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  < source > ( sample_size: Optional = 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: Tuple = ('CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'DownBlock2D') mid_block_type: Optional = 'UNetMidBlock2DCrossAttn' up_block_types: Tuple = ('UpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D') only_cross_attention: Union = False block_out_channels: Tuple = (320, 640, 1280, 1280) layers_per_block: Union = 2 downsample_padding: int = 1 mid_block_scale_factor: float = 1 dropout: float = 0.0 act_fn: str = 'silu' norm_num_groups: Optional = 32 norm_eps: float = 1e-05 cross_attention_dim: Union = 1280 transformer_layers_per_block: Union = 1 reverse_transformer_layers_per_block: Optional = None encoder_hid_dim: Optional = None encoder_hid_dim_type: Optional = None attention_head_dim: Union = 8 num_attention_heads: Union = None dual_cross_attention: bool = False use_linear_projection: bool = False class_embed_type: Optional = None addition_embed_type: Optional = None addition_time_embed_dim: Optional = None num_class_embeds: Optional = None upcast_attention: bool = False resnet_time_scale_shift: str = 'default' resnet_skip_time_act: bool = False resnet_out_scale_factor: float = 1.0 time_embedding_type: str = 'positional' time_embedding_dim: Optional = None time_embedding_act_fn: Optional = None timestep_post_act: Optional = None time_cond_proj_dim: Optional = None conv_in_kernel: int = 3 conv_out_kernel: int = 3 projection_class_embeddings_input_dim: Optional = None attention_type: str = 'default' class_embeddings_concat: bool = False mid_block_only_cross_attention: Optional = None cross_attention_norm: Optional = None addition_embed_type_num_heads: int = 64  )   Parameters   sample_size (int or Tuple[int, int], optional, defaults to None) —