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AI videoVideoGigaGAN is a generative video super-resolution (VSR) model aimed at enhancing video resolution while preserving high-frequency details and maintaining temporal consistency. It is built on the large-scale image upsampler GigaGAN and can upscale videos by up to 8 times (for example, from 128×128 to 1024×1024). The material notes that simply adding temporal modules to GigaGAN directly leads to severe temporal flickering, so the project introduces several techniques to suppress artifacts: adding temporal attention layers in the decoder modules, fusing features from an optical-flow-guided propagation module, using anti-aliasing modules in the encoder downsampling layers, and directly passing high-frequency features through skip connections to compensate for detail loss during the BlurPool process. Experiments show that, compared with existing state-of-the-art VSR models, VideoGigaGAN can generate video results with richer details and comparable temporal consistency, and is applicable to general videos of different categories. This material is a project introduction page and does not yet provide specific installation commands, usage steps, pricing, platform support, or limitation conditions and other operational details.