Lets build again

app.py

@@ -0,0 +1,21 @@
+import gradio as gr
+from stable_audio_tools import get_pretrained_model
+from stable_audio_tools.interface.gradio import create_ui
+import json 
+
+import torch
+
+def run():
+    torch.manual_seed(42)
+
+    interface = create_ui(
+        model_config_path = "model_config_float_conditioning_dit_all.json", 
+        ckpt_path="epoch=1292-step=602500.ckpt", 
+        model_half=False
+    )
+    interface.queue()
+    interface.launch(share=True)
+
+if __name__ == "__main__":
+    run()
+

defaults.ini

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

model_config_float_conditioning_dit_all.json

@@ -0,0 +1,208 @@
+{
+  "model_type": "diffusion_cond",
+  "sample_size": 1048576,
+  "sample_rate": 44100,
+  "audio_channels": 1,
+  "model": {
+    "pretransform": {
+      "type": "autoencoder",
+      "iterate_batch": true,
+      "config": {
+          "encoder": {
+              "type": "dac",
+              "config": {
+                  "in_channels": 1,
+                  "latent_dim": 128,
+                  "d_model": 128,
+                  "strides": [4, 4, 8, 8]
+              }
+          },
+          "decoder": {
+              "type": "dac",
+              "config": {
+                  "out_channels": 1,
+                  "latent_dim": 64,
+                  "channels": 1536,
+                  "rates": [8, 8, 4, 4]
+              }
+          },
+          "bottleneck": {
+              "type": "vae"
+          },
+          "latent_dim": 64,
+          "downsampling_ratio": 1024,
+          "io_channels": 1
+      }
+  },
+    "conditioning": {
+      "configs": [
+        {
+          "id": "latitude",
+          "type": "number",
+          "config": {
+            "min_val": -54.617412,
+            "max_val": -10.13994
+          }
+        },
+        {
+          "id": "longitude",
+          "type": "number",
+          "config": {
+            "min_val": 96.8233,
+            "max_val": 167.9619
+          }
+        },
+        {
+          "id": "temperature",
+          "type": "number",
+          "config": {
+            "min_val": -10.0,
+            "max_val": 55.0
+          }
+        },
+        {
+          "id": "humidity",
+          "type": "number",
+          "config": {
+            "min_val": 1,
+            "max_val": 100.0
+          }
+        },
+        {
+          "id": "wind_speed",
+          "type": "number",
+          "config": {
+            "min_val": 0,
+            "max_val": 50.0
+          }
+        },
+        {
+          "id": "pressure",
+          "type": "number",
+          "config": {
+            "min_val": 800.0,
+            "max_val": 1200.0
+          }
+        },
+        {
+          "id": "minutes_of_day",
+          "type": "number",
+          "config": {
+            "min_val": 0,
+            "max_val": 1439
+          }
+        },
+        {
+          "id": "day_of_year",
+          "type": "number",
+          "config": {
+            "min_val": 1,
+            "max_val": 366
+          }
+        },
+        {
+          "id": "seconds_start",
+          "type": "number",
+          "config": {
+            "min_val": 0,
+            "max_val": 512
+          }
+        },
+        {
+          "id": "seconds_total",
+          "type": "number",
+          "config": {
+            "min_val": 0,
+            "max_val": 512
+          }
+        }
+      ],
+      "cond_dim": 768
+    },
+    "diffusion": {
+      "cross_attention_cond_ids": ["latitude", "longitude", "temperature", "humidity", "wind_speed", "pressure", "minutes_of_day", "day_of_year","seconds_start", "seconds_total"],
+      "global_cond_ids": ["seconds_start", "seconds_total"],
+      "type": "dit",
+      "config": {
+        "io_channels": 64,
+        "embed_dim": 768,
+        "depth": 24,
+        "num_heads": 24,
+        "cond_token_dim": 768,
+        "global_cond_dim": 1536,
+        "project_cond_tokens": false,
+        "transformer_type": "continuous_transformer"
+      }
+    },
+    "io_channels": 64
+  },
+
+  "training": {
+    "use_ema": true,
+    "log_loss_info": false,
+    "optimizer_configs": {
+      "diffusion": {
+        "optimizer": {
+          "type": "AdamW",
+          "config": {
+            "lr": 5e-5,
+            "betas": [0.9, 0.999],
+            "weight_decay": 1e-3
+          }
+        },
+        "scheduler": {
+          "type": "InverseLR",
+          "config": {
+            "inv_gamma": 1000000,
+            "power": 0.5,
+            "warmup": 0.99
+          }
+        }
+      }
+    },
+    "demo": {
+      "demo_every": 2500,
+      "demo_steps": 100,
+      "num_demos": 3,
+      "demo_cfg_scales": [3, 5, 7],
+      "demo_cond": [
+        {
+          "latitude": -24.005512,
+          "longitude": 133.368348,
+          "temperature": 25.5,
+          "humidity": 60,
+          "wind_speed": 8,
+          "pressure": 1000,
+          "minutes_of_day": 400,
+          "day_of_year": 110,
+          "seconds_start": 0,
+          "seconds_total": 22
+        },
+        {
+          "latitude": -26.987815,
+          "longitude": 153.129068,
+          "temperature": 31.5,
+          "humidity": 70,
+          "wind_speed": 12,
+          "pressure": 1010,
+          "minutes_of_day": 600,
+          "day_of_year": 57,
+          "seconds_start": 0,
+          "seconds_total": 22
+        },
+        {
+          "latitude": -12.546364,
+          "longitude": 130.919605,
+          "temperature": 28.5,
+          "humidity": 60,
+          "wind_speed": 18,
+          "pressure": 1015,
+          "minutes_of_day": 1140,
+          "day_of_year": 280,
+          "seconds_start": 0,
+          "seconds_total": 22
+        }
+      ]
+    }
+  }
+}

pyproject.toml

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

requirements.txt

@@ -0,0 +1,34 @@
+--extra-index-url https://download.pytorch.org/whl/cu113
+torch
+aeiou
+alias-free-torch
+auraloss
+descript-audio-codec
+einops
+einops-exts
+ema-pytorch
+encodec
+gradio
+huggingface_hub
+importlib-resources
+k-diffusion
+laion-clap
+local-attention
+pandas
+pedalboard
+prefigure
+pytorch_lightning
+PyWavelets
+safetensors
+sentencepiece
+s3fs
+torch
+torchaudio
+torchmetrics
+tqdm
+transformers
+v-diffusion-pytorch
+vector-quantize-pytorch
+wandb
+webdataset
+x_transformers

run_gradio.py

@@ -0,0 +1,31 @@
+from stable_audio_tools import get_pretrained_model
+from stable_audio_tools.interface.gradio import create_ui
+import json 
+
+import torch
+
+def main(args):
+    torch.manual_seed(42)
+
+    interface = create_ui(
+        model_config_path = args.model_config, 
+        ckpt_path=args.ckpt_path, 
+        pretrained_name=args.pretrained_name, 
+        pretransform_ckpt_path=args.pretransform_ckpt_path,
+        model_half=args.model_half
+    )
+    interface.queue()
+    interface.launch(share=True, auth=(args.username, args.password) if args.username is not None else None)
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser(description='Run gradio interface')
+    parser.add_argument('--pretrained-name', type=str, help='Name of pretrained model', required=False)
+    parser.add_argument('--model-config', type=str, help='Path to model config', required=False)
+    parser.add_argument('--ckpt-path', type=str, help='Path to model checkpoint', required=False)
+    parser.add_argument('--pretransform-ckpt-path', type=str, help='Optional to model pretransform checkpoint', required=False)
+    parser.add_argument('--username', type=str, help='Gradio username', required=False)
+    parser.add_argument('--password', type=str, help='Gradio password', required=False)
+    parser.add_argument('--model-half', action='store_true', help='Whether to use half precision', required=False)
+    args = parser.parse_args()
+    main(args)

run_tests.py

@@ -0,0 +1,44 @@
+from stable_audio_tools import get_pretrained_model
+from stable_audio_tools.interface.testing import runTests
+print(runTests)  # Check if it prints a function reference
+
+
+import torch
+
+def main(args):
+    torch.manual_seed(42)
+    runTests(model_config_path = args.model_config, 
+        ckpt_path=args.ckpt_path, 
+        pretrained_name=args.pretrained_name, 
+        pretransform_ckpt_path=args.pretransform_ckpt_path,
+        model_half=args.model_half,
+        output_dir=args.output_dir,
+        json_dir=args.json_dir
+    )
+    
+
+
+
+
+if __name__ == "__main__":
+    import argparse
+    import sys
+    parser = argparse.ArgumentParser(description='Run generation tests')
+    parser.add_argument('--pretrained-name', type=str, help='Name of pretrained model', required=False)
+    parser.add_argument('--model-config', type=str, help='Path to model config', required=False)
+    parser.add_argument('--ckpt-path', type=str, help='Path to model checkpoint', required=False)
+    parser.add_argument('--pretransform-ckpt-path', type=str, help='Optional to model pretransform checkpoint', required=False)
+    parser.add_argument('--model-half', action='store_true', help='Whether to use half precision', required=False)
+    parser.add_argument('--output-dir', type=str, help='Path to output directory', required=True)
+    parser.add_argument('--json-dir', type=str, help='Path to directory containing JSON files', required=True)
+    print("Running tests")
+
+    print("Arguments provided:", sys.argv[1:])
+    
+    args = parser.parse_args()
+    print("Parsed arguments:", args)
+    main(args)
+
+    
+
+

scripts/ds_zero_to_pl_ckpt.py

@@ -0,0 +1,14 @@
+import argparse
+from lightning.pytorch.utilities.deepspeed import convert_zero_checkpoint_to_fp32_state_dict
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--save_path", type=str, help="Path to the zero checkpoint")
+    parser.add_argument("--output_path", type=str, help="Path to the output checkpoint", default="lightning_model.pt")
+    args = parser.parse_args()
+
+    # lightning deepspeed has saved a directory instead of a file
+    save_path = args.save_path
+    output_path = args.output_path
+    convert_zero_checkpoint_to_fp32_state_dict(save_path, output_path)

setup.py

@@ -0,0 +1,46 @@
+from setuptools import setup, find_packages
+
+setup(
+    name='stable-audio-tools',
+    version='0.0.12',
+    url='https://github.com/Stability-AI/stable-audio-tools.git',
+    author='Stability AI',
+    description='Training and inference tools for generative audio models from Stability AI',
+    packages=find_packages(),  
+    install_requires=[
+        'audiocraft==1.0.0',
+        'aeiou==0.0.20',
+        'alias-free-torch==0.0.6',
+        'auraloss==0.4.0',
+        'descript-audio-codec==1.0.0',
+        'einops==0.7.0',
+        'einops-exts==0.0.4',
+        'ema-pytorch==0.2.3',
+        'encodec==0.1.1',
+        'flash-attn>=2.5.0',
+        'gradio>=3.42.0',
+        'huggingface_hub',
+        'importlib-resources==5.12.0',
+        'k-diffusion==0.1.1',
+        'laion-clap==1.1.4',
+        'local-attention==1.8.6',
+        'pandas==2.0.2',
+        'pedalboard==0.7.4',
+        'prefigure==0.0.9',
+        'pytorch_lightning==2.1.0', 
+        'PyWavelets==1.4.1',
+        'safetensors',
+        'sentencepiece==0.1.99',
+        's3fs',
+        'torch>=2.0.1',
+        'torchaudio>=2.0.2',
+        'torchmetrics==0.11.4',
+        'tqdm',
+        'transformers==4.33.3',
+        'v-diffusion-pytorch==0.0.2',
+        'vector-quantize-pytorch==1.9.14',
+        'wandb==0.15.4',
+        'webdataset==0.2.48',
+        'x-transformers<1.27.0'
+    ],
+)

stable_audio_tools/__init__.py

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

stable_audio_tools/configs/dataset_configs/custom_metadata/custom_md_example.py

@@ -0,0 +1,4 @@
+def get_custom_metadata(info, audio):
+
+    # Use relative path as the prompt
+    return {"prompt": info["relpath"]}

stable_audio_tools/configs/dataset_configs/local_training_example.json

@@ -0,0 +1,11 @@
+{
+    "dataset_type": "audio_dir",
+    "datasets": [
+        {
+            "id": "my_audio",
+            "path": "/path/to/audio/dataset/"
+        }
+    ],
+    "custom_metadata_module": "/path/to/custom_metadata/custom_md_example.py",
+    "random_crop": true
+}

stable_audio_tools/configs/dataset_configs/s3_wds_example.json

@@ -0,0 +1,10 @@
+{
+    "dataset_type": "s3",
+    "datasets": [
+        {
+            "id": "s3-test",
+            "s3_path": "s3://my-bucket/datasets/webdataset/audio/"
+        }
+    ],
+    "random_crop": true
+}

stable_audio_tools/configs/model_configs/autoencoders/dac_2048_32_vae.json

@@ -0,0 +1,71 @@
+{
+    "model_type": "autoencoder",
+    "sample_size": 65536,
+    "sample_rate": 44100,
+    "audio_channels": 1,
+    "model": {
+        "encoder": {
+            "type": "dac",
+            "config": {
+                "latent_dim": 64,
+                "d_model": 128,
+                "strides": [4, 8, 8, 8]
+            }
+        },
+        "decoder": {
+            "type": "dac",
+            "config": {
+                "latent_dim": 32,
+                "channels": 1536,
+                "rates": [8, 8, 8, 4]
+            }
+        },
+        "bottleneck": {
+            "type": "vae"
+        },
+        "latent_dim": 32,
+        "downsampling_ratio": 2048,
+        "io_channels": 1
+    },
+    "training": {
+        "learning_rate": 1e-4,
+        "warmup_steps": 0,
+        "use_ema": false,
+        "loss_configs": {
+            "discriminator": {
+                "type": "encodec",
+                "config": {
+                    "filters": 32,
+                    "n_ffts": [2048, 1024, 512, 256, 128, 64, 32],
+                    "hop_lengths": [512, 256, 128, 64, 32, 16, 8],
+                    "win_lengths": [2048, 1024, 512, 256, 128, 64, 32]
+                },
+                "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
+                }
+            }
+        },
+        "demo": {
+            "demo_every": 2000
+        }
+    }
+}

stable_audio_tools/configs/model_configs/autoencoders/encodec_musicgen_rvq.json

@@ -0,0 +1,88 @@
+{
+    "model_type": "autoencoder",
+    "sample_size": 32000,
+    "sample_rate": 32000,
+    "audio_channels": 1,
+    "model": {
+        "encoder": {
+            "type": "seanet",
+            "config": {
+                "channels": 1,
+                "dimension": 128,
+                "n_filters": 64,
+                "ratios": [4, 4, 5, 8],
+                "n_residual_layers": 1,
+                "dilation_base": 2,
+                "lstm": 2,
+                "norm": "weight_norm"
+            }
+        },
+        "decoder": {
+            "type": "seanet",
+            "config": {
+                "channels": 1,
+                "dimension": 128,
+                "n_filters": 64,
+                "ratios": [4, 4, 5, 8],
+                "n_residual_layers": 1,
+                "dilation_base": 2,
+                "lstm": 2,
+                "norm": "weight_norm"
+            }
+        },
+        "bottleneck": {
+            "type": "rvq",
+            "config": {
+                "num_quantizers": 4,
+                "codebook_size": 2048,
+                "dim": 128,
+                "decay": 0.99,
+                "threshold_ema_dead_code": 2
+            }
+        },
+        "latent_dim": 128,
+        "downsampling_ratio": 640,
+        "io_channels": 1
+    },
+    "training": {
+        "learning_rate": 1e-4,
+        "warmup_steps": 0,
+        "use_ema": true,
+        "loss_configs": {
+            "discriminator": {
+                "type": "encodec",
+                "config": {
+                    "filters": 32,
+                    "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
+                }
+            }
+        },
+        "demo": {
+            "demo_every": 2000
+        }
+    }
+}

stable_audio_tools/configs/model_configs/autoencoders/stable_audio_1_0_vae.json

@@ -0,0 +1,111 @@
+{
+    "model_type": "autoencoder",
+    "sample_size": 65536,
+    "sample_rate": 44100,
+    "audio_channels": 2,
+    "model": {
+        "encoder": {
+            "type": "dac",
+            "config": {
+                "in_channels": 2,
+                "latent_dim": 128,
+                "d_model": 128,
+                "strides": [4, 4, 8, 8]
+            }
+        },
+        "decoder": {
+            "type": "dac",
+            "config": {
+                "out_channels": 2,
+                "latent_dim": 64,
+                "channels": 1536,
+                "rates": [8, 8, 4, 4]
+            }
+        },
+        "bottleneck": {
+            "type": "vae"
+        },
+        "latent_dim": 64,
+        "downsampling_ratio": 1024,
+        "io_channels": 2
+    },
+    "training": {
+        "learning_rate": 1e-4,
+        "warmup_steps": 0,
+        "use_ema": true,
+        "optimizer_configs": {
+            "autoencoder": {
+                "optimizer": {
+                    "type": "AdamW",
+                    "config": {
+                        "betas": [0.8, 0.99],
+                        "lr": 1e-4
+                    }
+                },
+                "scheduler": {
+                    "type": "ExponentialLR",
+                    "config": {
+                        "gamma": 0.999996
+                    }
+                }
+            },
+            "discriminator": {
+                "optimizer": {
+                    "type": "AdamW",
+                    "config": {
+                        "betas": [0.8, 0.99],
+                        "lr": 1e-4
+                    }
+                },
+                "scheduler": {
+                    "type": "ExponentialLR",
+                    "config": {
+                        "gamma": 0.999996
+                    }
+                }
+            }
+        },
+        "loss_configs": {
+            "discriminator": {
+                "type": "encodec",
+                "config": {
+                    "filters": 32,
+                    "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-6
+                }
+            }
+        },
+        "demo": {
+            "demo_every": 2000
+        }
+    }
+}

stable_audio_tools/configs/model_configs/autoencoders/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": 2000
+        }
+    }
+}

stable_audio_tools/configs/model_configs/dance_diffusion/dance_diffusion_base.json

@@ -0,0 +1,18 @@
+{
+    "model_type": "diffusion_uncond",
+    "sample_size": 65536,
+    "sample_rate": 48000,
+    "model": {
+        "type": "DAU1d",
+        "config": {
+            "n_attn_layers": 5
+        }
+    },
+    "training": {
+        "learning_rate": 1e-4,
+        "demo": {
+            "demo_every": 2000,
+            "demo_steps": 250
+        }
+    }
+}

stable_audio_tools/configs/model_configs/dance_diffusion/dance_diffusion_base_16k.json

@@ -0,0 +1,18 @@
+{
+    "model_type": "diffusion_uncond",
+    "sample_size": 65536,
+    "sample_rate": 16000,
+    "model": {
+        "type": "DAU1d",
+        "config": {
+            "n_attn_layers": 5
+        }
+    },
+    "training": {
+        "learning_rate": 1e-4,
+        "demo": {
+            "demo_every": 2000,
+            "demo_steps": 250
+        }
+    }
+}

stable_audio_tools/configs/model_configs/dance_diffusion/dance_diffusion_base_44k.json

@@ -0,0 +1,18 @@
+{
+    "model_type": "diffusion_uncond",
+    "sample_size": 65536,
+    "sample_rate": 44100,
+    "model": {
+        "type": "DAU1d",
+        "config": {
+            "n_attn_layers": 5
+        }
+    },
+    "training": {
+        "learning_rate": 4e-5,
+        "demo": {
+            "demo_every": 2000,
+            "demo_steps": 250
+        }
+    }
+}

stable_audio_tools/configs/model_configs/dance_diffusion/dance_diffusion_large.json

@@ -0,0 +1,18 @@
+{
+    "model_type": "diffusion_uncond",
+    "sample_size": 131072,
+    "sample_rate": 48000,
+    "model": {
+        "type": "DAU1d",
+        "config": {
+            "n_attn_layers": 5
+        }
+    },
+    "training": {
+        "learning_rate": 1e-4,
+        "demo": {
+            "demo_every": 2000,
+            "demo_steps": 250
+        }
+    }
+}

stable_audio_tools/configs/model_configs/txt2audio/musicgen_small_finetune.json

@@ -0,0 +1,22 @@
+{
+    "model_type": "musicgen",
+    "sample_size": 320000,
+    "sample_rate": 32000,
+    "audio_channels": 1,
+    "model": {
+        "pretrained": "small"
+    },
+    "training": {
+        "learning_rate": 1e-4,
+        "demo": {
+            "demo_every": 2000,
+            "demo_cond": [
+                {"prompt": "Keywords: Atmospheres, Orchestral Drone, Bass, Sci-Fi Ambient Soundscape, Synthesiser, Middle Eastern Vocal, dramatic piano"},
+                {"prompt": "Genre: Corporate|Instruments: Ukulele, Drums, Clapping, Glockenspiel"},
+                {"prompt": "Description: 116 BPM rock drums, drum track for a rock song"},
+                {"prompt": "A grand orchestral arrangement with thunderous percussion, epic brass fanfares, and soaring strings, creating a cinematic atmosphere fit for a heroic battle."}
+            ],
+            "demo_cfg_scales": [3, 6, 9]
+        }
+    }
+}

stable_audio_tools/configs/model_configs/txt2audio/stable_audio_1_0.json

@@ -0,0 +1,107 @@
+{
+    "model_type": "diffusion_cond",
+    "sample_size": 4194304,
+    "sample_rate": 44100,
+    "audio_channels": 2,
+    "model": {
+        "pretransform": {
+            "type": "autoencoder",
+            "iterate_batch": true,
+            "config": {
+                "encoder": {
+                    "type": "dac",
+                    "config": {
+                        "in_channels": 2,
+                        "latent_dim": 128,
+                        "d_model": 128,
+                        "strides": [4, 4, 8, 8]
+                    }
+                },
+                "decoder": {
+                    "type": "dac",
+                    "config": {
+                        "out_channels": 2,
+                        "latent_dim": 64,
+                        "channels": 1536,
+                        "rates": [8, 8, 4, 4]
+                    }
+                },
+                "bottleneck": {
+                    "type": "vae"
+                },
+                "latent_dim": 64,
+                "downsampling_ratio": 1024,
+                "io_channels": 2
+            }
+        },
+        "conditioning": {
+            "configs": [
+                {
+                    "id": "prompt",
+                    "type": "clap_text",
+                    "config": {
+                        "audio_model_type": "HTSAT-base",
+                        "enable_fusion": true,
+                        "clap_ckpt_path": "/path/to/clap.ckpt",
+                        "use_text_features": true,
+                        "feature_layer_ix": -2
+                    }
+                },
+                {
+                    "id": "seconds_start",
+                    "type": "int",
+                    "config": {
+                        "min_val": 0,
+                        "max_val": 512
+                    }
+                },
+                {
+                    "id": "seconds_total",
+                    "type": "int",
+                    "config": {
+                        "min_val": 0,
+                        "max_val": 512
+                    }
+                }
+            ],
+            "cond_dim": 768
+        },
+        "diffusion": {
+            "type": "adp_cfg_1d",
+            "cross_attention_cond_ids": ["prompt", "seconds_start", "seconds_total"],
+            "config": {
+                "in_channels": 64,
+                "context_embedding_features": 768,
+                "context_embedding_max_length": 79,
+                "channels": 256,
+                "resnet_groups": 16,
+                "kernel_multiplier_downsample": 2,
+                "multipliers": [4, 4, 4, 5, 5],
+                "factors": [1, 2, 2, 4],
+                "num_blocks": [2, 2, 2, 2],
+                "attentions": [1, 3, 3, 3, 3],
+                "attention_heads": 16,
+                "attention_multiplier": 4,
+                "use_nearest_upsample": false,
+                "use_skip_scale": true,
+                "use_context_time": true
+            }
+        },
+        "io_channels": 64
+    },
+    "training": {
+        "learning_rate": 4e-5,
+        "demo": {
+            "demo_every": 2000,
+            "demo_steps": 250,
+            "num_demos": 4,
+            "demo_cond": [
+                {"prompt": "A beautiful piano arpeggio", "seconds_start": 0, "seconds_total": 95},
+                {"prompt": "A tropical house track with upbeat melodies, a driving bassline, and cheery vibes", "seconds_start": 0, "seconds_total": 90},
+                {"prompt": "A cool 80s glam rock song with driving drums and distorted guitars", "seconds_start": 0, "seconds_total": 180},
+                {"prompt": "A grand orchestral arrangement with thunderous percussion, epic brass fanfares, and soaring strings, creating a cinematic atmosphere fit for a heroic battle.", "seconds_start": 0, "seconds_total": 60}
+            ],
+            "demo_cfg_scales": [3, 6, 9]
+        }
+    }
+}

stable_audio_tools/configs/model_configs/txt2audio/stable_audio_2_0.json

@@ -0,0 +1,127 @@
+{
+    "model_type": "diffusion_cond",
+    "sample_size": 12582912,
+    "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": "prompt",
+                    "type": "clap_text",
+                    "config": {
+                        "audio_model_type": "HTSAT-base",
+                        "enable_fusion": true,
+                        "clap_ckpt_path": "/path/to/clap.ckpt",
+                        "use_text_features": true,
+                        "feature_layer_ix": -2
+                    }
+                },
+                {
+                    "id": "seconds_start",
+                    "type": "number",
+                    "config": {
+                        "min_val": 0,
+                        "max_val": 512
+                    }
+                },
+                {
+                    "id": "seconds_total",
+                    "type": "number",
+                    "config": {
+                        "min_val": 0,
+                        "max_val": 512
+                    }
+                }
+            ],
+            "cond_dim": 768
+        },
+        "diffusion": {
+            "cross_attention_cond_ids": ["prompt", "seconds_start", "seconds_total"],
+            "global_cond_ids": ["seconds_start", "seconds_total"],
+            "type": "dit",
+            "config": {
+                "io_channels": 64,
+                "embed_dim": 1536,
+                "depth": 24,
+                "num_heads": 24,
+                "cond_token_dim": 768,
+                "global_cond_dim": 1536,
+                "project_cond_tokens": false,
+                "transformer_type": "continuous_transformer"
+            }
+        },
+        "io_channels": 64
+    },
+    "training": {
+        "use_ema": true,
+        "log_loss_info": false,
+        "optimizer_configs": {
+            "diffusion": {
+                "optimizer": {
+                    "type": "AdamW",
+                    "config": {
+                        "lr": 5e-5,
+                        "betas": [0.9, 0.999],
+                        "weight_decay": 1e-3
+                    }
+                },
+                "scheduler": {
+                    "type": "InverseLR",
+                    "config": {
+                        "inv_gamma": 1000000,
+                        "power": 0.5,
+                        "warmup": 0.99
+                    }
+                }
+            }
+        },
+        "demo": {
+            "demo_every": 2000,
+            "demo_steps": 250,
+            "num_demos": 4,
+            "demo_cond": [
+                {"prompt": "A beautiful piano arpeggio", "seconds_start": 0, "seconds_total": 80},
+                {"prompt": "A tropical house track with upbeat melodies, a driving bassline, and cheery vibes", "seconds_start": 0, "seconds_total": 250},
+                {"prompt": "A cool 80s glam rock song with driving drums and distorted guitars", "seconds_start": 0, "seconds_total": 180},
+                {"prompt": "A grand orchestral arrangement with thunderous percussion, epic brass fanfares, and soaring strings, creating a cinematic atmosphere fit for a heroic battle.", "seconds_start": 0, "seconds_total": 190}
+            ],
+            "demo_cfg_scales": [3, 6, 9]
+        }
+    }
+}

