LTX-2 VBVR LoRA - Video Reasoning:
LoRA fine-tuned weights for LTX-2.3 22B on the VBVR (A Very Big Video Reasoning Suite) dataset.
Training Data
To ensure training quality, we preprocessed the full 1,000,000 videos from the official dataset and randomly sample during training to maintain data diversity. We adopt the official parameters with batch_size=16 and rank=32 to prevent catastrophic forgetting caused by excessively large rank.
The VBVR dataset contains 200 reasoning task categories, with ~5,000 variants per task, totaling ~1M videos. Main task types include:
Object Trajectory: Objects moving to target positions
Physical Reasoning: Rolling balls, collisions, gravity
Causal Relationships: Conditional triggers, chain reactions
Spatial Relationships: Relative positions, path planning
Model Details:
Base Model : ltx-2.3-22b-dev
Training Method: LoRA Fine-tuning
LoRA Rank : 32
Effective Batch Size: 16
Mixed Precision : BF16
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LoRA Capabilities
This LoRA adapter enhances the base LTX-2 model for production video generation workflows:
Enhanced Complex Prompt Understanding: Accurately interprets multi-object, multi-condition prompts with detailed spatial descriptions and temporal sequences, reducing prompt misinterpretation in production scenarios.
Improved Motion Dynamics: Generates smooth, physically plausible object movements with natural acceleration, deceleration, and trajectory curves, avoiding robotic or unnatural motion patterns.
Temporal Consistency: Maintains object appearance, lighting, and scene coherence throughout the video sequence, reducing flickering and frame-to-frame artifacts common in generated videos.
Precise Timing Control: Enables accurate control over action duration, pacing, and synchronization between multiple moving elements based on prompt semantics.
Multi-Object Interaction: Handles complex scenes with multiple objects interacting simultaneously, including collisions, following, avoiding, and coordinated movements.
Camera and Framing Stability: Maintains consistent camera perspective and framing throughout the sequence, avoiding unwanted camera shake or unexpected viewpoint changes.
Training Configuration:
Learning Rate : 1e-4
Scheduler : Cosine
Gradient Accumulation : 16 steps
Gradient Clipping : 1.0
Optimizer : AdamW
Evaluation Metrics:
Training Steps~6,000
Final Loss~0.008
Loss Reduction 44% (from 0.014 to 0.008)
Dataset :
This model is trained on the VBVR (Video Benchmark for Video Reasoning) dataset from .