tremor VS stable-diffusion

Interesting it's open-source but they don't seem to highlight it on their home page, I had to search for their Github page to find it https://github.com/tremorlabs/tremor

Then when I came back to the home page and searched for "license", I confirmed it says "Apache-2.0 license" in light-grey, small font in the middle of the page.

I was hoping to see support for React Native but according to one of their issues]1], they need another contributor for it and "other JS frameworks"

[1] https://github.com/tremorlabs/tremor/issues/230

Ya i used https://tremor.so nextjs and tailwindcss

Neither! I deployed on nextjs and use https://tremor.so

I recently discovered https://github.com/tremorlabs/tremor a components lib aimed at building dashboards, it’s impressive how fast you can build a complete product with it … I built the ui for a python tasks scheduler in a couple of days: https://github.com/lucafaggianelli/mario-pype

Github stars: 6.6k License: MIT Links: Demo | Documentation | GitHub

If you have any other questions, reach out to me or open an issue on GitHub

Heya! I am one of the creators of the library. We have released many new features and improvements over the last few months. You can check out the project here: https://github.com/tremorlabs/tremor What's new? ◆ Override and extend root styles with tailwind classes ◆ All HTML attributes extended ◆ Ref forwarding ◆ Bundle size decreased by 25% → http://tremor.so/changelog

Which is a 1-click install of Stable Diffusion with an alternative web interface. You can choose a different approach but this one is pretty simple and I am new to this stuff.

an invisible watermarking of the outputs, to help viewers identify the images as machine-generated.

It sounds like you just want to set up Stable Diffusion to run locally. I don't think your computer's specs will be able to do it. You need a graphics card with a decent amount of VRAM. Stable diffusion is in Python as is almost every AI open source project I've seen. If you can get your hands on a system with an Nvidia RTX card with as much VRAM as possible, you're in business. I have an RTX 3060 with 12 gigs of VRAM and I can run stable diffusion and a whole variety of open source LLMs as well as other projects like face swap, Roop, tortoise TTS, sadtalker, etc...

Use specific GPU on multi GPU systems · Issue #87 · CompVis/stable-diffusion · GitHub

I dealt with this in one of my posts. At least SD 1.1 till 1.5 are all trained on a batch size of 2048. The version pretty much everyone uses (1.5) is first pretrained at a resolution of 256x256 for 237K steps on laion2B-en, at the end of those training steps it will have seen roughly 500M images in laion2B-en. After that it is pre-trained for 194K steps on laion-high-resolution at a resolution of 512x512, which is a subset of 170M images from laion5B. Finally it is trained for 1.110K steps on LAION aesthetic v2 5+. This is easily verified by taking a glance at the model card of SD 1.5. Though that one doesn't specify for part of the training exactly which aesthetic set was used for part of the training, for that you have to look at the CompVis github repo. Thus at the end of it all both the most recent images and the majority of images will have come from LAION aesthetic v2 5+ (seeing every image approx 4 times). Realistically a lot of the weights obtained from pretraining on 2B will have been lost, and only provided a good starting point for the weights.

stable diffusion · CompVis/stable-diffusion@2ff270f · GitHub

Here for example --> https://github.com/CompVis/stable-diffusion