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sayakpaul
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diffusers-qa-chatbot-artifacts

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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)