Create Stable Diffusion Renders of Design Assets (SVG and GLB)

Hacker News

glyf is a tool for creating high-quality renders of SVGs or 3D models using Stable Diffusion the goal is to give 2D and 3D designers more fine-tuned control over image generation by combining their source design asset with easy-to-understand render settings for example: upload an asset (SVG or 3D Model), describe the subject "a dragon", select a material "marble", select a setting "photo studio", and use the color picker to choose a palette for the scene then just click render to get your result glyf can be used for iconography, logo design, concept art, graphic design, typography, and much more looking for feedback on if this is helpful for your production process and any suggestions you have!

Visit product View on Hacker NewsNov 22, 2023

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

88%88% 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.

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

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · 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 · Missing: arr, mrr, revenue

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

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

Incorrect prediction on native model

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