Shortbread – Create AI comics in minutes

Hacker News

Just go to the link and click on "Start Creating". No signing in required. I built shortbread to help anyone to create comics / manga series. The onboarding process helps you kick start a page from 60%, then you can use your creativity to bring it to 1000% in a fully-controllable editor. Tech stack: GPT 3.5 Turbo - the comic script generation. It handled everything from layout, character, scene, SD prompts, to dialogue. SD 1.5 - We put up SD servers on GCP. For every comic we generate one large image and crop it into panels. Per the experiments of u/Deathmarkedadc on Reddit, this massively helps with consistency. The models are trained on anime scenes tho, and might not be so great with animals. Frontend: Next.js 13 on Vercel, React + Typescript. We built the entire editor from scratch to compose the comic (images, panels, speech bubbles, text) like a webpage. This allows you to edit and republish your comics like a website. You can dynamically generate panels as well. Try resizing a panel into a long narrow box and generate. Backend: Firebase. Sample comics: a japanese couple sits at dinner table. The husband told the wife a secret (link https://create.shortbread.ai/viewer/debdf25c-3f95-492a-952a-... ) An army of male soldiers fighting against an army of female soldiers in ancient china ( https://create.shortbread.ai/viewer/4566613c-7146-4ed7-9b8d-... ) a team of girls play volleyball against a team of boys ( https://create.shortbread.ai/viewer/aafc2f61-d008-4f3f-aa8f-... ) Next steps: - More pages - Fine panel-level control. Poses, control net, etc. - Multi-character. - Different styles. - Allows you to control character design. I’m Fengjiao Peng, founder and chief engineer at Shortbread. I was previously a webtoon artist. We want to build this into something you can create entire comics series / manga / webtoons with. Criticism and suggestions welcome!

Visit product View on Hacker NewsOct 6, 2023

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

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

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, 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.

Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

26%26% 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 · Strong signals: arr · Missing: mrr, revenue, profit

22%22% 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.

Incorrect prediction on native model

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