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Phoenix Upscaler vs SeedVR2

Phoenix Upscaler

phoenixlabs.space

SeedVR2

github.com

Side by side

FigurePhoenix UpscalerSeedVR2
Price from—Free
Free plan—Yes
Free trialYes—
Open source——
WebYes—
WindowsYesYes
macOS—Yes
LinuxYesYes
APIYes—
Self-hosted—Yes
Plans published51
Spec score · AI Video Restoration Software8.7 (#4)8.1 (#9)
PurposePhoenix Upscaler restores and upscales old video, including tapes, DVDs, film scans and phone clips.SeedVR2 is a one-step diffusion-transformer model for video restoration at any resolution.

Plans

Phoenix Upscaler

  • Cloud Standard$2.99, paid once
  • Cloud Enhanced$5.99, paid once
  • Desktop app$129, paid once
  • Phoenix Upscaler$129, paid once
  • Cloud Studio MaxPrice not published

Phoenix Upscaler pricing →

SeedVR2

  • SeedVR2Free

SeedVR2 pricing →

In their own words

Phoenix Upscaler

Phoenix Upscaler is software for restoring and enlarging older video recordings, including tapes, DVDs, film scans, and phone clips. It reduces noise, corrects interlaced motion, and rebuilds detail lost through compression. Processing can stay on your computer, without uploading video files. Standard, Enhanced, and Generative modes provide different restoration approaches, while Auto, VHS, Film, Faces, and Max presets provide starting points. MP4, MOV, MKV, and AVI are listed as supported formats. The desktop product runs on Windows and Linux; a web option is also listed. Upscaling reaches 4x, with output up to 4K, plus denoising and face recovery. The $129.00 USD once plan installs on three of your computers, removes the watermark, accepts any clip length, and includes updates. A free trial handles clips up to 60 seconds with a watermark. Cloud Restore uses datacenter GPUs and shows its per-job charge before payment.

Visit phoenixlabs.space

SeedVR2

SeedVR2 is an open-source model for restoring video at arbitrary resolutions, with a one-step diffusion-transformer approach that does not add a separate diffusion prior. Its official ComfyUI release can upscale both video and images and provides four nodes to control the pipeline. A standalone command-line interface supports batch and multi-GPU workflows. Inputs may be RGB or RGBA images and video; the documented model choices are 3B and 7B, with FP16, FP8, and quantized GGUF options. Model offloading, VAE tiling, and BlockSwap are available to manage memory during inference. The research describes adaptive window attention, which adjusts window size to output resolution, and adversarial post-training for high-resolution restoration. Standalone installation instructions cover Windows, macOS, and Linux, including Apple Silicon MPS support. Input height and width must be multiples of 32, and the demo crops resized inputs to meet that constraint. SeedVR2 is licensed under Apache 2.0, and released inference code and model weights are linked from the repository. The plan is free.

Visit github.com