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Using Stable Diffusion to generate predictable illustrations

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Using Stable Diffusion to generate predictable illustrations

Hey HN, We are Kirill and Ivan. We've been working together on various projects since 2014, and our latest one is something we're really excited about. Since October 2022 we've been working on NewArc.ai ( https://newarc.ai ), a tool that allows creating beautiful illustrations with ease. When Stable Diffusion was released publicly in September 2020, we played around with it, trying to find the best way to use it in the work of designers and illustrators. We realised that text-to-image generation in Stable Diffusion was great for inspiration, but not suitable for creating final images because of its creativity. That's why we combined image-to-image generation, ControlNet and the ability to train custom models (Dream Booth, LORA) to create a tool that allows illustrators to define composition, colour, and style to generate images with predictable results. Today we're releasing the first version of our service for illustrators, and here’s how it works: 1. Upload a sketch to define the composition and colours. It will allow you to generate predictable draft illustrations. 2. Select one of our standard styles or add your very own style. Generate dozens of drafts that match your style, colour, and composition requirements. 3. Choose the best draft illustration that matches your vision, then add the finishing touches in your favourite graphics editor to make it perfect. So illustrators can say goodbye to tedious work, save time and get more done or enjoy more freedom. Happy to further discuss details in the comments!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
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Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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