stable_audio_tools/data/dataset.py

@@ -0,0 +1,597 @@
+import importlib
+import numpy as np
+import io
+import os
+import posixpath
+import random
+import re
+import subprocess
+import time
+import torch
+import torchaudio
+import webdataset as wds
+
+from aeiou.core import is_silence
+from os import path
+from pedalboard.io import AudioFile
+from torchaudio import transforms as T
+from typing import Optional, Callable, List
+
+from .utils import Stereo, Mono, PhaseFlipper, PadCrop_Normalized_T
+
+AUDIO_KEYS = ("flac", "wav", "mp3", "m4a", "ogg", "opus")
+
+# fast_scandir implementation by Scott Hawley originally in https://github.com/zqevans/audio-diffusion/blob/main/dataset/dataset.py
+
+def fast_scandir(
+    dir:str,  # top-level directory at which to begin scanning
+    ext:list,  # list of allowed file extensions,
+    #max_size = 1 * 1000 * 1000 * 1000 # Only files < 1 GB
+    ):
+    "very fast `glob` alternative. from https://stackoverflow.com/a/59803793/4259243"
+    subfolders, files = [], []
+    ext = ['.'+x if x[0]!='.' else x for x in ext]  # add starting period to extensions if needed
+    try: # hope to avoid 'permission denied' by this try
+        for f in os.scandir(dir):
+            try: # 'hope to avoid too many levels of symbolic links' error
+                if f.is_dir():
+                    subfolders.append(f.path)
+                elif f.is_file():
+                    file_ext = os.path.splitext(f.name)[1].lower()
+                    is_hidden = os.path.basename(f.path).startswith(".")
+
+                    if file_ext in ext and not is_hidden:
+                        files.append(f.path)
+            except:
+                pass 
+    except:
+        pass
+
+    for dir in list(subfolders):
+        sf, f = fast_scandir(dir, ext)
+        subfolders.extend(sf)
+        files.extend(f)
+    return subfolders, files
+
+def keyword_scandir(
+    dir: str,  # top-level directory at which to begin scanning
+    ext: list,  # list of allowed file extensions
+    keywords: list,  # list of keywords to search for in the file name
+):
+    "very fast `glob` alternative. from https://stackoverflow.com/a/59803793/4259243"
+    subfolders, files = [], []
+    # make keywords case insensitive
+    keywords = [keyword.lower() for keyword in keywords]
+    # add starting period to extensions if needed
+    ext = ['.'+x if x[0] != '.' else x for x in ext]
+    banned_words = ["paxheader", "__macosx"]
+    try:  # hope to avoid 'permission denied' by this try
+        for f in os.scandir(dir):
+            try:  # 'hope to avoid too many levels of symbolic links' error
+                if f.is_dir():
+                    subfolders.append(f.path)
+                elif f.is_file():
+                    is_hidden = f.name.split("/")[-1][0] == '.'
+                    has_ext = os.path.splitext(f.name)[1].lower() in ext
+                    name_lower = f.name.lower()
+                    has_keyword = any(
+                        [keyword in name_lower for keyword in keywords])
+                    has_banned = any(
+                        [banned_word in name_lower for banned_word in banned_words])
+                    if has_ext and has_keyword and not has_banned and not is_hidden and not os.path.basename(f.path).startswith("._"):
+                        files.append(f.path)
+            except:
+                pass
+    except:
+        pass
+
+    for dir in list(subfolders):
+        sf, f = keyword_scandir(dir, ext, keywords)
+        subfolders.extend(sf)
+        files.extend(f)
+    return subfolders, files
+
+def get_audio_filenames(
+    paths: list,  # directories in which to search
+    keywords=None,
+    exts=['.wav', '.mp3', '.flac', '.ogg', '.aif', '.opus']
+):
+    "recursively get a list of audio filenames"
+    filenames = []
+    if type(paths) is str:
+        paths = [paths]
+    for path in paths:               # get a list of relevant filenames
+        if keywords is not None:
+            subfolders, files = keyword_scandir(path, exts, keywords)
+        else:
+            subfolders, files = fast_scandir(path, exts)
+        filenames.extend(files)
+    return filenames
+
+class SampleDataset(torch.utils.data.Dataset):
+    def __init__(
+        self, 
+        paths, 
+        sample_size=65536, 
+        sample_rate=48000, 
+        keywords=None, 
+        relpath=None, 
+        random_crop=True,
+        force_channels="stereo",
+        custom_metadata_fn: Optional[Callable[[str], str]] = None
+    ):
+        super().__init__()
+        self.filenames = []
+        self.relpath = relpath
+
+        self.augs = torch.nn.Sequential(
+            PhaseFlipper(),
+        )
+
+        self.pad_crop = PadCrop_Normalized_T(sample_size, sample_rate, randomize=random_crop)
+
+        self.force_channels = force_channels
+
+        self.encoding = torch.nn.Sequential(
+            Stereo() if self.force_channels == "stereo" else torch.nn.Identity(),
+            Mono() if self.force_channels == "mono" else torch.nn.Identity(),
+        )
+
+        self.filenames = get_audio_filenames(paths, keywords)
+
+        print(f'Found {len(self.filenames)} files')
+
+        self.sr = sample_rate
+
+        self.custom_metadata_fn = custom_metadata_fn
+
+    def load_file(self, filename):
+        ext = filename.split(".")[-1]
+
+        if ext == "mp3":
+            with AudioFile(filename) as f:
+                audio = f.read(f.frames)
+                audio = torch.from_numpy(audio)
+                in_sr = f.samplerate
+        else:
+            audio, in_sr = torchaudio.load(filename, format=ext)
+
+        if in_sr != self.sr:
+            resample_tf = T.Resample(in_sr, self.sr)
+            audio = resample_tf(audio)
+
+        return audio
+
+    def __len__(self):
+        return len(self.filenames)
+
+    def __getitem__(self, idx):
+        audio_filename = self.filenames[idx]
+        try:
+            start_time = time.time()
+            audio = self.load_file(audio_filename)
+
+            audio, t_start, t_end, seconds_start, seconds_total, padding_mask = self.pad_crop(audio)
+
+            # Run augmentations on this sample (including random crop)
+            if self.augs is not None:
+                audio = self.augs(audio)
+
+            audio = audio.clamp(-1, 1)
+
+            # Encode the file to assist in prediction
+            if self.encoding is not None:
+                audio = self.encoding(audio)
+
+            info = {}
+
+            info["path"] = audio_filename
+
+            if self.relpath is not None:
+                info["relpath"] = path.relpath(audio_filename, self.relpath)
+
+            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
+
+            if self.custom_metadata_fn is not None:
+                custom_metadata = self.custom_metadata_fn(info, audio)
+                info.update(custom_metadata)
+
+                if "__reject__" in info and info["__reject__"]:
+                    return self[random.randrange(len(self))]
+
+            return (audio, info)
+        except Exception as e:
+            print(f'Couldn\'t load file {audio_filename}: {e}')
+            return self[random.randrange(len(self))]
+
+def group_by_keys(data, keys=wds.tariterators.base_plus_ext, lcase=True, suffixes=None, handler=None):
+    """Return function over iterator that groups key, value pairs into samples.
+    :param keys: function that splits the key into key and extension (base_plus_ext)
+    :param lcase: convert suffixes to lower case (Default value = True)
+    """
+    current_sample = None
+    for filesample in data:
+        assert isinstance(filesample, dict)
+        fname, value = filesample["fname"], filesample["data"]
+        prefix, suffix = keys(fname)
+        if wds.tariterators.trace:
+            print(
+                prefix,
+                suffix,
+                current_sample.keys() if isinstance(current_sample, dict) else None,
+            )
+        if prefix is None:
+            continue
+        if lcase:
+            suffix = suffix.lower()
+        if current_sample is None or prefix != current_sample["__key__"]:
+            if wds.tariterators.valid_sample(current_sample):
+                yield current_sample
+            current_sample = dict(__key__=prefix, __url__=filesample["__url__"])
+        if suffix in current_sample:
+            print(f"{fname}: duplicate file name in tar file {suffix} {current_sample.keys()}")
+        if suffixes is None or suffix in suffixes:
+            current_sample[suffix] = value
+    if wds.tariterators.valid_sample(current_sample):
+        yield current_sample
+
+wds.tariterators.group_by_keys = group_by_keys
+
+# S3 code and WDS preprocessing code based on implementation by Scott Hawley originally in https://github.com/zqevans/audio-diffusion/blob/main/dataset/dataset.py
+
+def get_s3_contents(dataset_path, s3_url_prefix=None, filter='', recursive=True, debug=False, profile=None):
+    """
+    Returns a list of full S3 paths to files in a given S3 bucket and directory path.
+    """
+    # Ensure dataset_path ends with a trailing slash
+    if dataset_path != '' and not dataset_path.endswith('/'):
+        dataset_path += '/'
+    # Use posixpath to construct the S3 URL path
+    bucket_path = posixpath.join(s3_url_prefix or '', dataset_path)
+    # Construct the `aws s3 ls` command
+    cmd = ['aws', 's3', 'ls', bucket_path]
+
+    if profile is not None:
+        cmd.extend(['--profile', profile])
+
+    if recursive:
+        # Add the --recursive flag if requested
+        cmd.append('--recursive')
+    
+    # Run the `aws s3 ls` command and capture the output
+    run_ls = subprocess.run(cmd, capture_output=True, check=True)
+    # Split the output into lines and strip whitespace from each line
+    contents = run_ls.stdout.decode('utf-8').split('\n')
+    contents = [x.strip() for x in contents if x]
+    # Remove the timestamp from lines that begin with a timestamp
+    contents = [re.sub(r'^\S+\s+\S+\s+\d+\s+', '', x)
+                if re.match(r'^\S+\s+\S+\s+\d+\s+', x) else x for x in contents]
+    # Construct a full S3 path for each file in the contents list
+    contents = [posixpath.join(s3_url_prefix or '', x)
+                for x in contents if not x.endswith('/')]
+    # Apply the filter, if specified
+    if filter:
+        contents = [x for x in contents if filter in x]
+    # Remove redundant directory names in the S3 URL
+    if recursive:
+        # Get the main directory name from the S3 URL
+        main_dir = "/".join(bucket_path.split('/')[3:])
+        # Remove the redundant directory names from each file path
+        contents = [x.replace(f'{main_dir}', '').replace(
+            '//', '/') for x in contents]
+    # Print debugging information, if requested
+    if debug:
+        print("contents = \n", contents)
+    # Return the list of S3 paths to files
+    return contents
+
+
+def get_all_s3_urls(
+    names=[],           # list of all valid [LAION AudioDataset] dataset names
+    # list of subsets you want from those datasets, e.g. ['train','valid']
+    subsets=[''],
+    s3_url_prefix=None,  # prefix for those dataset names
+    recursive=True,     # recursively list all tar files in all subdirs
+    filter_str='tar',   # only grab files with this substring
+    # print debugging info -- note: info displayed likely to change at dev's whims
+    debug=False,
+    profiles={},        # dictionary of profiles for each item in names, e.g. {'dataset1': 'profile1', 'dataset2': 'profile2'}
+):
+    "get urls of shards (tar files) for multiple datasets in one s3 bucket"
+    urls = []
+    for name in names:
+        # If s3_url_prefix is not specified, assume the full S3 path is included in each element of the names list
+        if s3_url_prefix is None:
+            contents_str = name
+        else:
+            # Construct the S3 path using the s3_url_prefix and the current name value
+            contents_str = posixpath.join(s3_url_prefix, name)
+        if debug:
+            print(f"get_all_s3_urls: {contents_str}:")
+        for subset in subsets:
+            subset_str = posixpath.join(contents_str, subset)
+            if debug:
+                print(f"subset_str = {subset_str}")
+            # Get the list of tar files in the current subset directory
+            profile = profiles.get(name, None)
+            tar_list = get_s3_contents(
+                subset_str, s3_url_prefix=None, recursive=recursive, filter=filter_str, debug=debug, profile=profile)
+            for tar in tar_list:
+                # Escape spaces and parentheses in the tar filename for use in the shell command
+                tar = tar.replace(" ", "\ ").replace(
+                    "(", "\(").replace(")", "\)")
+                # Construct the S3 path to the current tar file
+                s3_path = posixpath.join(name, subset, tar) + " -"
+                # Construct the AWS CLI command to download the current tar file
+                if s3_url_prefix is None:
+                    request_str = f"pipe:aws s3 --cli-connect-timeout 0 cp {s3_path}"
+                else:
+                    request_str = f"pipe:aws s3 --cli-connect-timeout 0 cp {posixpath.join(s3_url_prefix, s3_path)}"
+                if profiles.get(name):
+                    request_str += f" --profile {profiles.get(name)}"
+                if debug:
+                    print("request_str = ", request_str)
+                # Add the constructed URL to the list of URLs
+                urls.append(request_str)
+    return urls
+
+
+def log_and_continue(exn):
+    """Call in an exception handler to ignore any exception, isssue a warning, and continue."""
+    print(f"Handling webdataset error ({repr(exn)}). Ignoring.")
+    return True
+
+
+def is_valid_sample(sample):
+    has_json = "json" in sample
+    has_audio = "audio" in sample
+    is_silent = is_silence(sample["audio"])
+    is_rejected = "__reject__" in sample["json"] and sample["json"]["__reject__"]
+
+    return has_json and has_audio and not is_silent and not is_rejected
+
+class S3DatasetConfig:
+    def __init__(
+        self,
+        id: str,
+        s3_path: str,
+        custom_metadata_fn: Optional[Callable[[str], str]] = None,
+        profile: Optional[str] = None,
+    ):
+        self.id = id
+        self.s3_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.s3_path],
+            s3_url_prefix=None,
+            recursive=True,
+            profiles={self.s3_path: self.profile} if self.profile else {},
+        )
+
+        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 S3WebDataLoader():
+    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]
+
+        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.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.s3_path in sample["__url__"]:
+                custom_metadata = dataset.custom_metadata_fn(sample["json"], audio)
+                sample["json"].update(custom_metadata)
+
+        if found_key != rewrite_key:   # rename long/weird key with its simpler counterpart
+            del sample[found_key]
+
+        sample["audio"] = audio
+
+        # Add audio to the metadata as well for conditioning
+        sample["json"]["audio"] = audio
+        
+        return sample
+
+def create_dataloader_from_config(dataset_config, batch_size, sample_size, sample_rate, audio_channels=2, num_workers=4):
+
+    dataset_type = dataset_config.get("dataset_type", None)
+
+    assert dataset_type is not None, "Dataset type must be specified in dataset config"
+
+    if audio_channels == 1:
+        force_channels = "mono"
+    else:
+        force_channels = "stereo"
+
+    if dataset_type == "audio_dir":
+
+        audio_dir_configs = dataset_config.get("datasets", None)
+
+        assert audio_dir_configs is not None, "Directory configuration must be specified in datasets[\"dataset\"]"
+
+        training_dirs = []
+
+        custom_metadata_fn = None
+        custom_metadata_module_path = dataset_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
+
+        for audio_dir_config in audio_dir_configs:
+            audio_dir_path = audio_dir_config.get("path", None)
+            assert audio_dir_path is not None, "Path must be set for local audio directory configuration"
+            training_dirs.append(audio_dir_path)
+
+        train_set = SampleDataset(
+            training_dirs,
+            sample_rate=sample_rate,
+            sample_size=sample_size,
+            random_crop=dataset_config.get("random_crop", True),
+            force_channels=force_channels,
+            custom_metadata_fn=custom_metadata_fn,
+            relpath=training_dirs[0] #TODO: Make relpath relative to each training dir
+        )
+
+        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 == "s3":
+        dataset_configs = []
+
+        for s3_config in dataset_config["datasets"]:
+
+            custom_metadata_fn = None
+            custom_metadata_module_path = s3_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
+
+            dataset_configs.append(
+                S3DatasetConfig(
+                    id=s3_config["id"],
+                    s3_path=s3_config["s3_path"],
+                    custom_metadata_fn=custom_metadata_fn,
+                    profile=s3_config.get("profile", None),
+                )
+            )
+
+        return S3WebDataLoader(
+            dataset_configs,
+            sample_rate=sample_rate,
+            sample_size=sample_size,
+            batch_size=batch_size,
+            random_crop=dataset_config.get("random_crop", True),
+            num_workers=num_workers,
+            persistent_workers=True,
+            force_channels=force_channels,
+            epoch_steps=dataset_config.get("epoch_steps", 2000),
+        ).data_loader

stable_audio_tools/data/utils.py

@@ -0,0 +1,96 @@
+import math
+import random
+import torch
+
+from torch import nn
+from typing import Tuple
+
+class PadCrop(nn.Module):
+    def __init__(self, n_samples, randomize=True):
+        super().__init__()
+        self.n_samples = n_samples
+        self.randomize = randomize
+
+    def __call__(self, signal):
+        n, s = signal.shape
+        start = 0 if (not self.randomize) else torch.randint(0, max(0, s - self.n_samples) + 1, []).item()
+        end = start + self.n_samples
+        output = signal.new_zeros([n, self.n_samples])
+        output[:, :min(s, self.n_samples)] = signal[:, start:end]
+        return output
+
+class PadCrop_Normalized_T(nn.Module):
+    
+    def __init__(self, n_samples: int, sample_rate: int, randomize: bool = True):
+        
+        super().__init__()
+        
+        self.n_samples = n_samples
+        self.sample_rate = sample_rate
+        self.randomize = randomize
+
+    def __call__(self, source: torch.Tensor) -> Tuple[torch.Tensor, float, float, int, int]:
+        
+        n_channels, n_samples = source.shape
+        
+        # If the audio is shorter than the desired length, pad it
+        upper_bound = max(0, n_samples - self.n_samples)
+        
+        # If randomize is False, always start at the beginning of the audio
+        offset = 0
+        if(self.randomize and n_samples > self.n_samples):
+            offset = random.randint(0, upper_bound)
+
+        # Calculate the start and end times of the chunk
+        t_start = offset / (upper_bound + self.n_samples)
+        t_end = (offset + self.n_samples) / (upper_bound + self.n_samples)
+
+        # Create the chunk
+        chunk = source.new_zeros([n_channels, self.n_samples])
+
+        # Copy the audio into the chunk
+        chunk[:, :min(n_samples, self.n_samples)] = source[:, offset:offset + self.n_samples]
+        
+        # Calculate the start and end times of the chunk in seconds
+        seconds_start = math.floor(offset / self.sample_rate)
+        seconds_total = math.ceil(n_samples / self.sample_rate)
+
+        # Create a mask the same length as the chunk with 1s where the audio is and 0s where it isn't
+        padding_mask = torch.zeros([self.n_samples])
+        padding_mask[:min(n_samples, self.n_samples)] = 1
+        
+        
+        return (
+            chunk,
+            t_start,
+            t_end,
+            seconds_start,
+            seconds_total,
+            padding_mask
+        )
+
+class PhaseFlipper(nn.Module):
+    "Randomly invert the phase of a signal"
+    def __init__(self, p=0.5):
+        super().__init__()
+        self.p = p
+    def __call__(self, signal):
+        return -signal if (random.random() < self.p) else signal
+        
+class Mono(nn.Module):
+  def __call__(self, signal):
+    return torch.mean(signal, dim=0, keepdims=True) if len(signal.shape) > 1 else signal
+
+class Stereo(nn.Module):
+  def __call__(self, signal):
+    signal_shape = signal.shape
+    # Check if it's mono
+    if len(signal_shape) == 1: # s -> 2, s
+        signal = signal.unsqueeze(0).repeat(2, 1)
+    elif len(signal_shape) == 2:
+        if signal_shape[0] == 1: #1, s -> 2, s
+            signal = signal.repeat(2, 1)
+        elif signal_shape[0] > 2: #?, s -> 2,s
+            signal = signal[:2, :]    
+
+    return signal

stable_audio_tools/inference/generation.py

@@ -0,0 +1,243 @@
+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
+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:
+    # k-diffusion denoising process go!
+    sampled = sample_k(model.model, noise, init_audio, mask, 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)
+    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)
+
+    # 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_tensors = 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
+
+    # Now the generative AI part:
+    # k-diffusion denoising process go!
+    sampled = sample_k(model.model, noise, init_audio, mask, steps, **sampler_kwargs, **conditioning_tensors, **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)
+
+    # Denoising process done. 
+    # If this is latent diffusion, decode latents back into audio
+    if model.pretransform is not None and not return_latents:
+        #cast sampled latents to pretransform dtype
+        sampled = sampled.to(next(model.pretransform.parameters()).dtype)
+        sampled = model.pretransform.decode(sampled)
+
+    # Return audio
+    return sampled
+
+# builds a softmask given the parameters
+# returns array of values 0 to 1, size sample_size, where 0 means noise / fresh generation, 1 means keep the input audio, 
+# and anything between is a mixture of old/new
+# ideally 0.5 is half/half mixture but i haven't figured this out yet
+def build_mask(sample_size, mask_args):
+    maskstart = math.floor(mask_args["maskstart"]/100.0 * sample_size)
+    maskend = math.ceil(mask_args["maskend"]/100.0 * sample_size)
+    softnessL = round(mask_args["softnessL"]/100.0 * sample_size)
+    softnessR = round(mask_args["softnessR"]/100.0 * sample_size)
+    marination = mask_args["marination"]
+    # use hann windows for softening the transition (i don't know if this is correct)
+    hannL = torch.hann_window(softnessL*2, periodic=False)[:softnessL]
+    hannR = torch.hann_window(softnessR*2, periodic=False)[softnessR:]
+    # build the mask. 
+    mask = torch.zeros((sample_size))
+    mask[maskstart:maskend] = 1
+    mask[maskstart:maskstart+softnessL] = hannL
+    mask[maskend-softnessR:maskend] = hannR
+    # marination finishes the inpainting early in the denoising schedule, and lets audio get changed in the final rounds
+    if marination > 0:        
+        mask = mask * (1-marination) 
+    #print(mask)
+    return mask

stable_audio_tools/inference/sampling.py

@@ -0,0 +1,170 @@
+import torch
+import math
+from tqdm import trange
+
+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(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)
+

