Hugging Face's logo

Diffusers documentation

UNet2DConditionModel

You are viewing v0.21.0 version. A newer version v0.35.1 is available.
Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started

UNet2DConditionModel

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 2D UNet conditional 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.

UNet2DConditionModel

class diffusers.UNet2DConditionModel

< >

( sample_size: typing.Optional[int] = None in_channels: int = 4 out_channels: int = 4 center_input_sample: bool = False flip_sin_to_cos: bool = True freq_shift: int = 0 down_block_types: typing.Tuple[str] = ('CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'DownBlock2D') mid_block_type: typing.Optional[str] = 'UNetMidBlock2DCrossAttn' up_block_types: typing.Tuple[str] = ('UpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D') only_cross_attention: typing.Union[bool, typing.Tuple[bool]] = False block_out_channels: typing.Tuple[int] = (320, 640, 1280, 1280) layers_per_block: typing.Union[int, typing.Tuple[int]] = 2 downsample_padding: int = 1 mid_block_scale_factor: float = 1 dropout: float = 0.0 act_fn: str = 'silu' norm_num_groups: typing.Optional[int] = 32 norm_eps: float = 1e-05 cross_attention_dim: typing.Union[int, typing.Tuple[int]] = 1280 transformer_layers_per_block: typing.Union[int, typing.Tuple[int]] = 1 encoder_hid_dim: typing.Optional[int] = None encoder_hid_dim_type: typing.Optional[str] = None attention_head_dim: typing.Union[int, typing.Tuple[int]] = 8 num_attention_heads: typing.Union[int, typing.Tuple[int], NoneType] = None dual_cross_attention: bool = False use_linear_projection: bool = False class_embed_type: typing.Optional[str] = None addition_embed_type: typing.Optional[str] = None addition_time_embed_dim: typing.Optional[int] = None num_class_embeds: typing.Optional[int] = None upcast_attention: bool = False resnet_time_scale_shift: str = 'default' resnet_skip_time_act: bool = False resnet_out_scale_factor: int = 1.0 time_embedding_type: str = 'positional' time_embedding_dim: typing.Optional[int] = None time_embedding_act_fn: typing.Optional[str] = None timestep_post_act: typing.Optional[str] = None time_cond_proj_dim: typing.Optional[int] = None conv_in_kernel: int = 3 conv_out_kernel: int = 3 projection_class_embeddings_input_dim: typing.Optional[int] = None attention_type: str = 'default' class_embeddings_concat: bool = False mid_block_only_cross_attention: typing.Optional[bool] = None cross_attention_norm: typing.Optional[str] = None addition_embed_type_num_heads = 64 )

