A fast, simple playground for Trellis 2 image generation

Hacker News

Trellis 2 is a new image generation model, but I found it surprisingly hard to quickly try, compare prompts, or understand what it’s good at without digging through docs or code. So I built a small, fast site that focuses on one thing: letting people experiment with Trellis 2 through real prompts and instant results. It’s intentionally simple: * No account required * Prompt-in, image-out * Optimized for people coming from search or just curious about the model This started as a way to explore Trellis 2 myself, and then I realized others might want the same thing. Would love feedback from people who are testing new models or building AI tooling.

Visit product View on Hacker NewsDec 24, 2025

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

82%82% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

41%41% 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

18%18% 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.

Correct prediction on native model

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