stable_audio_tools/inference/utils.py

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

stable_audio_tools/interface/gradio.py

@@ -0,0 +1,788 @@
+import gc
+import numpy as np
+import gradio as gr
+import json 
+import torch
+import torchaudio
+
+from aeiou.viz import audio_spectrogram_image
+from einops import rearrange
+from safetensors.torch import load_file
+from torch.nn import functional as F
+from torchaudio import transforms as T
+
+from ..inference.generation import generate_diffusion_cond, generate_diffusion_uncond
+from ..models.factory import create_model_from_config
+from ..models.pretrained import get_pretrained_model
+from ..models.utils import load_ckpt_state_dict
+from ..inference.utils import prepare_audio
+from ..training.utils import copy_state_dict
+
+# Define preset values
+presets = {
+    "Pied Currawong": {
+        "latitude": -33.6467,
+        "longitude": 150.3246,
+        "temperature": 12.43,
+        "humidity": 86,
+        "wind_speed": 0.66,
+        "pressure": 1013,
+        "minutes_of_day": 369,
+        "day_of_year": 297,
+    },
+    "Yellow-tailed Black Cockatoo": {
+        "latitude": -32.8334,
+        "longitude": 150.2001,
+        "temperature": 23.23,
+        "humidity": 45,
+        "wind_speed": 1.37,
+        "pressure": 1009,
+        "minutes_of_day": 986,
+        "day_of_year": 78,
+    },
+    "Australian Magpie": {
+        "latitude": -38.522,
+        "longitude": 145.3365,
+        "temperature": 18.75,
+        "humidity": 67,
+        "wind_speed": 1.5,
+        "pressure": 1023,
+        "minutes_of_day": 940,
+        "day_of_year": 307,
+    },
+    "Laughing Kookaburra": {
+        "latitude": -27.2685099,
+        "longitude": 152.8587437,
+        "temperature": 9.02,
+        "humidity": 94,
+        "wind_speed": 1.5,
+        "pressure": 1025,
+        "minutes_of_day": 320,
+        "day_of_year": 236,
+    }
+}
+
+def update_sliders(preset_name):
+    preset = presets[preset_name]
+    return (preset["latitude"], preset["longitude"], preset["temperature"], preset["humidity"], preset["wind_speed"], preset["pressure"], preset["minutes_of_day"], preset["day_of_year"])
+
+
+model = None
+sample_rate = 44100
+sample_size = 524288
+
+
+def load_model(model_config=None, model_ckpt_path=None, pretrained_name=None, pretransform_ckpt_path=None, device="cuda", model_half=False):
+    global model, sample_rate, sample_size
+    
+    if pretrained_name is not None:
+        print(f"Loading pretrained model {pretrained_name}")
+        model, model_config = get_pretrained_model(pretrained_name)
+
+    elif model_config is not None and model_ckpt_path is not None:
+        print(f"Creating model from config")
+        model = create_model_from_config(model_config)
+
+        print(f"Loading model checkpoint from {model_ckpt_path}")
+        # Load checkpoint
+        copy_state_dict(model, load_ckpt_state_dict(model_ckpt_path))
+        #model.load_state_dict(load_ckpt_state_dict(model_ckpt_path))
+
+    sample_rate = model_config["sample_rate"]
+    sample_size = model_config["sample_size"]
+
+    if pretransform_ckpt_path is not None:
+        print(f"Loading pretransform checkpoint from {pretransform_ckpt_path}")
+        model.pretransform.load_state_dict(load_ckpt_state_dict(pretransform_ckpt_path), strict=False)
+        print(f"Done loading pretransform")
+
+    model.to(device).eval().requires_grad_(False)
+
+    if model_half:
+        model.to(torch.float16)
+        
+    print(f"Done loading model")
+
+    return model, model_config
+
+def generate_cond(
+        seconds_start=0,
+        seconds_total=30,
+        latitude = 0.0,
+        longitude = 0.0,
+        temperature = 0.0,
+        humidity = 0.0,
+        wind_speed = 0.0,
+        pressure = 0.0,
+        minutes_of_day = 0.0,
+        day_of_year = 0.0,
+        cfg_scale=6.0,
+        steps=250,
+        preview_every=None,
+        seed=-1,
+        sampler_type="dpmpp-2m-sde",
+        sigma_min=0.03,
+        sigma_max=50,
+        cfg_rescale=0.4,
+        use_init=False,
+        init_audio=None,
+        init_noise_level=1.0,
+        mask_cropfrom=None,
+        mask_pastefrom=None,
+        mask_pasteto=None,
+        mask_maskstart=None,
+        mask_maskend=None,
+        mask_softnessL=None,
+        mask_softnessR=None,
+        mask_marination=None,
+        batch_size=1    
+    ):
+
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gc.collect()
+
+
+    global preview_images
+    preview_images = []
+    if preview_every == 0:
+        preview_every = None
+
+    # Return fake stereo audio
+    conditioning = [{"latitude": -latitude, "longitude": longitude, "temperature": temperature, "humidity": humidity, "wind_speed": wind_speed, "pressure": pressure, "minutes_of_day": minutes_of_day,"day_of_year": day_of_year, "seconds_start":seconds_start, "seconds_total": seconds_total }] * batch_size
+        
+    #Get the device from the model
+    device = next(model.parameters()).device
+
+    seed = int(seed)
+
+    if not use_init:
+        init_audio = None
+    
+    input_sample_size = sample_size
+
+    if init_audio is not None:
+        in_sr, init_audio = init_audio
+        # Turn into torch tensor, converting from int16 to float32
+        init_audio = torch.from_numpy(init_audio).float().div(32767)
+        
+        if init_audio.dim() == 1:
+            init_audio = init_audio.unsqueeze(0) # [1, n]
+        elif init_audio.dim() == 2:
+            init_audio = init_audio.transpose(0, 1) # [n, 2] -> [2, n]
+
+        if in_sr != sample_rate:
+            resample_tf = T.Resample(in_sr, sample_rate).to(init_audio.device)
+            init_audio = resample_tf(init_audio)
+
+        audio_length = init_audio.shape[-1]
+
+        if audio_length > sample_size:
+
+            input_sample_size = audio_length + (model.min_input_length - (audio_length % model.min_input_length)) % model.min_input_length
+
+        init_audio = (sample_rate, init_audio)
+
+    def progress_callback(callback_info):
+        global preview_images
+        denoised = callback_info["denoised"]
+        current_step = callback_info["i"]
+        sigma = callback_info["sigma"]
+
+        if (current_step - 1) % preview_every == 0:
+            if model.pretransform is not None:
+                denoised = model.pretransform.decode(denoised)
+            denoised = rearrange(denoised, "b d n -> d (b n)")
+            denoised = denoised.clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+            audio_spectrogram = audio_spectrogram_image(denoised, sample_rate=sample_rate)
+            preview_images.append((audio_spectrogram, f"Step {current_step} sigma={sigma:.3f})"))
+
+    # If inpainting, send mask args
+    # This will definitely change in the future
+    if mask_cropfrom is not None: 
+        mask_args = {
+            "cropfrom": mask_cropfrom,
+            "pastefrom": mask_pastefrom,
+            "pasteto": mask_pasteto,
+            "maskstart": mask_maskstart,
+            "maskend": mask_maskend,
+            "softnessL": mask_softnessL,
+            "softnessR": mask_softnessR,
+            "marination": mask_marination,
+        }
+    else:
+        mask_args = None 
+
+    # Do the audio generation
+    audio = generate_diffusion_cond(
+        model, 
+        conditioning=conditioning,
+        steps=steps,
+        cfg_scale=cfg_scale,
+        batch_size=batch_size,
+        sample_size=input_sample_size,
+        sample_rate=sample_rate,
+        seed=seed,
+        device=device,
+        sampler_type=sampler_type,
+        sigma_min=sigma_min,
+        sigma_max=sigma_max,
+        init_audio=init_audio,
+        init_noise_level=init_noise_level,
+        mask_args = mask_args,
+        callback = progress_callback if preview_every is not None else None,
+        scale_phi = cfg_rescale
+    )
+
+    # Convert to WAV file
+    audio = rearrange(audio, "b d n -> d (b n)")
+    audio = audio.to(torch.float32).div(torch.max(torch.abs(audio))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+    torchaudio.save("output.wav", audio, sample_rate)
+
+    # Let's look at a nice spectrogram too
+    audio_spectrogram = audio_spectrogram_image(audio, sample_rate=sample_rate)
+
+    return ("output.wav", [audio_spectrogram, *preview_images])
+
+def generate_uncond(
+        steps=250,
+        seed=-1,
+        sampler_type="dpmpp-2m-sde",
+        sigma_min=0.03,
+        sigma_max=50,
+        use_init=False,
+        init_audio=None,
+        init_noise_level=1.0,
+        batch_size=1,
+        preview_every=None
+        ):
+
+    global preview_images
+
+    preview_images = []
+
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gc.collect()
+
+    #Get the device from the model
+    device = next(model.parameters()).device
+
+    seed = int(seed)
+
+    if not use_init:
+        init_audio = None
+    
+    input_sample_size = sample_size
+
+    if init_audio is not None:
+        in_sr, init_audio = init_audio
+        # Turn into torch tensor, converting from int16 to float32
+        init_audio = torch.from_numpy(init_audio).float().div(32767)
+        
+        if init_audio.dim() == 1:
+            init_audio = init_audio.unsqueeze(0) # [1, n]
+        elif init_audio.dim() == 2:
+            init_audio = init_audio.transpose(0, 1) # [n, 2] -> [2, n]
+
+        if in_sr != sample_rate:
+            resample_tf = T.Resample(in_sr, sample_rate).to(init_audio.device)
+            init_audio = resample_tf(init_audio)
+
+        audio_length = init_audio.shape[-1]
+
+        if audio_length > sample_size:
+
+            input_sample_size = audio_length + (model.min_input_length - (audio_length % model.min_input_length)) % model.min_input_length
+
+        init_audio = (sample_rate, init_audio)
+
+    def progress_callback(callback_info):
+        global preview_images
+        denoised = callback_info["denoised"]
+        current_step = callback_info["i"]
+        sigma = callback_info["sigma"]
+
+        if (current_step - 1) % preview_every == 0:
+
+            if model.pretransform is not None:
+                denoised = model.pretransform.decode(denoised)
+
+            denoised = rearrange(denoised, "b d n -> d (b n)")
+
+            denoised = denoised.clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+
+            audio_spectrogram = audio_spectrogram_image(denoised, sample_rate=sample_rate)
+
+            preview_images.append((audio_spectrogram, f"Step {current_step} sigma={sigma:.3f})"))
+
+    audio = generate_diffusion_uncond(
+        model, 
+        steps=steps,
+        batch_size=batch_size,
+        sample_size=input_sample_size,
+        seed=seed,
+        device=device,
+        sampler_type=sampler_type,
+        sigma_min=sigma_min,
+        sigma_max=sigma_max,
+        init_audio=init_audio,
+        init_noise_level=init_noise_level,
+        callback = progress_callback if preview_every is not None else None
+    )
+
+    audio = rearrange(audio, "b d n -> d (b n)")
+
+    audio = audio.to(torch.float32).div(torch.max(torch.abs(audio))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+
+    torchaudio.save("output.wav", audio, sample_rate)
+
+    audio_spectrogram = audio_spectrogram_image(audio, sample_rate=sample_rate)
+
+    return ("output.wav", [audio_spectrogram, *preview_images])
+
+def generate_lm(
+        temperature=1.0,
+        top_p=0.95,
+        top_k=0,    
+        batch_size=1,
+        ):
+    
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gc.collect()
+
+    #Get the device from the model
+    device = next(model.parameters()).device
+
+    audio = model.generate_audio(
+        batch_size=batch_size,
+        max_gen_len = sample_size//model.pretransform.downsampling_ratio,
+        conditioning=None,
+        temp=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        use_cache=True
+    )
+
+    audio = rearrange(audio, "b d n -> d (b n)")
+
+    audio = audio.to(torch.float32).div(torch.max(torch.abs(audio))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+
+    torchaudio.save("output.wav", audio, sample_rate)
+
+    audio_spectrogram = audio_spectrogram_image(audio, sample_rate=sample_rate)
+
+    return ("output.wav", [audio_spectrogram])
+
+
+def create_uncond_sampling_ui(model_config):   
+    generate_button = gr.Button("Generate", variant='primary', scale=1)
+    
+    with gr.Row(equal_height=False):
+        with gr.Column():            
+            with gr.Row():
+                # Steps slider
+                steps_slider = gr.Slider(minimum=1, maximum=500, step=1, value=100, label="Steps")
+
+            with gr.Accordion("Sampler params", open=False):
+            
+                # Seed
+                seed_textbox = gr.Textbox(label="Seed (set to -1 for random seed)", value="-1")
+
+            # Sampler params
+                with gr.Row():
+                    sampler_type_dropdown = gr.Dropdown(["dpmpp-2m-sde", "dpmpp-3m-sde", "k-heun", "k-lms", "k-dpmpp-2s-ancestral", "k-dpm-2", "k-dpm-fast"], label="Sampler type", value="dpmpp-2m-sde")
+                    sigma_min_slider = gr.Slider(minimum=0.0, maximum=2.0, step=0.01, value=0.03, label="Sigma min")
+                    sigma_max_slider = gr.Slider(minimum=0.0, maximum=200.0, step=0.1, value=80, label="Sigma max")
+
+            with gr.Accordion("Init audio", open=False):
+                init_audio_checkbox = gr.Checkbox(label="Use init audio")
+                init_audio_input = gr.Audio(label="Init audio")
+                init_noise_level_slider = gr.Slider(minimum=0.0, maximum=100.0, step=0.01, value=0.1, label="Init noise level")
+
+        with gr.Column():
+            audio_output = gr.Audio(label="Output audio", interactive=False)
+            audio_spectrogram_output = gr.Gallery(label="Output spectrogram", show_label=False)
+            send_to_init_button = gr.Button("Send to init audio", scale=1)
+            send_to_init_button.click(fn=lambda audio: audio, inputs=[audio_output], outputs=[init_audio_input])
+    
+    generate_button.click(fn=generate_uncond, 
+        inputs=[
+            steps_slider, 
+            seed_textbox, 
+            sampler_type_dropdown, 
+            sigma_min_slider, 
+            sigma_max_slider,
+            init_audio_checkbox,
+            init_audio_input,
+            init_noise_level_slider,
+        ], 
+        outputs=[
+            audio_output, 
+            audio_spectrogram_output
+        ], 
+        api_name="generate")
+def create_conditioning_slider(min_val, max_val, default_value, label):
+    """
+    Create a Gradio slider for a given conditioning parameter.
+
+    Args:
+    - min_val: The minimum value for the slider.
+    - max_val: The maximum value for the slider.
+    - label: The label for the slider, which is displayed in the UI.
+
+    Returns:
+    - A gr.Slider object configured according to the provided parameters.
+    """
+    step = (max_val - min_val) / 1000
+    default_val = default_value
+    print(f"Creating slider for {label} with min_val={min_val}, max_val={max_val}, step={step}, default_val={default_val}")
+    return gr.Slider(minimum=min_val, maximum=max_val, step=step, value=default_val, label=label)
+
+def create_sampling_ui(model_config):
+    with gr.Row():
+        
+        generate_button = gr.Button("Generate", variant='primary', scale=1)
+    
+    model_conditioning_config = model_config["model"].get("conditioning", None)
+
+    has_seconds_start = False
+    has_seconds_total = False
+
+    if model_conditioning_config is not None:
+        for conditioning_config in model_conditioning_config["configs"]:
+            if conditioning_config["id"] == "seconds_start":
+                has_seconds_start = True
+            if conditioning_config["id"] == "seconds_total":
+                has_seconds_total = True
+
+    with gr.Row(equal_height=False):
+        with gr.Column():
+            with gr.Row():
+                
+                seconds_start_slider = gr.Slider(minimum=0, maximum=512, step=1, value=0, label="Seconds start", visible=has_seconds_start)
+           
+                seconds_total_slider = gr.Slider(minimum=0, maximum=22, step=1, value=sample_size//sample_rate, label="Seconds total", visible=has_seconds_total)
+            
+            with gr.Row():
+                # Steps slider
+                steps_slider = gr.Slider(minimum=1, maximum=500, step=1, value=250, label="Steps")
+
+                # Preview Every slider
+                preview_every_slider = gr.Slider(minimum=0, maximum=100, step=1, value=0, label="Preview Every")
+
+                # CFG scale 
+                cfg_scale_slider = gr.Slider(minimum=0.0, maximum=25.0, step=0.1, value=4.0, label="CFG scale")
+                
+            with gr.Accordion("Climate and location", open=True):
+                preset_dropdown = gr.Dropdown(choices=list(presets.keys()), label="Select Preset")
+
+                latitude_config = next((item for item in model_conditioning_config["configs"] if item["id"] == "latitude"), None)
+                if latitude_config:
+                    latitude_slider = create_conditioning_slider(
+                        min_val=latitude_config["config"]["min_val"],
+                        max_val=latitude_config["config"]["max_val"],
+                        default_value = -29.8913,
+                        label="latitude")
+                    
+                longitude_config = next((item for item in model_conditioning_config["configs"] if item["id"] == "longitude"), None)
+                if longitude_config:
+                    longitude_slider = create_conditioning_slider(
+                        min_val=longitude_config["config"]["min_val"],
+                        max_val=longitude_config["config"]["max_val"],
+                        default_value=152.4951,
+                        label="longitude")
+                    
+                temperature_config = next((item for item in model_conditioning_config["configs"] if item["id"] == "temperature"), None)
+                if temperature_config:
+                    temperature_slider = create_conditioning_slider(
+                        min_val=temperature_config["config"]["min_val"],
+                        max_val=temperature_config["config"]["max_val"],
+                        default_value=22.05,
+                        label="temperature")
+                    
+                humidity_config = next((item for item in model_conditioning_config["configs"] if item["id"] == "humidity"), None)
+                if humidity_config:
+                    humidity_slider = create_conditioning_slider(
+                        min_val=humidity_config["config"]["min_val"],
+                        max_val=humidity_config["config"]["max_val"],
+                        default_value=88,
+                        label="humidity")
+                    
+                wind_speed_config = next((item for item in model_conditioning_config["configs"] if item["id"] == "wind_speed"), None)
+                if wind_speed_config:
+                    wind_speed_slider = create_conditioning_slider(
+                        min_val=wind_speed_config["config"]["min_val"],
+                        max_val=wind_speed_config["config"]["max_val"],
+                        default_value=0.54,
+                        label="wind_speed")
+                    
+                pressure_config = next((item for item in model_conditioning_config["configs"] if item["id"] == "pressure"), None)
+                if pressure_config:
+                    pressure_slider = create_conditioning_slider(
+                        min_val=pressure_config["config"]["min_val"],
+                        max_val=pressure_config["config"]["max_val"],
+                        default_value=1021,
+                        label="pressure")
+                    
+                minutes_of_day_config = next((item for item in model_conditioning_config["configs"] if item["id"] == "minutes_of_day"), None)
+                if minutes_of_day_config:
+                    minutes_of_day_slider = create_conditioning_slider(
+                        min_val=minutes_of_day_config["config"]["min_val"],
+                        max_val=minutes_of_day_config["config"]["max_val"],
+                        default_value=1354,
+                        label="minutes_of_day")
+                    
+                day_of_year_config = next((item for item in model_conditioning_config["configs"] if item["id"] == "day_of_year"), None)
+                if day_of_year_config:
+                    day_of_year_slider = create_conditioning_slider(
+                        min_val=day_of_year_config["config"]["min_val"],
+                        max_val=day_of_year_config["config"]["max_val"],
+                        default_value=342,
+                        label="Day of year")
+
+            with gr.Accordion("Sampler params", open=False):
+            
+                # Seed
+                seed_textbox = gr.Textbox(label="Seed (set to -1 for random seed)", value="-1")
+
+                # Sampler params
+                with gr.Row():
+                    sampler_type_dropdown = gr.Dropdown(["dpmpp-2m-sde", "dpmpp-3m-sde", "k-heun", "k-lms", "k-dpmpp-2s-ancestral", "k-dpm-2", "k-dpm-fast"], label="Sampler type", value="dpmpp-2m-sde")
+                    sigma_min_slider = gr.Slider(minimum=0.0, maximum=2.0, step=0.01, value=0.03, label="Sigma min")
+                    sigma_max_slider = gr.Slider(minimum=0.0, maximum=200.0, step=0.1, value=50, label="Sigma max")
+                    cfg_rescale_slider = gr.Slider(minimum=0.0, maximum=1, step=0.01, value=0.4, label="CFG rescale amount")
+
+            
+            # Default generation tab
+            with gr.Accordion("Init audio", open=False):
+                init_audio_input = gr.Audio(label="Init audio")
+                init_noise_level_slider = gr.Slider(minimum=0.1, maximum=100.0, step=0.01, value=1.0, label="Init noise level")
+
+                inputs = [
+                    seconds_start_slider, 
+                    seconds_total_slider, 
+                    latitude_slider,
+                    longitude_slider,
+                    temperature_slider,
+                    humidity_slider,
+                    wind_speed_slider,
+                    pressure_slider,
+                    minutes_of_day_slider,
+                    day_of_year_slider,
+                    cfg_scale_slider, 
+                    steps_slider, 
+                    preview_every_slider, 
+                    seed_textbox, 
+                    sampler_type_dropdown, 
+                    sigma_min_slider, 
+                    sigma_max_slider,
+                    cfg_rescale_slider,
+                    init_noise_level_slider
+                ]
+
+        with gr.Column():
+            audio_output = gr.Audio(label="Output audio", interactive=False)
+            audio_spectrogram_output = gr.Gallery(label="Output spectrogram", show_label=False)
+    
+    generate_button.click(fn=generate_cond, 
+        inputs=inputs,
+        outputs=[
+            audio_output, 
+            audio_spectrogram_output
+        ], 
+        api_name="generate")
+
+    preset_dropdown.change(
+        fn=update_sliders,
+        inputs=[preset_dropdown],
+        outputs=[
+            latitude_slider,
+            longitude_slider,
+            temperature_slider,
+            humidity_slider,
+            wind_speed_slider,
+            pressure_slider,
+            minutes_of_day_slider,
+            day_of_year_slider
+        ]
+    )
+
+def create_txt2audio_ui(model_config):
+    with gr.Blocks() as ui:
+        with gr.Tab("Generation"):
+            create_sampling_ui(model_config) 
+        # with gr.Tab("Inpainting"):
+        #     create_sampling_ui(model_config, inpainting=True)    
+    return ui
+
+def create_diffusion_uncond_ui(model_config):
+    with gr.Blocks() as ui:
+        create_uncond_sampling_ui(model_config)
+    
+    return ui
+
+def autoencoder_process(audio, latent_noise, n_quantizers):
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gc.collect()
+
+    #Get the device from the model
+    device = next(model.parameters()).device
+
+    in_sr, audio = audio
+
+    audio = torch.from_numpy(audio).float().div(32767).to(device)
+
+    if audio.dim() == 1:
+        audio = audio.unsqueeze(0)
+    else:
+        audio = audio.transpose(0, 1)
+
+    audio = model.preprocess_audio_for_encoder(audio, in_sr)
+    # Note: If you need to do chunked encoding, to reduce VRAM, 
+    # then add these arguments to encode_audio and decode_audio: chunked=True, overlap=32, chunk_size=128
+    # To turn it off, do chunked=False
+    # Optimal overlap and chunk_size values will depend on the model. 
+    # See encode_audio & decode_audio in autoencoders.py for more info
+    # Get dtype of model
+    dtype = next(model.parameters()).dtype
+
+    audio = audio.to(dtype)
+
+    if n_quantizers > 0:
+        latents = model.encode_audio(audio, chunked=False, n_quantizers=n_quantizers)
+    else:
+        latents = model.encode_audio(audio, chunked=False)
+
+    if latent_noise > 0:
+        latents = latents + torch.randn_like(latents) * latent_noise
+
+    audio = model.decode_audio(latents, chunked=False)
+
+    audio = rearrange(audio, "b d n -> d (b n)")
+
+    audio = audio.to(torch.float32).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+
+    torchaudio.save("output.wav", audio, sample_rate)
+
+    return "output.wav"
+
+def create_autoencoder_ui(model_config):
+
+    is_dac_rvq = "model" in model_config and "bottleneck" in model_config["model"] and model_config["model"]["bottleneck"]["type"] in ["dac_rvq","dac_rvq_vae"]
+
+    if is_dac_rvq:
+        n_quantizers = model_config["model"]["bottleneck"]["config"]["n_codebooks"]
+    else:
+        n_quantizers = 0
+
+    with gr.Blocks() as ui:
+        input_audio = gr.Audio(label="Input audio")
+        output_audio = gr.Audio(label="Output audio", interactive=False)
+        n_quantizers_slider = gr.Slider(minimum=1, maximum=n_quantizers, step=1, value=n_quantizers, label="# quantizers", visible=is_dac_rvq)
+        latent_noise_slider = gr.Slider(minimum=0.0, maximum=10.0, step=0.001, value=0.0, label="Add latent noise")
+        process_button = gr.Button("Process", variant='primary', scale=1)
+        process_button.click(fn=autoencoder_process, inputs=[input_audio, latent_noise_slider, n_quantizers_slider], outputs=output_audio, api_name="process")
+
+    return ui
+
+def diffusion_prior_process(audio, steps, sampler_type, sigma_min, sigma_max):
+
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gc.collect()
+
+    #Get the device from the model
+    device = next(model.parameters()).device
+
+    in_sr, audio = audio
+
+    audio = torch.from_numpy(audio).float().div(32767).to(device)
+    
+    if audio.dim() == 1:
+        audio = audio.unsqueeze(0) # [1, n]
+    elif audio.dim() == 2:
+        audio = audio.transpose(0, 1) # [n, 2] -> [2, n]
+
+    audio = audio.unsqueeze(0)
+
+    audio = model.stereoize(audio, in_sr, steps, sampler_kwargs={"sampler_type": sampler_type, "sigma_min": sigma_min, "sigma_max": sigma_max})
+
+    audio = rearrange(audio, "b d n -> d (b n)")
+
+    audio = audio.to(torch.float32).div(torch.max(torch.abs(audio))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+
+    torchaudio.save("output.wav", audio, sample_rate)
+
+    return "output.wav"
+
+def create_diffusion_prior_ui(model_config):
+    with gr.Blocks() as ui:
+        input_audio = gr.Audio(label="Input audio")
+        output_audio = gr.Audio(label="Output audio", interactive=False)
+        # Sampler params
+        with gr.Row():
+            steps_slider = gr.Slider(minimum=1, maximum=500, step=1, value=100, label="Steps")
+            sampler_type_dropdown = gr.Dropdown(["dpmpp-2m-sde", "dpmpp-3m-sde", "k-heun", "k-lms", "k-dpmpp-2s-ancestral", "k-dpm-2", "k-dpm-fast"], label="Sampler type", value="dpmpp-2m-sde")
+            sigma_min_slider = gr.Slider(minimum=0.0, maximum=2.0, step=0.01, value=0.03, label="Sigma min")
+            sigma_max_slider = gr.Slider(minimum=0.0, maximum=200.0, step=0.1, value=80, label="Sigma max")
+        process_button = gr.Button("Process", variant='primary', scale=1)
+        process_button.click(fn=diffusion_prior_process, inputs=[input_audio, steps_slider, sampler_type_dropdown, sigma_min_slider, sigma_max_slider], outputs=output_audio, api_name="process")    
+
+    return ui
+
+def create_lm_ui(model_config):
+    with gr.Blocks() as ui:
+        output_audio = gr.Audio(label="Output audio", interactive=False)
+        audio_spectrogram_output = gr.Gallery(label="Output spectrogram", show_label=False)
+
+        # Sampling params
+        with gr.Row():
+            temperature_slider = gr.Slider(minimum=0, maximum=5, step=0.01, value=1.0, label="Temperature")
+            top_p_slider = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.95, label="Top p")
+            top_k_slider = gr.Slider(minimum=0, maximum=100, step=1, value=0, label="Top k")
+
+        generate_button = gr.Button("Generate", variant='primary', scale=1)
+        generate_button.click(
+            fn=generate_lm, 
+            inputs=[
+                temperature_slider, 
+                top_p_slider, 
+                top_k_slider
+            ], 
+            outputs=[output_audio, audio_spectrogram_output],
+            api_name="generate"
+        )
+
+    return ui
+
+def create_ui(model_config_path=None, ckpt_path=None, pretrained_name=None, pretransform_ckpt_path=None, model_half=False):
+
+    assert (pretrained_name is not None) ^ (model_config_path is not None and ckpt_path is not None), "Must specify either pretrained name or provide a model config and checkpoint, but not both"
+
+    if model_config_path is not None:
+        # Load config from json file
+        with open(model_config_path) as f:
+            model_config = json.load(f)
+    else:
+        model_config = None
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    _, model_config = load_model(model_config, ckpt_path, pretrained_name=pretrained_name, pretransform_ckpt_path=pretransform_ckpt_path, model_half=model_half, device=device)
+    
+    model_type = model_config["model_type"]
+
+    if model_type == "diffusion_cond":
+        ui = create_txt2audio_ui(model_config)
+    elif model_type == "diffusion_uncond":
+        ui = create_diffusion_uncond_ui(model_config)
+    elif model_type == "autoencoder" or model_type == "diffusion_autoencoder":
+        ui = create_autoencoder_ui(model_config)
+    elif model_type == "diffusion_prior":
+        ui = create_diffusion_prior_ui(model_config)
+    elif model_type == "lm":
+        ui = create_lm_ui(model_config)
+        
+    return ui

