ClickDiffusion: Harnessing LLMs for Interactive Precise Image Editing (2404.04376v1)

Abstract: Recently, researchers have proposed powerful systems for generating and manipulating images using natural language instructions. However, it is difficult to precisely specify many common classes of image transformations with text alone. For example, a user may wish to change the location and breed of a particular dog in an image with several similar dogs. This task is quite difficult with natural language alone, and would require a user to write a laboriously complex prompt that both disambiguates the target dog and describes the destination. We propose ClickDiffusion, a system for precise image manipulation and generation that combines natural language instructions with visual feedback provided by the user through a direct manipulation interface. We demonstrate that by serializing both an image and a multi-modal instruction into a textual representation it is possible to leverage LLMs to perform precise transformations of the layout and appearance of an image. Code available at https://github.com/poloclub/ClickDiffusion.

• The paper introduces ClickDiffusion, a novel system that fuses natural language and direct manipulation to achieve precise image editing.
• It utilizes a LLM-based framework with in-context learning to convert spatial inputs into textual cues, enabling fine-grained control over image transformations.
• Experimental evaluations show that ClickDiffusion outperforms text-only models in tasks requiring accurate object repositioning and layout adjustments.

ClickDiffusion: A Multi-Modal System for Image Editing

The paper "ClickDiffusion: Harnessing LLMs for Interactive Precise Image Editing" introduces ClickDiffusion, a sophisticated image editing system designed to enable precise image manipulations through the fusion of natural language instructions and direct manipulation interfaces. This approach addresses the inadequacies of text-only image editing systems, which often struggle with precision and specificity in spatial transformations.

Key Contributions

The authors present two main contributions:

1. ClickDiffusion System: This system allows users, notably artists and designers, to carry out precise image manipulations. By integrating natural language commands with direct manipulation, users can perform complex edits such as moving objects or changing their appearance with ease and precision. For example, a user can specify instructions like "move \thicksquarecyan to \starcyan and make it a golden retriever," thereby combining spatial and textual inputs for flexible editing.
2. LLM-Based Framework: The paper introduces a novel framework leveraging LLMs to integrate visual and textual instructions for image manipulation. By representing visual components such as mouse interactions textually, the framework harnesses the few-shot learning capabilities of LLMs, thus generalizing to various transformations without extensive retraining.

Background and Related Work

The paper situates its contributions within the landscape of recent advancements in image generation and editing. While diffusion models have become notable for generating high-fidelity imagery from textual inputs, their limitations lie in executing fine-grained transformations. ClickDiffusion addresses gaps left by such models, emphasizing local edits and layout adjustments.

Additionally, the system builds on grounded image generation, a technique that aligns object positioning with pre-defined layouts. Existing works like LLM Grounded Diffusion informed the approach, where serialized image layouts are processed by LLMs.

The integration of direct manipulation—a concept established in the domain of human-computer interaction—further strengthens the method. While related efforts have combined direct manipulation with natural language, ClickDiffusion uniquely extends this to real-world image editing.

Methodology

ClickDiffusion's methodology is centered on multi-modal instruction processing. The system converts user-provided spatial and textual inputs into a textual format suitable for LLMs. This innovative use of serialization enables the manipulation of intermediate image layouts instead of pixel-level modifications.

Key to their methodology is the use of in-context learning, where the LLM receives example tasks within its input context, thereby facilitating task completion without direct model retraining. This technique aids in maintaining the model's versatility and adaptability.

Evaluation

The efficacy of ClickDiffusion is evaluated through comparative studies against text-only baselines such as InstructPix2Pix and LLM Grounded Diffusion. The system demonstrates superior performance in tasks demanding precision, like distinguishing and relocating objects within complex images. The experiments highlight ClickDiffusion's ability to achieve precise outcomes with more concise and intuitive user inputs.

Implications and Future Work

ClickDiffusion offers significant implications for image editing, particularly in professional domains like digital art and design. The system's capacity to blend language flexibility with spatial specificity provides a robust tool for practitioners needing precise control over image content.

Future developments could explore more extensive user studies to quantify performance enhancements and integrate additional modalities. Expanding the framework to accommodate more complex interactions and leveraging advances in LLM architectures could further refine its capabilities.

In conclusion, ClickDiffusion exemplifies a forward-thinking approach to image manipulation, setting a precedent for future systems that aim to balance user-friendliness with technical precision. The integration of LLMs with direct manipulation interfaces stands as a promising direction in advancing AI-driven creative tools.