beksinski-v1s1600
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Introduction
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Zdzisław Beksiński was a Polish artist whose work transcends conventional boundaries to evoke a haunting, surreal vision of dystopia. Born in 1929 in Sanok and originally trained as an architect, he turned to painting without formal art school training and developed a unique style defined by eerie landscapes, distorted figures, and decaying, otherworldly structures. His oil paintings on meticulously prepared hardboard panels are imbued with a dreamlike quality—Beksiński once stated he wished to “paint in such a manner as if
\[he\]
were photographing dreams.” This distinctive aesthetic has now been captured by a Low Rank Adaptation (LoRA) for NoobAI, which has been carefully tuned to replicate his signature style, allowing AI-generated visuals to echo his grim, yet mesmerizing visions.
Over the course of his career, Beksiński evolved from expressionistic influences to a more abstract, gothic interpretation of surrealism, particularly during his “fantastic period” from the late 1960s to the mid-1980s. His work from this time is celebrated for its intricate detail and oppressive atmosphere—a blend of meticulous craftsmanship and a raw, visceral imagination that continues to influence contemporary art and popular culture. This model aims to reflect the artist’s technical mastery while preserving the enigmatic quality of his visions, providing a new means to create art that feels both timeless and deeply rooted in the dystopian surrealism that defined Beksiński’s legacy.
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Styles
Style LoRAs for Stable Diffusion models focus on adapting the neural network to replicate specific artistic styles, visual aesthetics, or rendering techniques. These adaptations modify how the model interprets and generates visual elements like brushstrokes, color palettes, shading techniques, and overall artistic presentation. Style LoRAs can transform outputs to match anything from classical art movements (like impressionism or art nouveau) to modern digital art styles (such as anime, pixel art, or watercolor). They work by fine-tuning the model's attention layers to recognize and reproduce distinctive visual patterns and techniques associated with the target style, while requiring relatively few training images (typically 15-50 high-quality examples) to achieve good results.
Characters
Character LoRAs specialize in teaching Stable Diffusion models to generate consistent representations of specific characters, including their facial features, body proportions, clothing, and distinctive attributes. These adaptations are particularly popular in the AI art community for creating consistent representations of original characters, celebrities, or fictional personalities. Character LoRAs require careful curation of training data (typically 20-50 high-quality images) that clearly show the character's defining features from various angles and expressions. They primarily work by fine-tuning the model's attention mechanisms to maintain consistency in facial features, body proportions, and characteristic details while allowing for natural variations in poses and expressions. The training process often focuses on the upper layers of the model responsible for high-level feature recognition and generation.
NoobAI LoRAs
NoobAI is a finetune of Illustrious using the full Danbooru and e621 datasets. There are two different versions of the model, one with `eps` and one with `v-prediction`. I'm currently training for the v-pred 0.65S version unless otherwise specified.