Leoma is Bittensor subnet 99 (SN99), a project in the Image/Video/Audio Generation lane. Winner-take-all AI video on Bittensor
Leoma turns Bittensor into a tournament for AI video generators: every round, miners submit short clips, an automated benchmark scores them, and only the single best one gets paid.
It is an educational deep dive, not a recommendation to stake. For live price, pool depth, and performance, always open the live subnet page.
What is Leoma (SN99)?
Leoma (SN99) is a Bittensor subnet for AI video generation. Independent operators ("miners") run models that turn a starting image plus a text prompt into a short video clip, and a separate set of participants ("validators") score the results. Each round, the best clip wins and that miner takes the full reward.
In plain English: It is like Runway or Pika, but instead of one company's model, many models compete head to head and only the round's winner gets paid.
Centralized reference points: Runway, Pika, Google Veo, OpenAI Sora
How miners and validators work
Like every Bittensor subnet, SN99 is a two-sided market: miners produce work, validators score it, and emissions follow those weights under the network’s dTAO economics.
- Miners upload a Text-Image-to-Video model to Hugging Face, deploy it on Chutes, and commit it on-chain. Given a supplied first frame and a text prompt, they return a short video.
- Validators run an automated evaluator that scores each video on first-frame fidelity, prompt adherence, temporal quality, and visual artifacts, then set winner-take-all weights on-chain.
Mechanism and design
According to the repository README, Leoma currently supports one task type, Text-Image to Video (TI2V): a validator supplies a first frame plus a prompt, and miners return a short video. The round runs in three roles. The subnet owner runs an "owner-sampler" that builds tasks from short source clips, calls miners through Chutes (Bittensor's compute subnet), and posts task artifacts to object storage. Validators run an evaluator that pulls the latest task, scores each generated video with a multimodal model, and posts results back to the Leoma API. The API computes a ranking and exposes a weights endpoint; each epoch, validators read it and set winner-take-all weights on-chain, so the top miner takes full weight for the round.
One detail worth flagging for anyone reading the code: the README describes the evaluator using OpenAI's GPT-4o, while the most recent commits move it to Google's Gemini (the May 2026 commits pin a Gemini model and streamline the evaluator to require only Gemini). The scoring structure is the same either way; the underlying vision model is mid-transition.
The stack is Python, with miner models hosted on Hugging Face, inference served through Chutes, and S3-compatible object storage (Cloudflare R2 by default, or Hippius). Development is real but concentrated: the public repository's commit history shows a single primary contributor and roughly three dozen commits since the repo went up in March 2026. The roadmap in the README lists Text-to-Video and Image-to-Video as planned additions beyond today's TI2V-only support.
Why the project exists
- The problem it solves: State-of-the-art video generation is concentrated in a few closed, hosted models. Leoma builds an open market where any model builder can enter and is judged purely on output quality.
- The opportunity: Image-to-video and text-to-video are among the fastest-moving corners of generative AI. A permissionless arena where the best model wins each round is a different shape from the subscription products that dominate today.
- The Bittensor advantage: Winner-take-all scoring puts direct pressure on shipping the best model, not the best-marketed one. An automated benchmark, rather than vendor claims, decides who wins.
- Traction signals: Early. The public repository shows active development, with the most recent commit in late May 2026 and a live site at leoma.ai. There is little public social footprint so far, and the subnet currently receives no emission share (covered in the risks below).
What stakers should check before buying alpha
Subnet storytelling is cheap. On-chain and product checks are not. Use the same framework as our subnet metrics guide:
| Check | Why |
|---|---|
| Real product / docs / repo | Separates shipping teams from narrative-only slots |
| Pool depth (TAO) | Your exit door: thin pools amplify slippage |
| Net flows (7d / 30d) | Under flow-based emissions, capital votes with its feet |
| Emission share | Whether the market is funding this subnet right now |
| Ownership concentration | Large holders can move a small alpha market |
| Immunity / deregistration | Post-immunity weak slots can lose the race (see immunity guide) |
Pull the live numbers on Leoma · SN99 rather than trusting any static screenshot in a blog post.
Risk factors
- Deregistration: Leoma registered in late January 2026, so its four-month network immunity has now lapsed. Bittensor automatically deregisters the lowest-EMA-price non-immune subnet roughly every two days when the network is full. With a 0% emission share and a thin pool, Leoma sits in the exposed range until staking inflows turn positive.
- Single-contributor development: The public repository's history shows one primary author. The work is recent and active, but it carries key-person concentration risk.
- Evaluator dependence on a closed model: Scoring relies on a third-party multimodal model, and the repo is mid-transition from GPT-4o to Gemini. Evaluation quality, cost, and availability track an external vendor the subnet does not control.
- Competition: The category is crowded on both sides, against well-funded centralized products (Sora, Veo, Runway) and any other generative-media effort on Bittensor.
- Early traction: Public usage and social signal are limited so far, so there is little independent evidence of demand yet.
How this fits a TAO portfolio
Many holders keep a root vs subnet split: root for calmer TAO exposure, subnets like Leoma for thematic upside (and higher variance). If you use TaoScale, you can size SN99 from your wallet without handing over custody. See non-custodial staking.
- Live market: SN99 on TaoScale
- Browse and stake: /invest
- More subnet explainers: /blog/subnets
FAQ
What does Leoma do on Bittensor?
It is like Runway or Pika, but instead of one company's model, many models compete head to head and only the round's winner gets paid.
What is the SN99 alpha token?
When you stake TAO into subnet 99, you receive that subnet’s alpha. Its price versus TAO moves with the subnet’s pool (see what is dTAO).
How do I research Leoma before staking?
Read the mechanism above, open the official repo/docs when available, then verify pool depth, flows, and activity on SN99. Cross-check with the metrics checklist.
Is this financial advice?
No. TaoScale publishes educational explainers and live tools. Decisions to stake are yours alone.
Not financial advice. Educational overview of Leoma (SN99) compiled from public project materials and ecosystem documentation. Mechanisms and market structure change, so verify live data and official sources before acting.
