SDK/Python Client for Automatic1111's Stable Diffusion WebUI

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Introducing auto1111sdk, a python package that encapsulates all the main features of the Automatic1111 Stable Diffusion WebUI. `pip install auto1111sdk` https://github.com/saketh12/Auto1111SDK Why we built this, what you can do with it right now, and where we plan to go below: Why we built this: As a developer using stable diffusion, you’ve probably used HuggingFace’s diffusers library. However, it just doesn’t make sense to use diffusers when the “gold standard” tool for stable diffusion is Automatic1111’s Stable Diffusion Web UI. We detail some limitations of diffusers here: https://flush-ai.gitbook.io/automatic-1111-sdk/auto-1111-sdk... What you can do with it right now: - Text-to-Image, Image-to-Image, Inpainting, and Outpainting pipelines. - Upscaling pipelines for ESRGAN and Real-ESRGAN - Download & run safetensor model files from Civitai Where we plan to go: We’re an open source project, and we intend to remain this way. Our next step is to add support for Stable Diffusion XL, Lora’s, and Dreambooth finetuning. We welcome all contributors! In the meantime, check out our docs and join our discord.

Visit product View on Hacker NewsJan 29, 2024

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

73%73% 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: open source, pipe, io · Missing: https docs, excited, just released

63%63% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

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

TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

48%48% 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

16%16% 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.

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