README.md

---
license: mit
base_model:
- Manojb/stable-diffusion-2-1-base
pipeline_tag: image-to-image
library_name: diffusers
---

# Towards Photorealistic and Efficient Bokeh Rendering via Diffusion Framework

This repository contains the model and code for **Towards Photorealistic and Efficient Bokeh Rendering via Diffusion Framework**, as presented in the paper:

[**Towards Photorealistic and Efficient Bokeh Rendering via Diffusion Framework**](https://arxiv.org/abs/2605.07429)

## Abstract

Existing mobile devices are constrained by compact optical designs, such as small apertures, which make it difficult to produce natural, optically realistic bokeh effects. Although recent learning-based methods have shown promising results, they still struggle with photos captured under high digital zoom levels, which often suffer from reduced resolution and loss of fine details. A naive solution is to enhance image quality before applying bokeh rendering, yet this two-stage pipeline reduces efficiency and introduces unnecessary error accumulation. To overcome these limitations, we propose MagicBokeh, a unified diffusion-based framework designed for high-quality and efficient bokeh rendering. Through an alternative training strategy and a focus-aware masked attention mechanism, our method jointly optimizes bokeh rendering and super-resolution, substantially improving both controllability and visual fidelity. Furthermore, we introduce degradation-aware depth module to enable more accurate depth estimation from low-quality inputs. Experimental results demonstrate that MagicBokeh efficiently produces photorealistic bokeh effects, particularly on real-world low-resolution images, paving the way for future advancements in bokeh rendering.

## Code and Usage

The official code and model are available at the following GitHub repository:
[https://github.com/vivoCameraResearch/MagicBokeh](https://github.com/vivoCameraResearch/MagicBokeh)