alt Hacker NewsI've been using MiniMax H3 on my M5 Pro 64GB MacBook Pro through ComfyUI. It works extremely well.
I had to modify the default ComfyUI workflows to use a GGUF quant (city96's ComfyUI-GGUF custom node, UnetLoaderGGUF in place of the stock loader) [0].
I use the model labeled Q5_K_M. There is Q8_0 available as well, which is 34GB and fits fine in 64GB unified memory if you keep resolution modest.
The main issue is speed, a ~9-second 480x864 clip at 20 steps takes me a bit over an hour. So this will be cool to try for the speed up alone.
There's a lot of great information and workflows available to follow on the r/StableDiffusion subreddit.
[0] https://huggingface.co/Abiray/MiniMax-H3-GGUF/tree/main/unet
Replies
> a ~9-second 480x864 clip at 20 steps takes me a bit over an hour
that's rough. for comparison, i tried the exact same parameters on my 5090 RTX and it took 2 minutes to generate.
i believe diffusion models are primarily compute bound so the macs aren't really the ideal hardware for this kind of stuff
This implementation is much faster on my M5 Max, like a few minutes for the same video, but on an M5 Max with 128GB, didn't test on M5 Pro. About memory, could be executed on 64GB with a few changes.
I wonder how much faster your m5 pro is compared to my M1 Max @ 64gb
What is the quality of the output like compared to something like Veo?
How much free space do you have left after running this llm model? Have you tried to develop your own model with the M5?
GGUF is outdated in the latest versions of Comfy-UI. If you want a good balance of size, speed and quality you should use the int8_convrot model from the official Comfy Org Repo https://huggingface.co/Comfy-Org/MiniMax-H3/tree/main/diffus...