sd-extension-system-info
root
sd-extension-system-info | root | |
---|---|---|
51 | 31 | |
262 | 2,430 | |
- | 1.5% | |
6.7 | 10.0 | |
7 days ago | 3 days ago | |
Python | C++ | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
sd-extension-system-info
- RTX 4070 vs rx 7800 xt
-
AMD for AI
I've been using both SD and various LLM on linux without any issue and have done so for months. Windows support is also starting to roll out slowly, with koboldcpp-rocm recently giving me 20-25+t/s for a13B even on windows. you can see what SD performance is like on sites like these. those numbers roughly match what i get on my RX6800 as well (8t/s).
-
Stable Diffusion in pure C/C++
That seems a lot worse than a 2060 SUPER with PyTorch in A1111.
https://vladmandic.github.io/sd-extension-system-info/pages/... (search for 2060 SUPER)
-
Iterations per second benchmarking question
But usually A1111 users use benchmark on this extension https://github.com/vladmandic/sd-extension-system-info
-
Best AMD SD Guide for 2023?
AMD SD = Setup Diaster? it was quite troublesome googling the few linux/amdgpu/rocm/sd vers/configs/params posts online. Also the whole PC may hang during generation which is bad for the harddisk. Your card is way more powerful so may not hang like mine. People are getting 8it/s https://vladmandic.github.io/sd-extension-system-info/pages/benchmark.html
-
Which one is better? Nvidia Tesla M40 vs Nvidia Tesla P4?
According to system info benchmark, M40 is like 1-2 it/s and P4 is barely better than that.
- Video card price/performance ratio
-
--medvram. Should I remove this flag? Running 3090
Anyway to properly "benchmark" the impacts different switches on your image generation speed, it is better to use the benchmarking utility from extension https://github.com/vladmandic/sd-extension-system-info (it also creates a very handy table of results from other users at https://vladmandic.github.io/sd-extension-system-info/pages/benchmark.html for you to compare with.
-
Searching for install guide for top performance setup on WSL2 (Automatic1111)
I can see that the top performance benchmark results on SD WebUI Benchmark Data (using RTX 4090), are obtained through WSL2 running Automatic1111 on a Linux dist and Python 3.10.11, along with PyTorch 2.1.0.dev+cu121 (like benchmark id: 4126)
-
Advice for Optimization on an RTX 8000
You should be able to compare based on the published benchmarks, just replicate the settings based on what's reported https://vladmandic.github.io/sd-extension-system-info/pages/benchmark.html
root
-
If you can't reproduce the model then it's not open-source
I think the process of data acquisition isn't so clear-cut. Take CERN as an example: they release loads of data from various experiments under the CC0 license [1]. This isn't just a few small datasets for classroom use; we're talking big-league data, like the entire first run data from LHCb [2].
On their portal, they don't just dump the data and leave you to it. They've got guides on analysis and the necessary tools (mostly open source stuff like ROOT [3] and even VMs). This means anyone can dive in. You could potentially discover something new or build on existing experiment analyses. This setup, with open data and tools, ticks the boxes for reproducibility. But does it mean people need to recreate the data themselves?
Ideally, yeah, but realistically, while you could theoretically rebuild the LHC (since most technical details are public), it would take an army of skilled people, billions of dollars, and years to do it.
This contrasts with open source models, where you can retrain models using data to get the weights. But getting hold of the data and the cost to reproduce the weights is usually prohibitive. I get that CERN's approach might seem to counter this, but remember, they're not releasing raw data (which is mostly noise), but a more refined version. Try downloading several petabytes of raw data if not; good luck with that. But for training something like a LLM, you might need the whole dataset, which in many cases have its own problems with copyrights…etc.
[1] https://opendata.cern.ch/docs/terms-of-use
[2] https://opendata.cern.ch/docs/lhcb-releases-entire-run1-data...
[3] https://root.cern/
- What software is used to generate plots/graphs like this seen in many particle physics papers?
-
Interactive GCC (igcc) is a read-eval-print loop (REPL) for C/C++
The odd part is that this is not just for fun. For many physicists when I was at CERN, a C++ REPL was a commonly used tool to interactively debug analyses to such a degree that many never compiled their code. Back then, I believe, it was some custom implementation included in ROOT (https://root.cern/). I even went out of my way to write C++ code compatible to it just so it could run with this implementation, otherwise some colleagues weren't interested in collaborating at all.
-
Stable Diffusion in pure C/C++
That Python ML code is calling C++ code running in the GPU, one more reason to use C++ across the whole stack.
CERN already used prototyping in C++, with ROOT and CINT, 20 years ago.
https://root.cern/
Nowadays it is even usable from Netbooks via Xeus.
It is more a matter of lack of exposure to C++ interpreters than anything else.
- Root: Analyzing Petabytes of Data, Scientifically
-
Aliens might be waiting for humans to solve a puzzle
Quantum computing is a pretty interesting science too. https://home.cern/news/press-release/knowledge-sharing/cern-quantum-technology-initiative-unveils-strategic-roadmap they have to deal with lots of data streaming too https://root.cern/
-
cppyy Generated Wrappers and Type Annotations
I'm a user of CERN's ROOT (https://root.cern/) and while I'd usually write in C++, I've been trying to write as much Python as I can recently to get a bit better in the language.
- Root: Analyzing Petabytes of Scientific Data
-
Span: how to cast pointer of pointer to other types?
I'm dealing with a C++ software called ROOT made by CERN, which is, if I'm not wrong, the only C++ API that we could use for data analysis such as plotting histograms, fitting multi-parameter functions and storing data in the size of TB to the disk and many more. That's the only reason why physicists still stick to this software. you can check here .
-
How exactly would you go about writing a program to simplify algebraic expressions?
Hey, I found something which could be useful: https://root.cern
What are some alternatives?
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
PyMesh - Geometry Processing Library for Python
tomesd - Speed up Stable Diffusion with this one simple trick!
xeus - Implementation of the Jupyter kernel protocol in C++
voltaML-fast-stable-diffusion - Beautiful and Easy to use Stable Diffusion WebUI
tfgo - Tensorflow + Go, the gopher way
stable-diffusion-webui-amdgpu - Stable Diffusion web UI
windows-telemetry-blocklist - Blocks outgoing Windows telemetry, compatible with Pi-Hole.
scribble-diffusion - Turn your rough sketch into a refined image using AI
decimal - Arbitrary-precision fixed-point decimal numbers in Go
HIP - HIP: C++ Heterogeneous-Compute Interface for Portability
apd - Arbitrary-precision decimals for Go