DiffusionLight-Turbo: Accelerated Light Probes for Free via Single-Pass Chrome Ball Inpainting
Worameth Chinchuthakun, Pakkapon Phongthawee, Amit Raj, Varun Jampani, Pramook Khungurn, Supasorn Suwajanakorn

TL;DR
DiffusionLight-Turbo presents a fast, stable, and generalizable method for estimating lighting from single LDR images by leveraging diffusion models and LoRA fine-tuning, significantly reducing computation time.
Contribution
The paper introduces DiffusionLight-Turbo, a novel approach that accelerates lighting estimation using diffusion models and LoRA techniques, enabling real-time performance with minimal quality loss.
Findings
Achieves 60x speedup over previous methods.
Produces convincing lighting estimates across diverse scenarios.
Demonstrates superior generalization to in-the-wild images.
Abstract
We introduce a simple yet effective technique for estimating lighting from a single low-dynamic-range (LDR) image by reframing the task as a chrome ball inpainting problem. This approach leverages a pre-trained diffusion model, Stable Diffusion XL, to overcome the generalization failures of existing methods that rely on limited HDR panorama datasets. While conceptually simple, the task remains challenging because diffusion models often insert incorrect or inconsistent content and cannot readily generate chrome balls in HDR format. Our analysis reveals that the inpainting process is highly sensitive to the initial noise in the diffusion process, occasionally resulting in unrealistic outputs. To address this, we first introduce DiffusionLight, which uses iterative inpainting to compute a median chrome ball from multiple outputs to serve as a stable, low-frequency lighting prior that…
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Taxonomy
TopicsImage Enhancement Techniques · Computer Graphics and Visualization Techniques · Advanced Vision and Imaging
