Add ThinkSound module files to repository

ThinkSound

@@ -1 +0,0 @@
-Subproject commit 600962ed922a87bf416a6c152a64a35756c9c97e

ThinkSound/__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

ThinkSound/configs/model_configs/stable_audio_2_0_vae.json

@@ -0,0 +1,122 @@
+{
+    "model_type": "autoencoder",
+    "sample_size": 65536,
+    "sample_rate": 44100,
+    "audio_channels": 2,
+    "model": {
+        "encoder": {
+            "type": "oobleck",
+            "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
+    },
+    "training": {
+        "learning_rate": 1.5e-4,
+        "warmup_steps": 0,
+        "use_ema": true,
+        "optimizer_configs": {
+            "autoencoder": {
+                "optimizer": {
+                    "type": "AdamW",
+                    "config": {
+                        "betas": [0.8, 0.99],
+                        "lr": 1.5e-4,
+                        "weight_decay": 1e-3
+                    }
+                },
+                "scheduler": {
+                    "type": "InverseLR",
+                    "config": {
+                        "inv_gamma": 200000,
+                        "power": 0.5,
+                        "warmup": 0.999
+                    }
+                }
+            },
+            "discriminator": {
+                "optimizer": {
+                    "type": "AdamW",
+                    "config": {
+                        "betas": [0.8, 0.99],
+                        "lr": 3e-4,
+                        "weight_decay": 1e-3
+                    }
+                },
+                "scheduler": {
+                    "type": "InverseLR",
+                    "config": {
+                        "inv_gamma": 200000,
+                        "power": 0.5,
+                        "warmup": 0.999
+                    }
+                }
+            }
+        },
+        "loss_configs": {
+            "discriminator": {
+                "type": "encodec",
+                "config": {
+                    "filters": 64,
+                    "n_ffts": [2048, 1024, 512, 256, 128],
+                    "hop_lengths": [512, 256, 128, 64, 32],
+                    "win_lengths": [2048, 1024, 512, 256, 128]
+                },
+                "weights": {
+                    "adversarial": 0.1,
+                    "feature_matching": 5.0
+                }
+            },
+            "spectral": {
+                "type": "mrstft",
+                "config": {
+                    "fft_sizes": [2048, 1024, 512, 256, 128, 64, 32],
+                    "hop_sizes": [512, 256, 128, 64, 32, 16, 8],
+                    "win_lengths": [2048, 1024, 512, 256, 128, 64, 32],
+                    "perceptual_weighting": true
+                },
+                "weights": {
+                    "mrstft": 1.0
+                }
+            },
+            "time": {
+                "type": "l1",
+                "weights": {
+                    "l1": 0.0
+                }
+            },
+            "bottleneck": {
+                "type": "kl",
+                "weights": {
+                    "kl": 1e-4
+                }
+            }
+        },
+        "demo": {
+            "demo_every": 10000
+        }
+    }
+}

ThinkSound/configs/model_configs/thinksound.json

@@ -0,0 +1,147 @@
+{
+    "model_type": "mm_diffusion_cond",
+    "sample_size": 397312,
+    "sample_rate": 44100,
+    "audio_channels": 2,
+    "model": {
+        "pretransform": {
+            "type": "autoencoder",
+            "iterate_batch": true,
+            "config": {
+                "encoder": {
+                    "type": "oobleck",
+                    "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": "metaclip_features",
+                    "type": "mm_unchang",
+                    "config": {
+                        "dim": 1024,
+                        "output_dim": 1024
+                        }
+                },
+                {
+                    "id": "metaclip_text_features",
+                    "type": "mm_unchang",
+                    "config": {
+                        "dim": 1024,
+                        "output_dim": 1024
+                    }
+                },
+                {
+                    "id": "sync_features",
+                    "type": "mm_unchang",
+                    "config": {
+                        "dim": 768,
+                        "output_dim": 768
+                    }
+                },
+                {
+                    "id": "t5_features",
+                    "type": "mm_unchang",
+                    "config": {
+                        "dim": 2048,
+                        "output_dim": 2048
+                    }
+                }
+            ],
+            "cond_dim": 768
+        },
+        "diffusion": {
+            "mm_cond_ids": ["metaclip_features", "sync_features", "metaclip_text_features","t5_features"],
+            "type": "mmdit",
+            "diffusion_objective": "rectified_flow",
+            "config": {
+                "latent_dim":64,
+                "clip_dim":1024,
+                "sync_dim":768,
+                "text_dim":2048,
+                "hidden_dim":1024,
+                "depth":21,
+                "fused_depth":14,
+                "num_heads":16,
+                "latent_seq_len":194,
+                "clip_seq_len":72,
+                "sync_seq_len":216,
+                "v2": true,
+                "kernel_size": 3
+            }
+        },
+        "io_channels": 64
+    },
+    "training": {
+        "use_ema": true,
+        "log_loss_info": false,
+        "cfg_dropout_prob": 0.2,
+        "pre_encoded": true,
+        "timestep_sampler": "logit_normal",
+        "optimizer_configs": {
+            "diffusion": {
+                "optimizer": {
+                    "type": "AdamW",
+                    "config": {
+                        "lr": 5e-5,
+                        "betas": [0.9, 0.95],
+                        "weight_decay": 1e-4,
+                        "eps": 1e-6
+                    }
+                },
+                "scheduler": {
+                    "type": "InverseLR",
+                    "config": {
+                        "inv_gamma": 1000000,
+                        "power": 0.5,
+                        "warmup": 0.99
+                    }
+                }
+            }
+        },
+        "demo": {
+            "demo_every": 5000,
+            "demo_steps": 24,
+            "num_demos": 10,
+            "demo_cond": [
+                "dataset/vggsound/video_latents_t5_clip_npz/test/0Cu33yBwAPg_000060.npz",
+                "dataset/vggsound/video_latents_t5_clip_npz/test/bmKtI808DsU_000009.npz",
+                "dataset/vggsound/video_latents_t5_clip_npz/test/VC0c22cJTbM_000424.npz",
+                "dataset/vggsound/video_latents_t5_clip_npz/test/F3gsbUTdc2U_000090.npz",
+                "dataset/vggsound/video_latents_t5_clip_npz/test/WatvT8A8iug_000100.npz",
+                "dataset/vggsound/video_latents_t5_clip_npz/test/0nvBTp-q7tU_000112.npz",
+                "dataset/vggsound/video_latents_t5_clip_npz/test/3-PFuDkTM48_000080.npz",
+                "dataset/vggsound/video_latents_t5_clip_npz/test/luSAuu-BoPs_000232.npz",
+                "dataset/vggsound/video_latents_t5_clip_npz/test/__8UJxW0aOQ_000002.npz",
+                "dataset/vggsound/video_latents_t5_clip_npz/test/_0m_YMpQayA_000168.npz"
+            ],
+            "demo_cfg_scales": [5]
+        }
+    }
+}

ThinkSound/configs/multimodal_dataset_demo.json

@@ -0,0 +1,53 @@
+{
+    "dataset_type": "multimodal_dir",
+    "video_datasets": [
+        {
+            "id": "vggsound",
+            "path": "dataset/vggsound/video_latents_t5_clip_npz/train",
+            "split_path": "dataset/vggsound/split_txt/train_cot.txt"
+        }
+    ],
+    "audio_datasets": [
+        {
+            "id": "audiostock",
+            "path": "dataset/Laion-Audio-630k/audiostock_latents_npz",
+            "split_path": "dataset/Laion-Audio-630k/split_txt/cot_audiostock_1.txt"
+        },
+        {
+            "id": "freesound_no_overlap",
+            "path": "dataset/Laion-Audio-630k/freesound_no_overlap_latents_npz",
+            "split_path": "dataset/Laion-Audio-630k/split_txt/cot_freesound.txt"
+        },
+        {
+            "id": "audioset_sl",
+            "path": "dataset/wavcaps/audioset_sl_latents_npz",
+            "split_path": "dataset/wavcaps/split_txt/cot_audio_sl_1.txt"
+        },
+        {
+            "id": "audiocaps",
+            "path": "dataset/1_audiocaps/audiocaps_latents_npz",
+            "split_path": "dataset/1_audiocaps/split_txt/train_cot.txt"
+        },
+        {
+            "id": "bbc",
+            "path": "dataset/Laion-Audio-630k/bbc_latents_npz",
+            "split_path": "dataset/Laion-Audio-630k/split_txt/cot_bbc_1.txt"
+        }
+    ],
+    "val_datasets": [
+        {
+            "id": "vggsound",
+            "path": "dataset/vggsound/video_latents_t5_clip_npz/test",
+            "split_path": "dataset/vggsound/split_txt/test_cot.txt"
+        }
+    ],
+    "test_datasets": [
+        {
+            "id": "vggsound",
+            "path": "cot_coarse",
+            "split_path": "cot_vgg_demo_caption.txt"
+        }
+    ],
+    "random_crop": true,
+    "input_type": "prompt"
+}

ThinkSound/data/datamodule.py

@@ -0,0 +1,194 @@
+import lightning as L
+from .dataset import LatentDataset, SampleDataset, VideoDataset, AudioDataset, MultiModalDataset, LocalDatasetConfig, collation_fn
+import importlib
+from torch.utils.data import DataLoader
+
+
+def get_configs(audio_configs):
+    configs = []
+    for config in audio_configs:
+        data_dir_path = config.get("path", None)
+        audio_dir_path = config.get("audio_dir", None)
+        split_path = config.get("split_path", None)
+        assert data_dir_path is not None, "Path must be set for local audio directory configuration"
+        
+        custom_metadata_fn = None
+        custom_metadata_module_path = config.get("custom_metadata_module", None)
+        
+        if custom_metadata_module_path:
+            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=config["id"],
+                path=data_dir_path,
+                split_path=split_path,
+                custom_metadata_fn=custom_metadata_fn,
+                audio_dir=audio_dir_path
+            )
+        )
+    return configs
+
+class DataModule(L.LightningDataModule):
+    def __init__(self, dataset_config, batch_size, test_batch_size, sample_size, sample_rate, audio_channels=2, num_workers=4,repeat_num=5,latent_length=194):
+        super().__init__()
+        dataset_type = dataset_config.get("dataset_type", None)
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.test_batch_size = test_batch_size
+        self.repeat_num = repeat_num
+        self.latent_length = latent_length
+        assert dataset_type is not None, "Dataset type must be specified in dataset config"
+
+        if audio_channels == 1:
+            force_channels = "mono"
+        elif audio_channels == 2:
+            force_channels = "stereo"
+        else:
+            force_channels = "foa"
+        val_dir_configs = dataset_config.get("val_datasets", None)
+        test_dir_configs = dataset_config.get("test_datasets", None)
+        configs = []
+        val_configs = []
+        test_configs = []
+        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 = get_configs(audio_dir_configs)
+            val_configs = get_configs(val_dir_configs)
+            test_configs = get_configs(test_dir_configs)
+        elif dataset_type == "latent_dir" or dataset_type == "video_dataset":
+            audio_dir_configs = dataset_config.get("datasets", None)
+            assert audio_dir_configs is not None, "Directory configuration must be specified in datasets[\"dataset\"]"
+            for i, dataset in enumerate((audio_dir_configs, val_dir_configs, test_dir_configs)):
+                for config in dataset:
+                    data_dir_path = config.get("path", None)
+                    audio_dir_path = config.get("audio_dir", None)
+                    split_path = config.get("split_path", None)
+                    assert data_dir_path is not None, "Path must be set for local audio directory configuration"
+                    
+                    content = LocalDatasetConfig(
+                        id=config["id"],
+                        path=data_dir_path,
+                        split_path=split_path,
+                        audio_dir=audio_dir_path,
+                        extra_cot=config.get("extra_cot", None)
+                    )
+                    if i == 0:
+                        configs.append(content)
+                    elif i == 1:
+                        val_configs.append(content)
+                    else:
+                        test_configs.append(content)
+        elif dataset_type == "multimodal_dir":
+            self.audio_configs = []
+            self.video_configs = []
+            audio_dir_configs = dataset_config.get("audio_datasets", None)
+            video_dir_configs = dataset_config.get("video_datasets", None)
+            assert audio_dir_configs is not None and video_dir_configs is not None, "Directory configuration must be specified in video_datasets and audio_datasets"
+            for i, dataset in enumerate((audio_dir_configs, video_dir_configs, val_dir_configs, test_dir_configs)):
+                for config in dataset:
+                    data_dir_path = config.get("path", None)
+                    audio_dir_path = config.get("audio_dir", None)
+                    split_path = config.get("split_path", None)
+                    assert data_dir_path is not None, "Path must be set for local audio directory configuration"
+                    print(f'extra cot: {config.get("extra_cot", None)}')
+                    content = LocalDatasetConfig(
+                        id=config["id"],
+                        path=data_dir_path,
+                        split_path=split_path,
+                        audio_dir=audio_dir_path,
+                        extra_cot=config.get("extra_cot", None)
+                    )
+                    if i == 0:
+                        self.audio_configs.append(content)
+                    elif i == 1:
+                        self.video_configs.append(content)
+                    elif i == 2:
+                        val_configs.append(content)
+                    else:
+                        test_configs.append(content)
+        self.dataset_type = dataset_type
+        self.configs = configs
+        self.val_configs = val_configs
+        self.test_configs = test_configs
+        self.sample_rate = sample_rate
+        self.sample_size = sample_size
+        self.random_crop = dataset_config.get("random_crop", True)
+        self.input_type = dataset_config.get("input_type", "video")
+        self.fps = dataset_config.get("fps", 4)
+        self.force_channels = force_channels
+        
+
+    def setup(self, stage: str):
+        if self.dataset_type == 'audio_dir':
+            dataset_class = SampleDataset
+        elif self.dataset_type == 'latent_dir':
+            dataset_class = LatentDataset
+        elif self.dataset_type == 'video_dataset':
+            dataset_class = VideoDataset
+        elif self.dataset_type == 'multimodal_dir':
+            dataset_class = VideoDataset
+
+        def create_dataset(configs, random_crop):
+            return dataset_class(
+                configs,
+                sample_rate=self.sample_rate,
+                sample_size=self.sample_size,
+                random_crop=random_crop,
+                input_type=self.input_type,
+                fps=self.input_type,
+                force_channels=self.force_channels,
+                latent_length=self.latent_length
+            )
+
+        if stage == 'fit':
+            if self.dataset_type != 'multimodal_dir':
+                self.train_set = create_dataset(self.configs, random_crop=self.random_crop)
+            else:
+                self.video_set = VideoDataset(
+                    self.video_configs,
+                    sample_rate=self.sample_rate,
+                    sample_size=self.sample_size,
+                    random_crop=self.random_crop,
+                    input_type=self.input_type,
+                    fps=self.input_type,
+                    force_channels=self.force_channels
+                )
+                self.audio_set = AudioDataset(
+                    self.audio_configs,
+                    sample_rate=self.sample_rate,
+                    sample_size=self.sample_size,
+                    random_crop=self.random_crop,
+                    input_type=self.input_type,
+                    fps=self.input_type,
+                    force_channels=self.force_channels
+                )
+                self.train_set = MultiModalDataset([self.video_set]*self.repeat_num, [self.audio_set])
+            self.val_set = create_dataset(self.val_configs, random_crop=False)
+        elif stage == 'validate':
+            self.val_set = create_dataset(self.val_configs, random_crop=False)
+        elif stage == 'predict':
+            self.test_set = create_dataset(self.test_configs, random_crop=False)
+
+    def train_dataloader(self):
+        return DataLoader(self.train_set, self.batch_size, shuffle=True,
+                                num_workers=self.num_workers, persistent_workers=True, pin_memory=True, drop_last=True, collate_fn=collation_fn)
+
+    def val_dataloader(self):
+        return DataLoader(self.val_set, self.batch_size, shuffle=False,
+                                num_workers=self.num_workers, persistent_workers=False, pin_memory=False, drop_last=False, collate_fn=collation_fn)
+
+    def predict_dataloader(self):
+        return DataLoader(self.test_set, batch_size=self.test_batch_size, shuffle=False,
+                                num_workers=self.num_workers, persistent_workers=False, pin_memory=False, drop_last=False, collate_fn=collation_fn)
+
+    # def predict_dataloader(self):
+    #     return DataLoader(self.mnist_predict, batch_size=self.batch_size)
+
+    # def teardown(self, stage: str):
+    #     # Used to clean-up when the run is finished
+    #     ...

