PhoneDiffusion – Local AI image generation for iOS

Hacker News

PhoneDiffusion is officially live Now you can generate AI images locally on iPhone with Stable Diffusion. Fast, private, account-free and specifically built for iOS. What you can do: ∙ Generate images in under 5 seconds on the newest iPhones ∙ Choose between fast SD 1.5 models and high quality SDXL models ∙ Use any of the 8 built-in styles ∙ Change steps, CFG and batch count ∙ Keep prompts and images on your device ∙ Use it with no account and no internet after setup Try PhoneDiffusion today: https://apps.apple.com/app/id6762061991

Visit product View on Hacker NewsMay 25, 2026

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Actual performance

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Launch Intel predictions

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

79%79% 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.

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

56%56% predicted probability of success on Product Hunt, 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.

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

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

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

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

nativeThis product was originally launched on this platform.

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

19%19% 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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