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39 lines
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39 lines
1.7 KiB
Markdown
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# Transformer2DModel
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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.
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When the input is **continuous**:
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1. Project the input and reshape it to `(batch_size, sequence_length, feature_dimension)`.
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2. Apply the Transformer blocks in the standard way.
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3. Reshape to image.
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When the input is **discrete**:
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> [!TIP]
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> 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.
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1. Convert input (classes of latent pixels) to embeddings and apply positional embeddings.
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2. Apply the Transformer blocks in the standard way.
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3. Predict classes of unnoised image.
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## Transformer2DModel
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[[autodoc]] Transformer2DModel
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## Transformer2DModelOutput
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[[autodoc]] models.modeling_outputs.Transformer2DModelOutput
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