Generating Customized Illustrated Instructions: A Novel Approach Combining Large Language Models and Text-to-Image Diffusion

Core Concepts

StackedDiffusion, a novel approach that combines large language models and text-to-image diffusion models, can generate customized illustrated instructions that are faithful to the goal, step-by-step text, and visually consistent across steps.

Abstract

The paper introduces the novel task of "Illustrated Instructions", which requires generating a sequence of images and text that together describe how to achieve a user's goal. The authors identify three key desiderata for this task: goal faithfulness, step faithfulness, and cross-image consistency.
To address this challenge, the authors propose StackedDiffusion, a model that leverages large language models (LLMs) and text-to-image (T2I) diffusion models. Key innovations include:

Separate encoding of goal and step text, with step-positional encoding, to better capture the relationship between text and images.
Simultaneous generation of all step images through spatial tiling, leveraging the priors learned by the T2I model to ensure cross-image consistency.
Adjustments to the training noise schedule to mitigate distribution shift between training and inference.

The authors evaluate StackedDiffusion on a new dataset of instructional articles from WikiHow, and show that it significantly outperforms various baselines, including frozen and finetuned T2I models, as well as recent multimodal LLMs. Notably, in 30% of cases, human evaluators even preferred StackedDiffusion's generations over ground truth human-created articles.
The paper also showcases new applications enabled by StackedDiffusion, such as personalized instructions, goal suggestion, and error correction - going beyond what is possible with static instructional articles.

Stats

StackedDiffusion generates images that are 81.7% faithful to the goal text, 61.5% faithful to the step text, and have 39.5% cross-image consistency, outperforming baselines.
In human evaluations, StackedDiffusion is preferred over baselines by wide margins, and is even preferred over ground truth articles in 30% of cases.

Quotes

"StackedDiffusion convincingly surpasses state-of-the-art models. Our final model even outperforms human-generated articles in 30% of cases, showing the strong potential of our approach."
"StackedDiffusion enables various new and exciting applications far beyond what static articles on the web can provide, such as personalized instructions complete with intermediate steps and pictures in response to a user's individual situation."

Key Insights Distilled From

Generating Illustrated Instructions

by Sachit Menon... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2312.04552.pdf

Deeper Inquiries

How can StackedDiffusion's performance be further improved, especially in terms of closing the gap with human-generated content

To further improve StackedDiffusion's performance and close the gap with human-generated content, several strategies can be implemented. Firstly, enhancing the text generation capabilities of the model can lead to more detailed and accurate step descriptions, aligning them more closely with human-written instructions. This can be achieved by fine-tuning the language model component of StackedDiffusion on a larger and more diverse dataset of instructional text. Additionally, incorporating a feedback mechanism where human evaluators provide input on generated content can help the model learn from its mistakes and improve over time.
Furthermore, focusing on improving the cross-image consistency aspect of the generated content can also enhance the overall quality of the instructions. By ensuring that all images in a set are visually coherent and consistent with each other, the instructions will be more visually appealing and easier to follow. This can be achieved by refining the image generation process to prioritize consistency across all generated visuals.
Lastly, exploring advanced techniques in multimodal fusion and attention mechanisms can help StackedDiffusion better integrate text and images, leading to more cohesive and informative illustrated instructions. By leveraging the latest advancements in multimodal AI research, StackedDiffusion can further refine its output and approach human-level quality in generating instructional content.

What other modalities, beyond images, could be incorporated into the Illustrated Instructions task to make the instructions even more comprehensive and engaging

Incorporating additional modalities beyond images can significantly enhance the comprehensiveness and engagement of the Illustrated Instructions task. One promising modality to consider is video, which can provide dynamic visual demonstrations of each step in the instructions. By generating step-by-step video guides alongside textual descriptions and static images, users can have a more immersive and interactive learning experience. This can be achieved by extending StackedDiffusion to generate video content based on the textual input, similar to how it generates images.
Another modality to consider is audio, which can be used to provide additional guidance and explanations for each step. By incorporating audio instructions or narrations along with text and visuals, StackedDiffusion can cater to users with different learning preferences and accessibility needs. This can make the instructions more inclusive and accessible to a wider audience.
Additionally, interactive elements such as clickable diagrams, 3D models, or virtual reality simulations can further enhance the instructional experience. By allowing users to interact with the content in a hands-on manner, StackedDiffusion can create more engaging and effective instructional materials that cater to diverse learning styles.

How could the techniques developed for StackedDiffusion be applied to generate other types of multimodal content, such as interactive tutorials or step-by-step video guides

The techniques developed for StackedDiffusion can be applied to generate other types of multimodal content, such as interactive tutorials or step-by-step video guides, by adapting the model architecture and training process. For interactive tutorials, the model can be modified to generate a combination of text, images, and interactive elements that guide users through a specific task or process. This can involve incorporating user interactions, feedback loops, and branching pathways based on user input.
For step-by-step video guides, StackedDiffusion can be extended to generate video sequences that demonstrate each step visually, accompanied by textual descriptions and annotations. By training the model on a dataset of instructional videos and associated text, StackedDiffusion can learn to generate coherent and informative video content that complements the textual instructions.
Moreover, the model can be further optimized for real-time generation and interaction, allowing users to receive immediate feedback and guidance as they progress through the tutorial or guide. By leveraging techniques such as reinforcement learning and active learning, StackedDiffusion can adapt its output based on user input and preferences, creating personalized and interactive multimodal content for a wide range of applications.