The Rise of 'Undictionaries': How Nonexistent Words Shape AI Image Generation

In a quietly groundbreaking exploration that has captivated the AI art community, independent researcher Evelyn Hightower has documented a peculiar phenomenon in Stable Diffusion and other CLIP-based generative models: the consistent visual output triggered by prompting the system with words that do not exist in any dictionary. Dubbed "Undictionaries," these nonce terms — such as "flibbertigibbet" or "snorgle" — produce remarkably stable, interpretable imagery despite having no linguistic foundation. Hightower’s two-and-a-half-year personal study, compiled into an informal yet meticulously detailed report, offers the first systematic taxonomy of these linguistic anomalies and their visual correlates.

Unlike traditional prompt engineering, which relies on known vocabulary and semantic relationships, Undictionaries operate in the blind spots of the model’s embedding space. Hightower observed that certain fabricated words, when consistently used across hundreds of generations, produced identical or highly similar visual themes — for example, "wumpus" consistently rendered as a glowing, furred creature with multiple eyes, while "crimble" evoked swirling, metallic textures reminiscent of rusted clockwork. These results suggest that the CLIP model, trained on vast corpora of image-text pairs, has inadvertently learned to associate phonetic and orthographic patterns with latent visual features, even in the absence of real-world referents.

While the original report is hosted on Google Drive and circulated primarily through Reddit’s r/StableDiffusion community, its implications extend far beyond niche art experimentation. The findings resonate with recent academic work on adversarial prompts and semantic drift in diffusion models, though Hightower’s approach is uniquely empirical and user-driven. Unlike institutional research, which often focuses on model robustness or safety, this grassroots inquiry reveals how end users can manipulate latent spaces through intuitive, almost playful experimentation — a form of "prompt archaeology."

Notably, Hightower’s methodology is accessible to non-technical users: he recommends starting with phonetically unusual or whimsical words, testing them across multiple seeds, and documenting output consistency. His nomenclature system — categorizing Undictionaries as "Stable," "Flickering," or "Chimeric" based on output reliability — provides a practical framework for others to replicate and expand upon. This democratization of AI probing stands in contrast to proprietary tools offered by commercial platforms, where prompt interactions are often obfuscated behind layers of abstraction.

However, the longevity of Undictionaries is uncertain. As industry leaders like OpenAI, Google, and Stability AI shift toward hybrid architectures that integrate large language models (LLMs) as intermediaries between prompts and latent spaces, the direct access to CLIP’s embedding layer may be phased out. Future models may filter or normalize prompt inputs to suppress such anomalies, prioritizing safety and coherence over serendipitous creativity. "This is a feature of CLIP-based models," Hightower writes, "and if the industry moves on, we’ll lose a fascinating window into how machines "think" in pixels, not words."

For now, Undictionaries remain a testament to the unexpected emergent behaviors of AI systems — and a reminder that sometimes, the most profound discoveries come not from peer-reviewed journals, but from curious individuals tinkering in the margins. As AI art continues to evolve, the legacy of these phantom words may endure not as tools, but as artifacts of a fleeting era when language was still a loose key to the machine’s imagination.

For those interested in exploring Undictionaries firsthand, Hightower’s full report is available at drive.google.com/file/d/178pCtLQcOSvwvE7BxgBVeyAicQNKSnlw/. The Reddit thread has amassed over 12,000 upvotes and hundreds of user-submitted examples, turning the study into a collaborative, crowdsourced lexicon of the unreal.