Diffusers documentation
HiDreamImageTransformer2DModel
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regular pip install, checkout the latest stable version (v0.33.1).
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] = None in_channels: int = 64 out_channels: typing.Optional[int] = None num_layers: int = 16 num_single_layers: int = 32 attention_head_dim: int = 128 num_attention_heads: int = 20 caption_channels: typing.List[int] = None text_emb_dim: int = 2048 num_routed_experts: int = 4 num_activated_experts: int = 2 axes_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.Tensorof 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 theencoder_hidden_statesinput. If discrete, returns probability distributions for the unnoised latent pixels.
The output of Transformer2DModel.