input, target
+
+class SpectralConvergenceLoss(torch.nn.Module):
+    """Spectral convergence loss module.
+
+    See [Arik et al., 2018](https://arxiv.org/abs/1808.06719).
+    """
+
+    def __init__(self):
+        super(SpectralConvergenceLoss, self).__init__()
+
+    def forward(self, x_mag, y_mag):
+        return (torch.norm(y_mag - x_mag, p="fro", dim=[-1, -2]) / torch.norm(y_mag, p="fro", dim=[-1, -2])).mean()
+
+class STFTMagnitudeLoss(torch.nn.Module):
+    """STFT magnitude loss module.
+
+    See [Arik et al., 2018](https://arxiv.org/abs/1808.06719)
+    and [Engel et al., 2020](https://arxiv.org/abs/2001.04643v1)
+
+    Log-magnitudes are calculated with `log(log_fac*x + log_eps)`, where `log_fac` controls the
+    compression strength (larger value results in more compression), and `log_eps` can be used
+    to control the range of the compressed output values (e.g., `log_eps>=1` ensures positive
+    output values). The default values `log_fac=1` and `log_eps=0` correspond to plain log-compression.
+
+    Args:
+        log (bool, optional): Log-scale the STFT magnitudes,
+            or use linear scale. Default: True
+        log_eps (float, optional): Constant value added to the magnitudes before evaluating the logarithm.
+            Default: 0.0
+        log_fac (float, optional): Constant multiplication factor for the magnitudes before evaluating the logarithm.
+            Default: 1.0
+        distance (str, optional): Distance function ["L1", "L2"]. Default: "L1"
+        reduction (str, optional): Reduction of the loss elements. Default: "mean"
+    """
+
+    def __init__(self, log=True, log_eps=0.0, log_fac=1.0, distance="L1", reduction="mean"):
+        super(STFTMagnitudeLoss, self).__init__()
+
+        self.log = log
+        self.log_eps = log_eps
+        self.log_fac = log_fac
+
+        if distance == "L1":
+            self.distance = torch.nn.L1Loss(reduction=reduction)
+        elif distance == "L2":
+            self.distance = torch.nn.MSELoss(reduction=reduction)
+        else:
+            raise ValueError(f"Invalid distance: '{distance}'.")
+
+    def forward(self, x_mag, y_mag):
+        if self.log:
+            x_mag = torch.log(self.log_fac * x_mag + self.log_eps)
+            y_mag = torch.log(self.log_fac * y_mag + self.log_eps)
+        return self.distance(x_mag, y_mag)
+
+
+class STFTLoss(torch.nn.Module):
+    """STFT loss module.
+
+    See [Yamamoto et al. 2019](https://arxiv.org/abs/1904.04472).
+
+    Args:
+        fft_size (int, optional): FFT size in samples. Default: 1024
+        hop_size (int, optional): Hop size of the FFT in samples. Default: 256
+        win_length (int, optional): Length of the FFT analysis window. Default: 1024
+        window (str, optional): Window to apply before FFT, can either be one of the window function provided in PyTorch
+            ['hann_window', 'bartlett_window', 'blackman_window', 'hamming_window', 'kaiser_window']
+            or any of the windows provided by [SciPy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.windows.get_window.html).
+            Default: 'hann_window'
+        w_sc (float, optional): Weight of the spectral convergence loss term. Default: 1.0
+        w_log_mag (float, optional): Weight of the log magnitude loss term. Default: 1.0
+        w_lin_mag_mag (float, optional): Weight of the linear magnitude loss term. Default: 0.0
+        w_phs (float, optional): Weight of the spectral phase loss term. Default: 0.0
+        sample_rate (int, optional): Sample rate. Required when scale = 'mel'. Default: None
+        scale (str, optional): Optional frequency scaling method, options include:
+            ['mel', 'chroma']
+            Default: None
+        n_bins (int, optional): Number of scaling frequency bins. Default: None.
+        perceptual_weighting (bool, optional): Apply perceptual A-weighting (Sample rate must be supplied). Default: False
+        scale_invariance (bool, optional): Perform an optimal scaling of the target. Default: False
+        eps (float, optional): Small epsilon value for stablity. Default: 1e-8
+        output (str, optional): Format of the loss returned.
+            'loss' : Return only the raw, aggregate loss term.
+            'full' : Return the raw loss, plus intermediate loss terms.
