diffusers/pr_14409/en/api/models/cogview3plus_transformer2d.md

CogView3PlusTransformer2DModel

A Diffusion Transformer model for 2D data from CogView3Plus was introduced in CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion by Tsinghua University & ZhipuAI.

The model can be loaded with the following code snippet.

from diffusers import CogView3PlusTransformer2DModel

transformer = CogView3PlusTransformer2DModel.from_pretrained("THUDM/CogView3Plus-3b", subfolder="transformer", dtype=torch.bfloat16).to("cuda")

CogView3PlusTransformer2DModel[[diffusers.CogView3PlusTransformer2DModel]]

diffusers.CogView3PlusTransformer2DModel[[diffusers.CogView3PlusTransformer2DModel]]

diffusers.CogView3PlusTransformer2DModel(patch_size: int = 2, in_channels: int = 16, num_layers: int = 30, attention_head_dim: int = 40, num_attention_heads: int = 64, out_channels: int = 16, text_embed_dim: int = 4096, time_embed_dim: int = 512, condition_dim: int = 256, pos_embed_max_size: int = 128, sample_size: int = 128)

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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

The Transformer model introduced in CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion.

forward[[diffusers.CogView3PlusTransformer2DModel.forward]]

forward(hidden_states: Tensor, encoder_hidden_states: Tensor, timestep: LongTensor, original_size: Tensor, target_size: Tensor, crop_coords: Tensor, return_dict: bool = True)

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Parameters:

hidden_states (torch.Tensor) : Input hidden_states of shape (batch size, channel, height, width).

encoder_hidden_states (torch.Tensor) : Conditional embeddings (embeddings computed from the input conditions such as prompts) of shape (batch_size, sequence_len, text_embed_dim)

timestep (torch.LongTensor) : Used to indicate denoising step.

original_size (torch.Tensor) : CogView3 uses SDXL-like micro-conditioning for original image size as explained in section 2.2 of https://huggingface.co/papers/2307.01952.

target_size (torch.Tensor) : CogView3 uses SDXL-like micro-conditioning for target image size as explained in section 2.2 of https://huggingface.co/papers/2307.01952.

crop_coords (torch.Tensor) : CogView3 uses SDXL-like micro-conditioning for crop coordinates as explained in section 2.2 of https://huggingface.co/papers/2307.01952.

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

Returns: torch.Tensor or ~models.transformer_2d.Transformer2DModelOutput

The denoised latents using provided inputs as conditioning.

The CogView3PlusTransformer2DModel forward method.

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

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

diffusers.models.modeling_outputs.Transformer2DModelOutput(sample: torch.Tensor)

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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.

The output of Transformer2DModel.