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Create your own AI videos with Stable Diffusion

Hacker News

Create your own AI videos with Stable Diffusion

Over Christmas break, I wanted to make an AI generated music video. I started going into the rabbit hole of tools like Video Killed The Radio Star, Deforum, and Warpfusion. What I learned was that these tools had a high learning curve. They required knowledge of Python notebooks, expensive compute, and knowledge of all of the settings and how to manipulate them. What I wanted was a cheaper, easier, and faster way to create high-quality videos. In January, I started prototyping products in Python notebooks that could automate the video production process. I started posting videos at @magichourai on Youtube, TikTok, FB, and IG. The videos have received millions of views, and I realized other people wanted this too. I asked a friend to help me productize this, and in June we launched magichour.ai. Currently there are three tools: Animated Music Videos (Deforum-style), Video-to-Video, and Face Swap (i.e. deep fakes). So far, a small number of users are using Video-to-Video every day, but overall usage is low and very few users are paid. This is the first product I’ve ever shipped publicly. I’d love to get feedback from HN on how to improve it and give more value to users.

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
91%91% 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: user, using · Missing: mac, agents, macos
86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video, users, way · Missing: mobile apps, ios, personal
65%65% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive
58%58% predicted probability of success on AppSumo, 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
55%55% 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
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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