ThinkSound/data/dataset.py

@@ -0,0 +1,1266 @@
+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
+import pandas as pd
+from aeiou.core import is_silence
+from os import path
+from pathlib import Path
+from pedalboard.io import AudioFile
+from torchaudio import transforms as T
+from typing import Optional, Callable, List
+import bisect
+
+from .utils import FOA, Stereo, Mono, PhaseFlipper, PadCrop_Normalized_T, PadCrop_Video_Normalized_T, PadCrop_Video_Hiera_Normalized_T, PadCrop_Video_Image_Normalized_T, PadCrop_DualVideo_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,
+        split_path: str,
+        audio_dir: str = None,
+        extra_cot: str = None,
+        custom_metadata_fn: Optional[Callable[[str], str]] = None
+    ):
+        self.id = id
+        self.path = path
+        self.split_path = split_path
+        self.audio_dir = audio_dir
+        self.custom_metadata_fn = custom_metadata_fn
+        self.extra_cot = extra_cot
+class SampleDataset(torch.utils.data.Dataset):
+    def __init__(
+        self, 
+        configs,
+        sample_size=65536, 
+        sample_rate=48000, 
+        keywords=None, 
+        random_crop=True,
+        input_type="prompt",
+        fps=4,
+        force_channels="stereo"
+    ):
+        super().__init__()
+        self.filenames = []
+
+        self.augs = torch.nn.Sequential(
+            PhaseFlipper(),
+        )
+
+        self.root_paths = []
+        if input_type == 'video':
+            self.pad_crop = PadCrop_Video_Normalized_T(sample_size, sample_rate, fps, randomize=random_crop)
+        elif input_type == 'video_hiera':
+            self.pad_crop = PadCrop_Video_Hiera_Normalized_T(sample_size, sample_rate, fps, randomize=random_crop)
+        elif input_type == 'video_image':
+            self.pad_crop = PadCrop_Video_Image_Normalized_T(sample_size, sample_rate, fps, randomize=random_crop)
+        elif input_type == 'dual_video':
+            self.pad_crop = PadCrop_DualVideo_Normalized_T(sample_size, sample_rate, fps, randomize=random_crop)
+        else:
+            self.pad_crop = PadCrop_Normalized_T(sample_size, sample_rate, randomize=random_crop)
+
+        self.force_channels = force_channels
+        print('######################')
+        print(f'input channels is: {force_channels}')
+        print('######################')
+        self.encoding = torch.nn.Sequential(
+            FOA() if self.force_channels == "foa" else torch.nn.Identity(),
+            Stereo() if self.force_channels == "stereo" else torch.nn.Identity(),
+            Mono() if self.force_channels == "mono" else torch.nn.Identity(),
+        )
+        self.input_type = input_type
+        self.sr = sample_rate
+        self.custom_metadata_fns = {}
+
+        for config in configs:
+            self.root_paths.append(config.path)
+            def add_prefix(s):
+                return str(os.path.join(config.path,f'{s.strip()}'))
+            with open(config.split_path,'r') as f:
+                item_names = f.readlines()
+            filenames = list(map(add_prefix, item_names))
+            self.filenames.extend(filenames) 
+            # 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:
+            try:
+                resample_tf = T.Resample(in_sr, self.sr)
+                audio = resample_tf(audio)
+            except:
+                print(f'{filename} resample errors')
+
+        assert not (torch.isnan(audio).any() or torch.isinf(audio).any()), f'file-{filename} contains nan or inf number, check it!'
+        return audio
+
+    def __len__(self):
+        return len(self.filenames)
+
+    def __getitem__(self, idx):
+        audio_filename = self.filenames[idx]
+        assert os.path.exists(audio_filename), f'{audio_filename}: file not exists'
+        try:
+            start_time = time.time()
+            audio = self.load_file(audio_filename)
+            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)
+
+
+            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))]
+            if self.input_type == 'video':
+                audio, video, t_start, t_end, seconds_start, seconds_total, padding_mask = self.pad_crop(audio, info['video'])
+                info['video'] = video
+            elif self.input_type == 'dual_video':
+                audio, video_360, video_fov, t_start, t_end, seconds_start, seconds_total, padding_mask = self.pad_crop(audio, info['video'], info['video_fov'])
+                info['video_360'] = video_360
+                info['video_fov'] = video_fov
+            else:
+                audio, t_start, t_end, seconds_start, seconds_total, padding_mask = self.pad_crop(audio)
+                assert not (torch.isnan(audio).any() or torch.isinf(audio).any()), f'file-{filename} contains nan or inf number, check it!'
+            # 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["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
+            
+            
+            return (audio, info)
+        except Exception as e:
+            print(f'Couldn\'t load file {audio_filename}: {e}')
+            return self[random.randrange(len(self))]
+
+class LatentDataset(torch.utils.data.Dataset):
+    def __init__(
+        self, 
+        configs,
+        sample_size=65536, 
+        sample_rate=48000, 
+        keywords=None, 
+        random_crop=True,
+        input_type="prompt",
+        fps=4,
+        force_channels="stereo"
+    ):
+        super().__init__()
+        self.filenames = []
+
+        self.augs = torch.nn.Sequential(
+            PhaseFlipper(),
+        )
+
+        self.root_paths = []
+
+        self.force_channels = force_channels
+        print('######################')
+        print(f'input channels is: {force_channels}')
+        print('######################')
+        self.encoding = torch.nn.Sequential(
+            FOA() if self.force_channels == "foa" else torch.nn.Identity(),
+            Stereo() if self.force_channels == "stereo" else torch.nn.Identity(),
+            Mono() if self.force_channels == "mono" else torch.nn.Identity(),
+        )
+        self.input_type = input_type
+        self.sr = sample_rate
+        for config in configs:
+            self.root_paths.append(config.path)
+            def add_prefix(s):
+                return str(os.path.join(config.path,f'{s.strip()}'))
+            with open(config.split_path,'r') as f:
+                item_names = f.readlines()
+            filenames = list(map(add_prefix, item_names))
+            self.filenames.extend(filenames) 
+            # self.filenames.extend(get_audio_filenames(config.path, keywords))
+            
+
+        print(f'Found {len(self.filenames)} files')
+
+    def load_file(self, filename, info):
+        # try:
+        npz_file = filename.replace('.pth','.npz')
+        if os.path.exists(filename) and '.npz' not in filename:
+            data = torch.load(filename, weights_only=False)
+        elif os.path.exists(npz_file): 
+            # print(filename)
+            npz_data = np.load(npz_file,allow_pickle=True)
+            data = {key: npz_data[key] for key in npz_data.files}
+            # print("data.keys()",data.keys())
+            for key in data.keys():
+                if isinstance(data[key], np.ndarray) and np.issubdtype(data[key].dtype, np.number):
+                    data[key] = torch.from_numpy(data[key])
+        else:
+            raise ValueError(f'error load file: {filename}')
+        info.update(data)
+        audio = data['latent']
+        # except:
+        #     print(f'error load file: {filename}')
+        return audio, info['metaclip_features']
+
+    def __len__(self):
+        return len(self.filenames)
+
+    def __getitem__(self, idx):
+        audio_filename = self.filenames[idx]
+        assert os.path.exists(audio_filename) or audio_filename.replace('.pth','.npz'), f'{audio_filename}: file not exists'
+        # try:
+        start_time = time.time()
+        info = {}
+        audio, video = self.load_file(audio_filename, info)
+        info["path"] = audio_filename
+
+        info['id'] = Path(audio_filename).stem
+        for root_path in self.root_paths:
+            if root_path in audio_filename:
+                info["relpath"] = path.relpath(audio_filename, root_path)
+        
+        return (audio, info)
+
+class AudioDataset(torch.utils.data.Dataset):
+    def __init__(
+        self, 
+        configs,
+        sample_size=65536, 
+        sample_rate=48000, 
+        keywords=None, 
+        random_crop=True,
+        input_type="prompt",
+        fps=4,
+        force_channels="stereo"
+    ):
+        super().__init__()
+        self.filenames = []
+
+        self.augs = torch.nn.Sequential(
+            PhaseFlipper(),
+        )
+
+        self.root_paths = []
+
+        self.force_channels = force_channels
+        print('######################')
+        print(f'input channels is: {force_channels}')
+        print('######################')
+        self.encoding = torch.nn.Sequential(
+            FOA() if self.force_channels == "foa" else torch.nn.Identity(),
+            Stereo() if self.force_channels == "stereo" else torch.nn.Identity(),
+            Mono() if self.force_channels == "mono" else torch.nn.Identity(),
+        )
+        self.fake_clip_features = torch.zeros(72, 1024)
+        self.fake_sync_features = torch.zeros(216, 768)
+        self.video_exist = torch.tensor(0, dtype=torch.bool)
+        self.input_type = input_type
+        self.sr = sample_rate
+        for config in configs:
+            self.root_paths.append(config.path)
+            def add_prefix(s):
+                return str(os.path.join(config.path,f'{s.strip()}'))
+            with open(config.split_path,'r') as f:
+                item_names = f.readlines()
+            filenames = list(map(add_prefix, item_names))
+            self.filenames.extend(filenames) 
+            # self.filenames.extend(get_audio_filenames(config.path, keywords))
+            
+
+        print(f'Found {len(self.filenames)} files')
+
+    def load_file(self, filename, info):
+        # try:
+        npz_file = filename.replace('.pth','.npz')
+        if os.path.exists(filename) and '.npz' not in filename:
+            data = torch.load(filename, weights_only=False)
+        elif os.path.exists(npz_file): 
+            # print(filename)
+            npz_data = np.load(npz_file,allow_pickle=True)
+            data = {key: npz_data[key] for key in npz_data.files}
+            # print("data.keys()",data.keys())
+            for key in data.keys():
+                if isinstance(data[key], np.ndarray) and np.issubdtype(data[key].dtype, np.number):
+                    data[key] = torch.from_numpy(data[key])
+        else:
+            raise ValueError(f'error load file: {filename}')
+        info.update(data)
+        audio = data['latent']
+        info['metaclip_features'] = self.fake_clip_features
+        info['sync_features'] = self.fake_sync_features
+        info['video_exist'] = self.video_exist
+        # except:
+        #     print(f'error load file: {filename}')
+        return audio, info['metaclip_features']
+
+    def __len__(self):
+        return len(self.filenames)
+
+    def __getitem__(self, idx):
+        audio_filename = self.filenames[idx]
+        assert os.path.exists(audio_filename) or audio_filename.replace('.pth','.npz'), f'{audio_filename}: file not exists'
+        # try:
+        start_time = time.time()
+        info = {}
+        audio, video = self.load_file(audio_filename, info)
+        info["path"] = audio_filename
+
+        info['id'] = Path(audio_filename).stem
+        for root_path in self.root_paths:
+            if root_path in audio_filename:
+                info["relpath"] = path.relpath(audio_filename, root_path)
+        
+        return (audio, info)
+
+class VideoDataset(torch.utils.data.Dataset):
+    def __init__(
+        self, 
+        configs,
+        sample_size=65536, 
+        sample_rate=48000, 
+        keywords=None, 
+        random_crop=True,
+        input_type="prompt",
+        fps=4,
+        force_channels="stereo",
+        latent_length=194,  # default latent length for video dataset
+    ):
+        self.latent_length = latent_length
+        super().__init__()
+        self.filenames = []
+        print(f'configs: {configs[0]}')
+        if configs[0].extra_cot is not None:
+            self.extra_cot = configs[0].extra_cot
+            print(f'load extra cot from {self.extra_cot}')
+        else:
+            self.extra_cot = None
+        self.augs = torch.nn.Sequential(
+            PhaseFlipper(),
+        )
+
+        self.root_paths = []
+
+        self.force_channels = force_channels
+        print('######################')
+        print(f'input channels is: {force_channels}')
+        print('######################')
+        self.encoding = torch.nn.Sequential(
+            FOA() if self.force_channels == "foa" else torch.nn.Identity(),
+            Stereo() if self.force_channels == "stereo" else torch.nn.Identity(),
+            Mono() if self.force_channels == "mono" else torch.nn.Identity(),
+        )
+        self.input_type = input_type
+        self.sr = sample_rate
+        self.video_exist = torch.tensor(1, dtype=torch.bool)
+        for config in configs:
+            self.root_paths.append(config.path)
+            def add_prefix(s):
+                return str(os.path.join(config.path,f'{s.strip()}'))
+            with open(config.split_path,'r') as f:
+                item_names = f.readlines()
+            filenames = list(map(add_prefix, item_names))
+            self.filenames.extend(filenames) 
+            # self.filenames.extend(get_audio_filenames(config.path, keywords))
+            
+
+        print(f'Found {len(self.filenames)} files')
+
+    def load_file(self, filename, info):
+        # try:
+        npz_file = filename.replace('.pth','.npz')
+        if os.path.exists(filename) and '.npz' not in filename:
+            data = torch.load(filename, weights_only=False)
+        elif os.path.exists(npz_file): 
+            # print(filename)
+            npz_data = np.load(npz_file,allow_pickle=True)
+            data = {key: npz_data[key] for key in npz_data.files}
+            # print("data.keys()",data.keys())
+            for key in data.keys():
+                if isinstance(data[key], np.ndarray) and np.issubdtype(data[key].dtype, np.number):
+                    data[key] = torch.from_numpy(data[key])
+            if self.extra_cot is not None:
+                extra_pth = filename.replace('.npz','.pth')
+                extra_pth = os.path.join(self.extra_cot, os.path.basename(extra_pth))
+                if os.path.exists(extra_pth):
+                    extra_data = torch.load(extra_pth, weights_only=False)
+                    for key in extra_data.keys():
+                        if isinstance(extra_data[key], torch.Tensor):
+                            # print(f'load extra cot {key}')
+                            data[key] = extra_data[key]
+        else:
+            raise ValueError(f'error load file: {filename}')
+        info.update(data)
+        if 'latent' in data.keys():
+            audio = data['latent']
+        else:
+            audio = torch.zeros(64,self.latent_length)
+        info['video_exist'] = self.video_exist
+        # except:
+        #     print(f'error load file: {filename}')
+        return audio, info['metaclip_features']
+
+    def __len__(self):
+        return len(self.filenames)
+
+    def __getitem__(self, idx):
+        audio_filename = self.filenames[idx]
+        assert os.path.exists(audio_filename) or audio_filename.replace('.pth','.npz'), f'{audio_filename}: file not exists'
+        # try:
+        start_time = time.time()
+        info = {}
+        audio, video = self.load_file(audio_filename, info)
+        info["path"] = audio_filename
+
+        info['id'] = Path(audio_filename).stem
+        for root_path in self.root_paths:
+            if root_path in audio_filename:
+                info["relpath"] = path.relpath(audio_filename, root_path)
+        
+        return (audio, info)
+
+# modified from https://pytorch.org/docs/stable/_modules/torch/utils/data/dataset.html#ConcatDataset
+class MultiModalDataset(torch.utils.data.Dataset):
+    datasets: list[torch.utils.data.Dataset]
+    cumulative_sizes: list[int]
+
+    @staticmethod
+    def cumsum(sequence):
+        r, s = [], 0
+        for e in sequence:
+            l = len(e)
+            r.append(l + s)
+            s += l
+        return r
+
+    def __init__(self, video_datasets: list[torch.utils.data.Dataset], audio_datasets: list[torch.utils.data.Dataset]):
+        super().__init__()
+        self.video_datasets = list(video_datasets)
+        self.audio_datasets = list(audio_datasets)
+        self.datasets = self.video_datasets + self.audio_datasets
+
+        self.cumulative_sizes = self.cumsum(self.datasets)
+        print(f'Found {self.cumulative_sizes[-1]} files')
+
+    def __len__(self):
+        return self.cumulative_sizes[-1]
+
+    def __getitem__(self, idx):
+        if idx < 0:
+            if -idx > len(self):
+                raise ValueError("absolute value of index should not exceed dataset length")
+            idx = len(self) + idx
+        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
+        if dataset_idx == 0:
+            sample_idx = idx
+        else:
+            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
+        return self.datasets[dataset_idx][sample_idx]
+
+    def compute_latent_stats(self) -> tuple[torch.Tensor, torch.Tensor]:
+        return self.video_datasets[0].compute_latent_stats()
+
+
+# class MultiModalDataset(torch.utils.data.Dataset):
+#     def __init__(
+#         self, 
+#         configs,
+#         sample_size=65536, 
+#         sample_rate=48000, 
+#         keywords=None, 
+#         random_crop=True,
+#         input_type="prompt",
+#         fps=4,
+#         force_channels="stereo"
+#     ):
+#         super().__init__()
+#         self.filenames = []
+#         self.captions = []
+#         self.caption_t5s = []
+#         self.ids = []
+#         self.augs = torch.nn.Sequential(
+#             PhaseFlipper(),
+#         )
+
+#         self.root_paths = []
+#         if input_type == 'video':
+#             self.pad_crop = PadCrop_Video_Normalized_T(sample_size, sample_rate, fps, randomize=random_crop)
+#         elif input_type == 'video_hiera':
+#             self.pad_crop = PadCrop_Video_Hiera_Normalized_T(sample_size, sample_rate, fps, randomize=random_crop)
+#         elif input_type == 'video_image':
+#             self.pad_crop = PadCrop_Video_Image_Normalized_T(sample_size, sample_rate, fps, randomize=random_crop)
+#         elif input_type == 'dual_video':
+#             self.pad_crop = PadCrop_DualVideo_Normalized_T(sample_size, sample_rate, fps, randomize=random_crop)
+#         else:
+#             self.pad_crop = PadCrop_Normalized_T(sample_size, sample_rate, randomize=random_crop)
+
+#         self.force_channels = force_channels
+#         print('######################')
+#         print(f'input channels is: {force_channels}')
+#         print('######################')
+#         self.encoding = torch.nn.Sequential(
+#             FOA() if self.force_channels == "foa" else torch.nn.Identity(),
+#             Stereo() if self.force_channels == "stereo" else torch.nn.Identity(),
+#             Mono() if self.force_channels == "mono" else torch.nn.Identity(),
+#         )
+#         self.input_type = input_type
+#         self.sr = sample_rate
+#         self.custom_metadata_fns = {}
+
+#         for config in configs:
+#             print(config.split_path)
+#             self.root_paths.append(config.path)
+#             def add_prefix(s):
+#                 return str(os.path.join(config.path,f'{s.strip()}'))
+#             with open(config.split_path,'r') as f:
+#                 item_names = f.readlines()
+#             csv_path = config.split_path.replace('.txt','.csv')
+#             df = pd.read_csv(csv_path)
+#             # 检查是否存在 'caption_t5' 列，如果不存在则创建并复制 'caption' 的值
+#             if 'caption_t5' not in df.columns:
+#                 df['caption_t5'] = df['caption']
+
+#             captions = df['caption'].tolist()
+#             caption_t5s = df['caption_t5'].tolist()
+#             filenames = list(map(add_prefix, item_names))
+#             assert len(captions) == len(caption_t5s) and len(captions) == len(filenames), f'{config.path} has wrong filename and caption'
+#             if config.id == 'vggsound':
+#                 self.filenames.extend(filenames*5)
+#                 self.captions.extend(captions*5)
+#                 self.caption_t5s.extend(caption_t5s*5)
+#                 self.ids.extend(df['id'].tolist()*5)
+#             else:
+#                 self.filenames.extend(filenames)
+#                 self.captions.extend(captions)
+#                 self.caption_t5s.extend(caption_t5s)
+#                 self.ids.extend(df['id'].tolist())
+#             # 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
+
+#         assert len(self.ids) == len(self.captions) and len(self.caption_t5s) == len(self.filenames), 'length need to be same'
+#         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:
+#             try:
+#                 resample_tf = T.Resample(in_sr, self.sr)
+#                 audio = resample_tf(audio)
+#             except:
+#                 print(f'{filename} resample errors')
+
+#         assert not (torch.isnan(audio).any() or torch.isinf(audio).any()), f'file-{filename} contains nan or inf number, check it!'
+#         return audio
+
+#     def __len__(self):
+#         return len(self.filenames)
+
+#     def __getitem__(self, idx):
+#         audio_filename = self.filenames[idx]
+#         id = self.ids[idx]
+#         assert str(id) == str(Path(audio_filename).stem), f'audio_file: {audio_filename} needs to be same as {id} '
+#         assert os.path.exists(audio_filename), f'{audio_filename}: file not exists'
+#         try:
+#             start_time = time.time()
+#             audio = self.load_file(audio_filename)
+#             caption = self.captions[idx]
+#             caption_t5 = self.caption_t5s[idx]
+#             if pd.isna(caption_t5) or caption_t5 == '':
+#                 caption_t5 = caption
+#             info = {}
+#             info["path"] = audio_filename
+#             info['caption'] = caption
+#             info['caption_t5'] = caption_t5
+
+#             for root_path in self.root_paths:
+#                 if root_path in audio_filename:
+#                     info["relpath"] = path.relpath(audio_filename, root_path)
+
+
+#             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))]
+#             # if self.input_type == 'video':
+#             #     audio, video, t_start, t_end, seconds_start, seconds_total, padding_mask = self.pad_crop(audio, info['clip_features'])
+#             #     info['clip_features'] = video
+#             # else:
+#             if info['flag']:
+#                 audio, t_start, t_end, seconds_start, seconds_total, padding_mask = self.pad_crop(audio,randomize=False)
+#             else:
+#                 audio, t_start, t_end, seconds_start, seconds_total, padding_mask = self.pad_crop(audio,randomize=True)
+#             assert not (torch.isnan(audio).any() or torch.isinf(audio).any()), f'file-{filename} contains nan or inf number, check it!'
+#             # 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["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
+            
+            
+#             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"
+    elif audio_channels == 2:
+        force_channels = "stereo"
+    else:
+        force_channels = "foa"
+
+    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)
+            split_path = audio_dir_config.get("split_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,
+                    split_path=split_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),
+            input_type=dataset_config.get("input_type", "video"),
+            fps=dataset_config.get("fps", 4),
+            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
+
+    elif dataset_type == "latent_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)
+            split_path = audio_dir_config.get("split_path", None)
+            assert audio_dir_path is not None, "Path must be set for local audio directory configuration"
+            
+            configs.append(
+                LocalDatasetConfig(
+                    id=audio_dir_config["id"],
+                    path=audio_dir_path,
+                    split_path=split_path,
+                )
+            )
+
+        train_set = LatentDataset(
+            configs,
+            sample_rate=sample_rate,
+            sample_size=sample_size,
+            random_crop=dataset_config.get("random_crop", True),
+            input_type=dataset_config.get("input_type", "video"),
+            fps=dataset_config.get("fps", 4),
+            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 == 'multimodal_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)
+            split_path = audio_dir_config.get("split_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,
+                    split_path=split_path,
+                    custom_metadata_fn=custom_metadata_fn
+                )
+            )
+
+        train_set = MultiModalDataset(
+            configs,
+            sample_rate=sample_rate,
+            sample_size=sample_size,
+            random_crop=dataset_config.get("random_crop", True),
+            input_type=dataset_config.get("input_type", "video"),
+            fps=dataset_config.get("fps", 4),
+            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)

