xla
InvokeAI
xla | InvokeAI | |
---|---|---|
8 | 239 | |
2,296 | 21,337 | |
1.7% | 1.4% | |
9.9 | 10.0 | |
5 days ago | 5 days ago | |
C++ | TypeScript | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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.
xla
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Who uses Google TPUs for inference in production?
> The PyTorch/XLA Team at Google
Meanwhile you have an issue from 5 years ago with 0 support
https://github.com/pytorch/xla/issues/202
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Google TPU v5p beats Nvidia H100
PyTorch has had an XLA backend for years. I don't know how performant it is though. https://pytorch.org/xla
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Why Did Google Brain Exist?
It's curtains for XLA, to be precise. And PyTorch officially supports XLA backend nowadays too ([1]), which kind of makes JAX and PyTorch standing on the same foundation.
1. https://github.com/pytorch/xla
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Accelerating AI inference?
Pytorch supports other kinds of accelerators (e.g. FPGA, and https://github.com/pytorch/glow), but unless you want to become a ML systems engineer and have money and time to throw away, or a business case to fund it, it is not worth it. In general, both pytorch and tensorflow have hardware abstractions that will compile down to device code. (XLA, https://github.com/pytorch/xla, https://github.com/pytorch/glow). TPUs and GPUs have very different strengths; so getting top performance requires a lot of manual optimizations. Considering the the cost of training LLM, it is time well spent.
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[D] Colab TPU low performance
While apparently TPUs can theoretically achieve great speedups, getting to the point where they beat a single GPU requires a lot of fiddling around and debugging. A specific setup is required to make it work properly. E.g., here it says that to exploit TPUs you might need a better CPU to keep the TPU busy, than the one in colab. The tutorials I looked at oversimplified the whole matter, the same goes for pytorch-lightning which implies switching to TPU is as easy as changing a single parameter. Furthermore, none of the tutorials I saw (even after specifically searching for that) went into detail about why and how to set up a GCS bucket for data loading.
- How to train large deep learning models as a startup
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Distributed Training Made Easy with PyTorch-Ignite
XLA on TPUs via pytorch/xla.
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[P] PyTorch for TensorFlow Users - A Minimal Diff
I don't know of any such trick except for using TensorFlow. In fact, I benchmarked PyTorch XLA vs TensorFlow and found that the former's performance was quite abysmal: PyTorch XLA is very slow on Google Colab. The developers' explanation, as I understood it, was that TF was using features not available to the PyTorch XLA developers and that they therefore could not compete on performance. The situation may be different today, I don't know really.
InvokeAI
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Stable Diffusion 3
Probably not, since I have no idea what you're talking about. I've just been using the models that InvokeAI (2.3, I only just now saw there's a 3.0) downloads for me [0]. The SD1.5 one is as good as ever, but the SD2 model introduces artifacts on (many, but not all) faces and copyrighted characters.
[0] https://github.com/invoke-ai/InvokeAI
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AMD Funded a Drop-In CUDA Implementation Built on ROCm: It's Open-Source
I actually used the rocm/pytorch image you also linked.
I'm not sure what you're pointing to with your reference to the Fedora-based images. I'm quite happy with my NixOS install and really don't want to switch to anything else. And as long as I have the correct kernel module, my host OS really shouldn't matter to run any of the images.
And I'm sure it can be made to work with many base images, my point was just that the dependency management around pytorch was in a bad state, where it is extremely easy to break.
> Anyways, hopefully this PR fixes the immediate issue: https://github.com/invoke-ai/InvokeAI/pull/5714/files
It does! At least for me. It is my PR after all ;)
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Can some expert analyze a github repo and tell us if it's really safe or not?
The data being flagged is not in that github repo, it's fetched from elsewhere and I don't fancy spending time looking for it. The alert is for 'Sirefef!cfg' which has been reported as a false positive with a bunch of other stable diffusion projects (https://www.reddit.com/r/StableDiffusion/comments/101zjec/trojanwin32sirefefcfg_an_apparently_common_false/, https://www.reddit.com/r/StableDiffusion/comments/xmhukb/trojan_in_waifudiffusion_model_file/, https://github.com/invoke-ai/InvokeAI/issues/2773 )
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What is the most effcient port of SD to mac?
I haven’t tried it recently, but InvokeAI runs on Mac. Invoke. I used to run on my MacBook, but have since gotten a Win laptop.
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Easy Stable Diffusion XL in your device, offline
There are already a number of local, inference options that are (crucially) open-source, with more robust feature sets.
And if the defense here is "but Auto1111 and Comfy don't have as user-friendly a UI", that's also already covered. https://github.com/invoke-ai/InvokeAI
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Ask HN: Selfhosted ChatGPT and Stable-diffusion like alternatives?
https://github.com/invoke-ai/InvokeAI should work on your machine. For LLM models, the smaller ones should run using llama.cpp, but I don't think you'll be happy comparing them to ChatGPT.
- 🚀 InvokeAI 3.4 now supports LCM & LCM-LoRAs and much more!
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Best ai image generator without a nsfw filter?
Stable Diffusion. /r/stablediffusion There are many tutorials on how to set it up locally and use it. InvokeAI is the easiest way to set it up. https://github.com/invoke-ai/InvokeAI
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What's the best stable diffusion client for base m1 MacBook air?
InvokeAI
- invoke-ai/InvokeAI
What are some alternatives?
NCCL - Optimized primitives for collective multi-GPU communication
stable-diffusion-webui - Stable Diffusion web UI
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
stable-diffusion
why-ignite - Why should we use PyTorch-Ignite ?
ControlNet - Let us control diffusion models!
pocketsphinx - A small speech recognizer
ComfyUI - The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
ignite - High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
dreambooth-gui
ompi - Open MPI main development repository
stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM