The Tool Desk
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Two questions that get confused
When an upscaled image looks wrong, most people ask a single question: “Is this a bad model?” That question bundles two things:
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- What detail the model generates. This depends on the model’s architecture, its training data, the degradations it was built to handle, and the scale it was asked to produce. Its output can contain invented lines, warped features, or halos around edges.
- Whether the browser or device can run that model effectively. This depends on the runtime (WebGPU, WebGL, or a fallback), the memory and GPU available, how the model is downloaded and cached, and how the work is split into tiles. A model can be excellent and still run slowly, or be split into tiles in a way that leaves seams.
Keeping these apart makes troubleshooting far easier. If the linework is wrong in the same way across every browser, the model is the likely cause. If the same model gives different timings or fails on one device, the runtime is the more likely cause.
What the model is trying to do
Real-ESRGAN, the family of models behind most current browser anime options, is trained on synthetic degradations. Its original paper, “Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data,” published in 2021, builds training pairs by applying a chain of simulated damage to clean images: blur, noise, compression, and resizing. The model then learns to reverse that damage. The authors state that this process is an approximation and does not cover every real-world degradation.
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That approximation has consequences for anime. A model trained on a mix of damage patterns will sometimes treat clean flat colour and crisp outlines as if they were degraded, and it will fill in the gaps with detail that was never in the source. The paper’s own limitations section names several failure types:
- Twisted lines. Curves and thin strokes can bend or wobble, which is especially visible in hair strands and outlines.
- GAN artifacts. The paper attributes unpleasant artifacts to the adversarial training used to make results look sharp.
- Out-of-distribution degradation. Inputs that differ from the training damage can produce complicated failures, and the authors say these may not be removed and may even be amplified.
The paper also reports benchmark numbers in its appendix. Those figures describe its own evaluation datasets and models. They do not compare browser tools or anime models against each other, so they should not be read as proof that one option beats another for your images.
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Anime model options in browser implementations
The open-source web-realesrgan project documents four Real-ESRGAN variants, plus Real-CUGAN options, in its browser implementation. Its model table lists the two anime-oriented Real-ESRGAN variants at 4×. The table below reflects what the project documents; scale information for the general-purpose variants is not stated in that table.
| Option label | Model | Intended content | Listed scale |
|---|---|---|---|
| anime_fast | RealESRGAN-animevideov3 | Anime (video-oriented variant) | 4× |
| anime_plus | RealESRGAN_x4plus_anime_6B | Anime (larger variant) | 4× |
| general_fast | RealESRGAN-general-x4v3 | General photographic and mixed content | Not stated in the project’s model table |
| general_plus | RealESRGAN_x4plus | General photographic and mixed content | Not stated in the project’s model table |
The project also documents Real-CUGAN options. Its documentation suggests Real-CUGAN is faster, but that is the maintainer’s recommendation for their implementation, not an independent comparison, so treat it as a starting point for your own timing tests.
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Labels such as “anime” and “plus” describe intended content and model size. They do not guarantee a better result on your particular image. Two anime models can produce noticeably different linework from the same source.
How browser execution changes the result
Running the model in a browser adds constraints that do not apply to a desktop tool. The following points are documented by the web-realesrgan project, and they describe that implementation rather than browser upscalers in general:
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- First-run downloads. The first run downloads the model and caches it in the browser’s IndexedDB storage. Expect a noticeable delay before the first result, and a much shorter wait afterwards.
- Tile-size-specific variants. Transformed models can require separate downloads for different tile sizes. Changing a tile setting can therefore mean a new download, not just a new calculation.
- Execution path. The project describes WebGPU and WebGL execution. Which one you get depends on your browser and hardware, so the same page can behave differently on two machines.
- Speed. The project reports that its browser implementation is slower than local execution with its official local repositories, and it recommends those local repositories when processing many images.
A separate browser service describes its own ONNX Runtime Web implementation. It uses WebGPU with a WebGL fallback, runs tiled inference, and routes images to 4× or 2× depending on source size and GPU tier. These are that service’s own claims about its implementation. They are not general properties of all browser upscalers, but they show why the same source can take different paths on different devices.
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How to compare outputs fairly
Model names are a poor guide to results. A fair comparison needs controlled conditions:
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- English dubbed, Japanese language, English subtitles
- Choose one source image that contains the features you care about: a face at a small size, thin hair strands, lettering, and a flat-colour area with clean edges.
- Set the same output scale for every option. If one model supports only 4× and another does not list a scale, compare them at a scale both support.
- Crop the same region from each output and view it at 100% zoom. Full-page previews hide the failures that matter.
- Record the browser, its version, the device, and whether the run used WebGPU or a fallback path.
- Time the first run separately from later runs, so that model download and cache loading do not distort the comparison.
Then inspect each crop against the following checklist:
- Do outlines stay continuous and straight where the source is straight?
- Are eyes, mouths, and small facial features the same shape as in the source?
- Do hair strands or thin lines gain or lose strokes, or does new texture appear where the source had flat colour?
- Is there ringing or a light halo along high-contrast edges?
- Does lettering stay readable, or does it turn into plausible but wrong shapes?
- Are edges softened or oversharpened compared with the source?
Common symptoms and what they usually point to
| What you see | Most likely source | What to try |
|---|---|---|
| Wobbly or twisted outlines | Model limitation (the paper names twisted lines) | Compare a different anime variant at the same scale; if the problem persists, accept the model’s limit or adjust the source. |
| Grainy or unnatural texture in flat areas | GAN-style artifacts | Test the same source with another model; reduce sharpening applied afterwards. |
| Faces changed or distorted | Model generating detail that was not in the source | Compare crops at the same scale; check against the original before using the result. |
| Seams or visible tile boundaries | Tile setting or tiled inference | Change the tile size, noting that a different tile size may need a new model download. |
| Very slow first result, faster later | Model download and IndexedDB caching | Time later runs separately before judging speed. |
| Slow on every run | Execution path (WebGL fallback or limited GPU) | Check which path your browser used; for large batches, move to a local tool. |
When a local or batch tool is the better choice
If you need to process many images, or your browser run is too slow to be practical, a local desktop tool is the more realistic option. Upscayl is one example. Its documentation describes local GPU processing, model selection, and batch operations. It is a desktop application, not a browser tool, so it addresses the speed and batch constraints above rather than the question of which model produces the best linework.
Upscayl’s README also says its model enhances images by guessing possible details, and it names Real-ESRGAN and Vulkan. That is the same reconstruction caveat that applies to every option in this article, so the same inspection checklist still applies to its output.
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- It establishes that Real-ESRGAN-based models generate detail by estimation, and that the original authors document specific failure types.
- It establishes which anime variants the
web-realesrganproject lists and at what scale for the two anime options. - It does not establish that any browser anime upscaler is the best overall. No controlled, independent comparison of browser and local anime upscaling was found, and the paper’s benchmark figures do not measure browser output.
- It does not verify current support for WebGPU or WebGL in every browser, and hardware performance will vary by device.
This article is based on project documentation and the original paper. It does not report a test of image outputs or browser workflows. Use the comparison method above on your own images before choosing a model for a real project.
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