# QwenImageTransformer2DModel

The model can be loaded with the following code snippet.

```python
from diffusers import QwenImageTransformer2DModel

transformer = QwenImageTransformer2DModel.from_pretrained("Qwen/QwenImage-20B", subfolder="transformer", torch_dtype=torch.bfloat16)
```

## QwenImageTransformer2DModel[[diffusers.QwenImageTransformer2DModel]]

#### diffusers.QwenImageTransformer2DModel[[diffusers.QwenImageTransformer2DModel]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_qwenimage.py#L479)

The Transformer model introduced in Qwen.

forwarddiffusers.QwenImageTransformer2DModel.forwardhttps://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_qwenimage.py#L566[{"name": "hidden_states", "val": ": Tensor"}, {"name": "encoder_hidden_states", "val": ": Tensor = None"}, {"name": "encoder_hidden_states_mask", "val": ": Tensor = None"}, {"name": "timestep", "val": ": LongTensor = None"}, {"name": "img_shapes", "val": ": typing.Optional[typing.List[typing.Tuple[int, int, int]]] = None"}, {"name": "txt_seq_lens", "val": ": typing.Optional[typing.List[int]] = None"}, {"name": "guidance", "val": ": Tensor = None"}, {"name": "attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "controlnet_block_samples", "val": " = None"}, {"name": "return_dict", "val": ": bool = True"}]- **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.
- **encoder_hidden_states_mask** (`torch.Tensor` of shape `(batch_size, text_sequence_length)`) --
  Mask of the input conditions.
- **timestep** ( `torch.LongTensor`) --
  Used to indicate denoising step.
- **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.0If `return_dict` is True, an `~models.transformer_2d.Transformer2DModelOutput` is returned, otherwise a
`tuple` where the first element is the sample tensor.

The `QwenTransformer2DModel` forward method.

**Parameters:**

patch_size (`int`, defaults to `2`) : 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 `60`) : The number of layers of dual 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 `3584`) : The number of dimensions to use for the joint attention (embedding/channel dimension of `encoder_hidden_states`).

guidance_embeds (`bool`, defaults to `False`) : Whether to use guidance embeddings for guidance-distilled variant of the model.

axes_dims_rope (`Tuple[int]`, defaults to `(16, 56, 56)`) : The dimensions to use for the rotary positional embeddings.

**Returns:**

If `return_dict` is True, an `~models.transformer_2d.Transformer2DModelOutput` is returned, otherwise a
`tuple` where the first element is the sample tensor.

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

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

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/modeling_outputs.py#L21)

The output of [Transformer2DModel](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel).

**Parameters:**

sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [Transformer2DModel](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel) is discrete) : The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability distributions for the unnoised latent pixels.

