IPAdapter-Instruct: Resolving Ambiguity in Image-based Conditioning using Instruct Prompts (2408.03209v2)

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.

• The paper introduces the novel IPAdapter-Instruct model that resolves ambiguity in image conditioning by integrating instruct prompts.
• It employs a cross-attention layer to align natural image conditions with specific user instructions, achieving performance on par with dedicated single-task models.
• The approach streamlines training and inference processes while supporting diverse tasks like style transfer, object extraction, and structural preservation.

IPAdapter-Instruct: Resolving Ambiguity in Image-based Conditioning using Instruct Prompts

Abstract:

The authors propose a novel approach named IPAdapter-Instruct to enhance control over image generation in diffusion models by introducing instruct prompts. This paper addresses the limitations of textual prompts and image-based conditioning methods like ControlNet and IPAdapter, which lack flexibility in interpreting user intent from the conditioning image. By combining natural image conditioning with instruct prompts, IPAdapter-Instruct allows users to specify how the conditioning image should influence the generated output, offering tasks such as style transfer, object extraction, and structural preservation. The approach is demonstrated to be efficient, maintaining quality comparable to dedicated per-task models while simplifying the training and inference process.

Introduction:

Diffusion models have emerged as a robust framework for image generation, surpassing earlier GAN-based approaches in training stability and applicability. However, the nuanced control over generated images using textual prompts often proves insufficient, necessitating methods like ControlNet and IPAdapter that leverage image-based conditioning. Despite their advancements, these models are limited to single conditioning interpretations, making them cumbersome for practical, multi-task workflows. The IPAdapter-Instruct model proposed in this paper aims to mitigate this by enabling a single model to handle multiple conditioning tasks with instruct prompts.

Methodology:

IPAdapter-Instruct builds on the IPAdapter+ architecture, incorporating an additional cross-attention layer that attends to instruct prompts encoded in the same CLIP space as the condition image. The model is trained across multiple datasets, each tailored to specific tasks, including image replication, style preservation, object extraction, structural preservation, and identity preservation. By leveraging a diverse range of instructions generated using ChatGPT4, the model learns to handle various conditioning interpretations efficiently.

Results:

The paper presents both qualitative and quantitative comparisons of IPAdapter-Instruct against single-task models and the original IPAdapter+. It demonstrates that IPAdapter-Instruct performs on par with or better than dedicated single-task models across various metrics such as CLIP-I, CLIP-T, CLIP-P, and CLIP-S. Moreover, the training and inference processes are streamlined, significantly reducing the complexity and cost associated with multi-model management.

Discussion:

IPAdapter-Instruct introduces a flexible and efficient approach to image-based conditioning in diffusion models, allowing users to specify conditioning tasks through instruct prompts. This enhances control over the generated images, catering to specific user intents such as style transfer and object extraction. The ability to combine this model with existing ControlNets and LoRA models further extends its utility, offering precise and versatile image generation capabilities.

Conclusion:

The IPAdapter-Instruct model addresses a critical limitation in image-based conditioning by disambiguating user intent through instruct prompts. This advancement not only streamlines the training and inference processes but also enhances the practical applicability of diffusion models in diverse image generation tasks. Future work could explore integrating pixel-precise and semantic conditioning within a unified framework, potentially expanding the model's versatility and control over generated outputs.