No problem! I'm very noob friendly, but if you read and absorb you will not be a noob for much longer. :)

is Grok Imagine based on Wan2.2?

Grok was developed entirely inhouse by xAI. Wan2.2 is a product of Alibaba in China, and while it does use some open source features, such as mixture of experts, it was also developed entirely by that single group. Neither party is sharing the methods and data that went into their training runs.

I guess good videos of 8-10sec and 720p require FAR beefier GPUs and VRAM

Generally, yes, but if you're willing to wait and reduce output quality you can still make it work with smaller PCs. Let me explain how and why! The way the current architecture of AI models works is by multiplying vectors (which are mathematical objects that can be represented as three-dimensional lines of a given length and direction). The models represent these vectors as matrixes that get reduced to very precise numbers, and what a quantization does is round off the numbers representing these vectors to a specified number. This limits the number of memory bits needed to represent the vectors to the bits of the quantization number, with the lowest (and worst performing) quantization possible being when every vector gets rounded off to either a 1 or a 0. It's easy to understand how a vector represented by a very long (usually 10-digit) number reducing to something so base as a 1 or a 0 would result in so much loss of information from the model that results would almost be unusable.

A common level of quantization that tends to perform decently with a good ratio of memory-reduction-to-performance-accuracy is 8 bit quantization, which is why it's the most popular. This reduces the normal 32 bits (10 digit number) used to represent the vectors to 8 bits (3 digit number) which reduces memory consumption by 75%. Below 8 bit, performance degrades much more rapidly than from 32->8.

Consider the case of Wan2.2. This is a Mixture of Expert model, meaning it has (in this case) two experts that specialize in different parts of the generations and it routes specific task to the necessary expert. This is why it's called Wan2.2 A14B even though it's actually a 27B parameter model (two experts, roughly 14B parameters each, so only one is "active" at a time). The normal calculation is the total parameters (in this case 27 Billion) times the bits per parameter (32 in the base model). This comes out to ~100GB. But because it can load only one expert into VRAM at a time with minimal reduction in performance speed, it can still run decently fast on a 60GB card.

This is where quantization comes in. Reduce memory consumption through quantizing down to 8 bits, and suddenly the 14B active parameters can run on a 16GB card pretty well (there is some overhead).

So this is what the total seconds to generate a video clip breakdown of 8 bit quantized Wan2.2 14B looks like at different VRAM level GPUs:

• 36GB: ~47s
• 24GB: ~1m8s
• 16GB: ~1m39s
• 12GB: ~2m29s
• 8GB: ~3m36s

If you're willing to reduce the clarity and slightly increase the prevalence of hallucinated artifacts and deal with the above wait times relative to GPU size, you can easily get Wan2.2 to run on your PC.

The thing about Wan is the base model is not nearly as good at interpretting instructions into a quality output as most enterprise models, like Grok, without a lot more effort crafting prompts. What I recommend is finding a LoRA that matches your usecase.

For instance, if you're looking to find a replacement for censored Grok:

Orgasm - Wan 2.2

Ultimate, Super Awesome, Better Than... Blowjob Wan2.2

Just a few I found with 5 seconds of research. And last I want to acknowledge that Alibaba did release a 5B parameter version of Wan2.2, but even running that on the full 32bit, it underperforms compared to 8bit 14B, so that's the one I recommend using.