ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting (2411.17176v1)

Abstract: Despite the significant advancements in text-to-image (T2I) generative models, users often face a trial-and-error challenge in practical scenarios. This challenge arises from the complexity and uncertainty of tedious steps such as crafting suitable prompts, selecting appropriate models, and configuring specific arguments, making users resort to labor-intensive attempts for desired images. This paper proposes Automatic T2I generation, which aims to automate these tedious steps, allowing users to simply describe their needs in a freestyle chatting way. To systematically study this problem, we first introduce ChatGenBench, a novel benchmark designed for Automatic T2I. It features high-quality paired data with diverse freestyle inputs, enabling comprehensive evaluation of automatic T2I models across all steps. Additionally, recognizing Automatic T2I as a complex multi-step reasoning task, we propose ChatGen-Evo, a multi-stage evolution strategy that progressively equips models with essential automation skills. Through extensive evaluation across step-wise accuracy and image quality, ChatGen-Evo significantly enhances performance over various baselines. Our evaluation also uncovers valuable insights for advancing automatic T2I. All our data, code, and models will be available in \url{https://chengyou-jia.github.io/ChatGen-Home}

Overview of "ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting"

The paper "ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting" introduces a new paradigm in the application and evaluation of text-to-image (T2I) systems by proposing an automated process that simplifies user interaction. Traditionally, generating images from textual input involves crafting precise prompts and selecting suitable models and configurations, a process overwhelmed by trial-and-error efforts, particularly for users without expert knowledge. The authors address this through an automated system named ChatGen, underpinned by a strategic multi-stage approach termed ChatGen-Evo, which simplifies this intricate sequence by taking freestyle chatting inputs and translating them into efficient generative steps.

Methodology

A cornerstone of the paper is the automated T2I generation through ChatGen, which the authors position as a complex multi-step reasoning task. The process begins with ChatGenBench, a benchmark designed specifically for evaluating such systems. ChatGenBench shows a robust dataset constructed from over 6,807 models, paired with freestyle user inputs mimicking real-world scenarios. This benchmark supports a broad spectrum of input types, including text-based, multimodal, and sequential historical data, providing a comprehensive evaluation framework and helping identify bottlenecks in the automation process.

Building upon this benchmark, the paper introduces ChatGen-Evo. Unlike conventional approaches that directly map input to output, ChatGen-Evo employs a multi-stage evolution strategy that develops automated T2I skills in phases: prompting craft, model selection using specialized ModelTokens, and argument configuration via in-context learning. This staged approach is shown to lead to superior performance, as it provides targeted feedback and breaks down the automation task into more manageable units, allowing the model to learn progressively more sophisticated reasoning skills without overwhelming complexity.

Evaluation

The paper evaluates ChatGen-Evo extensively through multiple settings on ChatGenBench, illustrating significant improvements over various baselines across step-wise metrics such as prompt rewriting fidelity, model selection accuracy, and argument configuration soundness. Notably, the system shows robustness in few-shot learning scenarios, highlighting the benefits of decomposing the problem into stages rather than a monolithic prediction task. This positions ChatGen-Evo as a promising candidate for applications where training data is sparse or user requirements are highly variable.

Image Quality Metrics

The authors employ a range of metrics to assess final image quality including FID Scores, CLIP Scores, Human Preference Score (HPS v2), and ImageReward, which collectively form a Unified Metric. ChatGen-Evo achieves higher scores than direct end-to-end systems, affirming the value of systematic, modular training strategies.

Implications and Future Directions

This research positions chat-based interaction as a viable mechanism to reduce user friction in T2I systems, facilitating broader adoption in non-expert communities. From a theoretical standpoint, the multi-stage evolution approach could serve as a blueprint for tackling other complex AI tasks where user interaction involves multiple decision points. Future developments could extend the principles of ChatGen in several directions:

1. Scaling Prompt Rewriting: Developing a deeper understanding of how prompt engineering affects text-image alignment could offer further insight, especially under conditions with minimal user input.
2. Complex Multimodal Integration: Exploring how to incorporate richer forms of user input beyond the current benchmarks could enhance the richness and applicability of the outputs.
3. Reasoning Augmentations and Tool Use: Introducing enhanced tool use models and incorporating external knowledge bases could further improve model selection and argument configuration, particularly in beginning users with diverse requirements.

In conclusion, this paper contributes significantly to the domain of automated generative models by advocating for a holistic, phased approach to problem-solving in T2I interactions, aiming to streamline user experience without compromising the quality or specificity of generated images.