ThinkSound/data/utils.py

@@ -0,0 +1,378 @@
+import math
+import random
+import torch
+import torch.nn.functional as F
+from torch import nn
+from typing import Tuple
+import numpy as np
+
+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, randomize=True) -> 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(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 PadCrop_Video_Normalized_T(nn.Module):
+    
+    def __init__(self, n_samples: int, sample_rate: int, fps: int, randomize: bool = True):
+        
+        super().__init__()
+        
+        self.n_samples = n_samples
+        self.sample_rate = sample_rate
+        self.randomize = randomize
+        self.fps = fps
+        self.n_frames = int(self.fps * self.n_samples / self.sample_rate)
+
+    def __call__(self, audio: torch.Tensor, video: torch.Tensor) -> Tuple[torch.Tensor, float, float, int, int]:
+        n_channels, n_samples = audio.shape
+        # print(video.shape)
+        n_frames, dim = video.shape
+        if not torch.is_tensor(video):
+            video = torch.from_numpy(video)
+        # If the audio is shorter than the desired length, pad it
+        audio_upper_bound = max(0, n_samples - self.n_samples)
+        video_upper_bound = int(max(0, n_frames - self.n_frames) * self.sample_rate / self.fps)
+        upper_bound = min(audio_upper_bound,video_upper_bound)
+        
+        # If randomize is False, always start at the beginning of the audio
+        offset = 0
+        if(self.randomize and n_samples > self.n_samples and n_frames > self.n_frames):
+            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)
+        frame_offset = int(self.fps * offset / self.sample_rate)
+        # frame_end = frame_offset + int(self.fps * self.n_samples / self.sample_rate)
+        # Create the chunk
+        chunk = audio.new_zeros([n_channels, self.n_samples])
+        video_chunk = video.new_zeros([self.n_frames, video.shape[1]])
+        # Copy the audio into the chunk
+        chunk[:, :min(n_samples, self.n_samples)] = audio[:, offset:offset + self.n_samples]
+        video_chunk[:min(n_frames, self.n_frames)] = video[frame_offset:frame_offset + self.n_frames,:]
+        # 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,
+            video_chunk,
+            t_start,
+            t_end,
+            seconds_start,
+            seconds_total,
+            padding_mask
+        )
+
+class PadCrop_Video_Image_Normalized_T(nn.Module):
+    
+    def __init__(self, n_samples: int, sample_rate: int, fps: int, randomize: bool = True):
+        
+        super().__init__()
+        
+        self.n_samples = n_samples
+        self.sample_rate = sample_rate
+        self.randomize = randomize
+        self.fps = fps
+        self.n_frames = int(self.fps * self.n_samples / self.sample_rate)
+
+    def __call__(self, audio: torch.Tensor, video: torch.Tensor) -> Tuple[torch.Tensor, float, float, int, int]:
+        n_channels, n_samples = audio.shape
+        # import ipdb
+        # ipdb.set_trace()
+        n_frames, channel, width, height= video.shape
+        video = torch.from_numpy(video)
+        # If the audio is shorter than the desired length, pad it
+        audio_upper_bound = max(0, n_samples - self.n_samples)
+        video_upper_bound = int(max(0, n_frames - self.n_frames) * self.sample_rate / self.fps)
+        upper_bound = min(audio_upper_bound,video_upper_bound)
+        
+        # If randomize is False, always start at the beginning of the audio
+        offset = 0
+        if(self.randomize and n_samples > self.n_samples and n_frames > self.n_frames):
+            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)
+        frame_offset = int(self.fps * offset / self.sample_rate)
+        # frame_end = frame_offset + int(self.fps * self.n_samples / self.sample_rate)
+        # Create the chunk
+        chunk = audio.new_zeros([n_channels, self.n_samples])
+        video_chunk = video.new_zeros([self.n_frames, channel, width, height])
+        # Copy the audio into the chunk
+        chunk[:, :min(n_samples, self.n_samples)] = audio[:, offset:offset + self.n_samples]
+        video_chunk[:min(n_frames, self.n_frames)] = video[frame_offset:frame_offset + self.n_frames]
+        # 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,
+            video_chunk,
+            t_start,
+            t_end,
+            seconds_start,
+            seconds_total,
+            padding_mask
+        )
+
+class PadCrop_Video_Hiera_Normalized_T(nn.Module):
+    
+    def __init__(self, n_samples: int, sample_rate: int, fps: int, randomize: bool = True):
+        
+        super().__init__()
+        
+        self.n_samples = n_samples
+        self.sample_rate = sample_rate
+        self.randomize = randomize
+        self.fps = fps
+        self.n_frames = int(self.fps * self.n_samples / self.sample_rate)
+
+    def __call__(self, audio: torch.Tensor, video: torch.Tensor) -> Tuple[torch.Tensor, float, float, int, int]:
+
+        n_channels, n_samples = audio.shape
+        n_frames, heigh, width, channel = video.shape
+        video = torch.from_numpy(video)
+        # If the audio is shorter than the desired length, pad it
+        audio_upper_bound = max(0, n_samples - self.n_samples)
+        video_upper_bound = int(max(0, n_frames - self.n_frames) * self.sample_rate / self.fps)
+        upper_bound = min(audio_upper_bound,video_upper_bound)
+        
+        # If randomize is False, always start at the beginning of the audio
+        offset = 0
+        if(self.randomize and n_samples > self.n_samples and n_frames > self.n_frames):
+            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)
+        frame_offset = int(self.fps * offset / self.sample_rate)
+        # frame_end = frame_offset + int(self.fps * self.n_samples / self.sample_rate)
+        # Create the chunk
+        chunk = audio.new_zeros([n_channels, self.n_samples])
+        video_chunk = video.new_zeros([self.n_frames, heigh, width, channel])
+        # Copy the audio into the chunk
+        chunk[:, :min(n_samples, self.n_samples)] = audio[:, offset:offset + self.n_samples]
+        video_chunk[:min(n_frames, self.n_frames)] = video[frame_offset:frame_offset + self.n_frames]
+        # video_chunk = video_chunk[None].permute(0, 4, 1, 2, 3).contiguous()
+        # print(video_chunk.shape)
+        # video_chunk = F.interpolate(
+        #     video_chunk[0],
+        #     size=(224, 224, 3),  # 输出的空间尺寸
+        #     scale_factor=(target_frames / video_tensor.shape[1], 1, 1),  # 时间轴的缩放因子
+        #     mode='trilinear',  # 使用三线性插值
+        #     align_corners=False
+        # )
+
+        # video_chunk = F.interpolate(video_chunk, size=(64, 224, 224), mode="trilinear")[0]
+        # video_chunk = video_chunk.view(3,4,16,224,224).transpose(0,1)
+        # 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,
+            video_chunk,
+            t_start,
+            t_end,
+            seconds_start,
+            seconds_total,
+            padding_mask
+        )
+
+class PadCrop_DualVideo_Normalized_T(nn.Module):
+    
+    def __init__(self, n_samples: int, sample_rate: int, fps: int, randomize: bool = True):
+        
+        super().__init__()
+        
+        self.n_samples = n_samples
+        self.sample_rate = sample_rate
+        self.randomize = randomize
+        self.fps = fps
+        self.n_frames = int(self.fps * self.n_samples / self.sample_rate)
+
+    def __call__(self, audio: torch.Tensor, video_360: torch.Tensor, video_fov: torch.Tensor) -> Tuple[torch.Tensor, float, float, int, int]:
+        n_channels, n_samples = audio.shape
+        # print(video.shape)
+        n_frames, dim = video_360.shape
+        video_360 = torch.from_numpy(video_360)
+        video_fov = torch.from_numpy(video_fov)
+        # If the audio is shorter than the desired length, pad it
+        audio_upper_bound = max(0, n_samples - self.n_samples)
+        video_upper_bound = int(max(0, n_frames - self.n_frames) * self.sample_rate / self.fps)
+        upper_bound = min(audio_upper_bound,video_upper_bound)
+        
+        # If randomize is False, always start at the beginning of the audio
+        offset = 0
+        if(self.randomize and n_samples > self.n_samples and n_frames > self.n_frames):
+            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)
+        frame_offset = int(self.fps * offset / self.sample_rate)
+        # frame_end = frame_offset + int(self.fps * self.n_samples / self.sample_rate)
+        # Create the chunk
+        chunk = audio.new_zeros([n_channels, self.n_samples])
+        video_360_chunk = video_360.new_zeros([self.n_frames, video_360.shape[1]])
+        video_fov_chunk = video_fov.new_zeros([self.n_frames, video_fov.shape[1]])
+        # Copy the audio into the chunk
+        chunk[:, :min(n_samples, self.n_samples)] = audio[:, offset:offset + self.n_samples]
+        video_360_chunk[:min(n_frames, self.n_frames)] = video_360[frame_offset:frame_offset + self.n_frames,:]
+        video_fov_chunk[:min(n_frames, self.n_frames)] = video_fov[frame_offset:frame_offset + self.n_frames,:]
+        # 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,
+            video_360_chunk,
+            video_fov_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
+
+class FOA(nn.Module):
+  def __call__(self, signal):
+    signal_shape = signal.shape
+    # Check if it's mono
+    if len(signal_shape) == 1:  # s -> (4, s)
+        foa = torch.zeros(4, signal_shape[0], device=signal.device)  # 与输入信号一致的设备类型
+        foa[0, :] = signal  # W通道: 全方位声源
+        foa[1, :] = 0  # X通道
+        foa[2, :] = 0  # Y通道
+        foa[3, :] = 0  # Z通道
+    elif len(signal_shape) == 2:
+        foa = torch.zeros(4, signal_shape[1], device=signal.device)  # 与输入信号一致的设备类型
+        if signal_shape[0] == 1:  # (1, s) -> (4, s)
+            foa[0, :] = signal[0]  # W通道: 全方位声源
+            foa[1, :] = 0  # X通道
+            foa[2, :] = 0  # Y通道
+            foa[3, :] = 0  # Z通道
+        elif signal_shape[0] == 2:  # (2, s) -> (4, s)
+            left = signal[0]
+            right = signal[1]
+            # 将立体声信号映射到FOA信号通道
+            foa[0, :] = (left + right) / np.sqrt(2)  # W通道: 全方位声源
+            foa[1,  :] = (left - right) / np.sqrt(2)  # X通道: 前后方向
+            foa[2, :] = 0  # Y通道: 左右方向，简单实现先置零
+            foa[3, :] = 0  # Z通道: 垂直方向，这里置零
+        else:
+            foa = signal
+
+    else:
+        raise ValueError(f"Unsupported signal shape: {signal_shape}")
+
+    assert foa.shape[0] == 4, f'inputs not FOA format'
+
+    return foa

ThinkSound/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
+    import ipdb
+    # ipdb.set_trace()
+    # 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(model.model, noise, steps, 0, **conditioning_inputs)
+        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

ThinkSound/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)

ThinkSound/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

ThinkSound/models/__init__.py

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

ThinkSound/models/autoencoders.py

@@ -0,0 +1,800 @@
+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 = {}
+        # import ipdb
+        # ipdb.set_trace()
+        if self.pretransform is not None and not skip_pretransform:
+            if self.pretransform.enable_grad:
+                if iterate_batch:
+                    audios = []
+                    for i in range(audio.shape[0]):
+                        audios.append(self.pretransform.encode(audio[i:i+1]))
+                    audio = torch.cat(audios, dim=0)
+                else:
+                    audio = self.pretransform.encode(audio)
+            else:
+                with torch.no_grad():
+                    if iterate_batch:
+                        audios = []
+                        for i in range(audio.shape[0]):
+                            audios.append(self.pretransform.encode(audio[i:i+1]))
+                        audio = torch.cat(audios, dim=0)
+                    else:
+                        audio = self.pretransform.encode(audio)
+
+        if self.encoder is not None:
+            if iterate_batch:
+                latents = []
+                for i in range(audio.shape[0]):
+                    latents.append(self.encoder(audio[i:i+1]))
+                latents = torch.cat(latents, dim=0)
+            else:
+                latents = self.encoder(audio)
+        else:
+            latents = audio
+
+        if self.bottleneck is not None:
+            # TODO: Add iterate batch logic, needs to merge the info dicts
+            latents, bottleneck_info = self.bottleneck.encode(latents, return_info=True, **kwargs)
+
+            info.update(bottleneck_info)
+        
+        if return_info:
+            return latents, info
+
+        return latents
+
+    def decode(self, latents, iterate_batch=False, **kwargs):
+
+        if self.bottleneck is not None:
+            if iterate_batch:
+                decoded = []
+                for i in range(latents.shape[0]):
+                    decoded.append(self.bottleneck.decode(latents[i:i+1]))
+                latents = torch.cat(decoded, dim=0)
+            else:
+                latents = self.bottleneck.decode(latents)
+
+        if iterate_batch:
+            decoded = []
+            for i in range(latents.shape[0]):
+                decoded.append(self.decoder(latents[i:i+1]))
+            decoded = torch.cat(decoded, dim=0)
+        else:
+            decoded = self.decoder(latents, **kwargs)
+
+        if self.pretransform is not None:
+            if self.pretransform.enable_grad:
+                if iterate_batch:
+                    decodeds = []
+                    for i in range(decoded.shape[0]):
+                        decodeds.append(self.pretransform.decode(decoded[i:i+1]))
+                    decoded = torch.cat(decodeds, dim=0)
+                else:
+                    decoded = self.pretransform.decode(decoded)
+            else:
+                with torch.no_grad():
+                    if iterate_batch:
+                        decodeds = []
+                        for i in range(latents.shape[0]):
+                            decodeds.append(self.pretransform.decode(decoded[i:i+1]))
+                        decoded = torch.cat(decodeds, dim=0)
+                    else:
+                        decoded = self.pretransform.decode(decoded)
+
+        if self.soft_clip:
+            decoded = torch.tanh(decoded)
+        
+        return decoded
+          
+    def decode_tokens(self, tokens, **kwargs):
+        '''
+        Decode discrete tokens to audio
+        Only works with discrete autoencoders
+        '''
+
+        assert isinstance(self.bottleneck, DiscreteBottleneck), "decode_tokens only works with discrete autoencoders"
+
+        latents = self.bottleneck.decode_tokens(tokens, **kwargs)
+
+        return self.decode(latents, **kwargs)
+        
+    
+    def preprocess_audio_for_encoder(self, audio, in_sr):
+        '''
+        Preprocess single audio tensor (Channels x Length) to be compatible with the encoder.
+        If the model is mono, stereo audio will be converted to mono.
+        Audio will be silence-padded to be a multiple of the model's downsampling ratio.
+        Audio will be resampled to the model's sample rate. 
+        The output will have batch size 1 and be shape (1 x Channels x Length)
+        '''
+        return self.preprocess_audio_list_for_encoder([audio], [in_sr])
+
+    def preprocess_audio_list_for_encoder(self, audio_list, in_sr_list):
+        '''
+        Preprocess a [list] of audio (Channels x Length) into a batch tensor to be compatable with the encoder. 
+        The audio in that list can be of different lengths and channels. 
+        in_sr can be an integer or list. If it's an integer it will be assumed it is the input sample_rate for every audio.
+        All audio will be resampled to the model's sample rate. 
+        Audio will be silence-padded to the longest length, and further padded to be a multiple of the model's downsampling ratio. 
+        If the model is mono, all audio will be converted to mono. 
+        The output will be a tensor of shape (Batch x Channels x Length)
+        '''
+        batch_size = len(audio_list)
+        if isinstance(in_sr_list, int):
+            in_sr_list = [in_sr_list]*batch_size
+        assert len(in_sr_list) == batch_size, "list of sample rates must be the same length of audio_list"
+        new_audio = []
+        max_length = 0
+        # resample & find the max length
+        for i in range(batch_size):
+            audio = audio_list[i]
+            in_sr = in_sr_list[i]
+            if len(audio.shape) == 3 and audio.shape[0] == 1:
+                # batchsize 1 was given by accident. Just squeeze it.
+                audio = audio.squeeze(0)
+            elif len(audio.shape) == 1:
+                # Mono signal, channel dimension is missing, unsqueeze it in
+                audio = audio.unsqueeze(0)
+            assert len(audio.shape)==2, "Audio should be shape (Channels x Length) with no batch dimension" 
+            # Resample audio
+            if in_sr != self.sample_rate:
+                resample_tf = T.Resample(in_sr, self.sample_rate).to(audio.device)
+                audio = resample_tf(audio)
+            new_audio.append(audio)
+            if audio.shape[-1] > max_length:
+                max_length = audio.shape[-1]
+        # Pad every audio to the same length, multiple of model's downsampling ratio
+        padded_audio_length = max_length + (self.min_length - (max_length % self.min_length)) % self.min_length
+        for i in range(batch_size):
+            # Pad it & if necessary, mixdown/duplicate stereo/mono channels to support model
+            new_audio[i] = prepare_audio(new_audio[i], in_sr=in_sr, target_sr=in_sr, target_length=padded_audio_length, 
+                target_channels=self.in_channels, device=new_audio[i].device).squeeze(0)
+        # convert to tensor 
+        return torch.stack(new_audio) 
+
+    def encode_audio(self, audio, chunked=False, overlap=32, chunk_size=128, **kwargs):
+        '''
+        Encode audios into latents. Audios should already be preprocesed by preprocess_audio_for_encoder.
+        If chunked is True, split the audio into chunks of a given maximum size chunk_size, with given overlap.
+        Overlap and chunk_size params are both measured in number of latents (not audio samples) 
+        # and therefore you likely could use the same values with decode_audio. 
+        A overlap of zero will cause discontinuity artefacts. Overlap should be => receptive field size. 
+        Every autoencoder will have a different receptive field size, and thus ideal overlap.
+        You can determine it empirically by diffing unchunked vs chunked output and looking at maximum diff.
+        The final chunk may have a longer overlap in order to keep chunk_size consistent for all chunks.
+        Smaller chunk_size uses less memory, but more compute.
+        The chunk_size vs memory tradeoff isn't linear, and possibly depends on the GPU and CUDA version
+        For example, on a A6000 chunk_size 128 is overall faster than 256 and 512 even though it has more chunks
+        '''
+        if not chunked:
+            # default behavior. Encode the entire audio in parallel
+            return self.encode(audio, **kwargs)
+        else:
+            # CHUNKED ENCODING
+            # samples_per_latent is just the downsampling ratio (which is also the upsampling ratio)
+            # import ipdb
+            # ipdb.set_trace()
+            samples_per_latent = self.downsampling_ratio
+            total_size = audio.shape[2] # in samples
+            print(f'audio shape: {audio.shape}')
+            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)
+            print(f'y_final shape: {y_final.shape}')
+            for i in range(num_chunks):
+                x_chunk = chunks[i,:]
+                # encode the chunk
+                y_chunk = self.encode(x_chunk)
+                print(f'y_chunk shape: {y_chunk.shape}')
+                # 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
+    )

ThinkSound/models/blocks.py

@@ -0,0 +1,430 @@
+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
+
+class ChannelLastConv1d(nn.Conv1d):
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x.permute(0, 2, 1)
+        x = super().forward(x)
+        x = x.permute(0, 2, 1)
+        return x
+
+
+# https://github.com/Stability-AI/sd3-ref
+class MLP(nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int = 256,
+    ):
+        """
+        Initialize the FeedForward module.
+
+        Args:
+            dim (int): Input dimension.
+            hidden_dim (int): Hidden dimension of the feedforward layer.
+            multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
+
+        Attributes:
+            w1 (ColumnParallelLinear): Linear transformation for the first layer.
+            w2 (RowParallelLinear): Linear transformation for the second layer.
+            w3 (ColumnParallelLinear): Linear transformation for the third layer.
+
+        """
+        super().__init__()
+        hidden_dim = int(2 * hidden_dim / 3)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+
+    def forward(self, x):
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+
+class ConvMLP(nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int = 256,
+        kernel_size: int = 3,
+        padding: int = 1,
+    ):
+        """
+        Initialize the FeedForward module.
+
+        Args:
+            dim (int): Input dimension.
+            hidden_dim (int): Hidden dimension of the feedforward layer.
+            multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
+
+        Attributes:
+            w1 (ColumnParallelLinear): Linear transformation for the first layer.
+            w2 (RowParallelLinear): Linear transformation for the second layer.
+            w3 (ColumnParallelLinear): Linear transformation for the third layer.
+
+        """
+        super().__init__()
+        hidden_dim = int(2 * hidden_dim / 3)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = ChannelLastConv1d(dim,
+                                    hidden_dim,
+                                    bias=False,
+                                    kernel_size=kernel_size,
+                                    padding=padding)
+        self.w2 = ChannelLastConv1d(hidden_dim,
+                                    dim,
+                                    bias=False,
+                                    kernel_size=kernel_size,
+                                    padding=padding)
+        self.w3 = ChannelLastConv1d(dim,
+                                    hidden_dim,
+                                    bias=False,
+                                    kernel_size=kernel_size,
+                                    padding=padding)
+
+    def forward(self, x):
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))

ThinkSound/models/bottleneck.py

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

ThinkSound/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)

