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Posted by Alexander Frömmgen, Staff Software Engineer, and Lera Kharatyan, Senior Software Engineer, Core Systems & Experiences Code-change reviews are a critical part of the software development process at scale, taking a significant amount of the code authors’ and the code reviewers’ time. As part… Read More »Resolving code review comments with ML Google AI Google AI Blog
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TL;DR: Text Prompt -> LLM -> Intermediate Representation (such as an image layout) -> Stable Diffusion -> Image.
Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, despite their impressive capabilities, diffusion models, such as Stable Diffusion, often struggle to accurately follow the prompts when spatial or common sense reasoning is required.
The following figure lists four scenarios in which Stable Diffusion falls short in generating images that accurately correspond to the given prompts, namely negation, numeracy, and attribute assignment, spatial relationships. In contrast, our method, LLM-grounded Diffusion (LMD), delivers much better prompt understanding in text-to-image generation in those scenarios.
Figure 1: LLM-grounded Diffusion enhances the prompt understanding ability of text-to-image diffusion models.
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