# GlmImageTransformer2DModel A Diffusion Transformer model for 2D data from [GlmImageTransformer2DModel] (TODO). ## GlmImageTransformer2DModel[[diffusers.GlmImageTransformer2DModel]] - **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` - **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. - **prior_token_id** (`torch.Tensor`) -- Token ids for the prior embedding lookup. - **prior_token_drop** (`torch.Tensor`) -- Boolean mask indicating which prior embeddings should be dropped (zeroed out). - **timestep** (`torch.LongTensor`) -- Used to indicate denoising step. - **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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - **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. - **kv_caches** (`GlmImageKVCache`, *optional*) -- Pre-computed key/value caches used to speed up inference. - **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 [GlmImageTransformer2DModel](/docs/diffusers/main/en/api/models/glm_image_transformer2d#diffusers.GlmImageTransformer2DModel) forward method.