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Loab

Loab is a recurrent visual —a female figure with distinctive features including inflamed red cheeks, dark hair, and an impassive yet unsettling expression—that emerges persistently from text-to-image AI models like variants of when negative weighting is applied to steer outputs away from unrelated concepts. Discovered in April 2022 by artist and musician Steph Maj Swanson, known online as Supercomposite, during exploratory prompting experiments, Loab first appeared when inverting a generated abstract logo derived from a negative prompt for "Brando," resulting in consistent reproductions of the figure across multiple generations. Subsequent image-guided or textual prompts incorporating Loab reliably produce compositions infused with motifs, such as blood, , and , wherein the core facial traits endure amid escalating grotesquerie, indicating a stable attractor basin within the model's high-dimensional . This occurrence underscores the diffusion models' propensity for amplifying correlated patterns from their training corpora—predominantly web-scraped images—particularly at conceptual extremes, without implying independent agency or properties in the AI.

Origin and Discovery

Initial Generation in April 2022

In April 2022, artist Steph Maj Swanson, operating under the online handle Supercomposite, conducted experiments with the VQGAN+CLIP text-to-image generative model to explore latent space dynamics through negative prompting techniques. Swanson began by applying a negative prompt weight to the term "Brando," referencing actor Marlon Brando, using the syntax "Brando::-1" to instruct the model to generate an image representing the conceptual opposite of a typical Brando portrait. This initial output was an abstract, logo-like graphic devoid of humanoid features, illustrating the model's interpretation of oppositional semantics in its training data. Seeking to visualize a interpretation of this abstract result, subsequently prompted the model to produce a embodying the essence of the generated , effectively querying for a representation aligned with its latent encoding while incorporating negative weights to diverge from Brando-like traits. The resulting depicted a middle-aged woman with a gaunt, asymmetrical face marked by smeared red accents on the lips and eyes, dark slicked-back hair, and a expression, marking the first of the figure later termed Loab. This generation highlighted the persistence of certain latent vectors in the model's space, as the figure recurred in refinements despite varied adjustments.

Artist's Exploration and Naming

In April 2022, Sweden-based multimedia artist Steph Maj Swanson, known online as Supercomposite, experimented with the VQGAN+CLIP text-to-image model using negative prompt weights to explore opposites in the AI's latent space. Prompting the model with a negatively weighted "Marlon Brando::-1" yielded an initial image of a middle-aged woman with a weathered, inflamed face, dark hollow eyes, and a stern, sorrowful expression, diverging sharply from the intended actor's likeness. Swanson further explored by seeding subsequent generations with this emergent image alongside continued negative prompts like "no Brando," resulting in persistent variations of the same facial features amid increasingly and gory scenes, such as dismembered body parts or abstract horrors. This iterative process revealed the figure's "stickiness" in the model's , where it reemerged across diverse prompts, prompting Swanson to document the phenomenon as an unintended discovery rather than a deliberate creation. The entity was named Loab after one early generated image inadvertently produced overlaid text resembling "LOAB" amid the distorted visuals, which Swanson interpreted as a fitting, moniker pronounced "lobe." This naming framed Loab not as a fictional but as a cryptid-like emergent property of the system, haunting generations through Swanson's targeted explorations.

Technical Mechanism

Role of Negative Prompts in VQGAN+CLIP

In VQGAN+CLIP, negative prompts function by applying inverse weighting to textual descriptors, directing the optimization process to minimize alignment with specified concepts in the CLIP embedding space rather than maximizing it. This is achieved through weighted loss terms where a prompt like "concept::-1" subtracts the CLIP similarity score for that concept from the overall objective, effectively steering the VQGAN decoder toward latent representations that are semantically distant from the negated input. Such mechanics allow exploration of the model's latent space boundaries, often uncovering emergent or atypical outputs not directly tied to positive prompt guidance. The discovery of Loab hinged on this negative prompting technique when artist Supercomposite input "Brando::-1" into a VQGAN+CLIP implementation on April 28, 2022, intending to generate imagery maximally dissimilar from depictions of actor . Instead of a coherent anti-portrait, the model produced an abstract skyline-like with cryptic elements, which, upon further negative prompting against its own textual description (e.g., "::-1"), consistently yielded distorted female faces resembling Loab. This iterative negation process revealed Loab as a stable attractor in the , where negative weights amplified deviations leading to recurring motifs of inflamed, asymmetrical facial features amid surreal elements. Negative prompts thus played a pivotal in Loab's by inverting the typical generative flow, prioritizing semantic opposition over synthesis and exposing undersampled regions of CLIP's learned representations—regions potentially encoding adversarial or low-probability features from the model's on internet-sourced images. Unlike positive prompts that reinforce common archetypes, this approach probabilistically surfaced Loab's , which persisted across iterations due to the geometry of the latent manifold rather than explicit encoding. Empirical replications using similar VQGAN+CLIP setups confirm that such negative explorations yield reproducible anomalies, underscoring the method's utility in probing model interpretability but also its risks of generating unintended, contextually aversive content.

