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Stable Diffusion works fine on a CPU - on an AMD Ryzen 5700, approx 90s per image (and I believe comparable or faster on my old i7-6700). If you want to kick off a batch in the background while you work on something else, that's plenty fast. (I use: https://github.com/brycedrennan/imaginAIry).
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Judoscale
Save 47% on cloud hosting with autoscaling that just works. Judoscale integrates with Django, FastAPI, Celery, and RQ to make autoscaling easy and reliable. Save big, and say goodbye to request timeouts and backed-up task queues.
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Yeah!
Here's the colab notebook, in case anyone is interested: https://github.com/TheLastBen/fast-stable-diffusion
I've trained a few smaller models using their Dreambooth notebook, but I think for 4000 training steps, an A100 will usually take 30-40min. I believe replicate also uses A100s for their dreambooth training jobs.
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Dreambooth-Stable-Diffusion
Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) by way of Textual Inversion (https://arxiv.org/abs/2208.01618) for Stable Diffusion (https://arxiv.org/abs/2112.10752). Tweaks focused on training faces, objects, and styles. (by JoePenna)
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sd-webui-lobe-theme
🅰️ Lobe theme - The modern theme for stable diffusion webui, exquisite interface design, highly customizable UI, and efficiency boosting features.
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openOutpaint
local offline javascript and html canvas outpainting gizmo for stable diffusion webUI API 🐠
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InfluxDB
InfluxDB high-performance time series database. Collect, organize, and act on massive volumes of high-resolution data to power real-time intelligent systems.