Parameters

  • sample_size (int or Tuple[int, int], optional, defaults to None) — Height and width of input/output sample.
  • in_channels (int, optional, defaults to 4) — Number of channels in the input sample.
  • out_channels (int, optional, defaults to 4) — Number of channels in the output.
  • center_input_sample (bool, optional, defaults to False) — Whether to center the input sample.
  • flip_sin_to_cos (bool, optional, defaults to False) — Whether to flip the sin to cos in the time embedding.
  • freq_shift (int, optional, defaults to 0) — The frequency shift to apply to the time embedding.
  • down_block_types (Tuple[str], optional, defaults to ("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")) — The tuple of downsample blocks to use.
  • mid_block_type (str, optional, defaults to "UNetMidBlock2DCrossAttn") — Block type for middle of UNet, it can be either UNetMidBlock2DCrossAttn or UNetMidBlock2DSimpleCrossAttn. If None, the mid block layer is skipped.
  • up_block_types (Tuple[str], optional, defaults to ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")) — The tuple of upsample blocks to use.
  • only_cross_attention(bool or Tuple[bool], optional, default to False) — Whether to include self-attention in the basic transformer blocks, see BasicTransformerBlock.
  • block_out_channels (Tuple[int], optional, defaults to (320, 640, 1280, 1280)) — The tuple of output channels for each block.
  • layers_per_block (int, optional, defaults to 2) — The number of layers per block.
  • downsample_padding (int, optional, defaults to 1) — The padding to use for the downsampling convolution.
  • mid_block_scale_factor (float, optional, defaults to 1.0) — The scale factor to use for the mid block.
  • dropout (float, optional, defaults to 0.0) — The dropout probability to use.
  • act_fn (str, optional, defaults to "silu") — The activation function to use.
  • norm_num_groups (int, optional, defaults to 32) — The number of groups to use for the normalization. If None, normalization and activation layers is skipped in post-processing.
  • norm_eps (float, optional, defaults to 1e-5) — The epsilon to use for the normalization.
  • cross_attention_dim (int or Tuple[int], optional, defaults to 1280) — The dimension of the cross attention features.
  • transformer_layers_per_block (int or Tuple[int], optional, defaults to 1) — The number of transformer blocks of type BasicTransformerBlock. Only relevant for CrossAttnDownBlock2D, CrossAttnUpBlock2D, UNetMidBlock2DCrossAttn.
  • encoder_hid_dim (int, optional, defaults to None) — If encoder_hid_dim_type is defined, encoder_hidden_states will be projected from encoder_hid_dim dimension to cross_attention_dim.
  • encoder_hid_dim_type (str, optional, defaults to None) — If given, the encoder_hidden_states and potentially other embeddings are down-projected to text embeddings of dimension cross_attention according to encoder_hid_dim_type.
  • attention_head_dim (int, optional, defaults to 8) — The dimension of the attention heads.
  • num_attention_heads (int, optional) — The number of attention heads. If not defined, defaults to attention_head_dim
  • resnet_time_scale_shift (str, optional, defaults to "default") — Time scale shift config for ResNet blocks (see ResnetBlock2D). Choose from default or scale_shift.
  • class_embed_type (str, optional, defaults to None) — The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None, "timestep", "identity", "projection", or "simple_projection".
  • addition_embed_type (str, optional, defaults to None) — Configures an optional embedding which will be summed with the time embeddings. Choose from None or “text”. “text” will use the TextTimeEmbedding layer. addition_time_embed_dim — (int, optional, defaults to None): Dimension for the timestep embeddings.
  • num_class_embeds (int, optional, defaults to None) — Input dimension of the learnable embedding matrix to be projected to time_embed_dim, when performing class conditioning with class_embed_type equal to None.
  • time_embedding_type (str, optional, defaults to positional) — The type of position embedding to use for timesteps. Choose from positional or fourier.
  • time_embedding_dim (int, optional, defaults to None) — An optional override for the dimension of the projected time embedding.
  • time_embedding_act_fn (str, optional, defaults to None) — Optional activation function to use only once on the time embeddings before they are passed to the rest of the UNet. Choose from silu, mish, gelu, and swish.
  • timestep_post_act (str, optional, defaults to None) — The second activation function to use in timestep embedding. Choose from silu, mish and gelu.
  • time_cond_proj_dim (int, optional, defaults to None) — The dimension of cond_proj layer in the timestep embedding.
  • conv_in_kernel (int, optional, default to 3) — The kernel size of conv_in layer.
  • conv_out_kernel (int, optional, default to 3) — The kernel size of conv_out layer.
  • projection_class_embeddings_input_dim (int, optional) — The dimension of the class_labels input when class_embed_type="projection". Required when class_embed_type="projection".
  • class_embeddings_concat (bool, optional, defaults to False) — Whether to concatenate the time embeddings with the class embeddings.
  • mid_block_only_cross_attention (bool, optional, defaults to None) — Whether to use cross attention with the mid block when using the UNetMidBlock2DSimpleCrossAttn. If only_cross_attention is given as a single boolean and mid_block_only_cross_attention is None, the only_cross_attention value is used as the value for mid_block_only_cross_attention. Default to False otherwise.

