diffusion2.py

from dataclasses import dataclass

@dataclass
class TrainingConfig:
    image_size = 512  # the generated image resolution
    train_batch_size = 2
    eval_batch_size = 4  # how many images to sample during evaluation
    num_epochs = 50
    gradient_accumulation_steps = 1
    learning_rate = 1e-4
    lr_warmup_steps = 500
    save_image_epochs = 10
    save_model_epochs = 30
    mixed_precision = "fp16"  # `no` for float32, `fp16` for automatic mixed precision
    output_dir = "skin_lesion_cancer_diffusion_512_V2"  # the model name locally and on the HF Hub

    push_to_hub = False  # whether to upload the saved model to the HF Hub
    hub_model_id = "<your-username>/<my-awesome-model>"  # the name of the repository to create on the HF Hub
    hub_private_repo = False
    overwrite_output_dir = True  # overwrite the old model when re-running the notebook
    seed = 0


config = TrainingConfig()
from datasets import load_dataset
from torchvision import transforms, datasets
# config.dataset_name = "huggan/smithsonian_butterflies_subset"
# dataset = load_dataset(config.dataset_name, split="train")

from torchvision import transforms

preprocess = transforms.Compose(
    [
        transforms.Resize((config.image_size, config.image_size)),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
import torch
from torch.utils.data import Subset
from torch.utils.data import DataLoader, Dataset
dataset = datasets.ImageFolder(root="data/isic", transform=preprocess)
# subset_indices = torch.randperm(len(dataset))[:100]
# subset = Subset(dataset, subset_indices)
train_dataloader = DataLoader(dataset, batch_size=config.train_batch_size, shuffle=True)


from diffusers import UNet2DModel

model = UNet2DModel(
    sample_size=config.image_size,  # the target image resolution
    in_channels=3,  # the number of input channels, 3 for RGB images
    out_channels=3,  # the number of output channels
    layers_per_block=2,  # how many ResNet layers to use per UNet block
    block_out_channels=(128, 128, 256, 256, 512, 512),  # the number of output channels for each UNet block
    down_block_types=(
        "DownBlock2D",  # a regular ResNet downsampling block
        "DownBlock2D",
        "DownBlock2D",
        "DownBlock2D",
        "AttnDownBlock2D",  # a ResNet downsampling block with spatial self-attention
        "DownBlock2D",
    ),
    up_block_types=(
        "UpBlock2D",  # a regular ResNet upsampling block
        "AttnUpBlock2D",  # a ResNet upsampling block with spatial self-attention
        "UpBlock2D",
        "UpBlock2D",
        "UpBlock2D",
        "UpBlock2D",
    ),
)
import torch
from PIL import Image
from diffusers import DDPMScheduler

noise_scheduler = DDPMScheduler(num_train_timesteps=1000)
from diffusers.optimization import get_cosine_schedule_with_warmup

optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate)
lr_scheduler = get_cosine_schedule_with_warmup(
    optimizer=optimizer,
    num_warmup_steps=config.lr_warmup_steps,
    num_training_steps=(len(train_dataloader) * config.num_epochs),
)

from diffusers import DDPMPipeline
from diffusers.utils import make_image_grid
import os

def evaluate(config, epoch, pipeline):
    # Sample some images from random noise (this is the backward diffusion process).
    # The default pipeline output type is `List[PIL.Image]`
    images = pipeline(
        batch_size=config.eval_batch_size,
        generator=torch.Generator(device='cpu').manual_seed(config.seed), # Use a separate torch generator to avoid rewinding the random state of the main training loop
    ).images

    # Make a grid out of the images
    image_grid = make_image_grid(images, rows=2, cols=2)

    # Save the images
    test_dir = os.path.join(config.output_dir, "samples")
    os.makedirs(test_dir, exist_ok=True)
    image_grid.save(f"{test_dir}/{epoch:04d}.png")
from accelerate import Accelerator
from huggingface_hub import create_repo, upload_folder
from tqdm.auto import tqdm
from pathlib import Path
import os
import torch.nn.functional as F

# Define the checkpoint directory
checkpoint_dir = Path(config.output_dir) / "checkpoints"
checkpoint_dir.mkdir(parents=True, exist_ok=True)
checkpoint_file = checkpoint_dir / "last_checkpoint.pth"

# Function to save the training state
def save_checkpoint(model, optimizer, lr_scheduler, epoch, global_step):
    checkpoint = {
        'model_state_dict': model.state_dict(),
        'optimizer_state_dict': optimizer.state_dict(),
        'lr_scheduler_state_dict': lr_scheduler.state_dict(),
        'epoch': epoch,
        'global_step': global_step
    }
    torch.save(checkpoint, checkpoint_file)

# Function to load the training state
def load_checkpoint(model, optimizer, lr_scheduler):
    if checkpoint_file.exists():
        checkpoint = torch.load(checkpoint_file)
        model.load_state_dict(checkpoint['model_state_dict'])
        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
        lr_scheduler.load_state_dict(checkpoint['lr_scheduler_state_dict'])
        epoch = checkpoint['epoch']
        global_step = checkpoint['global_step']
        print(f"Loaded checkpoint from epoch {epoch}, global step {global_step}")
    else:
        epoch = 0
        global_step = 0
    return model, optimizer, lr_scheduler, epoch, global_step

def train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler):
    # Initialize accelerator and tensorboard logging
    accelerator = Accelerator(
        mixed_precision=config.mixed_precision,
        gradient_accumulation_steps=config.gradient_accumulation_steps,
    )
    if accelerator.is_main_process:
        if config.output_dir is not None:
            os.makedirs(config.output_dir, exist_ok=True)
        
        accelerator.init_trackers("train_example")

    # Prepare everything
    model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        model, optimizer, train_dataloader, lr_scheduler
    )

    # Load checkpoint if it exists
    model, optimizer, lr_scheduler, start_epoch, global_step = load_checkpoint(model, optimizer, lr_scheduler)

    # Now you train the model
    for epoch in range(start_epoch, config.num_epochs):
        progress_bar = tqdm(total=len(train_dataloader), disable=not accelerator.is_local_main_process)
        progress_bar.set_description(f"Epoch {epoch}")
        for step, batch in enumerate(train_dataloader):
            clean_images = batch[0]
            noise = torch.randn(clean_images.shape, device=clean_images.device)
            bs = clean_images.shape[0]
            timesteps = torch.randint(
                0, noise_scheduler.config.num_train_timesteps, (bs,), device=clean_images.device,
                dtype=torch.int64
            )
            noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)

            with accelerator.accumulate(model):
                noise_pred = model(noisy_images, timesteps, return_dict=False)[0]
                loss = F.mse_loss(noise_pred, noise)
                accelerator.backward(loss)
                accelerator.clip_grad_norm_(model.parameters(), 1.0)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            progress_bar.update(1)
            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step}
            progress_bar.set_postfix(**logs)
            accelerator.log(logs, step=global_step)
            global_step += 1

        # Save checkpoint at the end of each epoch
        if accelerator.is_main_process:
            save_checkpoint(model, optimizer, lr_scheduler, epoch, global_step)

            # Optionally sample some demo images and save the model
            pipeline = DDPMPipeline(unet=accelerator.unwrap_model(model), scheduler=noise_scheduler)
            evaluate(config, epoch, pipeline)
            pipeline.save_pretrained(config.output_dir)


# train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)

from accelerate import notebook_launcher

args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)

notebook_launcher(train_loop, args, num_processes=1)