u/yasircivan91

Hi everyone,

This is another project from my local AI music video series.

Danse Nocturne is a 3 minute 37 second cinematic Ambient Deep Techno music video, generated almost entirely on my own hardware.

The workflow is the same:

• ACE-Step 1.5 Q8 GGUF

• LTX 2.3 Q6 GGUF
• ComfyUI
• LTX Director Node (Custom python coded on v1.3.9, see details below*)

• CapCut (editing)

*For this project I also modified (python coding) LTX Director 1.3.9 by replacing the local Gemma text-encoding step with calls to the free LTX API Gemma model, reducing local memory usage while keeping the rest of the workflow unchanged. To be able to do this, place the logic from the gemma_api_conditioning.py file into the _encoded_relay function inside ltx_director.py. You can easily have any AI do this.

Together with my previous project (Féfénié), these videos total 555 seconds of AI-generated footage.

Considering Seedance 2.0's published pricing (12 credits/second at 720p), reproducing this amount of video commercially would require approximately:

• 6,660 credits (720p)
• 16,650 credits (1080p)

...before accounting for failed generations and retries.

I'd really appreciate feedback on:

• video consistency
• pacing
• prompt design
• local AI workflows
• long-form AI video production

Video:
https://www.youtube.com/watch?v=ktcFzXYEBAs&list=PLHjIuYra8dUcesWVnh7ZtqSC1w2Ys9h0v&index=1

Hi everyone,

After my first local AI music video, I wanted to push things much further.

This time I created a 5 minute 38 second cinematic fantasy music video called Féfénié, generated almost entirely on my own PC.

My setup:

• GPU: RTX 3070 Ti Laptop (8GB VRAM)
• RAM: 16GB DDR5 4800MHz

Workflow:

• ACE-Step 1.5 Q8 GGUF (acestep.cpp)

• LTX 2.3 Q6 GGUF (ComfyUI)
• LTX Director Node (Custom python coded on v1.3.9, see details below*)

• CapCut (editing)

*One interesting experiment was modifying LTX Director 1.3.9.

I edited ltx_director.py so that, instead of loading a local Gemma Text Encoder model, it calls the free LTX API Gemma model. This offloads the Gemma text-encoding stage to the cloud, reducing local VRAM/RAM usage. It worked successfully, although the overall generation speed improvement was smaller than I expected.

Another reason I wanted to stay local is cost.

For comparison, Seedance 2.0 currently lists 720p generation at 12 credits per second.

My two finished music videos total 555 seconds.

That means, assuming perfect first-pass generation:

• 720p → 6,660 credits
• 1080p → 16,650 credits

Realistically, multiple attempts are needed, so the actual cost would likely be several times higher. Even a yearly Max subscription provides around 10,000 credits per month, which shows how expensive long-form AI video production can become.

This project convinced me that local generation still has a place, even on relatively modest hardware.

I'd love to hear your thoughts on:

• video consistency
• prompt workflow
• LTX Director
• local long-form AI video generation

Video:
https://www.youtube.com/watch?v=cwVTagYA4cg&list=PLHjIuYra8dUcesWVnh7ZtqSC1w2Ys9h0v&index=2

Hi everyone,

I wanted to push the limits of my budget rig (8GB VRAM / 16GB RAM) and created a full video and music project completely locally.

Here is the tech stack I used:

• Video: LTX 2.3 Q6 K M Quant GGUF (via ComfyUI) + Leonardo.ai (for initial character avatar)
• Audio/Vocals: ACE-Step 1.5 Q8 Quant GGUF
• Editor: CapCut (for cutting and transition effects)

This is a fan-made anthem for the Turkish National Team heading into the 2026 World Cup. It's my very first local AI generation, but I'm planning to make this a series. I'm currently working on French tracks and videos next!

What do you think about the audio quality and video consistency? Did you like it? Since I'm running this on low specs, any prompt or optimization tips for LTX 2.3 and ACE-Step would be highly appreciated!!

Link: https://youtube.com/watch?v=URJgiVueRWI&si=OdDXEDCKp6wXwJTt