conditioned_mnist.py

import torch
import torchvision
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
from diffusers import DDPMScheduler, UNet2DModel
from matplotlib import pyplot as plt
from tqdm.auto import tqdm

exp_name = 'bs128'
# exp_name = 'bs'


device = 'mps' if torch.backends.mps.is_available() else 'cuda' if torch.cuda.is_available() else 'cpu'
print(f'Using device: {device}')

# Load the dataset
dataset = torchvision.datasets.MNIST(root="mnist/", train=True, download=True, transform=torchvision.transforms.ToTensor())

# Feed it into a dataloader (batch size 8 here just for demo)
train_dataloader = DataLoader(dataset, batch_size=8, shuffle=True)

# View some examples
x, y = next(iter(train_dataloader))
print('Input shape:', x.shape)
print('Labels:', y)

plt.imsave(exp_name + '_1.png', torchvision.utils.make_grid(x)[0], cmap='Greys')

class ClassConditionedUnet(nn.Module):
  def __init__(self, num_classes=10, class_emb_size=4):
    super().__init__()
    
    # The embedding layer will map the class label to a vector of size class_emb_size
    self.class_emb = nn.Embedding(num_classes, class_emb_size)

    # Self.model is an unconditional UNet with extra input channels to accept the conditioning information (the class embedding)
    self.model = UNet2DModel(
        sample_size=28,           # the target image resolution
        in_channels=1 + class_emb_size, # Additional input channels for class cond.
        out_channels=1,           # the number of output channels
        layers_per_block=2,       # how many ResNet layers to use per UNet block
        block_out_channels=(32, 64, 64), 
        down_block_types=( 
            "DownBlock2D",        # a regular ResNet downsampling block
            "AttnDownBlock2D",    # a ResNet downsampling block with spatial self-attention
            "AttnDownBlock2D",
        ), 
        up_block_types=(
            "AttnUpBlock2D", 
            "AttnUpBlock2D",      # a ResNet upsampling block with spatial self-attention
            "UpBlock2D",          # a regular ResNet upsampling block
          ),
    )

  # Our forward method now takes the class labels as an additional argument
  def forward(self, x, t, class_labels):
    # Shape of x:
    bs, ch, w, h = x.shape
    
    # class conditioning in right shape to add as additional input channels
    class_cond = self.class_emb(class_labels) # Map to embedding dimension
    class_cond = class_cond.view(bs, class_cond.shape[1], 1, 1).expand(bs, class_cond.shape[1], w, h)
    # x is shape (bs, 1, 28, 28) and class_cond is now (bs, 4, 28, 28)

    # Net input is now x and class cond concatenated together along dimension 1
    net_input = torch.cat((x, class_cond), 1) # (bs, 5, 28, 28)

    # Feed this to the UNet alongside the timestep and return the prediction
    return self.model(net_input, t).sample # (bs, 1, 28, 28)


#scheduler
noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule='squaredcos_cap_v2')

#@markdown Training loop (10 Epochs):

# Redefining the dataloader to set the batch size higher than the demo of 8
train_dataloader = DataLoader(dataset, batch_size=128, shuffle=True)
# train_dataloader = DataLoader(dataset, batch_size=1024, shuffle=True)


# How many runs through the data should we do?
n_epochs = 10

# Our network 
net = ClassConditionedUnet().to(device)

# Our loss function
loss_fn = nn.MSELoss()

# The optimizer
opt = torch.optim.Adam(net.parameters(), lr=1e-3) 

# Keeping a record of the losses for later viewing
losses = []

# The training loop
for epoch in range(n_epochs):
    for x, y in tqdm(train_dataloader):
        
        # Get some data and prepare the corrupted version
        x = x.to(device) * 2 - 1 # Data on the GPU (mapped to (-1, 1))
        y = y.to(device)
        noise = torch.randn_like(x)
        timesteps = torch.randint(0, 999, (x.shape[0],)).long().to(device)
        noisy_x = noise_scheduler.add_noise(x, noise, timesteps)

        # Get the model prediction
        pred = net(noisy_x, timesteps, y) # Note that we pass in the labels y

        # Calculate the loss
        loss = loss_fn(pred, noise) # How close is the output to the noise

        # Backprop and update the params:
        opt.zero_grad()
        loss.backward()
        opt.step()

        # Store the loss for later
        losses.append(loss.item())

    # Print out the average of the last 100 loss values to get an idea of progress:
    avg_loss = sum(losses[-100:])/100
    print(f'Finished epoch {epoch}. Average of the last 100 loss values: {avg_loss:05f}')

# View the loss curve
plt.plot(losses)
plt.savefig(exp_name + '_losses.png')

print('training finished, begin sampling')
#@markdown Sampling some different digits:

# Prepare random x to start from, plus some desired labels y
x = torch.randn(80, 1, 28, 28).to(device)
y = torch.tensor([[i]*8 for i in range(10)]).flatten().to(device)

# Sampling loop
for i, t in tqdm(enumerate(noise_scheduler.timesteps)):

    # Get model pred
    with torch.no_grad():
        residual = net(x, t, y)  # Again, note that we pass in our labels y

    # Update sample with step
    x = noise_scheduler.step(residual, t, x).prev_sample

# Show the results
fig, ax = plt.subplots(1, 1, figsize=(12, 12))
plt.imsave(exp_name + '_sampled.png', torchvision.utils.make_grid(x.detach().cpu().clip(-1, 1), nrow=8)[0], cmap='Greys')