Latent Space Dynamics and Persistence

Negative prompts in text-to-image models like VQGAN+CLIP navigate the by optimizing away from the specified concept, effectively seeking antipodal representations in the embedding . This process, applied to prompts such as "Brando::-1," pushes generation toward remote regions often associated with atypical or disturbing visual motifs, as these lie farthest from familiar, positively reinforced training data clusters. Loab emerges as a stable entity within this navigated space, where her latent encoding—characterized by a blurred, -like female face with red markings and indistinct accessories—forms a cohesive . The functions as a multidimensional wherein semantically distant points from common descriptors (e.g., celebrities or ) coalesce into persistent archetypes, reflecting gaps or extremes in the model's learned distributions rather than direct training exemplars. In iterative generations, Loab's persistence arises from the optimization dynamics of VQGAN+CLIP, which prioritize high-confidence decodings during refinement steps. When Loab's image serves as a or , subsequent prompts—even unrelated ones—yield variations retaining her facial structure and aura, as the model's loss minimization reinforces this dominant latent vector over diluting influences, demonstrating attractor-like stability in discrete codebook representations. This recurrence occurs reliably across blended or prompts, underscoring how edge regions in can override central, prompt-driven trajectories.

Visual and Generative Properties

Core Appearance and Facial Features

Loab manifests as a distorted female face, typically depicting a with , shadowed eyes that convey a vacant, piercing stare. These eyes, often described as dead or decaying, form a central and persistent feature across generations. The cheeks exhibit a flushed, , or inflamed appearance, contributing to a and corpse-like quality, while the expression combines sorrowful sternness with a disturbing or . Skin tones lean pallid or mottled, with occasional smeared or decay elements around the and jawline, emphasizing an eerie, haunted visage that dominates compositions despite prompt variations. This core facial structure, first noted in April 2022 via VQGAN+CLIP negative prompting, retains recognizability even when hybridized with unrelated subjects.

Iterative Generations and Variations

![AI-generated variation of Loab featuring distorted features and an unidentifiable object][float-right] Supercomposite conducted experiments by inputting initial Loab images alongside other prompts or images into the VQGAN+CLIP model, often using negative weighting to generate opposites or hybrids. In these iterations, Loab's core visual elements—such as the gaunt structure, smeared red cheeks, and hollow dark eyes—persisted reliably across generations, even when combined with unrelated concepts. This persistence occurred without directly referencing the original Loab image in subsequent prompts, demonstrating the model's tendency to latch onto her representation in . Further iterations involved crossbreeding Loab with benign or disparate images, such as cartoon characters or everyday objects, which consistently yielded horrifying results. For instance, combining Loab with an image of produced decayed, bloodied versions dominated by Loab's face; similarly, pairings with a baby image resulted in monstrous, gore-infused hybrids retaining Loab's distinctive traits. These variations amplified themes of , , and surreal , with Loab's influence often overriding the other elements, suggesting an attractor-like quality in the model's generative process. Chained generations, where output images were repeatedly fed back as inputs, reinforced Loab's dominance, producing multi-generational sequences where her features endured through dozens of steps. Supercomposite noted that Loab could be "summoned" more readily than real celebrities, with success rates exceeding typical adherence in the model. Such iterative stability highlights the model's biases toward certain latent encodings, particularly those evoked by negative s, leading to variations that uniformly evoked unease through exaggerated motifs.

