mlsanityData to 14 Oct 2026

SDXL Finetuned with LoRA for Coloring Therapy: Generating Graphic Templates Inspired by United Arab Emirates Culture

arXiv 2403.05562 ↗First posted 20 Feb 2024cs.HC cs.AI cs.CYcited by 0

Not on the map. Why?

Abstract (arXiv)

A transformative approach to mental health therapy lies at the crossroads of cultural heritage and advanced technology. This paper introduces an innovative method that fuses machine learning techniques with traditional Emirati motifs, focusing on the United Arab Emirates (UAE). We utilize the Stable Diffusion XL (SDXL) model, enhanced with Low-Rank Adaptation (LoRA), to create culturally significant coloring templates featuring Al-Sadu weaving patterns. This novel approach leverages coloring therapy for its recognized stress-relieving benefits and embeds deep cultural resonance, making it a potent tool for therapeutic intervention and cultural preservation. Specifically targeting Generalized Anxiety Disorder (GAD), our method demonstrates significant potential in reducing associated symptoms. Additionally, the paper delves into the broader implications of color and music therapy, emphasizing the importance of culturally tailored content. The technical aspects of the SDXL model and its LoRA fine-tuning showcase its capability to generate high-quality, culturally specific images. This research stands at the forefront of integrating mental wellness practices with cultural heritage, providing a groundbreaking perspective on the synergy between technology, culture, and healthcare. In future work, we aim to employ biosignals to assess the level of engagement and effectiveness of color therapy. A key focus will be to examine the impact of the Emirati heritage Al Sadu art on Emirati individuals and compare their responses with those of other nationalities. This will provide deeper insights into the cultural specificity of therapeutic interventions and further the understanding of the unique interplay between cultural identity and mental health therapy.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 18 references that are in the map. First posted on arXiv 20 Feb 2024.

Builds on

4 of 18 references.

References · 4

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,356
  3. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  4. 2023Art and the science of generative AI: A deeper divecited by 95

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Style Transfer as Bias Mitigation: Diffusion Models for Synthetic Mental Health Text for Arabic
  2. 2025Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o
  3. 2025A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context
  4. 2026YAQIN: Culturally Sensitive, Agentic AI for Mental Healthcare Support Among Muslim Women in the UK
  5. 2026Digital Harf: A Clinically Integrated Multimodal AI System for Pervasive Arabic Speech and Language Therapy
  6. 2025Evaluating Large Language Models for Anxiety, Depression, and Stress Detection: Insights into Prompting Strategies and Synthetic Data
  7. 2025EmoArt: A Multidimensional Dataset for Emotion-Aware Artistic Generation
  8. 2026Poetic Heritage for Culturally Grounded Emotional Support: An Interaction Design Framework and Its Multimodal Agentic Instantiation ↗