# ChromaTransformer2DModel

A modified flux Transformer model from [Chroma](https://huggingface.co/lodestones/Chroma1-HD)

## ChromaTransformer2DModel[[diffusers.ChromaTransformer2DModel]]

- **patch_size** (`int`, defaults to `1`) --
  Patch size to turn the input data into small patches.
- **in_channels** (`int`, defaults to `64`) --
  The number of channels in the input.
- **out_channels** (`int`, *optional*, defaults to `None`) --
  The number of channels in the output. If not specified, it defaults to `in_channels`.
- **num_layers** (`int`, defaults to `19`) --
  The number of layers of dual stream DiT blocks to use.
- **num_single_layers** (`int`, defaults to `38`) --
  The number of layers of single stream DiT blocks to use.
- **attention_head_dim** (`int`, defaults to `128`) --
  The number of dimensions to use for each attention head.
- **num_attention_heads** (`int`, defaults to `24`) --
  The number of attention heads to use.
- **joint_attention_dim** (`int`, defaults to `4096`) --
  The number of dimensions to use for the joint attention (embedding/channel dimension of
  `encoder_hidden_states`).
- **axes_dims_rope** (`tuple[int]`, defaults to `(16, 56, 56)`) --
  The dimensions to use for the rotary positional embeddings.

The Transformer model introduced in Flux, modified for Chroma.

Reference: https://huggingface.co/lodestones/Chroma1-HD

- **hidden_states** (`torch.Tensor` of shape `(batch_size, image_sequence_length, in_channels)`) --
  Input `hidden_states`.
- **encoder_hidden_states** (`torch.Tensor` of shape `(batch_size, text_sequence_length, joint_attention_dim)`) --
  Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
- **timestep** ( `torch.LongTensor`) --
  Used to indicate denoising step.
- **img_ids** (`torch.Tensor`) --
  Image position ids used to compute the rotary positional embeddings.
- **txt_ids** (`torch.Tensor`) --
  Text position ids used to compute the rotary positional embeddings.
- **attention_mask** (`torch.Tensor`, *optional*) --
  Mask applied to `encoder_hidden_states` during attention.
- **controlnet_block_samples** (`list` of `torch.Tensor`, *optional*) --
  A list of tensors that if specified are added to the residuals of transformer blocks.
- **controlnet_single_block_samples** (`list` of `torch.Tensor`, *optional*) --
  A list of tensors that if specified are added to the residuals of single transformer blocks.
- **controlnet_blocks_repeat** (`bool`, *optional*, defaults to `False`) --
  Whether to repeat the controlnet block samples across all transformer blocks.
- **joint_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.If `return_dict` is True, an `~models.transformer_2d.Transformer2DModelOutput` is returned, otherwise a
`tuple` where the first element is the sample tensor.

The [FluxTransformer2DModel](/docs/diffusers/main/en/api/models/flux_transformer#diffusers.FluxTransformer2DModel) forward method.