stable_audio_tools/interface/testing.py

@@ -0,0 +1,409 @@
+import gc
+import numpy as np
+import json 
+import torch
+import torchaudio
+import os
+import re
+
+from aeiou.viz import audio_spectrogram_image
+from einops import rearrange
+from safetensors.torch import load_file
+from torch.nn import functional as F
+from torchaudio import transforms as T
+
+from ..inference.generation import generate_diffusion_cond, generate_diffusion_uncond
+from ..models.factory import create_model_from_config
+from ..models.pretrained import get_pretrained_model
+from ..models.utils import load_ckpt_state_dict
+from ..inference.utils import prepare_audio
+from ..training.utils import copy_state_dict
+
+
+model = None
+sample_rate = 44100
+sample_size = 524288
+
+def load_model(model_config=None, model_ckpt_path=None, pretrained_name=None, pretransform_ckpt_path=None, device="cuda", model_half=False):
+    global model, sample_rate, sample_size
+    
+    if pretrained_name is not None:
+        print(f"Loading pretrained model {pretrained_name}")
+        model, model_config = get_pretrained_model(pretrained_name)
+
+    elif model_config is not None and model_ckpt_path is not None:
+        print(f"Creating model from config")
+        model = create_model_from_config(model_config)
+
+        print(f"Loading model checkpoint from {model_ckpt_path}")
+        # Load checkpoint
+        copy_state_dict(model, load_ckpt_state_dict(model_ckpt_path))
+        #model.load_state_dict(load_ckpt_state_dict(model_ckpt_path))
+
+    sample_rate = model_config["sample_rate"]
+    sample_size = model_config["sample_size"]
+
+    if pretransform_ckpt_path is not None:
+        print(f"Loading pretransform checkpoint from {pretransform_ckpt_path}")
+        model.pretransform.load_state_dict(load_ckpt_state_dict(pretransform_ckpt_path), strict=False)
+        print(f"Done loading pretransform")
+
+    model.to(device).eval().requires_grad_(False)
+
+    if model_half:
+        model.to(torch.float16)
+        
+    print(f"Done loading model")
+
+    return model, model_config
+
+def generate_cond_with_path(
+        prompt,
+        negative_prompt=None,
+        seconds_start=0,
+        seconds_total=30,
+        latitude = 0.0,
+        longitude = 0.0,
+        temperature = 0.0,
+        humidity = 0.0,
+        wind_speed = 0.0,
+        pressure = 0.0,
+        minutes_of_day = 0.0,
+        day_of_year = 0.0,
+        cfg_scale=6.0,
+        steps=250,
+        preview_every=None,
+        seed=-1,
+        sampler_type="dpmpp-2m-sde",
+        sigma_min=0.03,
+        sigma_max=50,
+        cfg_rescale=0.4,
+        use_init=False,
+        init_audio=None,
+        init_noise_level=1.0,
+        mask_cropfrom=None,
+        mask_pastefrom=None,
+        mask_pasteto=None,
+        mask_maskstart=None,
+        mask_maskend=None,
+        mask_softnessL=None,
+        mask_softnessR=None,
+        mask_marination=None,
+        batch_size=1,
+        destination_folder=None,
+        file_name=None    
+    ):
+
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gc.collect()
+
+    print(f"Prompt: {prompt}")
+
+    global preview_images
+    preview_images = []
+    if preview_every == 0:
+        preview_every = None
+
+    # Return fake stereo audio
+    conditioning = [{"prompt": prompt, "latitude": latitude, "longitude": longitude, "temperature": temperature, "humidity": humidity, "wind_speed": wind_speed, "pressure": pressure, "minutes_of_day": minutes_of_day,"day_of_year": day_of_year, "seconds_start":seconds_start, "seconds_total": seconds_total }] * batch_size
+
+    if negative_prompt:
+        negative_conditioning = [{"prompt": negative_prompt, "latitude": latitude, "longitude": longitude, "temperature": temperature, "humidity": humidity, "wind_speed": wind_speed, "pressure": pressure, "minutes_of_day": minutes_of_day,"day_of_year": day_of_year, "seconds_start":seconds_start, "seconds_total": seconds_total}] * batch_size
+    else:
+        negative_conditioning = None
+        
+    #Get the device from the model
+    device = next(model.parameters()).device
+
+    seed = int(seed)
+
+    if not use_init:
+        init_audio = None
+    
+    input_sample_size = sample_size
+
+    if init_audio is not None:
+        in_sr, init_audio = init_audio
+        # Turn into torch tensor, converting from int16 to float32
+        init_audio = torch.from_numpy(init_audio).float().div(32767)
+        
+        if init_audio.dim() == 1:
+            init_audio = init_audio.unsqueeze(0) # [1, n]
+        elif init_audio.dim() == 2:
+            init_audio = init_audio.transpose(0, 1) # [n, 2] -> [2, n]
+
+        if in_sr != sample_rate:
+            resample_tf = T.Resample(in_sr, sample_rate).to(init_audio.device)
+            init_audio = resample_tf(init_audio)
+
+        audio_length = init_audio.shape[-1]
+
+        if audio_length > sample_size:
+
+            input_sample_size = audio_length + (model.min_input_length - (audio_length % model.min_input_length)) % model.min_input_length
+
+        init_audio = (sample_rate, init_audio)
+
+    def progress_callback(callback_info):
+        global preview_images
+        denoised = callback_info["denoised"]
+        current_step = callback_info["i"]
+        sigma = callback_info["sigma"]
+
+        if (current_step - 1) % preview_every == 0:
+            if model.pretransform is not None:
+                denoised = model.pretransform.decode(denoised)
+            denoised = rearrange(denoised, "b d n -> d (b n)")
+            denoised = denoised.clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+            audio_spectrogram = audio_spectrogram_image(denoised, sample_rate=sample_rate)
+            preview_images.append((audio_spectrogram, f"Step {current_step} sigma={sigma:.3f})"))
+
+    # If inpainting, send mask args
+    # This will definitely change in the future
+    if mask_cropfrom is not None: 
+        mask_args = {
+            "cropfrom": mask_cropfrom,
+            "pastefrom": mask_pastefrom,
+            "pasteto": mask_pasteto,
+            "maskstart": mask_maskstart,
+            "maskend": mask_maskend,
+            "softnessL": mask_softnessL,
+            "softnessR": mask_softnessR,
+            "marination": mask_marination,
+        }
+    else:
+        mask_args = None 
+
+    # Do the audio generation
+    audio = generate_diffusion_cond(
+        model, 
+        conditioning=conditioning,
+        negative_conditioning=negative_conditioning,
+        steps=steps,
+        cfg_scale=cfg_scale,
+        batch_size=batch_size,
+        sample_size=input_sample_size,
+        sample_rate=sample_rate,
+        seed=seed,
+        device=device,
+        sampler_type=sampler_type,
+        sigma_min=sigma_min,
+        sigma_max=sigma_max,
+        init_audio=init_audio,
+        init_noise_level=init_noise_level,
+        mask_args = mask_args,
+        callback = progress_callback if preview_every is not None else None,
+        scale_phi = cfg_rescale
+    )
+
+    # Convert to WAV file
+    audio = rearrange(audio, "b d n -> d (b n)")
+    audio = audio.to(torch.float32).div(torch.max(torch.abs(audio))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+    #save to the desired folder with the required filename and add the .wav extension
+    
+    if destination_folder is not None and file_name is not None:
+        torchaudio.save(f"{destination_folder}/{file_name}.wav", audio, sample_rate)
+        
+        
+
+    # Let's look at a nice spectrogram too
+    # audio_spectrogram = audio_spectrogram_image(audio, sample_rate=sample_rate)
+
+    # return ("output.wav", [audio_spectrogram, *preview_images])
+
+
+
+def generate_lm(
+        temperature=1.0,
+        top_p=0.95,
+        top_k=0,    
+        batch_size=1,
+        ):
+    
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gc.collect()
+
+    #Get the device from the model
+    device = next(model.parameters()).device
+
+    audio = model.generate_audio(
+        batch_size=batch_size,
+        max_gen_len = sample_size//model.pretransform.downsampling_ratio,
+        conditioning=None,
+        temp=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        use_cache=True
+    )
+
+    audio = rearrange(audio, "b d n -> d (b n)")
+
+    audio = audio.to(torch.float32).div(torch.max(torch.abs(audio))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+
+    torchaudio.save("output.wav", audio, sample_rate)
+
+    audio_spectrogram = audio_spectrogram_image(audio, sample_rate=sample_rate)
+
+    return ("output.wav", [audio_spectrogram])
+
+
+
+
+def autoencoder_process(audio, latent_noise, n_quantizers):
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gc.collect()
+
+    #Get the device from the model
+    device = next(model.parameters()).device
+
+    in_sr, audio = audio
+
+    audio = torch.from_numpy(audio).float().div(32767).to(device)
+
+    if audio.dim() == 1:
+        audio = audio.unsqueeze(0)
+    else:
+        audio = audio.transpose(0, 1)
+
+    audio = model.preprocess_audio_for_encoder(audio, in_sr)
+    # Note: If you need to do chunked encoding, to reduce VRAM, 
+    # then add these arguments to encode_audio and decode_audio: chunked=True, overlap=32, chunk_size=128
+    # To turn it off, do chunked=False
+    # Optimal overlap and chunk_size values will depend on the model. 
+    # See encode_audio & decode_audio in autoencoders.py for more info
+    # Get dtype of model
+    dtype = next(model.parameters()).dtype
+
+    audio = audio.to(dtype)
+
+    if n_quantizers > 0:
+        latents = model.encode_audio(audio, chunked=False, n_quantizers=n_quantizers)
+    else:
+        latents = model.encode_audio(audio, chunked=False)
+
+    if latent_noise > 0:
+        latents = latents + torch.randn_like(latents) * latent_noise
+
+    audio = model.decode_audio(latents, chunked=False)
+
+    audio = rearrange(audio, "b d n -> d (b n)")
+
+    audio = audio.to(torch.float32).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+
+    torchaudio.save("output.wav", audio, sample_rate)
+
+    return "output.wav"
+
+def load_and_generate(model_path, json_dir, output_dir):
+    """Load JSON files and generate audio for each set of conditions."""
+    # List all files in the json_dir
+    files = os.listdir(json_dir)
+    
+    # Filter for JSON files
+    json_files = [file for file in files if file.endswith('.json')]
+    
+    if not json_files:
+        print(f"No JSON files found in {json_dir}. Please check the directory path and file permissions.")
+        return
+
+    for json_filename in json_files:
+        json_file_path = os.path.join(json_dir, json_filename)
+        
+        try:
+            with open(json_file_path, 'r') as file:
+                data = json.load(file)
+        except Exception as e:
+            print(f"Failed to read or parse {json_file_path}: {e}")
+            continue
+        
+        # Print the JSON path
+        print(json_file_path)
+        
+        # Extract conditions from JSON
+        conditions = {
+            'birdSpecies': data['birdSpecies'],
+            'latitude': data['coord']['lat'],
+            'longitude': data['coord']['lon'],
+            'temperature': data['main']['temp'],
+            'humidity': data['main']['humidity'],
+            'pressure': data['main']['pressure'],
+            'wind_speed': data['wind']['speed'],
+            'day_of_year': data['dayOfYear'],
+            'minutes_of_day': data['minutesOfDay']
+        }
+        
+        # Extract base filename components
+        step_number = re.search(r'step=(\d+)', model_path).group(1)
+        bird_species = conditions['birdSpecies'].replace(' ', '_')
+        base_filename = f"{bird_species}_{os.path.splitext(json_filename)[0]}_{step_number}_cfg_scale_"
+        
+
+        
+        #An array of cfg scale values to test
+        cfg_scales = [1.8, 2.5, 4.0, 5.0, 12.0]
+        
+        # Generate audio we do this 4 times with a loop
+        for scale in cfg_scales:
+            generate_cond_with_path(prompt = "",
+            negative_prompt="",
+            seconds_start=0,
+            seconds_total=22,
+            latitude = conditions['latitude'],
+            longitude = conditions['longitude'],
+            temperature = conditions['temperature'],
+            humidity = conditions['humidity'],
+            wind_speed = conditions['wind_speed'],
+            pressure = conditions['pressure'],
+            minutes_of_day = conditions['minutes_of_day'],
+            day_of_year = conditions['day_of_year'],
+            cfg_scale=scale,
+            steps=250,
+            preview_every=None,
+            seed=-1,
+            sampler_type="dpmpp-2m-sde",
+            sigma_min=0.03,
+            sigma_max=50,
+            cfg_rescale=0.4,
+            use_init=False,
+            init_audio=None,
+            init_noise_level=1.0,
+            mask_cropfrom=None,
+            mask_pastefrom=None,
+            mask_pasteto=None,
+            mask_maskstart=None,
+            mask_maskend=None,
+            mask_softnessL=None,
+            mask_softnessR=None,
+            mask_marination=None,
+            batch_size=1,
+            destination_folder=output_dir,
+            file_name=base_filename + str(scale))
+            
+            
+def runTests(model_config_path=None, ckpt_path=None, pretrained_name=None, pretransform_ckpt_path=None, model_half=False, json_dir=None, output_dir=None):
+    assert (pretrained_name is not None) ^ (model_config_path is not None and ckpt_path is not None), "Must specify either pretrained name or provide a model config and checkpoint, but not both"
+
+    if model_config_path is not None:
+        # Load config from json file
+        with open(model_config_path) as f:
+            model_config = json.load(f)
+    else:
+        model_config = None
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    _, model_config = load_model(model_config, ckpt_path, pretrained_name=pretrained_name, pretransform_ckpt_path=pretransform_ckpt_path, model_half=model_half, device=device)
+
+        # Ensure output directory exists-    os.makedirs(args.output_dir, exist_ok=True)
+
+    # Process all JSON files and generate audio
+    load_and_generate(ckpt_path, json_dir, output_dir)  
+
+
+
+
+            
+
+ 

stable_audio_tools/models/__init__.py

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

stable_audio_tools/models/adp.py

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

stable_audio_tools/models/autoencoders.py

@@ -0,0 +1,800 @@
+import torch
+import math
+import numpy as np
+
+from torch import nn, sin, pow
+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 List, Literal, Dict, Any, Callable
+from einops import rearrange
+
+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, AutoencoderPretransform
+
+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
+
+        act = get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels)
+
+        padding = (dilation * (7-1)) // 2
+
+        self.layers = nn.Sequential(
+            act,
+            WNConv1d(in_channels=in_channels, out_channels=out_channels,
+                      kernel_size=7, dilation=dilation, padding=padding),
+            act,
+            WNConv1d(in_channels=out_channels, out_channels=out_channels,
+                      kernel_size=1)
+        )
+
+    def forward(self, x):
+        res = x
+        
+        # Disable checkpoint until tensor mismatch is fixed
+        #x = checkpoint(self.layers, x)
+        x = self.layers(x)
+
+
+class EncoderBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False):
+        super().__init__()
+
+        act = get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels)
+
+        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),
+            act,
+            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))
+
+        act = get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels)
+
+        self.layers = nn.Sequential(
+            act,
+            upsample_layer,
+            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+                         dilation=1, use_snake=use_snake),
+            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+                         dilation=3, use_snake=use_snake),
+            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+                         dilation=9, use_snake=use_snake),
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+class OobleckEncoder(nn.Module):
+    def __init__(self, 
+                 in_channels=2, 
+                 channels=128, 
+                 latent_dim=32, 
+                 c_mults = [1, 2, 4, 8], 
+                 strides = [2, 4, 8, 8],
+                 use_snake=False,
+                 antialias_activation=False
+        ):
+        super().__init__()
+          
+        c_mults = [1] + c_mults
+
+        self.depth = len(c_mults)
+
+        layers = [
+            WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3)
+        ]
+        
+        for i in range(self.depth-1):
+            layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)]
+
+        layers += [
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[-1] * channels),
+            WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1)
+        ]
+
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class OobleckDecoder(nn.Module):
+    def __init__(self, 
+                 out_channels=2, 
+                 channels=128, 
+                 latent_dim=32, 
+                 c_mults = [1, 2, 4, 8], 
+                 strides = [2, 4, 8, 8],
+                 use_snake=False,
+                 antialias_activation=False,
+                 use_nearest_upsample=False,
+                 final_tanh=True):
+        super().__init__()
+
+        c_mults = [1] + c_mults
+        
+        self.depth = len(c_mults)
+
+        layers = [
+            WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3),
+        ]
+        
+        for i in range(self.depth-1, 0, -1):
+            layers += [DecoderBlock(
+                in_channels=c_mults[i]*channels, 
+                out_channels=c_mults[i-1]*channels, 
+                stride=strides[i-1], 
+                use_snake=use_snake, 
+                antialias_activation=antialias_activation,
+                use_nearest_upsample=use_nearest_upsample
+                )
+            ]
+
+        layers += [
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[0] * channels),
+            WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False),
+            nn.Tanh() if final_tanh else nn.Identity()
+        ]
+
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+class DACEncoderWrapper(nn.Module):
+    def __init__(self, in_channels=1, **kwargs):
+        super().__init__()
+
+        from dac.model.dac import Encoder as DACEncoder
+
+        latent_dim = kwargs.pop("latent_dim", None)
+
+        encoder_out_dim = kwargs["d_model"] * (2 ** len(kwargs["strides"]))
+        self.encoder = DACEncoder(d_latent=encoder_out_dim, **kwargs)
+        self.latent_dim = latent_dim
+
+        # Latent-dim support was added to DAC after this was first written, and implemented differently, so this is for backwards compatibility
+        self.proj_out = nn.Conv1d(self.encoder.enc_dim, latent_dim, kernel_size=1) if latent_dim is not None else nn.Identity()
+
+        if in_channels != 1:
+            self.encoder.block[0] = WNConv1d(in_channels, kwargs.get("d_model", 64), kernel_size=7, padding=3)
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.proj_out(x)
+        return x
+
+class DACDecoderWrapper(nn.Module):
+    def __init__(self, latent_dim, out_channels=1, **kwargs):
+        super().__init__()
+
+        from dac.model.dac import Decoder as DACDecoder
+
+        self.decoder = DACDecoder(**kwargs, input_channel = latent_dim, d_out=out_channels)
+
+        self.latent_dim = latent_dim
+
+    def forward(self, x):
+        return self.decoder(x)
+
+class AudioAutoencoder(nn.Module):
+    def __init__(
+        self,
+        encoder,
+        decoder,
+        latent_dim,
+        downsampling_ratio,
+        sample_rate,
+        io_channels=2,
+        bottleneck: Bottleneck = None,
+        pretransform: Pretransform = None,
+        in_channels = None,
+        out_channels = None,
+        soft_clip = False
+    ):
+        super().__init__()
+
+        self.downsampling_ratio = downsampling_ratio
+        self.sample_rate = sample_rate
+
+        self.latent_dim = latent_dim
+        self.io_channels = io_channels
+        self.in_channels = io_channels
+        self.out_channels = io_channels
+
+        self.min_length = self.downsampling_ratio
+
+        if in_channels is not None:
+            self.in_channels = in_channels
+
+        if out_channels is not None:
+            self.out_channels = out_channels
+
+        self.bottleneck = bottleneck
+
+        self.encoder = encoder
+
+        self.decoder = decoder
+
+        self.pretransform = pretransform
+
+        self.soft_clip = soft_clip
+ 
+        self.is_discrete = self.bottleneck is not None and self.bottleneck.is_discrete
+
+    def encode(self, audio, return_info=False, skip_pretransform=False, iterate_batch=False, **kwargs):
+
+        info = {}
+
+        if self.pretransform is not None and not skip_pretransform:
+            if self.pretransform.enable_grad:
+                if iterate_batch:
+                    audios = []
+                    for i in range(audio.shape[0]):
+                        audios.append(self.pretransform.encode(audio[i:i+1]))
+                    audio = torch.cat(audios, dim=0)
+                else:
+                    audio = self.pretransform.encode(audio)
+            else:
+                with torch.no_grad():
+                    if iterate_batch:
+                        audios = []
+                        for i in range(audio.shape[0]):
+                            audios.append(self.pretransform.encode(audio[i:i+1]))
+                        audio = torch.cat(audios, dim=0)
+                    else:
+                        audio = self.pretransform.encode(audio)
+
+        if self.encoder is not None:
+            if iterate_batch:
+                latents = []
+                for i in range(audio.shape[0]):
+                    latents.append(self.encoder(audio[i:i+1]))
+                latents = torch.cat(latents, dim=0)
+            else:
+                latents = self.encoder(audio)
+        else:
+            latents = audio
+
+        if self.bottleneck is not None:
+            # TODO: Add iterate batch logic, needs to merge the info dicts
+            latents, bottleneck_info = self.bottleneck.encode(latents, return_info=True, **kwargs)
+
+            info.update(bottleneck_info)
+        
+        if return_info:
+            return latents, info
+
+        return latents
+
+    def decode(self, latents, iterate_batch=False, **kwargs):
+
+        if self.bottleneck is not None:
+            if iterate_batch:
+                decoded = []
+                for i in range(latents.shape[0]):
+                    decoded.append(self.bottleneck.decode(latents[i:i+1]))
+                decoded = torch.cat(decoded, dim=0)
+            else:
+                latents = self.bottleneck.decode(latents)
+
+        if iterate_batch:
+            decoded = []
+            for i in range(latents.shape[0]):
+                decoded.append(self.decoder(latents[i:i+1]))
+            decoded = torch.cat(decoded, dim=0)
+        else:
+            decoded = self.decoder(latents, **kwargs)
+
+        if self.pretransform is not None:
+            if self.pretransform.enable_grad:
+                if iterate_batch:
+                    decodeds = []
+                    for i in range(decoded.shape[0]):
+                        decodeds.append(self.pretransform.decode(decoded[i:i+1]))
+                    decoded = torch.cat(decodeds, dim=0)
+                else:
+                    decoded = self.pretransform.decode(decoded)
+            else:
+                with torch.no_grad():
+                    if iterate_batch:
+                        decodeds = []
+                        for i in range(latents.shape[0]):
+                            decodeds.append(self.pretransform.decode(decoded[i:i+1]))
+                        decoded = torch.cat(decodeds, dim=0)
+                    else:
+                        decoded = self.pretransform.decode(decoded)
+
+        if self.soft_clip:
+            decoded = torch.tanh(decoded)
+        
+        return decoded
+          
+    def decode_tokens(self, tokens, **kwargs):
+        '''
+        Decode discrete tokens to audio
+        Only works with discrete autoencoders
+        '''
+
+        assert isinstance(self.bottleneck, DiscreteBottleneck), "decode_tokens only works with discrete autoencoders"
+
+        latents = self.bottleneck.decode_tokens(tokens, **kwargs)
+
+        return self.decode(latents, **kwargs)
+        
+    
+    def preprocess_audio_for_encoder(self, audio, in_sr):
+        '''
+        Preprocess single audio tensor (Channels x Length) to be compatible with the encoder.
+        If the model is mono, stereo audio will be converted to mono.
+        Audio will be silence-padded to be a multiple of the model's downsampling ratio.
+        Audio will be resampled to the model's sample rate. 
+        The output will have batch size 1 and be shape (1 x Channels x Length)
+        '''
+        return self.preprocess_audio_list_for_encoder([audio], [in_sr])
+
+    def preprocess_audio_list_for_encoder(self, audio_list, in_sr_list):
+        '''
+        Preprocess a [list] of audio (Channels x Length) into a batch tensor to be compatable with the encoder. 
+        The audio in that list can be of different lengths and channels. 
+        in_sr can be an integer or list. If it's an integer it will be assumed it is the input sample_rate for every audio.
+        All audio will be resampled to the model's sample rate. 
+        Audio will be silence-padded to the longest length, and further padded to be a multiple of the model's downsampling ratio. 
+        If the model is mono, all audio will be converted to mono. 
+        The output will be a tensor of shape (Batch x Channels x Length)
+        '''
+        batch_size = len(audio_list)
+        if isinstance(in_sr_list, int):
+            in_sr_list = [in_sr_list]*batch_size
+        assert len(in_sr_list) == batch_size, "list of sample rates must be the same length of audio_list"
+        new_audio = []
+        max_length = 0
+        # resample & find the max length
+        for i in range(batch_size):
+            audio = audio_list[i]
+            in_sr = in_sr_list[i]
+            if len(audio.shape) == 3 and audio.shape[0] == 1:
+                # batchsize 1 was given by accident. Just squeeze it.
+                audio = audio.squeeze(0)
+            elif len(audio.shape) == 1:
+                # Mono signal, channel dimension is missing, unsqueeze it in
+                audio = audio.unsqueeze(0)
+            assert len(audio.shape)==2, "Audio should be shape (Channels x Length) with no batch dimension" 
+            # Resample audio
+            if in_sr != self.sample_rate:
+                resample_tf = T.Resample(in_sr, self.sample_rate).to(audio.device)
+                audio = resample_tf(audio)
+            new_audio.append(audio)
+            if audio.shape[-1] > max_length:
+                max_length = audio.shape[-1]
+        # Pad every audio to the same length, multiple of model's downsampling ratio
+        padded_audio_length = max_length + (self.min_length - (max_length % self.min_length)) % self.min_length
+        for i in range(batch_size):
+            # Pad it & if necessary, mixdown/duplicate stereo/mono channels to support model
+            new_audio[i] = prepare_audio(new_audio[i], in_sr=in_sr, target_sr=in_sr, target_length=padded_audio_length, 
+                target_channels=self.in_channels, device=new_audio[i].device).squeeze(0)
+        # convert to tensor 
+        return torch.stack(new_audio) 
+
+    def encode_audio(self, audio, chunked=False, overlap=32, chunk_size=128, **kwargs):
+        '''
+        Encode audios into latents. Audios should already be preprocesed by preprocess_audio_for_encoder.
+        If chunked is True, split the audio into chunks of a given maximum size chunk_size, with given overlap.
+        Overlap and chunk_size params are both measured in number of latents (not audio samples) 
+        # and therefore you likely could use the same values with decode_audio. 
+        A overlap of zero will cause discontinuity artefacts. Overlap should be => receptive field size. 
+        Every autoencoder will have a different receptive field size, and thus ideal overlap.
+        You can determine it empirically by diffing unchunked vs chunked output and looking at maximum diff.
+        The final chunk may have a longer overlap in order to keep chunk_size consistent for all chunks.
+        Smaller chunk_size uses less memory, but more compute.
+        The chunk_size vs memory tradeoff isn't linear, and possibly depends on the GPU and CUDA version
+        For example, on a A6000 chunk_size 128 is overall faster than 256 and 512 even though it has more chunks
+        '''
+        if not chunked:
+            # default behavior. Encode the entire audio in parallel
+            return self.encode(audio, **kwargs)
+        else:
+            # CHUNKED ENCODING
+            # samples_per_latent is just the downsampling ratio (which is also the upsampling ratio)
+            samples_per_latent = self.downsampling_ratio
+            total_size = audio.shape[2] # in samples
+            batch_size = audio.shape[0]
+            chunk_size *= samples_per_latent # converting metric in latents to samples
+            overlap *= samples_per_latent # converting metric in latents to samples
+            hop_size = chunk_size - overlap
+            chunks = []
+            for i in range(0, total_size - chunk_size + 1, hop_size):
+                chunk = audio[:,:,i:i+chunk_size]
+                chunks.append(chunk)
+            if i+chunk_size != total_size:
+                # Final chunk
+                chunk = audio[:,:,-chunk_size:]
+                chunks.append(chunk)
+            chunks = torch.stack(chunks)
+            num_chunks = chunks.shape[0]
+            # Note: y_size might be a different value from the latent length used in diffusion training
+            # because we can encode audio of varying lengths
+            # However, the audio should've been padded to a multiple of samples_per_latent by now.
+            y_size = total_size // samples_per_latent
+            # Create an empty latent, we will populate it with chunks as we encode them
+            y_final = torch.zeros((batch_size,self.latent_dim,y_size)).to(audio.device)
+            for i in range(num_chunks):
+                x_chunk = chunks[i,:]
+                # encode the chunk
+                y_chunk = self.encode(x_chunk)
+                # figure out where to put the audio along the time domain
+                if i == num_chunks-1:
+                    # final chunk always goes at the end
+                    t_end = y_size
+                    t_start = t_end - y_chunk.shape[2]
+                else:
+                    t_start = i * hop_size // samples_per_latent
+                    t_end = t_start + chunk_size // samples_per_latent
+                #  remove the edges of the overlaps
+                ol = overlap//samples_per_latent//2
+                chunk_start = 0
+                chunk_end = y_chunk.shape[2]
+                if i > 0:
+                    # no overlap for the start of the first chunk
+                    t_start += ol
+                    chunk_start += ol
+                if i < num_chunks-1:
+                    # no overlap for the end of the last chunk
+                    t_end -= ol
+                    chunk_end -= ol
+                # paste the chunked audio into our y_final output audio
+                y_final[:,:,t_start:t_end] = y_chunk[:,:,chunk_start:chunk_end]
+            return y_final
+    
+    def decode_audio(self, latents, chunked=False, overlap=32, chunk_size=128, **kwargs):
+        '''
+        Decode latents to audio. 
+        If chunked is True, split the latents into chunks of a given maximum size chunk_size, with given overlap, both of which are measured in number of latents. 
+        A overlap of zero will cause discontinuity artefacts. Overlap should be => receptive field size. 
+        Every autoencoder will have a different receptive field size, and thus ideal overlap.
+        You can determine it empirically by diffing unchunked vs chunked audio and looking at maximum diff.
+        The final chunk may have a longer overlap in order to keep chunk_size consistent for all chunks.
+        Smaller chunk_size uses less memory, but more compute.
+        The chunk_size vs memory tradeoff isn't linear, and possibly depends on the GPU and CUDA version
+        For example, on a A6000 chunk_size 128 is overall faster than 256 and 512 even though it has more chunks
+        '''
+        if not chunked:
+            # default behavior. Decode the entire latent in parallel
+            return self.decode(latents, **kwargs)
+        else:
+            # chunked decoding
+            hop_size = chunk_size - overlap
+            total_size = latents.shape[2]
+            batch_size = latents.shape[0]
+            chunks = []
+            for i in range(0, total_size - chunk_size + 1, hop_size):
+                chunk = latents[:,:,i:i+chunk_size]
+                chunks.append(chunk)
+            if i+chunk_size != total_size:
+                # Final chunk
+                chunk = latents[:,:,-chunk_size:]
+                chunks.append(chunk)
+            chunks = torch.stack(chunks)
+            num_chunks = chunks.shape[0]
+            # samples_per_latent is just the downsampling ratio
+            samples_per_latent = self.downsampling_ratio
+            # Create an empty waveform, we will populate it with chunks as decode them
+            y_size = total_size * samples_per_latent
+            y_final = torch.zeros((batch_size,self.out_channels,y_size)).to(latents.device)
+            for i in range(num_chunks):
+                x_chunk = chunks[i,:]
+                # decode the chunk
+                y_chunk = self.decode(x_chunk)
+                # figure out where to put the audio along the time domain
+                if i == num_chunks-1:
+                    # final chunk always goes at the end
+                    t_end = y_size
+                    t_start = t_end - y_chunk.shape[2]
+                else:
+                    t_start = i * hop_size * samples_per_latent
+                    t_end = t_start + chunk_size * samples_per_latent
+                #  remove the edges of the overlaps
+                ol = (overlap//2) * samples_per_latent
+                chunk_start = 0
+                chunk_end = y_chunk.shape[2]
+                if i > 0:
+                    # no overlap for the start of the first chunk
+                    t_start += ol
+                    chunk_start += ol
+                if i < num_chunks-1:
+                    # no overlap for the end of the last chunk
+                    t_end -= ol
+                    chunk_end -= ol
+                # paste the chunked audio into our y_final output audio
+                y_final[:,:,t_start:t_end] = y_chunk[:,:,chunk_start:chunk_end]
+            return y_final
+
+    
+class DiffusionAutoencoder(AudioAutoencoder):
+    def __init__(
+        self,
+        diffusion: ConditionedDiffusionModel,
+        diffusion_downsampling_ratio,
+        *args,
+        **kwargs
+    ):
+        super().__init__(*args, **kwargs)
+
+        self.diffusion = diffusion
+
+        self.min_length = self.downsampling_ratio * diffusion_downsampling_ratio
+
+        if self.encoder is not None:
+            # Shrink the initial encoder parameters to avoid saturated latents
+            with torch.no_grad():
+                for param in self.encoder.parameters():
+                    param *= 0.5
+
+    def decode(self, latents, steps=100):
+
+        upsampled_length = latents.shape[2] * self.downsampling_ratio
+
+        if self.bottleneck is not None:
+            latents = self.bottleneck.decode(latents)
+
+        if self.decoder is not None:
+            latents = self.decode(latents)
+    
+        # Upsample latents to match diffusion length
+        if latents.shape[2] != upsampled_length:
+            latents = F.interpolate(latents, size=upsampled_length, mode='nearest')
+
+        noise = torch.randn(latents.shape[0], self.io_channels, upsampled_length, device=latents.device)
+        decoded = sample(self.diffusion, noise, steps, 0, input_concat_cond=latents)
+
+        if self.pretransform is not None:
+            if self.pretransform.enable_grad:
+                decoded = self.pretransform.decode(decoded)
+            else:
+                with torch.no_grad():
+                    decoded = self.pretransform.decode(decoded)
+
+        return decoded
+        
+# AE factories
+
+def create_encoder_from_config(encoder_config: Dict[str, Any]):
+    encoder_type = encoder_config.get("type", None)
+    assert encoder_type is not None, "Encoder type must be specified"
+
+    if encoder_type == "oobleck":
+        encoder = OobleckEncoder(
+            **encoder_config["config"]
+        )
+    
+    elif encoder_type == "seanet":
+        from encodec.modules import SEANetEncoder
+        seanet_encoder_config = encoder_config["config"]
+
+        #SEANet encoder expects strides in reverse order
+        seanet_encoder_config["ratios"] = list(reversed(seanet_encoder_config.get("ratios", [2, 2, 2, 2, 2])))
+        encoder = SEANetEncoder(
+            **seanet_encoder_config
+        )
+    elif encoder_type == "dac":
+        dac_config = encoder_config["config"]
+
+        encoder = DACEncoderWrapper(**dac_config)
+    elif encoder_type == "local_attn":
+        from .local_attention import TransformerEncoder1D
+
+        local_attn_config = encoder_config["config"]
+
+        encoder = TransformerEncoder1D(
+            **local_attn_config
+        )
+    else:
+        raise ValueError(f"Unknown encoder type {encoder_type}")
+    
+    requires_grad = encoder_config.get("requires_grad", True)
+    if not requires_grad:
+        for param in encoder.parameters():
+            param.requires_grad = False
+
+    return encoder
+
+def create_decoder_from_config(decoder_config: Dict[str, Any]):
+    decoder_type = decoder_config.get("type", None)
+    assert decoder_type is not None, "Decoder type must be specified"
+
+    if decoder_type == "oobleck":
+        decoder = OobleckDecoder(
+            **decoder_config["config"]
+        )
+    elif decoder_type == "seanet":
+        from encodec.modules import SEANetDecoder
+
+        decoder = SEANetDecoder(
+            **decoder_config["config"]
+        )
+    elif decoder_type == "dac":
+        dac_config = decoder_config["config"]
+
+        decoder = DACDecoderWrapper(**dac_config)
+    elif decoder_type == "local_attn":
+        from .local_attention import TransformerDecoder1D
+
+        local_attn_config = decoder_config["config"]
+
+        decoder = TransformerDecoder1D(
+            **local_attn_config
+        )
+    else:
+        raise ValueError(f"Unknown decoder type {decoder_type}")
+    
+    requires_grad = decoder_config.get("requires_grad", True)
+    if not requires_grad:
+        for param in decoder.parameters():
+            param.requires_grad = False
+
+    return decoder
+
+def create_autoencoder_from_config(config: Dict[str, Any]):
+    
+    ae_config = config["model"]
+
+    encoder = create_encoder_from_config(ae_config["encoder"])
+    decoder = create_decoder_from_config(ae_config["decoder"])
+
+    bottleneck = ae_config.get("bottleneck", None)
+
+    latent_dim = ae_config.get("latent_dim", None)
+    assert latent_dim is not None, "latent_dim must be specified in model config"
+    downsampling_ratio = ae_config.get("downsampling_ratio", None)
+    assert downsampling_ratio is not None, "downsampling_ratio must be specified in model config"
+    io_channels = ae_config.get("io_channels", None)
+    assert io_channels is not None, "io_channels must be specified in model config"
+    sample_rate = config.get("sample_rate", None)
+    assert sample_rate is not None, "sample_rate must be specified in model config"
+
+    in_channels = ae_config.get("in_channels", None)
+    out_channels = ae_config.get("out_channels", None)
+
+    pretransform = ae_config.get("pretransform", None)
+
+    if pretransform is not None:
+        pretransform = create_pretransform_from_config(pretransform, sample_rate)
+
+    if bottleneck is not None:
+        bottleneck = create_bottleneck_from_config(bottleneck)
+
+    soft_clip = ae_config["decoder"].get("soft_clip", False)
+
+    return AudioAutoencoder(
+        encoder,
+        decoder,
+        io_channels=io_channels,
+        latent_dim=latent_dim,
+        downsampling_ratio=downsampling_ratio,
+        sample_rate=sample_rate,
+        bottleneck=bottleneck,
+        pretransform=pretransform,
+        in_channels=in_channels,
+        out_channels=out_channels,
+        soft_clip=soft_clip
+    )
+
+def create_diffAE_from_config(config: Dict[str, Any]):
+    
+    diffae_config = config["model"]
+
+    if "encoder" in diffae_config:
+        encoder = create_encoder_from_config(diffae_config["encoder"])
+    else:
+        encoder = None
+
+    if "decoder" in diffae_config:
+        decoder = create_decoder_from_config(diffae_config["decoder"])
+    else:
+        decoder = None
+
+    diffusion_model_type = diffae_config["diffusion"]["type"]
+
+    if diffusion_model_type == "DAU1d":
+        diffusion = DAU1DCondWrapper(**diffae_config["diffusion"]["config"])
+    elif diffusion_model_type == "adp_1d":
+        diffusion = UNet1DCondWrapper(**diffae_config["diffusion"]["config"])
+    elif diffusion_model_type == "dit":
+        diffusion = DiTWrapper(**diffae_config["diffusion"]["config"])
+
+    latent_dim = diffae_config.get("latent_dim", None)
+    assert latent_dim is not None, "latent_dim must be specified in model config"
+    downsampling_ratio = diffae_config.get("downsampling_ratio", None)
+    assert downsampling_ratio is not None, "downsampling_ratio must be specified in model config"
+    io_channels = diffae_config.get("io_channels", None)
+    assert io_channels is not None, "io_channels must be specified in model config"
+    sample_rate = config.get("sample_rate", None)
+    assert sample_rate is not None, "sample_rate must be specified in model config"
+
+    bottleneck = diffae_config.get("bottleneck", None)
+
+    pretransform = diffae_config.get("pretransform", None)
+
+    if pretransform is not None:
+        pretransform = create_pretransform_from_config(pretransform, sample_rate)
+
+    if bottleneck is not None:
+        bottleneck = create_bottleneck_from_config(bottleneck)
+
+    diffusion_downsampling_ratio = None,
+
+    if diffusion_model_type == "DAU1d":
+        diffusion_downsampling_ratio = np.prod(diffae_config["diffusion"]["config"]["strides"])
+    elif diffusion_model_type == "adp_1d":
+        diffusion_downsampling_ratio = np.prod(diffae_config["diffusion"]["config"]["factors"])
+    elif diffusion_model_type == "dit":
+        diffusion_downsampling_ratio = 1
+
+    return DiffusionAutoencoder(
+        encoder=encoder,
+        decoder=decoder,
+        diffusion=diffusion,
+        io_channels=io_channels,
+        sample_rate=sample_rate,
+        latent_dim=latent_dim,
+        downsampling_ratio=downsampling_ratio,
+        diffusion_downsampling_ratio=diffusion_downsampling_ratio,
+        bottleneck=bottleneck,
+        pretransform=pretransform
+    )

