The distributed
canvas of GPU compute.
CANAS.NET weaves millions of idle consumer GPUs into one mesh that renders generative diffusion — images and video — and settles it per frame on-chain. Requesters pay only for what they generate. Providers earn from silicon that would otherwise sit dark.
Submit a job. Watch the mesh render it.
This is the real thing: your prompt is priced in USDC, escrowed, routed to an actual provider in the live network, and rendered. It even shows up in the public job stream below.
Six steps from prompt
to settled frame.
CANAS borrows HTTP 402: an unpaid request gets a payment challenge, the consumer attaches proof, and the network does the rest — match, render, verify, settle.
Lock escrow
A job — prompt, model, sampler, seed — is submitted and payment is locked in a Solana escrow program. No accounts, no subscriptions.
Match a GPU
The scheduler routes by GPU model, VRAM, latency, price and reputation — to one provider, or several for premium jobs.
Render
The provider pulls a pinned container and content-addressed weights (Arweave/IPFS + CDN) and runs the diffusion inference.
Attest
The result is returned with an attestation: job id, parameter hash, model hash, a perceptual hash of the output, and a timestamp.
Verify
A sampled fraction is re-run by an independent verifier and compared by perceptual closeness. Canary jobs catch fabrication.
Settle
On acceptance the escrow releases — ~90% to the provider, ~5% to verifiers, ~5% to the protocol. Paid straight to wallet.
The network, rendering right now.
Every row below is a real diffusion job moving through escrow, scheduling, render and settlement — streamed straight from the mesh.
Live job stream
a samurai standing in a field of red spider lilies, matte painting, iridescent backlight, 35mm, masterpiece, shot on Hasselblad
a sprawling solarpunk metropolis, claymation still, moody chiaroscuro, octane render, sharp focus, trending on artstation
a cat slowly blinking in warm afternoon light, shallow depth of field
a portrait of an elven ranger, freckles, sharp eyes, comic book ink, volumetric god rays, octane render, film grain, wide angle
a witch brewing potions in a candle-lit cottage, vaporwave, soft rim lighting, ultra-detailed textures
a clockwork owl perched on a gear-driven tree, comic book ink, iridescent backlight, sharp focus
a surfer riding a wave of liquid mercury, pixel art, volumetric god rays, trending on artstation, wide angle
a derelict space station orbiting a gas giant, oil painting, neon glow, sharp focus
a colossal whale swimming through clouds, dark fantasy illustration, soft rim lighting, 8k
a phoenix rising from molten obsidian, baroque chiaroscuro, volumetric god rays, highly detailed, ultra-detailed textures, volumetric fog
ocean waves crashing on black volcanic rocks, slow motion
a cozy bookshop on a snowy evening, matte painting, candlelit warmth, highly detailed
a surfer riding a wave of liquid mercury, dark fantasy illustration, overcast diffuse light, film grain
a colossal whale swimming through clouds, vaporwave, iridescent backlight, bokeh, depth of field, 35mm
a vintage diner at the edge of the galaxy, comic book ink, iridescent backlight, volumetric fog
Provider map
24 regionsTop providers
by earningsWe don't pretend to prove a GPU.
We make lying unprofitable.
Bit-exact proof of diffusion across heterogeneous hardware is physically impossible. So CANAS trades cryptographic certainty for an economic & statistical one — reproducibility by closeness, canaries, redundancy, reputation and slashing.
Perceptual reproducibility
LPIPS · SSIM · pHashA verifier with the same pinned container re-runs the job and compares outputs by LPIPS / SSIM / pHash within tolerance — never bit-for-bit, because floating point diverges across hardware.
Canary jobs
known seed · known outputThe scheduler blends in reference prompts with a known seed and reference output. Any node fabricating or substituting results fails the canary and gets caught.
Redundant execution
N-of-M consensusPremium and high-value jobs run on several providers at once. Consensus is the perceptual cluster — outliers are rejected before settlement.
Optimistic + sampling
sampled · slashableMost jobs settle without re-checks; a random fraction is re-verified, and disputes resolve by redundant re-execution. Dishonesty is met with bond slashing.
honest limit — this is an economic and statistical guarantee, not a cryptographic proof of computation. For subjective visual output it's a fair trade; for safety-critical compute it isn't the right tool, and CANAS doesn't claim to be.
Your idle GPU is a render farm.
Most gaming GPUs sit dark for 18+ hours a day. Point yours at CANAS and it renders diffusion jobs for people who'd otherwise pay a centralized cloud — and you keep ~90% of every settlement.
Earnings estimate
indicativePaid in USDC, settled to your wallet each epoch · ~90% provider / 5% verifier / 5% protocol · estimates scale with real network demand.
Install the worker
One Docker container — diffusers / ComfyUI, CUDA, pinned versions. A light agent opens a reverse tunnel.
Stake a bond
Lock $CNSNET as a provider bond. It backs honest output and is slashable for fabrication.
Go online & earn
The scheduler routes jobs to you by GPU, VRAM and reputation. USDC settles to your wallet each epoch.
$CNSNET — a utility, not a yield machine.
Users pay in USDC for price stability. $CNSNET secures the network: it bonds providers, stakes verifiers, governs parameters, and unlocks priority. Value tracks job flow — not emissions.
Utility
- Provider bond — slashed for fabricated or substituted output
- Verifier stake — collateral for honest re-checks
- Governance — network params, fee rates, model set
- Priority routing & reduced fees for holders
Token allocation
Settlement split per job
214 providers bonded · paying protocol fees in $CNSNET costs less than in stablecoin.
From test mesh to planetary canvas.
Test mesh
- Permissioned provider set
- Baseline image generation
- Escrow on Solana devnet
Mainnet-beta
- Open provider onboarding & reputation
- Canary + sampled verification
- USDC settlement, per-frame pricing
Scheduler decentralization
- Open verifier set
- $CNSNET governance
- Video diffusion workflows
Maturity
- Redundant execution for premium jobs
- Expanded model catalog
- SLA tiers
Paint with the world's
idle GPUs.
Render diffusion for a fraction of cloud prices, or turn your gaming rig into a cash-flowing render node. Two sides, one canvas.