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What is OpenModelDB?
OpenModelDB is a community-driven database of AI upscaling models. Its stated goal is to make it easier to find and compare upscaling models than existing sources do, and it organizes a large catalog — the site lists 671 models — around the tasks those models are built for.
What you can do there
- Browse by subject and purpose. Models are tagged by content type (anime, cartoon, CGI, faces, game screenshots, game textures, manga, pixel art, realistic, text, video frame, and more) and by purpose (general upscaler, denoise, deblur, dehalo, deblocking, anti-aliasing, colorization, compression removal, JPEG artifacts, restoration, inpainting, and others).
- Compare candidates side by side. Each model entry carries details such as scale factor (1x, 2x, and so on), architecture names, and a description of what it was trained for.
- Contribute. The "Contribute" section indicates the catalog is maintained by its users rather than a single vendor.
A concrete example
The listing for "90s Sonic 2x (Small)" by pokepress describes a model aimed at footage from Adventures of Sonic the Hedgehog and Sonic SatAm, notes that those shows are cel-based animation with some panning segments above 24fps, and suggests deinterlacing with QTGMC for maximum quality. That is the kind of practical context OpenModelDB entries tend to include: not just a file, but the source material and workflow it suits. Another entry, "StarSample V2.0 Lite NS" by .derpy, targets My Little Pony: Friendship is Magic restoration and notes that V2.0 improved compression handling and reduced artifacting compared with V1.0.
Who it is for
- Restoration and remaster hobbyists working on old cartoons, anime, or film scans who need a model matched to a specific artifact.
- Video and image editors who want a starting point before testing models on their own footage.
- Researchers and tinkerers who want to see what architectures and training targets exist in the field.
The trade-off is typical of community databases: breadth and useful tagging versus uneven depth. Some entries are richly documented, while others are brief, and quality claims come from the model authors rather than independent testing. Treat the descriptions as a shortlist, then run two or three candidates on a representative clip or image before committing.
If you are new to this, start with the tag selector: pick your content type (for example, anime or realistic) and your problem (denoise, deblur, or general upscaling), then read the scale factor and description for the top few results.
How do I choose the right AI upscaling model for my specific content type?
Match the model to the content and the job, not to the model's popularity. OpenModelDB is built around that filtering problem: its browse view lets you narrow models by Subject (Anime, Cartoon, CGI, Faces, Game Screenshots, Game Textures, Manga, Pixel Art, Realistic, Text, Video Frame, AI Generated) and by Purpose (Anti-aliasing, Debanding, Deblur, Denoise, JPEG, Compression Removal, Restoration, General Upscaler, Texture Generation, and more), with an advanced tag selector when one tag isn't enough.
A practical selection routine
- Name the source, not just the genre. "Anime" is too broad; "1990s cel-animated TV, interlaced, VHS-sourced" points somewhere specific. On OpenModelDB, tags like Video Frame, Cartoon and Restoration separate models aimed at that case from models trained on clean digital art.
- Decide whether you are restoring or enlarging. Restoration models target artifacts (compression, halos, noise, JPEG blocking, deblurring). General upscalers target resolution. Running a restoration model at 4x because it happens to exist usually produces softer output than a purpose-built upscaler.
- Pick the scale factor you actually need. A 1x model cannot enlarge; a 2x model can be downscaled afterward, but you pay the cost of the larger pass. If you only need 1080p from 540p, a 2x model is the natural fit.
- Check the intended medium. Video Frame and animation-oriented models are often tuned for temporal consistency; still-image models may produce crisper single frames but flicker across a sequence.
- Read the model description for caveats. Authors frequently state the exact show, dataset or preprocessing they had in mind. Those notes are the fastest way to know whether your footage is in scope.
What the trade-offs look like
| Your situation | Better starting point | Why |
|---|---|---|
| Old cel animation, interlaced source | Cartoon + Video Frame + Restoration tags | Models trained on that era's compression and line art; deinterlacing is usually a separate step |
| Modern anime, real-time playback | Anime + Restoration, realtime-oriented entries | Speed matters more than maximum detail recovery |
| Live-action film grain, mild softness | Realistic + Deblur or General Upscaler | Grain-aware training avoids plastic skin and smeared texture |
| Scanned manga or text pages | Manga or Text + Dehalftone/JPEG | Preserves line edges and readable glyphs instead of hallucinating strokes |
| Pixel art or retro game sprites | Pixel Art | Prevents the model from smoothing intentional hard edges |
Deciding between two close candidates
Run the same short clip or crop through both and compare at 100% zoom on the parts you care about: faces, fine text, straight edges, and areas of flat color where artifacts show up as blotches. Watch for "AI smudging" — detail that looks plausible at a glance but dissolves on inspection. For video, check a few seconds across a cut or a pan, since frame-to-frame stability is where many otherwise sharp models fail.
A useful next step: open the browse page, apply your Subject and Purpose tags, then read two or three model descriptions before downloading anything. The descriptions usually tell you more about fit than any ranking does.
