T-DIG: Text-Conditioned Diversity for Image Generation
In this paper, we introduce T-DIG, a text-conditioned diversity framework for image generation and editing.
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Key points
- Text-to-image (T2I) platforms typically return several images per prompt so users can explore different visual interpretations and choose one that matches their intent.
- T-DIG is an iterative framework comprising multiple modules that lets users type prompts, inspect default generations, request variations using natural language, and continue refining outputs until they're satisfied.
- We also release the first dataset for text-conditioned prompt diversity in image generation, with the training and validation splits built from human-written axes and a test set drawn from real user interactions with T-DIG.
- We show that user-directed diversity exploration can be added to a wide range of image generation models, open and closed, without the need to access their weights.
Sources (1)
- [1]T-DIG: Text-Conditioned Diversity for Image GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 12:11 AM
In this paper, we introduce T-DIG, a text-conditioned diversity framework for image generation and editing.
Text-to-image (T2I) platforms typically return several images per prompt so users can explore different visual interpretations and choose one that matches their intent.
Extractive summary: sentences quoted from the sources.
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