Snappy VS InvokeAI

Another example of Nim being really fast is the supersnappy library. This library benchmarks faster than Google’s C or C++ Snappy implementation.

It doesn't destroy performance for the simple reason that nowadays memory access has higher latency than pure compute. If you need to use compute to produce some data to be stored in memory, your overall throughput could very well be faster than without compression.

There have been a large amount of innovation on fast compression in recent years. Traditional compression tools like gzip or xz are geared towards higher compression ratio, but memory compression tends to favor speed. Check out those algorithms:

* lz4: https://lz4.github.io/lz4/

* Google's snappy: https://github.com/google/snappy

* Facebook's zstd in fast mode: http://facebook.github.io/zstd/#benchmarks

Google released Snappy, which is extremely fast and robust (both at compression and decompression), but it's definitely not nearly as good (in terms of compression ratio). Google mostly uses it for real-time compression, for example of network messages - not for long-term storage.

Just compress it! Of course if you will you ZIP, players will able to just open this zip file and change whatever they want. But you can use less popular compression algorithms which are not supported by default Windows File Explorer. Snappy for example.

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

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 ;)

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 )

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.

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

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.

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

InvokeAI