SeedVR2 AI Image & Video Upscaler: 2K, 4K, 8K Output

SeedVR2 reconstructs an image or a short clip at a larger target size in one pass. For video it processes the sequence rather than independent frames, so flicker and drifting edges are less likely than with a frame-by-frame pipeline, but they are not impossible. The output is a new file at the target resolution; your original is never overwritten and stays the reference for comparison.

The model infers detail that was not in the file. That is useful on soft, clean sources and unreliable on anything the model cannot read: small lettering, logos, product serials, and thin repeating patterns can come back plausible but wrong. Treat every generated element as a proposal and verify the ones that carry meaning against the original.

Images arrive as JPEG, PNG, or WebP and video as MP4, WebM, or MOV, uploaded as a file or referenced by a public HTTPS link. Image jobs cap at 20 MB per file and video jobs at 500 MB, with up to 10 images or 3 clips in a single batch and a 200 MB total for the batch. A single video job stops at 10 minutes and is billed from a five-second minimum.

Your browser checks size and type first, so most rejections happen before anything is uploaded. The remaining checks happen on the server: it verifies the clip duration, reserves the credits for the job, and only then dispatches the work. Nothing is charged for a file that fails one of those checks, because no job was created for it.

An insufficient-credit answer is a purchase step rather than a failure: the route tells you how many credits the job needed and opens the packs that cover it. A video-too-long answer means the clamp at 10 minutes was hit, so split the file and submit the parts. An invalid-type answer names the format that was refused, and a login-required answer means the account is needed before the run can be dispatched.

A job that reaches the provider and then fails is refunded automatically, and the queue item reports the failure instead of leaving a silent gap. If you see a status check failure rather than a job failure, refresh the page: the queue is rebuilt from the account's history rather than from browser state, so a completed job is not lost by closing the tab.

Compare the output with the source at the destination size, not only zoomed in. Motion, faces, hair, fabric, and on-screen text are where diffusion artefacts appear first: look for crawling texture, halos, duplicated edges, and shapes that changed identity between frames. A sharper still frame does not prove a stable sequence.

Check the delivery details too: duration, frame dimensions, aspect ratio, and how the file behaves after the platform recompresses it. Keep the original master and any project file, because the upscaled clip is a derivative. When a batch goes wrong, the useful response is a smaller test on one representative file, not a second pass over the whole queue.