Vidaio follows the standard Bittensor subnet design with miners and validators cooperating via two “synapses” (query types).

• Miners: Run AI upscaling models on video data. Miners “enhance video quality using AI-driven upscaling techniques.” They may fine-tune open-source super-resolution models or develop new ones, and they handle upscaling requests from validators and end-users. In practice, miners take low-resolution video chunks (from synthetic or organic queries) and output high-resolution versions.
• Validators: Check that miners’ outputs meet quality standards. Validators feed controlled test videos to miners and “ensure miners deliver consistent, high-quality results by evaluating performance”. In a Synthetic Query, validators downscale a known high-resolution clip, send it to miners to upscale, then score the result using perceptual metrics (Vidaio uses VMAF and PIE-APP for this). In an Organic Query, real user videos are split into chunks and queued to miners; once upscaled, the pieces are reassembled and returned to the user. Validators then use a no-reference quality metric (Vidaio uses ClipIQA+ for organic jobs) to assess the output’s perceptual quality.
• Incentive Mechanism: Vidaio’s reward system is built on video-quality metrics. Validators periodically submit weight vectors reflecting each miner’s scores on the subnet. The Bittensor blockchain aggregates these vectors and applies the Yuma consensus algorithm, which combines each miner’s weighted score with its staked TAO to compute token emissions. Thus, miners earn more TAO by consistently producing high-VMAF/PIE-APP scores, and validators are themselves rewarded for honest scoring. This merit-based model (unique to each subnet) drives the network to optimize for perceptual video quality. (In benchmark tests, Vidaio’s upscaler outperformed leading centralized solutions – e.g. its ClipIQA+ score was 0.4697 vs. 0.4658 for Topaz – demonstrating strong perceptual performance.)

Products & Services

Vidaio’s primary offering is a video upscaling service accessible via its web portal. Users can create an account, upload a low-resolution video, and choose a target resolution (e.g. 4K) for AI enhancement. The interface shows the upload file, chosen quality, and expected processing time, and has an “Upscale Video” button to submit the job.

Vidaio’s web interface allows users to upload videos and select a target resolution (e.g. 4K) for AI-based upscaling. In addition to the web app, Vidaio has a fully open-source codebase and API. The GitHub repository (vidaio-subnet/vidaio-subnet) contains the core subnet code and setup guides for running miners or validators. The team plans to release a developer API/SDK so other apps can integrate Vidaio’s processing.

Technical Architecture

Vidaio’s tech stack combines decentralized blockchain components with AI/ML video pipelines. Core elements include:

• Bittensor Integration: The subnet runs on the Bittensor chain. Both miners and validators use the Bittensor Python SDK/CLI to register, stake, and communicate on-chain. Validators regularly send weight vectors to the chain, and the Bittensor network’s Yuma consensus module computes emissions. (This is standard for all Bittensor subnets.) Vidaio follows the dynamic TAO model, meaning TAO tokens staked into Vidaio (via the Bittensor wallet) generate dTAO reflecting Vidaio’s value and share in emissions.
• AI/ML Models: Vidaio’s upscaling engine uses state-of-the-art super-resolution models. The code references known open-source frameworks (e.g. Video2X, ESRGAN variants) and implements novel enhancements. Training and inference are done in Python (likely using PyTorch or TensorFlow libraries, given the requirements). The repository’s Docker setup suggests containerized deployment for reproducibility. For quality evaluation, Vidaio employs multiple metrics (VMAF, PIE-APP, TOPIQ, LPIPS, ClipIQA+, etc.) as shown in their documentation.
• Infrastructure: Although decentralized, typical miner nodes are GPU-equipped servers/instances running Vidaio’s Python mining software. The GitHub structure (folders like vidaio_subnet_core, services, docker) indicates a modular backend. The public web service (vidaio.io) likely runs on cloud servers using this core code plus a web frontend (React/Vue, since a frontend dev is on the team).
• Interoperability & Tooling: Vidaio interoperates seamlessly with the Bittensor network: it observes the metagraph of peers and follows the Bittensor protocol. It uses standard crypto wallets (multisig hotkeys) for TAO. Tools like Docker, the Bittensor CLI, and monitoring dashboards are part of the stack. The GitHub repo includes setup guides for validators and miners, as well as an “Incentive Mechanism Guide” (linked in the docs) to explain the scoring algorithm.