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c0af20c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | # 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
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