AI Image Upscaler With Super Resolution - AI Image Tools Tool

Overview

AI Image Upscaler With Super Resolution on Replicate is a collection of production-ready super-resolution and enhancement models (including Real-ESRGAN) that increase image resolution while reducing noise and restoring details. The collection groups restorative upscalers (for realistic sharpening and artifact removal) and generative upscalers (which can hallucinate plausible high-frequency detail) and exposes common options such as upscaling factor, face restoration toggles, and refinement parameters. ([replicate.com](https://replicate.com/collections/super-resolution)) Real-ESRGAN itself (deployed on Replicate as nightmareai/real-esrgan) is an adaptation of ESRGAN trained to handle real-world degradations using synthetic training data; it’s commonly used for 2× and 4× upscales and includes optional face-enhancement integration (GFPGAN) for improved facial detail. Replicate’s model page lists practical limits (max recommended input ~1440p) and shows large community usage, indicating broad adoption for both single-image and batch workflows. For deployers, Replicate supports running community models via the API or self-hosting with Cog/Docker. ([github.com](https://github.com/GeorgiosIoannouCoder/realesrgan?utm_source=openai))

Key Features

• 2× and 4× super-resolution upscales using Real-ESRGAN and similar models.
• Optional face restoration (GFPGAN) to improve facial details and reduce artifacts.
• Noise reduction and artifact removal during upscaling for cleaner outputs.
• Fast inference: example 2× upscale ~1.8s on Nvidia T4, ~0.7s on A100 (Replicate notes).
• Run via Replicate API or self-host with Cog/Docker for on-premise control.

Example Usage

Example (python):

import replicate

# Example: run the Real-ESRGAN model on Replicate (check model page for exact parameter names/version)
# Ensure REPLICATE_API_TOKEN is set in your environment

output = replicate.run(
    "nightmareai/real-esrgan",  # model identifier on Replicate
    input={
        "image": open("input.jpg", "rb"),
        "scale": 2,                # common values: 2 or 4 — verify available options on the model page
        "face_enhance": True      # optional: enable face restoration (GFPGAN) if supported
    }
)

# 'output' is typically a URL or file-like object to the upscaled image — save to disk
if isinstance(output, list):
    url_or_bytes = output[0]
else:
    url_or_bytes = output

# For many Replicate deployments the returned value is a URL; fetch/save as needed.
print("Result:", url_or_bytes)

# Notes: confirm parameter names and version from the model's API/README on Replicate before production use.
# See Replicate docs for Python client usage and community model examples. (Replicate docs / model pages)

Pricing

Replicate bills models either by runtime (per-second hardware pricing) or by input/output for some models. Example hardware rates published by Replicate: Nvidia T4 $0.000225/sec (~$0.81/hr), Nvidia A100 (80GB) $0.001400/sec (~$5.04/hr). Model-level cost may vary (some models charge per output); check the specific model page for per-run or per-output estimates. ([replicate.com](https://replicate.com/pricing))

Benchmarks

Replicate runs (nightmareai/real-esrgan): 82.9M runs (Source: https://replicate.com/nightmareai/real-esrgan (Replicate model page).)

Max recommended input resolution: 1440p (Source: Replicate model README for nightmareai/real-esrgan.)

Approx. latency for 2× upscale: ~1.8s on Nvidia T4; ~0.7s on Nvidia A100 (per 2× upscale, reported by Replicate collection guide) (Source: Replicate 'super-resolution' collection (performance notes).)

Replicate GPU pricing (examples): Nvidia T4 $0.000225/sec; Nvidia A100 (80GB) $0.001400/sec (Source: Replicate pricing page.)

Training approach (Real-ESRGAN): Trained on synthetic degradations to generalize to real-world images (Source: Real-ESRGAN GitHub / paper.)

Key Information

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