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UniTune: Text-Driven Image Editing by Fine Tuning a Diffusion Model on a Single Image (2210.09477v4)

Published 17 Oct 2022 in cs.CV, cs.GR, and cs.LG

Abstract: Text-driven image generation methods have shown impressive results recently, allowing casual users to generate high quality images by providing textual descriptions. However, similar capabilities for editing existing images are still out of reach. Text-driven image editing methods usually need edit masks, struggle with edits that require significant visual changes and cannot easily keep specific details of the edited portion. In this paper we make the observation that image-generation models can be converted to image-editing models simply by fine-tuning them on a single image. We also show that initializing the stochastic sampler with a noised version of the base image before the sampling and interpolating relevant details from the base image after sampling further increase the quality of the edit operation. Combining these observations, we propose UniTune, a novel image editing method. UniTune gets as input an arbitrary image and a textual edit description, and carries out the edit while maintaining high fidelity to the input image. UniTune does not require additional inputs, like masks or sketches, and can perform multiple edits on the same image without retraining. We test our method using the Imagen model in a range of different use cases. We demonstrate that it is broadly applicable and can perform a surprisingly wide range of expressive editing operations, including those requiring significant visual changes that were previously impossible.

PDF HTML Abstract

An Expert Overview of UniTune: Text-Driven Image Editing by Fine Tuning a Diffusion Model on a Single Image

The paper “UniTune: Text-Driven Image Editing by Fine Tuning a Diffusion Model on a Single Image” presents a novel approach to text-driven image editing that significantly enhances the capabilities of existing text-to-image models. Traditional text-driven image generation has achieved commendable results, but applying these capabilities to editing existing images has remained a challenge. The UniTune approach leverages the fine-tuning of large-scale diffusion models on a single image, achieving impressive results across various complex editing tasks.

Methodological Framework

UniTune distinguishes itself by converting text-to-image diffusion models into image editing tools. It achieves this transformation through a two-stage process. Firstly, the model is fine-tuned on a single image in conjunction with a unique text token. This fine-tuning process biases the image model towards the input image while maintaining the model's inherent capability for varied generative creativity. Secondly, the sampling process is adeptly modified to balance fidelity to the original image with adherence to the textual edit prompt. This is accomplished using classifier-free guidance and initialized sampling from a strategically noised version of the original image, akin to SDEdit techniques.

Empirical Results

UniTune demonstrates its capabilities across a broad range of editing tasks, including localized object additions, complex stylistic changes, and global transformations. It shows proficiency in maintaining both visual fidelity—retaining the original image's visual characteristics—and semantic fidelity—preserving the underlying meaning and context of the image. This robustness makes it particularly effective in generating local changes without necessitating additional inputs like masks or sketches, which are typically required by other methods.

The paper further establishes the effectiveness of UniTune by comparing it to existing methods such as SDedit. The evaluation includes qualitative and quantitative measures and reveals a substantial preference for UniTune, particularly in scenarios demanding substantial visual change.

Theoretical and Practical Implications

Theoretically, UniTune bridges the gap between image generation and editing, broadening our understanding of how single-instance tuning can modify model outputs. It demonstrates that biasing a model’s output distribution doesn't lead to catastrophic forgetting and that underlying generative capacities remain preserved. Practically, this extension can significantly enhance creative workflows, enabling flexible and intuitive image editing facilitated by natural language, making powerful editing accessible to non-experts.

Moreover, UniTune’s adaptability to different architectures like Stable Diffusion signifies its scalability and potential applicability across various platforms. This flexibility highlights its potential for integration into various application domains, including graphic design, creative media, and user-generated content platforms.

Future Directions

While the UniTune approach shows significant promise, several open questions remain. These include optimizing the balance between fidelity and expressiveness, enhancing generation speed, and ensuring consistency across different diffusion model architectures. Additionally, addressing societal implications related to potential biases and misuse of edited images continues to be an important consideration, necessitating careful oversight and further paper.

In conclusion, the UniTune method stands as a substantial advancement in image editing technology, leveraging fine-tuning on single instances to preserve model competency while facilitating nuanced and context-aware image editing. Its contributions are poised to inform future research directions and practical applications within the field of computer graphics and beyond.

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Authors (6)
  1. Dani Valevski (5 papers)
  2. Matan Kalman (3 papers)
  3. Eyal Molad (2 papers)
  4. Eyal Segalis (2 papers)
  5. Yossi Matias (61 papers)
  6. Yaniv Leviathan (8 papers)
Citations (27)
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