+            Default: 'loss'
+        reduction (str, optional): Specifies the reduction to apply to the output:
+            'none': no reduction will be applied,
+            'mean': the sum of the output will be divided by the number of elements in the output,
+            'sum': the output will be summed.
+            Default: 'mean'
+        mag_distance (str, optional): Distance function ["L1", "L2"] for the magnitude loss terms.
+        device (str, optional): Place the filterbanks on specified device. Default: None
+
+    Returns:
+        loss:
+            Aggreate loss term. Only returned if output='loss'. By default.
+        loss, sc_mag_loss, log_mag_loss, lin_mag_loss, phs_loss:
+            Aggregate and intermediate loss terms. Only returned if output='full'.
+    """
+
+    def __init__(
+        self,
+        fft_size: int = 1024,
+        hop_size: int = 256,
+        win_length: int = 1024,
+        window: str = "hann_window",
+        w_sc: float = 1.0,
+        w_log_mag: float = 1.0,
+        w_lin_mag: float = 0.0,
+        w_phs: float = 0.0,
+        sample_rate: float = None,
+        scale: str = None,
+        n_bins: int = None,
+        perceptual_weighting: bool = False,
+        scale_invariance: bool = False,
+        eps: float = 1e-8,
+        output: str = "loss",
+        reduction: str = "mean",
+        mag_distance: str = "L1",
+        device: Any = None,
+        **kwargs
+    ):
+        super().__init__()
+        self.fft_size = fft_size
+        self.hop_size = hop_size
+        self.win_length = win_length
+        self.window = get_window(window, win_length)
+        self.w_sc = w_sc
+        self.w_log_mag = w_log_mag
+        self.w_lin_mag = w_lin_mag
+        self.w_phs = w_phs
+        self.sample_rate = sample_rate
+        self.scale = scale
+        self.n_bins = n_bins
+        self.perceptual_weighting = perceptual_weighting
+        self.scale_invariance = scale_invariance
+        self.eps = eps
+        self.output = output
+        self.reduction = reduction
+        self.mag_distance = mag_distance
+        self.device = device
+
+        self.phs_used = bool(self.w_phs)
+
+        self.spectralconv = SpectralConvergenceLoss()
+        self.logstft = STFTMagnitudeLoss(
+            log=True,
+            reduction=reduction,
+            distance=mag_distance,
+            **kwargs
+        )
+        self.linstft = STFTMagnitudeLoss(
+            log=False,
+            reduction=reduction,
+            distance=mag_distance,
+            **kwargs
+        )
+
+        # setup mel filterbank
+        if scale is not None:
+            try:
+                import librosa.filters
+            except Exception as e:
+                print(e)
+                print("Try `pip install auraloss[all]`.")
+
+            if self.scale == "mel":
+                assert sample_rate != None  # Must set sample rate to use mel scale
+                assert n_bins <= fft_size  # Must be more FFT bins than Mel bins
+                fb = librosa.filters.mel(sr=sample_rate, n_fft=fft_size, n_mels=n_bins)
+                fb = torch.tensor(fb).unsqueeze(0)
+
+            elif self.scale == "chroma":
+                assert sample_rate != None  # Must set sample rate to use chroma scale
+                assert n_bins <= fft_size  # Must be more FFT bins than chroma bins
+                fb = librosa.filters.chroma(
+                    sr=sample_rate, n_fft=fft_size, n_chroma=n_bins
+                )
+
+            else:
+                raise ValueError(
+                    f"Invalid scale: {self.scale}. Must be 'mel' or 'chroma'."
+                )
+
+            self.register_buffer("fb", fb)
+
+        if scale is not None and device is not None:
+            self.fb = self.fb.to(self.device)  # move filterbank to device
+
+        if self.perceptual_weighting:
+            if sample_rate is None:
+                raise ValueError(
+                    f"`sample_rate` must be supplied when `perceptual_weighting = True`."
+                )
+            self.prefilter = FIRFilter(filter_type="aw", fs=sample_rate)
+
+    def stft(self, x):
+        """Perform STFT.
+        Args:
+            x (Tensor): Input signal tensor (B, T).
+
+        Returns:
+            Tensor: x_mag, x_phs
+                Magnitude and phase spectra (B, fft_size // 2 + 1, frames).
