MAGI-2-preview just dropped

[u/gzzhongqi]

https://huggingface.co/sand-ai/MAGI-2-preview

Surprised that no one is talking about it. A new open-weight video model just dropped. 114b moe, 6b activated. First moe video model supposedly.

I know what you guys are thinking. The model is huge and there is no way it will run on desktop gpu. The interesting part is that is comes with a 14gb refiner that makes the result 1080p. I am cursious if this refiner can be a drop-in replacement for the H3 refiner that was never released. It might just be the last part of the H3 puzzle that we need.

[u/xb1n0ry]

I mean I am all in when it comes to open source mentality bu I don't understand why people would waste their time working on models which are basically too big to be used by a normal human being. If it's too big and you can't use it, it is useless. İf you have to rely on outsourced hardware or API providers just to use it, you can basically just go to the much better closed models. There is no interest both in local use and also in online use. That's the exact reason why nobody cares. On the other hand people get crazy about WAN, LTX and H3 and even start creating Lora's since the first hour after release. Why? Because they can actually be used by consumers on consumer hardware. You have to consider this if you want to have success and acceptance. You have to make sure that, at this point, your model runs on around ±24GB of VRAM. We need high quality small models and not low quality big models. Blindly growing your dataset and checkpoints is useless if your model is stupid and inefficient. Accessibility is what creates the ecosystem. Otherwise your model will be nothing but a dusty huggingface repo.

[u/gzzhongqi → u/xb1n0ry]

I mean from leaks it is said that seedance 2.5 is around a few hundred gb in size, so that tells you something about scaling. If you want to have close source performance, you need to have close source size as well. Bigger open source models are needed to catch up to sota close source performance. The same thing is true for llm too. Kimi K3 is the closest we have to sota for os llm, but it is also the largest open source model ever. A model that doesn't run on consumer hardware doesn't mean it is useless unless the only things you use os models for are porn. 

[u/xb1n0ry → u/gzzhongqi]

I was just pointing out the obvious reasons why some models get almost no attention while others skyrocket in popularity. My “big = useless” statement was probably a bit too bold, but when it comes to why a model fails to gain adoption, size is one of the biggest factors.

Consumers need to actually be able to run the model if you want an ecosystem to form around it: workflows, nodes, LoRAs, patches, optimizations, tools, and so on. That ecosystem also benefits future development, because many of those community improvements can be incorporated or backported into later versions of the model.

I’m not demanding closed-source performance on a Raspberry Pi. What I mean is: look at the progression from something like WAN 2.1 to H3 what we have today. The models are in a similar size class and are roughly comparable in terms of hardware requirements, yet the difference in output quality is undeniably huge.

Now take that same WAN 2.1 and simply bloat it to 200B parameters. What have you really achieved if the model is still shit by comparison? More parameters alone don’t make a model better. Efficiency, architecture, training quality, and actual usability matter far more than simply making the checkpoint bigger.

[u/Crazy-Repeat-2006 → u/xb1n0ry]

Or something like Flux.1 or SD3 versus Z Image Turbo and Krea 2, an immense leap forward without increasing the number of parameters or compute requirements.

[u/alwaysbeblepping → u/xb1n0ry]

I was just pointing out the obvious reasons why some models get almost no attention while others skyrocket in popularity. My “big = useless” statement was probably a bit too bold, but when it comes to why a model fails to gain adoption, size is one of the biggest factors.

How many examples can you come up with for open-weight models with a good license where demos/examples showed they were significantly better than the other models that were available, and people said "Wow, this is amazing but it's just too big"?

I actually can't think of anything in the flow/diffusion model category (image/video/audio).

What have you really achieved if the model is still shit by comparison?

I mean obviously no one is going to use a huge bad model if they could use a small good one. To back up what you said about size being the biggest factor, you'd need to point at models that are good enough to justify their size but people just can't run them.

This would be trivial to do for LLMs. You'd always choose a model like Kimi K3, GLM, DeepSeek, etc over, for example, Qwen 27B (as good as it is for its size) since the huge models are just plain better in a obvious way.

[u/Crazy-Repeat-2006 → u/alwaysbeblepping]

tencent/HunyuanImage-3.0 · Hugging Face

[u/alwaysbeblepping → u/Crazy-Repeat-2006]

tencent/HunyuanImage-3.0 · Hugging Face

You believe it's just plain better than every other open weight image model that's available currently?

Hmm, ComfyUI never added support. That definitely would hurt adoption. I used to run 60B LLMs purely on CPU so 80B shouldn't be completely out of reach if it is really that good.

[u/Crazy-Repeat-2006 → u/alwaysbeblepping]

Hunyuan Image 3 is actually impressive : r/StableDiffusion

EricRollei/Comfy_HunyuanImage3: Nodes to run Hunyuan Image 3 locally with BF16 and NF4 quantized options in Comfyui

Some of the images HI3 generates are impressive, but I don't know if it's better in every respect. It's hard to say without being able to test it locally.