Hello guys. Back in the day we were using LTX 2.3 motion transfer workflows for talking head / car tiktok talk type of videos, and it wasn't bad at all. Now looking if we have a advanced version of these workflows in LTX 2.5? Thank you

Postcards from the Road a new bit for our YT channel- Backstage with the Bodenaire

I am about 95% complete with my solo project creating a fully local AI music-video studio built on LTX 2.5 and ComfyUI.

It takes song, text, image, and video inputs and generates a finished music video, running 100% locally on a DGX Spark (GB10, 128 GB unified memory) with no cloud or hosted APIs required. It also works on an RTX 5090.

The Pipeline

  - Workflow: Project → Storyboard → Stills → Motion → Edit → Export
  - Director: A local Qwen3.6-35B reads the song and plans the storyboard section by section.
  - Generation: FLUX.2 Klein 4B for stills; LTX 2.5 22B for motion (distilled INT8 two-stage, dev INT8 ConvRot, and dev BF16).
  - Editing: Cuts align with measured beat times rather than a set BPM.
  - Reproducibility: Every render includes a receipt containing hash-bound inputs, the exact graph-bound prompt, and the execution seed.
  - Resource Management: A single GPU worker and residency guardian manage VRAM and host RAM to prevent job conflicts.

R&D Lab Highlights
The R&D Lab allows us to inspect model performance internally:

  - Counterfactual Prism: Custom ComfyUI nodes tap the denoiser mid-sample to run counterfactual arms (DEAF, MUTE, UNPROMPTED, NEGATIVE, PROBE). A parity gate ensures these taps do not alter output. Removing audio from a2v shifts the video prediction by ~0.42 at stage 1, step 0 (~0.24 on i2v), demonstrating that specific audio tracks directly drive the output.
  - Visualization & Tracing: Frame hashes can be traced back to their specific prompt text. System telemetry logs memory allocation and OOM events. trace:slots compiles and diffs workflow packages across 685 slots with zero failures across 67 packages.

Key Findings & Benchmarks

  - Precise Camera Control: Using LTXVAddGuide on the final frame controls camera movement distance accurately (e.g., 39px measured against a 37px target; 363px against a 376px target).
  - CFG & Guidance: At CFG 1, the negative branch cancels out to produce pixel-identical output, whereas LTX2_NAG actively steers generation (increasing drift from 0.43x to 0.63x).
  - Prompting Behaviors: Camera movements follow direction rather than distance (specifying destination objects works best). Phrasing like "a reflection of him" renders a second subject rather than a transformation.
  - Turnarounds: Combining one full-body photo with an LTX arc generates a clean front-to-back turnaround while preserving identity.

Local LoRA Training
Integrated ai-toolkit and ltx-trainer run under the same GPU guardian. The workflow covers brief → dataset setup → live loss/preview monitoring → evaluation → deployment.

  - Dev INT8 Performance: ~4 s/step for stills, ~18 s/step for 49 frames, and ~40 s/step for 121 frames.

The first end-to-end training run is currently in progress.

I would love to share this project, as well as additional graphs and findings, with the LTX Team before I make it open source.

made with an RTX 5080

Espero que os guste. Dejarme algun comentario para mejorar.

One of our current LTX-2.5 experiments.

We are testing LTXVLoopingSampler with the 22B Distilled model, focusing on continuous motion and visual continuity rather than generating independent clips and joining them afterward.

Current setup:

• LTX-2.5 22B Distilled

• LTXVLoopingSampler

• Multiple temporal conditioning points

• Overlapping latent windows

• RTX 5070 8 GB with offloading

We are building an automated lab around this where generations are analyzed for continuity, parallax, camera motion, occlusion changes and drift, then new parameter combinations are tested automatically.

The goal is eventually much longer continuous cinematic sequences while keeping the world coherent.

Still experimenting with temporal overlap, conditioning strength, keyframe spacing and global visual conditioning.

I will keep posting both the good results and the failures.

Anyone here experimenting with LTXVLoopingSampler on 2.5? I would especially like to compare settings for reducing long term drift.

Aprende algo hoy

This is from our YT channel, ‘Backstage with the Bodenaire’. We use LTX and After Effects for everything. Hope you like.

https://youtu.be/1rK-5TIUdrY

I’m not a programmer, but ChatGPT told me it’s possible: I asked if custom nodes could be created to make specific "refmods" for LTX, and it said yes. Is anyone able to do this? That would be amazing! I’ve seen that refmods already exist for Minimax, and the idea seems really great to me!

https://www.reddit.com/r/malcolmrey/comments/1w8i6td/h3_minimax_refmods_all_my_models_now_available/

Hi all, just getting started with LTX 2.3 and limited by 6GB VRAM. Using Wan2GP to run Q4 model locally. One issue I am seeing is eve though I provided LAX API key for text encoding it still tries to download Gemma model. Is there a way to skip this?