Improving Comprehension of Text-to-Image Diffusion Models with Powerful Language Models – The Captivating Insights from Berkeley Artificial Intelligence Research Blog
Introduction:
Recent advancements in text-to-image generation with diffusion models have shown great potential in synthesizing realistic and diverse images. However, these models often struggle to accurately follow prompts that require spatial or common sense reasoning. In this blog post, we introduce a novel approach called LLM-grounded Diffusion (LMD) that enhances the prompt understanding ability of text-to-image diffusion models. By leveraging large language models (LLMs) as text-guided layout generators, we enable diffusion models to generate images conditioned on the layout. LMD offers additional capabilities such as dialog-based multi-round scene specification and support for prompts in languages not well-supported by the underlying diffusion model. We provide visualizations and comparisons to demonstrate the superiority of LMD in accurately generating images according to prompts that require both language and spatial reasoning. For more details, please visit our website and read the paper on arXiv.
Full Article: Improving Comprehension of Text-to-Image Diffusion Models with Powerful Language Models – The Captivating Insights from Berkeley Artificial Intelligence Research Blog
GPT-4 + Stable-Diffusion = ?: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models
Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, there are still challenges in accurately following prompts that require spatial or common sense reasoning.
Challenges Faced by Stable Diffusion Models
Stable Diffusion, a popular diffusion model, falls short in accurately generating images that correspond to given prompts in several scenarios. These scenarios include negation, numeracy, attribute assignment, and spatial relationships. In contrast, a new method called LLM-grounded Diffusion (LMD) has been developed, which delivers better prompt understanding in text-to-image generation for these scenarios.
The Solution: LMD
To address the challenges mentioned above, the LMD approach enhances diffusion models with spatial and common sense reasoning by incorporating off-the-shelf frozen large language models (LLMs). This is achieved through a two-stage generation process.
First, an LLM is adapted to be a text-guided layout generator. When provided with an image prompt, the LLM produces a scene layout consisting of bounding boxes and corresponding descriptions. Second, a diffusion model is guided by the layout using a novel controller to generate images. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization.
Additional Capabilities of LMD
Apart from enhancing prompt understanding, LMD also offers additional capabilities. It enables dialog-based multi-round scene specification, allowing users to provide additional information or clarifications to the LLM for generating images with updated layouts. LMD can also handle prompts in languages that are not well-supported by the underlying diffusion models.
Benefits and Visualizations
LMD outperforms the base diffusion model (Stable Diffusion 2.1) in accurately generating images that require both language and spatial reasoning. It also enables counterfactual text-to-image generation, which the base diffusion model cannot achieve. More evaluation and comparisons can be found in the work.
Conclusion
The LLM-grounded Diffusion (LMD) approach enhances the prompt understanding of text-to-image diffusion models by incorporating large language models and a two-stage generation process. It delivers better results in scenarios that require spatial or common sense reasoning and offers additional capabilities for dialog-based scene specification. For more details, visit the official website and read the paper on arXiv.
Summary: Improving Comprehension of Text-to-Image Diffusion Models with Powerful Language Models – The Captivating Insights from Berkeley Artificial Intelligence Research Blog
GPT-4 + Stable-Diffusion = ?: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models
Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, diffusion models often struggle to accurately follow prompts when spatial or common sense reasoning is required. To address this, we propose a novel two-stage generation process called LMD (LLM-grounded Diffusion). LMD equips diffusion models with enhanced spatial and common sense reasoning by using off-the-shelf frozen LLMs. This approach allows for efficient prompt understanding without the need for additional training costs. LMD also offers additional capabilities such as dialog-based multi-round scene specification and handling prompts in unsupported languages. Visualizations demonstrate the superiority of LMD in accurately generating images according to prompts that involve language and spatial reasoning. For more information, visit our website and read the paper on arXiv.
