A 2D Transformer model for image-like data. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of h

r""" A basic Transformer block. Parameters: dim (`int`): The number of channels in the input and output. num_attention_heads

A pose detect method for image-like data. Parameters: model_det: (str) serialized ONNX format model path, such

Convert any spelled-out numbers into arabic numbers, while handling: - remove any commas - keep the suffixes such as: `1960s`, `274th`,

Applies British-American spelling mappings as listed in [1]. [1] https://www.tysto.com/uk-us-spelling-list.html

Select the sample with the highest log probabilities, penalized using either a simple length normalization or Google NMT paper's length penal

A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens

The output of [`Transformer2DModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or

The output of [`UNet2DConditionModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):

A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks. Args: in_channels (`int`): T

detect human pose by dwpose

Tiny Autoencoder block used in [`AutoencoderTiny`]. It is a mini residual module consisting of plain conv + ReLU blocks. Args:

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