I think I'm finally at a point where I am ready to share something I've been working on to get some feedback. So any feedback would be greatly appreciated. This is not a 'finished' product by any means. It is still very much a work in progress with a good To-Do list. But some feedback and/or ideas would help me out.

First let me give you the premise for this whole thing. Before February I had done nothing with diffusion models. I started to get into it, downloaded ComfyUI, Z-image-turbo, and got hooked. In march I used my bonus from work to purchase a new laptop with a 5090 24GB VRAM / 64 GB system memory so I could start also playing around with learning video models.

(Why I'm doing this:) - All for fun.
I thought it would be fun to create 3D animated versions of my fiancée and our families, and then use them as the characters in a fantasy adventure story that is based on a fantasy version of her home country. So I set out going through the process of developing a story, the characters, world building, etc. My goal was to make something that is entertaining and also family friendly. I think to back to when my own kids were young and how we would enjoy watching things together.

I try to make every scene have a purpose whether it is revealing something about the story, the world, or a character. But….. Do I have a kitchen scene that exists so my 5 year old nephew can say "Hey! That's me!"? Yes. Yes I do.

I have a character that is a daydreamer and longs for some adventure in life. So I thought it would be fun to have a scene with one of those typical Disney "I want" songs. (Imagine Belle at the beginning of Beauty and the Beast) so I worked that in to help show her some of her personality and motivation.

(Quality of the work:)
I am a noob to all of this but I'm having an absolute blast. I don't have money coming out of my ears so I try to use local models as much as I can unless the scene calls for more than I can produce locally. I am not blaming models for my lack of experience for bad edits or if a scene does not flow well.  

(Character Voices:)
I know that voice consistency could be achieved if I took the time to do the voice acting / convert voice using a model / etc. but I do this in my spare time and I just don't have that much time. I have found that I can get between 80%-90% voice consistency by giving LTX consistent voice anchors for each character. For example, for the wizard Hazel, every time she speaks I use "the teenage girl in the blue wizard robes, says in teen girl's voice with a mid-range pitch, clear smooth texture, measured and articulate delivery, and a calm, thoughtful tone: "Nice to meet you, my name is Hazel."" Each character gets an anchor of (gender & age / pitch / texture / delivery / and tone. And tone is one you play with. Depending upon the conversation I might change her from "thoughtful tone:" to "playful tone:". And before anyone tries to argue that this technique does not work, just watch the video for yourself. Like I said it's not 100%, but I'll take it. BTW I also find that using the same voice anchors if I need a shot from Seedance seems to keep it within that 80%-90% range too.

(My To-Do list:)

1. Still several shots to remove the extra 'music' from. Way too many shots left (Thank you LTX. LOL)
2. Re-work the knight sparring scene to something that flows better
3. 2 scenes to complete and inject before the shift of setting to Seabreeze to make the transition flow.
4. Re-balance the dialogue to background music in some spots.

Looking for LTX suggestions:

I would love some suggestions on how to get better results from LTX in certain situations. Places like the dialogue shots (ex: 15:09 in the video) are where LTX really shines. BUT places like the 15:00 mark where the two people are simply walking forward and her face is melting into goo is where LTX drives me nuts. Maybe it's something I'm not doing correctly. I've seen people post some amazing things they've made with LTX but any time I attempt any real motion things turn nasty quick.

Hi everyone.

Today I want to share a problem I ran into while trying to generate high-resolution video with LTX, and a ComfyUI node I made to solve it.

First, for context, my test machine uses an RTX 5090 GPU.

But the point of this post is not to show off my hardware.

The issue was not only about running out of VRAM. Even on systems with enough VRAM, such as 3090, 4090, 5090, or RTX Pro 6000 setups, the default LTX workflow can start to break when the output resolution gets too large.

In my tests, once the resolution went beyond a certain stable range, the result did not just become slower. The video itself started to break. I saw color shifts, artifacts, and sometimes even broken character structure.

So I made a node that splits the latent into tiled regions and samples them separately.

It supports 1x2, 2x1, and 2x2 tiled sampling.

I think this will be especially useful for people using GPUs like the 3090, 4090, 5090, or RTX Pro 6000. Until now, even with enough VRAM, it was difficult to generate sharp ultra-high-resolution LTX videos reliably using only the default workflow.

That said, this node is not only for high-VRAM users.

Even 16GB VRAM users often try to make 9:16 vertical videos with heights around 1500 to 1900 pixels. In those cases, this node may also help handle higher resolutions more reliably.

The main idea is not to force LTX to process one huge latent at once. Instead, the node divides the large latent into smaller regions, samples them, and then combines the result back together.

With this method, I was able to generate much more stable high-resolution results than with the default LTX workflow.

Usage is simple.

Install Deno Custom Nodes, then replace or connect this tiled sampling node at the final high-res sampling stage of your existing LTX 2.3 workflow.

For wide videos, you can use 1x2.

For tall videos, you can use 2x1.

For very large resolutions such as 4K, you can try 2x2.

If you are using an Image-to-Video workflow with guide frames, connect the Crop Guide node after sampling to clean up the guide area, then continue with VAE Decode and Video Combine.

I also uploaded a YouTube video so the 4K results can be viewed with less quality loss.

If you are interested, please check the results there.

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

After researching and testing LTX 2.3 l wanted to share my findings and see if the community agrees:

Here is the hierarchy I'm seeing based on stability, motion, and structural consistency:

🏆 Tier 1: Video-to-Video (V2V) — The King

If you want absolute control over camera choreography, physical pacing, and complex human action, V2V is easily the best way to use 2.3. Using Depth or Pose conditioning modes locks down the physical mass and architecture of a scene perfectly, making character replacements or complex environmental re-texturing incredibly stable.

🥈 Tier 2: Detailed Text-to-Video (T2V) — The Reliable Standard

Thanks to the upgraded text connector, writing hyper-detailed, paragraph-length prompts (cinematic framing, lighting, exact materials like concrete/obsidian) yields gorgeous, organic results. The model actually choreographs the scene accurately, though you give up the absolute movement tracking you get with V2V.

🥉 Tier 3: Image-to-Video (I2V) — The Bottleneck

A simple I2V prompt feels like the worst approach right now. The model gets trapped in an architectural conflict—trying to perfectly preserve a pristine starting frame while simultaneously trying to invent motion out of thin air. It almost always results in a static "Ken Burns" pan or immediate visual melting/degradation after a few frames.

ik ik it' old card but i am confused i wanna buy a card for video gen that is under 1.5k us 3090 is a option but i want 32gb cards i hae tested rtx 4000 blackwell with ltx2.3 22b it did 1080 5sec in 1 min 18sec and 3090 did 4min for the same i wanna know how much v100 does to do the same and is there any alternative like how does r9700 performs

In this walkthrough, we show how to train an IC-LoRA that teaches LTX to colorize black-and-white footage using paired video data. You’ll see the full workflow from preparing clips and checking dataset structure to setting up the trainer, running a smoke test, monitoring validation clips, and testing the trained model on new footage.

The session also shows how Claude Code can help configure training runs, how IC-LoRAs differ from standard LoRAs, why clean training pairs matter, and how to catch file issues before starting a full run.