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>>> 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]), |
... ] |
... ) |
Use 🤗 Datasets’ set_transform method to apply the preprocess function on the fly during training: |
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>>> def transform(examples): |
... images = [preprocess(image.convert("RGB")) for image in examples["image"]] |
... return {"images": images} |
>>> dataset.set_transform(transform) |
Feel free to visualize the images again to confirm that they’ve been resized. Now you’re ready to wrap the dataset in a DataLoader for training! |
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>>> import torch |
>>> train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=config.train_batch_size, shuffle=True) |
Create a UNet2DModel |
Pretrained models in 🧨 Diffusers are easily created from their model class with the parameters you want. For example, to create a UNet2DModel: |
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>>> 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", |
... ), |
... ) |
It is often a good idea to quickly check the sample image shape matches the model output shape: |
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>>> sample_image = dataset[0]["images"].unsqueeze(0) |
>>> print("Input shape:", sample_image.shape) |
Input shape: torch.Size([1, 3, 128, 128]) |
>>> print("Output shape:", model(sample_image, timestep=0).sample.shape) |
Output shape: torch.Size([1, 3, 128, 128]) |
Great! Next, you’ll need a scheduler to add some noise to the image. |
Create a scheduler |
The scheduler behaves differently depending on whether you’re using the model for training or inference. During inference, the scheduler generates image from the noise. During training, the scheduler takes a model output - or a sample - from a specific point in the diffusion process and applies noise to the image according to a noise schedule and an update rule. |
Let’s take a look at the DDPMScheduler and use the add_noise method to add some random noise to the sample_image from before: |
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>>> import torch |
>>> from PIL import Image |
>>> from diffusers import DDPMScheduler |
>>> noise_scheduler = DDPMScheduler(num_train_timesteps=1000) |
>>> noise = torch.randn(sample_image.shape) |
>>> timesteps = torch.LongTensor([50]) |
>>> noisy_image = noise_scheduler.add_noise(sample_image, noise, timesteps) |
>>> Image.fromarray(((noisy_image.permute(0, 2, 3, 1) + 1.0) * 127.5).type(torch.uint8).numpy()[0]) |
The training objective of the model is to predict the noise added to the image. The loss at this step can be calculated by: |
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>>> import torch.nn.functional as F |
>>> noise_pred = model(noisy_image, timesteps).sample |
>>> loss = F.mse_loss(noise_pred, noise) |
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