AI Image Upscaler

Upload a small or soft image and get back a larger one. The model reconstructs detail rather than simply stretching pixels, which is the difference between an enlargement that holds up and one that turns to mush.

Start creating

How to upscale an image

Upload the best copy you have
Find the original file rather than the version that has been emailed, screenshotted and re-saved. Every step of that chain removed detail, and the upscaler can only rebuild from what is still present.
Upscale
The model works out what a higher-resolution version of the picture would plausibly look like: edges, texture, fine structure. It does not interpolate between the pixels it already has, which is all a simple resize can do.
Check it at 100%
Judge the result at full size, not zoomed to fit. Look at faces, text, patterned fabric and straight edges: those are where a reconstruction announces itself, and they are the details a viewer notices without knowing why.
Use it for whatever came next
The larger image lands in your library, ready to be edited or animated into a video clip. Clips can be finished with a script, voiceover and captions, then published on a schedule.

When upscaling is worth doing

Before you animate a still

The most important case here. An image-to-video model treats your picture as the first frame, so it inherits whatever softness is in it. Then it has to invent motion across detail that was never there. Sharpening the source before generating is far cheaper than trying to rescue the clip afterwards.

Before you crop

Every crop is a downscale in disguise: taking the middle third of an image throws away two thirds of the pixels. Upscaling first means the crop still has enough resolution to be usable at full size.

Old photographs and archive material

Scans, family photographs and material that predates high-resolution capture are usually small by modern standards. Enlarging them is what makes them usable in a video edit rather than a postage stamp on a large screen.

Assets you did not shoot

Logos pulled from a website, a client's only surviving product photograph, a still lifted from an old file. You cannot go back and capture these again, so reconstruction is the only route to a usable size.

Print and large displays

An image that looks fine on a phone can be well short of what a poster, a banner or a large screen needs. Resolution requirements scale with viewing size, and the shortfall only becomes obvious at the point it is expensive.

Older generated images

Pictures produced before higher-resolution models were available are often perfectly composed and simply too small. Upscaling recovers the work rather than asking you to recreate a composition you already liked.

Thumbnails and covers

A thumbnail is displayed at many sizes, and the largest one is unforgiving. Starting from a properly sized image means the same file works small and full-screen.

What is AI image upscaling?

AI image upscaling increases the pixel dimensions of an image while attempting to add plausible detail, rather than only enlarging what is already there. It is also called super-resolution.

A conventional resize cannot invent anything. Enlarging an image in ordinary software interpolates between existing pixels: it averages neighbours to fill the gaps, which is why a stretched photograph looks soft and slightly plastic. There is no new information in the result. Only the same information spread over a larger area.

An AI upscaler works differently. Having seen an enormous number of images, the model has learned what edges, skin, fabric, foliage and lettering tend to look like close up, and it uses that to reconstruct detail the enlargement would otherwise lack. The output has structure a resize cannot produce.

The honest caveat is that reconstruction is informed guesswork, not recovery. The upscaler is proposing what the detail probably was, and where the source is very degraded it will propose confidently and be wrong. Invented texture on a face, letters that resolve into the wrong letters. This matters for anything evidential or identifying, and matters much less for a background element that will be on screen for two seconds. Judge the result on its use.

Upscaling also cannot undo other damage. Motion blur, camera shake, heavy JPEG artefacts and blown highlights are missing information, not small information. Enlarging them produces larger versions of the same problems.

AI upscaling vs a normal resize vs generating it larger

Three ways to end up with a bigger image, and they fail in different places. A conventional resize stretches what exists. An AI upscaler reconstructs detail that plausibly belongs. Generating afresh at a higher resolution ignores your image entirely and makes a new one. Which is right depends on what has to survive.

Use a plain resize when you are going down, or barely going up. Downscaling loses nothing that matters and is always clean. A 4K image reduced for a vertical post is fine. Modest enlargements, on the order of a quarter or a third, are also often acceptable without any AI involved. The reason to reach for an upscaler is a real jump in size, or a source that is soft to begin with.

Use an AI upscaler when the specific picture must be preserved. This is the whole argument: it is the only one of the three options that keeps your actual image. The person in the photograph is still that person, the label still says what it said, the composition is unchanged. The picture has simply gained the detail it needed to hold up at a larger size.

Generate afresh at a higher resolution only when nothing in the frame is fixed. If the small image was a placeholder, or a background, or an idea rather than a record, describing it again at 4K gets you a genuinely high-resolution original rather than a reconstruction, and that will usually look better. It is the wrong answer the moment a real object, a real person or an approved composition is involved, because a fresh generation cannot return either of them.

