AI Image & Photo Restoration - AI Image Tools Tool

Overview

The "AI Image & Photo Restoration" collection on Replicate aggregates a curated set of open-source and research-driven models for restoring, enhancing, and repairing photographs. The collection brings together specialized models targeting face restoration, super-resolution (upscaling), colorization, denoising, deblurring, and inpainting, enabling multi-step pipelines where different models are chained for best results. Representative model families found in the collection include CodeFormer and GFPGAN for face-centric restoration, Real-ESRGAN (and other ESRGAN variants) for perceptual upscaling, and DeOldify-style approaches for automatic colorization. These models are commonly used in workflows such as: rescuing scanned analog photos, improving low-resolution surveillance or historical images, removing JPEG artifacts and noise, and selectively restoring facial detail while preserving identity. Practical considerations include GPU inference for reasonable latency, careful pre- and post-processing (face detection, alignment, mask-based blending), and verification for hallucinated details—especially for historical or forensic use. For the latest versions, parameters, and example runners, see the Replicate collection page and the upstream GitHub repositories (e.g., CodeFormer, GFPGAN, Real-ESRGAN, DeOldify).

Key Features

• Face restoration that recovers facial detail while preserving identity (e.g., CodeFormer, GFPGAN)
• Perceptual upscaling for sharper high-resolution outputs (Real-ESRGAN and ESRGAN variants)
• Automatic colorization routines for black-and-white photos (DeOldify-style models)
• Noise and JPEG artifact removal to clean scanned or highly compressed images
• Inpainting and mask-guided repair for missing or damaged regions (LaMa-style approaches)

Example Usage

Example (python):

import os
import replicate

# Set REPLICATE_API_TOKEN in your environment
client = replicate.Client(api_token=os.environ.get("REPLICATE_API_TOKEN"))

# Replace 'owner/model' with a model slug from the Replicate collection, e.g. 'sczhou/CodeFormer'
model_slug = "owner/model"
input_image_path = "path/to/low_res_photo.jpg"

# Simple run example: many Replicate models accept an "image" or "input" parameter
# Check the model's page on Replicate for exact input names and parameter options.
with open(input_image_path, "rb") as f:
    output = client.run(model_slug, input={"image": f})

print("Model output:", output)

# For larger images or multi-step pipelines (face restore + upscaling), run multiple models sequentially
# and pass intermediate outputs (URLs or bytes) to the next model.

Benchmarks

Representative models included: CodeFormer, GFPGAN, Real-ESRGAN, DeOldify and other restoration/upscaling models (Source: https://replicate.com/collections/ai-image-restoration)

Typical runtime environment: PyTorch-based models; GPU recommended for interactive use (NVIDIA CUDA-supported GPUs) (Source: https://replicate.com/collections/ai-image-restoration)

Common input/output formats: PNG/JPEG inputs, outputs preserved as PNG/JPEG; high-resolution outputs depend on model and memory (Source: https://replicate.com/collections/ai-image-restoration)

Key Information

• Category: Image Tools
• Type: AI Image Tools Tool