# based on https://github.com/huggingface/diffusers/blob/main/examples/train_unconditional.py import argparse import os import torch import torch.nn.functional as F from accelerate import Accelerator from accelerate.logging import get_logger from datasets import load_from_disk, load_dataset from diffusers import (DiffusionPipeline, DDPMScheduler, UNet2DModel, DDIMScheduler, AutoencoderKL) from diffusers.hub_utils import init_git_repo, push_to_hub from diffusers.optimization import get_scheduler from diffusers.training_utils import EMAModel from torchvision.transforms import ( CenterCrop, Compose, InterpolationMode, Normalize, Resize, ToTensor, ) import numpy as np from tqdm.auto import tqdm from librosa.util import normalize import sys sys.path.append('.') sys.path.append('..') from audiodiffusion.mel import Mel from audiodiffusion import LatentAudioDiffusionPipeline, AudioDiffusionPipeline logger = get_logger(__name__) def main(args): output_dir = os.environ.get("SM_MODEL_DIR", None) or args.output_dir logging_dir = os.path.join(output_dir, args.logging_dir) accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, log_with="tensorboard", logging_dir=logging_dir, ) # Handle the resolutions. try: args.resolution = (int(args.resolution), int(args.resolution)) except: try : args.resolution = tuple(int(x) for x in args.resolution.split(",")) if len(args.resolution) != 2: raise ValueError("Resolution must be a tuple of two integers or a single integer.") except: raise ValueError("Resolution must be a tuple of two integers or a single integer.") assert isinstance(args.resolution, tuple) if args.vae is not None: vqvae = AutoencoderKL.from_pretrained(args.vae) if args.from_pretrained is not None: model = DiffusionPipeline.from_pretrained(args.from_pretrained).unet else: model = UNet2DModel( sample_size=args.resolution if args.vae is None else args.latent_resolution, in_channels=1 if args.vae is None else vqvae.config['latent_channels'], out_channels=1 if args.vae is None else vqvae.config['latent_channels'], layers_per_block=2, block_out_channels=(128, 128, 256, 256, 512, 512), down_block_types=( "DownBlock2D", "DownBlock2D", "DownBlock2D", "DownBlock2D", "AttnDownBlock2D", "DownBlock2D", ), up_block_types=( "UpBlock2D", "AttnUpBlock2D", "UpBlock2D", "UpBlock2D", "UpBlock2D", "UpBlock2D", ), ) if args.scheduler == "ddpm": noise_scheduler = DDPMScheduler( num_train_timesteps=args.num_train_steps, tensor_format="pt") else: noise_scheduler = DDIMScheduler( num_train_timesteps=args.num_train_steps, tensor_format="pt") optimizer = torch.optim.AdamW( model.parameters(), lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) augmentations = Compose([ Resize(args.resolution, interpolation=InterpolationMode.BILINEAR), CenterCrop(args.resolution), ToTensor(), Normalize([0.5], [0.5]), ]) if args.dataset_name is not None: if os.path.exists(args.dataset_name): dataset = load_from_disk(args.dataset_name, args.dataset_config_name)["train"] else: dataset = load_dataset( args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, use_auth_token=True if args.use_auth_token else None, split="train", ) else: dataset = load_dataset( "imagefolder", data_dir=args.train_data_dir, cache_dir=args.cache_dir, split="train", ) def transforms(examples): if args.vae is not None and vqvae.config['in_channels'] == 3: images = [ augmentations(image.convert('RGB')) for image in examples["image"] ] else: images = [augmentations(image) for image in examples["image"]] return {"input": images} dataset.set_transform(transforms) train_dataloader = torch.utils.data.DataLoader( dataset, batch_size=args.train_batch_size, shuffle=True) lr_scheduler = get_scheduler( args.lr_scheduler, optimizer=optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=(len(train_dataloader) * args.num_epochs) // args.gradient_accumulation_steps, ) model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, lr_scheduler) ema_model = EMAModel( getattr(model, "module", model), inv_gamma=args.ema_inv_gamma, power=args.ema_power, max_value=args.ema_max_decay, ) if args.push_to_hub: repo = init_git_repo(args, at_init=True) if accelerator.is_main_process: run = os.path.split(__file__)[-1].split(".")[0] accelerator.init_trackers(run) mel = Mel(x_res=args.resolution[0], y_res=args.resolution[1], hop_length=args.hop_length) global_step = 0 for epoch in range(args.num_epochs): progress_bar = tqdm(total=len(train_dataloader), disable=not accelerator.is_local_main_process) progress_bar.set_description(f"Epoch {epoch}") if epoch < args.start_epoch: for step in range(len(train_dataloader)): optimizer.step() lr_scheduler.step() progress_bar.update(1) global_step += 1 if epoch == args.start_epoch - 1 and args.use_ema: ema_model.optimization_step = global_step continue model.train() for step, batch in enumerate(train_dataloader): clean_images = batch["input"] if args.vae is not None: vqvae.to(clean_images.device) with torch.no_grad(): clean_images = vqvae.encode( clean_images).latent_dist.sample() # Scale latent images to ensure approximately unit variance clean_images = clean_images * 0.18215 # Sample noise that we'll add to the images noise = torch.randn(clean_images.shape).to(clean_images.device) bsz = clean_images.shape[0] # Sample a random timestep for each image timesteps = torch.randint( 0, noise_scheduler.num_train_timesteps, (bsz, ), device=clean_images.device, ).long() # Add noise to the clean images according to the noise magnitude at each timestep # (this is the forward diffusion process) noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps) with