The practical ordering matters too. Upscale before you crop, not after. Cropping is a downscale, and doing it first means you upscale a smaller source for no reason. Upscale before you animate, because a video model inherits the frame you hand it and multiplies its problems across every subsequent frame. But run your edits on the original where you can: it is faster to change a background at the smaller size and upscale the finished picture than to upscale first and then edit a much larger file.

And when the option exists, take the higher-resolution original instead of any of the three. Generating at 4K in the first place, or finding the source file rather than the copy embedded in a slide deck, beats every reconstruction. Upscaling is what you do when that option has already gone.

Getting better results

Hunt down the original file
The version in a chat thread, a slide deck or a web page has usually been resized and re-compressed at least once. Ten minutes finding the source file will beat any amount of reconstruction, because you are starting with detail rather than asking for it back.
Upscale before you crop
A crop discards pixels. Cropping to a detail and then enlarging it means the upscaler is rebuilding from the small fraction you kept, where enlarging first gives it the whole frame to work from.
Edit first, upscale last
Background swaps and object removals are quicker and more predictable on the smaller file. Make the picture correct, then make it big. The final upscale then applies to the version you actually intend to use.
Do not expect blur to come back
Softness from a small file is missing detail and can be plausibly reconstructed. Motion blur, camera shake and blown highlights are lost information. Enlarging them gives you a bigger version of the same problem, so choose a different source frame if you have one.
Watch faces and text closely
These are the two things viewers read rather than glance at, and the two things a reconstruction is most likely to get subtly wrong. Check them at 100% before the image goes anywhere near a thumbnail or a client.
Match the size to the job
Enlarging far beyond what the output needs adds file weight and invented detail without adding usefulness. Work out the finished dimensions, including any crop or push-in. Aim a step above that, not several.
Compare against the original, side by side
It is easy to accept a big clean image as an improvement without checking what changed. Put the two next to each other: if a pattern has been reinvented or an expression has shifted, you want to know now rather than after publishing.
Keep the source
The upscale is one interpretation. Keeping the original means you can try again, or restart from it after an edit, instead of stacking reconstruction on top of reconstruction.

Where the upscaled image goes next

Kling 3 — Video model. Cinematic motion that holds a character across a cut. For animating the upscaled still.
Seedance 2.0 — Video model. ByteDance, up to 24 seconds. The long shots.
Grok Video — Video model. xAI. Fast image-to-video, and text-to-video.
Nano Banana 2 — Image model. Google's best stills. Generation and editing, to 4K. For the frame you upscale, or the fix afterwards.

Questions

How is this different from resizing an image?: A resize spreads the pixels you already have over a larger area, which is why enlargements look soft. An AI upscaler reconstructs detail that plausibly belongs in the picture: edges, texture, fine structure. The larger version has information the original file did not contain.
Will it make a blurry photo sharp?: Sometimes, and it depends on why the photo is blurry. Softness from a small or compressed file is often reconstructed well. Motion blur, camera shake and lost highlights are missing information rather than small information, and enlarging them produces a bigger version of the same fault.
Should I upscale before turning an image into a video?: Usually, yes. The still becomes the first frame of the clip, so its detail and cleanliness carry into every frame that follows. Fixing the source is much cheaper than trying to rescue generated motion built over detail that was never there.
Is this the same thing as the image generator?: No, and the difference is worth holding on to. The generator invents a new picture from a description and the editor changes part of a picture you have; both alter what is in the frame. Upscaling alters nothing but the size. The subject, the composition and the colours come back as they were, with more pixels behind them. The models listed on this page are what the rest of the product offers for the step after the enlargement, not the enlargement itself.
Is the added detail real?: It is plausible rather than recovered. The model is proposing what the detail probably looked like. That is fine for a background or a texture and worth scrutinising for anything identifying or evidential, so check faces and text at full size before you rely on the result.
Should I upscale before or after cropping?: Before. A crop throws pixels away, so cropping first leaves the upscaler with a smaller source to rebuild from. Enlarge the full frame, then take the part you want.
What kind of images work best?: Clean originals with a clear subject. Screenshots of screenshots, images saved repeatedly as JPEG, and anything already stretched have lost detail at each step, and the upscaler has correspondingly less to work with.
What can I do with the upscaled image?: It goes to your library, where it can be edited, animated into a video clip, or used in a sequence with a script, voiceover and captions. Finished videos can be published to YouTube, TikTok and Instagram on a schedule.