+        """
+        x_stft = torch.stft(
+            x,
+            self.fft_size,
+            self.hop_size,
+            self.win_length,
+            self.window,
+            return_complex=True,
+        )
+        x_mag = torch.sqrt(
+            torch.clamp((x_stft.real**2) + (x_stft.imag**2), min=self.eps)
+        )
+
+        # torch.angle is expensive, so it is only evaluated if the values are used in the loss
+        if self.phs_used:
+            x_phs = torch.angle(x_stft)
+        else:
+            x_phs = None
+
+        return x_mag, x_phs
+
+    def forward(self, input: torch.Tensor, target: torch.Tensor):
+        bs, chs, seq_len = input.size()
+
+        if self.perceptual_weighting:  # apply optional A-weighting via FIR filter
+            # since FIRFilter only support mono audio we will move channels to batch dim
+            input = input.view(bs * chs, 1, -1)
+            target = target.view(bs * chs, 1, -1)
+
+            # now apply the filter to both
+            self.prefilter.to(input.device)
+            input, target = self.prefilter(input, target)
+
+            # now move the channels back
+            input = input.view(bs, chs, -1)
+            target = target.view(bs, chs, -1)
+
+        # compute the magnitude and phase spectra of input and target
+        self.window = self.window.to(input.device)
+
+        x_mag, x_phs = self.stft(input.view(-1, input.size(-1)))
+        y_mag, y_phs = self.stft(target.view(-1, target.size(-1)))
+
+        # apply relevant transforms
+        if self.scale is not None:
+            self.fb = self.fb.to(input.device)
+            x_mag = torch.matmul(self.fb, x_mag)
+            y_mag = torch.matmul(self.fb, y_mag)
+
+        # normalize scales
+        if self.scale_invariance:
+            alpha = (x_mag * y_mag).sum([-2, -1]) / ((y_mag**2).sum([-2, -1]))
+            y_mag = y_mag * alpha.unsqueeze(-1)
+
+        # compute loss terms
+        sc_mag_loss = self.spectralconv(x_mag, y_mag) if self.w_sc else 0.0
+        log_mag_loss = self.logstft(x_mag, y_mag) if self.w_log_mag else 0.0
+        lin_mag_loss = self.linstft(x_mag, y_mag) if self.w_lin_mag else 0.0
+        phs_loss = torch.nn.functional.mse_loss(x_phs, y_phs) if self.phs_used else 0.0
+
+        # combine loss terms
+        loss = (
+            (self.w_sc * sc_mag_loss)
+            + (self.w_log_mag * log_mag_loss)
+            + (self.w_lin_mag * lin_mag_loss)
+            + (self.w_phs * phs_loss)
+        )
+
+        loss = apply_reduction(loss, reduction=self.reduction)
+
+        if self.output == "loss":
+            return loss
+        elif self.output == "full":
+            return loss, sc_mag_loss, log_mag_loss, lin_mag_loss, phs_loss
+
+class MultiResolutionSTFTLoss(torch.nn.Module):
+    """Multi resolution STFT loss module.
+
+    See [Yamamoto et al., 2019](https://arxiv.org/abs/1910.11480)
+
+    Args:
+        fft_sizes (list): List of FFT sizes.
+        hop_sizes (list): List of hop sizes.
+        win_lengths (list): List of window lengths.
+        window (str, optional): Window to apply before FFT, options include:
+            'hann_window', 'bartlett_window', 'blackman_window', 'hamming_window', 'kaiser_window']
+            Default: 'hann_window'
+        w_sc (float, optional): Weight of the spectral convergence loss term. Default: 1.0
+        w_log_mag (float, optional): Weight of the log magnitude loss term. Default: 1.0
+        w_lin_mag (float, optional): Weight of the linear magnitude loss term. Default: 0.0
+        w_phs (float, optional): Weight of the spectral phase loss term. Default: 0.0
+        sample_rate (int, optional): Sample rate. Required when scale = 'mel'. Default: None
+        scale (str, optional): Optional frequency scaling method, options include:
+            ['mel', 'chroma']
+            Default: None
+        n_bins (int, optional): Number of mel frequency bins. Required when scale = 'mel'. Default: None.
