Create your own DreamBooth models for $2. Share, try, or remix models

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I've been working on https://synapticpaint.com/ to make content creation easier and more accessible to people. As part of this, I've been building a DreamBooth ecosystem around the following features: - train: easily train DreamBooth models with no set up, just upload photos and go - use (generate): immediately use the models with one of the other Synaptic apps (Txt2Img or Synaptic Paint), or download the model for offline use - share: optionally publish your model so that other people can use it (DreamBooth models can handle not just people but also styles or objects) - discovery: browse existing DreamBooth models - remix: combine different models together (not implemented yet) There is a waitlist right now because I'm having trouble increasing my AWS limits, but if you're interested in playing with the technology email me (email in profile) and I'll send you an invite.

Visit product View on Hacker NewsNov 15, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, models · Missing: mac, agents, macos

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal

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

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

11%11% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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