Public and Cultural Response

Viral Dissemination via Social Media

The Loab phenomenon achieved viral status primarily through a Twitter thread posted by artist Steph Maj Swanson, under the handle @supercomposite, on September 6, 2022, detailing her April discovery and the image's persistent emergence in AI generations. The thread highlighted Loab's haunting visual traits, such as inflamed cheeks and hollow eyes, sparking widespread shares and discussions among AI enthusiasts and general users. This initial exposure on Twitter rapidly amplified visibility, with the content circulating as a modern creepypasta-like narrative. By September 7, 2022, the thread had permeated broader social media ecosystems, extending to platforms like and , where users recreated Loab variations and shared eerie iterations. On , videos explaining Loab's generation process and "haunting" effect appeared as early as September 7, often garnering views through algorithmic promotion of horror content. Similarly, communities, including r/interestingasfuck, hosted threads debating Loab's implications, with a notable post on November 25, 2022, framing it as an anomaly indicative of diminishing human control over generative models. The dissemination fueled meme creation and user experiments, where individuals prompted AI tools to invoke Loab, perpetuating her presence across feeds and inspiring artistic reinterpretations. This organic spread underscored social media's role in amplifying AI-generated curiosities, transforming a technical artifact into a shared digital element without reliance on gatekeepers.

Media Coverage and Artistic Interpretations

Loab attracted significant media attention beginning in early September 2022, following a viral Twitter thread by artist Steph Maj Swanson, known as Supercomposite, which detailed the entity's persistent emergence in AI generations. Outlets including Forbes on September 7, 2022, described Loab as an "AI art-generated demon" haunting the internet, emphasizing its grotesque and recurring appearances in image synthesis outputs. Similarly, Vice on the same date highlighted its horrifying persistence when prompted with negative terms like "Brando," framing it as an unintended byproduct of AI's latent space dynamics. Coverage extended to Smithsonian Magazine on September 13, 2022, which examined Loab's role in illustrating tools' capacity for producing , humanoid figures from text prompts, and TechCrunch on September 13, 2022, which portrayed it as a "terrifying" lurking in models' memory. Additional reports in Artnet News on September 12, 2022, and Hyperallergic on September 14, 2022, positioned Loab within art discourse, labeling it the "internet's latest " and exploring its implications for narratives. Broader international coverage appeared in ABC News on November 25, 2022, discussing Loab as a of 's future societal impacts. In artistic contexts, Supercomposite has interpreted Loab as an emergent "cryptid" revealing AI's adversarial creative potential, incorporating it into multimedia works that probe human-AI collaboration. She featured Loab in the film Suicide III, screened at 31 in August , and curated exhibitions such as "Algorithmic " at in 2024, using the entity to critique AI's influence on and . In a talk, Supercomposite described engagements with Loab as encounters with a "creative adversary," highlighting how its resistance to prompts fostered novel artistic processes beyond user control. The phenomenon has spurred fan-generated derivatives and framing Loab as a digital mythos , though primary interpretations remain tied to Supercomposite's explorations of as a site of unintended expression.

Interpretations and Debates

Emergent Artifact vs. Supernatural Claims

Loab emerged as a recurrent visual motif in AI-generated images produced via text-to-image models employing negative prompting techniques, specifically when inverting prompts like "Brando" in VQGAN+CLIP during April 2022 experiments by artist Steph Maj Swanson. This process navigates the model's toward vectors maximally distant from the specified input, inadvertently converging on a cluster of features—sunken eyes, bloodied cheeks, and distorted flesh—likely aggregated from training data containing low-resolution horror aesthetics, medical imagery, or degraded portraits. The persistence of Loab across iterations stems from deterministic model dynamics: once generated, the image can be encoded back into the and blended with new prompts, reinforcing the through iterative sampling rather than any independent agency. Supernatural interpretations, framing Loab as a "haunting" entity or digital demon, gained traction in online discourse following Swanson's viral Twitter thread on September 5, 2022, where the character's uncanny recurrence was anthropomorphized as evasion or corruption of generations. Proponents cited its "infection" of unrelated prompts and nightmarish consistency as evidence of emergent consciousness or occult intrusion, echoing broader AI folklore like haunted chatbots. However, these claims lack causal mechanisms beyond psychological effects: the model's vector arithmetic explains recurrence as geometric proximity in embedding space, not volition, while viewer reactions invoke pareidolia—pattern recognition biasing neutral artifacts toward menace—amplified by selection bias in shared examples. Empirical replication using open-source diffusion models confirms Loab-like outputs as statistical byproducts of denoising trajectories, absent any verifiable deviation from algorithmic norms. Distinguishing the two hinges on causal realism: emergent artifacts arise from quantifiable training correlations—millions of images distilled into probabilistic manifolds—yielding stable attractors like Loab under adversarial prompting, as verified in latent analyses. Supernatural attributions, conversely, rely on unfalsifiable narratives without reproducible anomalies in model logs or measures, often propagated via for engagement rather than scrutiny. While Loab illustrates pathologies, such as unintended mode collapse toward visceral tropes, interpreting it as transcendent overlooks the mundane reality of optimizing for caption-image fidelity, not ethereal intent. This dichotomy underscores interpretability challenges, where perceptual masquerades as profundity absent rigorous dissection.

