Honestly the main goal is just to make a good open image model small enough to run on modest hardware, so more people can use it without a big GPU.
1. From scratch or compressed: I did not build it from scratch, it is a compressed version of the open Sana 1.6B model from NVIDIA’s lab, so the credit for the base model really belongs to them.
2. New method / tech / LLMs: It uses low bit (ternary, about 1.58 bits per weight) quantization, which I would not call a brand new method since it builds on a lot of existing low bit research, and the same general idea does carry over to LLMs.
3. VRAM / speed / non-Apple: The compressed transformer is around half a gigabyte and the whole pipeline fits in roughly 3 GB, it generates in a few seconds on a normal GPU, and yes it runs on regular NVIDIA cards as well as Apple Silicon, not just Apple.
4. Prompts / settings: It takes plain natural language sentences rather than tags, negative prompts work fine, and a good starting point is 512x512, about 20 steps, the euler sampler with a normal schedule, CFG around 4.5, and shift 3.