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Feedback on Sketch Colourisation

Hacker News

Feedback on Sketch Colourisation

Hi I am looking for some feedback on our new project "Sketch Colourisation". The envisioned UI and objectives are -- * An artist should have greater control on how to colour a sketch. While a text-to-image model lacks this fine-grained control, a per-pixel colourisation pipeline makes sketch colourisation a laborious process with high-entry barrier. * What if an artist only draws a mask for a local region and specifies the colour palette for that local region? Then a neural network figures out how to colour the overall sketch -- while maintaining those local colour palette. [I would really like a feedback if the above UI (i.e., mask and local colour palette) makes sense to users/designers. As researchers, we often have the wrong idea of what is desired by end-users.] * On the exact implementation of the above concept, we designed a no-training based neural network framework -- and also make sure it runs on a Nvidia 4090. In other words, I will try to avoid any expensive training or inference -- which defeats the purpose of being useful to people (not just some research labs). * Note, I am not so bothered about the exact implementation (or whether it is "novel") -- as long as it is useful. * A shameless advertisement: The codebase ( https://github.com/CHAITron/sketchdeco-code.git ) is MIT License. It is no way near to being useful to people -- but I would really like to pursue this direction and your feedback/criticism will be immensely helpful. Thanks

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, training · Missing: mrr, revenue, profit
14%14% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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