Parameters

• patch_size (int, defaults to 2) — The size of the patches to use in the patch embedding layer.
• in_channels (int, defaults to 16) — The number of channels in the input.
• num_layers (int, defaults to 30) — The number of layers of Transformer blocks to use.
• attention_head_dim (int, defaults to 40) — The number of channels in each head.
• num_attention_heads (int, defaults to 64) — The number of heads to use for multi-head attention.
• out_channels (int, defaults to 16) — The number of channels in the output.
• text_embed_dim (int, defaults to 4096) — Input dimension of text embeddings from the text encoder.
• time_embed_dim (int, defaults to 512) — Output dimension of timestep embeddings.
• condition_dim (int, defaults to 256) — The embedding dimension of the input SDXL-style resolution conditions (original_size, target_size, crop_coords).
• pos_embed_max_size (int, defaults to 128) — The maximum resolution of the positional embeddings, from which slices of shape H x W are taken and added to input patched latents, where H and W are the latent height and width respectively. A value of 128 means that the maximum supported height and width for image generation is 128 * vae_scale_factor * patch_size => 128 * 8 * 2 => 2048.
• sample_size (int, defaults to 128) — The base resolution of input latents. If height/width is not provided during generation, this value is used to determine the resolution as sample_size * vae_scale_factor => 128 * 8 => 1024

Parameters

• sample (torch.Tensor of shape (batch_size, num_channels, height, width) or (batch size, num_vector_embeds - 1, num_latent_pixels) if Transformer2DModel is discrete) — The hidden states output conditioned on the encoder_hidden_states input. If discrete, returns probability distributions for the unnoised latent pixels.