| # Transformer2D | |
| A Transformer model for image-like data from [CompVis](https://huggingface.co/CompVis) that is based on the [Vision Transformer](https://huggingface.co/papers/2010.11929) introduced by Dosovitskiy et al. The [`Transformer2DModel`] accepts discrete (classes of vector embeddings) or continuous (actual embeddings) inputs. | |
| When the input is **continuous**: | |
| 1. Project the input and reshape it to `(batch_size, sequence_length, feature_dimension)`. | |
| 2. Apply the Transformer blocks in the standard way. | |
| 3. Reshape to image. | |
| When the input is **discrete**: | |
| <Tip> | |
| It is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised image don't contain a prediction for the masked pixel because the unnoised image cannot be masked. | |
| </Tip> | |
| 1. Convert input (classes of latent pixels) to embeddings and apply positional embeddings. | |
| 2. Apply the Transformer blocks in the standard way. | |
| 3. Predict classes of unnoised image. | |
| ## Transformer2DModel | |
| [[autodoc]] Transformer2DModel | |
| ## Transformer2DModelOutput | |
| [[autodoc]] models.transformer_2d.Transformer2DModelOutput | |