A conditional 2D UNet model that takes a noisy sample, conditional state, 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

< >

( sample: FloatTensor timestep: typing.Union[torch.Tensor, float, int] encoder_hidden_states: Tensor class_labels: typing.Optional[torch.Tensor] = None timestep_cond: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None added_cond_kwargs: typing.Union[typing.Dict[str, torch.Tensor], NoneType] = None down_block_additional_residuals: typing.Optional[typing.Tuple[torch.Tensor]] = None mid_block_additional_residual: typing.Optional[torch.Tensor] = None encoder_attention_mask: typing.Optional[torch.Tensor] = None return_dict: bool = True ) → UNet2DConditionOutput or tuple

Parameters

  • sample (torch.FloatTensor) — The noisy input tensor with the following shape (batch, channel, height, width).
  • timestep (torch.FloatTensor or float or int) — The number of timesteps to denoise an input.
  • encoder_hidden_states (torch.FloatTensor) — The encoder hidden states with shape (batch, sequence_length, feature_dim).
  • encoder_attention_mask (torch.Tensor) — A cross-attention mask of shape (batch, sequence_length) is applied to encoder_hidden_states. If True the mask is kept, otherwise if False it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to “discard” tokens.
  • return_dict (bool, optional, defaults to True) — Whether or not to return a UNet2DConditionOutput instead of a plain tuple.
  • cross_attention_kwargs (dict, optional) — A kwargs dictionary that if specified is passed along to the AttnProcessor. added_cond_kwargs — (dict, optional): A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that are passed along to the UNet blocks.

Returns

UNet2DConditionOutput or tuple

If return_dict is True, an UNet2DConditionOutput is returned, otherwise a tuple is returned where the first element is the sample tensor.

The UNet2DConditionModel forward method.

set_attention_slice

< >

( slice_size )

Parameters

  • slice_size (str or int or list(int), optional, defaults to "auto") — When "auto", input to the attention heads is halved, so attention is computed in two steps. If "max", maximum amount of memory is saved by running only one slice at a time. If a number is provided, uses as many slices as attention_head_dim // slice_size. In this case, attention_head_dim must be a multiple of slice_size.

Enable sliced attention computation.

When this option is enabled, the attention module splits the input tensor in slices to compute attention in several steps. This is useful for saving some memory in exchange for a small decrease in speed.

set_attn_processor

< >

( processor: typing.Union[diffusers.models.attention_processor.AttnProcessor, diffusers.models.attention_processor.AttnProcessor2_0, diffusers.models.attention_processor.XFormersAttnProcessor, diffusers.models.attention_processor.SlicedAttnProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor, diffusers.models.attention_processor.SlicedAttnAddedKVProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor2_0, diffusers.models.attention_processor.XFormersAttnAddedKVProcessor, diffusers.models.attention_processor.CustomDiffusionAttnProcessor, diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor, diffusers.models.attention_processor.LoRAAttnProcessor, diffusers.models.attention_processor.LoRAAttnProcessor2_0, diffusers.models.attention_processor.LoRAXFormersAttnProcessor, diffusers.models.attention_processor.LoRAAttnAddedKVProcessor, typing.Dict[str, typing.Union[diffusers.models.attention_processor.AttnProcessor, diffusers.models.attention_processor.AttnProcessor2_0, diffusers.models.attention_processor.XFormersAttnProcessor, diffusers.models.attention_processor.SlicedAttnProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor, diffusers.models.attention_processor.SlicedAttnAddedKVProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor2_0, diffusers.models.attention_processor.XFormersAttnAddedKVProcessor, diffusers.models.attention_processor.CustomDiffusionAttnProcessor, diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor, diffusers.models.attention_processor.LoRAAttnProcessor, diffusers.models.attention_processor.LoRAAttnProcessor2_0, diffusers.models.attention_processor.LoRAXFormersAttnProcessor, diffusers.models.attention_processor.LoRAAttnAddedKVProcessor]]] )

Parameters

  • processor (dict of AttentionProcessor or only AttentionProcessor) — The instantiated processor class or a dictionary of processor classes that will be set as the processor for all Attention layers.