ThinkSound/models/conditioners.py

@@ -0,0 +1,1005 @@
+#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 typing import Literal, Optional
+import os 
+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
+import numpy as np
+from einops import rearrange
+from transformers import AutoProcessor, AutoModel
+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 VideoHieraConditioner(Conditioner):
+    def __init__(self, 
+                 output_dim: int, 
+                 hiera_ckpt_path,
+                 project_out: bool = False,
+                 finetune: bool = False):
+        super().__init__(768, output_dim, project_out=project_out)
+
+        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:
+                from hiera import Hiera
+                import hiera
+                # model = hiera.hiera_base_16x224(pretrained=True, checkpoint="useful_ckpts/hiera_base_224.mae_in1k_ft_in1k") 
+                model = Hiera(
+                    num_classes=400,  # K400 has 400 classes
+                    input_size=(64, 224, 224),
+                    q_stride=[(1, 4, 4),(1,7,7),(1,2,2)],
+                    mask_unit_size=(1, 8, 8),
+                    patch_kernel=(3, 7, 7),
+                    patch_stride=(2, 4, 4),
+                    patch_padding=(1, 3, 3),
+                    sep_pos_embed=True,
+                )
+                state_dict = torch.load(hiera_ckpt_path)['model_state']
+                state_dict.pop('pos_embed_temporal', None)  # 如果不需要这个参数
+                model.load_state_dict(state_dict,strict=False)
+                if self.finetune:
+                    self.model = model
+                else: 
+                    self.__dict__["model"] = model
+
+                state_dict = model.state_dict()
+                self.model.load_state_dict(state_dict, strict=False)
+
+                if self.finetune:
+                    self.model.requires_grad_(True)
+                    self.model.train()
+                else:
+                    self.model.requires_grad_(False)
+                    self.model.train()
+
+            finally:
+                logging.disable(previous_level)
+
+
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def forward(self, x: tp.List[str], device: tp.Any = "cuda") -> tp.Any:
+        self.model.to(device)
+        import ipdb
+        ipdb.set_trace()
+        output, interm = model(x,return_intermediates=True)
+        
+        video_features = interm[-1]
+        return [self.proj_out(video_features), torch.ones(video_features.shape[0], 1).to(device)]
+
+class Video_Linear(Conditioner):
+    """ Transform the video feat encoder"""
+
+    def __init__(self, dim, output_dim):
+        super().__init__(dim, output_dim)
+        self.embedder = nn.Sequential(nn.Linear(dim, output_dim))
+
+    def forward(self, x, device: tp.Any = "cuda"):
+        # import ipdb
+        # ipdb.set_trace()
+        if not isinstance(x[0], torch.Tensor):
+            video_feats = []
+            for path in x:
+                if '.npy' in path:
+                    video_feats.append(torch.from_numpy(np.load(path)).to(device))
+                elif '.pth' in path:
+                    video_feats.append(torch.load(path)['metaclip_features'].to(device))
+                else:
+                    video_feats.append(torch.from_numpy(np.load(path)['feat']).to(device))
+            x = torch.stack(video_feats, dim=0).to(device)
+        else:
+            # Revise the shape here:
+            x = torch.stack(x, dim=0).to(device)
+
+        x = self.embedder(x)        # B x 117 x C
+        return [x, torch.ones(x.shape[0], 1).to(device)]
+
+class Video_Global(Conditioner):
+    """ Transform the video feat encoder"""
+
+    def __init__(self, dim, output_dim, global_dim=1536):
+        super().__init__(dim, output_dim)
+        self.embedder = nn.Sequential(nn.Linear(dim, output_dim))
+        self.global_proj = nn.Sequential(nn.Linear(output_dim, global_dim))
+
+    def forward(self, x, device: tp.Any = "cuda"):
+        # import ipdb
+        # ipdb.set_trace()
+        if not isinstance(x[0], torch.Tensor):
+            video_feats = []
+            for path in x:
+                if '.npy' in path:
+                    video_feats.append(torch.from_numpy(np.load(path)).to(device))
+                elif '.pth' in path:
+                    data = torch.load(path)
+                    video_feats.append(data['metaclip_features'].to(device))
+                else:
+                    video_feats.append(torch.from_numpy(np.load(path)['feat']).to(device))
+            x = torch.stack(video_feats, dim=0).to(device)
+        else:
+            # Revise the shape here:
+            x = torch.stack(x, dim=0).to(device)
+
+        x = self.embedder(x)        # B x 117 x C
+        global_x = self.global_proj(x.mean(dim=1))
+        return [x, torch.ones(x.shape[0], 1).to(device), global_x, torch.ones(global_x.shape[0], 1).to(device)]
+
+class Video_Sync(Conditioner):
+    """ Transform the video feat encoder"""
+
+    def __init__(self, dim, output_dim):
+        super().__init__(dim, output_dim)
+        self.embedder = nn.Sequential(nn.Linear(dim, output_dim))
+
+    def forward(self, x, device: tp.Any = "cuda"):
+        # import ipdb
+        # ipdb.set_trace()
+        if not isinstance(x[0], torch.Tensor):
+            video_feats = []
+            for path in x:
+                if '.npy' in path:
+                    video_feats.append(torch.from_numpy(np.load(path)).to(device))
+                elif '.pth' in path:
+                    video_feats.append(torch.load(path)['sync_features'].to(device))
+                else:
+                    video_feats.append(torch.from_numpy(np.load(path)['feat']).to(device))
+            x = torch.stack(video_feats, dim=0).to(device)
+        else:
+            # Revise the shape here:
+            x = torch.stack(x, dim=0).to(device)
+
+        x = self.embedder(x)        # B x 117 x C
+        return [x, torch.ones(x.shape[0], 1).to(device)]
+
+class Text_Linear(Conditioner):
+    """ Transform the video feat encoder"""
+
+    def __init__(self, dim, output_dim):
+        super().__init__(dim, output_dim)
+        self.embedder = nn.Sequential(nn.Linear(dim, output_dim))
+
+    def forward(self, x, device: tp.Any = "cuda"):
+        # import ipdb
+        # ipdb.set_trace()
+        if not isinstance(x[0], torch.Tensor):
+            video_feats = []
+            for path in x:
+                if '.npy' in path:
+                    video_feats.append(torch.from_numpy(np.load(path)).to(device))
+                elif '.pth' in path:
+                    video_feats.append(torch.load(path)['metaclip_text_features'].to(device))
+                else:
+                    video_feats.append(torch.from_numpy(np.load(path)['feat']).to(device))
+            x = torch.stack(video_feats, dim=0).to(device)
+        else:
+            # Revise the shape here:
+            x = torch.stack(x, dim=0).to(device)
+
+        x = self.embedder(x)        # B x 117 x C
+        return [x, torch.ones(x.shape[0], 1).to(device)]
+
+
+class mm_unchang(Conditioner):
+    """ Transform the video feat encoder"""
+
+    def __init__(self, dim, output_dim):
+        super().__init__(dim, output_dim)
+
+    def forward(self, x, device: tp.Any = "cuda"):
+        # import ipdb
+        # ipdb.set_trace()
+        if not isinstance(x[0], torch.Tensor):
+            video_feats = []
+            for path in x:
+                if '.npy' in path:
+                    video_feats.append(torch.from_numpy(np.load(path)).to(device))
+                elif '.pth' in path:
+                    video_feats.append(torch.load(path)['metaclip_features'].to(device))
+                else:
+                    video_feats.append(torch.from_numpy(np.load(path)['feat']).to(device))
+            x = torch.stack(video_feats, dim=0).to(device)
+        else:
+            # Revise the shape here:
+            x = torch.stack(x, dim=0).to(device)
+        return [x]
+
+class CLIPConditioner(Conditioner):
+
+    CLIP_MODELS = ["metaclip-base", "metaclip-b16", "metaclip-large", "metaclip-huge"]
+    
+    CLIP_MODEL_DIMS = {
+        "metaclip-base": 512,
+        "metaclip-b16": 512,
+        "metaclip-large": 768,
+        "metaclip-huge": 1024,
+    }
+
+    def __init__(
+            self,
+            dim: int,
+            output_dim: int,
+            clip_model_name: str = "metaclip-huge",
+            enable_grad: bool = False,
+            project_out: bool = False
+    ):
+        assert clip_model_name in self.CLIP_MODELS, f"Unknown CLIP model name: {clip_model_name}"
+        super().__init__(self.CLIP_MODEL_DIMS[clip_model_name], output_dim, project_out=project_out)
+        
+        self.enable_grad = enable_grad
+        model = AutoModel.from_pretrained(f"useful_ckpts/{clip_model_name}").train(enable_grad).requires_grad_(enable_grad).to(torch.float16)
+
+        
+            
+        if self.enable_grad:
+            self.model = model
+        else: 
+            self.__dict__["model"] = model
+
+
+    def forward(self, images: tp.List[str], device: tp.Union[torch.device, str]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+        
+        self.model.to(device)
+        self.proj_out.to(device)
+        # import ipdb
+        # ipdb.set_trace()
+
+        self.model.eval()
+        if not isinstance(images[0], torch.Tensor):
+            video_feats = []
+            for path in images:
+                if '.npy' in path:
+                    video_feats.append(torch.from_numpy(np.load(path)).to(device))
+                else:
+                    video_feats.append(torch.from_numpy(np.load(path)).to(device))
+            images = torch.stack(video_feats, dim=0).to(device)
+        else:
+            images = torch.stack(images, dim=0).to(device)    
+        bsz, t, c, h, w = images.shape
+        # 使用 rearrange 进行维度合并
+        images = rearrange(images, 'b t c h w -> (b t) c h w')
+        with torch.set_grad_enabled(self.enable_grad):
+            image_features = self.model.get_image_features(images)
+        image_features = rearrange(image_features, '(b t) d -> b t d', b=bsz, t=t)
+        image_features = self.proj_out(image_features)
+
+
+        return [image_features, torch.ones(image_features.shape[0], 1).to(device)]
+
+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-v1_1-xl", "google/t5-v1_1-xxl"]
+    
+    T5_MODEL_DIMS = {
+        "t5-small": 512,
+        "t5-base": 768,
+        "t5-large": 1024,
+        "t5-3b": 1024,
+        "t5-11b": 1024,
+        "t5-v1_1-xl": 2048,
+        "google/t5-v1_1-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 = 77,
+            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(os.path.join('useful_ckpts', t5_model_name))
+                model = T5EncoderModel.from_pretrained(os.path.join('useful_ckpts', 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
+
+def patch_clip(clip_model):
+    # a hack to make it output last hidden states
+    # https://github.com/mlfoundations/open_clip/blob/fc5a37b72d705f760ebbc7915b84729816ed471f/src/open_clip/model.py#L269
+    def new_encode_text(self, text, normalize: bool = False):
+        cast_dtype = self.transformer.get_cast_dtype()
+
+        x = self.token_embedding(text).to(cast_dtype)  # [batch_size, n_ctx, d_model]
+
+        x = x + self.positional_embedding.to(cast_dtype)
+        x = self.transformer(x, attn_mask=self.attn_mask)
+        x = self.ln_final(x)  # [batch_size, n_ctx, transformer.width]
+        return F.normalize(x, dim=-1) if normalize else x
+
+    clip_model.encode_text = new_encode_text.__get__(clip_model)
+    return clip_model
+
+class CLIPTextConditioner(Conditioner):
+    def __init__(
+            self,
+            output_dim: int,
+            max_length: str = 77,
+            enable_grad: bool = False,
+            project_out: bool = False
+    ):
+        super().__init__(1024, output_dim, project_out=project_out)
+        
+        from transformers import T5EncoderModel, AutoTokenizer
+        import open_clip
+        from open_clip import create_model_from_pretrained
+
+        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:
+                model = create_model_from_pretrained('hf-hub:apple/DFN5B-CLIP-ViT-H-14-384',cache_dir='useful_ckpts/DFN5B-CLIP-ViT-H-14-384',
+                                                           return_transform=False).train(enable_grad).requires_grad_(enable_grad).to(torch.float16)
+                model = patch_clip(model)
+                self.tokenizer = open_clip.get_tokenizer('ViT-H-14-378-quickgelu')  # same as 'ViT-H-14'
+            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
+        ).to(device)
+
+        # 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.encode_text(
+                encoded
+            )
+            
+        embeddings = self.proj_out(embeddings.float())
+
+        # embeddings = embeddings * attention_mask.unsqueeze(-1).float()
+
+        return embeddings, torch.ones(embeddings.shape[0], 1).to(device)
+
+def patch_clip(clip_model):
+    # a hack to make it output last hidden states
+    # https://github.com/mlfoundations/open_clip/blob/fc5a37b72d705f760ebbc7915b84729816ed471f/src/open_clip/model.py#L269
+    def new_get_text_features(self, input_ids=None, attention_mask=None, position_ids=None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None):
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        text_outputs = self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = text_outputs[0]
+        # pooled_output = text_outputs[1]
+        # text_features = self.text_projection(pooled_output)
+
+        return last_hidden_state
+
+    clip_model.get_text_features = new_get_text_features.__get__(clip_model)
+    return clip_model
+
+class MetaCLIPTextConditioner(Conditioner):
+    def __init__(
+            self,
+            output_dim: int,
+            max_length: str = 77,
+            enable_grad: bool = False,
+            project_out: bool = False
+    ):
+        super().__init__(1024, output_dim, project_out=project_out)
+        
+        from transformers import AutoModel
+        from transformers import AutoProcessor
+
+        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.model = AutoModel.from_pretrained("useful_ckpts/metaclip-huge")
+                self.model = patch_clip(self.model)
+                self.clip_processor = AutoProcessor.from_pretrained("useful_ckpts/metaclip-huge")
+            finally:
+                logging.disable(previous_level)
+
+
+    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.clip_processor(text=texts, return_tensors="pt", padding=True).to(device)
+
+        # input_ids = encoded["input_ids"].to(device)
+        attention_mask = encoded["attention_mask"].to(device).to(torch.bool)
+
+        self.model.eval()
+            
+        with torch.set_grad_enabled(self.enable_grad):
+            embeddings = self.model.get_text_features(
+                **encoded
+            )
+            
+        embeddings = self.proj_out(embeddings.float())
+
+        # embeddings = embeddings * attention_mask.unsqueeze(-1).float()
+
+        return embeddings, torch.ones(embeddings.shape[0],1).to(device)
+    
+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)
+            
+            cond_output = conditioner(conditioner_inputs, device)
+            if len(cond_output) == 1:
+                output[key] = cond_output[0]
+            elif len(cond_output) == 2:
+                output[key] = cond_output
+            elif len(cond_output) == 4:
+                output[key] = cond_output[:2]
+                output[f'{key}_g'] = cond_output[2:]
+
+        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 == "clip_text":
+            conditioners[id] = CLIPTextConditioner(**conditioner_config)
+        elif conditioner_type == "metaclip_text":
+            conditioners[id] = MetaCLIPTextConditioner(**conditioner_config)
+        elif conditioner_type == "clap_audio":
+            conditioners[id] = CLAPAudioConditioner(**conditioner_config)
+        elif conditioner_type == "video_linear":
+            conditioners[id] = Video_Linear(**conditioner_config)
+        elif conditioner_type == "video_global":
+            conditioners[id] = Video_Global(**conditioner_config)
+        elif conditioner_type == "video_sync":
+            conditioners[id] = Video_Sync(**conditioner_config)
+        elif conditioner_type == "text_linear":
+            conditioners[id] = Text_Linear(**conditioner_config)
+        elif conditioner_type == "video_clip":
+            conditioners[id] = CLIPConditioner(**conditioner_config)
+        elif conditioner_type == "video_hiera":
+            conditioners[id] = VideoHieraConditioner(**conditioner_config)
+        elif conditioner_type == "int":
+            conditioners[id] = IntConditioner(**conditioner_config)
+        elif conditioner_type == "number":
+            conditioners[id] = NumberConditioner(**conditioner_config)
+        elif conditioner_type == "phoneme":
+            conditioners[id] = PhonemeConditioner(**conditioner_config)
+        elif conditioner_type == "lut":
+            conditioners[id] = TokenizerLUTConditioner(**conditioner_config)
+        elif conditioner_type == "pretransform":
+            sample_rate = conditioner_config.pop("sample_rate", None)
+            assert sample_rate is not None, "Sample rate must be specified for pretransform conditioners"
+
+            pretransform = create_pretransform_from_config(conditioner_config.pop("pretransform_config"), sample_rate=sample_rate)
+
+            if conditioner_config.get("pretransform_ckpt_path", None) is not None:
+                pretransform.load_state_dict(load_ckpt_state_dict(conditioner_config.pop("pretransform_ckpt_path")))
+
+            conditioners[id] = PretransformConditioner(pretransform, **conditioner_config)
+        elif conditioner_type == "mm_unchang":
+            conditioners[id] = mm_unchang(**conditioner_config)
+        else:
+            raise ValueError(f"Unknown conditioner type: {conditioner_type}")
+
+    return MultiConditioner(conditioners, default_keys=default_keys)