stable_audio_tools/models/blocks.py

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

stable_audio_tools/models/bottleneck.py

@@ -0,0 +1,326 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from einops import rearrange
+from vector_quantize_pytorch import ResidualVQ, FSQ
+from dac.nn.quantize import ResidualVectorQuantize as DACResidualVQ
+
+class Bottleneck(nn.Module):
+    def __init__(self, is_discrete: bool = False):
+        super().__init__()
+
+        self.is_discrete = is_discrete
+
+    def encode(self, x, return_info=False, **kwargs):
+        raise NotImplementedError
+
+    def decode(self, x):
+        raise NotImplementedError
+
+class DiscreteBottleneck(Bottleneck):
+    def __init__(self, num_quantizers, codebook_size, tokens_id):
+        super().__init__(is_discrete=True)
+
+        self.num_quantizers = num_quantizers
+        self.codebook_size = codebook_size
+        self.tokens_id = tokens_id
+
+    def decode_tokens(self, codes, **kwargs):
+        raise NotImplementedError
+    
+class TanhBottleneck(Bottleneck):
+    def __init__(self):
+        super().__init__(is_discrete=False)
+        self.tanh = nn.Tanh()
+
+    def encode(self, x, return_info=False):
+        info = {}
+
+        x = torch.tanh(x)
+
+        if return_info:
+            return x, info
+        else:
+            return x
+
+    def decode(self, x):
+        return x
+
+def vae_sample(mean, scale):
+        stdev = nn.functional.softplus(scale) + 1e-4
+        var = stdev * stdev
+        logvar = torch.log(var)
+        latents = torch.randn_like(mean) * stdev + mean
+
+        kl = (mean * mean + var - logvar - 1).sum(1).mean()
+
+        return latents, kl
+
+class VAEBottleneck(Bottleneck):
+    def __init__(self):
+        super().__init__(is_discrete=False)
+
+    def encode(self, x, return_info=False, **kwargs):
+        info = {}
+
+        mean, scale = x.chunk(2, dim=1)
+
+        x, kl = vae_sample(mean, scale)
+
+        info["kl"] = kl
+
+        if return_info:
+            return x, info
+        else:
+            return x
+
+    def decode(self, x):
+        return x
+
+def compute_mean_kernel(x, y):
+        kernel_input = (x[:, None] - y[None]).pow(2).mean(2) / x.shape[-1]
+        return torch.exp(-kernel_input).mean()
+
+def compute_mmd(latents):
+    latents_reshaped = latents.permute(0, 2, 1).reshape(-1, latents.shape[1])
+    noise = torch.randn_like(latents_reshaped)
+
+    latents_kernel = compute_mean_kernel(latents_reshaped, latents_reshaped)
+    noise_kernel = compute_mean_kernel(noise, noise)
+    latents_noise_kernel = compute_mean_kernel(latents_reshaped, noise)
+    
+    mmd = latents_kernel + noise_kernel - 2 * latents_noise_kernel
+    return mmd.mean()
+
+class WassersteinBottleneck(Bottleneck):
+    def __init__(self, noise_augment_dim: int = 0):
+        super().__init__(is_discrete=False)
+
+        self.noise_augment_dim = noise_augment_dim
+    
+    def encode(self, x, return_info=False):
+        info = {}
+
+        if self.training and return_info:
+            mmd = compute_mmd(x)
+            info["mmd"] = mmd
+        
+        if return_info:
+            return x, info
+        
+        return x
+
+    def decode(self, x):
+
+        if self.noise_augment_dim > 0:
+            noise = torch.randn(x.shape[0], self.noise_augment_dim,
+                                x.shape[-1]).type_as(x)
+            x = torch.cat([x, noise], dim=1)
+
+        return x
+
+class L2Bottleneck(Bottleneck):
+    def __init__(self):
+        super().__init__(is_discrete=False)
+    
+    def encode(self, x, return_info=False):
+        info = {}
+
+        x = F.normalize(x, dim=1)
+
+        if return_info:
+            return x, info
+        else:
+            return x
+        
+    def decode(self, x):
+        return F.normalize(x, dim=1)
+        
+class RVQBottleneck(DiscreteBottleneck):
+    def __init__(self, **quantizer_kwargs):
+        super().__init__(num_quantizers = quantizer_kwargs["num_quantizers"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "quantizer_indices")
+        self.quantizer = ResidualVQ(**quantizer_kwargs)
+        self.num_quantizers = quantizer_kwargs["num_quantizers"]
+
+    def encode(self, x, return_info=False, **kwargs):
+        info = {}
+
+        x = rearrange(x, "b c n -> b n c")
+        x, indices, loss = self.quantizer(x)
+        x = rearrange(x, "b n c -> b c n")
+
+        info["quantizer_indices"] = indices
+        info["quantizer_loss"] = loss.mean()
+
+        if return_info:
+            return x, info
+        else:
+            return x
+        
+    def decode(self, x):
+        return x
+    
+    def decode_tokens(self, codes, **kwargs):
+        latents = self.quantizer.get_outputs_from_indices(codes)
+
+        return self.decode(latents, **kwargs)
+    
+class RVQVAEBottleneck(DiscreteBottleneck):
+    def __init__(self, **quantizer_kwargs):
+        super().__init__(num_quantizers = quantizer_kwargs["num_quantizers"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "quantizer_indices")
+        self.quantizer = ResidualVQ(**quantizer_kwargs)
+        self.num_quantizers = quantizer_kwargs["num_quantizers"]
+
+    def encode(self, x, return_info=False):
+        info = {}
+
+        x, kl = vae_sample(*x.chunk(2, dim=1))
+
+        info["kl"] = kl
+
+        x = rearrange(x, "b c n -> b n c")
+        x, indices, loss = self.quantizer(x)
+        x = rearrange(x, "b n c -> b c n")
+
+        info["quantizer_indices"] = indices
+        info["quantizer_loss"] = loss.mean()
+
+        if return_info:
+            return x, info
+        else:
+            return x
+        
+    def decode(self, x):
+        return x
+    
+    def decode_tokens(self, codes, **kwargs):
+        latents = self.quantizer.get_outputs_from_indices(codes)
+
+        return self.decode(latents, **kwargs)
+
+class DACRVQBottleneck(DiscreteBottleneck):
+    def __init__(self, quantize_on_decode=False, **quantizer_kwargs):
+        super().__init__(num_quantizers = quantizer_kwargs["n_codebooks"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "codes")
+        self.quantizer = DACResidualVQ(**quantizer_kwargs)
+        self.num_quantizers = quantizer_kwargs["n_codebooks"]
+        self.quantize_on_decode = quantize_on_decode
+
+    def encode(self, x, return_info=False, **kwargs):
+        info = {}
+
+        info["pre_quantizer"] = x
+
+        if self.quantize_on_decode:
+            return x, info if return_info else x
+
+        z, codes, latents, commitment_loss, codebook_loss = self.quantizer(x, **kwargs)
+
+        output = {
+            "z": z,
+            "codes": codes,
+            "latents": latents,
+            "vq/commitment_loss": commitment_loss,
+            "vq/codebook_loss": codebook_loss,
+        }
+
+        output["vq/commitment_loss"] /= self.num_quantizers
+        output["vq/codebook_loss"] /= self.num_quantizers
+
+        info.update(output)
+
+        if return_info:
+            return output["z"], info
+        
+        return output["z"]
+    
+    def decode(self, x):
+
+        if self.quantize_on_decode:
+            x = self.quantizer(x)[0]
+
+        return x
+    
+    def decode_tokens(self, codes, **kwargs):
+        latents, _, _ = self.quantizer.from_codes(codes)
+
+        return self.decode(latents, **kwargs)
+
+class DACRVQVAEBottleneck(DiscreteBottleneck):
+    def __init__(self, quantize_on_decode=False, **quantizer_kwargs):
+        super().__init__(num_quantizers = quantizer_kwargs["n_codebooks"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "codes")
+        self.quantizer = DACResidualVQ(**quantizer_kwargs)
+        self.num_quantizers = quantizer_kwargs["n_codebooks"]
+        self.quantize_on_decode = quantize_on_decode
+
+    def encode(self, x, return_info=False, n_quantizers: int = None):
+        info = {}
+
+        mean, scale = x.chunk(2, dim=1)
+
+        x, kl = vae_sample(mean, scale)
+
+        info["pre_quantizer"] = x
+        info["kl"] = kl
+
+        if self.quantize_on_decode:
+            return x, info if return_info else x
+
+        z, codes, latents, commitment_loss, codebook_loss = self.quantizer(x, n_quantizers=n_quantizers)
+
+        output = {
+            "z": z,
+            "codes": codes,
+            "latents": latents,
+            "vq/commitment_loss": commitment_loss,
+            "vq/codebook_loss": codebook_loss,
+        }
+
+        output["vq/commitment_loss"] /= self.num_quantizers
+        output["vq/codebook_loss"] /= self.num_quantizers
+
+        info.update(output)
+
+        if return_info:
+            return output["z"], info
+        
+        return output["z"]
+    
+    def decode(self, x):
+
+        if self.quantize_on_decode:
+            x = self.quantizer(x)[0]
+
+        return x
+
+    def decode_tokens(self, codes, **kwargs):
+        latents, _, _ = self.quantizer.from_codes(codes)
+
+        return self.decode(latents, **kwargs)
+    
+class FSQBottleneck(DiscreteBottleneck):
+    def __init__(self, dim, levels):
+        super().__init__(num_quantizers = 1, codebook_size = levels ** dim, tokens_id = "quantizer_indices")
+        self.quantizer = FSQ(levels=[levels] * dim)
+
+    def encode(self, x, return_info=False):
+        info = {}
+
+        x = rearrange(x, "b c n -> b n c")
+        x, indices = self.quantizer(x)
+        x = rearrange(x, "b n c -> b c n")
+
+        info["quantizer_indices"] = indices
+
+        if return_info:
+            return x, info
+        else:
+            return x
+        
+    def decode(self, x):
+        return x
+    
+    def decode_tokens(self, tokens, **kwargs):
+        latents = self.quantizer.indices_to_codes(tokens)
+
+        return self.decode(latents, **kwargs)

