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Using Stable Diffusion to show you in awesome outfits you can buy

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Using Stable Diffusion to show you in awesome outfits you can buy

Hey everyone! I'm Masha, Stanford alum and creator of Fotofit. Fotofit is a shopping app where you can see outfits in any style on you, and buy those outfits. I'm using an LLM to generate descriptions of outfits that fit your preferences, and using SD to generate the images of them. I came up with the idea after using social media for outfit inspiration. It was tough to find outfits in my style, and even if I did find them, I didn't know how they would look on me or how to buy the items to recreate that outfit. With Fotofit, you can search in natural language for whatever style / vibe you want ("Date night outfits for NYC winter", "Something in between formal and casual"), see thousands of outfits in that style on you, and tap to buy the items from your preferred brands, in your size and budget. It works for men's and women's styles. Please try the app, I'd love to hear your thoughts and feedback!

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Actual performance

6points
1comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
53%53% 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: using · Missing: mac, agents, macos
52%52% 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
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
37%37% 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
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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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