Papers by David Munechika

2 papers
DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models (2023.acl-long)

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Challenge: Recent advances in diffusion models have enabled high-quality image generation . generating images with desired details requires proper prompts .
Approach: They analyze syntactic and semantic characteristics of diffusion models and their prompts . they pinpoint specific hyperparameter values and prompt styles that can lead to model errors .
Outcome: The first large-scale text-to-image prompt dataset totals 6.5TB . it contains 14 million images generated by Stable Diffusion, 1.8 million unique prompts, and hyperparameters specified by real users.
Wordflow: Social Prompt Engineering for Large Language Models (2024.acl-demos)

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Challenge: Large language models (LLMs) require well-crafted prompts for effective use.
Approach: They propose a social prompt engineering paradigm that leverages social computing techniques to facilitate collaborative prompt design.
Outcome: The proposed paradigm leverages social computing techniques to facilitate prompt design.

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