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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-taskfine-tuning and optimization, require multiple novel views, or they inherentlyentangle preservation of real image identity, semantic coherence, andfaithfulness to text guidance. In this paper, we propose an optimization-freeand zero fine-tuning framework that applies complex and non-rigid edits to asingle real image via a text prompt, avoiding all the pitfalls described above.Using widely-available generic pre-trained text-to-image diffusion models, wedemonstrate the ability to modulate pose, scene, background, style, color, andeven racial identity in an extremely flexible manner through a single targettext detailing the desired edit. Furthermore, our method, which we name$\textit{Direct Inversion}$, proposes multiple intuitively configurablehyperparameters to allow for a wide range of types and extents of real imageedits. We prove our method's efficacy in producing high-quality, diverse,semantically coherent, and faithful real image edits through applying it on avariety of inputs for a multitude of tasks. We also formalize our method inwell-established theory, detail future experiments for further improvement, andcompare against state-of-the-art attempts.

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