diffusers/pr_13966/en/api/models/longcat_image_transformer2d.md

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", 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.