#
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", torch_dtype=torch.bfloat16).to("cuda")

##
CogView3PlusTransformer2DModel

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class diffusers.CogView3PlusTransformer2DModel

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( 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 )

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

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( hidden_states: Tensor encoder_hidden_states: Tensor timestep: LongTensor original_size: Tensor target_size: Tensor crop_coords: Tensor return_dict: bool = True ) →
`torch.Tensor` or `~models.transformer_2d.Transformer2DModelOutput`

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

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class diffusers.models.modeling_outputs.Transformer2DModelOutput

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( sample: torch.Tensor )

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.

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