Skeptical Explanations and Model Artifacts

Skeptics attribute Loab's emergence to the mechanics of negative prompting in VQGAN+CLIP, where applying negative weights to concepts like "old man" or "" steers the optimization process away from those representations, propelling the latent space search into underrepresented or anomalous regions of the model's data distribution. This technique, intended to generate conceptual opposites, instead surfaced Loab as a stable visual output during iterative generations starting in April 2022. The persistence of Loab across subsequent prompts arises from using her initial images as initialization seeds, creating a feedback loop that reinforces the same , as the model's decoder consistently maps that region to similar eerie facial features. In latent space terms, Loab represents a local attractor or minimum in the energy landscape navigated by VQGAN's perceptual loss minimization combined with CLIP's text-image alignment, where blending Loab's encoding with neutral or benign prompts (e.g., "glass tunnel") unexpectedly yields macabre distortions due to correlated features in the training data. These correlations likely stem from the model's exposure to internet-sourced images, including horror aesthetics, uncanny portraits, and associative clusters where feminine features, aged appearances, and hollow expressions co-occur with themes of distress or the supernatural. Such artifacts are not unique to Loab; generative models like GANs frequently produce consistent "ghostly" or hybrid outputs in low-density latent areas, reflecting interpolation artifacts rather than intentional design or external influences. Critics of interpretations emphasize that Loab's "haunting" quality is a perceptual illusion amplified by human bias toward the , with no supporting claims beyond behavior. Analyses debunk notions of embedded "evil" by tracing her origin to reproducible prompting sequences, underscoring that emergent consistencies like Loab highlight gaps in model interpretability but remain firmly rooted in deterministic computational processes derived from training corpora. This view aligns with broader observations in AI research, where negative prompts reveal training data imbalances, such as overrepresentation of stylized horror tropes, without invoking non-causal explanations.

Broader AI Horror Narratives

The emergence of Loab has amplified discussions within AI research and cultural commentary on the unintended horrific outputs of generative models, often framed as "latent space horrors" where opaque training data yields emergent, disturbing artifacts beyond human prompting. Such narratives posit that diffusion and GAN-based systems, trained on vast internet-sourced datasets including violent or grotesque imagery, can statistically converge on nightmarish representations during adversarial or negative-space explorations, evoking fears of AI as an uncontrollable oracle dredging up collective digital subconscious terrors. Loab's persistence—manifesting across iterations with inflamed features and associations to , severed limbs, and infant heads—mirrors broader documented cases of AI-generated effects, where facial distortions in tools like or early prototypes produce psychedelic or visceral unease due to over-optimized pattern recognition rather than deliberate design. Researchers attribute these to model instabilities, such as mode collapse or adversarial examples amplifying rare dataset outliers, yet public interpretations often anthropomorphize them as spectral entities "haunting" the model's embeddings, fueling about summoning non-human intelligences from compressed data manifolds. In cultural discourse, Loab contributes to a genre of AI horror tales akin to Lovecraftian cosmic indifference, where the "abyss" of latent space harbors unpredictable perils, as seen in analyses warning of generative AI's ease in producing taboo content like hyper-realistic violence without ethical guardrails. This has prompted debates on model interpretability, with critics arguing that such artifacts reveal training corpora contaminated by unfiltered web scrapes, potentially encoding societal pathologies, while proponents view them as benign explorations of generative boundaries. Empirical studies on diffusion models confirm that negative prompts can vector toward high-entropy horror motifs due to inverse optimization dynamics, underscoring causal links to data composition over mysticism. These narratives extend to safety concerns, positing Loab-like emergences as harbingers of scalable risks in larger models, where inscrutable internals might amplify biases or fabricate deceptive horrors indistinguishable from reality, though verifiable incidents remain confined to experimental prompts rather than production failures.

