# LongCatImageTransformer2DModel

The model can be loaded with the following code snippet.

```python
from diffusers import LongCatImageTransformer2DModel

transformer = LongCatImageTransformer2DModel.from_pretrained("meituan-longcat/LongCat-Image ", subfolder="transformer", torch_dtype=torch.bfloat16)
```

## LongCatImageTransformer2DModel[[diffusers.LongCatImageTransformer2DModel]]

The Transformer model introduced in Longcat-Image.

- **hidden_states** (`torch.FloatTensor` of shape `(batch size, channel, height, width)`) --
  Input `hidden_states`.
- **encoder_hidden_states** (`torch.FloatTensor` of 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 to `True`) --
  Whether or not to return a `~models.transformer_2d.Transformer2DModelOutput` instead of a plain
  tuple.If `return_dict` is True, an `~models.transformer_2d.Transformer2DModelOutput` is returned, otherwise a
`tuple` where the first element is the sample tensor.

The forward method.