Automatic 1111, but as a Python Package

Hacker News

I built an open souce, lightweight, fast Python library to run all of Automatic 1111. Compared to the API from Automatic 1111, the SDK is also much more lightweight than running all of stable diffusion webui just to use its API on localhost and takes up considerably less VRAM. Additionally, the API doesn't support upscaling, inpainting, outpainting, which we support right now. It also does not support extensions like controlnet, dreambooth that we plan to add. Compared to Huggingface diffusers, we are 2x faster and detail the many advantages we have over them here: https://flush-ai.gitbook.io/automatic-1111-sdk/auto-1111-sdk... . I would sincerely appreciate a star on our Github repository!! https://github.com/saketh12/Auto1111SDK

Visit product View on Hacker NewsJan 31, 2024

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

82%82% 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: user, open · Missing: mac, agents, macos

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

20%20% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

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