chfm / Ltx2.3-VBVR-lora-I2V

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Total runs: 65
24-hour runs: -3
7-day runs: -6
30-day runs: -24
Model's Last Updated: July 04 2026

Introduction of Ltx2.3-VBVR-lora-I2V

Model Details of Ltx2.3-VBVR-lora-I2V

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 100 reasoning task categories, with ~10,000 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
Item Details
Base Model ltx-2.3-22b-dev
Training Method LoRA Fine-tuning
LoRA Rank 32
Effective Batch Size 16
Mixed Precision BF16
TODO List
Dataset Release Plan
Dataset Videos Status
VBVR-96K 96,000 ✅ Released
VBVR-240K 240,000 ✅ Released
VBVR-reinforce 240K+150K ✅ Released
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
Stage 1: VBVR Foundation (96K)
Config Value
Dataset 96K VBVR general videos
Learning Rate 1e-4
Scheduler Cosine
Batch Size 1 × 16 (gradient accumulation)
Optimizer AdamW
Max Grad Norm 1.0
Target Modules to_q , to_k , to_v , to_out.0 , ff.net.0.proj , ff.net.2
Stage 2: VBVR Extended (240K)
Config Value
Dataset 240K general videos
Learning Rate 1e-4
Scheduler Cosine
Batch Size 1 × 16 (gradient accumulation)
Optimizer AdamW
Max Grad Norm 1.0
Target Modules to_q , to_k , to_v , to_out.0 , ff.net.0.proj , ff.net.2
Stage 3: General + Hard Reasoning (390K)
Config Value
Dataset 240K general videos + 150K high-difficulty reasoning videos
Learning Rate 5e-5
Scheduler Cosine
Batch Size 1 × 16 (gradient accumulation)
Optimizer AdamW
Max Grad Norm 1.0
Target Modules to_q , to_k , to_v , to_out.0 (FFN frozen)
Video Demo
Training Progress Comparison

Original Model

240K

390K

Dataset

This model is trained on the VBVR (Video Benchmark for Video Reasoning) dataset from video-reason.com .

Contact

For questions or suggestions, please open an issue on Hugging Face or contact the author directly.

Runs of chfm Ltx2.3-VBVR-lora-I2V on huggingface.co

65
Total runs
-3
24-hour runs
-2
3-day runs
-6
7-day runs
-24
30-day runs

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