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

• URL: http://arxiv.org/abs/2211.07825v1
• Date: Tue, 15 Nov 2022 01:07:38 GMT
• Title: Direct Inversion: Optimization-Free Text-Driven Real Image Editing with Diffusion Models
• Authors: Adham Elarabawy, Harish Kamath, Samuel Denton
• Abstract summary: 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. We prove our method's efficacy in producing high-quality, diverse, semantically coherent, and faithful real image edits.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rise of large, publicly-available text-to-image diffusion models, text-guided real image editing has garnered much research attention recently. Existing methods tend to either rely on some form of per-instance or per-task fine-tuning and optimization, require multiple novel views, or they inherently entangle preservation of real image identity, semantic coherence, and faithfulness to text guidance. In this paper, 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 target text detailing the desired edit. Furthermore, our method, which we name $\textit{Direct Inversion}$, proposes multiple intuitively configurable hyperparameters to allow for a wide range of types and extents of real image edits. We prove our method's efficacy in producing high-quality, diverse, semantically coherent, and faithful real image edits through applying it on a variety 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.

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