Fusion: Landscape and Beyond is an interdisciplinary art project that explores the relationship between memory, imagination, and Artificial Intelligence (AI) embodied in the century-long practices and discourse of Shan-Shui-Hua – Chinese landscape painting. It draws inspiration from the concept of Cultural Memory, where memories are selectively retrieved and updated based on present circumstances. The project considers text-to-image AI algorithms as analogous to Cultural Memory, as they generate diverse and imaginative images using pre-existing knowledge. In response to this analogy, the project introduces the concept of "AI memory" and situates it in the culturally significant Chinese landscape painting — a synthetic embodiment of creativity derived from the artist's memory.

Diversity plays both as a driving force and major inspiration for this project, which delves deeply into addressing the bias and the necessity for cultural diversity within the realm of machine-learning generative models for creative art. Recognizing that machines inherently exhibit bias stemming from their design and predominant use, it becomes essential to acknowledge and rectify such prejudices, particularly from a cultural standpoint. The initial phase of this project involves the fine-tuning of the Stable Diffusion model. The necessity for fine-tuning stems from the imperative to infuse a deeper cultural resonance within the AI's creations, ensuring they are not just technically accurate but also emotionally and culturally symbiotic. The Stable Diffusion model, while proficient in image generation, reflects its training on a more general and globally diverse dataset. By fine-tuning it, we delicately weave the intricacies of Shan-Shui-Hua's philosophy and aesthetic principles into the AI's fabric. This process not only counters the prevailing Western-centric perspectives but also fosters a generative space where technology and traditional Chinese artistry coalesce, manifesting works that are genuinely reflective of and rooted in Chinese cultural heritage.

The final output of this project – a video animation and a collection of scroll paintings generated using our fine-tuned models – demonstrates that by inserting more culturally diverse datasets, the bias in practical machine-learning creativity is significantly lessened. They are not only manifesting the possibility of increasing diversity in machine-learning generative models, and thus leading to better performances of pre-trained models, but also indicating the stylistic nuances of Chinese landscape painting fueled by AI’s unique synthetic ability. What’s more, this fusion of past, present, and future showcases another fundamental characteristic of AI, which is its inherent competence to bypass time. Viewers are presented with a captivating experience to “travel” through the river of time, seamlessly immersing them in contemporary re-embodiment of the rich, centuries-old tradition of artistic creativity, encapsulating the timeless essence of human expression and experience.