+        scale_invariance (bool, optional): Perform an optimal scaling of the target. Default: False
+    """
+
+    def __init__(
+        self,
+        fft_sizes: List[int] = [1024, 2048, 512],
+        hop_sizes: List[int] = [120, 240, 50],
+        win_lengths: List[int] = [600, 1200, 240],
+        window: str = "hann_window",
+        w_sc: float = 1.0,
+        w_log_mag: float = 1.0,
+        w_lin_mag: float = 0.0,
+        w_phs: float = 0.0,
+        sample_rate: float = None,
+        scale: str = None,
+        n_bins: int = None,
+        perceptual_weighting: bool = False,
+        scale_invariance: bool = False,
+        **kwargs,
+    ):
+        super().__init__()
+        assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)  # must define all
+        self.fft_sizes = fft_sizes
+        self.hop_sizes = hop_sizes
+        self.win_lengths = win_lengths
+
+        self.stft_losses = torch.nn.ModuleList()
+        for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
+            self.stft_losses += [
+                STFTLoss(
+                    fs,
+                    ss,
+                    wl,
+                    window,
+                    w_sc,
+                    w_log_mag,
+                    w_lin_mag,
+                    w_phs,
+                    sample_rate,
+                    scale,
+                    n_bins,
+                    perceptual_weighting,
+                    scale_invariance,
+                    **kwargs,
+                )
+            ]
+
+    def forward(self, x, y):
+        mrstft_loss = 0.0
+        sc_mag_loss, log_mag_loss, lin_mag_loss, phs_loss = [], [], [], []
+
+        for f in self.stft_losses:
+            if f.output == "full":  # extract just first term
+                tmp_loss = f(x, y)
+                mrstft_loss += tmp_loss[0]
+                sc_mag_loss.append(tmp_loss[1])
+                log_mag_loss.append(tmp_loss[2])
+                lin_mag_loss.append(tmp_loss[3])
+                phs_loss.append(tmp_loss[4])
+            else:
+                mrstft_loss += f(x, y)
+
+        mrstft_loss /= len(self.stft_losses)
+
+        if f.output == "loss":
+            return mrstft_loss
+        else:
+            return mrstft_loss, sc_mag_loss, log_mag_loss, lin_mag_loss, phs_loss
+
+
+class SumAndDifferenceSTFTLoss(torch.nn.Module):
+    """Sum and difference sttereo STFT loss module.
+
+    See [Steinmetz et al., 2020](https://arxiv.org/abs/2010.10291)
+
+    Args:
+        fft_sizes (List[int]): List of FFT sizes.
+        hop_sizes (List[int]): List of hop sizes.
+        win_lengths (List[int]): List of window lengths.
+        window (str, optional): Window function type.
+        w_sum (float, optional): Weight of the sum loss component. Default: 1.0
+        w_diff (float, optional): Weight of the difference loss component. Default: 1.0
+        perceptual_weighting (bool, optional): Apply perceptual A-weighting (Sample rate must be supplied). Default: False
+        mel_stft (bool, optional): Use Multi-resoltuion mel spectrograms. Default: False
+        n_mel_bins (int, optional): Number of mel bins to use when mel_stft = True. Default: 128
+        sample_rate (float, optional): Audio sample rate. Default: None
+        output (str, optional): Format of the loss returned.
+            'loss' : Return only the raw, aggregate loss term.
+            'full' : Return the raw loss, plus intermediate loss terms.
+            Default: 'loss'
+    """
+
+    def __init__(
+        self,
+        fft_sizes: List[int],
+        hop_sizes: List[int],
+        win_lengths: List[int],
+        window: str = "hann_window",
+        w_sum: float = 1.0,
+        w_diff: float = 1.0,
+        output: str = "loss",
+        **kwargs,
+    ):
+        super().__init__()
+        self.sd = SumAndDifference()
+        self.w_sum = w_sum
+        self.w_diff = w_diff
+        self.output = output
+        self.mrstft = MultiResolutionSTFTLoss(
+            fft_sizes,
+            hop_sizes,
+            win_lengths,
+            window,
+            **kwargs,
+        )
+
+    def forward(self, input: torch.Tensor, target: torch.Tensor):
+        """This loss function assumes batched input of stereo audio in the time domain.
+
+        Args:
+            input (torch.Tensor): Input tensor with shape (batch size, 2, seq_len).
+            target (torch.Tensor): Target tensor with shape (batch size, 2, seq_len).
+
+        Returns:
+            loss (torch.Tensor): Aggreate loss term. Only returned if output='loss'.
+            loss (torch.Tensor), sum_loss (torch.Tensor), diff_loss (torch.Tensor):
+                Aggregate and intermediate loss terms. Only returned if output='full'.
