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Generate Yourself – Consumerizing Stable Diffusion

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Generate Yourself – Consumerizing Stable Diffusion

Not all of us have the technological expertise or time to set up Stable Diffusion and train our own model. So we made it very easy to do so and start generating great pictures. Challenges: Scaling GPUs. Very hard to get large quotas from GPU providers. Other apps that are similar: We've tried competitor apps. A few gave very poor results (did not look like me at all), didn't allow you to generate your own photos with custom prompts, or were ridiculously overpriced. So we made ours cheap, ensured it gives accurate depictions, and can use any SFW prompts. Would love your feedback!

Visit productView on Hacker NewsDec 1, 2022
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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps · Missing: mac, agents, macos
82%82% 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
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
67%67% 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
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
46%46% 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
17%17% 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.

Incorrect prediction on native model

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