    If processor is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.

Sets the attention processor to use to compute attention.

set_default_attn_processor

< >

( )

Disables custom attention processors and sets the default attention implementation.

UNet2DConditionOutput

class diffusers.models.unet_2d_condition.UNet2DConditionOutput

< >

( sample: FloatTensor = None )

Parameters

  • sample (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — The hidden states output conditioned on encoder_hidden_states input. Output of last layer of model.

The output of UNet2DConditionModel.

FlaxUNet2DConditionModel

class diffusers.FlaxUNet2DConditionModel

< >

( sample_size: int = 32 in_channels: int = 4 out_channels: int = 4 down_block_types: typing.Tuple[str] = ('CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'DownBlock2D') up_block_types: typing.Tuple[str] = ('UpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D') only_cross_attention: typing.Union[bool, typing.Tuple[bool]] = False block_out_channels: typing.Tuple[int] = (320, 640, 1280, 1280) layers_per_block: int = 2 attention_head_dim: typing.Union[int, typing.Tuple[int]] = 8 num_attention_heads: typing.Union[int, typing.Tuple[int], NoneType] = None cross_attention_dim: int = 1280 dropout: float = 0.0 use_linear_projection: bool = False dtype: dtype = <class 'jax.numpy.float32'> flip_sin_to_cos: bool = True freq_shift: int = 0 use_memory_efficient_attention: bool = False parent: typing.Union[typing.Type[flax.linen.module.Module], typing.Type[flax.core.scope.Scope], typing.Type[flax.linen.module._Sentinel], NoneType] = <flax.linen.module._Sentinel object at 0x7f3306dc42e0> name: typing.Optional[str] = None )

Parameters

  • sample_size (int, optional) — The size of the input sample.
  • in_channels (int, optional, defaults to 4) — The number of channels in the input sample.
  • out_channels (int, optional, defaults to 4) — The number of channels in the output.
  • down_block_types (Tuple[str], optional, defaults to ("FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxDownBlock2D")) — The tuple of downsample blocks to use.
  • up_block_types (Tuple[str], optional, defaults to ("FlaxUpBlock2D", "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D")) — The tuple of upsample blocks to use.
  • block_out_channels (Tuple[int], optional, defaults to (320, 640, 1280, 1280)) — The tuple of output channels for each block.
  • layers_per_block (int, optional, defaults to 2) — The number of layers per block.
  • attention_head_dim (int or Tuple[int], optional, defaults to 8) — The dimension of the attention heads.
  • num_attention_heads (int or Tuple[int], optional) — The number of attention heads.
  • cross_attention_dim (int, optional, defaults to 768) — The dimension of the cross attention features.
  • dropout (float, optional, defaults to 0) — Dropout probability for down, up and bottleneck blocks.
  • flip_sin_to_cos (bool, optional, defaults to True) — Whether to flip the sin to cos in the time embedding.
  • freq_shift (int, optional, defaults to 0) — The frequency shift to apply to the time embedding.
  • use_memory_efficient_attention (bool, optional, defaults to False) — Enable memory efficient attention as described here.

A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample shaped output.

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

This model is also a Flax Linen flax.linen.Module subclass. Use it as a regular Flax Linen module and refer to the Flax documentation for all matters related to its general usage and behavior.

Inherent JAX features such as the following are supported:

FlaxUNet2DConditionOutput

class diffusers.models.unet_2d_condition_flax.FlaxUNet2DConditionOutput

< >

( sample: Array )

Parameters

  • sample (jnp.ndarray of shape (batch_size, num_channels, height, width)) — The hidden states output conditioned on encoder_hidden_states input. Output of last layer of model.

The output of FlaxUNet2DConditionModel.

replace

< >

( **updates )

“Returns a new object replacing the specified fields with new values.

←UNet2DModel UNet3DConditionModel→