ThinkSound/models/diffusion.py

@@ -0,0 +1,920 @@
+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 .mmdit import MMAudio
+from .factory import create_pretransform_from_config
+from .pretransforms import Pretransform
+from ..inference.generation import generate_diffusion_cond
+
+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] = [],
+            add_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.add_cond_ids = add_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
+        add_input = 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)
+            # import ipdb
+            # ipdb.set_trace()
+            cross_attention_input = torch.cat(cross_attention_input, dim=1)
+            cross_attention_masks = torch.cat(cross_attention_masks, dim=1)
+
+        if len(self.add_cond_ids) > 0:
+            # Concatenate all cross-attention inputs over the sequence dimension
+            # Assumes that the cross-attention inputs are of shape (batch, seq, channels)
+            add_input = []
+
+            for key in self.add_cond_ids:
+                add_in, _ = conditioning_tensors[key]
+
+                # Add sequence dimension if it's not there
+                if len(add_in.shape) == 2:
+                    add_in = add_in.unsqueeze(1)
+
+                add_input.append(add_in)
+
+            add_input = torch.cat(add_input, 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 = []
+            # import ipdb
+            # ipdb.set_trace()
+            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
+            if global_conds[0].shape[-1] == 768:
+                global_cond = torch.cat(global_conds, dim=-1)
+            else:
+                global_cond = sum(global_conds)
+
+            # 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,
+                "add_cond": add_input
+                }
+
+    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 MMDiTWrapper(ConditionedDiffusionModel):
+    def __init__(
+        self,
+        *args,
+        **kwargs
+    ):
+        super().__init__(supports_cross_attention=True, supports_global_cond=False, supports_input_concat=False)
+
+        self.model = MMAudio(*args, **kwargs)
+
+        # with torch.no_grad():
+        #     for param in self.model.parameters():
+        #         param *= 0.5
+
+    def forward(self,
+                x,
+                t,
+                clip_f,
+                sync_f,
+                text_f,
+                inpaint_masked_input=None,
+                t5_features=None,
+                metaclip_global_text_features=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):
+
+        # breakpoint()
+        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(
+            latent=x,
+            t=t,
+            clip_f=clip_f,
+            sync_f=sync_f,
+            text_f=text_f,
+            inpaint_masked_input=inpaint_masked_input,
+            t5_features=t5_features,
+            metaclip_global_text_features=metaclip_global_text_features,
+            cfg_scale=cfg_scale,
+            cfg_dropout_prob=cfg_dropout_prob,
+            scale_phi=scale_phi,
+            **kwargs)
+    
+class MMConditionedDiffusionModelWrapper(ConditionedDiffusionModel):
+    """
+    A diffusion model that takes in conditioning
+    """
+    def __init__(
+            self,
+            model: MMAudio,
+            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] = [],
+            add_cond_ids: tp.List[str] = [],
+            mm_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.add_cond_ids = add_cond_ids
+        self.min_input_length = min_input_length
+        self.mm_cond_ids = mm_cond_ids
+        
+        assert len(self.cross_attn_cond_ids) == 0, "cross_attn_cond_ids is not supported for MMDiTWrapper"
+        assert len(self.global_cond_ids) == 0, "global_cond_ids is not supported for MMDiTWrapper"
+        assert len(self.input_concat_ids) == 0, "input_concat_ids is not supported for MMDiTWrapper"
+        assert len(self.prepend_cond_ids) == 0, "prepend_cond_ids is not supported for MMDiTWrapper"
+        assert len(self.add_cond_ids) == 0, "add_cond_ids is not supported for MMDiTWrapper"
+        assert len(self.mm_cond_ids) > 0, "mm_cond_ids must be specified for MMDiTWrapper"
+        assert "metaclip_features" in self.mm_cond_ids, "clip_f must be specified in mm_cond_ids for MMDiTWrapper"
+        assert "sync_features" in self.mm_cond_ids, "sync_features must be specified in mm_cond_ids for MMDiTWrapper"
+        assert "metaclip_text_features" in self.mm_cond_ids, "metaclip_text_features must be specified in mm_cond_ids for MMDiTWrapper"
+        # assert len(self.mm_cond_ids) == 3, "mm_cond_ids must be clip_f sync_f text_f for MMDiTWrapper"
+
+    def get_conditioning_inputs(self, conditioning_tensors: tp.Dict[str, tp.Any], negative=False):
+        assert negative == False, "negative conditioning is not supported for MMDiTWrapper"
+        cross_attention_input = None
+        cross_attention_masks = None
+        global_cond = None
+        input_concat_cond = None
+        prepend_cond = None
+        prepend_cond_mask = None
+        add_input = None
+        inpaint_masked_input = None
+        t5_features = None
+        metaclip_global_text_features = None
+        clip_f = conditioning_tensors["metaclip_features"]
+        sync_f = conditioning_tensors["sync_features"]
+        text_f = conditioning_tensors["metaclip_text_features"]
+        if 'inpaint_masked_input' in conditioning_tensors.keys():
+            inpaint_masked_input = conditioning_tensors["inpaint_masked_input"]
+        if 't5_features' in conditioning_tensors.keys():
+            t5_features = conditioning_tensors["t5_features"]
+        if 'metaclip_global_text_features' in conditioning_tensors.keys():
+            metaclip_global_text_features = conditioning_tensors["metaclip_global_text_features"]
+        return {
+            "clip_f": clip_f,
+            "sync_f": sync_f,
+            "text_f": text_f,
+            "inpaint_masked_input": inpaint_masked_input,
+            "t5_features": t5_features,
+            "metaclip_global_text_features": metaclip_global_text_features
+            }
+
+    def forward(self, x: torch.Tensor, t: torch.Tensor, cond: tp.Dict[str, tp.Any], **kwargs):
+        # breakpoint()
+        # print(kwargs)
+        return self.model(x=x, t=t, **self.get_conditioning_inputs(cond), **kwargs)
+
+    def generate(self, *args, **kwargs):
+        return generate_diffusion_cond(self, *args, **kwargs)
+
+class DiTUncondWrapper(DiffusionModel):
+    def __init__(
+        self,
+        io_channels,
+        *args,
+        **kwargs
+    ):
+        super().__init__()
+
+        self.model = DiffusionTransformer(io_channels=io_channels, *args, **kwargs)
+
+        self.io_channels = io_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_infill_from_config(config: tp.Dict[str, tp.Any]):
+    diffusion_uncond_config = config["model"]
+
+
+    diffusion_config = diffusion_uncond_config.get('diffusion', {})
+    model_type = diffusion_config.get('type', None)
+    model_config = diffusion_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(
+            **model_config
+        )
+    
+    elif model_type == "adp_uncond_1d":
+
+        model = UNet1DUncondWrapper(
+            io_channels = io_channels,
+            **model_config
+        )
+    elif model_type == "dit":
+        model = DiTUncondWrapper(
+            **model_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)
+    elif diffusion_model_type == 'mmdit':
+        diffusion_model = MMDiTWrapper(**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', [])
+    add_cond_ids = diffusion_config.get('add_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', [])
+    mm_cond_ids = diffusion_config.get('mm_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 == "mm_diffusion_cond":
+        wrapper_fn = MMConditionedDiffusionModelWrapper
+        extra_kwargs["diffusion_objective"] = diffusion_objective
+        extra_kwargs["mm_cond_ids"] = mm_cond_ids
+
+    if model_type == "diffusion_cond" or model_type == "diffusion_cond_inpaint" or model_type == 'diffusion_infill':
+        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,
+        add_cond_ids=add_cond_ids,
+        pretransform=pretransform,
+        io_channels=io_channels,
+        **extra_kwargs
+    )

ThinkSound/models/dit.py

@@ -0,0 +1,439 @@
+import typing as tp
+
+import torch
+# from beartype.typing import Tuple
+from einops import rearrange
+from torch import nn
+from torch.nn import functional as F
+from x_transformers import ContinuousTransformerWrapper, Encoder
+from .mmmodules.model.low_level import MLP, ChannelLastConv1d, ConvMLP
+from .blocks import FourierFeatures
+from .transformer import ContinuousTransformer
+from .utils import mask_from_frac_lengths, resample
+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,
+        cond_ctx_dim=0,
+        depth=12,
+        num_heads=8,
+        transformer_type: tp.Literal["x-transformers", "continuous_transformer","mm_transformer"] = "x-transformers",
+        global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
+        frac_lengths_mask = (0.7, 1.),
+        ctx_drop: float = 0.1,
+        add_token_dim=0,
+        use_mlp=False,
+        **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),
+        )
+        self.use_mlp = use_mlp
+        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 add_token_dim > 0:
+            # Conditioning tokens
+
+            add_embed_dim = add_token_dim if not project_cond_tokens else embed_dim
+            self.to_add_embed = nn.Sequential(
+                nn.SiLU(),
+                ConvMLP(add_embed_dim, add_embed_dim * 4, kernel_size=3, padding=1),
+            )
+        else:
+            add_embed_dim = 0
+
+        if cond_ctx_dim > 0:
+            self.ctx_linear = nn.Linear(cond_ctx_dim*2, cond_ctx_dim, bias=True)
+            self.frac_lengths_mask = frac_lengths_mask
+            self.ctx_drop = ctx_drop
+
+        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
+        print("######################")
+        print(f'global type: {global_cond_type}')
+        print("######################")
+        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,
+        add_cond=None,
+        add_masks=None,
+        # x_ctx=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)
+            if len(global_embed.shape) == 3:
+                global_embed = torch.max(global_embed, dim=1).values
+
+        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-exact')
+
+            x = torch.cat([x, input_concat_cond], dim=1)
+
+        if add_cond is not None:
+            # Interpolate input_concat_cond to the same length as x
+
+            if self.use_mlp:
+                add_cond = self.to_add_embed(add_cond)
+            if add_cond.shape[1] != x.shape[2]:
+                # add_cond = add_cond.transpose(1,2)
+                # add_cond = F.interpolate(add_cond, (x.shape[2], ), mode='nearest-exact')
+                # add_cond = add_cond.transpose(1,2)
+                add_cond = resample(add_cond, x)
+
+        # Get the batch of timestep embeddings
+        timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None])) # (b, embed_dim)
+        # import ipdb
+        # ipdb.set_trace()
+        # 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, add_cond=add_cond, 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, add_cond=add_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,
+        add_cond=None,
+        cfg_scale=1.0,
+        cfg_dropout_prob=0.0,
+        causal=False,
+        scale_phi=0.0,
+        mask=None,
+        x_ctx=None,
+        ctx_mask=None,
+        return_info=False,
+        **kwargs):
+
+        assert causal == False, "Causal mode is not supported for DiffusionTransformer"
+        bsz, a, b = x.shape
+
+        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 add_cond is not None:
+                null_embed = torch.zeros_like(add_cond, device=add_cond.device)
+                dropout_mask = torch.bernoulli(torch.full((add_cond.shape[0], 1, 1), cfg_dropout_prob, device=add_cond.device)).to(torch.bool)
+                add_cond = torch.where(dropout_mask, null_embed, add_cond)
+
+        if cfg_scale != 1.0 and (cross_attn_cond is not None or prepend_cond is not None or add_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)
+         
+            batch_add_cond = None
+            
+            # Handle CFG for cross-attention conditioning
+            if add_cond is not None:
+
+                null_embed = torch.zeros_like(add_cond, device=add_cond.device)
+
+                
+                batch_add_cond = torch.cat([add_cond, null_embed], 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, 
+                # x_ctx=x_ctx,
+                input_concat_cond=batch_input_concat_cond, 
+                global_embed = batch_global_cond,
+                prepend_cond = batch_prepend_cond,
+                prepend_cond_mask = batch_prepend_cond_mask,
+                add_cond = batch_add_cond,
+                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,
+                add_cond=add_cond,
+                # x_ctx=x_ctx,
+                mask=mask,
+                return_info=return_info,
+                **kwargs
+            )

ThinkSound/models/embeddings.py

@@ -0,0 +1,85 @@
+import torch
+import torch.nn as nn
+
+# https://github.com/facebookresearch/DiT
+
+from typing import Union
+
+import torch
+from einops import rearrange
+from torch import Tensor
+
+# Ref: https://github.com/black-forest-labs/flux/blob/main/src/flux/math.py
+# Ref: https://github.com/lucidrains/rotary-embedding-torch
+
+
+def compute_rope_rotations(length: int,
+                           dim: int,
+                           theta: int,
+                           *,
+                           freq_scaling: float = 1.0,
+                           device: Union[torch.device, str] = 'cpu') -> Tensor:
+    assert dim % 2 == 0
+
+    with torch.amp.autocast(device_type='cuda', enabled=False):
+        pos = torch.arange(length, dtype=torch.float32, device=device)
+        freqs = 1.0 / (theta**(torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim))
+        freqs *= freq_scaling
+
+        rot = torch.einsum('..., f -> ... f', pos, freqs)
+        rot = torch.stack([torch.cos(rot), -torch.sin(rot), torch.sin(rot), torch.cos(rot)], dim=-1)
+        rot = rearrange(rot, 'n d (i j) -> 1 n d i j', i=2, j=2)
+        return rot
+
+
+def apply_rope(x: Tensor, rot: Tensor) -> tuple[Tensor, Tensor]:
+    with torch.amp.autocast(device_type='cuda', enabled=False):
+        _x = x.float()
+        _x = _x.view(*_x.shape[:-1], -1, 1, 2)
+        x_out = rot[..., 0] * _x[..., 0] + rot[..., 1] * _x[..., 1]
+        return x_out.reshape(*x.shape).to(dtype=x.dtype)
+
+
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+
+    def __init__(self, dim, frequency_embedding_size, max_period):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(frequency_embedding_size, dim),
+            nn.SiLU(),
+            nn.Linear(dim, dim),
+        )
+        self.dim = dim
+        self.max_period = max_period
+        assert dim % 2 == 0, 'dim must be even.'
+
+        with torch.autocast('cuda', enabled=False):
+            self.register_buffer("freqs",
+                1.0 / (10000**(torch.arange(0, frequency_embedding_size, 2, dtype=torch.float32) /
+                               frequency_embedding_size)),
+                persistent=False)
+            freq_scale = 10000 / max_period
+            self.freqs = freq_scale * self.freqs
+
+    def timestep_embedding(self, t):
+        """
+        Create sinusoidal timestep embeddings.
+        :param t: a 1-D Tensor of N indices, one per batch element.
+                          These may be fractional.
+        :param dim: the dimension of the output.
+        :param max_period: controls the minimum frequency of the embeddings.
+        :return: an (N, D) Tensor of positional embeddings.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
+
+        args = t[:, None].float() * self.freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        return embedding
+
+    def forward(self, t):
+        t_freq = self.timestep_embedding(t).to(t.dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb

ThinkSound/models/factory.py

@@ -0,0 +1,156 @@
+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_infill':
+    #     from .diffusion import create_diffusion_infill_from_config
+    #     return create_diffusion_infill_from_config(model_config)
+    elif model_type == 'diffusion_cond' or model_type == 'diffusion_cond_inpaint' or model_type == "diffusion_prior" or model_type == "diffusion_infill" or model_type == "mm_diffusion_cond":
+        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