What are the differences between models for anime, realistic, and cartoon upscaling?
Models are organized less by a single "anime vs. realistic vs. cartoon" switch and more by the subject and purpose tags you combine: subject tags include Anime, Cartoon, Realistic, CGI, Faces, Game Textures, Manga and Pixel Art; purpose tags include General Upscaler, Restoration, Denoise, Deblur, Dehalo, Compression Removal, Debanding and Texture Generation. At OpenModelDB, those tags are the main way to compare candidates, and the database currently lists 671 models.
What actually differs
- Training material and intent. An anime model is usually trained on animation frames, so it tends to preserve flat color regions, clean line art and stylized shading. A realistic model is trained on photographic or live-action content, where it must handle natural noise, skin texture, foliage and film grain without turning them into plastic. A cartoon model sits in between: cel animation, older TV masters, visible compression and sometimes interlacing. The Sonic 2x entries in the database are a concrete example — they are described as built for specific 1990s cartoon shows, with a note that deinterlacing may be needed first.
- What "good" looks like. For anime, the failure mode is usually smeared line work or lost detail in small faces and text. For realistic footage, over-smoothing skin, waxy faces and erased grain are the common complaints. For cartoons, ringing around black outlines, banding in flat backgrounds and leftover compression blocks matter most.
- Scale factor and speed. Some models are built for 1x restoration rather than enlargement; others target 2x or realtime use. The StarSample V2.0 Lite NS entry is described as originally viable mainly at 1x and improved for 2x in a later release, while Adore is described as a 2x realtime 1080p anime upscaler. Realtime models trade fine detail for speed; heavier architectures trade speed for cleaner output.
A practical comparison
| Content | Priority | Typical tags to start with | Main trade-off |
|---|---|---|---|
| Anime | Clean lines, stable flat color | Anime, Restoration | Realtime speed vs. line fidelity |
| Realistic | Natural texture, grain, skin | Realistic, Denoise or Deblur | Noise removal vs. lost micro-detail |
| Cartoon | Edge integrity, compression cleanup | Cartoon, Video Frame, Compression Removal | Aggressive cleanup vs. ringing and smearing |
How to choose
Start from the source, not the genre label. If you are working with a DVD-era cartoon rip, filter by Cartoon plus Video Frame and look at models trained on similar shows; if you are upscaling a modern anime Blu-ray, Anime plus Restoration is the better starting set. For live-action, decide first whether the problem is noise, blur or compression, then pick a purpose tag before comparing models.
A useful next step: run two or three candidate models on the same short clip — one clean scene, one dark or grainy scene, one with text or fine detail — and compare them at 100% zoom rather than full-frame. The model that wins on your worst scene is usually the safer default.
How can I contribute a new model to the OpenModelDB database?
OpenModelDB is a community-driven catalog of AI upscaling models, so contributions come from people who train or adapt models and want them listed alongside existing entries. The homepage presents "Contribute" as one of its four main sections, which is the intended starting point for adding a model.
What a contribution typically involves
A useful listing needs more than a file link. Based on how existing entries are structured, expect to provide:
- Model name and author — the display name and who made it.
- Scale factor — for example 1x, 2x, 4x.
- Architecture — such as SPAN, RealPLKSR or Real-CUGAN, as shown in current entries.
- Purpose and subject tags — categories like Anime, Cartoon, Realistic, Video Frame, Restoration or Denoise.
- A description — what the model is for, what it was trained on, and any practical notes. Existing entries often mention the source material, dataset improvements between versions, and caveats such as interlacing.
- A comparison or sample output — so users can judge quality rather than take a claim on trust.
Practical next step
Open the Contribute section on OpenModelDB and follow its submission guidance before preparing files. If you are deciding whether a model is worth listing, the test is whether it fills a gap: a model trained for a specific show, a particular compression artifact, or a niche subject is more useful to the database than another general-purpose upscaler with no distinguishing description. Write the entry the way you would want to read it when choosing between five similar models — state the target material, the scale, and the known weaknesses.
What do the different tags and purposes like 'Denoise' or 'Deblur' mean in the context of upscaling models?
These tags describe the specific defect a model was trained to fix, not just the fact that it enlarges an image. In upscaling, a model can be built for a clean 2x enlargement, or for repairing a particular artifact before or during enlargement. The purpose tag tells you which problem the model targets.
The main purpose categories
- General Upscaler — trained mainly to enlarge clean or lightly degraded images without targeting one specific artifact. A safe default when you don't know what's wrong with the source.
- Denoise — removes noise, such as film grain, sensor noise or compression speckle. Useful for old broadcasts, VHS captures or low-light footage. Over-denoising can erase fine texture, so it is a trade-off.
- Deblur — attempts to reverse softness caused by motion blur, focus miss or resampling. It can sharpen edges, but may also amplify ringing or halos if the source is not genuinely blurred.
- Debanding — smooths the stair-step colour bands seen in gradients, common in compressed video and anime skies.
