Multi-Hyperparameter Text-Guided Real Image Edits

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Computer Vision

Multi-Hyperparameter Text-Guided Real Image Edits

Direct Inversion: Optimization-Free Text-Driven Real Image Editing with Diffusion Models

We propose an optimization-free and zero fine-tuning framework that applies complex and non-rigid edits to a single real image via a text prompt, avoiding all the pitfalls described above.

Using widely-available generic pre-trained text-to-image diffusion models, we demonstrate the ability to modulate pose, scene, background, style, color, and even racial identity in an extremely flexible manner through a single targettext detailing the desired edit.

Furthermore, our method, which we name proposes multiple intuitively configurable hyperparameters to allow for a wide range of types and extents of real imageedits.

We prove our method s efficacy in producing high-quality, diverse, semantic coherent, and faithful real image edits through applying it on avariety of inputs for a multitude of tasks.

We also formalize our method in well-established theory, detail future experiments for further improvement, and compare against state-of-the-art attempts.

Authors

Adham Elarabawy, Harish Kamath, Samuel Denton

Related Topics

Text-to - image generation models

Text-to - image diffusion models

Per-instance or per-task fine-tuning

Optimization-free and zero fine-tuning framework

Generic pre-trained text-to - image diffusion models

Image-editing technique

Stable diffusion @ cite2

Per-instance training

Text-to - image machine learning models

Diffusion model implementations

Imagen @ cite1

Optimization-free, zero fine-tuning, text-based semantic image editing method

Dall-e @ cite0

Introduction

We present an optimization-free, zero fine-tuning, text-based semantic image editing method that can make flexible edits to both global and local structure, attributes, style, and much more-only requiring a single input image and text prompt.

Our method only requires an input image, along with a corresponding text prompt describing the desired edit.

Given these inputs, our method is able to elaborately modify the original image using the target edit text prompt-adding objects, as well as modulating scene, background, pose, style, color, and even race/ethnicity.

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Keep Up With Latest Trending Papers. Computer Science, AI and more.Subscribe

CC BY

Source arXiv

Computer Vision

Multi-Hyperparameter Text-Guided Real Image Edits

Direct Inversion: Optimization-Free Text-Driven Real Image Editing with Diffusion Models

We propose an optimization-free and zero fine-tuning framework that applies complex and non-rigid edits to a single real image via a text prompt, avoiding all the pitfalls described above.

Furthermore, our method, which we name proposes multiple intuitively configurable hyperparameters to allow for a wide range of types and extents of real imageedits.

We prove our method s efficacy in producing high-quality, diverse, semantic coherent, and faithful real image edits through applying it on avariety of inputs for a multitude of tasks.

We also formalize our method in well-established theory, detail future experiments for further improvement, and compare against state-of-the-art attempts.

Authors

Adham Elarabawy, Harish Kamath, Samuel Denton

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