stable_audio_tools/models/conditioners.py

@@ -0,0 +1,558 @@
+#Heavily influenced by https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/modules/conditioners.py
+
+import torch
+import logging, warnings
+import string
+import typing as tp
+import gc
+
+from .adp import NumberEmbedder
+from ..inference.utils import set_audio_channels
+from .factory import create_pretransform_from_config
+from .pretransforms import Pretransform
+from ..training.utils import copy_state_dict
+from .utils import load_ckpt_state_dict
+
+from torch import nn
+
+class Conditioner(nn.Module):
+    def __init__(
+            self,
+            dim: int,
+            output_dim: int,
+            project_out: bool = False,
+            ):
+        
+        super().__init__()
+
+        self.dim = dim
+        self.output_dim = output_dim
+        self.proj_out = nn.Linear(dim, output_dim) if (dim != output_dim or project_out) else nn.Identity()
+
+    def forward(self, x: tp.Any) -> tp.Any:
+        raise NotImplementedError()
+    
+class IntConditioner(Conditioner):
+    def __init__(self, 
+                output_dim: int,
+                min_val: int=0,
+                max_val: int=512
+                ):
+        super().__init__(output_dim, output_dim)
+
+        self.min_val = min_val
+        self.max_val = max_val
+        self.int_embedder = nn.Embedding(max_val - min_val + 1, output_dim).requires_grad_(True)
+
+    def forward(self, ints: tp.List[int], device=None) -> tp.Any:
+            
+            #self.int_embedder.to(device)
+    
+            ints = torch.tensor(ints).to(device)
+            ints = ints.clamp(self.min_val, self.max_val)
+    
+            int_embeds = self.int_embedder(ints).unsqueeze(1)
+    
+            return [int_embeds, torch.ones(int_embeds.shape[0], 1).to(device)]
+
+class NumberConditioner(Conditioner):
+    '''
+        Conditioner that takes a list of floats, normalizes them for a given range, and returns a list of embeddings
+    '''
+    def __init__(self, 
+                output_dim: int,
+                min_val: float=0,
+                max_val: float=1
+                ):
+        super().__init__(output_dim, output_dim)
+
+        self.min_val = min_val
+        self.max_val = max_val
+
+        self.embedder = NumberEmbedder(features=output_dim)
+
+    def forward(self, floats: tp.List[float], device=None) -> tp.Any:
+    
+            # Cast the inputs to floats
+            floats = [float(x) for x in floats]
+
+            floats = torch.tensor(floats).to(device)
+
+            floats = floats.clamp(self.min_val, self.max_val)
+    
+            normalized_floats = (floats - self.min_val) / (self.max_val - self.min_val)
+
+            # Cast floats to same type as embedder
+            embedder_dtype = next(self.embedder.parameters()).dtype
+            normalized_floats = normalized_floats.to(embedder_dtype)
+
+            float_embeds = self.embedder(normalized_floats).unsqueeze(1)
+    
+            return [float_embeds, torch.ones(float_embeds.shape[0], 1).to(device)]
+
+class CLAPTextConditioner(Conditioner):
+    def __init__(self, 
+                 output_dim: int, 
+                 clap_ckpt_path,
+                 use_text_features = False,
+                 feature_layer_ix: int = -1,
+                 audio_model_type="HTSAT-base", 
+                 enable_fusion=True,
+                 project_out: bool = False,
+                 finetune: bool = False):
+        super().__init__(768 if use_text_features else 512, output_dim, project_out=project_out)
+
+        self.use_text_features = use_text_features
+        self.feature_layer_ix = feature_layer_ix
+        self.finetune = finetune
+
+        # Suppress logging from transformers
+        previous_level = logging.root.manager.disable
+        logging.disable(logging.ERROR)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            try:
+                import laion_clap
+                from laion_clap.clap_module.factory import load_state_dict as clap_load_state_dict
+                
+                model = laion_clap.CLAP_Module(enable_fusion=enable_fusion, amodel=audio_model_type, device='cpu')
+
+                if self.finetune:
+                    self.model = model
+                else: 
+                    self.__dict__["model"] = model
+
+                state_dict = clap_load_state_dict(clap_ckpt_path)
+                self.model.model.load_state_dict(state_dict, strict=False)
+
+                if self.finetune:
+                    self.model.model.text_branch.requires_grad_(True)
+                    self.model.model.text_branch.train()
+                else:
+                    self.model.model.text_branch.requires_grad_(False)
+                    self.model.model.text_branch.eval()
+
+            finally:
+                logging.disable(previous_level)
+
+        del self.model.model.audio_branch
+
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def get_clap_features(self, prompts, layer_ix=-2, device: tp.Any = "cuda"):
+        prompt_tokens = self.model.tokenizer(prompts)
+        attention_mask = prompt_tokens["attention_mask"].to(device=device, non_blocking=True)
+        prompt_features = self.model.model.text_branch(
+            input_ids=prompt_tokens["input_ids"].to(device=device, non_blocking=True),
+            attention_mask=attention_mask,
+            output_hidden_states=True
+        )["hidden_states"][layer_ix]
+
+        return prompt_features, attention_mask
+
+    def forward(self, texts: tp.List[str], device: tp.Any = "cuda") -> tp.Any:
+        self.model.to(device)
+
+        if self.use_text_features:
+            if len(texts) == 1:
+                text_features, text_attention_mask = self.get_clap_features([texts[0], ""], layer_ix=self.feature_layer_ix, device=device)
+                text_features = text_features[:1, ...]
+                text_attention_mask = text_attention_mask[:1, ...]
+            else:
+                text_features, text_attention_mask = self.get_clap_features(texts, layer_ix=self.feature_layer_ix, device=device)
+            return [self.proj_out(text_features), text_attention_mask]
+
+        # Fix for CLAP bug when only one text is passed
+        if len(texts) == 1:
+            text_embedding = self.model.get_text_embedding([texts[0], ""], use_tensor=True)[:1, ...]
+        else:
+            text_embedding = self.model.get_text_embedding(texts, use_tensor=True)
+
+        text_embedding = text_embedding.unsqueeze(1).to(device)
+
+        return [self.proj_out(text_embedding), torch.ones(text_embedding.shape[0], 1).to(device)]
+
+class CLAPAudioConditioner(Conditioner):
+    def __init__(self, 
+                 output_dim: int, 
+                 clap_ckpt_path,
+                 audio_model_type="HTSAT-base", 
+                 enable_fusion=True,
+                 project_out: bool = False):
+        super().__init__(512, output_dim, project_out=project_out)
+
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+        # Suppress logging from transformers
+        previous_level = logging.root.manager.disable
+        logging.disable(logging.ERROR)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            try:
+                import laion_clap
+                from laion_clap.clap_module.factory import load_state_dict as clap_load_state_dict
+                
+                model = laion_clap.CLAP_Module(enable_fusion=enable_fusion, amodel=audio_model_type, device='cpu')
+
+                if self.finetune:
+                    self.model = model
+                else: 
+                    self.__dict__["model"] = model
+
+                state_dict = clap_load_state_dict(clap_ckpt_path)
+                self.model.model.load_state_dict(state_dict, strict=False)
+
+                if self.finetune:
+                    self.model.model.audio_branch.requires_grad_(True)
+                    self.model.model.audio_branch.train()
+                else:
+                    self.model.model.audio_branch.requires_grad_(False)
+                    self.model.model.audio_branch.eval()
+
+            finally:
+                logging.disable(previous_level)
+
+        del self.model.model.text_branch
+
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def forward(self, audios: tp.Union[torch.Tensor, tp.List[torch.Tensor], tp.Tuple[torch.Tensor]] , device: tp.Any = "cuda") -> tp.Any:
+
+        self.model.to(device)
+
+        if isinstance(audios, list) or isinstance(audios, tuple):
+            audios = torch.cat(audios, dim=0)
+
+        # Convert to mono
+        mono_audios = audios.mean(dim=1)
+
+        with torch.cuda.amp.autocast(enabled=False):
+            audio_embedding = self.model.get_audio_embedding_from_data(mono_audios.float(), use_tensor=True)
+
+        audio_embedding = audio_embedding.unsqueeze(1).to(device)
+
+        return [self.proj_out(audio_embedding), torch.ones(audio_embedding.shape[0], 1).to(device)]
+
+class T5Conditioner(Conditioner):
+
+    T5_MODELS = ["t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b",
+              "google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large",
+              "google/flan-t5-xl", "google/flan-t5-xxl"]
+    
+    T5_MODEL_DIMS = {
+        "t5-small": 512,
+        "t5-base": 768,
+        "t5-large": 1024,
+        "t5-3b": 1024,
+        "t5-11b": 1024,
+        "t5-xl": 2048,
+        "t5-xxl": 4096,
+        "google/flan-t5-small": 512,
+        "google/flan-t5-base": 768,
+        "google/flan-t5-large": 1024,
+        "google/flan-t5-3b": 1024,
+        "google/flan-t5-11b": 1024,
+        "google/flan-t5-xl": 2048,
+        "google/flan-t5-xxl": 4096,
+    }
+
+    def __init__(
+            self,
+            output_dim: int,
+            t5_model_name: str = "t5-base",
+            max_length: str = 128,
+            enable_grad: bool = False,
+            project_out: bool = False,
+    ):
+        assert t5_model_name in self.T5_MODELS, f"Unknown T5 model name: {t5_model_name}"
+        super().__init__(self.T5_MODEL_DIMS[t5_model_name], output_dim, project_out=project_out)
+        
+        from transformers import T5EncoderModel, AutoTokenizer
+
+        self.max_length = max_length
+        self.enable_grad = enable_grad
+
+        # Suppress logging from transformers
+        previous_level = logging.root.manager.disable
+        logging.disable(logging.ERROR)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            try:
+                # self.tokenizer = T5Tokenizer.from_pretrained(t5_model_name, model_max_length = max_length)
+                # model = T5EncoderModel.from_pretrained(t5_model_name, max_length=max_length).train(enable_grad).requires_grad_(enable_grad)
+                self.tokenizer = AutoTokenizer.from_pretrained(t5_model_name)
+                model = T5EncoderModel.from_pretrained(t5_model_name).train(enable_grad).requires_grad_(enable_grad).to(torch.float16)
+            finally:
+                logging.disable(previous_level)
+            
+        if self.enable_grad:
+            self.model = model
+        else: 
+            self.__dict__["model"] = model
+
+
+    def forward(self, texts: tp.List[str], device: tp.Union[torch.device, str]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+        
+        self.model.to(device)
+        self.proj_out.to(device)
+
+        encoded = self.tokenizer(
+            texts,
+            truncation=True,
+            max_length=self.max_length,
+            padding="max_length",
+            return_tensors="pt",
+        )
+
+        input_ids = encoded["input_ids"].to(device)
+        attention_mask = encoded["attention_mask"].to(device).to(torch.bool)
+
+        self.model.eval()
+            
+        with torch.cuda.amp.autocast(dtype=torch.float16) and torch.set_grad_enabled(self.enable_grad):
+            embeddings = self.model(
+                input_ids=input_ids, attention_mask=attention_mask
+            )["last_hidden_state"]    
+            
+        embeddings = self.proj_out(embeddings.float())
+
+        embeddings = embeddings * attention_mask.unsqueeze(-1).float()
+
+        return embeddings, attention_mask
+    
+class PhonemeConditioner(Conditioner):
+    """
+    A conditioner that turns text into phonemes and embeds them using a lookup table
+    Only works for English text
+
+    Args:
+        output_dim: the dimension of the output embeddings
+        max_length: the maximum number of phonemes to embed
+        project_out: whether to add another linear projection to the output embeddings
+    """
+
+    def __init__(
+            self,
+            output_dim: int,
+            max_length: int = 1024,
+            project_out: bool = False,
+    ):
+        super().__init__(output_dim, output_dim, project_out=project_out)
+        
+        from g2p_en import G2p
+
+        self.max_length = max_length
+
+        self.g2p = G2p()
+
+        # Reserving 0 for padding, 1 for ignored
+        self.phoneme_embedder = nn.Embedding(len(self.g2p.phonemes) + 2, output_dim)
+
+    def forward(self, texts: tp.List[str], device: tp.Union[torch.device, str]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+        
+        self.phoneme_embedder.to(device)
+        self.proj_out.to(device)
+
+        batch_phonemes = [self.g2p(text) for text in texts] # shape [batch_size, length]
+        
+        phoneme_ignore = [" ", *string.punctuation]
+
+        # Remove ignored phonemes and cut to max length
+        batch_phonemes = [[p if p not in phoneme_ignore else "_" for p in phonemes] for phonemes in batch_phonemes]
+
+        # Convert to ids
+        phoneme_ids = [[self.g2p.p2idx[p] + 2 if p in self.g2p.p2idx else 1 for p in phonemes] for phonemes in batch_phonemes]
+
+        #Pad to match longest and make a mask tensor for the padding
+        longest = max([len(ids) for ids in phoneme_ids])
+        phoneme_ids = [ids + [0] * (longest - len(ids)) for ids in phoneme_ids]
+        
+        phoneme_ids = torch.tensor(phoneme_ids).to(device)
+
+        # Convert to embeddings
+        phoneme_embeds = self.phoneme_embedder(phoneme_ids)
+        
+        phoneme_embeds = self.proj_out(phoneme_embeds)
+
+        return phoneme_embeds, torch.ones(phoneme_embeds.shape[0], phoneme_embeds.shape[1]).to(device)
+  
+class TokenizerLUTConditioner(Conditioner):
+    """
+    A conditioner that embeds text using a lookup table on a pretrained tokenizer's vocabulary
+
+    Args:
+        tokenizer_name: the name of the tokenizer from the Hugging Face transformers library
+        output_dim: the dimension of the output embeddings
+        max_length: the maximum length of the text to embed
+        project_out: whether to add another linear projection to the output embeddings
+    """
+
+    def __init__(
+            self,
+            tokenizer_name: str, # Name of a tokenizer from the Hugging Face transformers library
+            output_dim: int,
+            max_length: int = 1024,
+            project_out: bool = False,
+    ):
+        super().__init__(output_dim, output_dim, project_out=project_out)
+        
+        from transformers import AutoTokenizer
+
+         # Suppress logging from transformers
+        previous_level = logging.root.manager.disable
+        logging.disable(logging.ERROR)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            try:
+                self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+            finally:
+                logging.disable(previous_level)
+
+        self.max_length = max_length
+
+        self.token_embedder = nn.Embedding(len(self.tokenizer), output_dim)
+
+    def forward(self, texts: tp.List[str], device: tp.Union[torch.device, str]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+        self.proj_out.to(device)
+
+        encoded = self.tokenizer(
+            texts,
+            truncation=True,
+            max_length=self.max_length,
+            padding="max_length",
+            return_tensors="pt",
+        )
+
+        input_ids = encoded["input_ids"].to(device)
+        attention_mask = encoded["attention_mask"].to(device).to(torch.bool)
+    
+        embeddings = self.token_embedder(input_ids)
+            
+        embeddings = self.proj_out(embeddings)
+
+        embeddings = embeddings * attention_mask.unsqueeze(-1).float()
+
+        return embeddings, attention_mask
+
+class PretransformConditioner(Conditioner):
+    """
+    A conditioner that uses a pretransform's encoder for conditioning
+
+    Args:
+        pretransform: an instantiated pretransform to use for conditioning
+        output_dim: the dimension of the output embeddings
+    """
+    def __init__(self, pretransform: Pretransform, output_dim: int):
+        super().__init__(pretransform.encoded_channels, output_dim)
+
+        self.pretransform = pretransform
+
+    def forward(self, audio: tp.Union[torch.Tensor, tp.List[torch.Tensor], tp.Tuple[torch.Tensor]], device: tp.Union[torch.device, str]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
+
+        self.pretransform.to(device)
+        self.proj_out.to(device)
+
+        if isinstance(audio, list) or isinstance(audio, tuple):
+            audio = torch.cat(audio, dim=0)
+
+        # Convert audio to pretransform input channels
+        audio = set_audio_channels(audio, self.pretransform.io_channels)
+        
+        latents = self.pretransform.encode(audio)
+
+        latents = self.proj_out(latents)
+
+        return [latents, torch.ones(latents.shape[0], latents.shape[2]).to(latents.device)]
+
+class MultiConditioner(nn.Module):
+    """
+    A module that applies multiple conditioners to an input dictionary based on the keys
+
+    Args:
+        conditioners: a dictionary of conditioners with keys corresponding to the keys of the conditioning input dictionary (e.g. "prompt")
+        default_keys: a dictionary of default keys to use if the key is not in the input dictionary (e.g. {"prompt_t5": "prompt"})
+    """
+    def __init__(self, conditioners: tp.Dict[str, Conditioner], default_keys: tp.Dict[str, str] = {}):
+        super().__init__()
+
+        self.conditioners = nn.ModuleDict(conditioners)
+        self.default_keys = default_keys
+
+    def forward(self, batch_metadata: tp.List[tp.Dict[str, tp.Any]], device: tp.Union[torch.device, str]) -> tp.Dict[str, tp.Any]:
+        output = {}
+
+        for key, conditioner in self.conditioners.items():
+            condition_key = key
+
+            conditioner_inputs = []
+
+            for x in batch_metadata:
+
+                if condition_key not in x:
+                    if condition_key in self.default_keys:
+                        condition_key = self.default_keys[condition_key]
+                    else:
+                        raise ValueError(f"Conditioner key {condition_key} not found in batch metadata")
+
+                #Unwrap the condition info if it's a single-element list or tuple, this is to support collation functions that wrap everything in a list
+                if isinstance(x[condition_key], list) or isinstance(x[condition_key], tuple) and len(x[condition_key]) == 1:
+                    conditioner_inputs.append(x[condition_key][0])
+                else:
+                    conditioner_inputs.append(x[condition_key])
+            
+            output[key] = conditioner(conditioner_inputs, device)
+
+        return output
+    
+def create_multi_conditioner_from_conditioning_config(config: tp.Dict[str, tp.Any]) -> MultiConditioner:
+    """
+    Create a MultiConditioner from a conditioning config dictionary
+
+    Args:
+        config: the conditioning config dictionary
+        device: the device to put the conditioners on
+    """
+    conditioners = {}
+    cond_dim = config["cond_dim"]
+    
+    default_keys = config.get("default_keys", {})
+
+    for conditioner_info in config["configs"]:
+        id = conditioner_info["id"]
+
+        conditioner_type = conditioner_info["type"]
+
+        conditioner_config = {"output_dim": cond_dim}
+        
+        conditioner_config.update(conditioner_info["config"])
+
+        if conditioner_type == "t5":
+            conditioners[id] = T5Conditioner(**conditioner_config)
+        elif conditioner_type == "clap_text":
+            conditioners[id] = CLAPTextConditioner(**conditioner_config)
+        elif conditioner_type == "clap_audio":
+            conditioners[id] = CLAPAudioConditioner(**conditioner_config)
+        elif conditioner_type == "int":
+            conditioners[id] = IntConditioner(**conditioner_config)
+        elif conditioner_type == "number":
+            conditioners[id] = NumberConditioner(**conditioner_config)
+        elif conditioner_type == "phoneme":
+            conditioners[id] = PhonemeConditioner(**conditioner_config)
+        elif conditioner_type == "lut":
+            conditioners[id] = TokenizerLUTConditioner(**conditioner_config)
+        elif conditioner_type == "pretransform":
+            sample_rate = conditioner_config.pop("sample_rate", None)
+            assert sample_rate is not None, "Sample rate must be specified for pretransform conditioners"
+
+            pretransform = create_pretransform_from_config(conditioner_config.pop("pretransform_config"), sample_rate=sample_rate)
+
+            if conditioner_config.get("pretransform_ckpt_path", None) is not None:
+                pretransform.load_state_dict(load_ckpt_state_dict(conditioner_config.pop("pretransform_ckpt_path")))
+
+            conditioners[id] = PretransformConditioner(pretransform, **conditioner_config)
+        else:
+            raise ValueError(f"Unknown conditioner type: {conditioner_type}")
+
+    return MultiConditioner(conditioners, default_keys=default_keys)

stable_audio_tools/models/diffusion.py

@@ -0,0 +1,678 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+from functools import partial, reduce
+import numpy as np
+import typing as tp
+
+from .blocks import ResConvBlock, FourierFeatures, Upsample1d, Upsample1d_2, Downsample1d, Downsample1d_2, SelfAttention1d, SkipBlock, expand_to_planes
+from .conditioners import MultiConditioner, create_multi_conditioner_from_conditioning_config
+from .dit import DiffusionTransformer
+from .factory import create_pretransform_from_config
+from .pretransforms import Pretransform
+from ..inference.generation import generate_diffusion_cond
+
+from .adp import UNetCFG1d, UNet1d
+
+from time import time
+
+class Profiler:
+
+    def __init__(self):
+        self.ticks = [[time(), None]]
+
+    def tick(self, msg):
+        self.ticks.append([time(), msg])
+
+    def __repr__(self):
+        rep = 80 * "=" + "\n"
+        for i in range(1, len(self.ticks)):
+            msg = self.ticks[i][1]
+            ellapsed = self.ticks[i][0] - self.ticks[i - 1][0]
+            rep += msg + f": {ellapsed*1000:.2f}ms\n"
+        rep += 80 * "=" + "\n\n\n"
+        return rep
+
+class DiffusionModel(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, x, t, **kwargs):
+        raise NotImplementedError()
+
+class DiffusionModelWrapper(nn.Module):
+    def __init__(
+                self,
+                model: DiffusionModel,
+                io_channels,
+                sample_size,
+                sample_rate,
+                min_input_length,
+                pretransform: tp.Optional[Pretransform] = None,
+    ):
+        super().__init__()
+        self.io_channels = io_channels
+        self.sample_size = sample_size
+        self.sample_rate = sample_rate
+        self.min_input_length = min_input_length
+
+        self.model = model
+
+        if pretransform is not None:
+            self.pretransform = pretransform
+        else:
+            self.pretransform = None
+
+    def forward(self, x, t, **kwargs):
+        return self.model(x, t, **kwargs)
+
+class ConditionedDiffusionModel(nn.Module):
+    def __init__(self,
+                *args,
+                supports_cross_attention: bool = False,
+                supports_input_concat: bool = False,
+                supports_global_cond: bool = False,
+                supports_prepend_cond: bool = False,
+                **kwargs):
+        super().__init__(*args, **kwargs)
+        self.supports_cross_attention = supports_cross_attention
+        self.supports_input_concat = supports_input_concat
+        self.supports_global_cond = supports_global_cond
+        self.supports_prepend_cond = supports_prepend_cond
+
+    def forward(self,
+                x: torch.Tensor,
+                t: torch.Tensor,
+                cross_attn_cond: torch.Tensor = None,
+                cross_attn_mask: torch.Tensor = None,
+                input_concat_cond: torch.Tensor = None,
+                global_embed: torch.Tensor = None,
+                prepend_cond: torch.Tensor = None,
+                prepend_cond_mask: torch.Tensor = None,
+                cfg_scale: float = 1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = False,
+                rescale_cfg: bool = False,
+                **kwargs):
+        raise NotImplementedError()
+
+class ConditionedDiffusionModelWrapper(nn.Module):
+    """
+    A diffusion model that takes in conditioning
+    """
+    def __init__(
+            self,
+            model: ConditionedDiffusionModel,
+            conditioner: MultiConditioner,
+            io_channels,
+            sample_rate,
+            min_input_length: int,
+            pretransform: tp.Optional[Pretransform] = None,
+            cross_attn_cond_ids: tp.List[str] = [],
+            global_cond_ids: tp.List[str] = [],
+            input_concat_ids: tp.List[str] = [],
+            prepend_cond_ids: tp.List[str] = [],
+            ):
+        super().__init__()
+
+        self.model = model
+        self.conditioner = conditioner
+        self.io_channels = io_channels
+        self.sample_rate = sample_rate
+        self.pretransform = pretransform
+        self.cross_attn_cond_ids = cross_attn_cond_ids
+        self.global_cond_ids = global_cond_ids
+        self.input_concat_ids = input_concat_ids
+        self.prepend_cond_ids = prepend_cond_ids
+        self.min_input_length = min_input_length
+
+    def get_conditioning_inputs(self, cond: tp.Dict[str, tp.Any], negative=False):
+        cross_attention_input = None
+        cross_attention_masks = None
+        global_cond = None
+        input_concat_cond = None
+        prepend_cond = None
+        prepend_cond_mask = None
+
+        if len(self.cross_attn_cond_ids) > 0:
+            # Concatenate all cross-attention inputs over the sequence dimension
+            # Assumes that the cross-attention inputs are of shape (batch, seq, channels)
+            cross_attention_input = []
+            cross_attention_masks = []
+
+            for key in self.cross_attn_cond_ids:
+                cross_attn_in, cross_attn_mask = cond[key]
+
+                # Add sequence dimension if it's not there
+                if len(cross_attn_in.shape) == 2:
+                    cross_attn_in = cross_attn_in.unsqueeze(1)
+                    cross_attn_mask = cross_attn_mask.unsqueeze(1)
+
+                cross_attention_input.append(cross_attn_in)
+                cross_attention_masks.append(cross_attn_mask)
+
+            cross_attention_input = torch.cat(cross_attention_input, dim=1)
+            cross_attention_masks = torch.cat(cross_attention_masks, dim=1)
+
+        if len(self.global_cond_ids) > 0:
+            # Concatenate all global conditioning inputs over the channel dimension
+            # Assumes that the global conditioning inputs are of shape (batch, channels)
+            global_cond = torch.cat([cond[key][0] for key in self.global_cond_ids], dim=-1)
+            if len(global_cond.shape) == 3:
+                global_cond = global_cond.squeeze(1)
+
+        if 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([cond[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_cond = torch.cat([cond[key][0] for key in self.prepend_cond_ids], dim=1)
+            prepend_cond_mask = torch.cat([cond[key][1] for key in self.prepend_cond_ids], dim=1)
+
+        if negative:
+            return {
+                "negative_cross_attn_cond": cross_attention_input,
+                "negative_cross_attn_mask": cross_attention_masks,
+                "negative_global_cond": global_cond,
+                "negative_input_concat_cond": input_concat_cond
+            }
+        else:
+            return {
+                "cross_attn_cond": cross_attention_input,
+                "cross_attn_mask": cross_attention_masks,
+                "global_cond": global_cond,
+                "input_concat_cond": input_concat_cond,
+                "prepend_cond": prepend_cond,
+                "prepend_cond_mask": prepend_cond_mask
+            }
+
+    def forward(self, x: torch.Tensor, t: torch.Tensor, cond: tp.Dict[str, tp.Any], **kwargs):
+        return self.model(x, t, **self.get_conditioning_inputs(cond), **kwargs)
+
+    def generate(self, *args, **kwargs):
+        return generate_diffusion_cond(self, *args, **kwargs)
+
+class UNetCFG1DWrapper(ConditionedDiffusionModel):
+    def __init__(
+        self,
+        *args,
+        **kwargs
+    ):
+        super().__init__(supports_cross_attention=True, supports_global_cond=True, supports_input_concat=True)
+
+        self.model = UNetCFG1d(*args, **kwargs)
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self,
+                x,
+                t,
+                cross_attn_cond=None,
+                cross_attn_mask=None,
+                input_concat_cond=None,
+                global_cond=None,
+                cfg_scale=1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = False,
+                rescale_cfg: bool = False,
+                negative_cross_attn_cond=None,
+                negative_cross_attn_mask=None,
+                negative_global_cond=None,
+                negative_input_concat_cond=None,
+                prepend_cond=None,
+                prepend_cond_mask=None,
+                **kwargs):
+        p = Profiler()
+
+        p.tick("start")
+
+        channels_list = None
+        if input_concat_cond is not None:
+            channels_list = [input_concat_cond]
+
+        outputs = self.model(
+            x,
+            t,
+            embedding=cross_attn_cond,
+            embedding_mask=cross_attn_mask,
+            features=global_cond,
+            channels_list=channels_list,
+            embedding_scale=cfg_scale,
+            embedding_mask_proba=cfg_dropout_prob,
+            batch_cfg=batch_cfg,
+            rescale_cfg=rescale_cfg,
+            negative_embedding=negative_cross_attn_cond,
+            negative_embedding_mask=negative_cross_attn_mask,
+            **kwargs)
+
+        p.tick("UNetCFG1D forward")
+
+        #print(f"Profiler: {p}")
+        return outputs
+
+class UNet1DCondWrapper(ConditionedDiffusionModel):
+    def __init__(
+        self,
+        *args,
+        **kwargs
+    ):
+        super().__init__(supports_cross_attention=False, supports_global_cond=True, supports_input_concat=True)
+
+        self.model = UNet1d(*args, **kwargs)
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self,
+                x,
+                t,
+                input_concat_cond=None,
+                global_cond=None,
+                cross_attn_cond=None,
+                cross_attn_mask=None,
+                prepend_cond=None,
+                prepend_cond_mask=None,
+                cfg_scale=1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = False,
+                rescale_cfg: bool = False,
+                negative_cross_attn_cond=None,
+                negative_cross_attn_mask=None,
+                negative_global_cond=None,
+                negative_input_concat_cond=None,
+                **kwargs):
+
+        channels_list = None
+        if input_concat_cond is not None:
+
+            # Interpolate input_concat_cond to the same length as x
+            if input_concat_cond.shape[2] != x.shape[2]:
+                input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest')
+
+            channels_list = [input_concat_cond]
+
+        outputs = self.model(
+            x,
+            t,
+            features=global_cond,
+            channels_list=channels_list,
+            **kwargs)
+
+        return outputs
+
+class UNet1DUncondWrapper(DiffusionModel):
+    def __init__(
+        self,
+        in_channels,
+        *args,
+        **kwargs
+    ):
+        super().__init__()
+
+        self.model = UNet1d(in_channels=in_channels, *args, **kwargs)
+
+        self.io_channels = in_channels
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self, x, t, **kwargs):
+        return self.model(x, t, **kwargs)
+
+class DAU1DCondWrapper(ConditionedDiffusionModel):
+    def __init__(
+        self,
+        *args,
+        **kwargs
+    ):
+        super().__init__(supports_cross_attention=False, supports_global_cond=False, supports_input_concat=True)
+
+        self.model = DiffusionAttnUnet1D(*args, **kwargs)
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self,
+                x,
+                t,
+                input_concat_cond=None,
+                cross_attn_cond=None,
+                cross_attn_mask=None,
+                global_cond=None,
+                cfg_scale=1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = False,
+                rescale_cfg: bool = False,
+                negative_cross_attn_cond=None,
+                negative_cross_attn_mask=None,
+                negative_global_cond=None,
+                negative_input_concat_cond=None,
+                prepend_cond=None,
+                **kwargs):
+
+        return self.model(x, t, cond = input_concat_cond)
+
+class DiffusionAttnUnet1D(nn.Module):
+    def __init__(
+        self,
+        io_channels = 2,
+        depth=14,
+        n_attn_layers = 6,
+        channels = [128, 128, 256, 256] + [512] * 10,
+        cond_dim = 0,
+        cond_noise_aug = False,
+        kernel_size = 5,
+        learned_resample = False,
+        strides = [2] * 13,
+        conv_bias = True,
+        use_snake = False
+    ):
+        super().__init__()
+
+        self.cond_noise_aug = cond_noise_aug
+
+        self.io_channels = io_channels
+
+        if self.cond_noise_aug:
+            self.rng = torch.quasirandom.SobolEngine(1, scramble=True)
+
+        self.timestep_embed = FourierFeatures(1, 16)
+
+        attn_layer = depth - n_attn_layers
+
+        strides = [1] + strides
+
+        block = nn.Identity()
+
+        conv_block = partial(ResConvBlock, kernel_size=kernel_size, conv_bias = conv_bias, use_snake=use_snake)
+
+        for i in range(depth, 0, -1):
+            c = channels[i - 1]
+            stride = strides[i-1]
+            if stride > 2 and not learned_resample:
+                raise ValueError("Must have stride 2 without learned resampling")
+
+            if i > 1:
+                c_prev = channels[i - 2]
+                add_attn = i >= attn_layer and n_attn_layers > 0
+                block = SkipBlock(
+                    Downsample1d_2(c_prev, c_prev, stride) if (learned_resample or stride == 1) else Downsample1d("cubic"),
+                    conv_block(c_prev, c, c),
+                    SelfAttention1d(
+                        c, c // 32) if add_attn else nn.Identity(),
+                    conv_block(c, c, c),
+                    SelfAttention1d(
+                        c, c // 32) if add_attn else nn.Identity(),
+                    conv_block(c, c, c),
+                    SelfAttention1d(
+                        c, c // 32) if add_attn else nn.Identity(),
+                    block,
+                    conv_block(c * 2 if i != depth else c, c, c),
+                    SelfAttention1d(
+                        c, c // 32) if add_attn else nn.Identity(),
+                    conv_block(c, c, c),
+                    SelfAttention1d(
+                        c, c // 32) if add_attn else nn.Identity(),
+                    conv_block(c, c, c_prev),
+                    SelfAttention1d(c_prev, c_prev //
+                                    32) if add_attn else nn.Identity(),
+                    Upsample1d_2(c_prev, c_prev, stride) if learned_resample else Upsample1d(kernel="cubic")
+                )
+            else:
+                cond_embed_dim = 16 if not self.cond_noise_aug else 32
+                block = nn.Sequential(
+                    conv_block((io_channels + cond_dim) + cond_embed_dim, c, c),
+                    conv_block(c, c, c),
+                    conv_block(c, c, c),
+                    block,
+                    conv_block(c * 2, c, c),
+                    conv_block(c, c, c),
+                    conv_block(c, c, io_channels, is_last=True),
+                )
+        self.net = block
+
+        with torch.no_grad():
+            for param in self.net.parameters():
+                param *= 0.5
+
+    def forward(self, x, t, cond=None, cond_aug_scale=None):
+
+        timestep_embed = expand_to_planes(self.timestep_embed(t[:, None]), x.shape)
+
+        inputs = [x, timestep_embed]
+
+        if cond is not None:
+            if cond.shape[2] != x.shape[2]:
+                cond = F.interpolate(cond, (x.shape[2], ), mode='linear', align_corners=False)
+
+            if self.cond_noise_aug:
+                # Get a random number between 0 and 1, uniformly sampled
+                if cond_aug_scale is None:
+                    aug_level = self.rng.draw(cond.shape[0])[:, 0].to(cond)
+                else:
+                    aug_level = torch.tensor([cond_aug_scale]).repeat([cond.shape[0]]).to(cond)
+
+                # Add noise to the conditioning signal
+                cond = cond + torch.randn_like(cond) * aug_level[:, None, None]
+
+                # Get embedding for noise cond level, reusing timestamp_embed
+                aug_level_embed = expand_to_planes(self.timestep_embed(aug_level[:, None]), x.shape)
+
+                inputs.append(aug_level_embed)
+
+            inputs.append(cond)
+
+        outputs = self.net(torch.cat(inputs, dim=1))
+
+        return outputs
+
+class DiTWrapper(ConditionedDiffusionModel):
+    def __init__(
+        self,
+        *args,
+        **kwargs
+    ):
+        super().__init__(supports_cross_attention=True, supports_global_cond=False, supports_input_concat=False)
+
+        self.model = DiffusionTransformer(*args, **kwargs)
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self,
+                x,
+                t,
+                cross_attn_cond=None,
+                cross_attn_mask=None,
+                negative_cross_attn_cond=None,
+                negative_cross_attn_mask=None,
+                input_concat_cond=None,
+                negative_input_concat_cond=None,
+                global_cond=None,
+                negative_global_cond=None,
+                prepend_cond=None,
+                prepend_cond_mask=None,
+                cfg_scale=1.0,
+                cfg_dropout_prob: float = 0.0,
+                batch_cfg: bool = True,
+                rescale_cfg: bool = False,
+                scale_phi: float = 0.0,
+                **kwargs):
+
+        assert batch_cfg, "batch_cfg must be True for DiTWrapper"
+        assert negative_input_concat_cond is None, "negative_input_concat_cond is not supported for DiTWrapper"
+
+        return self.model(
+            x,
+            t,
+            cross_attn_cond=cross_attn_cond,
+            cross_attn_cond_mask=cross_attn_mask,
+            negative_cross_attn_cond=negative_cross_attn_cond,
+            negative_cross_attn_mask=negative_cross_attn_mask,
+            input_concat_cond=input_concat_cond,
+            prepend_cond=prepend_cond,
+            prepend_cond_mask=prepend_cond_mask,
+            cfg_scale=cfg_scale,
+            cfg_dropout_prob=cfg_dropout_prob,
+            scale_phi=scale_phi,
+            global_embed=global_cond,
+            **kwargs)
+
+class DiTUncondWrapper(DiffusionModel):
+    def __init__(
+        self,
+        in_channels,
+        *args,
+        **kwargs
+    ):
+        super().__init__()
+
+        self.model = DiffusionTransformer(io_channels=in_channels, *args, **kwargs)
+
+        self.io_channels = in_channels
+
+        with torch.no_grad():
+            for param in self.model.parameters():
+                param *= 0.5
+
+    def forward(self, x, t, **kwargs):
+        return self.model(x, t, **kwargs)
+
+def create_diffusion_uncond_from_config(config: tp.Dict[str, tp.Any]):
+    diffusion_uncond_config = config["model"]
+
+    model_type = diffusion_uncond_config.get('type', None)
+
+    diffusion_config = diffusion_uncond_config.get('config', {})
+
+    assert model_type is not None, "Must specify model type in config"
+
+    pretransform = diffusion_uncond_config.get("pretransform", None)
+
+    sample_size = config.get("sample_size", None)
+    assert sample_size is not None, "Must specify sample size in config"
+
+    sample_rate = config.get("sample_rate", None)
+    assert sample_rate is not None, "Must specify sample rate in config"
+
+    if pretransform is not None:
+        pretransform = create_pretransform_from_config(pretransform, sample_rate)
+        min_input_length = pretransform.downsampling_ratio
+    else:
+        min_input_length = 1
+
+    if model_type == 'DAU1d':
+
+        model = DiffusionAttnUnet1D(
+            **diffusion_config
+        )
+    
+    elif model_type == "adp_uncond_1d":
+
+        model = UNet1DUncondWrapper(
+            **diffusion_config
+        )
+
+    elif model_type == "dit":
+        model = DiTUncondWrapper(
+            **diffusion_config
+        )
+
+    else:
+        raise NotImplementedError(f'Unknown model type: {model_type}')
+
+    return DiffusionModelWrapper(model,
+                                io_channels=model.io_channels,
+                                sample_size=sample_size,
+                                sample_rate=sample_rate,
+                                pretransform=pretransform,
+                                min_input_length=min_input_length)
+
+def create_diffusion_cond_from_config(config: tp.Dict[str, tp.Any]):
+
+    model_config = config["model"]
+
+    model_type = config["model_type"]
+
+    diffusion_config = model_config.get('diffusion', None)
+    assert diffusion_config is not None, "Must specify diffusion config"
+
+    diffusion_model_type = diffusion_config.get('type', None)
+    assert diffusion_model_type is not None, "Must specify diffusion model type"
+
+    diffusion_model_config = diffusion_config.get('config', None)
+    assert diffusion_model_config is not None, "Must specify diffusion model config"
+
+    if diffusion_model_type == 'adp_cfg_1d':
+        diffusion_model = UNetCFG1DWrapper(**diffusion_model_config)
+    elif diffusion_model_type == 'adp_1d':
+        diffusion_model = UNet1DCondWrapper(**diffusion_model_config)
+    elif diffusion_model_type == 'dit':
+        diffusion_model = DiTWrapper(**diffusion_model_config)
+
+    io_channels = model_config.get('io_channels', None)
+    assert io_channels is not None, "Must specify io_channels in model config"
+
+    sample_rate = config.get('sample_rate', None)
+    assert sample_rate is not None, "Must specify sample_rate in config"
+
+    conditioning_config = model_config.get('conditioning', None)
+
+    conditioner = None
+    if conditioning_config is not None:
+        conditioner = create_multi_conditioner_from_conditioning_config(conditioning_config)
+
+    cross_attention_ids = diffusion_config.get('cross_attention_cond_ids', [])
+    global_cond_ids = diffusion_config.get('global_cond_ids', [])
+    input_concat_ids = diffusion_config.get('input_concat_ids', [])
+    prepend_cond_ids = diffusion_config.get('prepend_cond_ids', [])
+
+    pretransform = model_config.get("pretransform", None)
+
+    if pretransform is not None:
+        pretransform = create_pretransform_from_config(pretransform, sample_rate)
+        min_input_length = pretransform.downsampling_ratio
+    else:
+        min_input_length = 1
+
+    if diffusion_model_type == "adp_cfg_1d" or diffusion_model_type == "adp_1d":
+        min_input_length *= np.prod(diffusion_model_config["factors"])
+    elif diffusion_model_type == "dit":
+        min_input_length *= diffusion_model.model.patch_size
+
+    # Get the proper wrapper class
+
+    if model_type == "diffusion_cond" or model_type == "diffusion_cond_inpaint":
+        wrapper_fn = ConditionedDiffusionModelWrapper
+    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
+        elif prior_type == "source_separation":
+            from .diffusion_prior import SourceSeparationDiffusionPrior
+            wrapper_fn = SourceSeparationDiffusionPrior
+
+    return wrapper_fn(
+        diffusion_model,
+        conditioner,
+        min_input_length=min_input_length,
+        sample_rate=sample_rate,
+        cross_attn_cond_ids=cross_attention_ids,
+        global_cond_ids=global_cond_ids,
+        input_concat_ids=input_concat_ids,
+        prepend_cond_ids=prepend_cond_ids,
+        pretransform=pretransform,
+        io_channels=io_channels
+    )

