Maximizing Comprehension of Text-to-Image Diffusion Models using Large Language Models – Insights from the Berkeley Artificial Intelligence Research Blog.

Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models

The BAIR Blog highlights a recent advancement towards text-to-image generation with diffusion models. The Stable Diffusion model can generate highly realistic and diverse imagery. However, it can struggle to follow prompts when common sense or spatial reasoning is required. To solve this problem, the blog introduces LLM-grounded Diffusion (LMD), which has the enhanced prompt understanding ability of text-to-image diffusion models.

The Problem with Stable Diffusion

As incredible as the Stable Diffusion model is, it comes with its limitations. Diffusion models can sometimes generate images that don’t correspond to the provided prompts. For instance, given a prompt that requires spatial relationships or common sense reasoning, Stable Diffusion may fall short, resulting in inaccuracies.

Four Scenarios Highlight the Limitations of Stable Diffusion

The blog illustrates four scenarios where Stable Diffusion falls short.

Negation
Negation refers to prompts that require an opposite reaction to the normal expectation. For example, the text prompt could say, “a bird without wings.” Stable Diffusion can struggle to generate an image that accurately corresponds to such prompts.

Numeracy
Numeracy refers to when the prompt requires reasoning about numbers or quantities. For example, the prompt could say “a cupcake with fewer than ten sprinkles.” Stable Diffusion may struggle to produce the correct amount of sprinkles on the cupcake.

Attribute Assignment
Attribute Assignment refers to prompts that require an assignment of properties or attributes. For example, the prompt could say “a green apple.” Stable Diffusion may create an apple of the wrong shade of green or miss the green entirely in color.

Spatial Relationships
Spatial Relationships refer to when the prompt requires understanding of spatial relationships between objects. For example, the prompt could say “a pig behind a fence.” Stable Diffusion may not accurately depict the pig’s positioning in the image.

The Solution – LLM-grounded Diffusion (LMD)

The LMD method provides enhanced spatial and common sense reasoning to diffusion models using frozen LLMs in a two-stage generation process, supporting multi-modal dataset comprising intricate captions without training both LLMs and diffusion models.

Two-Stage Generation Process

The novel two-stage generation process of LMD includes providing an LLM as a text-guided layout generator, allowing it to output a layout in the form of bounding boxes and individual descriptions along with its current state. The second stage involves a novelty controller to generate images with conditions set in the layout following the first stage. The approach uses frozen pre-trained models without optimizing any LLM or diffusion model parameters.

LMD’s Additional Capabilities

Other benefits of this approach include dialog-based multi-round scene specifications, enabling the generation of a language Undersupported prompt, such as a prompt in Chinese. The adaptation of LMD accepts inputs of non-English prompts and generates layouts, descriptions of the boxes, and background in English.

Visualizations and Results

The blog compared LMD with the base diffusion model, demonstrating LMD’s superiority by accurately generating images necessitating both language and spatial reasoning and enabling counterfactual text-to-image generation.

Conclusion

The LMD methodology offers enhanced prompt understanding capabilities of text-to-image diffusion models by equipping them with improved spatial and common sense reasoning. Following the two-stage generation process, dialog-based multi-round scene specifications, and the ability to handle prompts in different languages further increase LMD’s utility.