Train your own image generator (Stable Diffusion) for free

Hacker News

I've had a blast playing with stable diffusion and I see all the potential it will bring to us. I released a service for training your model, just upload 20-30 images and you can have a model of someone or some object doing anything. You can train one model for free a month in a slower queue or you can train many models on a fast queue and with other features for a fee. Here is an example of using it for show product placement: https://app.88stacks.com/image/O6kReClOvrz7 and here is an example of using it for people: https://app.88stacks.com/image/nOpdvCwx6kb7 and an example for using it for styles: https://app.88stacks.com/image/zyjw6CmEgk2d The UI is rough, but I would love feedback on how to improve it for you. https://88stacks.com

Visit product View on Hacker NewsDec 12, 2022

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: model, models, using · Missing: mac, agents, macos

35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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