LLaMA-Factory VS StreamDiffusion

Depends what model you want to train, and how well you want your computer to keep working while you're doing it.

If you're interested in large language models there's a table of vram requirements for fine-tuning at [1] which says you could do the most basic type of fine-tuning on a 7B parameter model with 8GB VRAM.

You'll find that training takes quite a long time, and as a lot of the GPU power is going on training, your computer's responsiveness will suffer - even basic things like scrolling in your web browser or changing tabs uses the GPU, after all.

Spend a bit more and you'll probably have a better time.

[1] https://github.com/hiyouga/LLaMA-Factory?tab=readme-ov-file#...

Everyone does warmup before you measure. But measuring isn't always done right because we actually measure the GPU time only but some people naively use CPU time which is problematic because the process is asynchrenous. They have a few timing scripts though and I'm away from my GPU. There are some interesting things but they look like they know how to time. But it can also get confusing because is it considering batches or not. Some works do batch some do single. Only problem is when it isn't communicated correctly or left ambiguous.

Their paper is ambiguous unfortunately. Abstract, intro, and conclusion suggests single image by motivating with sequential generation (specifically mentioning metaverse). Experiment section says

> We note that we evaluate the throughput mainly via the average inference time per image through processing 100 images.

That implies batch along with their name Stream Batch...

Looking at the code I'm a bit confused. I'm away from my GPU so can't run. Maybe someone can let me know? This block[0] measures correctly but is using a downloaded image? Then just opens the image in the preprocess? (multi looks identical) This block[1] is using CPU? But running CPU. (there's another like this)

So I'm quite a bit confused tbh.

[0] https://github.com/cumulo-autumn/StreamDiffusion/blob/03e2a7...

[1] https://github.com/cumulo-autumn/StreamDiffusion/blob/03e2a7...