Diffusers documentation
LongCatImageTransformer2DModel
You are viewing main version, which requires installation from source. If you'd like
regular pip install, checkout the latest stable version (v0.40.0).
LongCatImageTransformer2DModel
The model can be loaded with the following code snippet.
from diffusers import LongCatImageTransformer2DModel
transformer = LongCatImageTransformer2DModel.from_pretrained("meituan-longcat/LongCat-Image ", subfolder="transformer", dtype=torch.bfloat16)LongCatImageTransformer2DModel
class diffusers.LongCatImageTransformer2DModel
< source >( patch_size: int = 1in_channels: int = 64num_layers: int = 19num_single_layers: int = 38attention_head_dim: int = 128num_attention_heads: int = 24joint_attention_dim: int = 3584pooled_projection_dim: int = 3584axes_dims_rope: list = [16, 56, 56] )
The Transformer model introduced in Longcat-Image.
forward
< source >( hidden_states: Tensorencoder_hidden_states: Tensor = Nonetimestep: LongTensor = Noneimg_ids: Tensor = Nonetxt_ids: Tensor = Noneguidance: Tensor = Nonereturn_dict: bool = True )
Parameters
- hidden_states (
torch.FloatTensorof shape(batch size, channel, height, width)) — Inputhidden_states. - encoder_hidden_states (
torch.FloatTensorof shape(batch size, sequence_len, embed_dims)) — 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. - guidance (
torch.Tensor, optional) — Guidance scale embedding used for guidance-distilled variants of the model. - return_dict (
bool, optional, defaults toTrue) — Whether or not to return a~models.transformer_2d.Transformer2DModelOutputinstead of a plain tuple.
The forward method.