FlashVSR vs SeedVR2
FlashVSR
github.comSeedVR2
github.comSide by side
| Figure | FlashVSR | SeedVR2 |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · AI Video Upscalers | 7.7 (#21) | 8.3 (#11) |
| License | The GitHub repository identifies its license as Apache-2.0. | The project is released under the Apache License 2.0. |
| Purpose | FlashVSR is a one-step diffusion framework for streaming video super-resolution. | SeedVR2 is a one-step diffusion-transformer model for video restoration at any resolution. |
| Support | The maintainers welcome feedback and issues, and list Junhao Zhuang as a contact. | The repository invites questions by email at [email protected]. |
Plans
In their own words
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.