Diffusers

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HiDreamImageTransformer2DModel

A Transformer model for image-like data from HiDream-I1.

The model can be loaded with the following code snippet.

from diffusers import HiDreamImageTransformer2DModel

transformer = HiDreamImageTransformer2DModel.from_pretrained("HiDream-ai/HiDream-I1-Full", subfolder="transformer", torch_dtype=torch.bfloat16)

HiDreamImageTransformer2DModel

class diffusers.HiDreamImageTransformer2DModel

< source >

( patch_size: typing.Optional[int] = Nonein_channels: int = 64out_channels: typing.Optional[int] = Nonenum_layers: int = 16num_single_layers: int = 32attention_head_dim: int = 128num_attention_heads: int = 20caption_channels: typing.List[int] = Nonetext_emb_dim: int = 2048num_routed_experts: int = 4num_activated_experts: int = 2axes_dims_rope: typing.Tuple[int, int] = (32, 32)max_resolution: typing.Tuple[int, int] = (128, 128)llama_layers: typing.List[int] = None )

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.

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