ThinkSound/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

ThinkSound/models/mmdit.py

@@ -0,0 +1,578 @@
+import logging
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import sys
+from .embeddings import compute_rope_rotations
+from .embeddings import TimestepEmbedder
+from .blocks import MLP, ChannelLastConv1d, ConvMLP
+from .transformer_layers import (FinalBlock, JointBlock, MMDitSingleBlock)
+from .utils import resample
+
+log = logging.getLogger()
+
+
+@dataclass
+class PreprocessedConditions:
+    clip_f: torch.Tensor
+    sync_f: torch.Tensor
+    text_f: torch.Tensor
+    clip_f_c: torch.Tensor
+    text_f_c: torch.Tensor
+
+
+class MMAudio(nn.Module):
+
+    def __init__(self,
+                 *,
+                 latent_dim: int,
+                 clip_dim: int,
+                 sync_dim: int,
+                 text_dim: int,
+                 hidden_dim: int,
+                 depth: int,
+                 fused_depth: int,
+                 num_heads: int,
+                 mlp_ratio: float = 4.0,
+                 latent_seq_len: int,
+                 clip_seq_len: int,
+                 sync_seq_len: int,
+                 text_seq_len: int = 77,
+                 latent_mean: Optional[torch.Tensor] = None,
+                 latent_std: Optional[torch.Tensor] = None,
+                 empty_string_feat: Optional[torch.Tensor] = None,
+                 v2: bool = False,
+                 kernel_size: int = 7,
+                 sync_kernel: int = 7,
+                 use_inpaint: bool = False,
+                 use_mlp: bool = False,
+                 cross_attend: bool = False,
+                 add_video: bool = False,
+                 triple_fusion: bool = False,
+                 gated_video: bool = False) -> None:
+        super().__init__()
+
+        self.v2 = v2
+        self.latent_dim = latent_dim
+        self._latent_seq_len = latent_seq_len
+        self._clip_seq_len = clip_seq_len
+        self._sync_seq_len = sync_seq_len
+        self._text_seq_len = text_seq_len
+        self.hidden_dim = hidden_dim
+        self.num_heads = num_heads
+        self.cross_attend = cross_attend
+        self.add_video = add_video
+        self.gated_video = gated_video
+        self.triple_fusion = triple_fusion
+        self.use_inpaint = use_inpaint
+        if self.gated_video:
+            self.gated_mlp = nn.Sequential(
+                nn.LayerNorm(hidden_dim * 2),
+                nn.Linear(hidden_dim*2, hidden_dim * 4, bias=False),
+                nn.SiLU(),
+                nn.Linear(hidden_dim * 4, hidden_dim, bias=False),
+                nn.Sigmoid()
+            )
+            # 初始化最后一层权重为零，促进初始均匀融合
+            nn.init.zeros_(self.gated_mlp[3].weight)
+        if self.triple_fusion:
+            self.gated_mlp_v = nn.Sequential(
+                nn.LayerNorm(hidden_dim * 3),
+                nn.Linear(hidden_dim*3, hidden_dim * 4, bias=False),
+                nn.SiLU(),
+                nn.Linear(hidden_dim * 4, hidden_dim, bias=False),
+                nn.Sigmoid()
+            )
+            self.gated_mlp_t = nn.Sequential(
+                nn.LayerNorm(hidden_dim * 3),
+                nn.Linear(hidden_dim*3, hidden_dim * 4, bias=False),
+                nn.SiLU(),
+                nn.Linear(hidden_dim * 4, hidden_dim, bias=False),
+                nn.Sigmoid()
+            )
+            nn.init.zeros_(self.gated_mlp_v[3].weight)
+            nn.init.zeros_(self.gated_mlp_t[3].weight)
+        if v2:
+            padding_size = (kernel_size - 1) // 2
+            if use_inpaint:
+                self.audio_input_proj = nn.Sequential(
+                    ChannelLastConv1d(latent_dim*2, hidden_dim, kernel_size=kernel_size, padding=padding_size),
+                    nn.SiLU(),
+                    ConvMLP(hidden_dim, hidden_dim * 4, kernel_size=kernel_size, padding=padding_size),
+                )
+            else:
+                self.audio_input_proj = nn.Sequential(
+                    ChannelLastConv1d(latent_dim, hidden_dim, kernel_size=kernel_size, padding=padding_size),
+                    nn.SiLU(),
+                    ConvMLP(hidden_dim, hidden_dim * 4, kernel_size=kernel_size, padding=padding_size),
+                )
+
+            self.clip_input_proj = nn.Sequential(
+                nn.Linear(clip_dim, hidden_dim),
+                nn.SiLU(),
+                ConvMLP(hidden_dim, hidden_dim * 4, kernel_size=3, padding=1),
+            )
+            sync_pad = (sync_kernel - 1) // 2
+            self.sync_input_proj = nn.Sequential(
+                ChannelLastConv1d(sync_dim, hidden_dim, kernel_size=sync_kernel, padding=sync_pad),
+                nn.SiLU(),
+                ConvMLP(hidden_dim, hidden_dim * 4, kernel_size=3, padding=1),
+            )
+
+            self.text_input_proj = nn.Sequential(
+                nn.Linear(text_dim, hidden_dim),
+                nn.SiLU(),
+                MLP(hidden_dim, hidden_dim * 4),
+            )
+        else:
+            self.audio_input_proj = nn.Sequential(
+                ChannelLastConv1d(latent_dim, hidden_dim, kernel_size=7, padding=3),
+                nn.SELU(),
+                ConvMLP(hidden_dim, hidden_dim * 4, kernel_size=7, padding=3),
+            )
+
+            self.clip_input_proj = nn.Sequential(
+                nn.Linear(clip_dim, hidden_dim),
+                ConvMLP(hidden_dim, hidden_dim * 4, kernel_size=3, padding=1),
+            )
+
+            self.sync_input_proj = nn.Sequential(
+                ChannelLastConv1d(sync_dim, hidden_dim, kernel_size=7, padding=3),
+                nn.SELU(),
+                ConvMLP(hidden_dim, hidden_dim * 4, kernel_size=3, padding=1),
+            )
+
+            self.text_input_proj = nn.Sequential(
+                nn.Linear(text_dim, hidden_dim),
+                MLP(hidden_dim, hidden_dim * 4),
+            )
+        
+        self.clip_cond_proj = nn.Linear(hidden_dim, hidden_dim)
+        if use_mlp:
+            self.text_cond_proj = nn.Sequential(
+                nn.Linear(1024, hidden_dim),
+                MLP(hidden_dim, hidden_dim * 4),
+            )
+        else:
+            self.text_cond_proj = nn.Linear(1024, hidden_dim)
+        self.global_cond_mlp = MLP(hidden_dim, hidden_dim * 4)
+        # each synchformer output segment has 8 feature frames
+        self.sync_pos_emb = nn.Parameter(torch.zeros((1, 1, 8, sync_dim)))
+
+        self.final_layer = FinalBlock(hidden_dim, latent_dim)
+
+        if v2:
+            self.t_embed = TimestepEmbedder(hidden_dim,
+                                            frequency_embedding_size=hidden_dim,
+                                            max_period=1)
+        else:
+            self.t_embed = TimestepEmbedder(hidden_dim,
+                                            frequency_embedding_size=256,
+                                            max_period=10000)
+        self.joint_blocks = nn.ModuleList([
+            JointBlock(hidden_dim,
+                       num_heads,
+                       mlp_ratio=mlp_ratio,
+                       pre_only=(i == depth - fused_depth - 1)) for i in range(depth - fused_depth)
+        ])
+
+        self.fused_blocks = nn.ModuleList([
+            MMDitSingleBlock(hidden_dim, num_heads, mlp_ratio=mlp_ratio, kernel_size=kernel_size, padding=padding_size, cross_attend=cross_attend)
+            for i in range(fused_depth)
+        ])
+
+        if empty_string_feat is None:
+            empty_string_feat = torch.zeros((77, 1024))
+        
+        empty_t5_feat = torch.zeros((77, 2048))
+
+        self.empty_string_feat = nn.Parameter(empty_string_feat, requires_grad=False)
+        self.empty_t5_feat = nn.Parameter(empty_t5_feat, requires_grad=False)
+        self.empty_clip_feat = nn.Parameter(torch.zeros(1, clip_dim), requires_grad=True)
+        self.empty_sync_feat = nn.Parameter(torch.zeros(1, sync_dim), requires_grad=True)
+
+        self.initialize_weights()
+        self.initialize_rotations()
+
+    def initialize_rotations(self):
+        base_freq = 1.0
+        latent_rot = compute_rope_rotations(self._latent_seq_len,
+                                            self.hidden_dim // self.num_heads,
+                                            10000,
+                                            freq_scaling=base_freq,
+                                            device=self.device)
+        clip_rot = compute_rope_rotations(self._clip_seq_len,
+                                          self.hidden_dim // self.num_heads,
+                                          10000,
+                                          freq_scaling=base_freq * self._latent_seq_len /
+                                          self._clip_seq_len,
+                                          device=self.device)
+
+        self.register_buffer("latent_rot", latent_rot, persistent=False)
+        self.register_buffer("clip_rot", clip_rot, persistent=False)
+
+    def update_seq_lengths(self, latent_seq_len: int, clip_seq_len: int, sync_seq_len: int) -> None:
+        self._latent_seq_len = latent_seq_len
+        self._clip_seq_len = clip_seq_len
+        self._sync_seq_len = sync_seq_len
+        self.initialize_rotations()
+
+    def initialize_weights(self):
+
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+
+        self.apply(_basic_init)
+
+        # Initialize timestep embedding MLP:
+        nn.init.normal_(self.t_embed.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.t_embed.mlp[2].weight, std=0.02)
+
+        # Zero-out adaLN modulation layers in DiT blocks:
+        for block in self.joint_blocks:
+            nn.init.constant_(block.latent_block.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(block.latent_block.adaLN_modulation[-1].bias, 0)
+            nn.init.constant_(block.clip_block.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(block.clip_block.adaLN_modulation[-1].bias, 0)
+            nn.init.constant_(block.text_block.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(block.text_block.adaLN_modulation[-1].bias, 0)
+        for block in self.fused_blocks:
+            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
+
+        # Zero-out output layers:
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
+        nn.init.constant_(self.final_layer.conv.weight, 0)
+        nn.init.constant_(self.final_layer.conv.bias, 0)
+
+        # empty string feat shall be initialized by a CLIP encoder
+        nn.init.constant_(self.sync_pos_emb, 0)
+        nn.init.constant_(self.empty_clip_feat, 0)
+        nn.init.constant_(self.empty_sync_feat, 0)
+
+    def preprocess_conditions(self, clip_f: torch.Tensor, sync_f: torch.Tensor,
+                              text_f: torch.Tensor, t5_features: torch.Tensor, metaclip_global_text_features: torch.Tensor) -> PreprocessedConditions:
+        """
+        cache computations that do not depend on the latent/time step
+        i.e., the features are reused over steps during inference
+        """
+        # breakpoint()
+        assert clip_f.shape[1] == self._clip_seq_len, f'{clip_f.shape=} {self._clip_seq_len=}'
+        assert sync_f.shape[1] == self._sync_seq_len, f'{sync_f.shape=} {self._sync_seq_len=}'
+        assert text_f.shape[1] == self._text_seq_len, f'{text_f.shape=} {self._text_seq_len=}'
+
+        bs = clip_f.shape[0]
+
+        # B * num_segments (24) * 8 * 768
+        num_sync_segments = self._sync_seq_len // 8
+        sync_f = sync_f.view(bs, num_sync_segments, 8, -1) + self.sync_pos_emb
+        sync_f = sync_f.flatten(1, 2)  # (B, VN, D)
+
+        # extend vf to match x
+        clip_f = self.clip_input_proj(clip_f)  # (B, VN, D)
+        sync_f = self.sync_input_proj(sync_f)  # (B, VN, D)
+
+        if t5_features is not None:
+
+            if metaclip_global_text_features is not None:
+                text_f_c = self.text_cond_proj(metaclip_global_text_features)  # (B, D)
+            else:
+                text_f_c = self.text_cond_proj(text_f.mean(dim=1))  # (B, D)
+            # 计算填充长度
+            padding_size = t5_features.size(2) - text_f.size(2)  # 渴望填充的数量
+            # 当确实需要填充的时候，确保填充是正数
+            if padding_size > 0:
+                # 填充 text_f 的特征维度两侧
+                text_f = F.pad(text_f, pad=(0, padding_size), mode='constant', value=0)  # 在最后一个维度上进行填充
+            else:
+                text_f = text_f  # 如果填充长度不是正数，则不需要填充
+            text_concat = torch.cat((text_f, t5_features), dim=1)
+            text_f = self.text_input_proj(text_concat)  # (B, VN, D)
+        else:
+            text_f = self.text_input_proj(text_f)  # (B, VN, D)
+            if metaclip_global_text_features is not None:
+                text_f_c = self.text_cond_proj(metaclip_global_text_features)  # (B, D)
+            else:
+                text_f_c = self.text_cond_proj(text_f.mean(dim=1))  # (B, D)
+
+        # upsample the sync features to match the audio
+        sync_f = sync_f.transpose(1, 2)  # (B, D, VN)
+        # sync_f = resample(sync_f, self._latent_seq_len)
+        sync_f = F.interpolate(sync_f, size=self._latent_seq_len, mode='nearest-exact')
+        sync_f = sync_f.transpose(1, 2)  # (B, N, D)
+
+        # get conditional features from the clip side
+        clip_f_c = self.clip_cond_proj(clip_f.mean(dim=1))  # (B, D)
+
+        return PreprocessedConditions(clip_f=clip_f,
+                                      sync_f=sync_f,
+                                      text_f=text_f,
+                                      clip_f_c=clip_f_c,
+                                      text_f_c=text_f_c)
+
+    def predict_flow(self, latent: torch.Tensor, t: torch.Tensor,
+                     conditions: PreprocessedConditions, inpaint_masked_input=None, cfg_scale:float=1.0,cfg_dropout_prob:float=0.0,scale_phi:float=0.0
+                     ) -> torch.Tensor:
+        """
+        for non-cacheable computations
+        """
+        # print(f'cfg_scale: {cfg_scale}, cfg_dropout_prob: {cfg_dropout_prob}, scale_phi: {scale_phi}')
+        assert latent.shape[1] == self._latent_seq_len, f'{latent.shape=} {self._latent_seq_len=}'
+        empty_conditions = None
+        if inpaint_masked_input is not None:
+            inpaint_masked_input = inpaint_masked_input.transpose(1,2)
+        clip_f = conditions.clip_f
+        sync_f = conditions.sync_f
+        text_f = conditions.text_f
+        clip_f_c = conditions.clip_f_c
+        text_f_c = conditions.text_f_c
+            
+        # breakpoint()
+        if inpaint_masked_input is not None:
+            latent = torch.cat([latent,inpaint_masked_input],dim=2)
+        latent = self.audio_input_proj(latent)  # (B, N, D)
+        global_c = self.global_cond_mlp(clip_f_c + text_f_c)  # (B, D)
+        # global_c = text_f_c
+        global_c = self.t_embed(t).unsqueeze(1) + global_c.unsqueeze(1)  # (B, D)
+        extended_c = global_c + sync_f
+
+        for block in self.joint_blocks:
+            latent, clip_f, text_f = block(latent, clip_f, text_f, global_c, extended_c,
+                                           self.latent_rot, self.clip_rot)  # (B, N, D)
+        if self.add_video:
+            if clip_f.shape[1] != latent.shape[1]:
+                clip_f = resample(clip_f, latent)
+
+            if self.triple_fusion:
+                text_f = torch.mean(text_f, dim=1, keepdim=True) # (bsz, 1, D)
+                text_f = text_f.expand(-1,latent.shape[1], -1) # (T_audio, D)
+                fusion = torch.concat((latent, clip_f, text_f),dim=-1)
+                gate_v = self.gated_mlp_v(fusion)
+                gate_t = self.gated_mlp_t(fusion)
+                # modulated_latent = gate * latent # 非对称设计
+                latent = latent + gate_v * clip_f + gate_t * text_f
+            elif self.gated_video:
+                fusion = torch.concat((latent, clip_f),dim=-1)
+                gate = self.gated_mlp(fusion)
+                modulated_latent = gate * latent # 非对称设计
+                latent = latent + modulated_latent
+            else:
+                latent = latent + clip_f
+        
+        for block in self.fused_blocks:
+            if self.cross_attend:
+                latent = block(latent, extended_c, self.latent_rot, context=text_f)
+            else:
+                latent = block(latent, extended_c, self.latent_rot)
+
+        # should be extended_c; this is a minor implementation error #55
+        flow = self.final_layer(latent, extended_c)  # (B, N, out_dim), remove t
+        return flow
+
+    def forward(self, latent: torch.Tensor, t: torch.Tensor, clip_f: torch.Tensor, sync_f: torch.Tensor,
+                text_f: torch.Tensor, inpaint_masked_input, t5_features, metaclip_global_text_features, cfg_scale:float,cfg_dropout_prob:float,scale_phi:float) -> torch.Tensor:
+        """
+        latent: (B, N, C) 
+        vf: (B, T, C_V)
+        t: (B,)
+        """
+        # breakpoint()
+        # print(f'cfg_scale: {cfg_scale}, cfg_dropout_prob: {cfg_dropout_prob}, scale_phi: {scale_phi}')
+        if self.use_inpaint and inpaint_masked_input is None:
+            inpaint_masked_input = torch.zeros_like(latent, device=latent.device)
+        latent = latent.permute(0, 2, 1)
+
+        if cfg_dropout_prob > 0.0:
+            if inpaint_masked_input is not None:
+                null_embed = torch.zeros_like(inpaint_masked_input,device=latent.device)
+                dropout_mask = torch.bernoulli(torch.full((inpaint_masked_input.shape[0], 1, 1), cfg_dropout_prob, device=latent.device)).to(torch.bool)
+                inpaint_masked_input = torch.where(dropout_mask, null_embed, inpaint_masked_input)
+
+            null_embed = torch.zeros_like(clip_f,device=latent.device)
+            dropout_mask = torch.bernoulli(torch.full((clip_f.shape[0], 1, 1), cfg_dropout_prob, device=latent.device)).to(torch.bool)
+            # clip_f = torch.where(dropout_mask, null_embed, clip_f)
+            clip_f = torch.where(dropout_mask, self.empty_clip_feat, clip_f)
+            null_embed = torch.zeros_like(sync_f,device=latent.device)
+            dropout_mask = torch.bernoulli(torch.full((sync_f.shape[0], 1, 1), cfg_dropout_prob, device=latent.device)).to(torch.bool)
+            # sync_f = torch.where(dropout_mask, null_embed, sync_f)
+            sync_f = torch.where(dropout_mask, self.empty_sync_feat, sync_f)
+            null_embed = torch.zeros_like(text_f,device=latent.device)
+            dropout_mask = torch.bernoulli(torch.full((text_f.shape[0], 1, 1), cfg_dropout_prob, device=latent.device)).to(torch.bool)
+            # text_f = torch.where(dropout_mask, null_embed, text_f)
+            text_f = torch.where(dropout_mask, self.empty_string_feat, text_f)
+            if t5_features is not None:
+                null_embed = torch.zeros_like(t5_features,device=latent.device)
+                dropout_mask = torch.bernoulli(torch.full((t5_features.shape[0], 1, 1), cfg_dropout_prob, device=latent.device)).to(torch.bool)
+                # t5_features = torch.where(dropout_mask, null_embed, t5_features)
+                t5_features = torch.where(dropout_mask, self.empty_t5_feat, t5_features)
+            if metaclip_global_text_features is not None:
+                null_embed = torch.zeros_like(metaclip_global_text_features,device=latent.device)
+                dropout_mask = torch.bernoulli(torch.full((metaclip_global_text_features.shape[0], 1), cfg_dropout_prob, device=latent.device)).to(torch.bool)
+                metaclip_global_text_features = torch.where(dropout_mask, null_embed, metaclip_global_text_features)
+            # null_embed = torch.zeros_like(clip_f_c,device=latent.device)
+            # dropout_mask = torch.bernoulli(torch.full((clip_f_c.shape[0], 1), cfg_dropout_prob, device=latent.device)).to(torch.bool)
+            # clip_f_c = torch.where(dropout_mask, null_embed, clip_f_c)
+            # null_embed = torch.zeros_like(text_f_c,device=latent.device)
+            # dropout_mask = torch.bernoulli(torch.full((text_f_c.shape[0], 1), cfg_dropout_prob, device=latent.device)).to(torch.bool)
+            # text_f_c = torch.where(dropout_mask, null_embed, text_f_c)
+
+        if cfg_scale != 1.0:
+            # empty_conditions = self.get_empty_conditions(latent.shape[0])
+            # breakpoint()
+            bsz = latent.shape[0]
+            latent = torch.cat([latent,latent], dim=0)
+            if inpaint_masked_input is not None:
+                empty_inpaint_masked_input = torch.zeros_like(inpaint_masked_input, device=latent.device)
+                inpaint_masked_input = torch.cat([inpaint_masked_input,empty_inpaint_masked_input], dim=0)
+            t = torch.cat([t, t], dim=0)
+            empty_clip_f = torch.zeros_like(clip_f, device=latent.device)
+            empty_sync_f = torch.zeros_like(sync_f, device=latent.device)
+            empty_text_f = torch.zeros_like(text_f, device=latent.device)
+
+            # clip_f = torch.cat([clip_f,empty_clip_f], dim=0)
+            # sync_f = torch.cat([sync_f,empty_sync_f], dim=0)
+            # text_f = torch.cat([text_f,empty_text_f], dim=0)
+            clip_f = safe_cat(clip_f,self.get_empty_clip_sequence(bsz), dim=0, match_dim=1)
+            sync_f = safe_cat(sync_f,self.get_empty_sync_sequence(bsz), dim=0, match_dim=1)
+            text_f = safe_cat(text_f,self.get_empty_string_sequence(bsz), dim=0, match_dim=1)
+            if t5_features is not None:
+                empty_t5_features = torch.zeros_like(t5_features, device=latent.device)
+                # t5_features = torch.cat([t5_features,empty_t5_features], dim=0)
+                t5_features = torch.cat([t5_features,self.get_empty_t5_sequence(bsz)], dim=0)
+            if metaclip_global_text_features is not None:
+                empty_metaclip_global_text_features = torch.zeros_like(metaclip_global_text_features, device=latent.device)
+                metaclip_global_text_features = torch.cat([metaclip_global_text_features,empty_metaclip_global_text_features], dim=0)
+            # metaclip_global_text_features = torch.cat([metaclip_global_text_features,metaclip_global_text_features], dim=0)
+            # clip_f_c = torch.cat([clip_f_c,empty_clip_f_c], dim=0)
+            # text_f_c = torch.cat([text_f_c,empty_text_f_c], dim=0)
+
+        conditions = self.preprocess_conditions(clip_f, sync_f, text_f, t5_features, metaclip_global_text_features)
+        flow = self.predict_flow(latent, t, conditions, inpaint_masked_input, cfg_scale,cfg_dropout_prob,scale_phi)
+        if cfg_scale != 1.0:
+            cond_output, uncond_output = torch.chunk(flow, 2, dim=0)
+            cfg_output = uncond_output + (cond_output - uncond_output) * cfg_scale
+            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)
+                flow = scale_phi * (cfg_output * (cond_out_std/out_cfg_std)) + (1-scale_phi) * cfg_output
+            else:
+                flow = cfg_output
+        flow = flow.permute(0, 2, 1)
+        return flow
+
+    def get_empty_string_sequence(self, bs: int) -> torch.Tensor:
+        return self.empty_string_feat.unsqueeze(0).expand(bs, -1, -1)
+
+    def get_empty_t5_sequence(self, bs: int) -> torch.Tensor:
+        return self.empty_t5_feat.unsqueeze(0).expand(bs, -1, -1)
+
+    def get_empty_clip_sequence(self, bs: int) -> torch.Tensor:
+        return self.empty_clip_feat.unsqueeze(0).expand(bs, self._clip_seq_len, -1)
+
+    def get_empty_sync_sequence(self, bs: int) -> torch.Tensor:
+        return self.empty_sync_feat.unsqueeze(0).expand(bs, self._sync_seq_len, -1)
+
+    def get_empty_conditions(
+            self,
+            bs: int,
+            *,
+            negative_text_features: Optional[torch.Tensor] = None) -> PreprocessedConditions:
+        if negative_text_features is not None:
+            empty_text = negative_text_features
+        else:
+            empty_text = self.get_empty_string_sequence(1)
+
+        empty_clip = self.get_empty_clip_sequence(1)
+        empty_sync = self.get_empty_sync_sequence(1)
+        conditions = self.preprocess_conditions(empty_clip, empty_sync, empty_text)
+        conditions.clip_f = conditions.clip_f.expand(bs, -1, -1)
+        conditions.sync_f = conditions.sync_f.expand(bs, -1, -1)
+        conditions.clip_f_c = conditions.clip_f_c.expand(bs, -1)
+        if negative_text_features is None:
+            conditions.text_f = conditions.text_f.expand(bs, -1, -1)
+            conditions.text_f_c = conditions.text_f_c.expand(bs, -1)
+
+        return conditions
+
+    def load_weights(self, src_dict) -> None:
+        if 't_embed.freqs' in src_dict:
+            del src_dict['t_embed.freqs']
+        if 'latent_rot' in src_dict:
+            del src_dict['latent_rot']
+        if 'clip_rot' in src_dict:
+            del src_dict['clip_rot']
+
+        self.load_state_dict(src_dict, strict=True)
+
+    @property
+    def device(self) -> torch.device:
+        return self.empty_clip_feat.device
+
+    @property
+    def latent_seq_len(self) -> int:
+        return self._latent_seq_len
+
+    @property
+    def clip_seq_len(self) -> int:
+        return self._clip_seq_len
+
+    @property
+    def sync_seq_len(self) -> int:
+        return self._sync_seq_len
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+def truncate_to_target(tensor, target_size, dim=1):
+    current_size = tensor.size(dim)
+    if current_size > target_size:
+        slices = [slice(None)] * tensor.dim()
+        slices[dim] = slice(0, target_size)
+        return tensor[slices]
+    return tensor
+
+def pad_to_target(tensor, target_size, dim=1, pad_value=0):
+    current_size = tensor.size(dim)
+    if current_size < target_size:
+        pad_size = target_size - current_size
+        
+        pad_config = [0, 0] * tensor.dim()
+        pad_index = 2 * (tensor.dim() - dim - 1) + 1
+        pad_config[pad_index] = pad_size
+        
+        return torch.nn.functional.pad(tensor, pad_config, value=pad_value)
+    return tensor
+
+
+def safe_cat(tensor1, tensor2, dim=0, match_dim=1):
+
+    target_size = tensor2.size(match_dim)
+
+    if tensor1.size(match_dim) > target_size:
+        tensor1 = truncate_to_target(tensor1, target_size, match_dim)
+        
+    else:
+        tensor1 = pad_to_target(tensor1, target_size, match_dim)
+    
+    return torch.cat([tensor1, tensor2], dim=dim)
+

ThinkSound/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

ThinkSound/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)

