LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generation (2308.05095v2)

Abstract: In the text-to-image generation field, recent remarkable progress in Stable Diffusion makes it possible to generate rich kinds of novel photorealistic images. However, current models still face misalignment issues (e.g., problematic spatial relation understanding and numeration failure) in complex natural scenes, which impedes the high-faithfulness text-to-image generation. Although recent efforts have been made to improve controllability by giving fine-grained guidance (e.g., sketch and scribbles), this issue has not been fundamentally tackled since users have to provide such guidance information manually. In this work, we strive to synthesize high-fidelity images that are semantically aligned with a given textual prompt without any guidance. Toward this end, we propose a coarse-to-fine paradigm to achieve layout planning and image generation. Concretely, we first generate the coarse-grained layout conditioned on a given textual prompt via in-context learning based on LLMs. Afterward, we propose a fine-grained object-interaction diffusion method to synthesize high-faithfulness images conditioned on the prompt and the automatically generated layout. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art models in terms of layout and image generation. Our code and settings are available at https://layoutLLM-t2i.github.io.

LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generation

The emergence of diffusion models in the field of text-to-image generation (T2I) represents a significant advancement in generating photorealistic images from textual descriptions. However, even state-of-the-art models like Stable Diffusion encounter challenges in accurately synthesizing images of complex scenes due to problematic spatial relation understanding and misalignment issues. In response to these challenges, the paper introduces LayoutLLM-T2I, a model that utilizes LLMs to guide T2I tasks through improved layout generation and semantic alignment.

Methodological Overview

The primary innovation in LayoutLLM-T2I is its two-stage approach, which involves generating a layout through LLMs based on a given textual prompt and subsequently guiding image generation via this layout. The paper seeks to address misalignment issues without requiring manual human guidance by leveraging the scene understanding capabilities of LLMs.

1. Text-to-Layout Induction: This phase employs in-context learning with LLMs, such as ChatGPT, to create a coarse layout based on a textual description. The process involves a feedback-based sampling mechanism that selects in-context examples to optimize layout induction. This strategy ensures that the layouts are both informative and aesthetically aligned with the prompts.
2. Layout-Guided Text-to-Image Generation: Leveraging the coarse layouts, the model further refines image synthesis using a diffusion-based approach. A layout-aware adapter is integrated into the diffusion process to incorporate the layout-generated objects and relations meaningfully into the generated images, ensuring a high degree of faithfulness to the given prompts.

Key Contributions

The paper makes several contributions to the field of text-to-image synthesis:

• In-context Learning for Layout Generation: By adapting LLMs for spatial reasoning, the model automates layout creation, improving the versatility and accuracy of the generated images without human intervention.
• Feedback-based Sampler for In-context Learning: This strategy optimizes the selection of in-context examples, beneficially influencing the scene understanding capabilities of LLMs in relation to T2I tasks.
• Relation-Aware Adapter for Diffusion Models: This novel integration within the diffusion model's framework allows for a nuanced interaction between textual descriptions, generated layouts, and visual tokens, effectively embedding semantic relations into the image synthesis process.

Empirical Results

The paper presents extensive experimental results demonstrating that LayoutLLM-T2I substantially outperforms existing models across several benchmarks. In the COCO2014 dataset, LayoutLLM-T2I achieved improvements in layout realism and alignment with numerical and relation-specific evaluations. These metrics include the Maximum IoU (mIoU) for layout similarity and cross-modal CLIP-based evaluations for image-text alignment. Additionally, the proposed model excels in generating complex scenes and accurately depicting objects and their interactions within a given spatial context.

Implications and Future Work

The findings underscore the potential of integrating LLM capabilities with diffusion models for enhanced text-to-image generation. This integration paves the way for efficiently generating high-fidelity images in response to complex and detailed natural language prompts. It also highlights the role of automated layout generation as a critical step in improving the semantic accuracy of T2I models.

Future research directions could explore optimizing the in-context learning framework for diverse datasets and incorporating additional feedback mechanisms to further enhance the alignment of generated images to user intent. Furthermore, extending the framework to accommodate more diverse datasets and adopting multimodal reinforcement learning strategies could result in even more robust and versatile models.