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README.md

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 license: mit
 ---
 
-# pOps: Photo-Inspired Diffusion Operators
+# pOps: Photo-Inspired Diffusion Operators
+
+<div align="center">
+
+[**Project Page**](https://popspaper.github.io/pOps/) **|** [**Paper**](https://popspaper.github.io/pOps/static/files/pOps_paper.pdf) **|** [**Code**](https://github.com/pOpsPaper/pOps)
+</div>
+
+---
+
+
+## Introduction
+
+<p align="center">
+<img src="https://popspaper.github.io/pOps/static/figures/teaser_pops.jpg" width="800px"/>  
+Different operators trained using pOps. Our method learns operators that are applied directly in the image embedding space, resulting in a variety of semantic operations that can then be realized as images using an image diffusion model.
+</p>
+
+## Trained Operators
+- [Texturing Operator](https://huggingface.co/pOpsPaper/operators/blob/main/models/texturing/learned_prior.pth): Given an image embedding of an object and an image embedding of a texture exemplar, paint the object with the provided texture.
+- [Scene Operator](https://huggingface.co/pOpsPaper/operators/blob/main/models/scene/learned_prior.pth): Given an image embedding of an object and an image embedding representing a scene layout, generate an image placing the object within a semantically similar scene.
+- [Union Operator](https://huggingface.co/pOpsPaper/operators/blob/main/models/union/learned_prior.pth): Given two image embeddings representing scenes with one or multiple objects, combine the objects appearing in the scenes into a single embedding composed of both objects.
+- [Instruct Operator](https://huggingface.co/pOpsPaper/operators/blob/main/models/instruct/learned_prior.pth): Given an image embedding of an object and a single-word adjective, apply the adjective to the image embedding, altering its characteristics accordingly.
+
+
+## Inference
+See the [pOps repo](https://github.com/pOpsPaper/pOps) for inference using the pretrained models
+