ThinkSound/models/transformer.py

@@ -0,0 +1,821 @@
+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: Literal['l2', 'ln', 'none'] = 'none',
+        natten_kernel_size = None
+    ):
+        super().__init__()
+        self.dim = dim
+        self.dim_heads = dim_heads
+        self.causal = causal
+
+        dim_kv = dim_context if dim_context is not None else dim
+        
+        self.num_heads = dim // dim_heads
+        self.kv_heads = dim_kv // dim_heads
+
+        if dim_context is not None:
+            self.to_q = nn.Linear(dim, dim, bias=False)
+            self.to_kv = nn.Linear(dim_kv, dim_kv * 2, bias=False)
+        else:
+            self.to_qkv = nn.Linear(dim, dim * 3, bias=False)
+
+        self.to_out = nn.Linear(dim, dim, bias=False)
+
+        if zero_init_output:
+            nn.init.zeros_(self.to_out.weight)
+
+        self.qk_norm = qk_norm
+
+        if self.qk_norm == "ln":
+            self.q_norm = nn.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6)
+            self.k_norm = nn.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6)
+        elif self.qk_norm == 'rns':
+            self.q_norm = nn.RMSNorm(dim_heads)
+            self.k_norm = nn.RMSNorm(dim_heads)
+
+        # 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 == "l2":
+            q = F.normalize(q, dim=-1)
+            k = F.normalize(k, dim=-1)
+        elif self.qk_norm == "ln":
+            q = self.q_norm(q)
+            k = self.k_norm(k)
+        elif self.qk_norm == "rns":
+            q = self.q_norm(q)
+            k = self.k_norm(k)
+
+        if rotary_pos_emb is not None and not has_context:
+            freqs, _ = rotary_pos_emb
+
+            q_dtype = q.dtype
+            k_dtype = k.dtype
+
+            q = q.to(torch.float32)
+            k = k.to(torch.float32)
+            freqs = freqs.to(torch.float32)
+
+            q = apply_rotary_pos_emb(q, freqs)
+            k = apply_rotary_pos_emb(k, freqs)
+
+            q = q.to(q_dtype)
+            k = k.to(k_dtype)
+        
+        input_mask = context_mask 
+
+        if input_mask is None and not has_context:
+            input_mask = mask
+
+        # determine masking
+        masks = []
+        final_attn_mask = None # The mask that will be applied to the attention matrix, taking all masks into account
+
+        if input_mask is not None:
+            input_mask = rearrange(input_mask, 'b j -> b 1 1 j')
+            masks.append(~input_mask)
+
+        # Other masks will be added here later
+
+        if len(masks) > 0:
+            final_attn_mask = ~or_reduce(masks)
+
+        n, device = q.shape[-2], q.device
+
+        causal = self.causal if causal is None else causal
+
+        if n == 1 and causal:
+            causal = False
+
+        if self.natten_kernel_size is not None:
+            if natten is None:
+                raise ImportError('natten not installed, please install natten to use neighborhood attention')
+            
+            dtype_in = q.dtype
+            q, k, v = map(lambda t: t.to(torch.float32), (q, k, v))
+
+            attn = natten.functional.natten1dqk(q, k, kernel_size = self.natten_kernel_size, dilation=1)
+
+            if final_attn_mask is not None:
+                attn = attn.masked_fill(final_attn_mask, -torch.finfo(attn.dtype).max)
+
+            attn = F.softmax(attn, dim=-1, dtype=torch.float32)
+
+            out = natten.functional.natten1dav(attn, v, kernel_size = self.natten_kernel_size, dilation=1).to(dtype_in)
+
+        # Prioritize Flash Attention 2
+        elif self.use_fa_flash:
+            assert final_attn_mask is None, 'masking not yet supported for Flash Attention 2'
+            # Flash Attention 2 requires FP16 inputs
+            fa_dtype_in = q.dtype
+            q, k, v = map(lambda t: rearrange(t, 'b h n d -> b n h d').to(torch.float16), (q, k, v))
+            
+            out = flash_attn_func(q, k, v, causal = causal)
+            
+            out = rearrange(out.to(fa_dtype_in), 'b n h d -> b h n d')
+
+        # Fall back to PyTorch implementation
+        elif self.use_pt_flash:
+            out = self.flash_attn(q, k, v, causal = causal, mask = final_attn_mask)
+
+        else:
+            # Fall back to custom implementation
+
+            if h != kv_h:
+                # Repeat interleave kv_heads to match q_heads
+                heads_per_kv_head = h // kv_h
+                k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
+
+            scale = 1. / (q.shape[-1] ** 0.5)
+
+            kv_einsum_eq = 'b j d' if k.ndim == 3 else 'b h j d'
+
+            dots = einsum(f'b h i d, {kv_einsum_eq} -> b h i j', q, k) * scale
+            
+            i, j, dtype = *dots.shape[-2:], dots.dtype
+
+            mask_value = -torch.finfo(dots.dtype).max
+
+            if final_attn_mask is not None:
+                dots = dots.masked_fill(~final_attn_mask, mask_value)
+
+            if causal:
+                causal_mask = self.create_causal_mask(i, j, device = device)
+                dots = dots.masked_fill(causal_mask, mask_value)
+
+            attn = F.softmax(dots, dim=-1, dtype=torch.float32)
+            attn = attn.type(dtype)
+
+            out = einsum(f'b h i j, {kv_einsum_eq} -> b h i d', attn, v)
+
+        # merge heads
+        out = rearrange(out, ' b h n d -> b n (h d)')
+
+        # Communicate between heads
+        
+        # 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,
+        add_cond = 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 add_cond is not None:
+            x = x + add_cond
+
+        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

ThinkSound/models/transformer_layers.py

@@ -0,0 +1,271 @@
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from einops.layers.torch import Rearrange
+
+from .embeddings import apply_rope
+from .blocks import MLP, ChannelLastConv1d, ConvMLP
+try:
+    from flash_attn import flash_attn_func, flash_attn_kvpacked_func
+    print('flash_attn installed, using Flash Attention')
+except ImportError as e:
+    print(e)
+    print('flash_attn not installed, disabling Flash Attention')
+    flash_attn_kvpacked_func = None
+    flash_attn_func = None
+
+def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor):
+    return x * (1 + scale) + shift
+
+
+def attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor):
+    # training will crash without these contiguous calls and the CUDNN limitation
+    # I believe this is related to https://github.com/pytorch/pytorch/issues/133974
+    # unresolved at the time of writing
+    fa_dtype_in = q.dtype
+
+    q = q.contiguous()
+    k = k.contiguous()
+    v = v.contiguous()
+    out = F.scaled_dot_product_attention(q, k, v)
+    out = rearrange(out, 'b h n d -> b n (h d)').contiguous()
+    return out
+    q, k, v = map(lambda t: rearrange(t, 'b h n d -> b n h d').to(torch.bfloat16), (q, k, v))
+    # print(f"q dtype: {q.dtype}")
+    # print(f"k dtype: {k.dtype}")
+    # print(f"v dtype: {v.dtype}")
+    # breakpoint()
+    out = flash_attn_func(q, k, v)
+    out = rearrange(out.to(fa_dtype_in), 'b n h d -> b n (h d)')
+    # out = rearrange(out.to(fa_dtype_in), 'b h n d -> b n (h d)').contiguous()
+    return out
+
+
+class SelfAttention(nn.Module):
+
+    def __init__(self, dim: int, nheads: int):
+        super().__init__()
+        self.dim = dim
+        self.nheads = nheads
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=True)
+        self.q_norm = nn.RMSNorm(dim // nheads)
+        self.k_norm = nn.RMSNorm(dim // nheads)
+
+        self.split_into_heads = Rearrange('b n (h d j) -> b h n d j',
+                                          h=nheads,
+                                          d=dim // nheads,
+                                          j=3)
+
+    def pre_attention(
+            self, x: torch.Tensor,
+            rot: Optional[torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        # x: batch_size * n_tokens * n_channels
+        qkv = self.qkv(x)
+        q, k, v = self.split_into_heads(qkv).chunk(3, dim=-1)
+        q = q.squeeze(-1)
+        k = k.squeeze(-1)
+        v = v.squeeze(-1)
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        if rot is not None:
+            q = apply_rope(q, rot)
+            k = apply_rope(k, rot)
+
+        return q, k, v
+
+    def forward(
+            self,
+            x: torch.Tensor,  # batch_size * n_tokens * n_channels
+    ) -> torch.Tensor:
+        q, v, k = self.pre_attention(x)
+        out = attention(q, k, v)
+        return out
+
+class CrossAttention(nn.Module):
+
+    def __init__(self, dim: int, nheads: int):
+        super().__init__()
+        self.dim = dim
+        self.nheads = nheads
+
+        self.to_q = nn.Linear(dim, dim, bias=False)
+        self.to_kv = nn.Linear(dim, dim * 2, bias=False)
+        self.q_norm = nn.RMSNorm(dim // nheads)
+        self.k_norm = nn.RMSNorm(dim // nheads)
+
+        self.split_q_into_heads = Rearrange('b n (h d) -> b h n d',
+                                          h=nheads,
+                                          d=dim // nheads)
+        self.split_kv_into_heads = Rearrange('b n (h d j) -> b h n d j',
+                                          h=nheads,
+                                          d=dim // nheads,
+                                          j=2)
+
+    def pre_attention(
+            self, x: torch.Tensor,
+            context: Optional[torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        # x: batch_size * n_tokens * n_channels
+        q = self.to_q(x)
+        kv = self.to_kv(context)
+        q = self.split_q_into_heads(q)
+        k, v = self.split_kv_into_heads(kv).chunk(2, dim=-1)
+        q = q.squeeze(-1)
+        k = k.squeeze(-1)
+        v = v.squeeze(-1)
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+
+        return q, k, v
+
+    def forward(
+            self,
+            x: torch.Tensor, context=None
+    ) -> torch.Tensor:
+        q, v, k = self.pre_attention(x, context=context)
+        out = attention(q, k, v)
+        return out
+
+
+class MMDitSingleBlock(nn.Module):
+
+    def __init__(self,
+                 dim: int,
+                 nhead: int,
+                 mlp_ratio: float = 4.0,
+                 pre_only: bool = False,
+                 kernel_size: int = 7,
+                 padding: int = 3,
+                 cross_attend: bool = False):
+        super().__init__()
+        self.norm1 = nn.LayerNorm(dim, elementwise_affine=False)
+        self.attn = SelfAttention(dim, nhead)
+        if cross_attend:
+            self.cross_attn = CrossAttention(dim, nhead)
+        self.pre_only = pre_only
+        if pre_only:
+            self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(dim, 2 * dim, bias=True))
+        else:
+            if kernel_size == 1:
+                self.linear1 = nn.Linear(dim, dim)
+            else:
+                self.linear1 = ChannelLastConv1d(dim, dim, kernel_size=kernel_size, padding=padding)
+            self.norm2 = nn.LayerNorm(dim, elementwise_affine=False)
+
+            if kernel_size == 1:
+                self.ffn = MLP(dim, int(dim * mlp_ratio))
+            else:
+                self.ffn = ConvMLP(dim,
+                                   int(dim * mlp_ratio),
+                                   kernel_size=kernel_size,
+                                   padding=padding)
+
+            self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(dim, 6 * dim, bias=True))
+
+    def pre_attention(self, x: torch.Tensor, c: torch.Tensor, rot: Optional[torch.Tensor]):
+        # x: BS * N * D
+        # cond: BS * D
+        modulation = self.adaLN_modulation(c)
+        if self.pre_only:
+            (shift_msa, scale_msa) = modulation.chunk(2, dim=-1)
+            gate_msa = shift_mlp = scale_mlp = gate_mlp = None
+        else:
+            (shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp,
+             gate_mlp) = modulation.chunk(6, dim=-1)
+
+        x = modulate(self.norm1(x), shift_msa, scale_msa)
+        q, k, v = self.attn.pre_attention(x, rot)
+        return (q, k, v), (gate_msa, shift_mlp, scale_mlp, gate_mlp)
+
+    def post_attention(self, x: torch.Tensor, attn_out: torch.Tensor, c: tuple[torch.Tensor], context=None):
+        if self.pre_only:
+            return x
+
+        (gate_msa, shift_mlp, scale_mlp, gate_mlp) = c
+        x = x + self.linear1(attn_out) * gate_msa
+        
+        if context is not None:
+            x = x + self.cross_attn(x, context=context)
+
+        r = modulate(self.norm2(x), shift_mlp, scale_mlp)
+        x = x + self.ffn(r) * gate_mlp
+
+        return x
+
+    def forward(self, x: torch.Tensor, cond: torch.Tensor,
+                rot: Optional[torch.Tensor], context: torch.Tensor = None) -> torch.Tensor:
+        # x: BS * N * D
+        # cond: BS * D
+        x_qkv, x_conditions = self.pre_attention(x, cond, rot)
+        attn_out = attention(*x_qkv)
+        x = self.post_attention(x, attn_out, x_conditions, context = context)
+
+        return x
+
+
+class JointBlock(nn.Module):
+
+    def __init__(self, dim: int, nhead: int, mlp_ratio: float = 4.0, pre_only: bool = False):
+        super().__init__()
+        self.pre_only = pre_only
+        self.latent_block = MMDitSingleBlock(dim,
+                                             nhead,
+                                             mlp_ratio,
+                                             pre_only=False,
+                                             kernel_size=3,
+                                             padding=1)
+        self.clip_block = MMDitSingleBlock(dim,
+                                           nhead,
+                                           mlp_ratio,
+                                           pre_only=pre_only,
+                                           kernel_size=3,
+                                           padding=1)
+        self.text_block = MMDitSingleBlock(dim, nhead, mlp_ratio, pre_only=pre_only, kernel_size=1)
+
+    def forward(self, latent: torch.Tensor, clip_f: torch.Tensor, text_f: torch.Tensor,
+                global_c: torch.Tensor, extended_c: torch.Tensor, latent_rot: torch.Tensor,
+                clip_rot: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        # latent: BS * N1 * D
+        # clip_f: BS * N2 * D
+        # c: BS * (1/N) * D
+        x_qkv, x_mod = self.latent_block.pre_attention(latent, extended_c, latent_rot)
+        c_qkv, c_mod = self.clip_block.pre_attention(clip_f, global_c, clip_rot)
+        t_qkv, t_mod = self.text_block.pre_attention(text_f, global_c, rot=None)
+
+        latent_len = latent.shape[1]
+        clip_len = clip_f.shape[1]
+        text_len = text_f.shape[1]
+
+        joint_qkv = [torch.cat([x_qkv[i], c_qkv[i], t_qkv[i]], dim=2) for i in range(3)]
+
+        attn_out = attention(*joint_qkv)
+        x_attn_out = attn_out[:, :latent_len]
+        c_attn_out = attn_out[:, latent_len:latent_len + clip_len]
+        t_attn_out = attn_out[:, latent_len + clip_len:]
+
+        latent = self.latent_block.post_attention(latent, x_attn_out, x_mod)
+        if not self.pre_only:
+            clip_f = self.clip_block.post_attention(clip_f, c_attn_out, c_mod)
+            text_f = self.text_block.post_attention(text_f, t_attn_out, t_mod)
+
+        return latent, clip_f, text_f
+
+
+class FinalBlock(nn.Module):
+
+    def __init__(self, dim, out_dim):
+        super().__init__()
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(dim, 2 * dim, bias=True))
+        self.norm = nn.LayerNorm(dim, elementwise_affine=False)
+        self.conv = ChannelLastConv1d(dim, out_dim, kernel_size=7, padding=3)
+
+    def forward(self, latent, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)
+        latent = modulate(self.norm(latent), shift, scale)
+        latent = self.conv(latent)
+        return latent

ThinkSound/models/utils.py

@@ -0,0 +1,164 @@
+import torch
+from safetensors.torch import load_file
+from torch import nn, Tensor, einsum, IntTensor, FloatTensor, BoolTensor
+from torchcubicspline import natural_cubic_spline_coeffs, NaturalCubicSpline
+from torch.nn.utils import remove_weight_norm
+
+def load_ckpt_state_dict(ckpt_path, prefix=None):
+    if ckpt_path.endswith(".safetensors"):
+        state_dict = load_file(ckpt_path)
+    else:
+        state_dict = torch.load(ckpt_path, map_location="cpu")["state_dict"]
+
+    # 过滤特定前缀的state_dict
+    filtered_state_dict = {k.replace(f'{prefix}',''): v for k, v in state_dict.items() if k.startswith(prefix)} if prefix is not None else state_dict
+
+    return filtered_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
+
+
+# mask construction helpers
+
+def mask_from_start_end_indices(
+    seq_len: int,
+    start: Tensor,
+    end: Tensor
+):
+    assert start.shape == end.shape
+    device = start.device
+
+    seq = torch.arange(seq_len, device = device, dtype = torch.long)
+    seq = seq.reshape(*((-1,) * start.ndim), seq_len)
+    seq = seq.expand(*start.shape, seq_len)
+
+    mask = seq >= start[..., None].long()
+    mask &= seq < end[..., None].long()
+    return mask
+
+def mask_from_frac_lengths(
+    seq_len: int,
+    frac_lengths: Tensor
+):
+    device = frac_lengths.device
+
+    lengths = (frac_lengths * seq_len).long()
+    max_start = seq_len - lengths
+
+    rand = torch.zeros_like(frac_lengths, device = device).float().uniform_(0, 1)
+    start = (max_start * rand).clamp(min = 0)
+    end = start + lengths
+
+    return mask_from_start_end_indices(seq_len, start, end)
+
+def _build_spline(video_feat, video_t, target_t):
+    # 三次样条插值核心实现
+    coeffs = natural_cubic_spline_coeffs(video_t, video_feat.permute(0,2,1))
+    spline = NaturalCubicSpline(coeffs)
+    return spline.evaluate(target_t).permute(0,2,1)
+
+def resample(video_feat, audio_latent):
+    """
+    9s
+    video_feat: [B, 72, D]
+    audio_latent: [B, D', 194] or int
+    """
+    B, Tv, D = video_feat.shape
+    
+    if isinstance(audio_latent, torch.Tensor):
+        # audio_latent is a tensor
+        if audio_latent.shape[1] != D:
+            Ta = audio_latent.shape[1]
+        else:
+            Ta = audio_latent.shape[2]
+    elif isinstance(audio_latent, int):
+        # audio_latent is an int
+        Ta = audio_latent
+    else:
+        raise TypeError("audio_latent must be either a tensor or an int")
+    
+    # 构建时间戳 (关键改进点)
+    video_time = torch.linspace(0, 9, Tv, device=video_feat.device)
+    audio_time = torch.linspace(0, 9, Ta, device=video_feat.device)
+    
+    # 三维化处理 (Batch, Feature, Time)
+    video_feat = video_feat.permute(0, 2, 1)  # [B, D, Tv]
+    
+    # 三次样条插值
+    aligned_video = _build_spline(video_feat, video_time, audio_time)  # [B, D, Ta]
+    return aligned_video.permute(0, 2, 1)  # [B, Ta, D]

ThinkSound/training/__init__.py

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

ThinkSound/training/autoencoders.py

@@ -0,0 +1,504 @@
+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, SpatialSTFTLoss
+# import pytorch_lightning as pl
+import lightning as L
+from lightning.pytorch.callbacks import Callback
+from ..models.autoencoders import AudioAutoencoder
+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(L.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)
+        elif self.autoencoder.out_channels == 4:
+            # self.sdstft = SpatialSTFTLoss(sample_rate=sample_rate, **stft_loss_args)
+            self.sdstft = 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),
+                ]
+            elif self.autoencoder.out_channels == 4:
+                # self.gen_loss_modules += [
+                #     AuralossLoss(self.lrstft, 'reals', 'decoded', name='stft_loss', weight=self.loss_config['spectral']['weights']['mrstft']),
+                # ]
+                # Add left and right channel reconstruction losses in addition to the sum and difference
+                self.gen_loss_modules += [
+                    AuralossLoss(self.sdstft, 'reals_w', 'decoded_w', name='stft_loss_w', weight=self.loss_config['spectral']['weights']['mrstft']/4),
+                    AuralossLoss(self.sdstft, 'reals_x', 'decoded_x', name='stft_loss_x', weight=self.loss_config['spectral']['weights']['mrstft']/4),
+                    AuralossLoss(self.sdstft, 'reals_y', 'decoded_y', name='stft_loss_y', weight=self.loss_config['spectral']['weights']['mrstft']/4),
+                    AuralossLoss(self.sdstft, 'reals_z', 'decoded_z', name='stft_loss_z', weight=self.loss_config['spectral']['weights']['mrstft']/4),
+                ]
+
+            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, :]
+        elif self.autoencoder.out_channels == 4:
+            loss_info["decoded_w"] = decoded[:, 0:1, :]
+            loss_info["decoded_x"] = decoded[:, 1:2, :]
+            loss_info["decoded_y"] = decoded[:, 2:3, :]
+            loss_info["decoded_z"] = decoded[:, 3:4, :]
+            loss_info["reals_w"] = reals[:, 0:1, :]
+            loss_info["reals_x"] = reals[:, 1:2, :]
+            loss_info["reals_y"] = reals[:, 2:3, :]
+            loss_info["reals_z"] = reals[:, 3:4, :]
+
+        # 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:
+
+            # import ipdb
+            # ipdb.set_trace()
+            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(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'demos/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