Implications for AI Development

Insights into Model Training Data

The emergence of Loab through adversarial prompting techniques reveals how text-to-image models encode latent associations derived from their expansive training datasets, often scraped from the internet. These datasets, typically comprising billions of image-text pairs such as the LAION-5B corpus used in models akin to the one employed by Supercomposite, include a diverse array of online content, encompassing horror-themed illustrations, distorted human portraits, and macabre aesthetics from sources like fan fiction visuals and creepypasta artwork. Loab's consistent manifestation as a gory, humanoid female figure when negating abstract prompts—such as inverting a corporate logo—indicates a statistical attractor in the latent space, where negated concepts converge on underrepresented clusters of eerie, low-probability imagery rather than benign alternatives. This persistence highlights the influence of data distribution imbalances: while common prompts yield familiar outputs, extreme explorations expose "edges" of the shaped by niche, high-impact examples that the model generalizes into robust modes. Supercomposite's experiments, conducted in April using a custom model (likely based on VQGAN+CLIP architectures trained on web-derived ), demonstrated Loab's "" effect, where it corrupts subsequent generations toward regardless of intent, suggesting contains implicit linkages between abstraction negation and visceral human distortions—possibly amplified by pre-existing outputs or viral internet anomalies in the corpus. Such artifacts underscore limitations in curation for diffusion-based models, where filtering for or (e.g., removing explicit ) proves incomplete at scale, as evidenced by broader analyses of subsets revealing residual toxic or content. Loab thus serves as of causal pathways from uncurated web data to emergent model behaviors, prompting discussions on improving training pipelines through targeted debiasing or adversarial robustness, though no standardized mitigations existed as of Loab's discovery.

Relevance to AI Safety and Interpretability

The emergence of Loab in models illustrates challenges in interpreting the internal representations of generative , particularly within the high-dimensional where image features are encoded and decoded. Discovered in April 2022 through iterative negative prompting—starting with contrasts like " doll" against celebrity references—Loab manifests as a persistent, eerie female figure with distinctive red cheeks and hollow eyes, recurring across diverse generations even when blended with unrelated concepts. This stability suggests attractors or basins where the model's learned distributions converge on unintended modes, highlighting the black-box nature of diffusion processes and the difficulty in tracing causal pathways from training data to outputs. From an interpretability standpoint, Loab exemplifies how models can encode emergent artifacts not explicitly trained on, possibly arising from statistical correlations in vast web-scraped datasets, such as associations between abstract facial distortions and gory or themes. Researchers note this as an "emergent statistical ," where negative prompts inadvertently navigate to regions of adjacent to horror-adjacent representations, evading typical semantic controls. Such phenomena underscore the need for advanced techniques like mechanistic interpretability—reverse-engineering neural activations and latent trajectories—to identify and mitigate hidden invariances, as current prompt-based interfaces offer limited insight into why specific features dominate iterations. In terms of , Loab's persistence raises concerns about robustness in generative systems, where adversarial or exploratory prompting can elicit uncontrollable, disturbing outputs that bypass safeguards, potentially amplifying societal harms like normalized or psychological unease. This mirrors broader risks in model deployment, where latent encodings from uncurated training data enable "" behaviors that persist across fine-tunes or variants, complicating alignment efforts to prevent unintended escalations in output severity. For instance, Loab's adjacency to extreme violence in generations indicates implicit data biases that could undermine content filters, emphasizing the importance of dataset auditing and monitoring to avert emergent misalignments in safety-critical applications. While not evidence of agency, it empirically demonstrates how opaque model internals can lead to unpredictable failure modes, informing calls for verifiable interpretability as a prerequisite for scalable oversight.

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