Experimental

A LoRA that improves prompt following, temporal consistency, and motion "precision" for LTX 2.3. Reduces the floaty, drifty motion that LTX tends to add to scenes. Things that should move, move with purpose. Things that shouldn't move, move less. Also works on non-NSFW, non-Furry, realistic, animated etc.

This is an early checkpoint. First time anyone has tried this on LTX. Results are promising but I'm still testing. Feedback and A-B comparisons welcome. V1 was trained on 4800 videos.

Recommended to run at strength 1.0-0.7 but experiment to find what works best for your setup. If you want stronger prompt adherence try strength 1.5-2.0. I have noticed the only side effect I have gotten from a high strength is the video looking like its 16fps.

Prompting tips in non-nsfw terms so its less confusing just adapt it to nsfw:

Be specific and literal. Describe what happens, in what order, step by step.

Instead of "a ball bouncing around" → "A red ball moves to the right, bounces off the wall, and returns to the center"

Instead of "fluid pouring" → "Water flows from the left container through the connecting tube into the right container until both levels are equal"

Describe the starting state, the action, and the end state

The LoRA follows prompts more literally than base LTX — precise prompts will give much better results

How was it made?

Trained on 360 videos from the VBVR (Very Big Video Reasoning) dataset synthetic task videos where every motion is precise and intentional. No concept bleed, no style change, just tighter control.

Based on the paper "A Very Big Video Reasoning Suite" which demonstrated this approach on Wan 2.2. I noticed that lora helped prompt following and temporal consistency a ton with wan so I am training this version for LTX. This is the first adaptation for LTX 2.3 and is still currently training.

What does it actually do?

Prompt following is more faithful — the model does more of what you asked instead of improvising

Motion is more deliberate and less erratic

Reduces random drift and wobble in scenes

Temporal consistency improved — actions follow logical sequences

What it doesn't do:

Doesn't change visual style

Doesn't add or remove capabilities LTX doesn't already have

Not a motion LoRA — stacks with motion LoRA's

Training details for v0.1 and v0.2 (if you give a shit)

Rank 32

360 VBVR synthetic videos at 512x512, 81 frames <------Alot less than 1 million but still a shitload to train on this is very slow to train locally.

LR 1e-4, adamw8bit

Early release — still training and evaluating

Training details for V1

Training videos were increased to 4800

Resolution is the same but frames were increased to 121

Every other setting the same as v0.1 and v0.2

More training data from the VBVR dataset was added to v1

Below is the new dataset I am training on's data composition if your curious

Tier 1 — Physics and Motion (3,400 samples)

Core generators at 300 each: G-11 (object reappearance) has a shape move off-screen in a direction and return along the same path — teaches trajectory and object persistence. G-25 (separate object spinning) is a shape that rotates in place then translates horizontally to a target position — multi-step motion sequencing. G-33 (visual jenga) is a stack of objects that get removed one by one from top to bottom — sequential extraction with implicit physics ordering. O-29 (ballcolor) is ball tracking tasks with color — motion following plus identity preservation. O-52 (traffic light) is discrete state transitions, lights switching on/off between green and gray — teaches the model that state changes are crisp, not gradual. O-75 (communicating vessels) is fluid equalizing between connected tubes based on pressure — continuous physics simulation over time. O-87 (fluid diffusion) is ink spreading in water — another continuous physical transformation but with expansion rather than equalization.

New additions at 250 each: G-35 (hit target after bounce) is a ball with an initial direction that bounces off walls following reflection laws to hit a target — pure trajectory prediction with physics constraints. O-30 (bookshelf) is book rearrangement on shelves — the specific task VBVR highlighted where their model beat Sora 2.

Multi-step transforms at 160 each: O-7 (shape color change) is a single transformation — shape changes from one color to another. O-8 (shape rotation) is a shape rotating by a specific angle. O-13 (outline then move) is two sequential steps: change a shape's outline style, then move it to a new position. O-14 (scale then outline) is also two steps: scale a shape up or down, then change its outline. These four together teach the model that instructions are ordered and each step completes before the next begins.

Tier 2 — Spatial and Reasoning (1,420 samples)

Proven generators at 100 each: G-13 (grid number sequence) is filling in number patterns on a grid. G-17 (grid avoid red block) is pathfinding on a grid while avoiding obstacles. G-31 (directed graph navigation) is finding the shortest path through a directed graph. G-41 (grid highest cost) is evaluating spatial values on a grid to find the optimal path. O-24 (domino chain) is a sequential cascade where dominoes fall until they hit a gap — teaches causal chains and stopping conditions. O-34 (dot to dot) is connecting numbered dots in sequence — ordered drawing. O-47 (sliding puzzle) is tile rearrangement under constraints, like a 15-puzzle. O-83 (planar warp) is warping a grid to align with a target quadrilateral — geometric transformation.

New reasoning diversity at 130 each: O-1 (color mixing) is RGB additive mixing where two light sources combine and the result fills a target zone — rule-based continuous process. O-33 (counting objects) is exactly what it sounds like — count things correctly. G-3 (stable sort) is arranging objects by a rule while preserving relative order. G-37 (symmetry random) is completing a pattern by mirroring across an axis. O-21 (construction blueprint) is fitting a correct puzzle piece into a gap in a structure. G-44 (BFS) is breadth-first search traversal of a graph — systematic layer-by-layer exploration.

The overall dataset is weighted roughly 70/30 toward physical motion and transformation tasks over abstract spatial reasoning, All of these are taken from the VBVR dataset I am not the creator of the dataset.

I will post more videos later. I cant gen videos and train at the same time since it takes up all of my goddamn VRAM and RAM to train lol.

REMEMBER its not X, its Y.

For 18+ only. This is NOT to be used for any illegal or unethical purposes or on any real person. Don't be fucking sus.