diffusers/pr_13881/en/api/models/cogview4_transformer2d.md

CogView4Transformer2DModel

A Diffusion Transformer model for 2D data from CogView4

The model can be loaded with the following code snippet.

from diffusers import CogView4Transformer2DModel

transformer = CogView4Transformer2DModel.from_pretrained("THUDM/CogView4-6B", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")

CogView4Transformer2DModel[[diffusers.CogView4Transformer2DModel]]

• 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

• hidden_states (torch.Tensor of shape (batch_size, in_channels, height, width)) -- Input hidden_states.

• encoder_hidden_states (torch.Tensor of shape (batch_size, sequence_len, embed_dims)) -- Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.

• timestep (torch.LongTensor) -- Used to indicate denoising step.

• original_size (torch.Tensor) -- Original image size conditioning.

• target_size (torch.Tensor) -- Target image size conditioning.

• crop_coords (torch.Tensor) -- Crop coordinates conditioning.

• attention_kwargs (dict, optional) -- A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor.

• return_dict (bool, optional, defaults to True) -- Whether or not to return a ~models.transformer_2d.Transformer2DModelOutput instead of a plain tuple.

• attention_mask (torch.Tensor, optional) -- Mask applied to attention scores.

• image_rotary_emb (tuple of torch.Tensor, optional) -- Pre-computed rotary positional embeddings.If return_dict is True, an ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a tuple where the first element is the sample tensor.

The CogView4Transformer2DModel forward method.

Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

• 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.

The output of Transformer2DModel.