- Deblock — reduces the square blocking artifacts from heavy video compression.
- Dehalo — reduces bright or dark rings around high-contrast edges, often left by earlier sharpening.
- Dedither — removes intentional dithering patterns used to fake gradients in older or limited-palette images.
- Dehalftone — removes halftone dot patterns, as found in scanned comics, manga or printed material.
- JPEG — targets the blocky and ringing artifacts of JPEG compression.
- Restoration — a broader label for repairing aged, degraded or damaged material, often combining several of the above.
- Colorization — adds colour to grayscale or monochrome sources.
- Inpainting — fills in missing or removed regions rather than enlarging the whole frame.
- Texture Generation — invents plausible surface detail where the source has none. This is generative, so results can differ from the original content.
- Anti-aliasing — softens jagged diagonal edges, common in game textures and pixel art.
- Compression Removal — a general tag for undoing compression artifacts such as blocking, ringing and banding together.
- DDS — relates to the DDS texture format, typically for game texture workflows.
- Pretrained — indicates a model supplied already trained, ready to use rather than requiring your own training.
- Research — models intended for experimentation or comparison rather than routine production use.
Subject tags are separate
Tags like Anime, Cartoon, Manga, Pixel Art, Faces, Realistic, CGI, Game Textures, Game Screenshots, Video Frame or Text describe the content the model was trained on. They matter because a model tuned for cel animation often handles flat colour and line art well but performs poorly on photographic skin or film grain, and vice versa.
A practical example
A 1990s cartoon capture with interlacing and compression noise needs a different toolchain from a clean modern photo. On OpenModelDB, a listing such as the 90s Sonic 2x models by pokepress is tagged Cartoon, Restoration and Video Frame, and its own description notes that deinterlacing with QTGMC is advisable for maximum quality. That combination tells you it was built for aged animation footage, not for general photography.
How to choose
- Identify the dominant defect: noise, blur, banding, blocking, halftone or compression.
- Match the purpose tag to that defect.
- Match the subject tag to your content type.
- Compare sample images at the intended scale factor, since a model may be strong at 2x and weak at 4x.
- Test on a short clip or a few frames before committing to a full batch.
As a next step, open OpenModelDB and filter by purpose first, then subject, then review the sample comparisons for the models that remain.
How do I compare the performance and quality of different upscaling models?
Compare models on OpenModelDB by matching the model's stated purpose and subject tags to your source material first, then judging quality within that narrow group. A model trained for 1990s cel animation will usually beat a general-purpose upscaler on that footage and lose badly on live-action film grain, so cross-category rankings are misleading.
OpenModelDB is a community-driven database of AI upscaling models, currently listing 671 models with browse filters for subject (Anime, CGI, Faces, Game Textures, Manga, Pixel Art, Realistic, Video Frame and more) and purpose (Denoise, Deblur, Dehalo, Debanding, JPEG, Compression Removal, Restoration, General Upscaler). Those two axes are the practical comparison unit.
A workable comparison routine
- Fix your inputs. Note resolution, whether the source is interlaced, and the dominant artifact (compression, halos, noise, softness). Interlacing matters: the 90s Sonic models by pokepress are described as being for cel-based animation where some segments use panning above 24fps, and the entry suggests deinterlacing with QTGMC first.
- Filter by subject and purpose, not by popularity. Narrow to models whose descriptions mention your content type and your main defect.
- Compare within a family. Successors often document what changed. StarSample V2.0 Lite NS by .derpy is described as improving 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."
- Check the scale factor against your target. A 1x restoration model and a 2x upscaler solve different problems. Adore by renarchi is listed as a 2x realtime 1080p anime upscaler and an improved successor to the Fallin models.
- Test on clips, not stills, where motion is involved. Temporal stability and flicker rarely show up in a single frame.
What to look at per model
| Signal | Why it matters |
|---|---|
| Subject tags | Predicts whether textures and edges match your content |
| Purpose tags | Tells you whether it removes artifacts or mainly enlarges |
| Scale factor | Determines whether it fits your output resolution |
| Architecture name | Groups models built on the same base, useful for like-for-like comparison |
| Description notes | Often names the training source, e.g. specific shows or art styles |
Practical example
Suppose you are restoring a DVD rip of a 1990s cartoon. Filter to Cartoon and Restoration, and you will land on entries like the 90s Sonic 2x models, one small and one large, both by pokepress and both scoped to Adventures of Sonic the Hedgehog and Sonic SatAm. The small and large variants give you a direct speed-versus-quality trade-off on identical training data, which is a cleaner comparison than pitting two unrelated models against each other. Run both on the same 30-second clip containing panning, then compare edge stability and how much line art wobbles.
As a next step, pick two models that share subject and purpose tags but differ in architecture or scale, and diff them on one clip that contains your worst artifact. Prefer the model whose description explicitly names your content type; treat general-purpose upscalers as the fallback when nothing specific exists.
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