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patrickvonplaten merged 1 commit into from
Jun 7, 2023
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[docs] add image processor documentation #3710

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2 changes: 2 additions & 0 deletions docs/source/en/_toctree.yml
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Expand Up @@ -146,6 +146,8 @@
title: Loaders
- local: api/utilities
title: Utilities
- local: api/image_processor
title: Vae Image Processor
title: Main Classes
- sections:
- local: api/pipelines/overview
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22 changes: 22 additions & 0 deletions docs/source/en/api/image_processor.mdx
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<!--Copyright 2023 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->

# Image Processor for VAE

Image processor provides a unified API for Stable Diffusion pipelines to prepare their image inputs for VAE encoding, as well as post-processing their outputs once decoded. This includes transformations such as resizing, normalization, and conversion between PIL Image, PyTorch, and Numpy arrays.

All pipelines with VAE image processor will accept image inputs in the format of PIL Image, PyTorch tensor, or Numpy array, and will able to return outputs in the format of PIL Image, Pytorch tensor, and Numpy array based on the `output_type` argument from the user. Additionally, the User can pass encoded image latents directly to the pipeline, or ask the pipeline to return latents as output with `output_type = 'pt'` argument. This allows you to take the generated latents from one pipeline and pass it to another pipeline as input, without ever having to leave the latent space. It also makes it much easier to use multiple pipelines together, by passing PyTorch tensors directly between different pipelines.


## VaeImageProcessor

[[autodoc]] image_processor.VaeImageProcessor