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AI Anime Image Upscaler

Upscale anime, manga, and drawn artwork 2x or 4x with a model tuned for line art, keeping edges crisp and colors flat.

Mehmet Demiray Published Updated
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Runs in your browser, nothing is uploaded. The AI model (about 2.5 MB) downloads once on first use.
Both take the same time. 2× gives the same quality at half the 4× size
PNG keeps sharp lines best, JPEG and WebP make smaller files

Tuned for anime, manga, and drawn artwork with flat colors and clean lines. For photographs, use our AI Image Upscaler instead.

Why Anime Needs Its Own Upscaler

Drawn artwork breaks the assumptions that photo upscalers rely on. A photograph is full of soft gradients, sensor noise, and organic texture, and photo models learn to reproduce exactly that. Feed them a clean anime frame and they treat flat color fields as texture waiting to be invented, smearing gradients into outlines and adding grain where the artist drew none.

An anime-tuned model learns the opposite priorities. Lines must stay thin and continuous, color fields must stay perfectly flat, and the hard boundary between the two must stay hard. This tool uses the anime variant of the Real-ESRGAN family, trained on animation frames, so enlarged artwork keeps the crisp, deliberate look of the original instead of drifting toward photographic softness.

The practical rule is simple. Screenshots from anime, manga pages, digital illustrations, stickers, and game art belong here. Photographs belong in the AI Image Upscaler, which is trained for exactly the textures this model deliberately suppresses.

Small Model, Fast Start

Animation compresses well as a learning problem. Flat colors and clean lines need far less network capacity than photographic texture, so the anime model weighs only about 2.5 MB, a fraction of the photo model. It downloads in moments on the first use, is stored by your browser, and never needs downloading again.

Everything runs on your own device. The image is cut into overlapping tiles, each tile passes through the network, and the pieces are stitched back seamlessly. On machines with a modern graphics chip the browser uses WebGPU and a typical image finishes in a few seconds. Without one, a slower processor fallback produces the identical result.

The network itself always enlarges four times in one pass. When you pick 2 times, the tool simply runs the same pass and scales the result down by half, which is why both options take the same amount of time. You are never paying extra for the smaller output, so choose purely by the size you need.

PNG or WebP for Line Art

Line art punishes lossy compression more visibly than photos do. JPEG in particular introduces faint ringing around hard edges, exactly where anime images are most sensitive. PNG stores the upscaled result losslessly and is the recommended default for artwork you care about, especially if it will be edited again.

WebP is a strong middle ground. At the quality level this tool uses, WebP artifacts on line art are hard to spot, and files come out several times smaller than PNG. For posting in chats and communities where file size matters, WebP is usually the practical choice. JPEG remains available for places that accept nothing else.

Inputs up to 2,048 pixels on the longest side are accepted, and the output reaches up to 8,192 pixels at 4 times. That is enough to turn a 640 by 360 screenshot into a wallpaper, or a small sticker into print-ready art.

What Upscales Well and What Struggles

Clean digital art is the ideal input. Screenshots taken directly from video, exported illustrations, and original drawings enlarge beautifully, with outlines that stay sharp at 4 times. Manga scans are more mixed. A good scan works well, but heavy JPEG compression in old scans gets interpreted as detail, and the model can etch those artifacts into the enlarged page. When a scan looks blocky, running it through the AI Image Denoiser first often rescues it.

Pixel art is a special case. This model smooths and rounds, which is the opposite of what pixel art usually wants. If the goal is bigger pixels with the blocky charm intact, nearest-neighbor resizing preserves that look better than any AI.

Mixed images, such as a photo with an anime character composited in, will lean toward the drawn look everywhere. And if you want to create new artwork rather than enlarge existing images, the AI Anime Image Generator generates original anime-style pictures from a text prompt.

The ones we answer the most.

How do I upscale an anime image?

Drop the image in, choose 2 times or 4 times, pick a format, and press the button. The anime-tuned AI enlarges it while keeping lines crisp and colors flat, and you download the result directly.

Do I need an account to use this?

No. There is no signup, no watermark, and no limit on how many images you process. The model downloads once, about 2.5 MB, and everything runs in your browser.

What makes this different from a normal photo upscaler?

The training data. This model learned from animation frames, so it keeps outlines sharp and color fields clean. A photo model like the AI Image Upscaler adds photographic texture, which softens and dirties drawn artwork.

Is this like waifu2x?

Same purpose, newer model family. Both enlarge anime-style images with AI, but this tool uses the more recent Real-ESRGAN anime architecture and runs entirely in your browser without uploading anything.

Why do 2x and 4x take the same time?

The network always enlarges 4 times internally. The 2 times option simply halves that result afterwards, so the heavy AI work is identical in both modes.

Why did my manga scan come out worse than expected?

Compression artifacts in old scans get amplified as if they were drawn detail. Denoise the scan first with the AI Image Denoiser, then upscale the cleaned version. A cleaner source makes a dramatic difference.

Does it work on pixel art?

It runs, but it smooths the pixels, which usually ruins the style. For pixel art, plain nearest-neighbor enlargement in an image editor preserves the blocky look better.

Is my artwork uploaded anywhere?

No. The model runs on your own device and the image never leaves your browser. That also applies to the one-time model download, which flows only from the server to you.

References

  1. Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data · arXiv · Xintao Wang, Liangbin Xie, Chao Dong, Ying Shan · 2021
  2. Real-ESRGAN reference implementation (realesr-animevideov3 model) · GitHub · Xintao Wang · 2022
  3. ONNX Runtime Web documentation · ONNX Runtime