InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User Instructions (2305.18047v1)

Abstract: Recent works have explored text-guided image editing using diffusion models and generated edited images based on text prompts. However, the models struggle to accurately locate the regions to be edited and faithfully perform precise edits. In this work, we propose a framework termed InstructEdit that can do fine-grained editing based on user instructions. Our proposed framework has three components: language processor, segmenter, and image editor. The first component, the language processor, processes the user instruction using a LLM. The goal of this processing is to parse the user instruction and output prompts for the segmenter and captions for the image editor. We adopt ChatGPT and optionally BLIP2 for this step. The second component, the segmenter, uses the segmentation prompt provided by the language processor. We employ a state-of-the-art segmentation framework Grounded Segment Anything to automatically generate a high-quality mask based on the segmentation prompt. The third component, the image editor, uses the captions from the language processor and the masks from the segmenter to compute the edited image. We adopt Stable Diffusion and the mask-guided generation from DiffEdit for this purpose. Experiments show that our method outperforms previous editing methods in fine-grained editing applications where the input image contains a complex object or multiple objects. We improve the mask quality over DiffEdit and thus improve the quality of edited images. We also show that our framework can accept multiple forms of user instructions as input. We provide the code at https://github.com/QianWangX/InstructEdit.

An Analysis of InstructEdit: Enhancing Diffusion-based Image Editing with User Instructions

The paper "InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User Instructions" presents a novel framework that enhances diffusion-based image editing through the integration of user instructions. The authors propose InstructEdit, a methodology composed of three primary components: a language processor, a segmenter, and an image editor. The overarching goal is to perform fine-grained image editing by accurately identifying and transforming specific image regions based on user directives.

Methodological Components

1. Language Processor: The framework uses a LLM, specifically ChatGPT, to process user instructions. The task involves parsing user directives into prompts suitable for image segmentation and captioning. This component can integrate BLIP2, a vision-LLM, which aids in deciphering unclear instructions by providing a descriptive context from the input image.
2. Segmenter: Utilizing the Grounded Segment Anything Model, which combines capabilities from a segmentation model (Segment Anything) and an open-set object detector (Grounded DINO), the segmenter aims to generate precise masks. This step circumvents the need for manual masks and enhances the editing precision in situations involving complex and multiple objects.
3. Image Editor: The editing phase leverages Stable Diffusion and features mask-guided image generation. By combining caption-derived prompts and the segmenter's masks, the model performs controlled and targeted image manipulations, maintaining a balance between fidelity to the input image and adherence to the user instruction.

Results and Observations

Quantitative and qualitative assessments reveal that InstructEdit surpasses conventional methods like MDP-$\boldsymbol\epsilon_t$, InstructPix2Pix, and DiffEdit, particularly in scenarios requiring precise control over segmented image regions. The paper attributes the enhanced performance to the segmenter's ability to generate improved mask quality, which significantly impacts the resultant image edits in scenarios with multiple or complex object arrangements.

The framework successfully handles editing types traditionally challenging for diffusion models, particularly when specified regions must be meticulously preserved or altered per user commands. InstructEdit shows robust adaptability to various instruction forms, demonstrating the innate flexibility of LLM integration in processing instructions.

Discussion and Future Implications

While the framework showcases promising advancements in user-led image editing, limitations remain. The reliance on probabilistic LLMs occasionally leads to inaccuracies in instruction parsing, especially when descriptions are vague or misleading. Furthermore, the framework's fidelity to maintaining structural elements during extensive edits necessitates further enhancements to address deformational transformations effectively.

The paper hints at potential future work expanding upon InstructEdit's abilities to encompass video editing and cross-domain applications where instructional precision is pivotal. As the landscape of AI-driven content manipulation broadens, ethical considerations will also play a critical role, influencing the responsible deployment and use of such technologies.

InstructEdit stands as a significant contribution to image editing methodologies, emphasizing the role of grounded extents in diffusion-based models. Through the inclusion of user instructions, the paper illuminates a path toward more interactive and customizable AI content generation, with implications extending into various fields like digital art, media production, and even augmented reality implementations.