ThinkSound/training/diffusion.py

@@ -0,0 +1,1076 @@
+# import pytorch_lightning as pl
+import lightning as L
+from lightning.pytorch.callbacks import Callback
+import sys, gc
+import random
+import torch
+import torchaudio
+import typing as tp
+import wandb
+# from beartype.typing import Tuple
+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 .autoencoders import create_loss_modules_from_bottleneck
+from .losses import AuralossLoss, MSELoss, MultiLoss
+from .utils import create_optimizer_from_config, create_scheduler_from_config, mask_from_frac_lengths, generate_mask, generate_channel_mask
+import os
+from pathlib import Path
+from time import time
+import numpy as np
+
+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(L.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.amp.autocast('cuda'):
+            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(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.amp.autocast('cuda'):
+                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 DiffusionInfillTrainingWrapper(L.LightningModule):
+    '''
+    Wrapper for training an unconditional audio diffusion model (like Dance Diffusion).
+    '''
+    def __init__(
+            self,
+            model: ConditionedDiffusionModelWrapper,
+            lr: float = 1e-4,
+            optimizer_configs: dict = None,
+            pre_encoded: bool = False,
+            frac_lengths_mask = (0.7, 1.),
+            min_span_len = 10,
+            timestep_sampler: tp.Literal["uniform", "logit_normal"] = "uniform",
+            diffusion_objective = 'rectified_flow',
+            ctx_drop: float = 0.1,
+            r_drop: float = 0.0,
+    ):
+        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
+        )
+
+        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.rng = torch.quasirandom.SobolEngine(1, scramble=True)
+        self.frac_lengths_mask = frac_lengths_mask
+        self.min_span_len = min_span_len
+        self.timestep_sampler = timestep_sampler
+        self.ctx_drop = ctx_drop
+        self.r_drop = r_drop
+        self.diffusion_objective = diffusion_objective
+        print(f'Training in the {diffusion_objective} formulation')
+        loss_modules = [
+            MSELoss("v",
+                     "targets",
+                     weight=1.0,
+                     name="mse_loss",
+                     mask_key="mask"
+                )
+        ]
+
+        self.losses = MultiLoss(loss_modules)
+
+        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
+        if reals.ndim == 4 and reals.shape[0] == 1:
+            reals = reals[0]
+        # import ipdb
+        # ipdb.set_trace()
+        p_drop = torch.rand(1).item()
+        # r_drop = torch.rand(1).item()
+        # if p_drop >= self.ctx_drop and self.r_drop > 0.0 and r_drop < self.r_drop:
+            # generate_channel_mask(reals)
+
+        diffusion_input = reals
+        assert torch.all(torch.isfinite(diffusion_input)), "Non-finite values detected in diffusion_input"
+        p = Profiler()
+        loss_info = {}
+        if not self.pre_encoded:
+            loss_info["audio_reals"] = diffusion_input
+            
+        p.tick("setup")
+
+        conditioning = {}
+
+        p.tick("conditioning")
+        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
+
+        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
+        # alphas, sigmas = get_alphas_sigmas(t)
+        # 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
+        # x_ctx = diffusion_input.detach().clone().transpose(1,2)
+        bsz, dim, seq_len = diffusion_input.shape
+        
+
+        if p_drop < self.ctx_drop:
+            ctx_mask = torch.ones((bsz, seq_len), device = diffusion_input.device, dtype = torch.bool)
+        # elif self.r_drop > 0.0 and r_drop < self.r_drop:
+        #     ctx_mask = torch.zeros((bsz, seq_len), device=diffusion_input.device, dtype=torch.bool)
+        else:
+            # 计算 frac_lengths 提前使用
+            frac_lengths = torch.zeros((bsz,), device=diffusion_input.device).uniform_(*self.frac_lengths_mask)
+            # if self.r_drop > 0.0 and r_drop < self.r_drop:
+            #     import ipdb
+            #     ipdb.set_trace()
+                
+            #     ctx_mask = torch.zeros((bsz, seq_len), device=diffusion_input.device, dtype=torch.bool)
+            # else:
+            ctx_mask = generate_mask(bsz, seq_len, frac_lengths, self.min_span_len)
+
+        if ctx_mask.dim() == 2:
+            ctx_mask = ctx_mask.unsqueeze(1)
+        masked_sequence = diffusion_input * ~ctx_mask
+        conditioning['x_ctx'] = [masked_sequence]
+        if self.diffusion_objective == "v":
+            targets = noise * alphas - diffusion_input * sigmas
+        elif self.diffusion_objective == "rectified_flow":
+            targets = noise - diffusion_input
+        with torch.amp.autocast('cuda'):
+            p.tick("amp")
+            v = self.diffusion(noised_inputs, t, cond=conditioning)
+            p.tick("diffusion")
+            loss_info.update({
+                "v": v,
+                "targets": targets,
+                "mask": ctx_mask.squeeze(-1)
+            })
+            # import ipdb
+            # ipdb.set_trace()
+            loss, losses = self.losses(loss_info)
+        
+        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")
+        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 DiffusionInfillDemoCallback(Callback):
+    def __init__(self, 
+                 demo_dl,
+                 demo_every=2000,
+                 num_demos=8,
+                 demo_steps=250,
+                 sample_rate=48000
+    ):
+        super().__init__()
+        self.demo_dl = iter(demo_dl)
+        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
+
+        
+        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]
+
+            demo_reals = demo_reals.to(module.device)
+            reals = demo_reals
+            log_dict = {}
+
+            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.amp.autocast('cuda'):
+                        demo_reals = module.diffusion.pretransform.encode(demo_reals)
+
+            demo_samples = demo_reals.shape[2]
+
+            # Get conditioning
+            conditioning = {}
+
+            noise = torch.randn([demo_reals.shape[0], module.diffusion.io_channels, demo_samples]).to(module.device)
+            frac_lengths = torch.zeros((demo_reals.shape[0],), device = module.device).uniform_(*(0.3,0.5))
+            ctx_mask = generate_mask(demo_reals.shape[0],demo_reals.shape[2], frac_lengths, module.min_span_len)
+            # x_ctx = (demo_reals * ~ctx_mask.unsqueeze(1)).transpose(1,2)
+            x_ctx = demo_reals * ~ctx_mask.unsqueeze(1)
+
+            conditioning['x_ctx'] = [x_ctx]
+            # x_ctx_mask = x_ctx * ~ctx_mask.unsqueeze(-1)
+            if module.diffusion.pretransform is not None:
+                log_dict[f'demo_masked_input'] = wandb.Image(tokens_spectrogram_image(x_ctx.cpu()))
+            else:
+                log_dict[f'demo_masked_input'] = wandb.Image(audio_spectrogram_image(rearrange(x_ctx, "b c t -> c (b t)").mul(32767).to(torch.int16).cpu()))
+            cond_inputs = module.diffusion.get_conditioning_inputs(conditioning)
+            with torch.amp.autocast('cuda'):
+                if module.diffusion_objective == "v":
+                    fakes = sample(module.diffusion_ema, noise, self.demo_steps, 0)
+                elif module.diffusion_objective == "rectified_flow":
+                    fakes = sample_discrete_euler(module.diffusion_ema, noise, self.demo_steps, **cond_inputs)
+                # fakes = sample(module.diffusion_ema, noise, self.demo_steps, 0)
+
+                if module.diffusion.pretransform is not None:
+                    fakes = module.diffusion.pretransform.decode(fakes)
+
+            # #Interleave reals and fakes
+            # reals_fakes = rearrange([reals, fakes], 'i b d n -> (b i) d n')
+            # Put the demos together
+            fakes = rearrange(fakes, 'b d n -> d (b n)')
+
+            
+            filename = f'results/audio_ssl/demo_ssl_{trainer.global_step:08}.wav'
+            os.makedirs(Path(filename).parent,exist_ok=True)
+            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(L.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,
+            diffusion_objective: tp.Literal["rectified_flow", "v"] = "v",
+            pre_encoded: bool = False,
+            cfg_dropout_prob = 0.1,
+            timestep_sampler: tp.Literal["uniform", "logit_normal"] = "uniform",
+            max_mask_segments = 0,
+    ):
+        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
+        print(f'Training in the {self.diffusion_objective} formulation with timestep sampler: {timestep_sampler}')
+
+        self.max_mask_segments = max_mask_segments
+            
+        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
+        # import ipdb
+        # ipdb.set_trace()
+        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.amp.autocast('cuda'):
+
+            conditioning = self.diffusion.conditioner(metadata, self.device)
+            
+
+        video_exist = torch.stack([item['video_exist'] for item in metadata],dim=0)
+        conditioning['metaclip_features'][~video_exist] = self.diffusion.model.model.empty_clip_feat
+        conditioning['sync_features'][~video_exist] = self.diffusion.model.model.empty_sync_feat
+        # 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.amp.autocast('cuda') and torch.set_grad_enabled(self.diffusion.pretransform.enable_grad):
+                    self.diffusion.pretransform.train(self.diffusion.pretransform.enable_grad)
+                    
+                    diffusion_input = self.diffusion.pretransform.encode(diffusion_input)
+                    p.tick("pretransform")
+
+                    # If mask_padding is on, interpolate the padding masks to the size of the pretransformed input
+                    if use_padding_mask:
+                        padding_masks = F.interpolate(padding_masks.unsqueeze(1).float(), size=diffusion_input.shape[2], mode="nearest").squeeze(1).bool()
+            else:            
+                # Apply scale to pre-encoded latents if needed, as the pretransform encode function will not be run
+                if hasattr(self.diffusion.pretransform, "scale") and self.diffusion.pretransform.scale != 1.0:
+                    diffusion_input = diffusion_input / self.diffusion.pretransform.scale
+
+        if self.max_mask_segments > 0:
+            # 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))
+        # import ipdb
+        # ipdb.set_trace()
+        # 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.amp.autocast('cuda'):
+            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 validation_step(self, batch, batch_idx):
+        reals, metadata = batch
+        # breakpoint()
+        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
+
+
+        with torch.amp.autocast('cuda'):
+
+            conditioning = self.diffusion.conditioner(metadata, self.device)
+        
+        video_exist = torch.stack([item['video_exist'] for item in metadata],dim=0)
+        conditioning['metaclip_features'][~video_exist] = self.diffusion.model.model.empty_clip_feat
+        conditioning['sync_features'][~video_exist] = self.diffusion.model.model.empty_sync_feat
+
+        if self.diffusion.pretransform is not None:
+
+            if not self.pre_encoded:
+                self.diffusion.pretransform.to(self.device)
+                with torch.amp.autocast('cuda') and torch.set_grad_enabled(self.diffusion.pretransform.enable_grad):
+                    self.diffusion.pretransform.train(self.diffusion.pretransform.enable_grad)
+                    
+                    diffusion_input = self.diffusion.pretransform.encode(diffusion_input)
+            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.max_mask_segments > 0:
+            # 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
+
+
+        with torch.amp.autocast('cuda'):
+            output = self.diffusion(noised_inputs, t, cond=conditioning, cfg_dropout_prob = 0.0)
+
+            loss_info.update({
+                "output": output,
+                "targets": targets,
+            })
+
+            loss, losses = self.losses(loss_info)
+
+
+        log_dict = {
+            'val_loss': loss.detach(),
+        }
+
+        self.log_dict(log_dict, prog_bar=True, batch_size=diffusion_input.size(0))
+
+    def predict_step(self, batch, batch_idx):
+        reals, metadata = batch
+        ids = [item['id'] for item in metadata]
+        batch_size, length = reals.shape[0], reals.shape[2]
+        print(f"Predicting {batch_size} samples with length {length} for ids: {ids}")
+        with torch.amp.autocast('cuda'):
+            conditioning = self.diffusion.conditioner(metadata, self.device)
+        
+        video_exist = torch.stack([item['video_exist'] for item in metadata],dim=0)
+        conditioning['metaclip_features'][~video_exist] = self.diffusion.model.model.empty_clip_feat
+        conditioning['sync_features'][~video_exist] = self.diffusion.model.model.empty_sync_feat
+
+        cond_inputs = self.diffusion.get_conditioning_inputs(conditioning)
+        if batch_size > 1:
+            noise_list = []
+            for _ in range(batch_size):
+                noise_1 = torch.randn([1, self.diffusion.io_channels, length]).to(self.device)  # 每次生成推进RNG状态
+                noise_list.append(noise_1)
+            noise = torch.cat(noise_list, dim=0)
+        else:
+            noise = torch.randn([batch_size, self.diffusion.io_channels, length]).to(self.device)
+        with torch.amp.autocast('cuda'):
+
+            model = self.diffusion.model
+            if self.diffusion_objective == "v":
+                fakes = sample(model, noise, 24, 0, **cond_inputs, cfg_scale=5, batch_cfg=True)
+            elif self.diffusion_objective == "rectified_flow":
+                import time
+                start_time = time.time()
+                fakes = sample_discrete_euler(model, noise, 24, **cond_inputs, cfg_scale=5, batch_cfg=True)
+                end_time = time.time()
+                execution_time = end_time - start_time
+                print(f"执行时间: {execution_time:.2f} 秒")
+            if self.diffusion.pretransform is not None:
+                fakes = self.diffusion.pretransform.decode(fakes)
+
+        audios = fakes.to(torch.float32).div(torch.max(torch.abs(fakes))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+        return audios
+        # # Put the demos together
+        # fakes = rearrange(fakes, 'b d n -> d (b n)')
+
+    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 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(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 '.pth' in demo_cond[0]:
+            demo_cond_data = []
+            for path in demo_cond:
+                # info = {}
+                data = torch.load(path, weights_only=True)
+                if 'caption_t5' not in data.keys():
+                    data['caption_t5'] = data['caption']
+                data['seconds_start'] = 0
+                data['seconds_total'] = 10
+                demo_cond_data.append(data)
+            demo_cond = demo_cond_data
+        elif '.npz' in demo_cond[0]:
+            demo_cond_data = []
+            for path in demo_cond:
+                # info = {}
+                npz_data = np.load(path,allow_pickle=True)
+                data = {key: npz_data[key] for key in npz_data.files}
+                for key in data.keys():
+                    # print(key)
+                    if isinstance(data[key], np.ndarray) and np.issubdtype(data[key].dtype, np.number):
+                        data[key] = torch.from_numpy(data[key])
+
+                demo_cond_data.append(data)
+            demo_cond = demo_cond_data
+        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.amp.autocast('cuda'):
+                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.amp.autocast('cuda'):
+                    # model = module.diffusion_ema.model if module.diffusion_ema is not None else module.diffusion.model
+                    model = 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'demos/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()

ThinkSound/training/factory.py

@@ -0,0 +1,262 @@
+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_infill':
+        from .diffusion import DiffusionInfillTrainingWrapper
+        return DiffusionInfillTrainingWrapper(
+            model, 
+            lr=training_config.get("learning_rate", None),
+            optimizer_configs=training_config.get("optimizer_configs", None),
+            pre_encoded=training_config.get("pre_encoded", False),
+            frac_lengths_mask=training_config.get("frac_lengths_mask", (0.7, 1.)),
+            min_span_len=training_config.get("min_span_len", 10),
+            timestep_sampler = training_config.get("timestep_sampler", "uniform"),
+            ctx_drop = training_config.get("ctx_drop", 0.1),
+            r_drop = training_config.get("r_drop", 0.0)
+        )
+    elif model_type == 'diffusion_cond' or model_type == 'mm_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),
+            diffusion_objective=training_config.get("diffusion_objective","v"),
+            cfg_dropout_prob = training_config.get("cfg_dropout_prob", 0.1),
+            timestep_sampler = training_config.get("timestep_sampler", "uniform"),
+            max_mask_segments = training_config.get("max_mask_segments", 0)
+        )
+    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_infill':
+        from .diffusion import DiffusionInfillDemoCallback
+        return DiffusionInfillDemoCallback(
+            demo_every=demo_config.get("demo_every", 2000), 
+            demo_steps=demo_config.get("demo_steps", 250), 
+            sample_rate=model_config["sample_rate"],
+            **kwargs
+        )
+    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" or model_type == 'mm_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}')

ThinkSound/training/losses/__init__.py

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

ThinkSound/training/losses/auraloss.py

@@ -0,0 +1,691 @@
+# 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 compute_direction(w, x, y, z):
+    # 计算各个声道的权重
+    phi = torch.atan2(y, x)
+    theta = torch.atan2(torch.sqrt(x**2 + y**2), z)
+    return phi.unsqueeze(1), theta.unsqueeze(1)
+
+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 = [], [], [], []
+        # import ipdb
+        # ipdb.set_trace()
+        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
+
+class SpatialSTFTLoss(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_phi: float = 1.0,
+        w_theta: float = 1.0,
+        output: str = "loss",
+        **kwargs,
+    ):
+        super().__init__()
+        self.w_phi = w_phi
+        self.w_theta = w_theta
+        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()
+
+        w_o, x_o, y_o, z_o = input[:, 0], input[:, 1], input[:, 2], input[:, 3]
+        w_r, x_r, y_r, z_r = target[:, 0], target[:, 1], target[:, 2], target[:, 3]
+        
+        phi_o, theta_o = compute_direction(w_o, x_o, y_o, z_o)
+        phi_r, theta_r = compute_direction(w_r, x_r, y_r, z_r)
+
+        # compute error in STFT domain
+        phi_loss = self.mrstft(phi_o, phi_r)
+        theta_loss = self.mrstft(theta_o, theta_r)
+        loss = ((self.w_phi * phi_loss) + (self.w_theta * theta_loss)) / 2
+
+        if self.output == "loss":
+            return loss
+        elif self.output == "full":
+            return loss, sum_loss, diff_loss

ThinkSound/training/losses/losses.py

@@ -0,0 +1,100 @@
+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

ThinkSound/training/utils.py

@@ -0,0 +1,200 @@
+import torch
+import os
+from torch import nn, Tensor, einsum, IntTensor, FloatTensor, BoolTensor
+import random 
+
+
+
+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()
+
+    # 创建一个列表存储不匹配的参数
+    missing_keys = []
+    unexpected_keys = []
+    # 手动加载并检查不匹配的参数
+    for key in state_dict:
+        if key not in model_state_dict:
+            unexpected_keys.append(key)
+        elif state_dict[key].shape != model_state_dict[key].shape:
+            unexpected_keys.append(key)
+
+    for key in model_state_dict:
+        if key not in state_dict:
+            missing_keys.append(key)
+
+    # 打印不匹配的参数
+    print("Missing keys in state_dict:", missing_keys)
+    print("Unexpected keys in state_dict:", unexpected_keys)
+    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
+
+# mask construction helpers
+
+def mask_from_start_end_indices(
+    seq_len: int,
+    start: Tensor,
+    end: Tensor
+):
+    assert start.shape == end.shape
+    device = start.device
+
+    seq = torch.arange(seq_len, device = device, dtype = torch.long)
+    seq = seq.reshape(*((-1,) * start.ndim), seq_len)
+    seq = seq.expand(*start.shape, seq_len)
+
+    mask = seq >= start[..., None].long()
+    mask &= seq < end[..., None].long()
+    return mask
+
+def mask_from_frac_lengths(
+    seq_len: int,
+    frac_lengths: Tensor
+):
+    device = frac_lengths.device
+
+    lengths = (frac_lengths * seq_len).long()
+    max_start = seq_len - lengths
+
+    rand = torch.zeros_like(frac_lengths, device = device).float().uniform_(0, 1)
+    start = (max_start * rand).clamp(min = 0)
+    end = start + lengths
+
+    return mask_from_start_end_indices(seq_len, start, end)
+
+def generate_mask(batch_size, seq_len, frac_lengths, min_span_len):
+    # 计算需要掩盖的起始数量
+    n_mask = (frac_lengths * seq_len // min_span_len).long()  # 每个 span 为 10
+    # 初始化掩码张量，初始为全 0（未掩盖）
+    mask_tensor = torch.zeros((batch_size, seq_len), device=frac_lengths.device, dtype=torch.bool)
+    
+    for b in range(batch_size):
+        # 随机挑选起始帧
+        start_frames = random.sample(range(0, seq_len - min_span_len + 1), n_mask[b])  # 0 到 seq_len-10 的范围
+        
+        for start in start_frames:
+            # 将 span 为 10 的区域标记为 1（掩盖）
+            mask_tensor[b, start:start + 10] = 1.0
+    
+    return mask_tensor
+
+def generate_channel_mask(diffusion_input):    
+
+    # 如果 r_drop 小于 threshold，则对每个样本选择一个随机声道进行完全 mask
+    batchsize, num_channels, dim = diffusion_input.shape
+    for i in range(batchsize):
+        channel_means = torch.mean(torch.abs(diffusion_input[i]), dim=1)  # Mean of the absolute values for each channel
+        # Determine if any channel is 'small enough'
+        if torch.all(channel_means > 0.01):
+            # If all channels are not 'small enough', apply the mask
+            channel = torch.randint(num_channels, (1,)).item()
+            diffusion_input[i, channel, :] = 1e-8  # Mask the channel by setting its values
+        else:
+            # Optionally log that at least one channel is 'small enough' and no mask is applied
+            print(f"Sample {i}: At least one channel is 'small enough', skipping masking.")
+
+    return diffusion_input
+