What Is OpenModelDB?

OpenModelDB is a community-driven database of AI upscaling models, hosted at openmodeldb.info. It exists to make finding and comparing upscaling models easier than scattered forum posts, GitHub repos, and Discord threads allow. If you already know you need an upscaler but don't know which model fits your source material, OpenModelDB is built for that decision. If you want to publish or document a model you trained, it also has a contribution path.

What you can actually do on the site

The homepage states the goal directly: "We aim to provide a better way to find and compare models than existing sources." In practice that means three activities, reflected in the site's own navigation: Browse, Learn, and Contribute.

• Browse — filter and search the model catalog.
• Learn — background material on upscaling concepts and model behavior.
• Contribute — add or improve model entries.

The catalog is not a small curated list. The homepage reports 671 models at the time of writing, which is the main reason a database structure matters here: at that scale, browsing by hand stops working and filtering becomes the primary tool.

How the catalog is organized

Models are tagged along two independent axes, which is what makes comparison possible rather than just search.

Subject — what the model was built to handle:

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

Purpose — what the model is supposed to fix or produce:

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

There is also an Advanced tag selector for combining these conditions rather than picking one at a time. This matters because real tasks are usually compound: restoring a compressed cartoon video frame is a different filter combination than cleaning up pixel art.

What a model entry tells you

Each entry pairs the model with its intended use and its architecture, which is the information you need before downloading anything. From the listings visible on the homepage:

Two things are worth noticing in that table. First, the same source material can have multiple entries at different sizes or architectures — the Sonic pair differs only in that respect, so the choice between them is a tradeoff, not a correctness question. Second, entries carry practical caveats. The Sonic models note that some segments use panning to exceed 24fps, so the author recommends QTGMC deinterlacing if you want maximum quality. That kind of note is the difference between a model working and a model disappointing you.

The StarSample entry shows how version notes are handled: V2.0 is described as improving on V1.0's dataset across the board, moving the models from realistically viable only at 1x to competent at 2x, with better compression handling and less artifacting and "AI smudging." The dataset draws from a wider variety of sources despite being smaller than V1.0 when tiled. That is the level of detail to read before assuming a newer version is automatically the right pick for your footage.

Choosing a model: the decision path

1. Identify your source type. Anime, live-action realistic footage, game textures, and pixel art each point to different subject tags.
2. Identify the defect or goal. If the problem is compression artifacts, filter for Compression Removal or JPEG rather than General Upscaler.
3. Check the scale factor. A 2x model and a 1x model are not interchangeable for a given pipeline; the Sonic and Adore entries are both 2x, while StarSample V2.0 is discussed at both 1x and 2x with different results.
4. Read the notes, not just the tags. Deinterlacing requirements, source-specific training, and version caveats live in the description.
5. Compare candidates on the same dimensions. When two models target your material, compare architecture, scale, and stated purpose side by side before testing.

When OpenModelDB is the right tool — and when it isn't

It fits when you have a defined upscaling task and need to find or compare candidate models, or when you want to document a model for others. It is less useful if you are looking for a single recommended model with no filtering — the value is in the tags and comparison structure, not in a ranked list. It also won't tell you how to run a model inside your specific toolchain; that belongs to the model's own documentation.

If you contribute, the same structure applies in reverse: a useful entry states the subject, the purpose, the scale, the architecture, and the conditions under which the model performs well or poorly.