+        """
+        assert input.shape == target.shape  # must have same shape
+        bs, chs, seq_len = input.size()
+
+        # compute sum and difference signals for both
+        input_sum, input_diff = self.sd(input)
+        target_sum, target_diff = self.sd(target)
+
+        # compute error in STFT domain
+        sum_loss = self.mrstft(input_sum, target_sum)
+        diff_loss = self.mrstft(input_diff, target_diff)
+        loss = ((self.w_sum * sum_loss) + (self.w_diff * diff_loss)) / 2
+
+        if self.output == "loss":
+            return loss
+        elif self.output == "full":
+            return loss, sum_loss, diff_loss

stable/build/lib/stable_audio_tools/training/losses/losses.py

@@ -0,0 +1,101 @@
+import typing as tp
+
+from torch.nn import functional as F
+from torch import nn
+
+class LossModule(nn.Module):
+    def __init__(self, name: str, weight: float = 1.0):
+        super().__init__()
+
+        self.name = name
+        self.weight = weight
+
+    def forward(self, info, *args, **kwargs):
+        raise NotImplementedError
+    
+class ValueLoss(LossModule):
+    def __init__(self, key: str, name, weight: float = 1.0):
+        super().__init__(name=name, weight=weight)
+
+        self.key = key
+    
+    def forward(self, info):
+        return self.weight * info[self.key]
+
+class L1Loss(LossModule):
+    def __init__(self, key_a: str, key_b: str, weight: float = 1.0, mask_key: str = None, name: str = 'l1_loss'):
+        super().__init__(name=name, weight=weight)
+
+        self.key_a = key_a
+        self.key_b = key_b
+
+        self.mask_key = mask_key
+    
+    def forward(self, info):
+        mse_loss = F.l1_loss(info[self.key_a], info[self.key_b], reduction='none')    
+
+        if self.mask_key is not None and self.mask_key in info:
+            mse_loss = mse_loss[info[self.mask_key]]
+
+        mse_loss = mse_loss.mean()
+
+        return self.weight * mse_loss
+    
+class MSELoss(LossModule):
+    def __init__(self, key_a: str, key_b: str, weight: float = 1.0, mask_key: str = None, name: str = 'mse_loss'):
+        super().__init__(name=name, weight=weight)
+
+        self.key_a = key_a
+        self.key_b = key_b
+
+        self.mask_key = mask_key
+    
+    def forward(self, info):
+        mse_loss = F.mse_loss(info[self.key_a], info[self.key_b], reduction='none')    
+
+        if self.mask_key is not None and self.mask_key in info and info[self.mask_key] is not None:
+            mask = info[self.mask_key]
+
+            if mask.ndim == 2 and mse_loss.ndim == 3:
+                mask = mask.unsqueeze(1)
+
+            if mask.shape[1] != mse_loss.shape[1]:
+                mask = mask.repeat(1, mse_loss.shape[1], 1)
+
+            mse_loss = mse_loss[mask]
+
+        mse_loss = mse_loss.mean()
+
+        return self.weight * mse_loss
+    
+class AuralossLoss(LossModule):
+    def __init__(self, auraloss_module, input_key: str, target_key: str, name: str, weight: float = 1):
+        super().__init__(name, weight)
+
+        self.auraloss_module = auraloss_module
+
+        self.input_key = input_key
+        self.target_key = target_key
+
+    def forward(self, info):
+        loss = self.auraloss_module(info[self.input_key], info[self.target_key])
+
+        return self.weight * loss
+    
+class MultiLoss(nn.Module):
+    def __init__(self, losses: tp.List[LossModule]):
+        super().__init__()
+
+        self.losses = nn.ModuleList(losses)
+
+    def forward(self, info):
+        total_loss = 0
+
+        losses = {}
+
+        for loss_module in self.losses:
+            module_loss = loss_module(info)
+            total_loss += module_loss
+            losses[loss_module.name] = module_loss
+
+        return total_loss, losses

stable/build/lib/stable_audio_tools/training/utils.py

@@ -0,0 +1,111 @@
+import torch
+import os
+
+def get_rank():
+    """Get rank of current process."""
