A Stable Diffusion desktop frontend with inpainting, img2img and more

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I was frustrated with laggy notebook stable diffusion demos. Plus they usually didn't have all the features I wanted (for example some of them only had inpainting and some only had img2img, so if I wanted both I had to repeatedly copy images between notebooks). So I made this desktop frontend which has much smoother performance than notebook alternatives and integrates image generation, inpainting and img2img into the same workflow. See a video demo here: https://user-images.githubusercontent.com/6392321/191858568-... Features include: * Can run locally or connect to a google colab server * Ability to erase * Ability to paint custom colors into the image. It is useful both for img2img (you can sketch a rough prototype and reimagine it into something nice) and inpainting (for example, you can paint a pixel red and it forces Stable Diffusion to put something red in there) * Infinite undo/redo * You can import your other images into a scratch pad and paste them into main image after erasing/cropping/scaling it * Increase image size (by padding with transparent empty margins) for outpainting

Visit product View on Hacker NewsSep 23, 2022

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

69%69% 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.

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

21%21% 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.

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