accelerator.accumulate(model): # Predict the noise residual noise_pred = model(noisy_images, timesteps)["sample"] loss = F.mse_loss(noise_pred, noise) accelerator.backward(loss) if accelerator.sync_gradients: accelerator.clip_grad_norm_(model.parameters(), 1.0) optimizer.step() lr_scheduler.step() if args.use_ema: ema_model.step(model) optimizer.zero_grad() progress_bar.update(1) global_step += 1 logs = { "loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step, } if args.use_ema: logs["ema_decay"] = ema_model.decay progress_bar.set_postfix(**logs) accelerator.log(logs, step=global_step) progress_bar.close() accelerator.wait_for_everyone() # Generate sample images for visual inspection if accelerator.is_main_process: if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1: if args.vae is not None: pipeline = LatentAudioDiffusionPipeline( unet=accelerator.unwrap_model( ema_model.averaged_model if args.use_ema else model ), vqvae=vqvae, scheduler=noise_scheduler) else: pipeline = AudioDiffusionPipeline( unet=accelerator.unwrap_model( ema_model.averaged_model if args.use_ema else model ), scheduler=noise_scheduler, ) # save the model if args.push_to_hub: try: push_to_hub( args, pipeline, repo, commit_message=f"Epoch {epoch}", blocking=False, ) except NameError: # current version of diffusers has a little bug pass else: pipeline.save_pretrained(output_dir) if epoch % args.save_images_epochs == 0 or epoch == args.num_epochs - 1: generator = torch.manual_seed(42) # run pipeline in inference (sample random noise and denoise) images, (sample_rate, audios) = pipeline( mel=mel, generator=generator, batch_size=args.eval_batch_size, ) # denormalize the images and save to tensorboard images = np.array([ np.frombuffer(image.tobytes(), dtype="uint8").reshape( (len(image.getbands()), image.height, image.width)) for image in images ]) accelerator.trackers[0].writer.add_images( "test_samples", images, epoch) for _, audio in enumerate(audios): accelerator.trackers[0].writer.add_audio( f"test_audio_{_}", normalize(audio), epoch, sample_rate=sample_rate, ) accelerator.wait_for_everyone() accelerator.end_training() if __name__ == "__main__": parser = argparse.ArgumentParser( description="Simple example of a training script.") parser.add_argument("--local_rank", type=int, default=-1) parser.add_argument("--dataset_name", type=str, default=None) parser.add_argument("--dataset_config_name", type=str, default=None) parser.add_argument( "--train_data_dir", type=str, default=None, help="A folder containing the training data.", ) parser.add_argument("--output_dir", type=str, default="ddpm-model-64") parser.add_argument("--overwrite_output_dir", type=bool, default=False) parser.add_argument("--cache_dir", type=str, default=None) parser.add_argument("--resolution", type=str, default="256") parser.add_argument("--train_batch_size", type=int, default=16) parser.add_argument("--eval_batch_size", type=int, default=16) parser.add_argument("--num_epochs", type=int, default=100) parser.add_argument("--save_images_epochs", type=int, default=10) parser.add_argument("--save_model_epochs", type=int, default=10) parser.add_argument("--gradient_accumulation_steps", type=int, default=1) parser.add_argument("--learning_rate", type=float, default=1e-4) parser.add_argument("--lr_scheduler", type=str, default="cosine") parser.add_argument("--lr_warmup_steps", type=int, default=500) parser.add_argument("--adam_beta1", type=float, default=0.95) parser.add_argument("--adam_beta2", type=float, default=0.999) parser.add_argument("--adam_weight_decay", type=float, default=1e-6) parser.add_argument("--adam_epsilon", type=float, default=1e-08) parser.add_argument("--use_ema", type=bool, default=True) parser.add_argument("--ema_inv_gamma", type=float, default=1.0) parser.add_argument("--ema_power", type=float, default=3 / 4) parser.add_argument("--ema_max_decay", type=float, default=0.9999) parser.add_argument("--push_to_hub", type=bool, default=False) parser.add_argument("--use_auth_token", type=bool, default=False) parser.add_argument("--hub_token", type=str, default=None) parser.add_argument("--hub_model_id", type=str, default=None) parser.add_argument("--hub_private_repo", type=bool, default=False) parser.add_argument("--logging_dir", type=str, default="logs") parser.add_argument( "--mixed_precision", type=str, default="no", choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose" "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." "and an Nvidia Ampere GPU."), ) parser.add_argument("--hop_length", type=int, default=512) parser.add_argument("--from_pretrained", type=str, default=None) parser.add_argument("--start_epoch", type=int, default=0) parser.add_argument("--num_train_steps", type=int, default=1000) parser.add_argument("--latent_resolution", type=int, default=None) parser.add_argument("--scheduler", type=str, default="ddpm", help="ddpm or ddim") parser.add_argument("--vae", type=str, default=None, help="pretrained VAE model for latent diffusion") args = parser.parse_args() env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) if env_local_rank != -1 and env_local_rank != args.local_rank: args.local_rank = env_local_rank if args.dataset_name is None and args.train_data_dir is None: raise ValueError( "You must specify either a dataset name from the hub or a train data directory." ) if args.dataset_name is not None and args.dataset_name == args.hub_model_id: raise ValueError( "The local dataset name must be different from the hub model id.") main(args)