Diffusers

Join the Hugging Face community

and get access to the augmented documentation experience

Collaborate on models, datasets and Spaces

Faster examples with accelerated inference

Switch between documentation themes

to get started

UNet1DModel

The UNet model was originally introduced by Ronneberger et al. for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a diffusion system because it facilitates the actual diffusion process. There are several variants of the UNet model in 🤗 Diffusers, depending on it’s number of dimensions and whether it is a conditional model or not. This is a 1D UNet model.

The abstract from the paper is:

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.

UNet1DModel

class diffusers.UNet1DModel

< source >

( sample_size: int = 65536 sample_rate: Optional = None in_channels: int = 2 out_channels: int = 2 extra_in_channels: int = 0 time_embedding_type: str = 'fourier' flip_sin_to_cos: bool = True use_timestep_embedding: bool = False freq_shift: float = 0.0 down_block_types: Tuple = ('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D') up_block_types: Tuple = ('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip') mid_block_type: Tuple = 'UNetMidBlock1D' out_block_type: str = None block_out_channels: Tuple = (32, 32, 64) act_fn: str = None norm_num_groups: int = 8 layers_per_block: int = 1 downsample_each_block: bool = False )

Parameters

sample_size (int, optional) — Default length of sample. Should be adaptable at runtime.
in_channels (int, optional, defaults to 2) — Number of channels in the input sample.
out_channels (int, optional, defaults to 2) — Number of channels in the output.
extra_in_channels (int, optional, defaults to 0) — Number of additional channels to be added to the input of the first down block. Useful for cases where the input data has more channels than what the model was initially designed for.
time_embedding_type (str, optional, defaults to "fourier") — Type of time embedding to use.
freq_shift (float, optional, defaults to 0.0) — Frequency shift for Fourier time embedding.
flip_sin_to_cos (bool, optional, defaults to False) — Whether to flip sin to cos for Fourier time embedding.
down_block_types (Tuple[str], optional, defaults to ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")) — Tuple of downsample block types.
up_block_types (Tuple[str], optional, defaults to ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")) — Tuple of upsample block types.
block_out_channels (Tuple[int], optional, defaults to (32, 32, 64)) — Tuple of block output channels.
mid_block_type (str, optional, defaults to "UNetMidBlock1D") — Block type for middle of UNet.
out_block_type (str, optional, defaults to None) — Optional output processing block of UNet.
act_fn (str, optional, defaults to None) — Optional activation function in UNet blocks.
norm_num_groups (int, optional, defaults to 8) — The number of groups for normalization.
layers_per_block (int, optional, defaults to 1) — The number of layers per block.
downsample_each_block (int, optional, defaults to False) — Experimental feature for using a UNet without upsampling.

A 1D UNet model that takes a noisy sample and a timestep and returns a sample shaped output.

This model inherits from ModelMixin. Check the superclass documentation for it’s generic methods implemented for all models (such as downloading or saving).

forward

< source >

( sample: Tensor timestep: Union return_dict: bool = True ) → UNet1DOutput or tuple

Parameters

sample (torch.Tensor) — The noisy input tensor with the following shape (batch_size, num_channels, sample_size).
timestep (torch.Tensor or float or int) — The number of timesteps to denoise an input.
return_dict (bool, optional, defaults to True) — Whether or not to return a UNet1DOutput instead of a plain tuple.

Returns

UNet1DOutput or tuple

If return_dict is True, an UNet1DOutput is returned, otherwise a tuple is returned where the first element is the sample tensor.

The UNet1DModel forward method.

UNet1DOutput

class diffusers.models.unets.unet_1d.UNet1DOutput

< source >

( sample: Tensor )

Parameters

sample (torch.Tensor of shape (batch_size, num_channels, sample_size)) — The hidden states output from the last layer of the model.

The output of UNet1DModel.

< > Update on GitHub

←StableCascadeUNet UNet2DModel→