Weird brandon – Stable Diffusion-generated Joe Bidens

Hacker News

I've been working with stable diffusion for an upcoming course I'm developing with my PhD advisor, and in the course of that we wanted to talk about the effects of modifiers on the prompts w.r.t. the generated images. I put this site together as a demo of the effects of randomly selected modifiers on a single well-represented figure in the stock stable diffusion model's training data, Joe Biden, as a riff on the "dark Brandon" meme that's been going around. The source is available at https://github.com/pkage/weirdbrandon

Visit product View on Hacker NewsOct 21, 2022

Share card

Actual performance

1points

1comments

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

67%67% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

55%55% 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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

19%19% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Markov Generated HN Titles37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Markov Generated HN Titles

Hacker News2

Hasgo, generated generic functions for Go37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Hasgo, generated generic functions for Go

Hacker News1

Tubey Racers, a randomly generated championship you can cheer for54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Tubey Racers, a randomly generated championship you can cheer for

Hacker News1

Nimiq Identicons – Cutely generated cartoon identicons46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Nimiq Identicons – Cutely generated cartoon identicons

Hacker News12

My first statically generated website48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

My first statically generated website

Hacker News2

Bear2panda model generated by CycleGAN49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Bear2panda model generated by CycleGAN

Hacker News1

Trippy Haiku generated with ChatGPT and DALL-E46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Trippy Haiku generated with ChatGPT and DALL-E

Hacker News1

Auto-generated algorithms40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Auto-generated algorithms

Hacker News2

Auto generated HackerShirts37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Auto generated HackerShirts

Hacker News1

An Occam to Go transpiler (LLM-generated)52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

An Occam to Go transpiler (LLM-generated)

Hacker News2

Browse all products Analyze your launch