ELODIN: Naming Concepts in Embedding Spaces
This addresses the problem of concept coherence and contamination in text-to-image synthesis for users needing more precise control, representing an incremental advancement.
The paper tackles the lack of fine-grained control in text-to-image synthesis by proposing a method to generate reusable concepts that expand natural language for better control, finding it to be a significant improvement over text-only prompts.
Despite recent advancements, the field of text-to-image synthesis still suffers from lack of fine-grained control. Using only text, it remains challenging to deal with issues such as concept coherence and concept contamination. We propose a method to enhance control by generating specific concepts that can be reused throughout multiple images, effectively expanding natural language with new words that can be combined much like a painter's palette. Unlike previous contributions, our method does not copy visuals from input data and can generate concepts through text alone. We perform a set of comparisons that finds our method to be a significant improvement over text-only prompts.