+    
+    print(os.environ.keys())
+
+    if "SLURM_PROCID" in os.environ:
+        return int(os.environ["SLURM_PROCID"])
+
+    if not torch.distributed.is_available() or not torch.distributed.is_initialized():
+        return 0
+    
+    return torch.distributed.get_rank()
+
+class InverseLR(torch.optim.lr_scheduler._LRScheduler):
+    """Implements an inverse decay learning rate schedule with an optional exponential
+    warmup. When last_epoch=-1, sets initial lr as lr.
+    inv_gamma is the number of steps/epochs required for the learning rate to decay to
+    (1 / 2)**power of its original value.
+    Args:
+        optimizer (Optimizer): Wrapped optimizer.
+        inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1.
+        power (float): Exponential factor of learning rate decay. Default: 1.
+        warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable)
+            Default: 0.
+        final_lr (float): The final learning rate. Default: 0.
+        last_epoch (int): The index of last epoch. Default: -1.
+        verbose (bool): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+    """
+
+    def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., final_lr=0.,
+                 last_epoch=-1, verbose=False):
+        self.inv_gamma = inv_gamma
+        self.power = power
+        if not 0. <= warmup < 1:
+            raise ValueError('Invalid value for warmup')
+        self.warmup = warmup
+        self.final_lr = final_lr
+        super().__init__(optimizer, last_epoch, verbose)
+
+    def get_lr(self):
+        if not self._get_lr_called_within_step:
+            import warnings
+            warnings.warn("To get the last learning rate computed by the scheduler, "
+                          "please use `get_last_lr()`.")
+
+        return self._get_closed_form_lr()
+
+    def _get_closed_form_lr(self):
+        warmup = 1 - self.warmup ** (self.last_epoch + 1)
+        lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power
+        return [warmup * max(self.final_lr, base_lr * lr_mult)
+                for base_lr in self.base_lrs]
+
+def copy_state_dict(model, state_dict):
+    """Load state_dict to model, but only for keys that match exactly.
+
+    Args:
+        model (nn.Module): model to load state_dict.
+        state_dict (OrderedDict): state_dict to load.
+    """
+    model_state_dict = model.state_dict()
+    for key in state_dict:
+        if key in model_state_dict and state_dict[key].shape == model_state_dict[key].shape:
+            if isinstance(state_dict[key], torch.nn.Parameter):
+                # backwards compatibility for serialized parameters
+                state_dict[key] = state_dict[key].data
+            model_state_dict[key] = state_dict[key]
+        
+    model.load_state_dict(model_state_dict, strict=False)
+
+def create_optimizer_from_config(optimizer_config, parameters):
+    """Create optimizer from config.
+
+    Args:
+        parameters (iterable): parameters to optimize.
+        optimizer_config (dict): optimizer config.
+
+    Returns:
+        torch.optim.Optimizer: optimizer.
+    """
+
+    optimizer_type = optimizer_config["type"]
+
+    if optimizer_type == "FusedAdam":
+        from deepspeed.ops.adam import FusedAdam
+        optimizer = FusedAdam(parameters, **optimizer_config["config"])
+    else:
+        optimizer_fn = getattr(torch.optim, optimizer_type)
+        optimizer = optimizer_fn(parameters, **optimizer_config["config"])
+    return optimizer
+
+def create_scheduler_from_config(scheduler_config, optimizer):
+    """Create scheduler from config.
+
+    Args:
+        scheduler_config (dict): scheduler config.
+        optimizer (torch.optim.Optimizer): optimizer.
+
+    Returns:
+        torch.optim.lr_scheduler._LRScheduler: scheduler.
