pOpsPaper/operators

pOps: Photo-Inspired Diffusion Operators

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**Project Page** | **Paper** | **Code** </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: Given an image embedding of an object and an image embedding of a texture exemplar, paint the object with the provided texture.
• —Scene Operator: 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: 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: 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 for inference using the pretrained models