Top AI Face Restoration Models – A Hands-On Comparison

Restoring old and low-resolution photos has become incredibly effective thanks to recent advances in AI. In this post, we compare some of the most powerful face restoration models available today: GFPGAN, CodeFormer, Flux Kontext, and the tools available on Upsampler.com.

🔧 Upsampler.com — Best All-Around Tool

Upsampler.com is a generative AI-based platform that offers several specialized tools for image and face restoration. It combines restoration, enhancement, upscaling, and optional facial repair in a seamless online workflow.

Key Features:

• Smart Upscale Tool: Enhances low-resolution images while preserving structure at a lower creativity value. Use the "Enhance Faces" option for better face restoration in low-res images.
• Image Restoration Tool: Designed for heavily degraded images, removes scratches or artifacts, and can colorize black-and-white photos.

✅ Best for: Casual users, businesses, old photos, colorization, face enhancement in upscaling.

📸 Smart Upscale Before & After Example:

📸 Image Restoration Before & After Examples:

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🧠 GFPGAN v1.4 – Consistent Results with Open-Source

• GitHub: TencentARC/GFPGAN

GFPGAN is a GAN-based face restoration model developed by Tencent ARC, built on StyleGAN2. It’s trained specifically for real-world blind face restoration, making it ideal for general use cases with moderately degraded images.

Pros:

• Preserves facial identity well
• Consistent, reliable results
• Lightweight and fast

Cons:

• May struggle with severely damaged or very low-res faces
• Requires additional upscaling model for background upsampling

✅ Best for: Users looking for stable, identity-preserving face enhancement.

📸 Before & After Example:

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🧬 CodeFormer – High Detail, More Creative

• GitHub: sczhou/CodeFormer

CodeFormer is another strong face restoration model that can handle poor-quality images, but with a twist: it often reconstructs faces creatively, leading to more detail, but sometimes less identity fidelity.

Pros:

• Strong at reconstructing heavily damaged faces
• Adjustable fidelity setting
• High-quality, realistic outputs

Cons:

• Sometimes significantly alter facial features
• Requires additional upscaling model for background upsampling

✅ Best for: Artistic restoration, high-detail reconstructions when identity isn't critical.

📸 Before & After Example:

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🧪 Flux Kontext Pro – Experimental but Promising

Flux Kontext is a newer image-to-image model developed by Black Forest Labs. It allows you to guide the restoration using a prompt, giving you more flexibility, but also more unpredictability.

Pros:

• Can remove scratches and colorize images
• Prompt-based customization

Cons:

• Limited to 1-megapixel resolution
• Identity consistency is hit-or-miss
• Varies a lot depending on prompt and image

✅ Best for: Creative projects, experiments, stylized restorations.

📸 Before & After Example:

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🏁 Final Thoughts

If you're restoring cherished family photos, we recommend starting with Upsampler.com or GFPGAN. For more creative applications or if you're experimenting with styles, CodeFormer and Flux Kontext might surprise you.