How to Run Generative Refocusing AI Locally

Introduction

Generative Refocusing Modern photography has always been limited by a physical constraint: focus must be decided at the moment of capture. Once the shutter clicks, the depth of field is locked forever. But what if focus could be changed after the photo is taken—without special hardware or multi-view cameras?

Generative Refocusing introduces a breakthrough approach that uses deep generative models to synthetically refocus images after capture. Powered by neural rendering and learned depth representations, this technique allows users to dynamically shift focus points and depth-of-field effects from a single image.

This blog explores how Generative Refocusing works, why it matters, and how developers can experiment with the open-source Genfocus implementation.

What Is Generative Refocusing?

Generative Refocusing is an AI-driven computational photography technique that recreates realistic focus changes using neural networks rather than optical lenses.

Instead of relying on:

Light-field cameras
Multi-lens systems
Stereo image capture

this method works from a single RGB image and predicts how the scene would look if the focus plane were changed.

Key Idea

The model learns a continuous depth-aware representation of the scene and uses generative synthesis to simulate optical blur realistically.

Why Generative Refocusing Is Important

Traditional refocusing methods often fail because:

Depth estimation is inaccurate
Blur looks artificial or uniform
Foreground/background separation is noisy

Generative Refocusing solves these by learning blur as a function of depth and context, producing smooth and photorealistic results.

Major Benefits

📷 Post-capture focus control
🎨 Cinematic depth-of-field effects
🧠 AI-learned optical realism
📱 No special camera hardware required
🎥 Applications in photography, film, and AR

This section walks you through setting up Genfocus on your local machine and running the interactive refocusing demo using Gradio.

Prerequisites

Before you begin, make sure you have:

Python 3.12
Conda (Miniconda or Anaconda)
Git
CUDA-enabled GPU (recommended for best performance)
Internet access to download pretrained models

1. Installation

Clone the Repository

Start by cloning the Genfocus repository and navigating into the project directory:

https://github.com/rayray9999/Genfocus

git clone git@github.com:rayray9999/Genfocus.git
cd Genfocus

Create and Activate Conda Environment

Create a clean Python environment to avoid dependency conflicts:

conda create -n Genfocus python=3.12
conda activate Genfocus

Install Dependencies

Install all required Python packages using pip:

pip install -r requirements.txt

This will install core dependencies such as PyTorch, Gradio, and supporting vision libraries.

2. Download Pre-trained Weights

Genfocus relies on multiple pretrained models to generate realistic refocusing effects.

Make sure you are in the Genfocus root directory before running the commands below.

Download Main Models

wget https://huggingface.co/nycu-cplab/Genfocus-Model/resolve/main/bokehNet.safetensors
wget https://huggingface.co/nycu-cplab/Genfocus-Model/resolve/main/deblurNet.safetensors

These models handle:

Bokeh generation
Defocus correction

Download Auxiliary Depth Model

mkdir -p checkpoints
cd checkpoints
wget https://huggingface.co/nycu-cplab/Genfocus-Model/resolve/main/checkpoints/depth_pro.pt
cd ..

This depth model enables accurate depth-aware blur synthesis.

3. Run the Gradio Demo

Genfocus provides an interactive Gradio web interface for testing generative refocusing in real time.

Important Note

⚠️ This project uses FLUX.1-dev.
You must request access and authenticate locally before running the demo.

Launch the Demo

python demo.py

Access the Web Interface

Once the server starts, open your browser and navigate to:

http://127.0.0.1:7860

You can now:

Upload an image
Select a virtual focus plane
Adjust refocus parameters
Generate photorealistic depth-of-field effects

Tips for Best Results

Use high-resolution images with clear foreground/background separation
Portrait or object-centric images work best
Run on GPU for faster inference
Experiment with different focal depths for cinematic effects

Troubleshooting

CUDA errors → Verify PyTorch GPU installation
Model not found → Ensure all .safetensors and .pt files are in correct directories
FLUX access error → Confirm authentication is completed successfully

Final Thoughts

This tutorial allows developers, researchers, and AI enthusiasts to quickly explore Generative Refocusing without building a pipeline from scratch. With Genfocus, refocusing becomes a post-processing AI capability, unlocking new possibilities in photography, media production, and neural rendering research.

Inside the Genfocus Project

The Genfocus repository provides a research-grade implementation of generative refocusing using deep learning.

Core Components

Neural Depth Representation – Learns continuous depth instead of hard segmentation
Generative Blur Synthesis – Produces realistic defocus instead of Gaussian blur
End-to-End Training Pipeline – Learns refocusing directly from data
PyTorch-based Architecture – Easy for researchers and developers to extend

What Makes It Unique?

Unlike traditional depth-based refocusing pipelines, Genfocus:

Avoids explicit depth maps in output
Preserves fine details like hair and edges
Produces natural bokeh transitions

How Generative Refocusing Works (High-Level)

Input Image
A single RGB image is fed into the network.
Latent Scene Encoding
The model encodes scene structure and depth cues into a latent space.
Focus Plane Selection
A virtual focal distance is selected programmatically.
Generative Rendering
The network synthesizes a new image with realistic depth-of-field blur.
Photorealistic Output
The final image appears as if captured with a different lens focus.

Practical Applications

📸 Photography & Mobile Cameras

Portrait mode enhancement
Post-shot refocus editing
DSLR-style bokeh on smartphones

🎬 Film & Media Production

Cinematic focus pulls in post-production
Depth-based storytelling effects

🥽 AR / VR & Metaverse

Depth-aware scene rendering
Focus simulation for immersive environments

🧪 Research & AI Vision

Neural rendering experiments
Scene understanding and depth modeling

Getting Started with Genfocus

If you’re a developer or researcher, Genfocus is a great playground for exploring neural rendering and computational photography.

Requirements (Typical)

Python
PyTorch
CUDA-enabled GPU (recommended)
Image datasets for training/testing

You can explore the codebase, experiment with pretrained models, and integrate generative refocusing into your own AI pipelines.

Expert Insight: Why This Matters for the Future

Generative Refocusing represents a paradigm shift:

Cameras no longer define the final image
AI becomes a virtual lens
Photography evolves from capture → computation

As neural rendering matures, we’re moving toward a future where focus, lighting, and optics are fully programmable—long after the photo is taken.

Conclusion

Generative Refocusing is more than a visual trick—it’s a glimpse into the future of AI-powered imaging. With projects like Genfocus, developers and researchers can experiment with cutting-edge techniques that blur the line between photography and generation.

If you’re interested in computer vision, neural rendering, or next-generation photography, this is a project worth exploring.

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