GPT-4 + Stable-Diffusion = ?: Improving Prompt Comprehension in Text-to-Image Diffusion Models using Powerful Language Models
Introduction:
The advancements in text-to-image generation with diffusion models have brought about impressive results in synthesizing realistic and diverse images. However, these models often struggle to accurately follow prompts that require spatial or common sense reasoning. This is where our solution, LLM-grounded Diffusion (LMD), comes in. LMD enhances spatial and common sense reasoning in diffusion models by using off-the-shelf frozen LLMs. It utilizes a two-stage generation process, where an LLM is adapted to generate a text-guided layout, and then a diffusion model generates images based on that layout. LMD also allows for dialog-based multi-round scene specification and supports prompts in languages that are not well-supported by the underlying diffusion model. With LMD, prompt understanding in text-to-image generation is greatly improved. For more information and visualizations, visit our website and read our paper on arXiv.
Full Article: GPT-4 + Stable-Diffusion = ?: Improving Prompt Comprehension in Text-to-Image Diffusion Models using Powerful Language Models
Title: Enhancing Prompt Understanding in Text-to-Image Generation with LLM-grounded Diffusion
Introduction:
Recent advances in text-to-image generation have resulted in the synthesis of highly realistic and diverse images. However, there are still challenges when it comes to accurately following prompts that require spatial or common sense reasoning. In this article, we discuss a solution called LLM-grounded Diffusion (LMD), which enhances the prompt understanding ability of text-to-image diffusion models.
The Limitations of Stable Diffusion Models:
Diffusion models like Stable Diffusion struggle to generate images that accurately correspond to given prompts in scenarios such as negation, numeracy, attribute assignment, and spatial relationships. These limitations led to the development of LMD, which delivers better prompt understanding in text-to-image generation.
The Two-Stage Generation Process:
To address these limitations efficiently and cost-effectively, we equipped diffusion models with enhanced spatial and common sense reasoning using off-the-shelf frozen LLMs (Large Language Models). The two-stage generation process involves adapting an LLM to be a text-guided layout generator and steering a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any parameter optimization.
LMD’s Additional Capabilities:
In addition to enhancing prompt understanding, LMD offers dialog-based multi-round scene specification, enabling additional clarifications and modifications for each prompt. Furthermore, LMD can handle prompts in languages not well-supported by the underlying diffusion model.
Visualizations:
Through visualizations, we demonstrate the superiority of LMD compared to the base diffusion model. LMD outperforms the base diffusion model in accurately generating images that require both language and spatial reasoning. It also enables counterfactual text-to-image generation that the base diffusion model is unable to generate.
Conclusion:
LLM-grounded Diffusion (LMD) presents a novel solution for enhancing prompt understanding in text-to-image generation. By leveraging off-the-shelf frozen LLMs, LMD improves spatial and common sense reasoning capabilities while maintaining efficiency and cost-effectiveness. For more information about LMD, visit our website and read the full research paper.
BibTex:
If LLM-grounded Diffusion inspires your work, please cite it as:
@article{lian2023llmgrounded,
title={LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models},
author={Lian, Long and Li, Boyi and Yala, Adam and Darrell, Trevor},
journal={arXiv preprint arXiv:2305.13655},
year={2023}
}
Disclaimer:
This article was initially published on the BAIR blog and appears here with the authors’ permission.
Summary: GPT-4 + Stable-Diffusion = ?: Improving Prompt Comprehension in Text-to-Image Diffusion Models using Powerful Language Models
Recent advancements in text-to-image generation have resulted in highly realistic and diverse images. However, diffusion models often struggle with prompts that require spatial reasoning or common sense understanding. To address this, the authors propose LLM-grounded Diffusion (LMD), which enhances prompt understanding by equipping diffusion models with off-the-shelf frozen LLMs. LMD uses a two-stage generation process, where an LLM generates a scene layout based on a text prompt, and a diffusion model generates an image conditioned on the layout. LMD also has additional capabilities such as dialog-based multi-round scene specification and support for non-well-supported languages. The authors provide visualizations and comparisons to validate the effectiveness of LMD. For more details, readers can visit their website and read the full paper.
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