Noiselith, the most powerful tool to try out SDXL

Hacker News

Hello HN, we're excited to introduce Noiselith, a new beta program we've just launched. This tool harnesses the power of Stable Diffusion models right on your local machine. We're still in beta and actively developing, so we'd greatly appreciate your feedback. Free download available at https://noiselith.com . Key Features: - Simplified installation: Just double-click the installation file. - Redesigned image creation process for ease of use. - Integrated model management and image gallery. - Compatibility with generation options from other well-known tools. More features coming soon!

Visit product View on Hacker NewsOct 17, 2023

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

92%92% 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.

AppSumoStrong fit for a featured deal · Strong signals: soon · Missing: plus, platform, intuitive

68%68% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

41%41% 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 · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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: active · Missing: arr, mrr, revenue

8%8% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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