schizo_denoise: Use vszipcl/cu for NLMeans

[TheFeelTrain]

Also moved the args into a dict so they aren't repeated 3 times.

In my testing for this PR I found vszipcl is actually slightly slower but surely julek speeds that up some more. (Clueless)
Jetpack already removed knlmeans, nlm-cuda, and nlm-hip so it still makes sense to still do this..
Jaded-Encoding-Thaumaturgy/vs-jetpack@5fdcfcf

For Nvidia users vszipcu is a huge upgrade at least.

core.knlm.KNLMeansCL(clip, a=2, d=2, h=0.2, channels="UV")                # 129.68 fps
core.nlm_hip.NLMeans(clip, a=2, d=2, h=0.2, channels="UV", num_streams=2) # 268.85 fps
core.vszipcl.NLMeans(clip, a=2, d=2, h=0.2, channels="UV", num_streams=2) # 263.59 fps
core.vszipcu.NLMeans(clip, a=2, d=2, h=0.2, channels="UV", num_streams=2) # 384.75 fps

[Vodes]

Should this actually be default lol
I know all the benchmarks used this but is it worth putting more resources into nlm in the context of a real script

[TheFeelTrain]

jetpack has nl_means default to num_streams=2 with it. https://github.com/Jaded-Encoding-Thaumaturgy/vs-jetpack/blob/5fdcfcf1533ab1094c95af802eb95b8d6017a180/vsdenoise/nlm.py#L108

A lot of other filters also default to 2. Bilateral is even 4 and it seems to be fine in a full script. I've been running it with 2 since the nlm-hip commit. If you want could also make it kwargs instead and default to 1.

num_streams=kwargs.pop("num_streams", 1)

Although I didn't originally because that is going to conflict with bm3d in vszip cl/cu, lol.
Best might be a num_streams=[2,2] type argument when that happens I suppose.

Also sidenote is there any reason why nlmeans is cuda[0] and bm3d is cuda[1] while everything else is the opposite?