How to Find an AI Upscaling Model on OpenModelDB

Updated

OpenModelDB is a community-driven database of AI upscaling models, and the fastest way to find one is to use its Browse filters. Start by choosing a Subject (what your source footage or image contains) and a Purpose (what you want the model to do), then narrow the results with the Advanced tag selector. At the time of writing the database lists 671 models, so filtering is the practical way to reach a shortlist rather than scrolling the full catalog.

Start with the two main filters

The Browse page exposes two primary filter groups, and combining them is what turns a large catalog into a usable list.

Subject — what is in your source

Pick the category that matches your material:

  • AI Generated
  • Anime
  • Cartoon
  • CGI
  • Faces
  • Game Screenshots
  • Game Textures
  • Manga
  • Pixel Art
  • Realistic
  • Text
  • Video Frame

Purpose — what you want the model to do

Pick the task you need performed:

  • Anti-aliasing
  • Colorization
  • Compression Removal
  • DDS
  • Debanding
  • Deblur
  • Dedither
  • Dehalftone
  • Dehalo
  • Denoise
  • General Upscaler
  • Inpainting
  • JPEG
  • Pretrained
  • Research
  • Restoration
  • Texture Generation

A useful habit is to set Subject first, because a model trained on anime line art behaves very differently from one trained on photographic faces. Then add Purpose to remove models that solve a different problem.

Use the Advanced tag selector

The Advanced tag selector is where you refine beyond the two main groups. Use it when your first pass returns too many results or results that are close but not quite right.

Practical ways to narrow:

  • Add tags that describe your specific defect (for example a compression or haloing problem) rather than a broad category.
  • Combine a broad Subject with a narrow Purpose when the broad Subject alone returns too much.
  • Remove one filter at a time if you drop to zero results, so you can see which constraint was too strict.

Read the model card before you commit

Each result carries metadata that tells you whether the model actually fits your case. The listing shows the model name, author, scale factor, architecture, and tags. For example, a result might read:

  • 2x 90s Sonic 2x (Small) by pokepress — designed for upscaling footage from Adventures of Sonic the Hedgehog and Sonic SatAm (1993–1994). The description notes these are cel-based animation but that some segments use panning to achieve >24fps, so you may want to use QTGMC to deinterlace for maximum quality. Tags: Cartoon, Restoration, Video Frame, RealPLKSR.
  • 2x Adore by renarchi — a 2x model for a realtime 1080p anime upscaler, described as an improved successor to the Fallin models using the same architecture and purpose. Tags: Anime, Restoration, SPAN.
  • 1x StarSample V2.0 Lite NS by .derpy. — a model for restoring My Little Pony: Friendship is Magic that also works decently on similar art. The description states V2.0 improves on V1.0's dataset, moving from being realistically viable only at 1x to being far more competent at 2x, with better compression understanding and less artifacting and "AI smudging."

What to check on each card:

Field Why it matters
Scale factor (1x, 2x, etc.) Tells you the output size you should expect
Architecture Indicates the model family and how it is run
Tags Confirms the subject and purpose match
Description Explains the intended source and any preprocessing advice

A worked example

Suppose you are restoring a cel-animated cartoon from the early 1990s and the source is interlaced.

  1. Set Subject to Cartoon.
  2. Set Purpose to Restoration.
  3. Open the Advanced tag selector and add Video Frame if you are working frame-by-frame.
  4. Read the remaining cards and prefer one whose description names your type of source — the 90s Sonic models above explicitly target 1993–1994 cel animation.
  5. Note any preprocessing the description recommends, such as deinterlacing with QTGMC before upscaling.

If your material is anime rather than Western cartoon, switch Subject to Anime and look for models like Adore that are described for realtime 1080p anime upscaling.

Common sticking points

  • Zero results. You have over-constrained. Drop the Advanced tags first, then relax Purpose, then Subject.
  • Results that look right but perform poorly. The Subject tag is broad; the description often names a narrower source. Match the description to your actual footage.
  • Ignoring the scale factor. A 1x model will not enlarge your image; it is for restoration at the same resolution.
  • Skipping preprocessing. Some model descriptions assume prior steps such as deinterlacing. Doing those first changes the result.

When to use OpenModelDB

Use it when you already know what your source contains and what defect you want fixed, and you want to compare candidate models rather than guess. Its stated aim is to make finding and comparing models better than existing sources, and the Subject/Purpose/tag structure is what delivers that. If you are still deciding what kind of model you need, browse by Subject alone first to see what categories exist, then add Purpose once you recognize your task.