+    """
+    if scheduler_config["type"] == "InverseLR":
+        scheduler_fn = InverseLR
+    else:
+        scheduler_fn = getattr(torch.optim.lr_scheduler, scheduler_config["type"])
+    scheduler = scheduler_fn(optimizer, **scheduler_config["config"])
+    return scheduler

stable/config_adapter.json

@@ -0,0 +1,124 @@
+{
+    "model_type": "diffusion_cond",
+    "sample_size": 2097152,
+    "sample_rate": 44100,
+    "audio_channels": 2,
+    "model": {
+        "pretransform": {
+            "type": "autoencoder",
+            "iterate_batch": true,
+            "config": {
+                "encoder": {
+                    "type": "oobleck",
+                    "requires_grad": false,
+                    "config": {
+                        "in_channels": 2,
+                        "channels": 128,
+                        "c_mults": [1, 2, 4, 8, 16],
+                        "strides": [2, 4, 4, 8, 8],
+                        "latent_dim": 128,
+                        "use_snake": true
+                    }
+                },
+                "decoder": {
+                    "type": "oobleck",
+                    "config": {
+                        "out_channels": 2,
+                        "channels": 128,
+                        "c_mults": [1, 2, 4, 8, 16],
+                        "strides": [2, 4, 4, 8, 8],
+                        "latent_dim": 64,
+                        "use_snake": true,
+                        "final_tanh": false
+                    }
+                },
+                "bottleneck": {
+                    "type": "vae"
+                },
+                "latent_dim": 64,
+                "downsampling_ratio": 2048,
+                "io_channels": 2
+            }
+        },
+        "conditioning": {
+            "configs": [
+                {
+                    "id": "prompt",
+                    "type": "t5",
+                    "config": {
+                        "t5_model_name": "t5-base",
+                        "max_length": 128
+                    }
+                },
+                {
+                    "id": "seconds_start",
+                    "type": "number",
+                    "config": {
+                        "min_val": 0,
+                        "max_val": 512
+                    }
+                },
+                {
+                    "id": "seconds_total",
+                    "type": "number",
+                    "config": {
+                        "min_val": 0,
+                        "max_val": 512
+                    }
+                }
+            ],
+            "cond_dim": 768
+        },
+        "diffusion": {
+            "cross_attention_cond_ids": ["prompt", "seconds_start", "seconds_total"],
+            "global_cond_ids": ["seconds_start", "seconds_total"],
+            "type": "dit",
+            "config": {
+                "io_channels": 64,
+                "embed_dim": 1536,
+                "depth": 24,
+                "num_heads": 24,
+                "cond_token_dim": 768,
+                "global_cond_dim": 1536,
+                "project_cond_tokens": false,
+                "transformer_type": "continuous_transformer",
+                "adapter_present": true
+            }
+        },
+        "io_channels": 64
+    },
+    "training": {
+        "use_ema": true,
+        "log_loss_info": false,
+        "optimizer_configs": {
+            "diffusion": {
+                "adapter_present": true,
+                "optimizer": {
+                    "type": "AdamW",
+                    "config": {
+                        "lr": 3e-3,
+                        "betas": [0.9, 0.999],
+                        "weight_decay": 1e-3
+                    }
+                },
+                "scheduler": {
+                    "type": "InverseLR",
+                    "config": {
+                        "inv_gamma": 1000000,
+                        "power": 0.5,
+                        "warmup": 0.99
+                    }
+                }
+            }
+        },
+        "demo": {
+            "demo_every": 15,
+            "demo_steps": 250,
+            "num_demos": 1,
+            "demo_cond": [
+                {"prompt": "Amen break 174 BPM", "seconds_start": 0, "seconds_total": 12}
+            ],
+            "demo_cfg_scales": [7]
+        }
+    }
+}

stable/convert_json.py

@@ -0,0 +1,44 @@
+import json
+import sys
+
+def update_path_in_json(input_file, output_file, new_path):
+    # Read the input JSON file
+    try:
+        with open(input_file, 'r') as infile:
+            data = json.load(infile)
+    except FileNotFoundError:
+        print(f"Input file {input_file} not found.")
+        sys.exit(1)
+    except json.JSONDecodeError:
+        print(f"Error decoding JSON from the input file {input_file}.")
+        sys.exit(1)
+
+    # Update the path
+    try:
+        data['datasets'][0]['path'] = new_path
+    except KeyError as e:
+        print(f"Key error: {e}")
+        sys.exit(1)
+    except IndexError as e:
+        print(f"Index error: {e}")
+        sys.exit(1)
+
+    # Write the updated JSON to the output file
+    try:
+        with open(output_file, 'w') as outfile:
+            json.dump(data, outfile, indent=4)
+    except IOError as e:
+        print(f"Error writing to the output file {output_file}: {e}")
+        sys.exit(1)
+
+    print(f"Path updated successfully in {output_file}")
+
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser(description='Convert JSON for fine-tuning.')
+    parser.add_argument('--input_json', type=str, help='Name of the dataset', required=True)
+    parser.add_argument('--output_json', type=str, help='Path to the input CSV', required=True)
+    parser.add_argument('--new_path', type=str, help='Path to output JSON', required=True)
+    args = parser.parse_args()
+    update_path_in_json(args.input_json, args.output_json, args.new_path)