How AI Photo Restoration Actually Works (And Why Newer Tools Do It Better)

Most AI photo restoration tools on the market are built the same way: six or seven small open-source models bolted together, each one handling a single job. Our tool works differently. It uses two frontier image models — Seedream and Nano Banana 2 on Ultra quality — where each model can do what used to take an entire pipeline.

The difference shows up in the details. Older pipelines are why so many restored photos come back with plastic-looking skin, a face that sort of resembles the person but feels off, or colors that confidently get your grandfather's uniform wrong. Every handoff in the chain is a place for small errors to pile up.

If you've tried more than one restoration tool and wondered why some feel like yearbook retouching and others feel like a real rescue of the photo you remember, the answer is mostly which generation of AI is doing the work.

How the older generation of tools work

Most restoration products you've heard of — the big mainstream apps, the heritage brands, the GitHub-based indie tools — share roughly the same architecture under the hood. A damaged photo goes in on one side, and it passes through six stages before coming out the other. Each stage is a different small model doing one narrow job.

The stages, in order, usually look like this:

1. Denoise. Smooth out grain, scanner noise, and film artifacts without blurring edges.
2. Inpaint damage. Detect scratches, tears, and missing patches, then fill them using the surrounding context.
3. Super-resolve. Upscale the image 2× or 4× and invent plausible fine detail that wasn't in the original pixels.
4. Face-restore. Find each face, crop it out, run it through a dedicated face model, and blend it back in.
5. Colorize. Optional. Guess plausible colors for a black-and-white image.
6. Composite. Stitch everything back together so the seams don't show.

Each of those stages is typically handled by a separate open-source model — names you may have seen if you've looked under the hood of other tools: Real-ESRGAN for upscaling, CodeFormer or GFPGAN for faces, LaMa for scratch filling, DeOldify for colorization. They're capable pieces of software, and the researchers who built them did excellent work. But they were each designed to solve one narrow problem, and they have to be stitched together to restore a whole photo.

The telephone-game problem

The catch with chaining six models is that every stage makes decisions based on what the previous stage handed it — not on the original photo. Small errors compound.

If the denoiser smooths too aggressively, the super-resolver has less real detail to work with and has to invent more. If the scratch-repair stage misses a crease, the upscaler faithfully upscales the crease. If the face model pulls features toward an average, the composite stage blends that average back into the photo of a specific person. By the time the image reaches the end, it's been touched by six different networks, none of which could see the whole picture.

Inpainting — filling in damaged areas using the surrounding context — is a good illustration. An older tool first has to generate a damage mask (the middle image above), then feed both the photo and the mask into an inpainting model. That model only sees the masked region. It doesn't know anything about the face two inches away, the lighting in the room, or what year the photo was taken. It just fills the hole.

Upscaling has the same issue. The super-resolution stage is brilliant at one thing: taking a small image and making it larger while inventing plausible fine detail. But it also invents detail faithfully over whatever came in — including leftover scratches, denoising artifacts, or mis-repaired edges from earlier stages. If the stages in front of it did imperfect work, the upscaler amplifies that imperfection.

The face-restoration sub-pipeline

Faces get their own mini-pipeline inside the bigger one, because human eyes are brutally sensitive to face quality. In older tools, every face is detected, cropped out to a fixed size, sent through a face-specific model, then blended back into the main image.

This is where a lot of the "looks like a different person" complaints come from. The face model has never seen the rest of the photo. It's working from a cropped square in isolation, and it's been trained on millions of other faces, so it tends to pull features toward what it's seen most often. Composite that averaged-out face back into your grandmother's photo and it technically is a restored face — just not quite hers.

CodeFormer is a representative example of how this generation of face-restoration models works. It learns a library of face features — roughly a thousand eye, nose, and mouth patterns — during training. When it sees a damaged input, it picks the closest matches from that library and reconstructs the face from them. It's clever engineering. But fundamentally it's guessing what your grandmother's eyes looked like from a menu of eyes it's seen before. A frontier model has a much larger, more flexible understanding of faces and doesn't have to fall back to a fixed menu.

How our approach is different

BestPhoto's restoration tool uses two frontier image models — Seedream and Nano Banana 2, running in Ultra quality mode — instead of a long chain of specialist networks. Each of these models can take a damaged photo, look at all of it at once, and return a restored version in one coherent pass.

What "frontier model" actually means

A frontier model is one of the largest and most capable image models available today. It's been trained on enormous and diverse datasets — many orders of magnitude beyond what a single-purpose restoration network learns from. That breadth is what lets it handle the whole job in one step. It already knows what damage looks like, what faces look like, what 1950s film grain looks like, what fabric should do in a wedding photo, and how to put all of that together without needing a separate specialist for each piece.

One coherent pass instead of a six-stage chain

Because a frontier model sees the whole photo at once, there are no handoffs to lose information across. Damage repair, face restoration, detail recovery, and color decisions are all made together. The model can use the lighting in the corner of the room to inform how skin tones should read. It can use the shape of a collar to guess whether an invented hair strand is plausible. That kind of cross-referencing is something a chain of narrow models simply can't do — each one only sees its own stage.

What Ultra quality adds

Ultra quality mode runs the restoration at higher internal resolution with more processing time per photo. It takes longer to return a result — measurably more seconds — but the output preserves more real detail and invents less. For old photos with tiny faces, delicate fabrics, or complex damage, that extra processing is the difference between a restoration that looks right and one that looks close but wrong.

The broader shift from GANs to diffusion

The older restoration tools are mostly built on GAN-based models — an earlier generation of image AI that's fast but tends to hallucinate confidently when uncertain. The newer frontier models are diffusion-based, which is both more controllable and better at knowing what it doesn't know. That's the underlying reason newer tools produce fewer "made-up" features in the final result.

Where the difference is most visible

Frequently asked questions