Stretch iPhone to Its Limit: 2GiB Stable Diffusion Model Runs Locally on Device

• Snappy

  5 6,103 6.2 C++

  A fast compressor/decompressor

  

  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

• InfluxDB

  www.influxdata.com featured

  Purpose built for real-time analytics at any scale. InfluxDB Platform is powered by columnar analytics, optimized for cost-efficient storage, and built with open data standards.

  

• xnu

  49 1,663 1.9 C

  

  iOS uses memory compression but not swap. iOS devices actually have special CPU instructions to speed up compression of up to page size increments specifically to aid in this model [1]

  [1] https://github.com/apple-oss-distributions/xnu/blob/bb611c8f...

• InvokeAI

  240 22,870 10.0 TypeScript

  InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.

  

• stable-diffusion-webui

  2,810 139,678 9.8 Python

  Stable Diffusion web UI

  Automatic1111's stable diffusion web gui works with apple silicon: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki...

• maple-diffusion

  7 789 10.0 Swift

  Stable Diffusion inference on iOS / macOS using MPSGraph

  yeah, running the full decoder takes a while. though, since the "latent" is just 4 channels and pretty close to representing RGB, you can use a linear combination of latent channels and get a basic (grainy, low-res) preview image like this [0] without much trouble. I expect you could go further, and train a shallow conv-only decoder to get nicer preview results, but I'm not sure if anyone's bothered yet.

  [0] https://github.com/madebyollin/maple-diffusion

• SaaSHub

  www.saashub.com featured

  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

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