CodeFormer logo

CodeFormer

research
Robust face restoration model for old photos and AI generated portraits, published by S Lab, widely used to recover identity and details while keeping naturalness controls for artistic workflows.
face-restoration upscale ai-image
research
Category
Beginner
Difficulty
Active
Status
Web App
Type

What is CodeFormer?

Discover how CodeFormer can enhance your workflow

CodeFormer is a research model for blind face restoration that balances fidelity and naturalness, making it useful for reviving old or low quality portraits and for improving faces generated by diffusion models. The open source implementation runs on PyTorch with CUDA acceleration and can be tried in hosted demos or integrated locally. Users can adjust a weight to favor identity preservation or smoothness, then export results for further editing. The repo includes instructions and requirements, and a permissive research license governs usage. Creative teams use CodeFormer in pipelines with upscalers, inpainting and color correction to reach realistic results from damaged or synthetic inputs. For production, always review outputs for artifacts and match terms to your commercial context.

Key Capabilities

What makes CodeFormer powerful

Identity Preserving Model

Apply CodeFormer to reconstruct faces from low quality inputs, tuning toward identity fidelity or toward smooth naturalness for stylistic needs.

Implementation Level Professional

Pipelines and GUIs

Use hosted demos, community GUIs or command line notebooks to slot the model into diffusion and editing workflows.

Implementation Level Basic

CUDA and Batching

Leverage GPU acceleration for faster turnarounds and batch archival jobs while watching VRAM limits.

Implementation Level Basic

Post Process Steps

Follow with upscalers, color and inpainting to reach print or delivery quality on demanding projects.

Implementation Level Basic

Key Features

What makes CodeFormer stand out

  • Blind face restoration that balances fidelity and naturalness via tunable weight
  • PyTorch implementation with CUDA acceleration and requirements listed
  • Hosted demos and community ports for quick trials
  • Use in diffusion pipelines to improve AI faces
  • Command line and notebook examples for batch work
  • Identity aware restoration helpful for old photos
  • Works with upscalers and denoisers downstream
  • Active community forks and GUIs for easier use

Use Cases

How CodeFormer can help you

  • Restoring old scanned portraits with damage
  • Improving diffusion generated faces in composites
  • Prepping portraits before upscale and print
  • Reviving low bitrate webcam headshots
  • Cleaning dataset faces for research
  • Batch processing archives via notebooks
  • Photo lab services for clients
  • Education in image restoration techniques

Perfect For

creators, photo labs, researchers and hobbyists who need a proven face restoration step inside AI or archival workflows

Tags

open-source python research analysis insights

Plans & Pricing

Free

Get Started Update this tool

Visit official site for current pricing

Quick Information

Category research
Pricing Model Free plan
Last Updated 3/19/2026

Compare CodeFormer with Alternatives

See how CodeFormer stacks up against similar tools

CodeFormer VS A/B Smartly CodeFormer VS AI21 Labs CodeFormer VS Aleph Alpha

Frequently Asked Questions

Is CodeFormer free to use commercially?
The original repository carries an S Lab license focused on research use. Check license terms and any hosted service’s policies before commercial deployments.
Do I need a powerful GPU?
A modern CUDA capable GPU speeds results, though small jobs can run on CPU or hosted demos at slower speeds.
Can it perfectly reconstruct a face?
No, it is a restoration model that estimates details. Always review for artifacts and avoid misleading edits in sensitive contexts.
How does it compare to generic upscalers?
It targets faces specifically, often delivering more believable eyes and features than general purpose upscalers.
Can I batch restore an archive?
Yes, use notebook scripts or CLI to process folders and tune parameters per set.
Will it work on profile or occluded faces?
Performance drops with heavy occlusion or extreme angles. Try multiple settings and combine with inpainting.
Is there a GUI for non coders?
Yes, community GUIs and web demos exist, which are easier for occasional users than local setup.
What file formats are supported?
Inputs and outputs are common formats like PNG and JPG via scripts or GUIs, with no special container required.

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