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GlmImageTransformer2DModel

A Diffusion Transformer model for 2D data from [GlmImageTransformer2DModel] (TODO).

GlmImageTransformer2DModel

class diffusers.GlmImageTransformer2DModel

< source >

( patch_size: int = 2 in_channels: int = 16 out_channels: int = 16 num_layers: int = 30 attention_head_dim: int = 40 num_attention_heads: int = 64 text_embed_dim: int = 1472 time_embed_dim: int = 512 condition_dim: int = 256 prior_vq_quantizer_codebook_size: int = 16384 )

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 1472) — 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

Update on GitHub

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