stable_audio_tools/models/diffusion_prior.py

@@ -0,0 +1,151 @@
+from enum import Enum
+import typing as tp
+
+from .diffusion import ConditionedDiffusionModelWrapper
+from ..inference.generation import generate_diffusion_cond
+from ..inference.utils import prepare_audio
+
+import torch
+from torch.nn import functional as F
+from torchaudio import transforms as T
+
+# Define prior types enum
+class PriorType(Enum):
+    MonoToStereo = 1
+    SourceSeparation = 2
+
+class DiffusionPrior(ConditionedDiffusionModelWrapper):
+    def __init__(self, *args, prior_type: PriorType=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.prior_type = prior_type  
+
+class MonoToStereoDiffusionPrior(DiffusionPrior):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, prior_type=PriorType.MonoToStereo, **kwargs)
+
+    def stereoize(
+        self, 
+        audio: torch.Tensor, # (batch, channels, time)
+        in_sr: int,
+        steps: int,
+        sampler_kwargs: dict = {},
+    ):
+        """
+        Generate stereo audio from mono audio using a pre-trained diffusion prior
+
+        Args:
+            audio: The mono audio to convert to stereo
+            in_sr: The sample rate of the input audio
+            steps: The number of diffusion steps to run
+            sampler_kwargs: Keyword arguments to pass to the diffusion sampler
+        """
+
+        device = audio.device
+
+        sample_rate = self.sample_rate
+
+        # Resample input audio if necessary
+        if in_sr != sample_rate:
+            resample_tf = T.Resample(in_sr, sample_rate).to(audio.device)
+            audio = resample_tf(audio)
+
+        audio_length = audio.shape[-1]
+
+        # Pad input audio to be compatible with the model
+        min_length = self.min_input_length
+        padded_input_length = audio_length + (min_length - (audio_length % min_length)) % min_length
+
+        # Pad input audio to be compatible with the model
+        if padded_input_length > audio_length:
+            audio = F.pad(audio, (0, padded_input_length - audio_length))
+
+        # Make audio mono, duplicate to stereo
+        dual_mono = audio.mean(1, keepdim=True).repeat(1, 2, 1)
+
+        if self.pretransform is not None:
+            dual_mono = self.pretransform.encode(dual_mono)
+
+        conditioning = {"source": [dual_mono]}
+
+        stereo_audio = generate_diffusion_cond(
+            self, 
+            conditioning_tensors=conditioning,
+            steps=steps,
+            sample_size=padded_input_length,
+            sample_rate=sample_rate,
+            device=device,
+            **sampler_kwargs,
+        ) 
+
+        return stereo_audio
+
+
+class SourceSeparationDiffusionPrior(DiffusionPrior):
+    """
+    A diffusion prior model made for conditioned source separation
+    """
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, prior_type=PriorType.SourceSeparation, **kwargs)
+
+    def separate(
+        self, 
+        mixed_audio: torch.Tensor, # (batch, channels, time)
+        in_sr: int,
+        steps: int,
+        conditioning: dict = None,
+        conditioning_tensors: tp.Optional[dict] = None,
+        sampler_kwargs: dict = {},
+    ):
+        """
+        Separate audio sources based on conditioning using a pre-trained diffusion prior
+
+        Args:
+            mixed_audio: The mixed audio to separate
+            in_sr: The sample rate of the input audio
+            steps: The number of diffusion steps to run
+            conditioning: The conditioning to use for source separation
+            conditioning_tensors: Pre-computed conditioning tensors to use for source separation. If provided, conditioning is ignored.
+            sampler_kwargs: Keyword arguments to pass to the diffusion sampler
+        """
+
+        device = mixed_audio.device
+
+        sample_rate = self.sample_rate
+
+        # Resample input audio if necessary
+        if in_sr != sample_rate:
+            resample_tf = T.Resample(in_sr, sample_rate).to(mixed_audio.device)
+            mixed_audio = resample_tf(mixed_audio)
+
+        audio_length = mixed_audio.shape[-1]
+
+        # Pad input audio to be compatible with the model
+        min_length = self.min_input_length
+        padded_input_length = audio_length + (min_length - (audio_length % min_length)) % min_length
+
+        # Pad input audio to be compatible with the model
+        if padded_input_length > audio_length:
+            mixed_audio = F.pad(mixed_audio, (0, padded_input_length - audio_length))
+
+        if self.pretransform is not None:
+            mixed_audio = self.pretransform.encode(mixed_audio)
+
+        # Conditioning
+        assert conditioning is not None or conditioning_tensors is not None, "Must provide either conditioning or conditioning_tensors for conditioned source separation"
+        if conditioning_tensors is None:
+            conditioning_tensors = self.conditioner(conditioning, device)
+        
+        # Pass in the mixture audio as conditioning
+        conditioning_tensors["source"] = [mixed_audio]
+
+        stereo_audio = generate_diffusion_cond(
+            self, 
+            conditioning_tensors=conditioning_tensors,
+            steps=steps,
+            sample_size=padded_input_length,
+            sample_rate=sample_rate,
+            device=device,
+            **sampler_kwargs,
+        ) 
+
+        return stereo_audio

stable_audio_tools/models/discriminators.py

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

stable_audio_tools/models/dit.py

@@ -0,0 +1,358 @@
+import typing as tp
+
+import torch
+
+from einops import rearrange
+from torch import nn
+from torch.nn import functional as F
+from x_transformers import ContinuousTransformerWrapper, Encoder
+
+from .blocks import FourierFeatures
+from .transformer import ContinuousTransformer        
+
+class DiffusionTransformer(nn.Module):
+    def __init__(self, 
+        io_channels=32, 
+        patch_size=1,
+        embed_dim=768,
+        cond_token_dim=0,
+        project_cond_tokens=True,
+        global_cond_dim=0,
+        input_concat_dim=0,
+        prepend_cond_dim=0,
+        depth=12,
+        num_heads=8,
+        transformer_type: tp.Literal["x-transformers", "continuous_transformer"] = "x-transformers",
+        global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
+        **kwargs):
+
+        super().__init__()
+        
+        self.cond_token_dim = cond_token_dim
+
+        # Timestep embeddings
+        timestep_features_dim = 256
+
+        self.timestep_features = FourierFeatures(1, timestep_features_dim)
+
+        self.to_timestep_embed = nn.Sequential(
+            nn.Linear(timestep_features_dim, embed_dim, bias=True),
+            nn.SiLU(),
+            nn.Linear(embed_dim, embed_dim, bias=True),
+        )
+
+        if cond_token_dim > 0:
+            # Conditioning tokens
+
+            cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim
+            self.to_cond_embed = nn.Sequential(
+                nn.Linear(cond_token_dim, cond_embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(cond_embed_dim, cond_embed_dim, bias=False)
+            )
+        else:
+            cond_embed_dim = 0
+
+        if global_cond_dim > 0:
+            # Global conditioning
+            self.to_global_embed = nn.Sequential(
+                nn.Linear(global_cond_dim, embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(embed_dim, embed_dim, bias=False)
+            )
+
+        if prepend_cond_dim > 0:
+            # Prepend conditioning
+            self.to_prepend_embed = nn.Sequential(
+                nn.Linear(prepend_cond_dim, embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(embed_dim, embed_dim, bias=False)
+            )
+
+        self.input_concat_dim = input_concat_dim
+
+        dim_in = io_channels + self.input_concat_dim
+
+        self.patch_size = patch_size
+
+        # Transformer
+
+        self.transformer_type = transformer_type
+
+        self.global_cond_type = global_cond_type
+
+        if self.transformer_type == "x-transformers":
+            self.transformer = ContinuousTransformerWrapper(
+                dim_in=dim_in * patch_size,
+                dim_out=io_channels * patch_size,
+                max_seq_len=0, #Not relevant without absolute positional embeds
+                attn_layers = Encoder(
+                    dim=embed_dim,
+                    depth=depth,
+                    heads=num_heads,
+                    attn_flash = True,
+                    cross_attend = cond_token_dim > 0,
+                    dim_context=None if cond_embed_dim == 0 else cond_embed_dim,
+                    zero_init_branch_output=True,
+                    use_abs_pos_emb = False,
+                    rotary_pos_emb=True,
+                    ff_swish = True,
+                    ff_glu = True,
+                    **kwargs
+                )
+            )
+
+        elif self.transformer_type == "continuous_transformer":
+
+            global_dim = None
+
+            if self.global_cond_type == "adaLN":
+                # The global conditioning is projected to the embed_dim already at this point
+                global_dim = embed_dim
+
+            self.transformer = ContinuousTransformer(
+                dim=embed_dim,
+                depth=depth,
+                dim_heads=embed_dim // num_heads,
+                dim_in=dim_in * patch_size,
+                dim_out=io_channels * patch_size,
+                cross_attend = cond_token_dim > 0,
+                cond_token_dim = cond_embed_dim,
+                global_cond_dim=global_dim,
+                **kwargs
+            )
+             
+        else:
+            raise ValueError(f"Unknown transformer type: {self.transformer_type}")
+
+        self.preprocess_conv = nn.Conv1d(dim_in, dim_in, 1, bias=False)
+        nn.init.zeros_(self.preprocess_conv.weight)
+        self.postprocess_conv = nn.Conv1d(io_channels, io_channels, 1, bias=False)
+        nn.init.zeros_(self.postprocess_conv.weight)
+
+    def _forward(
+        self, 
+        x, 
+        t, 
+        mask=None,
+        cross_attn_cond=None,
+        cross_attn_cond_mask=None,
+        input_concat_cond=None,
+        global_embed=None,
+        prepend_cond=None,
+        prepend_cond_mask=None,
+        **kwargs):
+
+        if cross_attn_cond is not None:
+            cross_attn_cond = self.to_cond_embed(cross_attn_cond)
+
+        if global_embed is not None:
+            # Project the global conditioning to the embedding dimension
+            global_embed = self.to_global_embed(global_embed)
+
+        prepend_inputs = None 
+        prepend_mask = None
+        prepend_length = 0
+        if prepend_cond is not None:
+            # Project the prepend conditioning to the embedding dimension
+            prepend_cond = self.to_prepend_embed(prepend_cond)
+            
+            prepend_inputs = prepend_cond
+            if prepend_cond_mask is not None:
+                prepend_mask = prepend_cond_mask
+
+        if input_concat_cond is not None:
+
+            # Interpolate input_concat_cond to the same length as x
+            if input_concat_cond.shape[2] != x.shape[2]:
+                input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest')
+
+            x = torch.cat([x, input_concat_cond], dim=1)
+
+        # Get the batch of timestep embeddings
+        timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None])) # (b, embed_dim)
+
+        # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists
+        if global_embed is not None:
+            global_embed = global_embed + timestep_embed
+        else:
+            global_embed = timestep_embed
+
+        # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer
+        if self.global_cond_type == "prepend":
+            if prepend_inputs is None:
+                # Prepend inputs are just the global embed, and the mask is all ones
+                prepend_inputs = global_embed.unsqueeze(1)
+                prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)
+            else:
+                # Prepend inputs are the prepend conditioning + the global embed
+                prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1)
+                prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1)
+
+            prepend_length = prepend_inputs.shape[1]
+
+        x = self.preprocess_conv(x) + x
+
+        x = rearrange(x, "b c t -> b t c")
+
+        extra_args = {}
+
+        if self.global_cond_type == "adaLN":
+            extra_args["global_cond"] = global_embed
+
+        if self.patch_size > 1:
+            x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size)
+
+        if self.transformer_type == "x-transformers" or self.transformer_type == "continuous_transformer":
+            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **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
+
+        return output
+
+    def forward(
+        self, 
+        x, 
+        t, 
+        cross_attn_cond=None,
+        cross_attn_cond_mask=None,
+        negative_cross_attn_cond=None,
+        negative_cross_attn_mask=None,
+        input_concat_cond=None,
+        global_embed=None,
+        negative_global_embed=None,
+        prepend_cond=None,
+        prepend_cond_mask=None,
+        cfg_scale=1.0,
+        cfg_dropout_prob=0.0,
+        causal=False,
+        scale_phi=0.0,
+        mask=None,
+        **kwargs):
+
+        assert causal == False, "Causal mode is not supported for DiffusionTransformer"
+
+        if cross_attn_cond_mask is not None:
+            cross_attn_cond_mask = cross_attn_cond_mask.bool()
+
+            cross_attn_cond_mask = None # Temporarily disabling conditioning masks due to kernel issue for flash attention
+
+        if prepend_cond_mask is not None:
+            prepend_cond_mask = prepend_cond_mask.bool()
+
+        # CFG dropout
+        if cfg_dropout_prob > 0.0:
+            if cross_attn_cond is not None:
+                null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)
+                dropout_mask = torch.bernoulli(torch.full((cross_attn_cond.shape[0], 1, 1), cfg_dropout_prob, device=cross_attn_cond.device)).to(torch.bool)
+                cross_attn_cond = torch.where(dropout_mask, null_embed, cross_attn_cond)
+
+            if prepend_cond is not None:
+                null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)
+                dropout_mask = torch.bernoulli(torch.full((prepend_cond.shape[0], 1, 1), cfg_dropout_prob, device=prepend_cond.device)).to(torch.bool)
+                prepend_cond = torch.where(dropout_mask, null_embed, prepend_cond)
+
+
+        if cfg_scale != 1.0 and (cross_attn_cond is not None or prepend_cond is not None):
+            # Classifier-free guidance
+            # Concatenate conditioned and unconditioned inputs on the batch dimension            
+            batch_inputs = torch.cat([x, x], dim=0)
+            batch_timestep = torch.cat([t, t], dim=0)
+
+            if global_embed is not None:
+                batch_global_cond = torch.cat([global_embed, global_embed], dim=0)
+            else:
+                batch_global_cond = None
+
+            if input_concat_cond is not None:
+                batch_input_concat_cond = torch.cat([input_concat_cond, input_concat_cond], dim=0)
+            else:
+                batch_input_concat_cond = None
+
+            batch_cond = None
+            batch_cond_masks = None
+            
+            # Handle CFG for cross-attention conditioning
+            if cross_attn_cond is not None:
+
+                null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)
+
+                # For negative cross-attention conditioning, replace the null embed with the negative cross-attention conditioning
+                if negative_cross_attn_cond is not None:
+
+                    # If there's a negative cross-attention mask, set the masked tokens to the null embed
+                    if negative_cross_attn_mask is not None:
+                        negative_cross_attn_mask = negative_cross_attn_mask.to(torch.bool).unsqueeze(2)
+
+                        negative_cross_attn_cond = torch.where(negative_cross_attn_mask, negative_cross_attn_cond, null_embed)
+                    
+                    batch_cond = torch.cat([cross_attn_cond, negative_cross_attn_cond], dim=0)
+
+                else:
+                    batch_cond = torch.cat([cross_attn_cond, null_embed], dim=0)
+
+                if cross_attn_cond_mask is not None:
+                    batch_cond_masks = torch.cat([cross_attn_cond_mask, cross_attn_cond_mask], dim=0)
+               
+            batch_prepend_cond = None
+            batch_prepend_cond_mask = None
+
+            if prepend_cond is not None:
+
+                null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)
+
+                batch_prepend_cond = torch.cat([prepend_cond, null_embed], dim=0)
+                           
+                if prepend_cond_mask is not None:
+                    batch_prepend_cond_mask = torch.cat([prepend_cond_mask, prepend_cond_mask], dim=0)
+         
+
+            if mask is not None:
+                batch_masks = torch.cat([mask, mask], dim=0)
+            else:
+                batch_masks = None
+            
+            batch_output = self._forward(
+                batch_inputs, 
+                batch_timestep, 
+                cross_attn_cond=batch_cond, 
+                cross_attn_cond_mask=batch_cond_masks, 
+                mask = batch_masks, 
+                input_concat_cond=batch_input_concat_cond, 
+                global_embed = batch_global_cond,
+                prepend_cond = batch_prepend_cond,
+                prepend_cond_mask = batch_prepend_cond_mask,
+                **kwargs)
+
+            cond_output, uncond_output = torch.chunk(batch_output, 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)
+
+                return scale_phi * (cfg_output * (cond_out_std/out_cfg_std)) + (1-scale_phi) * cfg_output
+
+            else:
+
+                return cfg_output
+            
+        else:
+            return self._forward(
+                x,
+                t,
+                cross_attn_cond=cross_attn_cond, 
+                cross_attn_cond_mask=cross_attn_cond_mask, 
+                input_concat_cond=input_concat_cond, 
+                global_embed=global_embed, 
+                prepend_cond=prepend_cond, 
+                prepend_cond_mask=prepend_cond_mask,
+                mask=mask,
+                **kwargs
+            )

stable_audio_tools/models/factory.py

@@ -0,0 +1,149 @@
+import json
+
+def create_model_from_config(model_config):
+    model_type = model_config.get('model_type', None)
+
+    assert model_type is not None, 'model_type must be specified in model config'
+
+    if model_type == 'autoencoder':
+        from .autoencoders import create_autoencoder_from_config
+        return create_autoencoder_from_config(model_config)
+    elif model_type == 'diffusion_uncond':
+        from .diffusion import create_diffusion_uncond_from_config
+        return create_diffusion_uncond_from_config(model_config)
+    elif model_type == 'diffusion_cond' or model_type == 'diffusion_cond_inpaint' or model_type == "diffusion_prior":
+        from .diffusion import create_diffusion_cond_from_config
+        return create_diffusion_cond_from_config(model_config)
+    elif model_type == 'diffusion_autoencoder':
+        from .autoencoders import create_diffAE_from_config
+        return create_diffAE_from_config(model_config)
+    elif model_type == 'musicgen':
+        from .musicgen import create_musicgen_from_config
+        return create_musicgen_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
+        return TanhBottleneck()
+    elif bottleneck_type == 'vae':
+        from .bottleneck import VAEBottleneck
+        return 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"])
+
+        return RVQBottleneck(**quantizer_params)
+    elif bottleneck_type == "dac_rvq":
+        from .bottleneck import DACRVQBottleneck
+
+        return 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"])
+
+        return RVQVAEBottleneck(**quantizer_params)
+        
+    elif bottleneck_type == 'dac_rvq_vae':
+        from .bottleneck import DACRVQVAEBottleneck
+        return DACRVQVAEBottleneck(**bottleneck_config["config"])
+    elif bottleneck_type == 'l2_norm':
+        from .bottleneck import L2Bottleneck
+        return L2Bottleneck()
+    elif bottleneck_type == "wasserstein":
+        from .bottleneck import WassersteinBottleneck
+        return WassersteinBottleneck(**bottleneck_config.get("config", {}))
+    elif bottleneck_type == "fsq":
+        from .bottleneck import FSQBottleneck
+        return FSQBottleneck(**bottleneck_config["config"])
+    else:
+        raise NotImplementedError(f'Unknown bottleneck type: {bottleneck_type}')

