#
QwenImageTransformer2DModel

The model can be loaded with the following code snippet.

    from diffusers import QwenImageTransformer2DModel

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

##
QwenImageTransformer2DModel

###
class diffusers.QwenImageTransformer2DModel

< source >

( patch_size: int = 2 in_channels: int = 64 out_channels: typing.Optional[int] = 16 num_layers: int = 60 attention_head_dim: int = 128 num_attention_heads: int = 24 joint_attention_dim: int = 3584 guidance_embeds: bool = False axes_dims_rope: typing.Tuple[int, int, int] = (16, 56, 56) )

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.

The Transformer model introduced in Qwen.

####
forward

< source >

( hidden_states: Tensor encoder_hidden_states: Tensor = None encoder_hidden_states_mask: Tensor = None timestep: LongTensor = None img_shapes: typing.Optional[typing.List[typing.Tuple[int, int, int]]] = None txt_seq_lens: typing.Optional[typing.List[int]] = None guidance: Tensor = None attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None controlnet_block_samples = None return_dict: bool = True )

Parameters

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

The `QwenTransformer2DModel` forward method.

##
Transformer2DModelOutput

###
class diffusers.models.modeling_outputs.Transformer2DModelOutput

< source >

( 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.

< > Update on GitHub