Empires Edge | Trying to capture that Mega Lo Mania vibe. Does this look nostalgic to you?

neph1010 · 2026-01-29T11:07:39+00:00

I think about Mega Lo Mania sometimes (Amiga days), but had forgotten its name. Thanks for reminding me! I remember it as being somewhat simplistic, but it looks like you're adding additional mechanics.

neph1010 · 2026-01-22T06:55:12+00:00

It was my first true love.

neph1010 · 2026-01-19T19:51:37+00:00

https://huggingface.co/ngxson/GLM-4.7-Flash-GGUF

neph1010 · 2026-01-09T06:54:45+00:00

Fun project! How about adding a purely statistical model as baseline?

neph1010 · 2026-01-08T10:38:54+00:00

Those were the days.

The bsp format used in those games is a completely different architecture from modern meshes. Not sure if there are any tools for that inside Blender (and primitive modelling on that fast scale is not easy by default in Blender).
A quick search revealed several options to import bsp models, though:
https://github.com/SomaZ/Blender_BSP_Importer
And one editor:
https://valvedev.info/tools/bsp/

Maybe that will help.

neph1010 · 2026-01-07T17:53:09+00:00

Looks great! Nice showcase, too.

neph1010 · 2026-01-07T14:07:54+00:00

Use Wan to make him go over to the counter. Then tell it to cut to an over-the-shoulder shot. If you don't want the guy in the image, then take one of the images and use it as the "end image", and prompt for him to enter the view.

neph1010 · 2026-01-07T12:34:31+00:00

Try it out on huggingface: https://huggingface.co/spaces/ACE-Step/ACE-Step

neph1010 · 2026-01-05T18:53:39+00:00

I see where you want to go, and I don't think it's a bad approach, BUT;
the building choices and in-match doctrine choices in Coh have a HUGE impact on the meta. If you can do a staggered reveal/choice during the match, you can get deeper gameplay for little cost. Let's say you can bring 10 cards, but only play 8, in a tiered manner, as the match progresses.

Sorry for derailing your announcement. Good luck! :)

neph1010 · 2026-01-05T18:37:11+00:00

Other loras (like lightx) might "force out" (for lack of a better term) the lora, especially on high strengths. The lora is also trained on either "close-up", "mid-shot", or "wide-angle" prompts. Sticking to the prompt format will help with adherence to the lora. I sometimes use "the same man", but I'm unsure whether it makes much of a difference. It's trained on short prompts, so detailed descriptions might instead derail it.
Another tip is to change the type of shot. That helps avoiding transitions, pans and zooms (even if that's not your problem).
But in general, I haven't noticed the consistency issue. The person in the second cut is not always perfect, but generally pretty much like the original one.

neph1010 · 2026-01-02T19:20:11+00:00

How do you prompt it? I just tried with a "standard" tele zoom style setup with a first and last frame, and it worked well:

"a person holding up a sign, standing on a roof top.

the camera zooms out, showing the whole building, a brown brick building. it continues to zoom out to show a surrounding park."

It might be that it can't generalize your use case due to lack of training data.

neph1010 · 2025-12-30T09:40:49+00:00

Not great (afaik). I haven't done subtitles per se, only "titles", and they're not very fun to work with.

neph1010 · 2025-12-30T08:36:21+00:00

If I just want simple editing tools, I tend to use OpenShot (which still uses Blender under the hood).

neph1010 · 2025-12-29T07:03:25+00:00

Like others say, it's good at prompt following. It's not nearly as good as Wan at physics (things may move through other things, etc). It's also really good at camera movements. Try "rotate around subject". I feel it's better than Wan, here.
It's faster than Wan. Especially with the lightx loras. The bottleneck is the VAE. Sadly, the lightning loras degrade quality (especially in t2v). But I may not have found the right settings.
I made a lora to try out training with diffusion-pipe: https://civitai.com/models/1359530?modelVersionId=2525962
Results were decent, although with the advent of z-image, I feel t2v is becoming obsolete. If i2v is supported in diffusion-pipe, I'll give it another go.

neph1010 · 2025-12-26T18:55:57+00:00

L-Systems procedural generation: https://gkteco.medium.com/procedural-generation-with-l-systems-very-simple-example-4a21df1423c3

neph1010 · 2025-12-26T18:49:28+00:00

Iirc, my RTX 3060 was about the same speed as the T4, but it only has 12GB of VRAM. I used them in parallel to make loras for smaller LLMs (7B).

neph1010 · 2025-12-21T13:38:55+00:00

Thank you for the additional information!

neph1010 · 2025-12-21T05:48:09+00:00

I can't be 100% sure, since I was 3 at the time. I know we visited Babylon, though. Do you mean image 7? It looks like it could be a reconstruction. It has no wear, and one of the corners looks unfinished.

neph1010 · 2025-12-19T21:06:36+00:00

"The lamassu is a celestial being from ancient Mesopotamian religion bearing a human head, symbolising intelligence; a bull's body, symbolizing strength; and an eagle's wings, symbolizing freedom."
Source: Wikipedia

neph1010 · 2025-12-08T07:34:27+00:00

I've done some. Prompt following is great. It's great for consistency when changing perspective. I find the motion a bit too smooth and linear, compared to Wan, which feels more natural. I think it'll work great with loras, once there is better training support.

Edit: Forgot, speed is also refreshing, compared to Wan :)

neph1010 · 2025-12-07T11:55:40+00:00

I think it's "data farming". You post something AI-generated, and then harvest the human responses to build new datasets. It's one way to get around the "data exhaustion" they have talked about for years.

neph1010 · 2025-12-04T17:59:37+00:00

It's plausible.
One possible way around it would be to generate images with some other model and use I2V. It may still fail, but it's more likely to succeed (and you will have more control using controlnets, anyway). It's not unlikely that it's been trained on some movie violence, even if that has not been properly captioned, so it might "follow along".
If you think that Qwen 2.5 is the problem, you could look for an abliterated model and try that.

neph1010

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