SUPIR vs SeedVR2: an image restorer meets a video upscaler

Choose SUPIR when the job is a still photo and the result must be produced on your own hardware under a non-commercial use case. SUPIR is purpose-built for photo-realistic image restoration and brings strong generative detail to damaged photographs. The cost is a heavy local dependency stack and a license that reserves commercial use to the licensor.

Choose SeedVR2 when the job involves video, when you want a permissive Apache-2.0 license, or when you want a hosted browser path instead of provisioning a GPU. SeedVR2 also restores images, so it can substitute for many still-photo tasks, but its center of gravity is video and the hosted upscaler workflow rather than a dedicated photo-restoration Gradio app.

Do not choose either from a paper figure. Run the same degraded still through both where hardware and license allow, define rejection rules in advance, and compare output at the same dimensions. The correct model is the one that passes your actual footage at an acceptable total cost and under a license your use case can actually accept.

SUPIR is an image restoration method. Its paper, demo, and repository all center on a single degraded image in and a restored image out. There is no video path in the official release, so temporal consistency across frames is not a design goal. If you feed it a sequence, you feed it frames one at a time and accept per-frame decisions.

SeedVR2 is published for video restoration and also handles images. The hosted SeedVR2.net upscaler accepts a video file or URL, restores and upscales it, and returns a finished video. That video-native positioning is the main reason a SeedVR2 comparison belongs next to a still-image model rather than being a drop-in replacement for it.

The practical rule follows from modality: a damaged photo can be tested against both; a damaged video clip is SeedVR2 territory, and SUPIR is not a video upscaler. Mixing the two up is the most common mistake in this comparison.

SUPIR ships under a non-commercial-use declaration and a separate software license agreement. The repository states the software is for non-commercial purposes, and commercial use requires prior written permission from the authors. That makes SUPIR unsuitable for revenue-generating processing, client work, or embedding in a paid product without a separate license.

SeedVR2 is published by ByteDance under Apache-2.0. That permissive license allows commercial use, modification, and redistribution within its terms. The hosted SeedVR2.net service is a separate commercial product, but the underlying model license is not the gate that SUPIR's license is.

License differences are not a quality ranking, but they are a go/no-go gate for many teams. If your restoration work earns money or touches client material, read both licenses and assume nothing: a permissive model license still does not waive your data-handling obligations for the media itself.

SUPIR deploys as a local Python project with a Gradio demo. You clone the repository, install a conda environment, download the SUPIR checkpoints plus SDXL and LLaVA dependencies, and launch the demo. There is an official online app at suppixel.ai, but the canonical open-source path is self-hosted and heavier than a single weight file.

SeedVR2 has a hosted browser upscaler as its lowest-friction door: upload or reference a clip, pick a resolution, read the credit preflight, submit, and download. For offline work, a maintained ComfyUI template runs the official weights on a local GPU. An API path through fal or WaveSpeed also exists for the same model family.

The deployment split changes the skill and time cost, not just the money. SUPIR expects a Python environment and a multi-part checkpoint download. SeedVR2's hosted path expects only a browser, while its local path expects ComfyUI plus the model files. Match the deployment to the operator and the machine you actually have.

SUPIR's documented dependency list loads SDXL alongside LLaVA CLIP encoders and a LLaVA 13B model. That is a heavy stack, so a high-VRAM card is expected in practice and the setup is closer to running a large generative model than a compact upscaler. The published README also lists multiple checkpoints to place before the demo runs.

SeedVR2's local path still needs real VRAM, and the site is explicit that 8GB is not a promise. The lighter entry point is the 3B INT8 checkpoint, with the 7B variant as a heavier alternative. The hosted SeedVR2.net path sidesteps local VRAM entirely by moving inference to a provider, which is the key difference when no workstation GPU exists.

Meeting a published minimum does not predict throughput on your resolution, codec, and clip length. Time a representative shot on the exact machine, and for hosted SeedVR2 include upload and queue time. Compare elapsed human-to-delivery time, not just a model-loading claim.

A SUPIR run is prompt-guided and image-focused: the demo exposes upscale ratio, sampling steps, control strength, color-fix type, and additive or negative prompts that steer the reconstruction toward a photographic look. That guidance is powerful for a damaged photo, but it also means the operator can steer the result away from the source, so identity and texture need checking.

A SeedVR2 hosted run is deliberately shorter: source, resolution, credit preflight, submit, download. There are fewer operator knobs, which suits video and occasional jobs. Review is the same discipline in both: check faces, small text, hair and fabric texture, and motion consistency, and keep the source next to the output.

In both tools, a single flattering still proves nothing. Build a small test set with a face in motion, small text, a dark compressed region, and fine repeating texture. Define rejection rules before viewing, compare at the same output dimensions, and let the result differ by source type if it genuinely does.