Klarity vs SeedVR2

Klarity is a free desktop tool for restoring images and videos locally and offline on Linux and Windows. Its modes include denoising, deblurring, upscaling, cleaning and full restoration, along with frame generation and related clean-frame and full-frame options. It supports JPG, JPEG, PNG, BMP, TIFF, TIF and WebP images, and MP4, AVI, MOV, MKV, WebM, FLV, WMV and M4V videos. Video processing requires FFmpeg. Users can work through a graphical interface or interactive and direct command-line modes. Heavy mode downloads about 888 MB of models; Lite downloads about 204 MB and is described as 20x faster. The listed minimum requirements are a 2-core CPU, 4 GB RAM and 2 GB storage, and CPU processing works without a GPU. AI models download on first use. Klarity uses NAFNet for denoising and deblurring, Real-HAT-GAN or Real-ESRGAN for upscaling, and RIFE for frame generation. The repository lists an MIT license for Klarity and separate licenses for model components. CPU-only desktop binaries are listed for Linux x86_64 and Windows x86_64.

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.