IPAdapter-Instruct: Resolving Ambiguity in Image-based Conditioning using Instruct Prompts
Ciara Rowles, Shimon Vainer, Dante De Nigris, Slava Elizarov, Konstantin Kutsy, Simon Donné
2024-08-07
Summary
This paper presents IPAdapter-Instruct, a new method that improves how AI can generate and manipulate images by using instructional prompts to clarify what the user wants.
What's the problem?
When using AI to create or edit images, it can be hard for the model to understand exactly what the user means. Text prompts often fail to accurately describe details like styles or specific features in images, leading to confusion and unsatisfactory results. Existing methods like ControlNet and IPAdapter help by conditioning the AI on images instead of just text, but they struggle when users want to perform multiple tasks at once, requiring different models for each task.
What's the solution?
The authors developed IPAdapter-Instruct, which combines natural image conditioning with 'Instruct' prompts. This allows the AI to switch between different interpretations of the same image, such as transferring styles or extracting objects. By doing this, the model can learn to handle multiple tasks without losing quality compared to models designed for specific tasks. Essentially, it makes the AI more flexible and capable of understanding various requests from users.
Why it matters?
This research is important because it enhances the ability of AI systems to work with images in a more intuitive way. By resolving ambiguity in user instructions, IPAdapter-Instruct can lead to better image generation and editing tools, making them more useful for artists, designers, and anyone who works with visual content.
Abstract
Diffusion models continuously push the boundary of state-of-the-art image generation, but the process is hard to control with any nuance: practice proves that textual prompts are inadequate for accurately describing image style or fine structural details (such as faces). ControlNet and IPAdapter address this shortcoming by conditioning the generative process on imagery instead, but each individual instance is limited to modeling a single conditional posterior: for practical use-cases, where multiple different posteriors are desired within the same workflow, training and using multiple adapters is cumbersome. We propose IPAdapter-Instruct, which combines natural-image conditioning with ``Instruct'' prompts to swap between interpretations for the same conditioning image: style transfer, object extraction, both, or something else still? IPAdapterInstruct efficiently learns multiple tasks with minimal loss in quality compared to dedicated per-task models.