stable_audio_tools/models/lm.py

@@ -0,0 +1,531 @@
+from dataclasses import dataclass
+import torch
+from tqdm.auto import trange
+import typing as tp
+from einops import rearrange
+from torch import nn
+
+from .autoencoders import AudioAutoencoder
+from .conditioners import MultiConditioner, create_multi_conditioner_from_conditioning_config
+from .factory import create_pretransform_from_config
+from .lm_backbone import AudioLMBackbone, XTransformersAudioLMBackbone, ContinuousTransformerAudioLMBackbone
+from .pretransforms import Pretransform, AutoencoderPretransform, PretrainedDACPretransform, AudiocraftCompressionPretransform
+from .utils import multinomial, sample_top_k, sample_top_p
+
+from audiocraft.modules.codebooks_patterns import (
+    CodebooksPatternProvider,
+    DelayedPatternProvider,
+    MusicLMPattern,
+    ParallelPatternProvider,
+    UnrolledPatternProvider,
+    VALLEPattern,
+)
+
+# Copied and modified from https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/models/lm.py under MIT license
+# License can be found in LICENSES/LICENSE_META.txt
+
+@dataclass
+class LMOutput:
+    # The logits are already re-aligned with the input codes
+    # hence no extra shift is required, e.g. when computing CE
+    logits: torch.Tensor  # [B, K, T, card]
+    mask: torch.Tensor  # [B, K, T]
+
+# Wrapper for a multi-codebook language model
+# Handles patterns and quantizer heads
+class AudioLanguageModel(nn.Module):
+    def __init__(
+            self, 
+            pattern_provider: CodebooksPatternProvider, 
+            backbone: AudioLMBackbone,
+            num_quantizers: int,
+            codebook_size: int
+        ):
+        super().__init__()
+
+        self.pattern_provider = pattern_provider
+        self.backbone = backbone
+        self.num_quantizers = num_quantizers
+        self.codebook_size = codebook_size
+
+        self.masked_token_id = codebook_size
+
+        # Per-quantizer embedders
+        # Add one for the mask embed
+        self.embeds = nn.ModuleList([nn.Embedding(codebook_size + 1, backbone.embed_dim) for _ in range(num_quantizers)])
+
+        # Per-quantizer output heads
+        self.quantizer_heads = nn.ModuleList([
+            nn.Linear(backbone.embed_dim, codebook_size) for _ in range(num_quantizers)
+        ])
+
+    def forward(self,
+            sequence: torch.Tensor, #[batch, seq_len, 
+            prepend_cond=None, #[batch, seq, channels]
+            prepend_cond_mask=None,
+            cross_attn_cond=None, #[batch, seq, channels],
+            **kwargs
+        ):
+
+        batch, num_quantizers, seq_len = sequence.shape
+
+        assert num_quantizers == self.num_quantizers, "Number of quantizers in sequence must match number of quantizers in model"
+
+        backbone_input = sum([self.embeds[i](sequence[:, i]) for i in range(num_quantizers)]) # [batch, seq_len, embed_dim]
+
+        output = self.backbone(
+            backbone_input,
+            cross_attn_cond=cross_attn_cond,
+            prepend_cond=prepend_cond,
+            prepend_cond_mask=prepend_cond_mask,
+            **kwargs
+        ) # [batch, seq_len, embed_dim]
+
+        # Run output through quantizer heads
+        logits = torch.stack([self.quantizer_heads[i](output) for i in range(num_quantizers)], dim=1) # [batch, num_quantizers, seq_len, codebook_size]
+
+        return logits
+    
+    def compute_logits(
+            self, 
+            codes, #[batch, num_quantizers, seq_len]
+            **kwargs):
+        """
+        Compute logits for a batch of codes, optionally conditioning on cross-attention and prepend conditioning
+        Handles translation between input sequence and pattern-shifted sequence
+        Only used during training
+        """
+        
+        batch, _, seq_len = codes.shape
+
+        pattern = self.pattern_provider.get_pattern(seq_len)
+
+        # Apply the token pattern to the codes, shifting the codes as needed and masking out invalid steps
+        shifted_codes, _, _ = pattern.build_pattern_sequence(
+            codes,
+            self.masked_token_id,
+            keep_only_valid_steps=True
+        )
+
+        # Run the model to get logits for each quantizer [batch, num_quantizers, seq_len, codebook_size]
+        logits = self(shifted_codes, **kwargs)
+
+        # Rearrange logits to prepare to revert pattern
+        logits = rearrange(logits, "b n s c -> b c n s")
+
+        # Revert sequence logits back to original sequence length, removing masked steps
+        logits, _, logits_mask = pattern.revert_pattern_logits(
+            logits, float('nan'), keep_only_valid_steps=True
+        )
+
+        logits = rearrange(logits, "b c n t -> b n t c")
+
+        logits_mask = logits_mask[None, :, :].expand(batch, -1, -1) # [batch, num_quantizers, seq_len]
+
+        return LMOutput(logits=logits, mask=logits_mask)
+
+# Conditioning and generation wrapper for a multi-codebook language model
+# Handles conditioning, CFG, generation, and encoding/decoding
+class AudioLanguageModelWrapper(nn.Module):
+    def __init__(
+            self, 
+            pretransform: Pretransform,
+            lm: AudioLanguageModel,
+            sample_rate: int,
+            min_input_length: int,
+            conditioner: MultiConditioner = None,
+            cross_attn_cond_ids: tp.List[str] = [],
+            prepend_cond_ids: tp.List[str] = [],
+            global_cond_ids: tp.List[str] = []
+        ):
+        super().__init__()
+        
+        assert pretransform.is_discrete, "Pretransform must be discrete"
+        self.pretransform = pretransform
+
+        self.pretransform.requires_grad_(False)
+        self.pretransform.eval()
+
+        if isinstance(self.pretransform, AutoencoderPretransform):
+            self.num_quantizers = self.pretransform.model.bottleneck.num_quantizers
+            self.codebook_size = self.pretransform.model.bottleneck.codebook_size
+        elif isinstance(self.pretransform, PretrainedDACPretransform):
+            self.num_quantizers = self.pretransform.model.num_quantizers
+            self.codebook_size = self.pretransform.model.codebook_size
+        elif isinstance(self.pretransform, AudiocraftCompressionPretransform):
+            self.num_quantizers = self.pretransform.num_quantizers
+            self.codebook_size = self.pretransform.codebook_size
+        else:
+            raise NotImplementedError(f"Unrecognized pretransform type {type(self.pretransform)}")
+
+        self.conditioner = conditioner
+
+        self.lm = lm
+
+        self.sample_rate = sample_rate
+        self.min_input_length = min_input_length
+
+        self.cross_attn_cond_ids = cross_attn_cond_ids
+        self.prepend_cond_ids = prepend_cond_ids
+        self.global_cond_ids = global_cond_ids
+    
+    def get_conditioning_inputs(self, cond: tp.Dict[str, tp.Any], negative=False):
+        cross_attention_input = None
+        prepend_cond = None
+        prepend_cond_mask = None
+        global_cond = None
+
+        if len(self.cross_attn_cond_ids) > 0:
+            # Concatenate all cross-attention inputs over the sequence dimension
+            # Assumes that the cross-attention inputs are of shape (batch, seq, channels)
+            cross_attention_input = torch.cat([cond[key][0] for key in self.cross_attn_cond_ids], dim=1)
+
+        if len(self.prepend_cond_ids) > 0:
+            # Concatenate all prepend conditioning inputs over the sequence dimension
+            # Assumes that the prepend conditioning inputs are of shape (batch, seq, channels)
+            prepend_cond = torch.cat([cond[key][0] for key in self.prepend_cond_ids], dim=1)
+            prepend_cond_mask = torch.cat([cond[key][1] for key in self.prepend_cond_ids], dim=1)
+
+        if len(self.global_cond_ids) > 0:
+            # Concatenate all global conditioning inputs over the channel dimension
+            # Assumes that the global conditioning inputs are of shape (batch, channels)
+            global_cond = torch.cat([cond[key][0] for key in self.global_cond_ids], dim=-1)
+            if len(global_cond.shape) == 3:
+                global_cond = global_cond.squeeze(1)
+
+        if negative:
+            return {
+                "negative_cross_attn_cond": cross_attention_input,
+                "negative_prepend_cond": prepend_cond,
+                "negative_prepend_cond_mask": prepend_cond_mask,
+                "negative_global_cond": global_cond
+            }
+        else:
+            return {
+                "cross_attn_cond": cross_attention_input,
+                "prepend_cond": prepend_cond,
+                "prepend_cond_mask": prepend_cond_mask,
+                "global_cond": global_cond
+            }
+        
+    def compute_logits(
+            self, 
+            codes, 
+            condition_tensors=None, 
+            cfg_dropout_prob=0.0,
+            **kwargs
+        ):
+        """
+        Compute logits for a batch of codes, and translates from conditioning inputs to model inputs
+        Handles CFG dropout
+        """
+
+        if condition_tensors is None:
+            condition_tensors = {}
+
+        conditioning_inputs = self.get_conditioning_inputs(condition_tensors)
+
+        cross_attn_cond = conditioning_inputs["cross_attn_cond"]
+        prepend_cond = conditioning_inputs["prepend_cond"]
+        prepend_cond_mask = conditioning_inputs["prepend_cond_mask"]
+        global_cond = conditioning_inputs["global_cond"]
+
+        if cfg_dropout_prob > 0.0:
+            if cross_attn_cond is not None:
+                null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)
+                dropout_mask = torch.bernoulli(torch.full((cross_attn_cond.shape[0], 1, 1), cfg_dropout_prob, device=cross_attn_cond.device)).to(torch.bool)
+                cross_attn_cond = torch.where(dropout_mask, null_embed, cross_attn_cond)
+        
+            if prepend_cond is not None:
+                null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)
+                dropout_mask = torch.bernoulli(torch.full((prepend_cond.shape[0], 1, 1), cfg_dropout_prob, device=prepend_cond.device)).to(torch.bool)
+                prepend_cond = torch.where(dropout_mask, null_embed, prepend_cond)
+
+            if global_cond is not None:
+                null_embed = torch.zeros_like(global_cond, device=global_cond.device)
+                dropout_mask = torch.bernoulli(torch.full((global_cond.shape[0], 1), cfg_dropout_prob, device=global_cond.device)).to(torch.bool)
+                global_cond = torch.where(dropout_mask, null_embed, global_cond)
+
+        return self.lm.compute_logits(codes, cross_attn_cond=cross_attn_cond, prepend_cond=prepend_cond, prepend_cond_mask=prepend_cond_mask, global_cond=global_cond, **kwargs)
+    
+    def _sample_next_token(
+            self, 
+            sequence, #[batch, num_quantizers, seq_len]
+            conditioning_tensors=None, 
+            cross_attn_use_cfg=True,
+            prepend_use_cfg=True,
+            global_use_cfg=True,
+            cfg_scale=1.0,
+            top_k=250,
+            top_p=0.0,
+            temp=1.0,
+            **kwargs
+        ):
+        """
+        Sample the next token for a batch of codes, and translates from conditioning inputs to model inputs
+        Handles CFG inference
+        """
+
+        if conditioning_tensors is None:
+            conditioning_tensors = {}
+
+        conditioning_inputs = self.get_conditioning_inputs(conditioning_tensors)
+
+        cross_attn_cond = conditioning_inputs["cross_attn_cond"]
+        prepend_cond = conditioning_inputs["prepend_cond"]
+        prepend_cond_mask = conditioning_inputs["prepend_cond_mask"]
+        global_cond = conditioning_inputs["global_cond"]
+
+        if cfg_scale != 1.0:
+            
+            # Batch size is doubled to account for negative samples
+            sequence = torch.cat([sequence, sequence], dim=0)
+
+            if cross_attn_cond is not None and cross_attn_use_cfg:
+                null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)
+
+                cross_attn_cond = torch.cat([cross_attn_cond, null_embed], dim=0)
+            
+            if prepend_cond is not None and prepend_use_cfg:
+                null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)
+
+                prepend_cond = torch.cat([prepend_cond, null_embed], dim=0) 
+
+                if prepend_cond_mask is not None:
+                    prepend_cond_mask = torch.cat([prepend_cond_mask, prepend_cond_mask], dim=0)
+
+            if global_cond is not None and global_use_cfg:
+                null_embed = torch.zeros_like(global_cond, device=global_cond.device)
+
+                global_cond = torch.cat([global_cond, null_embed], dim=0)
+
+        logits = self.lm(sequence, cross_attn_cond=cross_attn_cond, prepend_cond=prepend_cond, prepend_cond_mask=prepend_cond_mask, global_cond=global_cond, **kwargs)
+
+        if cfg_scale != 1.0:
+            cond_logits, uncond_logits = logits.chunk(2, dim=0)
+
+            logits = uncond_logits + (cond_logits - uncond_logits) * cfg_scale
+
+        logits = rearrange(logits, "b n s c -> b n c s") # [batch, num_quantizers, codebook_size, seq_len]
+        
+        # Grab the logits for the last step
+        logits = logits[:, :, :, -1] # [batch, num_quantizers, codebook_size]
+
+        # Apply top-k or top-p sampling
+
+        if temp > 0:
+            probs = torch.softmax(logits / temp, dim=-1)
+
+            if top_p > 0.0:
+                next_token = sample_top_p(probs, p=top_p)
+            elif top_k > 0:
+                next_token = sample_top_k(probs, k=top_k)
+            else:
+                next_token = multinomial(probs, num_samples=1)
+
+        else:
+            next_token = torch.argmax(logits, dim=-1, keepdim=True) # [batch, num_quantizers, 1]
+
+        return next_token
+
+    @torch.no_grad()
+    def generate(
+        self,
+        max_gen_len: int = 256,
+        batch_size: tp.Optional[int] = None,
+        init_data: tp.Optional[torch.Tensor] = None,
+        conditioning: tp.Optional[tp.Dict[str, tp.Any]] = None,
+        conditioning_tensors: tp.Optional[tp.Dict[str, tp.Any]] = None,
+        callback: tp.Optional[tp.Callable[[int, int], None]] = None,
+        use_cache: bool = True,
+        cfg_scale: float = 1.0,
+        **kwargs
+    ):
+        device = next(self.parameters()).device
+
+        if conditioning_tensors is None and conditioning is not None:
+            # Convert conditioning inputs to conditioning tensors
+            conditioning_tensors = self.conditioner(conditioning, device)
+
+        # Check that batch size is consistent across inputs
+        possible_batch_sizes = []
+
+        if batch_size is not None:
+            possible_batch_sizes.append(batch_size)
+        elif init_data is not None:
+            possible_batch_sizes.append(init_data.shape[0])
+        elif conditioning_tensors is not None:
+            # Assume that the first conditioning tensor has the batch dimension
+            possible_batch_sizes.append(conditioning_tensors[list(conditioning_tensors.keys())[0]][0].shape[0])
+        else:
+            possible_batch_sizes.append(1)
+
+        assert [x == possible_batch_sizes[0] for x in possible_batch_sizes], "Batch size must be consistent across inputs"
+
+        batch_size = possible_batch_sizes[0]
+        
+        if init_data is None:
+            # Initialize with zeros
+            assert batch_size > 0
+            init_data = torch.zeros((batch_size, self.num_quantizers, 0), device=device, dtype=torch.long)
+
+        batch_size, num_quantizers, seq_len = init_data.shape
+
+        start_offset = seq_len
+        assert start_offset < max_gen_len, "init data longer than max gen length"
+
+        pattern = self.lm.pattern_provider.get_pattern(max_gen_len)
+
+        unknown_token = -1
+
+        # Initialize the generated codes with the init data, padded with unknown tokens
+        gen_codes = torch.full((batch_size, num_quantizers, max_gen_len), unknown_token, device=device, dtype=torch.long)
+        gen_codes[:, :, :start_offset] = init_data # [batch, num_quantizers, max_gen_len]
+
+        gen_sequence, _, mask = pattern.build_pattern_sequence(gen_codes, self.lm.masked_token_id) # [batch, num_quantizers, gen_sequence_len]
+
+        start_offset_sequence = pattern.get_first_step_with_timesteps(start_offset)
+        assert start_offset_sequence is not None
+
+        # Generation
+        prev_offset = 0
+        gen_sequence_len = gen_sequence.shape[-1]
+
+        # Reset generation cache
+        if use_cache and self.lm.backbone.use_generation_cache:
+            self.lm.backbone.reset_generation_cache(max_gen_len, batch_size if cfg_scale == 1.0 else batch_size * 2)
+
+        for offset in trange(start_offset_sequence, gen_sequence_len):
+
+            # Get the full sequence up to the current offset
+            curr_sequence = gen_sequence[..., prev_offset:offset]
+
+            next_token = self._sample_next_token(
+                curr_sequence,
+                conditioning_tensors=conditioning_tensors,
+                use_cache=use_cache,
+                cfg_scale=cfg_scale,
+                **kwargs
+            )
+
+            valid_mask = mask[..., offset:offset+1].expand(batch_size, -1, -1)
+            next_token[~valid_mask] = self.lm.masked_token_id
+
+            # Update the generated sequence with the next token
+            gen_sequence[..., offset:offset+1] = torch.where(
+                gen_sequence[..., offset:offset+1] == unknown_token,
+                next_token, 
+                gen_sequence[..., offset:offset+1]
+            )
+
+            if use_cache and self.lm.backbone.use_generation_cache:
+                # Only update the offset if caching is being used
+                prev_offset = offset
+
+                self.lm.backbone.update_generation_cache(offset)
+
+            if callback is not None:
+                # Callback to report progress
+                # Pass in the offset relative to the start of the sequence, and the length of the current sequence
+                callback(1 + offset - start_offset_sequence, gen_sequence_len - start_offset_sequence)
+
+        assert not (gen_sequence == unknown_token).any(), "Unknown tokens in generated sequence"
+
+        out_codes, _, out_mask = pattern.revert_pattern_sequence(gen_sequence, special_token=unknown_token)
+
+        # sanity checks over the returned codes and corresponding masks
+        assert (out_codes[..., :max_gen_len] != unknown_token).all()
+        assert (out_mask[..., :max_gen_len] == 1).all()
+
+        #out_codes = out_codes[..., 0:max_gen_len]
+
+        return out_codes
+    
+
+    def generate_audio(
+        self,
+        **kwargs
+    ):
+        """
+        Generate audio from a batch of codes
+        """
+
+        codes = self.generate(**kwargs)
+
+        audio = self.pretransform.decode_tokens(codes)
+
+        return audio
+
+
+def create_audio_lm_from_config(config):
+    model_config = config.get('model', None)
+    assert model_config is not None, 'model config must be specified in config'
+
+    sample_rate = config.get('sample_rate', None)
+    assert sample_rate is not None, "Must specify sample_rate in config"
+    
+    lm_config = model_config.get('lm', None)
+    assert lm_config is not None, 'lm config must be specified in model config'
+
+    codebook_pattern = lm_config.get("codebook_pattern", "delay")
+
+    pattern_providers = {
+        'parallel': ParallelPatternProvider,
+        'delay': DelayedPatternProvider,
+        'unroll': UnrolledPatternProvider,
+        'valle': VALLEPattern,
+        'musiclm': MusicLMPattern,
+    }
+
+    pretransform_config = model_config.get("pretransform", None)
+    
+    pretransform = create_pretransform_from_config(pretransform_config, sample_rate)
+
+    assert pretransform.is_discrete, "Pretransform must be discrete"
+
+    min_input_length = pretransform.downsampling_ratio
+
+    pattern_provider = pattern_providers[codebook_pattern](n_q=pretransform.num_quantizers)
+
+    conditioning_config = model_config.get('conditioning', None)
+
+    conditioner = None
+    if conditioning_config is not None:
+        conditioner = create_multi_conditioner_from_conditioning_config(conditioning_config)
+
+    cross_attn_cond_ids = lm_config.get('cross_attention_cond_ids', [])
+    prepend_cond_ids = lm_config.get('prepend_cond_ids', [])
+    global_cond_ids = lm_config.get('global_cond_ids', [])
+
+    lm_type = lm_config.get("type", None)
+    lm_model_config = lm_config.get("config", None)
+
+    assert lm_type is not None, "Must specify lm type in lm config"
+    assert lm_model_config is not None, "Must specify lm model config in lm config"
+
+    if lm_type == "x-transformers":
+        backbone = XTransformersAudioLMBackbone(**lm_model_config)
+    elif lm_type == "continuous_transformer":
+        backbone = ContinuousTransformerAudioLMBackbone(**lm_model_config)
+    else:
+        raise NotImplementedError(f"Unrecognized lm type {lm_type}")
+
+    lm = AudioLanguageModel(
+        pattern_provider=pattern_provider,
+        backbone=backbone,
+        num_quantizers=pretransform.num_quantizers,
+        codebook_size=pretransform.codebook_size
+    )
+
+    model = AudioLanguageModelWrapper(
+        pretransform=pretransform,
+        lm=lm,
+        conditioner=conditioner,
+        sample_rate=sample_rate,
+        min_input_length=min_input_length,
+        cross_attn_cond_ids=cross_attn_cond_ids,
+        prepend_cond_ids=prepend_cond_ids,
+        global_cond_ids=global_cond_ids
+    )
+
+    return model

stable_audio_tools/models/lm_backbone.py

@@ -0,0 +1,157 @@
+import torch
+from torch import nn
+from x_transformers import ContinuousTransformerWrapper, Decoder
+
+from .transformer import ContinuousTransformer
+
+# Interface for backbone of a language model
+# Handles conditioning and cross-attention
+# Does not have to deal with patterns or quantizer heads
+class AudioLMBackbone(nn.Module):
+    def __init__(self, embed_dim: int, use_generation_cache=False, **kwargs):
+        super().__init__()
+
+        self.embed_dim = embed_dim
+        self.use_generation_cache = use_generation_cache
+
+    def forward(
+        self, 
+        x, 
+        cross_attn_cond=None, 
+        prepend_cond=None, 
+        prepend_cond_mask=None,
+        global_cond=None,
+        use_cache=False,
+        **kwargs
+        ):
+        raise NotImplementedError
+    
+    def reset_generation_cache(
+        self,
+        max_seq_len, 
+        batch_size,
+        dtype=None
+    ):
+        pass
+
+    def update_generation_cache(
+        self,
+        seqlen_offset
+    ):
+        pass
+
+class XTransformersAudioLMBackbone(AudioLMBackbone):
+    def __init__(self,
+                 embed_dim: int,
+                 cross_attn_cond_dim: int = 0,
+                 prepend_cond_dim: int = 0,
+                 **kwargs):
+        super().__init__(embed_dim=embed_dim)
+
+        # Embeddings are done in the AudioLanguageModel, so we use the continuous-input transformer
+        self.model = ContinuousTransformerWrapper(
+            dim_in=embed_dim,
+            dim_out=embed_dim,
+            max_seq_len=0, #Not relevant without absolute positional embeds,
+            attn_layers=Decoder(
+                dim=embed_dim,
+                attn_flash = True,
+                cross_attend = cross_attn_cond_dim > 0,
+                zero_init_branch_output=True,
+                use_abs_pos_emb = False,
+                rotary_pos_emb=True,
+                ff_swish = True,
+                ff_glu = True,
+                **kwargs
+            )
+        )
+
+        if prepend_cond_dim > 0:
+            # Prepend conditioning
+            self.to_prepend_embed = nn.Sequential(
+                nn.Linear(prepend_cond_dim, embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(embed_dim, embed_dim, bias=False)
+            )
+
+        if cross_attn_cond_dim > 0:
+            # Cross-attention conditioning
+            self.to_cross_attn_embed = nn.Sequential(
+                nn.Linear(cross_attn_cond_dim, embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(embed_dim, embed_dim, bias=False)
+            )
+
+    def forward(self, x, mask=None, prepend_cond=None, prepend_cond_mask=None, cross_attn_cond=None, global_cond=None, use_cache=False):
+
+        prepend_length = 0
+        if prepend_cond is not None:
+            # Project the prepend conditioning to the embedding dimension
+            prepend_cond = self.to_prepend_embed(prepend_cond)
+            prepend_length = prepend_cond.shape[1]
+
+            if prepend_cond_mask is not None:
+                # Cast mask to bool
+                prepend_cond_mask = prepend_cond_mask.bool()
+
+        if cross_attn_cond is not None:
+            # Project the cross-attention conditioning to the embedding dimension
+            cross_attn_cond = self.to_cross_attn_embed(cross_attn_cond)
+
+        return self.model(x, mask=mask, context=cross_attn_cond, prepend_embeds=prepend_cond, prepend_mask=prepend_cond_mask)[:, prepend_length:, :]
+    
+class ContinuousTransformerAudioLMBackbone(AudioLMBackbone):
+    def __init__(self,
+                 embed_dim: int,
+                 cross_attn_cond_dim: int = 0,
+                 prepend_cond_dim: int = 0,
+                 project_cross_attn_cond: bool = False,
+                 **kwargs):
+        super().__init__(embed_dim=embed_dim)
+
+        # Embeddings are done in the AudioLanguageModel, so we use the continuous-input transformer
+        self.model = ContinuousTransformer(
+            dim=embed_dim,
+            dim_in=embed_dim,
+            dim_out=embed_dim,
+            cross_attend = cross_attn_cond_dim > 0,
+            cond_token_dim = embed_dim if project_cross_attn_cond else cross_attn_cond_dim,
+            causal=True,
+            **kwargs
+        )
+
+        if prepend_cond_dim > 0:
+            # Prepend conditioning
+            self.to_prepend_embed = nn.Sequential(
+                nn.Linear(prepend_cond_dim, embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(embed_dim, embed_dim, bias=False)
+            )
+
+        if cross_attn_cond_dim > 0 and project_cross_attn_cond:
+            # Cross-attention conditioning
+            self.to_cross_attn_embed = nn.Sequential(
+                nn.Linear(cross_attn_cond_dim, embed_dim, bias=False),
+                nn.SiLU(),
+                nn.Linear(embed_dim, embed_dim, bias=False)
+            )
+        else:
+            self.to_cross_attn_embed = nn.Identity()
+
+    def forward(self, x, mask=None, prepend_cond=None, prepend_cond_mask=None, cross_attn_cond=None, global_cond=None, use_cache=False):
+
+        prepend_length = 0
+        if prepend_cond is not None:
+            # Project the prepend conditioning to the embedding dimension
+            prepend_cond = self.to_prepend_embed(prepend_cond)
+            prepend_length = prepend_cond.shape[1]
+
+            if prepend_cond_mask is not None:
+                # Cast mask to bool
+                prepend_cond_mask = prepend_cond_mask.bool()
+
+        if cross_attn_cond is not None:
+            # Project the cross-attention conditioning to the embedding dimension
+            cross_attn_cond = self.to_cross_attn_embed(cross_attn_cond)
+
+        return self.model(x, mask=mask, context=cross_attn_cond, prepend_embeds=prepend_cond, prepend_mask=prepend_cond_mask)[:, prepend_length:, :]

stable_audio_tools/models/local_attention.py

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

stable_audio_tools/models/musicgen.py

@@ -0,0 +1,161 @@
+import torch
+import typing as tp
+from audiocraft.models import MusicGen, CompressionModel, LMModel
+import audiocraft.quantization as qt
+from .autoencoders import AudioAutoencoder
+from .bottleneck import DACRVQBottleneck, DACRVQVAEBottleneck
+
+from audiocraft.modules.codebooks_patterns import (
+    DelayedPatternProvider,
+    MusicLMPattern,
+    ParallelPatternProvider,
+    UnrolledPatternProvider,
+    VALLEPattern,
+)
+
+from audiocraft.modules.conditioners import (
+    ConditionFuser,
+    ConditioningProvider,
+    T5Conditioner,
+)
+
+def create_musicgen_from_config(config):
+    model_config = config.get('model', None)
+    assert model_config is not None, 'model config must be specified in config'
+
+    if model_config.get("pretrained", False):
+        model = MusicGen.get_pretrained(model_config["pretrained"], device="cpu")
+
+        if model_config.get("reinit_lm", False):
+            model.lm._init_weights("gaussian", "current", True)
+    
+        return model
+    
+    # Create MusicGen model from scratch
+    compression_config = model_config.get('compression', None)
+    assert compression_config is not None, 'compression config must be specified in model config'
+
+    compression_type = compression_config.get('type', None)
+    assert compression_type is not None, 'type must be specified in compression config'
+
+    if compression_type == 'pretrained':
+        compression_model = CompressionModel.get_pretrained(compression_config["config"]["name"])
+    elif compression_type == "dac_rvq_ae":
+        from .autoencoders import create_autoencoder_from_config
+        autoencoder = create_autoencoder_from_config({"model": compression_config["config"], "sample_rate": config["sample_rate"]})
+        autoencoder.load_state_dict(torch.load(compression_config["ckpt_path"], map_location="cpu")["state_dict"])
+        compression_model = DACRVQCompressionModel(autoencoder)
+    
+    lm_config = model_config.get('lm', None)
+    assert lm_config is not None, 'lm config must be specified in model config'
+
+    codebook_pattern = lm_config.pop("codebook_pattern", "delay")
+
+    pattern_providers = {
+        'parallel': ParallelPatternProvider,
+        'delay': DelayedPatternProvider,
+        'unroll': UnrolledPatternProvider,
+        'valle': VALLEPattern,
+        'musiclm': MusicLMPattern,
+    }
+
+    pattern_provider = pattern_providers[codebook_pattern](n_q=compression_model.num_codebooks)
+
+    conditioning_config = model_config.get("conditioning", {})
+
+    condition_output_dim = conditioning_config.get("output_dim", 768)
+
+    condition_provider = ConditioningProvider(
+        conditioners = {
+            "description": T5Conditioner(
+                name="t5-base",
+                output_dim=condition_output_dim,
+                word_dropout=0.3,
+                normalize_text=False,
+                finetune=False,
+                device="cpu"
+            )
+        }
+    )
+
+    condition_fuser = ConditionFuser(fuse2cond={
+        "cross": ["description"],
+        "prepend": [],
+        "sum": []
+        })
+
+    lm = LMModel(
+        pattern_provider = pattern_provider,
+        condition_provider = condition_provider,
+        fuser = condition_fuser,
+        n_q = compression_model.num_codebooks,
+        card = compression_model.cardinality,
+        **lm_config
+    )
+
+
+    model = MusicGen(
+        name = model_config.get("name", "musicgen-scratch"),
+        compression_model = compression_model,
+        lm = lm,
+        max_duration=30
+    )
+
+    return model
+
+class DACRVQCompressionModel(CompressionModel):
+    def __init__(self, autoencoder: AudioAutoencoder):
+        super().__init__()
+        self.model = autoencoder.eval()
+
+        assert isinstance(self.model.bottleneck, DACRVQBottleneck) or isinstance(self.model.bottleneck, DACRVQVAEBottleneck), "Autoencoder must have a DACRVQBottleneck or DACRVQVAEBottleneck"
+
+        self.n_quantizers = self.model.bottleneck.num_quantizers
+
+    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
+        raise NotImplementedError("Forward and training with DAC RVQ not supported")
+
+    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
+        _, info = self.model.encode(x, return_info=True, n_quantizers=self.n_quantizers)
+        codes = info["codes"]
+        return codes, None
+
+    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
+        assert scale is None
+        z_q = self.decode_latent(codes)
+        return self.model.decode(z_q)
+
+    def decode_latent(self, codes: torch.Tensor):
+        """Decode from the discrete codes to continuous latent space."""
+        return self.model.bottleneck.quantizer.from_codes(codes)[0]
+
+    @property
+    def channels(self) -> int:
+        return self.model.io_channels
+
+    @property
+    def frame_rate(self) -> float:
+        return self.model.sample_rate / self.model.downsampling_ratio
+
+    @property
+    def sample_rate(self) -> int:
+        return self.model.sample_rate
+
+    @property
+    def cardinality(self) -> int:
+        return self.model.bottleneck.quantizer.codebook_size
+
+    @property
+    def num_codebooks(self) -> int:
+        return self.n_quantizers
+
+    @property
+    def total_codebooks(self) -> int:
+        self.model.bottleneck.num_quantizers
+
+    def set_num_codebooks(self, n: int):
+        """Set the active number of codebooks used by the quantizer.
+        """
+        assert n >= 1
+        assert n <= self.total_codebooks
+        self.n_quantizers = n

stable_audio_tools/models/pqmf.py

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