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Can someone ELI5 why the safetensor file is 23.8 GB, given the 12B parameter model? Does the model use closer to 24 GB of VRAM or 12 GB of VRAM. I've always associated a 1 billion parameter = 1 GB of VRAM. Is this estimate inaccurate?
That's a good ballpark for something quantized to 8 bits per parameter. But you can 2x/4x that for 16 and 32 bit.
Helpful blog post for understanding what kind of data is needed for these models! Does this have any application for generating realistic scenes for robotics training?
- cost per image - latency per image
Hope you guys can add it somewhere!
Hi! I'm lead researcher on Krea-1. FLUX.1 Krea is a 12B rectified flow model distilled from Krea-1, designed to be compatible with FLUX architecture. Happy to answer any technical questions :)
what does " designed to be compatible with FLUX architecture" mean and why is that important?
I usually use https://github.com/axolotl-ai-cloud/axolotl on Lambda/Together for working with these types of models. Curious what others are using? What is the quickest way to get started? They mention Pre-training and Post-training but sadly didnt provide any reference starter scripts.
Check this out: https://github.com/krea-ai/flux-krea
Let me see if we can add more details on the blog post and thanks for the flag!
For the Krea team that might be reading: I was trying to evaluate Krea for my image gen use case, and couldn't find: - cost per image - latency per image Hope you guys can add it somewhere!
Though we wanted to keep this technical blogpost free from marketing fluff, but maybe we over-did it.
However, sometimes it's hard to give an exact price per image, as it depends on resolution, number of steps, whether a LoRA is being used or not, etc.
Earlier quoted context omitted.
That's a good ballpark for something quantized to 8 bits per parameter. But you can 2x/4x that for 16 and 32 bit.
I've never seen a 32 bit model. There's bound to be a few of them, but it's hardly a normal precision.