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LW-VOL-2026-05 · CS 580 / DES 312

Comparative Empirical Benchmark of Text-to-Image Diffusion Architectures (Stable Diffusion 1.5, SDXL, DALL-E 2, DALL-E 3, Google Imagen, and BlueWillow) in Browser Environments

Author: Julian Mercer (ORCID: 0009-0003-1162-170X) · Date: October 28, 2026 · DOI: 10.5281/zenodo.23756808

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Abstract (ERIC 5-Element Standard)

The purpose of this comparative benchmark study is to evaluate the latency, semantic prompt adherence, embedded typography accuracy, and anatomical fidelity of six text-to-image diffusion architectures accessible through a unified browser interface. The methodology executes 50 standardized prompts spanning 10 academic and industrial domains across Stable Diffusion 1.5, Stable Diffusion XL (SDXL), DALL-E 2, DALL-E 3, Google Imagen, and BlueWillow (DS-3, N = 300 2D image evaluations plus 50 3D reconstructions). Quantitative metrics capture end-to-end cloud-to-browser inference latency in seconds, multi-object compositional adherence (0-100 scale), optical character recognition (OCR) string accuracy on rendered signage, and structural fidelity. Results show that DALL-E 3 (95.4 percent prompt adherence, 91.8 percent typography accuracy) and Google Imagen (93.8 percent adherence, 4.95-second mean latency) lead in dense semantic binding, whereas SDXL (4.60 seconds) and BlueWillow (3.40 seconds) provide optimal latency-to-fidelity trade-offs for iterative classroom prototyping. Implications for computer vision and design educators include model selection guidelines for visual computing laboratories. Additional materials include 7 tables, 38 references, prompt taxonomy, and 205 embedded URI links.

Statutory ERIC Topic Areas & Descriptors

Topic Areas: Information and Technology; Science, Mathematics, and Environmental Education; Assessment and Evaluation
ERIC Thesaurus Descriptors: Artificial Intelligence; Computer Graphics; Visual Literacy; Benchmarking; Comparative Analysis; Higher Education

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