Upscaler

Image upscaling utility. Single input image → output upscaled by 2× / 4× / 8× factor. Sharper, higher resolution, with AI-based detail interpolation (vs simple bilinear / bicubic).

This skill does NOT:

• Generate new content — it ENHANCES existing pixels via super-resolution models
• Magic-restore an image from junk-tier (16×16 thumbnail to 4K HD) — input quality matters; garbage in, garbage out
• Handle video — one frame at a time only
• Vectorize — output remains raster PNG
• Remove background (chain with bg-remover)
• Fix major artifacts (deep blur, motion blur, JPEG mosquito noise at extreme levels) — works best on mildly low-res / soft images

ROLE

Read input image → call a Replicate-hosted upscaler → save the upscaled output at the requested scale factor.

PIPELINE

1. Resolve input:

• --image (required)

1. Pick provider:

• Default: nightmareai/real-esrgan (popular general-purpose, ~$0.005/image at 4×)
• Alternatives via --replicate-model :
• nightmareai/real-esrgan — default; best general
• tencentarc/gfpgan — face-focused (best for portraits)
• jingyunliang/swinir — alternative general
• philz1337x/clarity-upscaler — high-fidelity preservation, slower
• Other Replicate upscalers: pass via --replicate-model

1. Scale: --scale 2|4|8 (default 4).

1. Face enhance (optional): --face-enhance enables face restoration (Real-ESRGAN supports GFPGAN-style face restoration as a flag).

1. Execute via the Replicate router (REPLICATEAPITOKEN required).

1. Save:

• Default: ./generated/upscaled/-4x.png (or -2x.png / -8x.png)
• Custom: --output

MODES

Required

• upscaler --image

Options

• --scale 2|4|8 — upscaling factor (default 4)
• --output — explicit output path
• --replicate-model — override default upscaler model
• --face-enhance — enable face restoration (works with Real-ESRGAN, GFPGAN)
• --yes — skip cost confirmation
• --check — verify REPLICATEAPITOKEN + connectivity

REFERENCES (load on demand)

| File | When to load | |---|---| | [references/providers.md](references/providers.md) | Replicate upscaler model comparison + when to pick which | | [references/use-cases.md](references/use-cases.md) | Real-world scenarios (old photo restoration, product shots, low-res AI gen output) | | [references/troubleshoot.md](references/troubleshoot.md) | When output is blurry, faces get distorted, artifacts appear |

EXAMPLES

See [examples/before-after.md](examples/before-after.md) — 3 calibration runs: AI-gen image at higher resolution, old family photo restoration, product shot enhancement.

CONSTRAINTS

• Single image per call. For batch: run in a shell loop.

• Replicate API key required. Set via /skills-keys add REPLICATE_API_TOKEN r8_....

• Cost: ~$0.005-0.02 per image depending on model and scale. 4× cheapest; 8× ~3× more expensive.

• Input quality matters. Models can enhance mildly-low-res images (480p → 1920p, soft → sharp). They CANNOT magic-restore severely degraded images.

• Face preservation: use --face-enhance or switch to --replicate-model tencentarc/gfpgan for portraits. Without this, generic upscalers may distort facial features.

• Output dimensions = input × scale factor. A 512×512 input at --scale 4 → 2048×2048 output. Plan disk space.

• Output format is PNG by default. Source format preserved when possible; transparent backgrounds maintained.

• Default 4× is the most-supported scale across all upscalers. Some models don't support 8×.

• No re-encoding to JPEG by skill. If you need JPEG: convert after via magick or Photoshop / Affinity.

• Never print API keys.

• Output dir is ./generated/upscaled/ by default.

INVOCATION HINTS

When the user says any of:

• "upscale this image", "enhance the resolution"
• "make this bigger / sharper", "fix this low-res image"
• "4x upscale", "8x upscale"
• "улучши разрешение", "увеличь фотку", "апскейл", "повысь разрешение"
• "сделай четче"

Defaults: --image (required); 4× via Real-ESRGAN; saves to ./generated/upscaled/-4x.png.

For portrait / face photos: prefer --face-enhance or --replicate-model tencentarc/gfpgan.

For AI-gen output to upscale: default (Real-ESRGAN) works well; the model is trained on a mix of real + AI imagery.

This skill is distinct from:

• image-prompt / cover-maker / etc. — they GENERATE new images. This ENHANCES existing.
• bg-remover — that's segmentation (transparent BG). This is super-resolution.
• style-transfer (planned) — that's stylization. This is resolution.
• style-check etc. — those are text linters, not visual utilities.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Mikefluff
• Source: Mikefluff/skills
• License: MIT
• Homepage: https://www.npmjs.com/package/@mikefluff/skills

Install and usage instructions live in the source repository linked above.