ali-dbhub VS ml-stable-diffusion

ChatGPT Mac/Windows App for SQL https://github.com/alibaba/Chat2DB

TLDR: Miniconda + Diffusers + Electron + Excalidraw

One of the most important aspects of creating this tool was finding a stable diffusion locally inference solution. There are many solutions available, such as https://github.com/leejet/stable-diffusion.cpp and https://github.com/apple/ml-stable-diffusion. I tested the C++ version, but the inference speed was very slow, and Metal GPU support still had issues (you can find relevant issues in their repo). Ultimately, I decided to use python to run it because PyTorch is mature and MPS support is well-established. And I chose Miniconda, because it can create a small, isolated Python environment to run the program.

The AI model should run in the background so that we can continuously produce images while you draw. We need to find an RPC method to enable communication between the Python process and Electron's Node.js process. The easiest way is to run a Python HTTP server, but the memory usage is too high. We need a more lightweight solution, so I used xmlrpc for memory efficiency, although there might be better alternatives that I'm unaware of. The AI inference part is handled by diffusers, which is great, but I had to apply some custom patches to make it work in this situation. This can be a bit challenging if you're not familiar with Python.

For the frontend, I initially used a low-level canvas library and tried to implement a drawing pad from scratch. However, it had too many details, so I chose a more mature option: Excalidraw. It's fantastic, with the only shortcoming being limited API support.

Finally, I combined all these technologies in Electron, ensuring they work smoothly on both the main process and the renderer process.

Ok, Is DrawingPics free to use?

Not sure how that got in here. Apple released CoreML Stable Diffusion library a little over a year ago [1]. Hugging Face released their version of the example app for the CoreML Stable Diffusion library [2].

The app should be able to run on iPhone 14 Pro, I believe the requirements is about 6-8Gb of RAM. And I was not able to run it on iPhone 13 Mini, because it has only 4Gb of RAM.

- [1] https://github.com/apple/ml-stable-diffusion

Where are you seeing a Stable Diffusion example? I'm familiar with Apple's CoreML Implementation of StableDiffusion, but is there something else in the SD world available for download now as part of MLX?

Other features and improvements to the repo https://github.com/apple/ml-stable-diffusion

M1! https://github.com/apple/ml-stable-diffusion

M2 MBA can run Stable Diffusion and LLaMa comfortably, which means generating your potential game/image asset locally. They're pretty much impractical in 7340.

Interestingly these are OpenCL kernels so in theory some of the optimizations might run out-of-the-box on CPUs.

It would be instructive to compare their speedups on the iPhone to the Apple CoreML implementation: https://github.com/apple/ml-stable-diffusion

Stable Diffusion runs great on my M1 Macs. The Draw Things app makes it really easy to run too. You also can’t disregard that Apple’s M chips actually have dedicated neural processing for ML/AI. This actual makes a Mac more affordable in this category because you don’t need to purchase a beefy graphics card. Not to mention that Apple has even optimized their software specifically for Stable Diffusion (related GitHub). Draw Things can take advantage of this. There’s a few guides to running the web UI on M1 too. I prefer the Draw Things app because of how easy it is to use, but the web UI is also nice because of all of the plugins and workflows that the community has built over time.

LINKS: ml-stable-diffusion: https://github.com/apple/ml-stable-diffusion Diffusers (HuggingFace Mac App): https://apps.apple.com/app/diffusers/id1666309574?mt=12